librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-03 18:27:40	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 550 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-03 18:26:42	card stringlengths 11 1.01M
dmavkgo/vilt_finetuned_200	dmavkgo	2024-06-04T05:02:13Z	63	0	transformers	[ "transformers", "safetensors", "vilt", "visual-question-answering", "generated_from_trainer", "dataset:vqa", "base_model:dandelin/vilt-b32-mlm", "base_model:finetune:dandelin/vilt-b32-mlm", "license:apache-2.0", "endpoints_compatible", "region:us" ]	visual-question-answering	2024-06-04T03:32:11Z	--- license: apache-2.0 base_model: dandelin/vilt-b32-mlm tags: - generated_from_trainer datasets: - vqa model-index: - name: vilt_finetuned_200 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vilt_finetuned_200 This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on the vqa dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
richardkelly/Qwen-Qwen1.5-1.8B-1717476207	richardkelly	2024-06-04T05:01:47Z	142	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T04:43:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
FuturisticVibes/Meta-Llama-3-70B-Instruct-abliterated-v3.5-6.0bpw-h8-exl2	FuturisticVibes	2024-06-04T04:58:52Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-06-04T04:51:48Z	--- library_name: transformers license: llama3 --- I have no idea what I’m doing… if this causes the apocalypse someone please let me know. Meta-Llama-3-70B-Instruct-abliterated-v3.5 6.0bpw h8 EXL2 Includes [measurement.json](https://huggingface.co/FuturisticVibes/Meta-Llama-3-70B-Instruct-abliterated-v3.5-6.0bpw-h8-exl2/tree/measurement) file for further quantization Up next is a new, old, long dead, but never forgotten friend… Assuming I can put enough money into RunPod to rent an H100 for a bit… Original Model: https://huggingface.co/failspy/Meta-Llama-3-70B-Instruct-abliterated-v3.5 # Original Model Card # Llama-3-70B-Instruct-abliterated-v3.5 Model Card [My original Jupyter "cookbook" to replicate the methodology can be found here](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb) [My personal library o' code used](https://github.com/FailSpy/abliterator) (WIP, looking to improve and generalize) This is [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more. ## V3.5? Second try. I felt that the V3 methodology of 70B wasn't well applied, and u/Nexesenex on reddit kinda confirmed my suspicions. So go blame them. :P This one has only a single layer modified(!) and that seems to have completely eliminated moralizing disclaimers. I hope you'll find this model better than 70B-V3! As well, this also fixes the tokenizer. ## Hang on, "abliteration"? Orthogonalization? Ablation? What is this? TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out. TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model. As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes. Ablate + obliterated = Abliterated Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization. ## A little more on the methodology, and why this is interesting To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt. Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights. > Why this over fine-tuning? Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage. As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.) Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques. It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa. I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity. > Okay, fine, but why V3? There's no V2 70B? Well, I released a V2 a while back for 8B under Cognitive Computations. It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model. I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations. So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's such an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.) ## Quirkiness awareness notice This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects. If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored. Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can.
mradermacher/Llama3-13B-lingyang-v1-GGUF	mradermacher	2024-06-04T04:56:59Z	49	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "Llama3", "en", "base_model:wwe180/Llama3-13B-lingyang-v1", "base_model:quantized:wwe180/Llama3-13B-lingyang-v1", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-04T04:10:40Z	--- base_model: wwe180/Llama3-13B-lingyang-v1 language: - en library_name: transformers license: - other quantized_by: mradermacher tags: - mergekit - merge - Llama3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/wwe180/Llama3-13B-lingyang-v1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q2_K.gguf) \| Q2_K \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.IQ3_XS.gguf) \| IQ3_XS \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q3_K_S.gguf) \| Q3_K_S \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.IQ3_S.gguf) \| IQ3_S \| 6.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.IQ3_M.gguf) \| IQ3_M \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q3_K_M.gguf) \| Q3_K_M \| 6.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q3_K_L.gguf) \| Q3_K_L \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.IQ4_XS.gguf) \| IQ4_XS \| 7.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q4_K_S.gguf) \| Q4_K_S \| 7.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q4_K_M.gguf) \| Q4_K_M \| 8.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q5_K_S.gguf) \| Q5_K_S \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q5_K_M.gguf) \| Q5_K_M \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q6_K.gguf) \| Q6_K \| 11.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q8_0.gguf) \| Q8_0 \| 14.2 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mssma/ko-solar-10.7b-v0.8	mssma	2024-06-04T04:50:40Z	62	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T04:41:44Z	--- library_name: transformers license: apache-2.0 language: - ko --- # usage ``` from transformers import AutoModelForCausalLM, AutoTokenizer import torch path = "mssma/ko-solar-10.7b-v0.8" model = AutoModelForCausalLM.from_pretrained( path, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) tokenizer = AutoTokenizer.from_pretrained(path) ```
vaibhavchavan/flan-t5-small-finetuned-xsum	vaibhavchavan	2024-06-04T04:45:04Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-30T03:20:29Z	--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-finetuned-xsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-small-finetuned-xsum This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 3.5714 - Rouge2: 1.2195 - Rougel: 3.5714 - Rougelsum: 3.5714 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 1 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 2.0 \| 2 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 3.0 \| 3 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 4.0 \| 4 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 5.0 \| 5 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 6.0 \| 6 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 7.0 \| 7 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 8.0 \| 8 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 9.0 \| 9 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 10.0 \| 10 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 11.0 \| 11 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 12.0 \| 12 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 13.0 \| 13 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 14.0 \| 14 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 15.0 \| 15 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 16.0 \| 16 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 17.0 \| 17 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 18.0 \| 18 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 19.0 \| 19 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 20.0 \| 20 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 21.0 \| 21 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 22.0 \| 22 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 23.0 \| 23 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 24.0 \| 24 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 25.0 \| 25 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 26.0 \| 26 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 27.0 \| 27 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 28.0 \| 28 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 29.0 \| 29 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 30.0 \| 30 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 31.0 \| 31 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 32.0 \| 32 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 33.0 \| 33 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 34.0 \| 34 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 35.0 \| 35 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 36.0 \| 36 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 37.0 \| 37 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 38.0 \| 38 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 39.0 \| 39 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 40.0 \| 40 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 41.0 \| 41 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 42.0 \| 42 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 43.0 \| 43 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 44.0 \| 44 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 45.0 \| 45 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 46.0 \| 46 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 47.0 \| 47 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 48.0 \| 48 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 49.0 \| 49 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 50.0 \| 50 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 51.0 \| 51 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 52.0 \| 52 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 53.0 \| 53 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 54.0 \| 54 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 55.0 \| 55 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 56.0 \| 56 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 57.0 \| 57 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 58.0 \| 58 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 59.0 \| 59 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 60.0 \| 60 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 61.0 \| 61 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 62.0 \| 62 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 63.0 \| 63 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 64.0 \| 64 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 65.0 \| 65 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 66.0 \| 66 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 67.0 \| 67 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 68.0 \| 68 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 69.0 \| 69 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 70.0 \| 70 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 71.0 \| 71 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 72.0 \| 72 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 73.0 \| 73 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 74.0 \| 74 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 75.0 \| 75 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 76.0 \| 76 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 77.0 \| 77 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 78.0 \| 78 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 79.0 \| 79 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 80.0 \| 80 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 81.0 \| 81 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 82.0 \| 82 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 83.0 \| 83 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 84.0 \| 84 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 85.0 \| 85 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 86.0 \| 86 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 87.0 \| 87 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 88.0 \| 88 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 89.0 \| 89 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 90.0 \| 90 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 91.0 \| 91 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 92.0 \| 92 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 93.0 \| 93 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 94.0 \| 94 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 95.0 \| 95 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 96.0 \| 96 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 97.0 \| 97 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 98.0 \| 98 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 99.0 \| 99 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 100.0 \| 100 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 101.0 \| 101 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 102.0 \| 102 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 103.0 \| 103 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 104.0 \| 104 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 105.0 \| 105 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 106.0 \| 106 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 107.0 \| 107 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 108.0 \| 108 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 109.0 \| 109 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 110.0 \| 110 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 111.0 \| 111 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 112.0 \| 112 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 113.0 \| 113 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 114.0 \| 114 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 115.0 \| 115 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 116.0 \| 116 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 117.0 \| 117 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 118.0 \| 118 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 119.0 \| 119 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 120.0 \| 120 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 121.0 \| 121 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 122.0 \| 122 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 123.0 \| 123 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 124.0 \| 124 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 125.0 \| 125 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 126.0 \| 126 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 127.0 \| 127 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 128.0 \| 128 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 129.0 \| 129 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 130.0 \| 130 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 131.0 \| 131 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 132.0 \| 132 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 133.0 \| 133 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 134.0 \| 134 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 135.0 \| 135 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 136.0 \| 136 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 137.0 \| 137 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 138.0 \| 138 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 139.0 \| 139 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 140.0 \| 140 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 141.0 \| 141 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 142.0 \| 142 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 143.0 \| 143 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 144.0 \| 144 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 145.0 \| 145 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 146.0 \| 146 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 147.0 \| 147 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 148.0 \| 148 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 149.0 \| 149 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 150.0 \| 150 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 151.0 \| 151 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 152.0 \| 152 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 153.0 \| 153 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 154.0 \| 154 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 155.0 \| 155 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 156.0 \| 156 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 157.0 \| 157 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 158.0 \| 158 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 159.0 \| 159 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 160.0 \| 160 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 161.0 \| 161 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 162.0 \| 162 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 163.0 \| 163 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 164.0 \| 164 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 165.0 \| 165 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 166.0 \| 166 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 167.0 \| 167 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 168.0 \| 168 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 169.0 \| 169 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 170.0 \| 170 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 171.0 \| 171 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 172.0 \| 172 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 173.0 \| 173 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 174.0 \| 174 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 175.0 \| 175 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 176.0 \| 176 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 177.0 \| 177 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 178.0 \| 178 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 179.0 \| 179 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 180.0 \| 180 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 181.0 \| 181 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 182.0 \| 182 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 183.0 \| 183 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 184.0 \| 184 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 185.0 \| 185 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 186.0 \| 186 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 187.0 \| 187 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 188.0 \| 188 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 189.0 \| 189 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 190.0 \| 190 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 191.0 \| 191 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 192.0 \| 192 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 193.0 \| 193 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 194.0 \| 194 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 195.0 \| 195 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 196.0 \| 196 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 197.0 \| 197 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 198.0 \| 198 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 199.0 \| 199 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 200.0 \| 200 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 201.0 \| 201 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 202.0 \| 202 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 203.0 \| 203 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 204.0 \| 204 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 205.0 \| 205 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 206.0 \| 206 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 207.0 \| 207 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 208.0 \| 208 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 209.0 \| 209 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 210.0 \| 210 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 211.0 \| 211 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 212.0 \| 212 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 213.0 \| 213 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 214.0 \| 214 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 215.0 \| 215 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 216.0 \| 216 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 217.0 \| 217 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 218.0 \| 218 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 219.0 \| 219 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 220.0 \| 220 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 221.0 \| 221 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 222.0 \| 222 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 223.0 \| 223 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 224.0 \| 224 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 225.0 \| 225 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 226.0 \| 226 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 227.0 \| 227 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 228.0 \| 228 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 229.0 \| 229 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 230.0 \| 230 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 231.0 \| 231 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 232.0 \| 232 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 233.0 \| 233 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 234.0 \| 234 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 235.0 \| 235 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 236.0 \| 236 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 237.0 \| 237 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 238.0 \| 238 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 239.0 \| 239 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 240.0 \| 240 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 241.0 \| 241 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 242.0 \| 242 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 243.0 \| 243 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 244.0 \| 244 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 245.0 \| 245 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 246.0 \| 246 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 247.0 \| 247 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 248.0 \| 248 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 249.0 \| 249 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 250.0 \| 250 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 251.0 \| 251 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 252.0 \| 252 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 253.0 \| 253 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 254.0 \| 254 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 255.0 \| 255 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 256.0 \| 256 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 257.0 \| 257 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 258.0 \| 258 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 259.0 \| 259 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 260.0 \| 260 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 261.0 \| 261 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 262.0 \| 262 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 263.0 \| 263 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 264.0 \| 264 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 265.0 \| 265 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 266.0 \| 266 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 267.0 \| 267 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 268.0 \| 268 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 269.0 \| 269 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 270.0 \| 270 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 271.0 \| 271 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 272.0 \| 272 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 273.0 \| 273 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 274.0 \| 274 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 275.0 \| 275 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 276.0 \| 276 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 277.0 \| 277 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 278.0 \| 278 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 279.0 \| 279 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 280.0 \| 280 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 281.0 \| 281 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 282.0 \| 282 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 283.0 \| 283 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 284.0 \| 284 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 285.0 \| 285 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 286.0 \| 286 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 287.0 \| 287 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 288.0 \| 288 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 289.0 \| 289 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 290.0 \| 290 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 291.0 \| 291 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 292.0 \| 292 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 293.0 \| 293 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 294.0 \| 294 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 295.0 \| 295 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 296.0 \| 296 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 297.0 \| 297 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 298.0 \| 298 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 299.0 \| 299 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 300.0 \| 300 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 301.0 \| 301 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 302.0 \| 302 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 303.0 \| 303 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 304.0 \| 304 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 305.0 \| 305 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 306.0 \| 306 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 307.0 \| 307 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 308.0 \| 308 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 309.0 \| 309 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 310.0 \| 310 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 311.0 \| 311 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 312.0 \| 312 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 313.0 \| 313 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 314.0 \| 314 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 315.0 \| 315 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 316.0 \| 316 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 317.0 \| 317 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 318.0 \| 318 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 319.0 \| 319 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 320.0 \| 320 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 321.0 \| 321 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 322.0 \| 322 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 323.0 \| 323 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 324.0 \| 324 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 325.0 \| 325 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 326.0 \| 326 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 327.0 \| 327 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 328.0 \| 328 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 329.0 \| 329 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 330.0 \| 330 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 331.0 \| 331 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 332.0 \| 332 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 333.0 \| 333 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 334.0 \| 334 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 335.0 \| 335 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 336.0 \| 336 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 337.0 \| 337 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 338.0 \| 338 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 339.0 \| 339 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 340.0 \| 340 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 341.0 \| 341 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 342.0 \| 342 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 343.0 \| 343 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 344.0 \| 344 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 345.0 \| 345 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 346.0 \| 346 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 347.0 \| 347 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 348.0 \| 348 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 349.0 \| 349 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 350.0 \| 350 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 351.0 \| 351 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 352.0 \| 352 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 353.0 \| 353 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 354.0 \| 354 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 355.0 \| 355 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 356.0 \| 356 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 357.0 \| 357 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 358.0 \| 358 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 359.0 \| 359 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 360.0 \| 360 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 361.0 \| 361 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 362.0 \| 362 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 363.0 \| 363 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 364.0 \| 364 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 365.0 \| 365 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 366.0 \| 366 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 367.0 \| 367 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 368.0 \| 368 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 369.0 \| 369 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 370.0 \| 370 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 371.0 \| 371 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 372.0 \| 372 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 373.0 \| 373 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 374.0 \| 374 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 375.0 \| 375 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 376.0 \| 376 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 377.0 \| 377 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 378.0 \| 378 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 379.0 \| 379 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 380.0 \| 380 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 381.0 \| 381 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 382.0 \| 382 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 383.0 \| 383 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 384.0 \| 384 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 385.0 \| 385 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 386.0 \| 386 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 387.0 \| 387 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 388.0 \| 388 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 389.0 \| 389 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 390.0 \| 390 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 391.0 \| 391 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 392.0 \| 392 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 393.0 \| 393 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 394.0 \| 394 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 395.0 \| 395 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 396.0 \| 396 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 397.0 \| 397 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 398.0 \| 398 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 399.0 \| 399 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 400.0 \| 400 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 401.0 \| 401 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 402.0 \| 402 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 403.0 \| 403 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 404.0 \| 404 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 405.0 \| 405 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 406.0 \| 406 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 407.0 \| 407 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 408.0 \| 408 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 409.0 \| 409 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 410.0 \| 410 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 411.0 \| 411 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 412.0 \| 412 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 413.0 \| 413 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 414.0 \| 414 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 415.0 \| 415 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 416.0 \| 416 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 417.0 \| 417 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 418.0 \| 418 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 419.0 \| 419 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 420.0 \| 420 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 421.0 \| 421 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 422.0 \| 422 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 423.0 \| 423 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 424.0 \| 424 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 425.0 \| 425 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 426.0 \| 426 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 427.0 \| 427 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 428.0 \| 428 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 429.0 \| 429 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 430.0 \| 430 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 431.0 \| 431 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 432.0 \| 432 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 433.0 \| 433 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 434.0 \| 434 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 435.0 \| 435 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 436.0 \| 436 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 437.0 \| 437 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 438.0 \| 438 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 439.0 \| 439 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 440.0 \| 440 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 441.0 \| 441 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 442.0 \| 442 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 443.0 \| 443 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 444.0 \| 444 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 445.0 \| 445 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 446.0 \| 446 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 447.0 \| 447 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 448.0 \| 448 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 449.0 \| 449 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 450.0 \| 450 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 451.0 \| 451 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 452.0 \| 452 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 453.0 \| 453 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 454.0 \| 454 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 455.0 \| 455 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 456.0 \| 456 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 457.0 \| 457 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 458.0 \| 458 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 459.0 \| 459 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 460.0 \| 460 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 461.0 \| 461 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 462.0 \| 462 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 463.0 \| 463 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 464.0 \| 464 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 465.0 \| 465 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 466.0 \| 466 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 467.0 \| 467 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 468.0 \| 468 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 469.0 \| 469 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 470.0 \| 470 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 471.0 \| 471 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 472.0 \| 472 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 473.0 \| 473 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 474.0 \| 474 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 475.0 \| 475 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 476.0 \| 476 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 477.0 \| 477 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 478.0 \| 478 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 479.0 \| 479 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 480.0 \| 480 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 481.0 \| 481 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 482.0 \| 482 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 483.0 \| 483 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 484.0 \| 484 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 485.0 \| 485 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 486.0 \| 486 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 487.0 \| 487 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 488.0 \| 488 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 489.0 \| 489 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 490.0 \| 490 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 491.0 \| 491 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 492.0 \| 492 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 493.0 \| 493 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 494.0 \| 494 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 495.0 \| 495 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 496.0 \| 496 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 497.0 \| 497 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 498.0 \| 498 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| No log \| 499.0 \| 499 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 500.0 \| 500 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 501.0 \| 501 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 502.0 \| 502 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 503.0 \| 503 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 504.0 \| 504 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 505.0 \| 505 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 506.0 \| 506 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 507.0 \| 507 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 508.0 \| 508 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 509.0 \| 509 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 510.0 \| 510 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 511.0 \| 511 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 512.0 \| 512 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 513.0 \| 513 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 514.0 \| 514 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 515.0 \| 515 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 516.0 \| 516 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 517.0 \| 517 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 518.0 \| 518 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 519.0 \| 519 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 520.0 \| 520 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 521.0 \| 521 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 522.0 \| 522 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 523.0 \| 523 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 524.0 \| 524 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 525.0 \| 525 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 526.0 \| 526 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 527.0 \| 527 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 528.0 \| 528 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 529.0 \| 529 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 530.0 \| 530 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 531.0 \| 531 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 532.0 \| 532 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 533.0 \| 533 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 534.0 \| 534 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 535.0 \| 535 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 536.0 \| 536 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 537.0 \| 537 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 538.0 \| 538 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 539.0 \| 539 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 540.0 \| 540 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 541.0 \| 541 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 542.0 \| 542 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 543.0 \| 543 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 544.0 \| 544 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 545.0 \| 545 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 546.0 \| 546 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 547.0 \| 547 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 548.0 \| 548 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 549.0 \| 549 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 550.0 \| 550 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 551.0 \| 551 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 552.0 \| 552 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 553.0 \| 553 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 554.0 \| 554 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 555.0 \| 555 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 556.0 \| 556 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 557.0 \| 557 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 558.0 \| 558 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 559.0 \| 559 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 560.0 \| 560 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 561.0 \| 561 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 562.0 \| 562 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 563.0 \| 563 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 564.0 \| 564 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 565.0 \| 565 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 566.0 \| 566 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 567.0 \| 567 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 568.0 \| 568 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 569.0 \| 569 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 570.0 \| 570 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 571.0 \| 571 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 572.0 \| 572 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 573.0 \| 573 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 574.0 \| 574 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 575.0 \| 575 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 576.0 \| 576 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 577.0 \| 577 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 578.0 \| 578 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 579.0 \| 579 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 580.0 \| 580 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 581.0 \| 581 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 582.0 \| 582 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 583.0 \| 583 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 584.0 \| 584 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 585.0 \| 585 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 586.0 \| 586 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 587.0 \| 587 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 588.0 \| 588 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 589.0 \| 589 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 590.0 \| 590 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 591.0 \| 591 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 592.0 \| 592 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 593.0 \| 593 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 594.0 \| 594 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 595.0 \| 595 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 596.0 \| 596 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 597.0 \| 597 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 598.0 \| 598 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 599.0 \| 599 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 600.0 \| 600 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 601.0 \| 601 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 602.0 \| 602 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 603.0 \| 603 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 604.0 \| 604 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 605.0 \| 605 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 606.0 \| 606 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 607.0 \| 607 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 608.0 \| 608 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 609.0 \| 609 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 610.0 \| 610 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 611.0 \| 611 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 612.0 \| 612 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 613.0 \| 613 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 614.0 \| 614 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 615.0 \| 615 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 616.0 \| 616 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 617.0 \| 617 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 618.0 \| 618 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 619.0 \| 619 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 620.0 \| 620 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 621.0 \| 621 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 622.0 \| 622 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 623.0 \| 623 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 624.0 \| 624 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 625.0 \| 625 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 626.0 \| 626 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 627.0 \| 627 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 628.0 \| 628 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 629.0 \| 629 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 630.0 \| 630 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 631.0 \| 631 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 632.0 \| 632 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 633.0 \| 633 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 634.0 \| 634 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 635.0 \| 635 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 636.0 \| 636 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 637.0 \| 637 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 638.0 \| 638 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 639.0 \| 639 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 640.0 \| 640 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 641.0 \| 641 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 642.0 \| 642 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 643.0 \| 643 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 644.0 \| 644 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 645.0 \| 645 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 646.0 \| 646 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 647.0 \| 647 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 648.0 \| 648 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 649.0 \| 649 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 650.0 \| 650 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 651.0 \| 651 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 652.0 \| 652 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 653.0 \| 653 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 654.0 \| 654 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 655.0 \| 655 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 656.0 \| 656 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 657.0 \| 657 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 658.0 \| 658 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 659.0 \| 659 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 660.0 \| 660 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 661.0 \| 661 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 662.0 \| 662 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 663.0 \| 663 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 664.0 \| 664 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 665.0 \| 665 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 666.0 \| 666 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 667.0 \| 667 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 668.0 \| 668 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 669.0 \| 669 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 670.0 \| 670 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 671.0 \| 671 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 672.0 \| 672 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 673.0 \| 673 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 674.0 \| 674 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 675.0 \| 675 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 676.0 \| 676 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 677.0 \| 677 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 678.0 \| 678 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 679.0 \| 679 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 680.0 \| 680 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 681.0 \| 681 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 682.0 \| 682 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 683.0 \| 683 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 684.0 \| 684 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 685.0 \| 685 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 686.0 \| 686 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 687.0 \| 687 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 688.0 \| 688 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 689.0 \| 689 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 690.0 \| 690 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 691.0 \| 691 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 692.0 \| 692 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 693.0 \| 693 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 694.0 \| 694 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 695.0 \| 695 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 696.0 \| 696 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 697.0 \| 697 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 698.0 \| 698 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 699.0 \| 699 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 700.0 \| 700 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 701.0 \| 701 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 702.0 \| 702 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 703.0 \| 703 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 704.0 \| 704 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 705.0 \| 705 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 706.0 \| 706 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 707.0 \| 707 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 708.0 \| 708 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 709.0 \| 709 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 710.0 \| 710 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 711.0 \| 711 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 712.0 \| 712 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 713.0 \| 713 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 714.0 \| 714 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 715.0 \| 715 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 716.0 \| 716 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 717.0 \| 717 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 718.0 \| 718 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 719.0 \| 719 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 720.0 \| 720 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 721.0 \| 721 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 722.0 \| 722 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 723.0 \| 723 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 724.0 \| 724 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 725.0 \| 725 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 726.0 \| 726 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 727.0 \| 727 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 728.0 \| 728 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 729.0 \| 729 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 730.0 \| 730 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 731.0 \| 731 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 732.0 \| 732 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 733.0 \| 733 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 734.0 \| 734 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 735.0 \| 735 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 736.0 \| 736 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 737.0 \| 737 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 738.0 \| 738 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 739.0 \| 739 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 740.0 \| 740 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 741.0 \| 741 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 742.0 \| 742 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 743.0 \| 743 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 744.0 \| 744 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 745.0 \| 745 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 746.0 \| 746 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 747.0 \| 747 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 748.0 \| 748 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 749.0 \| 749 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 750.0 \| 750 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 751.0 \| 751 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 752.0 \| 752 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 753.0 \| 753 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 754.0 \| 754 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 755.0 \| 755 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 756.0 \| 756 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 757.0 \| 757 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 758.0 \| 758 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 759.0 \| 759 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 760.0 \| 760 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 761.0 \| 761 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 762.0 \| 762 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 763.0 \| 763 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 764.0 \| 764 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 765.0 \| 765 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 766.0 \| 766 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 767.0 \| 767 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 768.0 \| 768 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 769.0 \| 769 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 770.0 \| 770 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 771.0 \| 771 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 772.0 \| 772 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 773.0 \| 773 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 774.0 \| 774 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 775.0 \| 775 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 776.0 \| 776 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 777.0 \| 777 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 778.0 \| 778 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 779.0 \| 779 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 780.0 \| 780 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 781.0 \| 781 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 782.0 \| 782 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 783.0 \| 783 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 784.0 \| 784 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 785.0 \| 785 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 786.0 \| 786 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 787.0 \| 787 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 788.0 \| 788 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 789.0 \| 789 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 790.0 \| 790 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 791.0 \| 791 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 792.0 \| 792 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 793.0 \| 793 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 794.0 \| 794 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 795.0 \| 795 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 796.0 \| 796 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 797.0 \| 797 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 798.0 \| 798 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 799.0 \| 799 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 800.0 \| 800 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 801.0 \| 801 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 802.0 \| 802 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 803.0 \| 803 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 804.0 \| 804 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 805.0 \| 805 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 806.0 \| 806 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 807.0 \| 807 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 808.0 \| 808 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 809.0 \| 809 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 810.0 \| 810 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 811.0 \| 811 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 812.0 \| 812 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 813.0 \| 813 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 814.0 \| 814 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 815.0 \| 815 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 816.0 \| 816 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 817.0 \| 817 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 818.0 \| 818 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 819.0 \| 819 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 820.0 \| 820 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 821.0 \| 821 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 822.0 \| 822 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 823.0 \| 823 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 824.0 \| 824 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 825.0 \| 825 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 826.0 \| 826 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 827.0 \| 827 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 828.0 \| 828 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 829.0 \| 829 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 830.0 \| 830 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 831.0 \| 831 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 832.0 \| 832 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 833.0 \| 833 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 834.0 \| 834 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 835.0 \| 835 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 836.0 \| 836 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 837.0 \| 837 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 838.0 \| 838 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 839.0 \| 839 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 840.0 \| 840 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 841.0 \| 841 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 842.0 \| 842 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 843.0 \| 843 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 844.0 \| 844 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 845.0 \| 845 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 846.0 \| 846 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 847.0 \| 847 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 848.0 \| 848 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 849.0 \| 849 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 850.0 \| 850 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 851.0 \| 851 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 852.0 \| 852 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 853.0 \| 853 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 854.0 \| 854 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 855.0 \| 855 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 856.0 \| 856 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 857.0 \| 857 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 858.0 \| 858 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 859.0 \| 859 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 860.0 \| 860 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 861.0 \| 861 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 862.0 \| 862 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 863.0 \| 863 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 864.0 \| 864 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 865.0 \| 865 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 866.0 \| 866 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 867.0 \| 867 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 868.0 \| 868 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 869.0 \| 869 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 870.0 \| 870 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 871.0 \| 871 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 872.0 \| 872 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 873.0 \| 873 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 874.0 \| 874 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 875.0 \| 875 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 876.0 \| 876 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 877.0 \| 877 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 878.0 \| 878 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 879.0 \| 879 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 880.0 \| 880 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 881.0 \| 881 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 882.0 \| 882 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 883.0 \| 883 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 884.0 \| 884 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 885.0 \| 885 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 886.0 \| 886 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 887.0 \| 887 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 888.0 \| 888 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 889.0 \| 889 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 890.0 \| 890 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 891.0 \| 891 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 892.0 \| 892 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 893.0 \| 893 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 894.0 \| 894 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 895.0 \| 895 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 896.0 \| 896 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 897.0 \| 897 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 898.0 \| 898 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 899.0 \| 899 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 900.0 \| 900 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 901.0 \| 901 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 902.0 \| 902 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 903.0 \| 903 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 904.0 \| 904 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 905.0 \| 905 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 906.0 \| 906 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 907.0 \| 907 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 908.0 \| 908 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 909.0 \| 909 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 910.0 \| 910 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 911.0 \| 911 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 912.0 \| 912 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 913.0 \| 913 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 914.0 \| 914 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 915.0 \| 915 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 916.0 \| 916 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 917.0 \| 917 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 918.0 \| 918 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 919.0 \| 919 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 920.0 \| 920 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 921.0 \| 921 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 922.0 \| 922 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 923.0 \| 923 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 924.0 \| 924 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 925.0 \| 925 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 926.0 \| 926 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 927.0 \| 927 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 928.0 \| 928 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 929.0 \| 929 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 930.0 \| 930 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 931.0 \| 931 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 932.0 \| 932 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 933.0 \| 933 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 934.0 \| 934 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 935.0 \| 935 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 936.0 \| 936 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 937.0 \| 937 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 938.0 \| 938 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 939.0 \| 939 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 940.0 \| 940 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 941.0 \| 941 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 942.0 \| 942 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 943.0 \| 943 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 944.0 \| 944 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 945.0 \| 945 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 946.0 \| 946 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 947.0 \| 947 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 948.0 \| 948 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 949.0 \| 949 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 950.0 \| 950 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 951.0 \| 951 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 952.0 \| 952 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 953.0 \| 953 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 954.0 \| 954 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 955.0 \| 955 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 956.0 \| 956 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 957.0 \| 957 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 958.0 \| 958 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 959.0 \| 959 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 960.0 \| 960 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 961.0 \| 961 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 962.0 \| 962 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 963.0 \| 963 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 964.0 \| 964 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 965.0 \| 965 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 966.0 \| 966 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 967.0 \| 967 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 968.0 \| 968 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 969.0 \| 969 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 970.0 \| 970 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 971.0 \| 971 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 972.0 \| 972 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 973.0 \| 973 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 974.0 \| 974 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 975.0 \| 975 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 976.0 \| 976 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 977.0 \| 977 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 978.0 \| 978 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 979.0 \| 979 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 980.0 \| 980 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 981.0 \| 981 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 982.0 \| 982 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 983.0 \| 983 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 984.0 \| 984 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 985.0 \| 985 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 986.0 \| 986 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 987.0 \| 987 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 988.0 \| 988 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 989.0 \| 989 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 990.0 \| 990 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 991.0 \| 991 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 992.0 \| 992 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 993.0 \| 993 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 994.0 \| 994 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 995.0 \| 995 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 996.0 \| 996 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 997.0 \| 997 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 998.0 \| 998 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 999.0 \| 999 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1000.0 \| 1000 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1001.0 \| 1001 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1002.0 \| 1002 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1003.0 \| 1003 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1004.0 \| 1004 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1005.0 \| 1005 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1006.0 \| 1006 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1007.0 \| 1007 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1008.0 \| 1008 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1009.0 \| 1009 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1010.0 \| 1010 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1011.0 \| 1011 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1012.0 \| 1012 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1013.0 \| 1013 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1014.0 \| 1014 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1015.0 \| 1015 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1016.0 \| 1016 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1017.0 \| 1017 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1018.0 \| 1018 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1019.0 \| 1019 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1020.0 \| 1020 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1021.0 \| 1021 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1022.0 \| 1022 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1023.0 \| 1023 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1024.0 \| 1024 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1025.0 \| 1025 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1026.0 \| 1026 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1027.0 \| 1027 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1028.0 \| 1028 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1029.0 \| 1029 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1030.0 \| 1030 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1031.0 \| 1031 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1032.0 \| 1032 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1033.0 \| 1033 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1034.0 \| 1034 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1035.0 \| 1035 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1036.0 \| 1036 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1037.0 \| 1037 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1038.0 \| 1038 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1039.0 \| 1039 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1040.0 \| 1040 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1041.0 \| 1041 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1042.0 \| 1042 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1043.0 \| 1043 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1044.0 \| 1044 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1045.0 \| 1045 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1046.0 \| 1046 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1047.0 \| 1047 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1048.0 \| 1048 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1049.0 \| 1049 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1050.0 \| 1050 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1051.0 \| 1051 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1052.0 \| 1052 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1053.0 \| 1053 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1054.0 \| 1054 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1055.0 \| 1055 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1056.0 \| 1056 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1057.0 \| 1057 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1058.0 \| 1058 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1059.0 \| 1059 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1060.0 \| 1060 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1061.0 \| 1061 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1062.0 \| 1062 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1063.0 \| 1063 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1064.0 \| 1064 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1065.0 \| 1065 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1066.0 \| 1066 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1067.0 \| 1067 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1068.0 \| 1068 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1069.0 \| 1069 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1070.0 \| 1070 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1071.0 \| 1071 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1072.0 \| 1072 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1073.0 \| 1073 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1074.0 \| 1074 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1075.0 \| 1075 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1076.0 \| 1076 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1077.0 \| 1077 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1078.0 \| 1078 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1079.0 \| 1079 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1080.0 \| 1080 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1081.0 \| 1081 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1082.0 \| 1082 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1083.0 \| 1083 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1084.0 \| 1084 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1085.0 \| 1085 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1086.0 \| 1086 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1087.0 \| 1087 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1088.0 \| 1088 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1089.0 \| 1089 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1090.0 \| 1090 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1091.0 \| 1091 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1092.0 \| 1092 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1093.0 \| 1093 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1094.0 \| 1094 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1095.0 \| 1095 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1096.0 \| 1096 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1097.0 \| 1097 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1098.0 \| 1098 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1099.0 \| 1099 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1100.0 \| 1100 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1101.0 \| 1101 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1102.0 \| 1102 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1103.0 \| 1103 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1104.0 \| 1104 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1105.0 \| 1105 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1106.0 \| 1106 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1107.0 \| 1107 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1108.0 \| 1108 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1109.0 \| 1109 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1110.0 \| 1110 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1111.0 \| 1111 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1112.0 \| 1112 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1113.0 \| 1113 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1114.0 \| 1114 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1115.0 \| 1115 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1116.0 \| 1116 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1117.0 \| 1117 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1118.0 \| 1118 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1119.0 \| 1119 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1120.0 \| 1120 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1121.0 \| 1121 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1122.0 \| 1122 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1123.0 \| 1123 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1124.0 \| 1124 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1125.0 \| 1125 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1126.0 \| 1126 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1127.0 \| 1127 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1128.0 \| 1128 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1129.0 \| 1129 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1130.0 \| 1130 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1131.0 \| 1131 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1132.0 \| 1132 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1133.0 \| 1133 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1134.0 \| 1134 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1135.0 \| 1135 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1136.0 \| 1136 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1137.0 \| 1137 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1138.0 \| 1138 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1139.0 \| 1139 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1140.0 \| 1140 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1141.0 \| 1141 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1142.0 \| 1142 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1143.0 \| 1143 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1144.0 \| 1144 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1145.0 \| 1145 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1146.0 \| 1146 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1147.0 \| 1147 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1148.0 \| 1148 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1149.0 \| 1149 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1150.0 \| 1150 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1151.0 \| 1151 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1152.0 \| 1152 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1153.0 \| 1153 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1154.0 \| 1154 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1155.0 \| 1155 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1156.0 \| 1156 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1157.0 \| 1157 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1158.0 \| 1158 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1159.0 \| 1159 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1160.0 \| 1160 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1161.0 \| 1161 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1162.0 \| 1162 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1163.0 \| 1163 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1164.0 \| 1164 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1165.0 \| 1165 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1166.0 \| 1166 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1167.0 \| 1167 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1168.0 \| 1168 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1169.0 \| 1169 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1170.0 \| 1170 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1171.0 \| 1171 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1172.0 \| 1172 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1173.0 \| 1173 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1174.0 \| 1174 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1175.0 \| 1175 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1176.0 \| 1176 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1177.0 \| 1177 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1178.0 \| 1178 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1179.0 \| 1179 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1180.0 \| 1180 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1181.0 \| 1181 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1182.0 \| 1182 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1183.0 \| 1183 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1184.0 \| 1184 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1185.0 \| 1185 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1186.0 \| 1186 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1187.0 \| 1187 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1188.0 \| 1188 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1189.0 \| 1189 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1190.0 \| 1190 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1191.0 \| 1191 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1192.0 \| 1192 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1193.0 \| 1193 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1194.0 \| 1194 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1195.0 \| 1195 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1196.0 \| 1196 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1197.0 \| 1197 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1198.0 \| 1198 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1199.0 \| 1199 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1200.0 \| 1200 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1201.0 \| 1201 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1202.0 \| 1202 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1203.0 \| 1203 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1204.0 \| 1204 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1205.0 \| 1205 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1206.0 \| 1206 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1207.0 \| 1207 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1208.0 \| 1208 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1209.0 \| 1209 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1210.0 \| 1210 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1211.0 \| 1211 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1212.0 \| 1212 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1213.0 \| 1213 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1214.0 \| 1214 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1215.0 \| 1215 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1216.0 \| 1216 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1217.0 \| 1217 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1218.0 \| 1218 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1219.0 \| 1219 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1220.0 \| 1220 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1221.0 \| 1221 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1222.0 \| 1222 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1223.0 \| 1223 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1224.0 \| 1224 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1225.0 \| 1225 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1226.0 \| 1226 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1227.0 \| 1227 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1228.0 \| 1228 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1229.0 \| 1229 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1230.0 \| 1230 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1231.0 \| 1231 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1232.0 \| 1232 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1233.0 \| 1233 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1234.0 \| 1234 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1235.0 \| 1235 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1236.0 \| 1236 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1237.0 \| 1237 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1238.0 \| 1238 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1239.0 \| 1239 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1240.0 \| 1240 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1241.0 \| 1241 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1242.0 \| 1242 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1243.0 \| 1243 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1244.0 \| 1244 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1245.0 \| 1245 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1246.0 \| 1246 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1247.0 \| 1247 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1248.0 \| 1248 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1249.0 \| 1249 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1250.0 \| 1250 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1251.0 \| 1251 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1252.0 \| 1252 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1253.0 \| 1253 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1254.0 \| 1254 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1255.0 \| 1255 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1256.0 \| 1256 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1257.0 \| 1257 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1258.0 \| 1258 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1259.0 \| 1259 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1260.0 \| 1260 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1261.0 \| 1261 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1262.0 \| 1262 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1263.0 \| 1263 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1264.0 \| 1264 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1265.0 \| 1265 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1266.0 \| 1266 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1267.0 \| 1267 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1268.0 \| 1268 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1269.0 \| 1269 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1270.0 \| 1270 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1271.0 \| 1271 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1272.0 \| 1272 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1273.0 \| 1273 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1274.0 \| 1274 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1275.0 \| 1275 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1276.0 \| 1276 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1277.0 \| 1277 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1278.0 \| 1278 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1279.0 \| 1279 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1280.0 \| 1280 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1281.0 \| 1281 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1282.0 \| 1282 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1283.0 \| 1283 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1284.0 \| 1284 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1285.0 \| 1285 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1286.0 \| 1286 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1287.0 \| 1287 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1288.0 \| 1288 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1289.0 \| 1289 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1290.0 \| 1290 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1291.0 \| 1291 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1292.0 \| 1292 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1293.0 \| 1293 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1294.0 \| 1294 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1295.0 \| 1295 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1296.0 \| 1296 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1297.0 \| 1297 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1298.0 \| 1298 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1299.0 \| 1299 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1300.0 \| 1300 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1301.0 \| 1301 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1302.0 \| 1302 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1303.0 \| 1303 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1304.0 \| 1304 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1305.0 \| 1305 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1306.0 \| 1306 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1307.0 \| 1307 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1308.0 \| 1308 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1309.0 \| 1309 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1310.0 \| 1310 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1311.0 \| 1311 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1312.0 \| 1312 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1313.0 \| 1313 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1314.0 \| 1314 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1315.0 \| 1315 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1316.0 \| 1316 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1317.0 \| 1317 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1318.0 \| 1318 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1319.0 \| 1319 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1320.0 \| 1320 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1321.0 \| 1321 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1322.0 \| 1322 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1323.0 \| 1323 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1324.0 \| 1324 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1325.0 \| 1325 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1326.0 \| 1326 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1327.0 \| 1327 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1328.0 \| 1328 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1329.0 \| 1329 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1330.0 \| 1330 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1331.0 \| 1331 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1332.0 \| 1332 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1333.0 \| 1333 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1334.0 \| 1334 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1335.0 \| 1335 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1336.0 \| 1336 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1337.0 \| 1337 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1338.0 \| 1338 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1339.0 \| 1339 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1340.0 \| 1340 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1341.0 \| 1341 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1342.0 \| 1342 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1343.0 \| 1343 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1344.0 \| 1344 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1345.0 \| 1345 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1346.0 \| 1346 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1347.0 \| 1347 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1348.0 \| 1348 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1349.0 \| 1349 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1350.0 \| 1350 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1351.0 \| 1351 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1352.0 \| 1352 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1353.0 \| 1353 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1354.0 \| 1354 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1355.0 \| 1355 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1356.0 \| 1356 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1357.0 \| 1357 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1358.0 \| 1358 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1359.0 \| 1359 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1360.0 \| 1360 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1361.0 \| 1361 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1362.0 \| 1362 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1363.0 \| 1363 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1364.0 \| 1364 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1365.0 \| 1365 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1366.0 \| 1366 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1367.0 \| 1367 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1368.0 \| 1368 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1369.0 \| 1369 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1370.0 \| 1370 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1371.0 \| 1371 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1372.0 \| 1372 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1373.0 \| 1373 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1374.0 \| 1374 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1375.0 \| 1375 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1376.0 \| 1376 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1377.0 \| 1377 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1378.0 \| 1378 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1379.0 \| 1379 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1380.0 \| 1380 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1381.0 \| 1381 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1382.0 \| 1382 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1383.0 \| 1383 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1384.0 \| 1384 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1385.0 \| 1385 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1386.0 \| 1386 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1387.0 \| 1387 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1388.0 \| 1388 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1389.0 \| 1389 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1390.0 \| 1390 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1391.0 \| 1391 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1392.0 \| 1392 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1393.0 \| 1393 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1394.0 \| 1394 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1395.0 \| 1395 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1396.0 \| 1396 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1397.0 \| 1397 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1398.0 \| 1398 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1399.0 \| 1399 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1400.0 \| 1400 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1401.0 \| 1401 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1402.0 \| 1402 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1403.0 \| 1403 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1404.0 \| 1404 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1405.0 \| 1405 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1406.0 \| 1406 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1407.0 \| 1407 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1408.0 \| 1408 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1409.0 \| 1409 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1410.0 \| 1410 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1411.0 \| 1411 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1412.0 \| 1412 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1413.0 \| 1413 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1414.0 \| 1414 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1415.0 \| 1415 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1416.0 \| 1416 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1417.0 \| 1417 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1418.0 \| 1418 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1419.0 \| 1419 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1420.0 \| 1420 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1421.0 \| 1421 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1422.0 \| 1422 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1423.0 \| 1423 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1424.0 \| 1424 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1425.0 \| 1425 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1426.0 \| 1426 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1427.0 \| 1427 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1428.0 \| 1428 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1429.0 \| 1429 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1430.0 \| 1430 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1431.0 \| 1431 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1432.0 \| 1432 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1433.0 \| 1433 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1434.0 \| 1434 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1435.0 \| 1435 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1436.0 \| 1436 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1437.0 \| 1437 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1438.0 \| 1438 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1439.0 \| 1439 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1440.0 \| 1440 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1441.0 \| 1441 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1442.0 \| 1442 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1443.0 \| 1443 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1444.0 \| 1444 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1445.0 \| 1445 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1446.0 \| 1446 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1447.0 \| 1447 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1448.0 \| 1448 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1449.0 \| 1449 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1450.0 \| 1450 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1451.0 \| 1451 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1452.0 \| 1452 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1453.0 \| 1453 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1454.0 \| 1454 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1455.0 \| 1455 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1456.0 \| 1456 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1457.0 \| 1457 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1458.0 \| 1458 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1459.0 \| 1459 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1460.0 \| 1460 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1461.0 \| 1461 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1462.0 \| 1462 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1463.0 \| 1463 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1464.0 \| 1464 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1465.0 \| 1465 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1466.0 \| 1466 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1467.0 \| 1467 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1468.0 \| 1468 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1469.0 \| 1469 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1470.0 \| 1470 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1471.0 \| 1471 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1472.0 \| 1472 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1473.0 \| 1473 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1474.0 \| 1474 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1475.0 \| 1475 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1476.0 \| 1476 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1477.0 \| 1477 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1478.0 \| 1478 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1479.0 \| 1479 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1480.0 \| 1480 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1481.0 \| 1481 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1482.0 \| 1482 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1483.0 \| 1483 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1484.0 \| 1484 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1485.0 \| 1485 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1486.0 \| 1486 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1487.0 \| 1487 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1488.0 \| 1488 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1489.0 \| 1489 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1490.0 \| 1490 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1491.0 \| 1491 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1492.0 \| 1492 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1493.0 \| 1493 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1494.0 \| 1494 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1495.0 \| 1495 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1496.0 \| 1496 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1497.0 \| 1497 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1498.0 \| 1498 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1499.0 \| 1499 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1500.0 \| 1500 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1501.0 \| 1501 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1502.0 \| 1502 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1503.0 \| 1503 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1504.0 \| 1504 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1505.0 \| 1505 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1506.0 \| 1506 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1507.0 \| 1507 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1508.0 \| 1508 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1509.0 \| 1509 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1510.0 \| 1510 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1511.0 \| 1511 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1512.0 \| 1512 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1513.0 \| 1513 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1514.0 \| 1514 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1515.0 \| 1515 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1516.0 \| 1516 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1517.0 \| 1517 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1518.0 \| 1518 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1519.0 \| 1519 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1520.0 \| 1520 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1521.0 \| 1521 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1522.0 \| 1522 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1523.0 \| 1523 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1524.0 \| 1524 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1525.0 \| 1525 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1526.0 \| 1526 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1527.0 \| 1527 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1528.0 \| 1528 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1529.0 \| 1529 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1530.0 \| 1530 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1531.0 \| 1531 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1532.0 \| 1532 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1533.0 \| 1533 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1534.0 \| 1534 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1535.0 \| 1535 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1536.0 \| 1536 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1537.0 \| 1537 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1538.0 \| 1538 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1539.0 \| 1539 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1540.0 \| 1540 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1541.0 \| 1541 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1542.0 \| 1542 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1543.0 \| 1543 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1544.0 \| 1544 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1545.0 \| 1545 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1546.0 \| 1546 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1547.0 \| 1547 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1548.0 \| 1548 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1549.0 \| 1549 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1550.0 \| 1550 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1551.0 \| 1551 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1552.0 \| 1552 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1553.0 \| 1553 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1554.0 \| 1554 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1555.0 \| 1555 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1556.0 \| 1556 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1557.0 \| 1557 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1558.0 \| 1558 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1559.0 \| 1559 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1560.0 \| 1560 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1561.0 \| 1561 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1562.0 \| 1562 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1563.0 \| 1563 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1564.0 \| 1564 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1565.0 \| 1565 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1566.0 \| 1566 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1567.0 \| 1567 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1568.0 \| 1568 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1569.0 \| 1569 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1570.0 \| 1570 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1571.0 \| 1571 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1572.0 \| 1572 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1573.0 \| 1573 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1574.0 \| 1574 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1575.0 \| 1575 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1576.0 \| 1576 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1577.0 \| 1577 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1578.0 \| 1578 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1579.0 \| 1579 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1580.0 \| 1580 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1581.0 \| 1581 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1582.0 \| 1582 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1583.0 \| 1583 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1584.0 \| 1584 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1585.0 \| 1585 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1586.0 \| 1586 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1587.0 \| 1587 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1588.0 \| 1588 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1589.0 \| 1589 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1590.0 \| 1590 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1591.0 \| 1591 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1592.0 \| 1592 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1593.0 \| 1593 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1594.0 \| 1594 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1595.0 \| 1595 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1596.0 \| 1596 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1597.0 \| 1597 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1598.0 \| 1598 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1599.0 \| 1599 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1600.0 \| 1600 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1601.0 \| 1601 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1602.0 \| 1602 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1603.0 \| 1603 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1604.0 \| 1604 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1605.0 \| 1605 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1606.0 \| 1606 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1607.0 \| 1607 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1608.0 \| 1608 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1609.0 \| 1609 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1610.0 \| 1610 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1611.0 \| 1611 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1612.0 \| 1612 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1613.0 \| 1613 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1614.0 \| 1614 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1615.0 \| 1615 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1616.0 \| 1616 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1617.0 \| 1617 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1618.0 \| 1618 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1619.0 \| 1619 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1620.0 \| 1620 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1621.0 \| 1621 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1622.0 \| 1622 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1623.0 \| 1623 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1624.0 \| 1624 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1625.0 \| 1625 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1626.0 \| 1626 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1627.0 \| 1627 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1628.0 \| 1628 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1629.0 \| 1629 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1630.0 \| 1630 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1631.0 \| 1631 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1632.0 \| 1632 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1633.0 \| 1633 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1634.0 \| 1634 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1635.0 \| 1635 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1636.0 \| 1636 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1637.0 \| 1637 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1638.0 \| 1638 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1639.0 \| 1639 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1640.0 \| 1640 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1641.0 \| 1641 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1642.0 \| 1642 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1643.0 \| 1643 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1644.0 \| 1644 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1645.0 \| 1645 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1646.0 \| 1646 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1647.0 \| 1647 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1648.0 \| 1648 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1649.0 \| 1649 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1650.0 \| 1650 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1651.0 \| 1651 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1652.0 \| 1652 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1653.0 \| 1653 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1654.0 \| 1654 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1655.0 \| 1655 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1656.0 \| 1656 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1657.0 \| 1657 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1658.0 \| 1658 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1659.0 \| 1659 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1660.0 \| 1660 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1661.0 \| 1661 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1662.0 \| 1662 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1663.0 \| 1663 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1664.0 \| 1664 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1665.0 \| 1665 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1666.0 \| 1666 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1667.0 \| 1667 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1668.0 \| 1668 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1669.0 \| 1669 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1670.0 \| 1670 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1671.0 \| 1671 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1672.0 \| 1672 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1673.0 \| 1673 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1674.0 \| 1674 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1675.0 \| 1675 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1676.0 \| 1676 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1677.0 \| 1677 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1678.0 \| 1678 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1679.0 \| 1679 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1680.0 \| 1680 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1681.0 \| 1681 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1682.0 \| 1682 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1683.0 \| 1683 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1684.0 \| 1684 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1685.0 \| 1685 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1686.0 \| 1686 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1687.0 \| 1687 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1688.0 \| 1688 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1689.0 \| 1689 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1690.0 \| 1690 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1691.0 \| 1691 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1692.0 \| 1692 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1693.0 \| 1693 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1694.0 \| 1694 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1695.0 \| 1695 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1696.0 \| 1696 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1697.0 \| 1697 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1698.0 \| 1698 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1699.0 \| 1699 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1700.0 \| 1700 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1701.0 \| 1701 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1702.0 \| 1702 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1703.0 \| 1703 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1704.0 \| 1704 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1705.0 \| 1705 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1706.0 \| 1706 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1707.0 \| 1707 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1708.0 \| 1708 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1709.0 \| 1709 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1710.0 \| 1710 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1711.0 \| 1711 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1712.0 \| 1712 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1713.0 \| 1713 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1714.0 \| 1714 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1715.0 \| 1715 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1716.0 \| 1716 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1717.0 \| 1717 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1718.0 \| 1718 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1719.0 \| 1719 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1720.0 \| 1720 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1721.0 \| 1721 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1722.0 \| 1722 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1723.0 \| 1723 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1724.0 \| 1724 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1725.0 \| 1725 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1726.0 \| 1726 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1727.0 \| 1727 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1728.0 \| 1728 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1729.0 \| 1729 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1730.0 \| 1730 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1731.0 \| 1731 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1732.0 \| 1732 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1733.0 \| 1733 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1734.0 \| 1734 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1735.0 \| 1735 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1736.0 \| 1736 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1737.0 \| 1737 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1738.0 \| 1738 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1739.0 \| 1739 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1740.0 \| 1740 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1741.0 \| 1741 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1742.0 \| 1742 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1743.0 \| 1743 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1744.0 \| 1744 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1745.0 \| 1745 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1746.0 \| 1746 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1747.0 \| 1747 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1748.0 \| 1748 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1749.0 \| 1749 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1750.0 \| 1750 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1751.0 \| 1751 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1752.0 \| 1752 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1753.0 \| 1753 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1754.0 \| 1754 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1755.0 \| 1755 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1756.0 \| 1756 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1757.0 \| 1757 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1758.0 \| 1758 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1759.0 \| 1759 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1760.0 \| 1760 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1761.0 \| 1761 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1762.0 \| 1762 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1763.0 \| 1763 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1764.0 \| 1764 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1765.0 \| 1765 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1766.0 \| 1766 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1767.0 \| 1767 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1768.0 \| 1768 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1769.0 \| 1769 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1770.0 \| 1770 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1771.0 \| 1771 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1772.0 \| 1772 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1773.0 \| 1773 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1774.0 \| 1774 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1775.0 \| 1775 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1776.0 \| 1776 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1777.0 \| 1777 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1778.0 \| 1778 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1779.0 \| 1779 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1780.0 \| 1780 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1781.0 \| 1781 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1782.0 \| 1782 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1783.0 \| 1783 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1784.0 \| 1784 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1785.0 \| 1785 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1786.0 \| 1786 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1787.0 \| 1787 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1788.0 \| 1788 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1789.0 \| 1789 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1790.0 \| 1790 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1791.0 \| 1791 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1792.0 \| 1792 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1793.0 \| 1793 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1794.0 \| 1794 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1795.0 \| 1795 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1796.0 \| 1796 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1797.0 \| 1797 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1798.0 \| 1798 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1799.0 \| 1799 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1800.0 \| 1800 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1801.0 \| 1801 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1802.0 \| 1802 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1803.0 \| 1803 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1804.0 \| 1804 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1805.0 \| 1805 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1806.0 \| 1806 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1807.0 \| 1807 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1808.0 \| 1808 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1809.0 \| 1809 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1810.0 \| 1810 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1811.0 \| 1811 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1812.0 \| 1812 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1813.0 \| 1813 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1814.0 \| 1814 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1815.0 \| 1815 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1816.0 \| 1816 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1817.0 \| 1817 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1818.0 \| 1818 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1819.0 \| 1819 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1820.0 \| 1820 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1821.0 \| 1821 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1822.0 \| 1822 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1823.0 \| 1823 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1824.0 \| 1824 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1825.0 \| 1825 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1826.0 \| 1826 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1827.0 \| 1827 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1828.0 \| 1828 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1829.0 \| 1829 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1830.0 \| 1830 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1831.0 \| 1831 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1832.0 \| 1832 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1833.0 \| 1833 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1834.0 \| 1834 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1835.0 \| 1835 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1836.0 \| 1836 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1837.0 \| 1837 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1838.0 \| 1838 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1839.0 \| 1839 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1840.0 \| 1840 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1841.0 \| 1841 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1842.0 \| 1842 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1843.0 \| 1843 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1844.0 \| 1844 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1845.0 \| 1845 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1846.0 \| 1846 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1847.0 \| 1847 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1848.0 \| 1848 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1849.0 \| 1849 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1850.0 \| 1850 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1851.0 \| 1851 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1852.0 \| 1852 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1853.0 \| 1853 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1854.0 \| 1854 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1855.0 \| 1855 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1856.0 \| 1856 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1857.0 \| 1857 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1858.0 \| 1858 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1859.0 \| 1859 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1860.0 \| 1860 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1861.0 \| 1861 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1862.0 \| 1862 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1863.0 \| 1863 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1864.0 \| 1864 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1865.0 \| 1865 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1866.0 \| 1866 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1867.0 \| 1867 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1868.0 \| 1868 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1869.0 \| 1869 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1870.0 \| 1870 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1871.0 \| 1871 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1872.0 \| 1872 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1873.0 \| 1873 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1874.0 \| 1874 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1875.0 \| 1875 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1876.0 \| 1876 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1877.0 \| 1877 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1878.0 \| 1878 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1879.0 \| 1879 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1880.0 \| 1880 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1881.0 \| 1881 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1882.0 \| 1882 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1883.0 \| 1883 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1884.0 \| 1884 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1885.0 \| 1885 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1886.0 \| 1886 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1887.0 \| 1887 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1888.0 \| 1888 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1889.0 \| 1889 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1890.0 \| 1890 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1891.0 \| 1891 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1892.0 \| 1892 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1893.0 \| 1893 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1894.0 \| 1894 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1895.0 \| 1895 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1896.0 \| 1896 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1897.0 \| 1897 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1898.0 \| 1898 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1899.0 \| 1899 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1900.0 \| 1900 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1901.0 \| 1901 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1902.0 \| 1902 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1903.0 \| 1903 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1904.0 \| 1904 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1905.0 \| 1905 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1906.0 \| 1906 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1907.0 \| 1907 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1908.0 \| 1908 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1909.0 \| 1909 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1910.0 \| 1910 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1911.0 \| 1911 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1912.0 \| 1912 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1913.0 \| 1913 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1914.0 \| 1914 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1915.0 \| 1915 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1916.0 \| 1916 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1917.0 \| 1917 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1918.0 \| 1918 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1919.0 \| 1919 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1920.0 \| 1920 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1921.0 \| 1921 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1922.0 \| 1922 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1923.0 \| 1923 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1924.0 \| 1924 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1925.0 \| 1925 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1926.0 \| 1926 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1927.0 \| 1927 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1928.0 \| 1928 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1929.0 \| 1929 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1930.0 \| 1930 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1931.0 \| 1931 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1932.0 \| 1932 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1933.0 \| 1933 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1934.0 \| 1934 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1935.0 \| 1935 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1936.0 \| 1936 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1937.0 \| 1937 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1938.0 \| 1938 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1939.0 \| 1939 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1940.0 \| 1940 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1941.0 \| 1941 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1942.0 \| 1942 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1943.0 \| 1943 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1944.0 \| 1944 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1945.0 \| 1945 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1946.0 \| 1946 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1947.0 \| 1947 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1948.0 \| 1948 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1949.0 \| 1949 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1950.0 \| 1950 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1951.0 \| 1951 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1952.0 \| 1952 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1953.0 \| 1953 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1954.0 \| 1954 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1955.0 \| 1955 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1956.0 \| 1956 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1957.0 \| 1957 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1958.0 \| 1958 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1959.0 \| 1959 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1960.0 \| 1960 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1961.0 \| 1961 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1962.0 \| 1962 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1963.0 \| 1963 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1964.0 \| 1964 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1965.0 \| 1965 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1966.0 \| 1966 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1967.0 \| 1967 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1968.0 \| 1968 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1969.0 \| 1969 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1970.0 \| 1970 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1971.0 \| 1971 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1972.0 \| 1972 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1973.0 \| 1973 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1974.0 \| 1974 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1975.0 \| 1975 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1976.0 \| 1976 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1977.0 \| 1977 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1978.0 \| 1978 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1979.0 \| 1979 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1980.0 \| 1980 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1981.0 \| 1981 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1982.0 \| 1982 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1983.0 \| 1983 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1984.0 \| 1984 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1985.0 \| 1985 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1986.0 \| 1986 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1987.0 \| 1987 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1988.0 \| 1988 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1989.0 \| 1989 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1990.0 \| 1990 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1991.0 \| 1991 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1992.0 \| 1992 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1993.0 \| 1993 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1994.0 \| 1994 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1995.0 \| 1995 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1996.0 \| 1996 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1997.0 \| 1997 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1998.0 \| 1998 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 1999.0 \| 1999 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| \| 0.0 \| 2000.0 \| 2000 \| nan \| 3.5714 \| 1.2195 \| 3.5714 \| 3.5714 \| 19.0 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
mradermacher/Machroom-3B-model_stock-GGUF	mradermacher	2024-06-04T04:36:07Z	6	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-04T04:25:44Z	--- base_model: DreadPoor/Machroom-3B-model_stock language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DreadPoor/Machroom-3B-model_stock <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q2_K.gguf) \| Q2_K \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.IQ3_XS.gguf) \| IQ3_XS \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.IQ3_S.gguf) \| IQ3_S \| 1.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q3_K_S.gguf) \| Q3_K_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.IQ3_M.gguf) \| IQ3_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q3_K_M.gguf) \| Q3_K_M \| 1.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q3_K_L.gguf) \| Q3_K_L \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.IQ4_XS.gguf) \| IQ4_XS \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q4_K_S.gguf) \| Q4_K_S \| 1.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q4_K_M.gguf) \| Q4_K_M \| 1.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q5_K_S.gguf) \| Q5_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q5_K_M.gguf) \| Q5_K_M \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q6_K.gguf) \| Q6_K \| 2.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q8_0.gguf) \| Q8_0 \| 3.1 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.f16.gguf) \| f16 \| 5.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
MubarakB/zxCm3h8ADcB3R0ve2rgC	MubarakB	2024-06-04T04:35:51Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2024-06-04T04:35:47Z	--- library_name: peft base_model: NousResearch/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2	Zoyd	2024-06-04T04:34:37Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-06-03T22:38:53Z	--- license: llama3 --- Exllamav2 quant (exl2 / 5.0 bpw) made with ExLlamaV2 v0.1.3 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)</center> \| <center>18625 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)</center> \| <center>20645 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)</center> \| <center>24211 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)</center> \| <center>27784 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)</center> \| <center>29572 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)</center> \| <center>31359 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)</center> \| <center>33139 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)</center> \| <center>38500 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)</center> \| <center>45805 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)</center> \| <center>49410 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)</center> \| <center>54655 MB</center> \| <center>8</center> \|
hdve/google-gemma-2b-1717475491	hdve	2024-06-04T04:33:55Z	141	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T04:31:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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bamaxi/ruBert-base-sakha	bamaxi	2024-06-04T04:28:42Z	127	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-06-03T23:19:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MubarakB/T7KGvt4x8LnHYdJN9MQ0	MubarakB	2024-06-04T04:21:09Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2024-06-04T04:21:05Z	--- library_name: peft base_model: NousResearch/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2	Zoyd	2024-06-04T04:20:09Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-06-03T17:18:31Z	--- license: llama3 --- Exllamav2 quant (exl2 / 3.5 bpw) made with ExLlamaV2 v0.1.3 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)</center> \| <center>18625 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)</center> \| <center>20645 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)</center> \| <center>24211 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)</center> \| <center>27784 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)</center> \| <center>29572 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)</center> \| <center>31359 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)</center> \| <center>33139 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)</center> \| <center>38500 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)</center> \| <center>45805 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)</center> \| <center>49410 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)</center> \| <center>54655 MB</center> \| <center>8</center> \|
SEHYONG/Llama-3-Open-Ko-8B-Instruct-kookmin7	SEHYONG	2024-06-04T04:11:42Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:SEHYONG/Llama-3-Open-Ko-8B-Instruct-kookmin6", "base_model:finetune:SEHYONG/Llama-3-Open-Ko-8B-Instruct-kookmin6", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T04:05:37Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: SEHYONG/Llama-3-Open-Ko-8B-Instruct-kookmin6 --- # Uploaded model - Developed by: SEHYONG - License: apache-2.0 - Finetuned from model : SEHYONG/Llama-3-Open-Ko-8B-Instruct-kookmin6 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Rudra360/Emoji_Suggester	Rudra360	2024-06-04T04:09:27Z	0	0	spacy	[ "spacy", "en", "region:us" ]	null	2024-06-03T14:17:44Z	--- language: - en library_name: spacy --- # Emoji Suggester Emoji Suggester is a tool designed to recommend relevant emojis based on incoming messages from social media apps, enhancing expressiveness and engagement in your conversations. The suggestions are powered by a model trained on a dataset of Twitter messages. ## Table of Contents - [Installation](#installation) - [Usage](#usage) - [Contributing](#contributing) - [License](#license) - [Contact](#contact) ## Installation To install Emoji Suggester, follow these steps: 1. Clone the repository: ```bash git clone https://huggingface.co/Rudra360/Emoji_Suggester or ```bash git clone [email protected]:Rudra360/Emoji_Suggester.git ## Usage Change the Directory 1. go to emoji_suggester ```bash cd Emoji_Suggester Then the run the follwing script 2. from util import predict 3. message = "I'm so happy today!" suggested_emojis = predict(message) print(suggested_emojis)
hdve/Qwen-Qwen1.5-7B-1717473930	hdve	2024-06-04T04:08:43Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T04:06:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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amazon/MegaBeam-Mistral-7B-300k	amazon	2024-06-04T04:06:40Z	6,628	16	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-05-13T02:30:08Z	--- license: apache-2.0 inference: false --- # MegaBeam-Mistral-7B-300k Model MegaBeam-Mistral-7B-300k is a fine-tuned [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) language model that supports input contexts up to 320k tokens. MegaBeam-Mistral-7B-300k can be deployed on a single AWS `g5.48xlarge` instance using serving frameworks such as [vLLM](https://github.com/vllm-project/vllm), Sagemaker [DJL](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-models-frameworks-djl-serving.html) endpoint, and others. Similarities and differences beween MegaBeam-Mistral-7B-300k and [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) are summarized below: \|Model\|Max context length\| rope_theta\| prompt template\| \|----------\|-------------:\|------------:\|------------:\| \| [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) \| 32K \| 1e6 \| [instruction format](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2#instruction-format)\| \| MegaBeam-Mistral-7B-300k \| 320K \| 25e6 \| AS ABOVE\| ## Evaluations [InfiniteBench: Extending Long Context Evaluation Beyond 100K Tokens](https://github.com/OpenBMB/InfiniteBench) _InfiniteBench is a cutting-edge benchmark tailored for evaluating the capabilities of language models to process, understand, and reason over super long contexts (100k+ tokens)_. We therefore evaluated MegaBeam-Mistral-7B-300k, [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), [Llama-3-8B-Instruct-262k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k), and [Llama3-70B-1M](https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-1048k) on InfiniteBench. The InfiniteBench authors also evaluated SOTA proprietary and open-source LLMs on InfiniteBench. We thus combined both results in the table below. \| Task Name \| MegaBeam-Mistral-7B-300k \| Mistral-7B-Instruct-v0.2 \| Llama-3-8B-Instruct-262k \| Llama3-70B-1M \| GPT-4-1106-preview \| YaRN-Mistral-7B \| Kimi-Chat \| Claude 2 \| Yi-6B-200K \| Yi-34B-200K \| Chatglm3-6B-128K \| \| ---------------- \| ---------------- \| ---------------- \| ---------------- \| ---------------- \| ------ \| --------------- \| --------- \| -------- \| -----------\| -----------\| -----------\| \| Retrieve.PassKey \| 100% \| 75.76% \| 98.30% \| 81.35% \| 100% \| 92.71% \| 98.14% \| 97.80% \| 100.00% \| 100.00% \| 92.20% \| \| Retrieve.Number \| 96.10% \| 25.25% \| 97.79% \| 97.62% \| 100% \| 56.61% \| 95.42% \| 98.14% \| 94.92% \| 100.00% \| 80.68% \| \| Retrieve.KV \| 0% \| 0% \| 3.40% \| 3% \| 89.00% \| < 5% \| 53.60% \| 65.40% \| < 5% \| < 5% \| < 5% \| \| En.Sum \| 29.39% \| 22.13% \| 16.40% \| 20.72% \| 14.73% \| 9.09% \| 17.93% \| 14.45% \| < 5% \| < 5% \|< 5% \| \| En.QA \| 14.93% \| 4.93% \| 13.20% \| 16.52% \| 22.22% \| 9.55% \| 16.52% \| 11.97% \| 9.20% \| 12.17% \|< 5% \| \| En.MC \| 51.52% \| 7.80% \| 50.65% \| 62% \| 67.25% \| 27.95% \| 72.49% \| 62.88% \| 36.68% \|38.43% \|10.48% \| \| En.Dia \| 9.50% \| 3.50% \| 1% \| 12.50% \| 8.50% \| 7.50% \| 11.50% \| 46.50% \| < 5% \|< 5% \|< 5% \| \| Zh.QA \| 10.71% \| 3.43% \| 19.02% \| 26% \| 25.96% \| 14.43% \| 17.93% \| 9.64% \| 15.07% \|13.61% \|< 5% \| \| Code.Debug \| 27.41% \| 11.60% \| 22.08% \| 23.85% \| 39.59% \| < 5% \| 18.02% \| < 5% \| < 5% \|< 5% \|< 5% \| \| Code.Run \| 1.75% \| 0.25% \| 0% \| 0% \| 23.25% \| < 5% \| < 5% \| < 5% \| < 5% \|< 5% \|< 5% \| \| Math.Calc \| 0% \| 0% \| 0% \| 0% \| < 5% \| < 5% \| < 5% \| < 5% \| < 5% \|< 5% \|< 5% \| \| Math.Find \| 24.28% \| 26.28% \| 15.40% \| 30% \| 60.00% \| 17.14% \| 12.57% \| 32.29% \| < 5% \|25.71% \|7.71% \| \| Average \| 30.70% \| 15.08% \| 28.10% \| 31.13% \| 46.08% \| 20.41% \| 34.93% \| 37.21% \| 22.78% \|25.41% \|17.59% \| The 12 evaluation tasks are summarized below (as per [InfiniteBench]((https://github.com/OpenBMB/InfiniteBench))) \| Task Name \| Context \| # Examples \| Avg Input Tokens \| Avg Output Tokens \| Description \| \| -------------------- \| ------------- \| ---------- \| ---------------- \| ----------------- \| ------------------------------------------------------------------------------------------- \| \| En.Sum \| Fake Book \| 103 \| 171.5k \| 1.1k \| Summarization of a fake book created with core entity substitution. \| \| En.QA \| Fake Book \| 351 \| 192.6k \| 4.8 \| Free-form question answering based on the fake book. \| \| En.MC \| Fake Book \| 229 \| 184.4k \| 5.3 \| Multiple choice questions derived from the fake book. \| \| En.Dia \| Script \| 200 \| 103.6k \| 3.4 \| Identification of talkers in partially anonymized scripts. \| \| Zh.QA \| New Book \| 175 \| 2068.6k \| 6.3 \| Question answering on a set of newly collected books. \| \| Code.Debug \| Code Document \| 394 \| 114.7k \| 4.8 \| Finding which function in a code repo contains an crashing error (in multiple choice form). \| \| Code.Run \| Synthetic \| 400 \| 75.2k \| 1.3 \| Simulating execution of multiple simple, synthetic functions. \| \| Math.Calc \| Synthetic \| 50 \| 43.9k \| 43.9k \| Calculations involving super-long arithmetic equations. \| \| Math.Find \| Synthetic \| 350 \| 87.9k \| 1.3 \| Finding special integers in a lengthy list. \| \| Retrieve.PassKey \| Synthetic \| 590 \| 122.4k \| 2.0 \| Retrieving hidden keys in a noisy long context. \| \| Retrieve.Number \| Synthetic \| 590 \| 122.4k \| 4.0 \| Locating repeated hidden numbers in a noisy long context. \| \| Retrieve.KV \| Synthetic \| 500 \| 89.9k \| 22.7 \| Finding the corresponding value from a dictionary and a key. \| ## Serve MegaBeam-Mistral-7B-300k on EC2 instances ## On an AWS `g5.48xlarge` instance, upgrade vLLM to the latest version as per [documentation on vLLM](https://vllm.readthedocs.io/en/latest/). ### Start the server ```shell python3 -m vllm.entrypoints.openai.api_server --model amazon/MegaBeam-Mistral-7B-300k --tensor-parallel-size 8 ``` Important Note - We have set the `max_position_embeddings` in the [`config.json`](config.json) to 288,800 in order to fit model's KV-cache on a single `g5.48xlarge` instance, which has 8 x A10 GPUs (24GB RAM per GPU). On an instance with larger GPU RAM (e.g. `p4d.24xlarge`), feel free to increase the value of the `max_position_embeddings`(e.g. to 350K), which the model should be able to process. ### Run the client ```python from openai import OpenAI # Modify OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( # defaults to os.environ.get("OPENAI_API_KEY") api_key=openai_api_key, base_url=openai_api_base, ) models = client.models.list() model = models.data[0].id chat_completion = client.chat.completions.create( messages = [ {"role": "user", "content": "What is your favourite condiment?"}, # insert your long context here {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} # insert your long context here ], model=model, ) print("Chat completion results:") print(chat_completion) ``` ### Deploy the model on a SageMaker Endpoint ### To deploy MegaBeam-Mistral-7B-300k on a SageMaker endpoint, please follow this [SageMaker DJL deployment guide](https://docs.djl.ai/docs/demos/aws/sagemaker/large-model-inference/sample-llm/vllm_deploy_mistral_7b.html). Run the following Python code in a SageMaker notebook (with each block running in a separate cell) ```python import sagemaker from sagemaker import Model, image_uris, serializers, deserializers sagemaker_session = sagemaker.Session() region = sagemaker_session.boto_region_name role = sagemaker.get_execution_role() %%writefile serving.properties engine=Python option.model_id=amazon/MegaBeam-Mistral-7B-300k option.dtype=bf16 option.task=text-generation option.rolling_batch=vllm option.tensor_parallel_degree=8 option.device_map=auto %%sh mkdir mymodel mv serving.properties mymodel/ tar czvf mymodel.tar.gz mymodel/ rm -rf mymodel image_uri = image_uris.retrieve( framework="djl-deepspeed", region=region, version="0.27.0" ) s3_code_prefix = "megaBeam-mistral-7b-300k/code" bucket = sagemaker_session.default_bucket() # bucket to house artifacts code_artifact = sagemaker_session.upload_data("mymodel.tar.gz", bucket, s3_code_prefix) print(f"S3 Code or Model tar ball uploaded to --- > {code_artifact}") model = Model(image_uri=image_uri, model_data=code_artifact, role=role) instance_type = "ml.g5.48xlarge" endpoint_name = sagemaker.utils.name_from_base("megaBeam-mistral-7b-300k") model.deploy(initial_instance_count=1, instance_type=instance_type, endpoint_name=endpoint_name ) # our requests and responses will be in json format so we specify the serializer and the deserializer predictor = sagemaker.Predictor( endpoint_name=endpoint_name, sagemaker_session=sagemaker_session, serializer=serializers.JSONSerializer(), ) # test the endpoint input_str = """<s>[INST] What is your favourite condiment? [/INST] Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " [INST] Do you have mayonnaise recipes? [/INST]""" predictor.predict( {"inputs": input_str, "parameters": {"max_new_tokens": 75}} ) ``` ### Invoke the model on a SageMaker Endpoint ### To use MegaBeam-Mistral-7B-300k on a SageMaker endpoint, please try following this example: ```python import boto3 import json def call_endpoint(text:str, endpoint_name:str): client = boto3.client("sagemaker-runtime") parameters = { "max_new_tokens": 450, "do_sample": True, "temperature": 0.7, } payload = {"inputs": text, "parameters": parameters} response = client.invoke_endpoint( EndpointName=endpoint_name, Body=json.dumps(payload), ContentType="application/json" ) output = json.loads(response["Body"].read().decode()) result = output["generated_text"] return result # please insert your long prompt/document content here prompt = """<s>[INST] What are the main challenges to support long contexts for a Large Language Model? [/INST]""" #print(prompt) endpoint_name = "megaBeam-mistral-7b-300k-2024-05-13-14-23-41-219" # please use a valid endpoint name result = call_endpoint(prompt, endpoint_name) print(result) ``` ## Limitations ## Before using the MegaBeam-Mistral-7B-300k model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content. ## The AWS Contributors ## Chen Wu, Yin Song, Verdi March, Eden Duthie
richardkelly/Qwen-Qwen1.5-0.5B-1717473352	richardkelly	2024-06-04T04:00:37Z	141	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T03:55:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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cgus/AlchemistCoder-DS-6.7B-exl2	cgus	2024-06-04T03:59:59Z	5	0	transformers	[ "transformers", "llama", "text-generation", "code generation", "conversational", "arxiv:2405.19265", "base_model:internlm/AlchemistCoder-DS-6.7B", "base_model:quantized:internlm/AlchemistCoder-DS-6.7B", "license:apache-2.0", "autotrain_compatible", "4-bit", "exl2", "region:us" ]	text-generation	2024-06-03T23:59:24Z	--- license: apache-2.0 base_model: internlm/AlchemistCoder-DS-6.7B inference: false tags: - code generation --- # AlchemistCoder-DS-6.7B-exl2 Original model: [AlchemistCoder-DS-6.7B](https://huggingface.co/internlm/AlchemistCoder-DS-6.7B) Model creator: [InternLM](https://huggingface.co/internlm) ## Quants [4bpw h6 (main)](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/main) [4.25bpw h6](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/4.25bpw-h6) [4.65bpw h6](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/4.65bpw-h6) [5bpw h6](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/5bpw-h6) [6bpw h6](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/6bpw-h6) [8bpw h8](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/8bpw-h8) ## Quantization notes Made with Exllamav2 0.1.3 with the default dataset. ## How to run This model is meant to be used with Exllamav2 loader that requires the model to be fully loaded into GPU VRAM. It primarily requires a Nvidia RTX card on Windows/Linux or AMD card on Linux. If you want to use this model but your system doesn't meet these requirements, you should look for GGUF versions of the model. It can be used with apps like: [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) [KoboldAI](https://github.com/henk717/KoboldAI) [ExUI](https://github.com/turboderp/exui) [lollms-webui](https://github.com/ParisNeo/lollms-webui) # Original model card # AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data [[🤗 HuggingFace](https://huggingface.co/internlm/AlchemistCoder-DS-6.7B)] [[📃 Paper](https://arxiv.org/abs/2405.19265)] [[🌐 Project Page](https://internlm.github.io/AlchemistCoder/)] ## ✨ Highlights > Abstract: Open-source Large Language Models (LLMs) and their specialized variants, particularly Code LLMs, have recently delivered impressive performance. However, previous Code LLMs are typically fine-tuned on single-source data with limited quality and diversity, which may insufficiently elicit the potential of pre-trained Code LLMs. In this paper, we present AlchemistCoder, a series of Code LLMs with enhanced code generation and generalization capabilities fine-tuned on multi-source data. To achieve this, we pioneer to unveil inherent conflicts among the various styles and qualities in multi-source code corpora and introduce data-specific prompts with hindsight relabeling, termed AlchemistPrompts, to harmonize different data sources and instruction-response pairs. Additionally, we propose incorporating the data construction process into the fine-tuning data as code comprehension tasks, including instruction evolution, data filtering, and code review. Extensive experiments demonstrate that AlchemistCoder holds a clear lead among all models of the same size (6.7B/7B) and rivals or even surpasses larger models (15B/33B/70B), showcasing the efficacy of our method in refining instruction-following capabilities and advancing the boundaries of code intelligence. - AlchemistPrompts: Designed as data-specific prompts for harmonizing inherent conflicts in multi-source data and mitigating the instruction/response misalignment at a fined-grained level. - Code Comprehenstion Tasks: Sourced from the process of data construction, consisting of instruction evolution, data filtering, and code review. - Harmonized Multi-source Data: Instruction tuned on 200M tokens, including 6 types of high-quality data. - Superior Model Performance: Surpassing all the open-source models of the same size (6.7/7B), and rivaling or even beating larger models (15B/33B/70B/ChatGPT) on 6 code benchmarks. - Advanced generic capabilities: Demonstrated by the significant improvements on MMLU, BBH, and GSM8K. ## 🚀 Quick Start ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("internlm/AlchemistCoder-DS-6.7B", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("internlm/AlchemistCoder-DS-6.7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() model = model.eval() input_text = "Implement the Dijkstra algorithm in Python" inputs = tokenizer(input_text, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_length=128) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` The above code will generate the following result: ```python import heapq def dijkstra(graph, start, end): """ Find the shortest path between two nodes in a graph using Dijkstra's algorithm. Args: graph (dict): A dictionary representing the graph, where the keys are nodes and the values are dictionaries containing the neighboring nodes and their edge weights. start: The starting node. end: The target node. Returns: list: The shortest path from the start node to the target node as a list of nodes. Raises: ValueError: If either the start or end node is not present in the graph. """ # Check if start and end nodes are in the graph if start not in graph: raise ValueError("Start node is not present in the graph.") if end not in graph: raise ValueError("End node is not present in the graph.") # Initialize the distance dictionary with infinite distances for all nodes distances = {node: float('inf') for node in graph} # Set the distance of the start node to 0 distances[start] = 0 # Initialize the heap with the start node heap = [(0, start)] # Initialize the previous dictionary to keep track of the path previous = {} while heap: # Pop the node with the smallest distance from the heap current_distance, current_node = heapq.heappop(heap) # If the current node is the end node, we have found the shortest path if current_node == end: # Reconstruct the path by following the previous nodes path = [] while current_node in previous: path.append(current_node) current_node = previous[current_node] path.append(start) # Reverse the path to get the correct order return path[::-1] # Iterate over the neighboring nodes and update their distances for neighbor, weight in graph[current_node].items(): new_distance = current_distance + weight # If a shorter path is found, update the distance and previous node if new_distance < distances[neighbor]: distances[neighbor] = new_distance previous[neighbor] = current_node heapq.heappush(heap, (new_distance, neighbor)) # If there is no path between the start and end nodes, return an empty list return [] ``` > The `dijkstra` function takes three arguments: `graph`, `start`, and `end`. The `graph` argument is a dictionary representing the graph, where the keys are nodes and the values are dictionaries containing the neighboring nodes and their edge weights. The `start` argument is the starting node, and the `end` argument is the target node. > The function first checks if the start and end nodes are present in the graph. If either node is not present, a `ValueError` is raised. > The function then initializes a `distances` dictionary with infinite distances for all nodes. It sets the distance of the start node to 0. It also initializes a heap with the start node and a `previous` dictionary to keep track of the path. > The algorithm then iterates over the nodes in the heap. For each node, it checks if it is the end node. If it is, the function reconstructs the path by following the previous nodes and returns the shortest path as a list of nodes in the correct order. > If the current node is not the end node, the algorithm iterates over its neighboring nodes and updates their distances if a shorter path is found. It also updates the `previous` dictionary to keep track of the path. > If there is no path between the start and end nodes, the function returns an empty list. > Note that this implementation assumes that the graph is a directed graph, and it uses a heap data structure to efficiently select the node with the smallest distance at each step. ## 🧪 Evaluation and Fine-tune Please refer to [AlchemistCoder](https://github.com/InternLM/AlchemistCoder) and [InternLM*](https://github.com/InternLM/InternLM/tree/main). ## 😃 Acknowledgments AlchemistCoder* is built with [InternLM](https://github.com/InternLM) and [OpenCompass](https://github.com/open-compass). Thanks for their awesome work! ## 📧 Contact If you have any questions, please create an issue on this repository or contact us at: - [email protected] - [email protected] ## 🌟 Citation If you find our work useful, please consider citing: ```bibtex @misc{song2024alchemistcoder, title={AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data}, author={Zifan Song and Yudong Wang and Wenwei Zhang and Kuikun Liu and Chengqi Lyu and Demin Song and Qipeng Guo and Hang Yan and Dahua Lin and Kai Chen and Cairong Zhao}, year={2024}, eprint={2405.19265}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
sahilkumar4ai/misteral-finetuned-samsum	sahilkumar4ai	2024-06-04T03:53:07Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-06-04T03:18:00Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ model-index: - name: misteral-finetuned-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sahilthegnius/huggingface/runs/7be8cmf0) # misteral-finetuned-samsum This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - training_steps: 259 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
stannisozbov/stann-speechtotext-medium-tr-03_06	stannisozbov	2024-06-04T03:51:08Z	6	1	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "tr", "dataset:common_voice_17_0", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-03T15:36:16Z	--- language: - tr license: apache-2.0 base_model: openai/whisper-medium tags: - hf-asr-leaderboard - generated_from_trainer datasets: - common_voice_17_0 metrics: - wer model-index: - name: stann-speechtotext-medium-tr-03_06 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_17_0 type: common_voice_17_0 config: tr split: None args: tr metrics: - name: Wer type: wer value: 32.78548922762497 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # stann-speechtotext-medium-tr-03_06 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_17_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2244 - Wer: 32.7855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.0307 \| 1.3784 \| 1000 \| 0.2063 \| 30.0843 \| \| 0.0131 \| 2.7567 \| 2000 \| 0.2025 \| 35.2159 \| \| 0.0016 \| 4.1351 \| 3000 \| 0.2244 \| 32.7855 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2	Zoyd	2024-06-04T03:41:15Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-06-03T14:47:50Z	--- license: llama3 --- Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.1.3 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)</center> \| <center>18625 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)</center> \| <center>20645 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)</center> \| <center>24211 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)</center> \| <center>27784 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)</center> \| <center>29572 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)</center> \| <center>31359 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)</center> \| <center>33139 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)</center> \| <center>38500 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)</center> \| <center>45805 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)</center> \| <center>49410 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)</center> \| <center>54655 MB</center> \| <center>8</center> \|
srbdtwentyfour/mystery-llama-3-8b-v2	srbdtwentyfour	2024-06-04T03:39:26Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-03T08:18:31Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: srbdtwentyfour - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF	bartowski	2024-06-04T03:26:56Z	150	0	null	[ "gguf", "text-generation", "en", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-06-04T03:09:56Z	--- language: - en license: llama3 quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of Llama-3-Instruct-8B-SimPO-ExPO Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3070">b3070</a> for quantization. Original model: https://huggingface.co/chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) ## Prompt format ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Llama-3-Instruct-8B-SimPO-ExPO-Q8_0.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q8_0.gguf) \| Q8_0 \| 8.54GB \| Extremely high quality, generally unneeded but max available quant. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-Q6_K.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q6_K.gguf) \| Q6_K \| 6.59GB \| Very high quality, near perfect, recommended. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-Q5_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q5_K_M.gguf) \| Q5_K_M \| 5.73GB \| High quality, recommended. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-Q5_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q5_K_S.gguf) \| Q5_K_S \| 5.59GB \| High quality, recommended. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-Q4_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q4_K_M.gguf) \| Q4_K_M \| 4.92GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-Q4_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q4_K_S.gguf) \| Q4_K_S \| 4.69GB \| Slightly lower quality with more space savings, recommended. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-IQ4_XS.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ4_XS.gguf) \| IQ4_XS \| 4.44GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_L.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_L.gguf) \| Q3_K_L \| 4.32GB \| Lower quality but usable, good for low RAM availability. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_M.gguf) \| Q3_K_M \| 4.01GB \| Even lower quality. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-IQ3_M.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ3_M.gguf) \| IQ3_M \| 3.78GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_S.gguf) \| Q3_K_S \| 3.66GB \| Low quality, not recommended. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-IQ3_XS.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ3_XS.gguf) \| IQ3_XS \| 3.51GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-IQ3_XXS.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ3_XXS.gguf) \| IQ3_XXS \| 3.27GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-Q2_K.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q2_K.gguf) \| Q2_K \| 3.17GB \| Very low quality but surprisingly usable. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-IQ2_M.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ2_M.gguf) \| IQ2_M \| 2.94GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-IQ2_S.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ2_S.gguf) \| IQ2_S \| 2.75GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Llama-3-Instruct-8B-SimPO-ExPO-IQ2_XS.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ2_XS.gguf) \| IQ2_XS \| 2.60GB \| Very low quality, uses SOTA techniques to be usable. \| ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF --include "Llama-3-Instruct-8B-SimPO-ExPO-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF --include "Llama-3-Instruct-8B-SimPO-ExPO-Q8_0.gguf/" --local-dir Llama-3-Instruct-8B-SimPO-ExPO-Q8_0 ``` You can either specify a new local-dir (Llama-3-Instruct-8B-SimPO-ExPO-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
Ariffiq99/CRAB_COPA_KUCI_xlm_roberta_base_finetuned	Ariffiq99	2024-06-04T03:25:02Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:Ariffiq99/COPA_KUCI_xlm_roberta_base_finetuned", "base_model:finetune:Ariffiq99/COPA_KUCI_xlm_roberta_base_finetuned", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2024-06-04T02:57:25Z	--- license: mit base_model: Ariffiq99/COPA_KUCI_xlm_roberta_base_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: CRAB_COPA_KUCI_xlm_roberta_base_finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # CRAB_COPA_KUCI_xlm_roberta_base_finetuned This model is a fine-tuned version of [Ariffiq99/COPA_KUCI_xlm_roberta_base_finetuned](https://huggingface.co/Ariffiq99/COPA_KUCI_xlm_roberta_base_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1600 - F1: 0.7417 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.2245 \| 1.0 \| 2880 \| 0.9044 \| 0.6875 \| \| 1.1396 \| 2.0 \| 5760 \| 1.0192 \| 0.7042 \| \| 1.039 \| 3.0 \| 8640 \| 1.1395 \| 0.7222 \| \| 0.8411 \| 4.0 \| 11520 \| 1.1650 \| 0.7389 \| \| 0.7471 \| 5.0 \| 14400 \| 1.1235 \| 0.7361 \| \| 0.9344 \| 6.0 \| 17280 \| 1.1646 \| 0.7375 \| \| 0.7564 \| 7.0 \| 20160 \| 1.0863 \| 0.7417 \| \| 0.7116 \| 8.0 \| 23040 \| 1.1600 \| 0.7417 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2	Zoyd	2024-06-04T03:19:34Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-06-03T23:58:04Z	--- license: llama3 --- Exllamav2 quant (exl2 / 6.0 bpw) made with ExLlamaV2 v0.1.3 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)</center> \| <center>18625 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)</center> \| <center>20645 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)</center> \| <center>24211 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)</center> \| <center>27784 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)</center> \| <center>29572 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)</center> \| <center>31359 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)</center> \| <center>33139 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)</center> \| <center>38500 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)</center> \| <center>45805 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)</center> \| <center>49410 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)</center> \| <center>54655 MB</center> \| <center>8</center> \|
wgd9527/sparseocc-v299-occ-seg-flow	wgd9527	2024-06-04T03:14:33Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-31T09:35:38Z	--- license: apache-2.0 ---
ALI-B/phi3-mini	ALI-B	2024-06-04T03:10:28Z	77	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T03:07:14Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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HuggingFaceFW/ablation-exp-dedup-global_minhash-350BT	HuggingFaceFW	2024-06-04T03:10:19Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-03T23:35:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2	Zoyd	2024-06-04T03:08:17Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-06-03T21:15:12Z	--- license: llama3 --- Exllamav2 quant (exl2 / 4.25 bpw) made with ExLlamaV2 v0.1.3 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)</center> \| <center>18625 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)</center> \| <center>20645 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)</center> \| <center>24211 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)</center> \| <center>27784 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)</center> \| <center>29572 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)</center> \| <center>31359 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)</center> \| <center>33139 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)</center> \| <center>38500 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)</center> \| <center>45805 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)</center> \| <center>49410 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)</center> \| <center>54655 MB</center> \| <center>8</center> \|
Abhinay45/outputs	Abhinay45	2024-06-04T03:08:04Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "dataset:yahma/alpaca-cleaned", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:adapter:unsloth/llama-3-8b-bnb-4bit", "license:llama2", "region:us" ]	null	2024-06-04T03:05:36Z	--- license: llama2 library_name: peft tags: - trl - sft - unsloth - generated_from_trainer base_model: unsloth/llama-3-8b-bnb-4bit datasets: - yahma/alpaca-cleaned model-index: - name: Alpaca + Llama-3 8b Unsloth 2x faster finetuning. results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Alpaca + Llama-3 8b Unsloth 2x faster finetuning. This model is a fine-tuned version of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) on the alpaca dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 60 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
lucalolee/ppo-LunarLander-v2-1	lucalolee	2024-06-04T03:04:20Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-06-04T03:04:01Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 276.00 +/- 24.50 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
turnipseason/latext5	turnipseason	2024-06-04T02:58:53Z	108	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "math", "normalization", "ru", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-26T02:36:06Z	--- license: mit language: - ru library_name: transformers pipeline_tag: text2text-generation tags: - math - normalization --- ### Описание: Модель для нормализации русскоязычных текстов, содержащих математические сущности, в формат LaTeX. Модель является дообученной на переведённом&аугментированном датасете "[Mathematics Stack Exchange API Q&A Data](https://zenodo.org/records/1414384)" версией модели [cointegrated/rut5-small](https://huggingface.co/cointegrated/rut5-small). ### Description: This is a model for mathematical text normalization in Russian, based on the [cointegrated/rut5-small](https://huggingface.co/cointegrated/rut5-small) paraphraser. The model was created by finetuning the paraphraser on a translated&augmented "[Mathematics Stack Exchange API Q&A Data](https://zenodo.org/records/1414384)" dataset. Пример использования: --- Usage example: --- ``` python import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from IPython.display import display, Math, Latex model_dir = "turnipseason/latext5" model = AutoModelForSeq2SeqLM.from_pretrained(model_dir) tokenizer = AutoTokenizer.from_pretrained(model_dir) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model.to(device) def get_latex(text): inputs = tokenizer(text, return_tensors='pt').to(device) with torch.no_grad(): hypotheses = model.generate( **inputs, do_sample=True, num_return_sequences=1, repetition_penalty=1.2, max_length=len(text), num_beams=10, early_stopping=True ) for h in hypotheses: display(Latex(tokenizer.decode(h, skip_special_tokens=True))) text = '''лямбда прописная квадрат минус три равно десять игрек куб При этом шинус икс равен интеграл от экспоненты до трёх игрек штрих''' get_latex(text) ```
John6666/pony-pencil-sdxl	John6666	2024-06-04T02:57:30Z	19	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-24T12:12:06Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime --- Original model is [here](https://huggingface.co/bluepen5805/pony_pencil-XL).
ehottl/distilbert-base-uncased-distilled-clinc	ehottl	2024-06-04T02:56:15Z	111	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-04T02:54:19Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2792 - Accuracy: 0.9439 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.2695 \| 1.0 \| 318 \| 1.6200 \| 0.7197 \| \| 1.264 \| 2.0 \| 636 \| 0.8322 \| 0.8616 \| \| 0.6826 \| 3.0 \| 954 \| 0.4907 \| 0.9077 \| \| 0.4228 \| 4.0 \| 1272 \| 0.3628 \| 0.9326 \| \| 0.3128 \| 5.0 \| 1590 \| 0.3137 \| 0.9413 \| \| 0.2644 \| 6.0 \| 1908 \| 0.2946 \| 0.9439 \| \| 0.2424 \| 7.0 \| 2226 \| 0.2846 \| 0.9439 \| \| 0.2299 \| 8.0 \| 2544 \| 0.2806 \| 0.9439 \| \| 0.2253 \| 9.0 \| 2862 \| 0.2792 \| 0.9439 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2.post303 - Datasets 2.19.1 - Tokenizers 0.15.2
Zery/MV-LLaVA-7B	Zery	2024-06-04T02:55:57Z	21	3	transformers	[ "transformers", "pytorch", "share4v", "text-generation", "image-text-to-text", "en", "dataset:Zery/BS-Objaverse", "dataset:Lin-Chen/ShareGPT4V", "arxiv:2406.00093", "license:apache-2.0", "autotrain_compatible", "region:us" ]	image-text-to-text	2024-05-13T07:18:35Z	--- inference: false pipeline_tag: image-text-to-text license: apache-2.0 datasets: - Zery/BS-Objaverse - Lin-Chen/ShareGPT4V language: - en --- <br> <br> # MV-LLaVA-7B Model Card ## Model details Model type: MV-LLaVA-7B is an open-source chatbot for 3D multi-view images trained by fine-tuning CLIP vision tower and LLaMA/Vicuna on GPT4-Vision-assisted [BS-Objaverse](https://huggingface.co/datasets/Zery/BS-Objaverse) data and [ShareGPT4V](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) data. Model date: MV-LLaVA-7B was trained in Apr, 2024. Paper or resources for more information: [[Project](https://sunzey.github.io/Bootstrap3D/)] [[Paper](https://huggingface.co/papers/2406.00093)] [[Code](https://github.com/SunzeY/Bootstrap3D)] ## Usage You can directly utilize this model as we provide in our [[repository](https://github.com/SunzeY/Bootstrap3D/tree/main/MV_LLaVA)]. ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. ## Intended use Primary intended uses: The primary use of ShareGPT4V-7B is research on large multimodal models and chatbots for 3D content. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 1.2M ShareGPT4V-PT data - 30K GPT4-Vision-generated multi-view image-text pairs - LLaVA instruction-tuning data
wenlianghuang/sample_phi3_finetune_example	wenlianghuang	2024-06-04T02:52:43Z	104	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "trl", "sft", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-03T07:03:36Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2	Zoyd	2024-06-04T02:52:17Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-06-03T13:33:55Z	--- license: llama3 --- Exllamav2 quant (exl2 / 2.2 bpw) made with ExLlamaV2 v0.1.3 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)</center> \| <center>18625 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)</center> \| <center>20645 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)</center> \| <center>24211 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)</center> \| <center>27784 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)</center> \| <center>29572 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)</center> \| <center>31359 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)</center> \| <center>33139 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)</center> \| <center>38500 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)</center> \| <center>45805 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)</center> \| <center>49410 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)</center> \| <center>54655 MB</center> \| <center>8</center> \|
martinsinnona/visdecode_vega_2	martinsinnona	2024-06-04T02:42:03Z	51	0	transformers	[ "transformers", "safetensors", "pix2struct", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-06-04T02:01:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Chanakan5591/llama-3-typhoon-v1.5-8b-nf4	Chanakan5591	2024-06-04T02:40:51Z	79	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-06-04T02:36:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ahmedesmail16/Paper_compared-beit-base	ahmedesmail16	2024-06-04T02:36:12Z	211	0	transformers	[ "transformers", "tensorboard", "safetensors", "beit", "image-classification", "generated_from_trainer", "base_model:microsoft/beit-base-patch16-224-pt22k-ft22k", "base_model:finetune:microsoft/beit-base-patch16-224-pt22k-ft22k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-06-04T00:17:01Z	--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224-pt22k-ft22k tags: - generated_from_trainer metrics: - accuracy model-index: - name: Paper_compared-beit-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Paper_compared-beit-base This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5363 - Accuracy: 0.8409 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 1.6803 \| 0.9492 \| 14 \| 0.9171 \| 0.7156 \| \| 0.8219 \| 1.9661 \| 29 \| 0.5230 \| 0.8330 \| \| 0.2323 \| 2.9831 \| 44 \| 0.5110 \| 0.8047 \| \| 0.1112 \| 4.0 \| 59 \| 0.4968 \| 0.8138 \| \| 0.0387 \| 4.9492 \| 73 \| 0.5502 \| 0.8093 \| \| 0.0232 \| 5.9661 \| 88 \| 0.5506 \| 0.8296 \| \| 0.0096 \| 6.9831 \| 103 \| 0.5341 \| 0.8431 \| \| 0.0068 \| 8.0 \| 118 \| 0.6003 \| 0.8149 \| \| 0.0046 \| 8.9492 \| 132 \| 0.5298 \| 0.8409 \| \| 0.0051 \| 9.4915 \| 140 \| 0.5363 \| 0.8409 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1
ehottl/distilbert-base-uncased-finetuned-clinc	ehottl	2024-06-04T02:36:06Z	113	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-04T02:24:15Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8020 - Accuracy: 0.9158 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.3069 \| 1.0 \| 318 \| 3.3020 \| 0.7177 \| \| 2.6569 \| 2.0 \| 636 \| 1.9007 \| 0.8468 \| \| 1.5836 \| 3.0 \| 954 \| 1.1867 \| 0.8881 \| \| 1.0474 \| 4.0 \| 1272 \| 0.8876 \| 0.9116 \| \| 0.8287 \| 5.0 \| 1590 \| 0.8020 \| 0.9158 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2.post303 - Datasets 2.19.1 - Tokenizers 0.15.2
warwavn/vit-base-patch16-224-in21k-finetuned-lora-food101	warwavn	2024-06-04T02:35:22Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-04T02:29:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
flammenai/Mahou-1.3a-mistral-7B-GGUF	flammenai	2024-06-04T02:26:13Z	0	1	transformers	[ "transformers", "gguf", "dataset:flammenai/MahouMix-v1", "base_model:flammenai/Mahou-1.3a-mistral-7B", "base_model:quantized:flammenai/Mahou-1.3a-mistral-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-02T03:39:19Z	--- library_name: transformers license: apache-2.0 base_model: - flammenai/Mahou-1.3a-mistral-7B datasets: - flammenai/MahouMix-v1 --- ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.3a-mistral-7B Mahou is designed to provide short messages in a conversational context. It is capable of casual conversation and character roleplay. ### Chat Format This model has been trained to use ChatML format. ``` <\|im_start\|>system {{system}}<\|im_end\|> <\|im_start\|>{{char}} {{message}}<\|im_end\|> <\|im_start\|>{{user}} {{message}}<\|im_end\|> ``` ### Roleplay Format - Speech without quotes. - Actions in `asterisks` ``` leans against wall cooly so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. ``` ### SillyTavern Settings 1. Use ChatML for the Context Template. 2. Enable Instruct Mode. 3. Use the [Mahou preset](https://huggingface.co/datasets/flammenai/Mahou-ST-ChatML-Instruct/raw/main/Mahou.json). 4. Recommended Additonal stopping strings: `["\n", "<\|", "</"]` ### Method DPO finetuned for 6 epochs using an A100 on Google Colab. [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne)
Ariffiq99/CRAB_COPA_KUCI_xlm_roberta_large_finetuned	Ariffiq99	2024-06-04T02:25:44Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:Ariffiq99/COPA_KUCI_xlm_roberta_large_finetuned", "base_model:finetune:Ariffiq99/COPA_KUCI_xlm_roberta_large_finetuned", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2024-06-04T00:05:24Z	--- license: mit base_model: Ariffiq99/COPA_KUCI_xlm_roberta_large_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: CRAB_COPA_KUCI_xlm_roberta_large_finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # CRAB_COPA_KUCI_xlm_roberta_large_finetuned This model is a fine-tuned version of [Ariffiq99/COPA_KUCI_xlm_roberta_large_finetuned](https://huggingface.co/Ariffiq99/COPA_KUCI_xlm_roberta_large_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2852 - F1: 0.7250 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.1412 \| 1.0 \| 2880 \| 1.4904 \| 0.675 \| \| 1.0659 \| 2.0 \| 5760 \| 1.7656 \| 0.6986 \| \| 0.9118 \| 3.0 \| 8640 \| 1.4802 \| 0.7083 \| \| 0.8833 \| 4.0 \| 11520 \| 0.9360 \| 0.7208 \| \| 0.9054 \| 5.0 \| 14400 \| 1.3935 \| 0.7111 \| \| 0.8062 \| 6.0 \| 17280 \| 1.1927 \| 0.7194 \| \| 0.8188 \| 7.0 \| 20160 \| 1.1275 \| 0.7278 \| \| 0.7608 \| 8.0 \| 23040 \| 1.2852 \| 0.7250 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
TTTXXX01/LS-zephyr-7b-sft-full	TTTXXX01	2024-06-04T02:25:00Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:alignment-handbook/zephyr-7b-sft-full", "base_model:finetune:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-03T18:08:07Z	--- license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: LS-zephyr-7b-sft-full results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # LS-zephyr-7b-sft-full This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 3 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - total_train_batch_size: 9 - total_eval_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ShenaoZ/DPO-Zephyr-7B	ShenaoZ	2024-06-04T02:24:29Z	10	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "base_model:finetune:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-31T17:13:41Z	--- license: mit base_model: HuggingFaceH4/mistral-7b-sft-beta tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: DPO-Zephyr-7B results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DPO-Zephyr-7B This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1
Carlosslocar/test6	Carlosslocar	2024-06-04T02:23:35Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-06-04T02:17:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Larbz-7/swin-tiny-patch4-window7-224-finetuned-eurosat	Larbz-7	2024-06-04T02:22:07Z	219	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-06-03T23:03:14Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1335 - Accuracy: 0.5414 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 2.3862 \| 0.9994 \| 788 \| 2.2541 \| 0.5365 \| \| 2.1651 \| 2.0 \| 1577 \| 2.1688 \| 0.5395 \| \| 2.1559 \| 2.9981 \| 2364 \| 2.1335 \| 0.5414 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
hdve/Qwen-Qwen1.5-1.8B-1717467486	hdve	2024-06-04T02:20:23Z	140	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T02:18:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
rubenamtz0/llama-3-8b-lora-law2entity	rubenamtz0	2024-06-04T02:19:37Z	15	1	peft	[ "peft", "safetensors", "gguf", "llama", "axolotl", "generated_from_trainer", "dataset:rubenamtz0/law_entity_recognition", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2024-06-02T01:21:16Z	--- license: llama3 library_name: peft tags: - axolotl - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B model-index: - name: llama-3-8b-lora-law2entity results: [] datasets: - rubenamtz0/law_entity_recognition --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3-8B model_type: LlamaForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: true load_in_4bit: false strict: false datasets: - path: rubenamtz0/law_entity_recognition type: alpaca dataset_prepared_path: val_set_size: 0.1 output_dir: ./outputs/lora-law hub_model_id: rubenamtz0/llama-3-8b-lora-law2entity sequence_len: 4096 sample_packing: true pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: wandb_project: entity-relationship-claim-ft wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 4 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true s2_attention: warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: <\|end_of_text\|> ``` </details><br> # llama-3-8b-lora-law2entity This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the rubenamtz0/law_entity_recognition dataset. It achieves the following results on the evaluation set: - Loss: 0.1490 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - total_eval_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.2735 \| 0.05 \| 1 \| 0.2923 \| \| 0.2852 \| 0.25 \| 5 \| 0.2742 \| \| 0.2007 \| 0.5 \| 10 \| 0.2015 \| \| 0.1742 \| 0.75 \| 15 \| 0.1807 \| \| 0.1854 \| 1.0 \| 20 \| 0.1688 \| \| 0.159 \| 1.1125 \| 25 \| 0.1630 \| \| 0.1444 \| 1.3625 \| 30 \| 0.1592 \| \| 0.1479 \| 1.6125 \| 35 \| 0.1565 \| \| 0.1505 \| 1.8625 \| 40 \| 0.1538 \| \| 0.1369 \| 2.1125 \| 45 \| 0.1518 \| \| 0.1348 \| 2.2125 \| 50 \| 0.1512 \| \| 0.1287 \| 2.4625 \| 55 \| 0.1510 \| \| 0.1359 \| 2.7125 \| 60 \| 0.1498 \| \| 0.1367 \| 2.9625 \| 65 \| 0.1491 \| \| 0.1218 \| 3.075 \| 70 \| 0.1491 \| \| 0.1285 \| 3.325 \| 75 \| 0.1493 \| \| 0.1307 \| 3.575 \| 80 \| 0.1490 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.1.2+cu118 - Datasets 2.19.1 - Tokenizers 0.19.1
kiatkock/sentiment_pc_oversampler	kiatkock	2024-06-04T02:15:32Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:ahmedrachid/FinancialBERT-Sentiment-Analysis", "base_model:finetune:ahmedrachid/FinancialBERT-Sentiment-Analysis", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-30T07:03:44Z	--- base_model: ahmedrachid/FinancialBERT-Sentiment-Analysis tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: sentiment_pc_oversampler results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment_pc_oversampler This model is a fine-tuned version of [ahmedrachid/FinancialBERT-Sentiment-Analysis](https://huggingface.co/ahmedrachid/FinancialBERT-Sentiment-Analysis) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3909 - Accuracy: 0.9291 - F1: 0.9288 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 0.1134 \| 50 \| 0.5293 \| 0.8154 \| 0.8173 \| \| No log \| 0.2268 \| 100 \| 0.4512 \| 0.8222 \| 0.8224 \| \| No log \| 0.3401 \| 150 \| 0.4212 \| 0.8356 \| 0.8364 \| \| No log \| 0.4535 \| 200 \| 0.3978 \| 0.8395 \| 0.8400 \| \| No log \| 0.5669 \| 250 \| 0.3745 \| 0.8631 \| 0.8642 \| \| No log \| 0.6803 \| 300 \| 0.3593 \| 0.8667 \| 0.8675 \| \| No log \| 0.7937 \| 350 \| 0.3203 \| 0.8821 \| 0.8826 \| \| No log \| 0.9070 \| 400 \| 0.3130 \| 0.8880 \| 0.8889 \| \| No log \| 1.0204 \| 450 \| 0.3052 \| 0.8903 \| 0.8904 \| \| 0.3514 \| 1.1338 \| 500 \| 0.3216 \| 0.8948 \| 0.8954 \| \| 0.3514 \| 1.2472 \| 550 \| 0.3178 \| 0.8979 \| 0.8981 \| \| 0.3514 \| 1.3605 \| 600 \| 0.3366 \| 0.8874 \| 0.8877 \| \| 0.3514 \| 1.4739 \| 650 \| 0.3108 \| 0.8951 \| 0.8950 \| \| 0.3514 \| 1.5873 \| 700 \| 0.2551 \| 0.9198 \| 0.9200 \| \| 0.3514 \| 1.7007 \| 750 \| 0.3358 \| 0.8911 \| 0.8907 \| \| 0.3514 \| 1.8141 \| 800 \| 0.2812 \| 0.9127 \| 0.9125 \| \| 0.3514 \| 1.9274 \| 850 \| 0.2443 \| 0.9240 \| 0.9239 \| \| 0.3514 \| 2.0408 \| 900 \| 0.3059 \| 0.9183 \| 0.9182 \| \| 0.3514 \| 2.1542 \| 950 \| 0.3161 \| 0.9155 \| 0.9152 \| \| 0.1587 \| 2.2676 \| 1000 \| 0.2733 \| 0.9237 \| 0.9235 \| \| 0.1587 \| 2.3810 \| 1050 \| 0.3252 \| 0.9141 \| 0.9137 \| \| 0.1587 \| 2.4943 \| 1100 \| 0.3257 \| 0.9141 \| 0.9140 \| \| 0.1587 \| 2.6077 \| 1150 \| 0.2836 \| 0.9254 \| 0.9253 \| \| 0.1587 \| 2.7211 \| 1200 \| 0.3176 \| 0.9166 \| 0.9163 \| \| 0.1587 \| 2.8345 \| 1250 \| 0.3335 \| 0.9232 \| 0.9228 \| \| 0.1587 \| 2.9478 \| 1300 \| 0.3076 \| 0.9257 \| 0.9254 \| \| 0.1587 \| 3.0612 \| 1350 \| 0.3169 \| 0.9269 \| 0.9264 \| \| 0.1587 \| 3.1746 \| 1400 \| 0.3627 \| 0.9240 \| 0.9238 \| \| 0.1587 \| 3.2880 \| 1450 \| 0.4074 \| 0.9127 \| 0.9118 \| \| 0.0731 \| 3.4014 \| 1500 \| 0.3580 \| 0.9251 \| 0.9247 \| \| 0.0731 \| 3.5147 \| 1550 \| 0.3802 \| 0.9240 \| 0.9235 \| \| 0.0731 \| 3.6281 \| 1600 \| 0.3705 \| 0.9257 \| 0.9253 \| \| 0.0731 \| 3.7415 \| 1650 \| 0.3177 \| 0.9362 \| 0.9361 \| \| 0.0731 \| 3.8549 \| 1700 \| 0.3563 \| 0.9314 \| 0.9310 \| \| 0.0731 \| 3.9683 \| 1750 \| 0.4248 \| 0.9158 \| 0.9154 \| \| 0.0731 \| 4.0816 \| 1800 \| 0.3535 \| 0.9314 \| 0.9310 \| \| 0.0731 \| 4.1950 \| 1850 \| 0.3568 \| 0.9308 \| 0.9305 \| \| 0.0731 \| 4.3084 \| 1900 \| 0.4044 \| 0.9266 \| 0.9264 \| \| 0.0731 \| 4.4218 \| 1950 \| 0.3598 \| 0.9331 \| 0.9327 \| \| 0.0358 \| 4.5351 \| 2000 \| 0.3909 \| 0.9291 \| 0.9288 \| \| 0.0358 \| 4.6485 \| 2050 \| 0.3725 \| 0.9325 \| 0.9322 \| \| 0.0358 \| 4.7619 \| 2100 \| 0.3953 \| 0.9305 \| 0.9303 \| \| 0.0358 \| 4.8753 \| 2150 \| 0.3902 \| 0.9305 \| 0.9302 \| \| 0.0358 \| 4.9887 \| 2200 \| 0.3960 \| 0.9286 \| 0.9282 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
lemon-mint/ko-tokenizer-experiment-003	lemon-mint	2024-06-04T02:14:47Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-04T02:14:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
zaynu/llama2-finetune	zaynu	2024-06-04T01:56:39Z	5	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T01:35:17Z	--- license: apache-2.0 ---
apwic/nerui-lora-r16-1	apwic	2024-06-04T01:54:08Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-28T13:06:00Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerui-lora-r16-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nerui-lora-r16-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0342 - Location Precision: 0.9316 - Location Recall: 0.9397 - Location F1: 0.9356 - Location Number: 116 - Organization Precision: 0.9484 - Organization Recall: 0.9304 - Organization F1: 0.9393 - Organization Number: 158 - Person Precision: 0.984 - Person Recall: 0.9919 - Person F1: 0.9880 - Person Number: 124 - Overall Precision: 0.9547 - Overall Recall: 0.9523 - Overall F1: 0.9535 - Overall Accuracy: 0.9896 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Location Precision \| Location Recall \| Location F1 \| Location Number \| Organization Precision \| Organization Recall \| Organization F1 \| Organization Number \| Person Precision \| Person Recall \| Person F1 \| Person Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------------:\|:---------------:\|:-----------:\|:---------------:\|:----------------------:\|:-------------------:\|:---------------:\|:-------------------:\|:----------------:\|:-------------:\|:---------:\|:-------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 1.0545 \| 1.0 \| 96 \| 0.6622 \| 0.0 \| 0.0 \| 0.0 \| 116 \| 0.0 \| 0.0 \| 0.0 \| 158 \| 0.0 \| 0.0 \| 0.0 \| 124 \| 0.0 \| 0.0 \| 0.0 \| 0.8394 \| \| 0.64 \| 2.0 \| 192 \| 0.5206 \| 0.0 \| 0.0 \| 0.0 \| 116 \| 0.5 \| 0.0127 \| 0.0247 \| 158 \| 0.0 \| 0.0 \| 0.0 \| 124 \| 0.3333 \| 0.0050 \| 0.0099 \| 0.8400 \| \| 0.503 \| 3.0 \| 288 \| 0.3728 \| 0.0833 \| 0.0086 \| 0.0156 \| 116 \| 0.3625 \| 0.1835 \| 0.2437 \| 158 \| 0.36 \| 0.2903 \| 0.3214 \| 124 \| 0.3438 \| 0.1658 \| 0.2237 \| 0.8718 \| \| 0.3537 \| 4.0 \| 384 \| 0.2518 \| 0.3947 \| 0.2586 \| 0.3125 \| 116 \| 0.4885 \| 0.5380 \| 0.5120 \| 158 \| 0.5521 \| 0.7258 \| 0.6272 \| 124 \| 0.4964 \| 0.5151 \| 0.5055 \| 0.9198 \| \| 0.2513 \| 5.0 \| 480 \| 0.1812 \| 0.6111 \| 0.5690 \| 0.5893 \| 116 \| 0.5979 \| 0.7342 \| 0.6591 \| 158 \| 0.8028 \| 0.9194 \| 0.8571 \| 124 \| 0.6667 \| 0.7437 \| 0.7031 \| 0.9498 \| \| 0.1948 \| 6.0 \| 576 \| 0.1359 \| 0.7438 \| 0.7759 \| 0.7595 \| 116 \| 0.7368 \| 0.7975 \| 0.7660 \| 158 \| 0.8905 \| 0.9839 \| 0.9349 \| 124 \| 0.7879 \| 0.8492 \| 0.8174 \| 0.9657 \| \| 0.1623 \| 7.0 \| 672 \| 0.1109 \| 0.7917 \| 0.8190 \| 0.8051 \| 116 \| 0.7619 \| 0.8101 \| 0.7853 \| 158 \| 0.9104 \| 0.9839 \| 0.9457 \| 124 \| 0.8175 \| 0.8668 \| 0.8415 \| 0.9701 \| \| 0.1397 \| 8.0 \| 768 \| 0.0954 \| 0.8083 \| 0.8362 \| 0.8220 \| 116 \| 0.7976 \| 0.8481 \| 0.8221 \| 158 \| 0.9389 \| 0.9919 \| 0.9647 \| 124 \| 0.8449 \| 0.8894 \| 0.8666 \| 0.9739 \| \| 0.1266 \| 9.0 \| 864 \| 0.0877 \| 0.8189 \| 0.8966 \| 0.8560 \| 116 \| 0.8155 \| 0.8671 \| 0.8405 \| 158 \| 0.9318 \| 0.9919 \| 0.9609 \| 124 \| 0.8525 \| 0.9146 \| 0.8824 \| 0.9761 \| \| 0.1157 \| 10.0 \| 960 \| 0.0731 \| 0.8607 \| 0.9052 \| 0.8824 \| 116 \| 0.8519 \| 0.8734 \| 0.8625 \| 158 \| 0.9609 \| 0.9919 \| 0.9762 \| 124 \| 0.8883 \| 0.9196 \| 0.9037 \| 0.9800 \| \| 0.1111 \| 11.0 \| 1056 \| 0.0673 \| 0.8760 \| 0.9138 \| 0.8945 \| 116 \| 0.8606 \| 0.8987 \| 0.8793 \| 158 \| 0.9685 \| 0.9919 \| 0.9801 \| 124 \| 0.8983 \| 0.9322 \| 0.9149 \| 0.9813 \| \| 0.1044 \| 12.0 \| 1152 \| 0.0635 \| 0.8760 \| 0.9138 \| 0.8945 \| 116 \| 0.8554 \| 0.8987 \| 0.8765 \| 158 \| 0.9685 \| 0.9919 \| 0.9801 \| 124 \| 0.8961 \| 0.9322 \| 0.9138 \| 0.9811 \| \| 0.098 \| 13.0 \| 1248 \| 0.0578 \| 0.8898 \| 0.9052 \| 0.8974 \| 116 \| 0.8589 \| 0.8861 \| 0.8723 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9042 \| 0.9246 \| 0.9143 \| 0.9816 \| \| 0.0939 \| 14.0 \| 1344 \| 0.0559 \| 0.875 \| 0.9052 \| 0.8898 \| 116 \| 0.8642 \| 0.8861 \| 0.8750 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9020 \| 0.9246 \| 0.9132 \| 0.9819 \| \| 0.091 \| 15.0 \| 1440 \| 0.0558 \| 0.8824 \| 0.9052 \| 0.8936 \| 116 \| 0.8402 \| 0.8987 \| 0.8685 \| 158 \| 0.9685 \| 0.9919 \| 0.9801 \| 124 \| 0.8916 \| 0.9296 \| 0.9102 \| 0.9816 \| \| 0.088 \| 16.0 \| 1536 \| 0.0555 \| 0.875 \| 0.9052 \| 0.8898 \| 116 \| 0.8452 \| 0.8987 \| 0.8712 \| 158 \| 0.9535 \| 0.9919 \| 0.9723 \| 124 \| 0.8873 \| 0.9296 \| 0.9080 \| 0.9811 \| \| 0.0857 \| 17.0 \| 1632 \| 0.0523 \| 0.8824 \| 0.9052 \| 0.8936 \| 116 \| 0.8868 \| 0.8924 \| 0.8896 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9156 \| 0.9271 \| 0.9213 \| 0.9846 \| \| 0.0809 \| 18.0 \| 1728 \| 0.0498 \| 0.8678 \| 0.9052 \| 0.8861 \| 116 \| 0.8659 \| 0.8987 \| 0.8820 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9024 \| 0.9296 \| 0.9158 \| 0.9833 \| \| 0.0773 \| 19.0 \| 1824 \| 0.0482 \| 0.8898 \| 0.9052 \| 0.8974 \| 116 \| 0.8827 \| 0.9051 \| 0.8938 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9160 \| 0.9322 \| 0.9240 \| 0.9844 \| \| 0.0765 \| 20.0 \| 1920 \| 0.0521 \| 0.8833 \| 0.9138 \| 0.8983 \| 116 \| 0.8571 \| 0.9114 \| 0.8834 \| 158 \| 0.9685 \| 0.9919 \| 0.9801 \| 124 \| 0.8988 \| 0.9372 \| 0.9176 \| 0.9822 \| \| 0.0754 \| 21.0 \| 2016 \| 0.0484 \| 0.875 \| 0.9052 \| 0.8898 \| 116 \| 0.8735 \| 0.9177 \| 0.8951 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9075 \| 0.9372 \| 0.9221 \| 0.9841 \| \| 0.072 \| 22.0 \| 2112 \| 0.0469 \| 0.875 \| 0.9052 \| 0.8898 \| 116 \| 0.8606 \| 0.8987 \| 0.8793 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9024 \| 0.9296 \| 0.9158 \| 0.9835 \| \| 0.0689 \| 23.0 \| 2208 \| 0.0440 \| 0.8898 \| 0.9052 \| 0.8974 \| 116 \| 0.8944 \| 0.9114 \| 0.9028 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9208 \| 0.9347 \| 0.9277 \| 0.9844 \| \| 0.0697 \| 24.0 \| 2304 \| 0.0456 \| 0.8974 \| 0.9052 \| 0.9013 \| 116 \| 0.8968 \| 0.8797 \| 0.8882 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9244 \| 0.9221 \| 0.9233 \| 0.9846 \| \| 0.0656 \| 25.0 \| 2400 \| 0.0436 \| 0.8983 \| 0.9138 \| 0.9060 \| 116 \| 0.8812 \| 0.8924 \| 0.8868 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9181 \| 0.9296 \| 0.9238 \| 0.9846 \| \| 0.0658 \| 26.0 \| 2496 \| 0.0427 \| 0.8974 \| 0.9052 \| 0.9013 \| 116 \| 0.8704 \| 0.8924 \| 0.8812 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9134 \| 0.9271 \| 0.9202 \| 0.9841 \| \| 0.065 \| 27.0 \| 2592 \| 0.0421 \| 0.9052 \| 0.9052 \| 0.9052 \| 116 \| 0.8834 \| 0.9114 \| 0.8972 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9208 \| 0.9347 \| 0.9277 \| 0.9855 \| \| 0.0613 \| 28.0 \| 2688 \| 0.0418 \| 0.8833 \| 0.9138 \| 0.8983 \| 116 \| 0.8882 \| 0.9051 \| 0.8966 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9163 \| 0.9347 \| 0.9254 \| 0.9855 \| \| 0.0591 \| 29.0 \| 2784 \| 0.0398 \| 0.9060 \| 0.9138 \| 0.9099 \| 116 \| 0.8882 \| 0.9051 \| 0.8966 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9231 \| 0.9347 \| 0.9288 \| 0.9874 \| \| 0.06 \| 30.0 \| 2880 \| 0.0395 \| 0.9060 \| 0.9138 \| 0.9099 \| 116 \| 0.8994 \| 0.9051 \| 0.9022 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9277 \| 0.9347 \| 0.9312 \| 0.9865 \| \| 0.0566 \| 31.0 \| 2976 \| 0.0386 \| 0.8983 \| 0.9138 \| 0.9060 \| 116 \| 0.8827 \| 0.9051 \| 0.8938 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9185 \| 0.9347 \| 0.9265 \| 0.9863 \| \| 0.0566 \| 32.0 \| 3072 \| 0.0392 \| 0.8889 \| 0.8966 \| 0.8927 \| 116 \| 0.9045 \| 0.8987 \| 0.9016 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9248 \| 0.9271 \| 0.9260 \| 0.9857 \| \| 0.0566 \| 33.0 \| 3168 \| 0.0398 \| 0.8992 \| 0.9224 \| 0.9106 \| 116 \| 0.9045 \| 0.8987 \| 0.9016 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9277 \| 0.9347 \| 0.9312 \| 0.9865 \| \| 0.0568 \| 34.0 \| 3264 \| 0.0396 \| 0.9224 \| 0.9224 \| 0.9224 \| 116 \| 0.8951 \| 0.9177 \| 0.9062 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9305 \| 0.9422 \| 0.9363 \| 0.9871 \| \| 0.0532 \| 35.0 \| 3360 \| 0.0379 \| 0.8983 \| 0.9138 \| 0.9060 \| 116 \| 0.9051 \| 0.9051 \| 0.9051 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9277 \| 0.9347 \| 0.9312 \| 0.9871 \| \| 0.052 \| 36.0 \| 3456 \| 0.0403 \| 0.9231 \| 0.9310 \| 0.9270 \| 116 \| 0.9012 \| 0.9241 \| 0.9125 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9332 \| 0.9472 \| 0.9401 \| 0.9879 \| \| 0.0516 \| 37.0 \| 3552 \| 0.0386 \| 0.8983 \| 0.9138 \| 0.9060 \| 116 \| 0.9 \| 0.9114 \| 0.9057 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9256 \| 0.9372 \| 0.9313 \| 0.9874 \| \| 0.0497 \| 38.0 \| 3648 \| 0.0378 \| 0.8992 \| 0.9224 \| 0.9106 \| 116 \| 0.8994 \| 0.9051 \| 0.9022 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9256 \| 0.9372 \| 0.9313 \| 0.9879 \| \| 0.052 \| 39.0 \| 3744 \| 0.0366 \| 0.9138 \| 0.9138 \| 0.9138 \| 116 \| 0.9006 \| 0.9177 \| 0.9091 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9303 \| 0.9397 \| 0.9350 \| 0.9885 \| \| 0.0472 \| 40.0 \| 3840 \| 0.0367 \| 0.9138 \| 0.9138 \| 0.9138 \| 116 \| 0.8987 \| 0.8987 \| 0.8987 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9298 \| 0.9322 \| 0.9310 \| 0.9868 \| \| 0.0486 \| 41.0 \| 3936 \| 0.0388 \| 0.9076 \| 0.9310 \| 0.9191 \| 116 \| 0.9074 \| 0.9304 \| 0.9187 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9310 \| 0.9497 \| 0.9403 \| 0.9882 \| \| 0.047 \| 42.0 \| 4032 \| 0.0375 \| 0.9068 \| 0.9224 \| 0.9145 \| 116 \| 0.9161 \| 0.8987 \| 0.9073 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9347 \| 0.9347 \| 0.9347 \| 0.9874 \| \| 0.0481 \| 43.0 \| 4128 \| 0.0380 \| 0.8983 \| 0.9138 \| 0.9060 \| 116 \| 0.9051 \| 0.9051 \| 0.9051 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9277 \| 0.9347 \| 0.9312 \| 0.9860 \| \| 0.0468 \| 44.0 \| 4224 \| 0.0391 \| 0.9231 \| 0.9310 \| 0.9270 \| 116 \| 0.9062 \| 0.9177 \| 0.9119 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9353 \| 0.9447 \| 0.94 \| 0.9876 \| \| 0.0473 \| 45.0 \| 4320 \| 0.0366 \| 0.8992 \| 0.9224 \| 0.9106 \| 116 \| 0.9045 \| 0.8987 \| 0.9016 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9277 \| 0.9347 \| 0.9312 \| 0.9868 \| \| 0.0441 \| 46.0 \| 4416 \| 0.0372 \| 0.9 \| 0.9310 \| 0.9153 \| 116 \| 0.9006 \| 0.9177 \| 0.9091 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9261 \| 0.9447 \| 0.9353 \| 0.9887 \| \| 0.0441 \| 47.0 \| 4512 \| 0.0375 \| 0.9224 \| 0.9224 \| 0.9224 \| 116 \| 0.9068 \| 0.9241 \| 0.9154 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9353 \| 0.9447 \| 0.94 \| 0.9887 \| \| 0.0416 \| 48.0 \| 4608 \| 0.0359 \| 0.9237 \| 0.9397 \| 0.9316 \| 116 \| 0.9363 \| 0.9304 \| 0.9333 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9475 \| 0.9523 \| 0.9499 \| 0.9898 \| \| 0.0446 \| 49.0 \| 4704 \| 0.0355 \| 0.9153 \| 0.9310 \| 0.9231 \| 116 \| 0.8931 \| 0.8987 \| 0.8959 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9279 \| 0.9372 \| 0.9325 \| 0.9876 \| \| 0.0425 \| 50.0 \| 4800 \| 0.0366 \| 0.9160 \| 0.9397 \| 0.9277 \| 116 \| 0.9 \| 0.9114 \| 0.9057 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9307 \| 0.9447 \| 0.9377 \| 0.9887 \| \| 0.0422 \| 51.0 \| 4896 \| 0.0364 \| 0.9153 \| 0.9310 \| 0.9231 \| 116 \| 0.9167 \| 0.9051 \| 0.9108 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9373 \| 0.9397 \| 0.9385 \| 0.9871 \| \| 0.0409 \| 52.0 \| 4992 \| 0.0357 \| 0.9145 \| 0.9224 \| 0.9185 \| 116 \| 0.9074 \| 0.9304 \| 0.9187 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9332 \| 0.9472 \| 0.9401 \| 0.9896 \| \| 0.0414 \| 53.0 \| 5088 \| 0.0359 \| 0.9231 \| 0.9310 \| 0.9270 \| 116 \| 0.9136 \| 0.9367 \| 0.9250 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9381 \| 0.9523 \| 0.9451 \| 0.9901 \| \| 0.0403 \| 54.0 \| 5184 \| 0.0353 \| 0.9231 \| 0.9310 \| 0.9270 \| 116 \| 0.8963 \| 0.9304 \| 0.9130 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9310 \| 0.9497 \| 0.9403 \| 0.9896 \| \| 0.0393 \| 55.0 \| 5280 \| 0.0352 \| 0.9145 \| 0.9224 \| 0.9185 \| 116 \| 0.9136 \| 0.9367 \| 0.9250 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9356 \| 0.9497 \| 0.9426 \| 0.9898 \| \| 0.0405 \| 56.0 \| 5376 \| 0.0359 \| 0.9237 \| 0.9397 \| 0.9316 \| 116 \| 0.9430 \| 0.9430 \| 0.9430 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9501 \| 0.9573 \| 0.9537 \| 0.9901 \| \| 0.0404 \| 57.0 \| 5472 \| 0.0370 \| 0.9160 \| 0.9397 \| 0.9277 \| 116 \| 0.9371 \| 0.9430 \| 0.9401 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9454 \| 0.9573 \| 0.9513 \| 0.9896 \| \| 0.0398 \| 58.0 \| 5568 \| 0.0355 \| 0.9316 \| 0.9397 \| 0.9356 \| 116 \| 0.9308 \| 0.9367 \| 0.9338 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9476 \| 0.9548 \| 0.9512 \| 0.9904 \| \| 0.0382 \| 59.0 \| 5664 \| 0.0355 \| 0.9397 \| 0.9397 \| 0.9397 \| 116 \| 0.9551 \| 0.9430 \| 0.9490 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9597 \| 0.9573 \| 0.9585 \| 0.9904 \| \| 0.0396 \| 60.0 \| 5760 \| 0.0344 \| 0.9160 \| 0.9397 \| 0.9277 \| 116 \| 0.9125 \| 0.9241 \| 0.9182 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9356 \| 0.9497 \| 0.9426 \| 0.9893 \| \| 0.0362 \| 61.0 \| 5856 \| 0.0356 \| 0.9231 \| 0.9310 \| 0.9270 \| 116 \| 0.9226 \| 0.9051 \| 0.9137 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9421 \| 0.9397 \| 0.9409 \| 0.9879 \| \| 0.037 \| 62.0 \| 5952 \| 0.0360 \| 0.9237 \| 0.9397 \| 0.9316 \| 116 \| 0.9167 \| 0.9051 \| 0.9108 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9398 \| 0.9422 \| 0.9410 \| 0.9882 \| \| 0.0386 \| 63.0 \| 6048 \| 0.0364 \| 0.9310 \| 0.9310 \| 0.9310 \| 116 \| 0.9367 \| 0.9367 \| 0.9367 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9499 \| 0.9523 \| 0.9511 \| 0.9896 \| \| 0.0365 \| 64.0 \| 6144 \| 0.0360 \| 0.9153 \| 0.9310 \| 0.9231 \| 116 \| 0.9412 \| 0.9114 \| 0.9260 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9470 \| 0.9422 \| 0.9446 \| 0.9887 \| \| 0.0347 \| 65.0 \| 6240 \| 0.0354 \| 0.9237 \| 0.9397 \| 0.9316 \| 116 \| 0.9416 \| 0.9177 \| 0.9295 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9496 \| 0.9472 \| 0.9484 \| 0.9887 \| \| 0.0393 \| 66.0 \| 6336 \| 0.0366 \| 0.9397 \| 0.9397 \| 0.9397 \| 116 \| 0.9355 \| 0.9177 \| 0.9265 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9520 \| 0.9472 \| 0.9496 \| 0.9887 \| \| 0.0359 \| 67.0 \| 6432 \| 0.0348 \| 0.9316 \| 0.9397 \| 0.9356 \| 116 \| 0.9241 \| 0.9241 \| 0.9241 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.945 \| 0.9497 \| 0.9474 \| 0.9893 \| \| 0.0331 \| 68.0 \| 6528 \| 0.0347 \| 0.9316 \| 0.9397 \| 0.9356 \| 116 \| 0.9177 \| 0.9177 \| 0.9177 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9425 \| 0.9472 \| 0.9449 \| 0.9890 \| \| 0.0344 \| 69.0 \| 6624 \| 0.0341 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9363 \| 0.9304 \| 0.9333 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9521 \| 0.9497 \| 0.9509 \| 0.9898 \| \| 0.0349 \| 70.0 \| 6720 \| 0.0345 \| 0.9397 \| 0.9397 \| 0.9397 \| 116 \| 0.9427 \| 0.9367 \| 0.9397 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9548 \| 0.9548 \| 0.9548 \| 0.9901 \| \| 0.0349 \| 71.0 \| 6816 \| 0.0354 \| 0.9310 \| 0.9310 \| 0.9310 \| 116 \| 0.9299 \| 0.9241 \| 0.9270 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9472 \| 0.9472 \| 0.9472 \| 0.9885 \| \| 0.0342 \| 72.0 \| 6912 \| 0.0343 \| 0.9237 \| 0.9397 \| 0.9316 \| 116 \| 0.9299 \| 0.9241 \| 0.9270 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.945 \| 0.9497 \| 0.9474 \| 0.9887 \| \| 0.0333 \| 73.0 \| 7008 \| 0.0354 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9241 \| 0.9241 \| 0.9241 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9472 \| 0.9472 \| 0.9472 \| 0.9890 \| \| 0.0332 \| 74.0 \| 7104 \| 0.0346 \| 0.9231 \| 0.9310 \| 0.9270 \| 116 \| 0.9241 \| 0.9241 \| 0.9241 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9425 \| 0.9472 \| 0.9449 \| 0.9893 \| \| 0.0346 \| 75.0 \| 7200 \| 0.0342 \| 0.9310 \| 0.9310 \| 0.9310 \| 116 \| 0.9245 \| 0.9304 \| 0.9274 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.945 \| 0.9497 \| 0.9474 \| 0.9896 \| \| 0.0334 \| 76.0 \| 7296 \| 0.0346 \| 0.9224 \| 0.9224 \| 0.9224 \| 116 \| 0.925 \| 0.9367 \| 0.9308 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9426 \| 0.9497 \| 0.9462 \| 0.9904 \| \| 0.034 \| 77.0 \| 7392 \| 0.0350 \| 0.9397 \| 0.9397 \| 0.9397 \| 116 \| 0.9299 \| 0.9241 \| 0.9270 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9497 \| 0.9497 \| 0.9497 \| 0.9896 \| \| 0.0341 \| 78.0 \| 7488 \| 0.0340 \| 0.9316 \| 0.9397 \| 0.9356 \| 116 \| 0.9363 \| 0.9304 \| 0.9333 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9499 \| 0.9523 \| 0.9511 \| 0.9904 \| \| 0.033 \| 79.0 \| 7584 \| 0.0348 \| 0.9304 \| 0.9224 \| 0.9264 \| 116 \| 0.925 \| 0.9367 \| 0.9308 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.945 \| 0.9497 \| 0.9474 \| 0.9896 \| \| 0.0308 \| 80.0 \| 7680 \| 0.0337 \| 0.9138 \| 0.9138 \| 0.9138 \| 116 \| 0.9193 \| 0.9367 \| 0.9279 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9378 \| 0.9472 \| 0.9425 \| 0.9898 \| \| 0.031 \| 81.0 \| 7776 \| 0.0341 \| 0.9224 \| 0.9224 \| 0.9224 \| 116 \| 0.9193 \| 0.9367 \| 0.9279 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9403 \| 0.9497 \| 0.9450 \| 0.9901 \| \| 0.0315 \| 82.0 \| 7872 \| 0.0340 \| 0.9237 \| 0.9397 \| 0.9316 \| 116 \| 0.9363 \| 0.9304 \| 0.9333 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9475 \| 0.9523 \| 0.9499 \| 0.9904 \| \| 0.0321 \| 83.0 \| 7968 \| 0.0343 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9367 \| 0.9367 \| 0.9367 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9523 \| 0.9523 \| 0.9523 \| 0.9901 \| \| 0.0317 \| 84.0 \| 8064 \| 0.0340 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9367 \| 0.9367 \| 0.9367 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9523 \| 0.9523 \| 0.9523 \| 0.9901 \| \| 0.0324 \| 85.0 \| 8160 \| 0.0340 \| 0.9145 \| 0.9224 \| 0.9185 \| 116 \| 0.9187 \| 0.9304 \| 0.9245 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9378 \| 0.9472 \| 0.9425 \| 0.9893 \| \| 0.0317 \| 86.0 \| 8256 \| 0.0339 \| 0.9316 \| 0.9397 \| 0.9356 \| 116 \| 0.9423 \| 0.9304 \| 0.9363 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9523 \| 0.9523 \| 0.9523 \| 0.9901 \| \| 0.0308 \| 87.0 \| 8352 \| 0.0347 \| 0.9316 \| 0.9397 \| 0.9356 \| 116 \| 0.9423 \| 0.9304 \| 0.9363 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9523 \| 0.9523 \| 0.9523 \| 0.9898 \| \| 0.0311 \| 88.0 \| 8448 \| 0.0344 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9367 \| 0.9367 \| 0.9367 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9523 \| 0.9523 \| 0.9523 \| 0.9898 \| \| 0.0295 \| 89.0 \| 8544 \| 0.0346 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9427 \| 0.9367 \| 0.9397 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9896 \| \| 0.0304 \| 90.0 \| 8640 \| 0.0343 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9427 \| 0.9367 \| 0.9397 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9896 \| \| 0.0315 \| 91.0 \| 8736 \| 0.0343 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9427 \| 0.9367 \| 0.9397 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9896 \| \| 0.0314 \| 92.0 \| 8832 \| 0.0342 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9427 \| 0.9367 \| 0.9397 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9896 \| \| 0.0322 \| 93.0 \| 8928 \| 0.0340 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9427 \| 0.9367 \| 0.9397 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9898 \| \| 0.0303 \| 94.0 \| 9024 \| 0.0343 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9367 \| 0.9367 \| 0.9367 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9523 \| 0.9523 \| 0.9523 \| 0.9898 \| \| 0.0316 \| 95.0 \| 9120 \| 0.0343 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9367 \| 0.9367 \| 0.9367 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9523 \| 0.9523 \| 0.9523 \| 0.9898 \| \| 0.0317 \| 96.0 \| 9216 \| 0.0342 \| 0.9391 \| 0.9310 \| 0.9351 \| 116 \| 0.9427 \| 0.9367 \| 0.9397 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9896 \| \| 0.0321 \| 97.0 \| 9312 \| 0.0341 \| 0.9316 \| 0.9397 \| 0.9356 \| 116 \| 0.9484 \| 0.9304 \| 0.9393 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9898 \| \| 0.0295 \| 98.0 \| 9408 \| 0.0342 \| 0.9316 \| 0.9397 \| 0.9356 \| 116 \| 0.9484 \| 0.9304 \| 0.9393 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9898 \| \| 0.031 \| 99.0 \| 9504 \| 0.0341 \| 0.9316 \| 0.9397 \| 0.9356 \| 116 \| 0.9484 \| 0.9304 \| 0.9393 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9898 \| \| 0.0299 \| 100.0 \| 9600 \| 0.0342 \| 0.9316 \| 0.9397 \| 0.9356 \| 116 \| 0.9484 \| 0.9304 \| 0.9393 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9896 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
vaiv/GeM2-Llamion-14B-Chat	vaiv	2024-06-04T01:49:33Z	2,245	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-13T08:43:21Z	--- license: apache-2.0 --- # GeM2-Llamion-14B We have released Llamion as GeM 2.0, the second series of generative models developed by VAIV Company to address the our principal business needs. Llamion (Llamafied Orion) is derived from transforming the [Orion model](https://huggingface.co/OrionStarAI/Orion-14B-Chat) into [the standard LLaMA architecture](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py) through parameter mapping and offline knowledge transfer. Further technical specifications and study results will be detailed in our upcoming paper, available on this page. <!-- Note that this model has NOT been contaminated to artificially inflate its scores for the Open LLM Leaderboards, unlike some recent models which have been intentionally tainted. --> ![vaiv_png](./vaiv.png) ### Contributors - VAIV Company AI Lab ([vaiv.kr](https://www.vaiv.kr/))
hdve/Qwen-Qwen1.5-0.5B-1717465528	hdve	2024-06-04T01:46:32Z	139	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T01:46:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ArashAhmadian/rloo_tldr_6.9b_defaultclip_512bs_05kl	ArashAhmadian	2024-06-04T01:37:51Z	5	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T01:34:26Z	--- tags: - generated_from_trainer model-index: - name: rloo_tldr_6.9b_defaultclip_512bs_05kl results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rloo_tldr_6.9b_defaultclip_512bs_05kl This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - num_epochs: 3.0 ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Trofish/Korean_syllable_roberta_512	Trofish	2024-06-04T01:34:41Z	126	0	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "ko", "dataset:klue/klue", "arxiv:2105.09680", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-13T11:23:10Z	--- license: apache-2.0 datasets: - klue/klue language: - ko metrics: - f1 - accuracy - pearsonr --- # RoBERTa-base Korean ## 모델 설명 이 RoBERTa 모델은 다양한 한국어 텍스트 데이터셋에서 음절 단위로 사전 학습되었습니다. 자체 구축한 한국어 음절 단위 vocab을 사용하였습니다. ## 아키텍처 - 모델 유형: RoBERTa - 아키텍처: RobertaForMaskedLM - 모델 크기: 512 hidden size, 8 hidden layers, 8 attention heads - max_position_embeddings: 514 - intermediate_size: 2,048 - vocab_size: 1,428 ## 학습 데이터 사용된 데이터셋은 다음과 같습니다: - 모두의말뭉치: 채팅, 게시판, 일상대화, 뉴스, 방송대본, 책 등 - AIHUB: SNS, 유튜브 댓글, 도서 문장 - 기타: 나무위키, 한국어 위키피디아 총 합산된 데이터는 약 11GB 입니다. (4B tokens) ## 학습 상세 - BATCH_SIZE: 196 (GPU당) - ACCUMULATE: 20 - Total_BATCH_SIZE: 8232 - MAX_STEPS: 12,500 - *TRAIN_STEPS BATCH_SIZE: 100M - WARMUP_STEPS: 2,400 - 최적화: AdamW, LR 1e-3, BETA (0.9, 0.98), eps 1e-6 - 학습률 감쇠: linear - 사용된 하드웨어: 2x A6000ada GPU ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a0fd6fd3149e05bc5260dd/S-3zdDXVMZnyEVrZdQ7J3.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a0fd6fd3149e05bc5260dd/3VwE53iLqKtc-gMQXOV_L.png) ## 성능 평가 - KLUE benchmark test를 통해서 성능을 평가했습니다. - klue-roberta-base에 비해서 매우 작은 크기라 성능이 낮기는 하지만 hidden size 512인 모델은 크기 대비 좋은 성능을 보였습니다. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a0fd6fd3149e05bc5260dd/I8e60cf9w-IQCHDgKiooq.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a0fd6fd3149e05bc5260dd/hkc5ko9Vo-pkKmtouN7xc.png) ## 사용 방법 ### tokenizer의 경우 wordpiece가 아닌 syllable 단위이기에 AutoTokenizer가 아니라 SyllableTokenizer를 사용해야 합니다. ### (레포에서 제공하고 있는 syllabletokenizer.py를 가져와서 사용해야 합니다.) ```python from transformers import AutoModel, AutoTokenizer from syllabletokenizer import SyllableTokenizer # 모델과 토크나이저 불러오기 model = AutoModelForMaskedLM.from_pretrained("Trofish/korean_syllable_roberta") tokenizer = SyllableTokenizer(vocab_file='vocab.json',tokenizer_kwargs) # 텍스트를 토큰으로 변환하고 예측 수행 inputs = tokenizer("여기에 한국어 텍스트 입력", return_tensors="pt") outputs = model(inputs) ``` ## Citation klue** ``` @misc{park2021klue, title={KLUE: Korean Language Understanding Evaluation}, author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho}, year={2021}, eprint={2105.09680}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
apwic/nerui-lora-r8-0	apwic	2024-06-04T01:26:47Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-28T12:12:41Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerui-lora-r8-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nerui-lora-r8-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0463 - Location Precision: 0.8462 - Location Recall: 0.9362 - Location F1: 0.8889 - Location Number: 94 - Organization Precision: 0.8667 - Organization Recall: 0.8563 - Organization F1: 0.8614 - Organization Number: 167 - Person Precision: 1.0 - Person Recall: 0.9854 - Person F1: 0.9926 - Person Number: 137 - Overall Precision: 0.9059 - Overall Recall: 0.9196 - Overall F1: 0.9127 - Overall Accuracy: 0.9848 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Location Precision \| Location Recall \| Location F1 \| Location Number \| Organization Precision \| Organization Recall \| Organization F1 \| Organization Number \| Person Precision \| Person Recall \| Person F1 \| Person Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------------:\|:---------------:\|:-----------:\|:---------------:\|:----------------------:\|:-------------------:\|:---------------:\|:-------------------:\|:----------------:\|:-------------:\|:---------:\|:-------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 1.1434 \| 1.0 \| 96 \| 0.7069 \| 0.0 \| 0.0 \| 0.0 \| 94 \| 0.0 \| 0.0 \| 0.0 \| 167 \| 0.0 \| 0.0 \| 0.0 \| 137 \| 0.0 \| 0.0 \| 0.0 \| 0.8343 \| \| 0.6699 \| 2.0 \| 192 \| 0.5760 \| 0.0 \| 0.0 \| 0.0 \| 94 \| 1.0 \| 0.0060 \| 0.0119 \| 167 \| 0.0 \| 0.0 \| 0.0 \| 137 \| 0.25 \| 0.0025 \| 0.0050 \| 0.8348 \| \| 0.5654 \| 3.0 \| 288 \| 0.4641 \| 0.0 \| 0.0 \| 0.0 \| 94 \| 0.4118 \| 0.0419 \| 0.0761 \| 167 \| 0.2414 \| 0.0511 \| 0.0843 \| 137 \| 0.3043 \| 0.0352 \| 0.0631 \| 0.8420 \| \| 0.4481 \| 4.0 \| 384 \| 0.3466 \| 0.2353 \| 0.0426 \| 0.0721 \| 94 \| 0.3578 \| 0.2335 \| 0.2826 \| 167 \| 0.3774 \| 0.4380 \| 0.4054 \| 137 \| 0.3614 \| 0.2588 \| 0.3016 \| 0.8793 \| \| 0.3376 \| 5.0 \| 480 \| 0.2613 \| 0.4058 \| 0.2979 \| 0.3436 \| 94 \| 0.5105 \| 0.5808 \| 0.5434 \| 167 \| 0.5081 \| 0.6861 \| 0.5839 \| 137 \| 0.4932 \| 0.5503 \| 0.5202 \| 0.9202 \| \| 0.2611 \| 6.0 \| 576 \| 0.2025 \| 0.5909 \| 0.5532 \| 0.5714 \| 94 \| 0.5588 \| 0.6826 \| 0.6146 \| 167 \| 0.6905 \| 0.8467 \| 0.7607 \| 137 \| 0.6130 \| 0.7085 \| 0.6573 \| 0.9406 \| \| 0.2071 \| 7.0 \| 672 \| 0.1615 \| 0.7021 \| 0.7021 \| 0.7021 \| 94 \| 0.6649 \| 0.7605 \| 0.7095 \| 167 \| 0.8224 \| 0.9124 \| 0.8651 \| 137 \| 0.7277 \| 0.7990 \| 0.7617 \| 0.9555 \| \| 0.1767 \| 8.0 \| 768 \| 0.1337 \| 0.7872 \| 0.7872 \| 0.7872 \| 94 \| 0.7120 \| 0.7844 \| 0.7464 \| 167 \| 0.9306 \| 0.9781 \| 0.9537 \| 137 \| 0.8033 \| 0.8518 \| 0.8268 \| 0.9644 \| \| 0.1601 \| 9.0 \| 864 \| 0.1165 \| 0.7980 \| 0.8404 \| 0.8187 \| 94 \| 0.7351 \| 0.8144 \| 0.7727 \| 167 \| 0.9306 \| 0.9781 \| 0.9537 \| 137 \| 0.8154 \| 0.8769 \| 0.8450 \| 0.9671 \| \| 0.1406 \| 10.0 \| 960 \| 0.1041 \| 0.7573 \| 0.8298 \| 0.7919 \| 94 \| 0.7816 \| 0.8144 \| 0.7977 \| 167 \| 0.9371 \| 0.9781 \| 0.9571 \| 137 \| 0.8286 \| 0.8744 \| 0.8509 \| 0.9693 \| \| 0.1283 \| 11.0 \| 1056 \| 0.0951 \| 0.8021 \| 0.8191 \| 0.8105 \| 94 \| 0.7865 \| 0.8383 \| 0.8116 \| 167 \| 0.9371 \| 0.9781 \| 0.9571 \| 137 \| 0.8417 \| 0.8819 \| 0.8613 \| 0.9704 \| \| 0.1229 \| 12.0 \| 1152 \| 0.0895 \| 0.8019 \| 0.9043 \| 0.8500 \| 94 \| 0.8 \| 0.8383 \| 0.8187 \| 167 \| 0.9375 \| 0.9854 \| 0.9609 \| 137 \| 0.8471 \| 0.9045 \| 0.8748 \| 0.9715 \| \| 0.1116 \| 13.0 \| 1248 \| 0.0831 \| 0.83 \| 0.8830 \| 0.8557 \| 94 \| 0.8314 \| 0.8563 \| 0.8437 \| 167 \| 0.9371 \| 0.9781 \| 0.9571 \| 137 \| 0.8675 \| 0.9045 \| 0.8856 \| 0.9743 \| \| 0.1077 \| 14.0 \| 1344 \| 0.0769 \| 0.8571 \| 0.8936 \| 0.875 \| 94 \| 0.8409 \| 0.8862 \| 0.8630 \| 167 \| 0.9504 \| 0.9781 \| 0.9640 \| 137 \| 0.8819 \| 0.9196 \| 0.9004 \| 0.9760 \| \| 0.1045 \| 15.0 \| 1440 \| 0.0758 \| 0.8333 \| 0.9043 \| 0.8673 \| 94 \| 0.8430 \| 0.8683 \| 0.8555 \| 167 \| 0.9371 \| 0.9781 \| 0.9571 \| 137 \| 0.8729 \| 0.9146 \| 0.8933 \| 0.9760 \| \| 0.1 \| 16.0 \| 1536 \| 0.0753 \| 0.8365 \| 0.9255 \| 0.8788 \| 94 \| 0.8111 \| 0.8743 \| 0.8415 \| 167 \| 0.9437 \| 0.9781 \| 0.9606 \| 137 \| 0.8615 \| 0.9221 \| 0.8908 \| 0.9746 \| \| 0.0961 \| 17.0 \| 1632 \| 0.0690 \| 0.8586 \| 0.9043 \| 0.8808 \| 94 \| 0.8563 \| 0.8922 \| 0.8739 \| 167 \| 0.9571 \| 0.9781 \| 0.9675 \| 137 \| 0.8910 \| 0.9246 \| 0.9075 \| 0.9785 \| \| 0.0981 \| 18.0 \| 1728 \| 0.0676 \| 0.86 \| 0.9149 \| 0.8866 \| 94 \| 0.8523 \| 0.8982 \| 0.8746 \| 167 \| 0.9504 \| 0.9781 \| 0.9640 \| 137 \| 0.8873 \| 0.9296 \| 0.9080 \| 0.9782 \| \| 0.0916 \| 19.0 \| 1824 \| 0.0653 \| 0.8333 \| 0.9043 \| 0.8673 \| 94 \| 0.8647 \| 0.8802 \| 0.8724 \| 167 \| 0.9640 \| 0.9781 \| 0.9710 \| 137 \| 0.8905 \| 0.9196 \| 0.9048 \| 0.9790 \| \| 0.0899 \| 20.0 \| 1920 \| 0.0637 \| 0.8586 \| 0.9043 \| 0.8808 \| 94 \| 0.8563 \| 0.8922 \| 0.8739 \| 167 \| 0.9640 \| 0.9781 \| 0.9710 \| 137 \| 0.8932 \| 0.9246 \| 0.9086 \| 0.9790 \| \| 0.0856 \| 21.0 \| 2016 \| 0.0656 \| 0.8113 \| 0.9149 \| 0.8600 \| 94 \| 0.8580 \| 0.8683 \| 0.8631 \| 167 \| 0.9571 \| 0.9781 \| 0.9675 \| 137 \| 0.8795 \| 0.9171 \| 0.8979 \| 0.9773 \| \| 0.0844 \| 22.0 \| 2112 \| 0.0621 \| 0.8416 \| 0.9043 \| 0.8718 \| 94 \| 0.8563 \| 0.8922 \| 0.8739 \| 167 \| 0.9571 \| 0.9781 \| 0.9675 \| 137 \| 0.8867 \| 0.9246 \| 0.9053 \| 0.9782 \| \| 0.0816 \| 23.0 \| 2208 \| 0.0608 \| 0.85 \| 0.9043 \| 0.8763 \| 94 \| 0.8647 \| 0.8802 \| 0.8724 \| 167 \| 0.9571 \| 0.9781 \| 0.9675 \| 137 \| 0.8927 \| 0.9196 \| 0.9059 \| 0.9798 \| \| 0.0803 \| 24.0 \| 2304 \| 0.0591 \| 0.8586 \| 0.9043 \| 0.8808 \| 94 \| 0.8671 \| 0.8982 \| 0.8824 \| 167 \| 0.9571 \| 0.9781 \| 0.9675 \| 137 \| 0.8956 \| 0.9271 \| 0.9111 \| 0.9796 \| \| 0.0793 \| 25.0 \| 2400 \| 0.0577 \| 0.85 \| 0.9043 \| 0.8763 \| 94 \| 0.8824 \| 0.8982 \| 0.8902 \| 167 \| 0.9710 \| 0.9781 \| 0.9745 \| 137 \| 0.9044 \| 0.9271 \| 0.9156 \| 0.9818 \| \| 0.0744 \| 26.0 \| 2496 \| 0.0576 \| 0.8529 \| 0.9255 \| 0.8878 \| 94 \| 0.8706 \| 0.8862 \| 0.8783 \| 167 \| 0.9710 \| 0.9781 \| 0.9745 \| 137 \| 0.9 \| 0.9271 \| 0.9134 \| 0.9818 \| \| 0.0761 \| 27.0 \| 2592 \| 0.0571 \| 0.8416 \| 0.9043 \| 0.8718 \| 94 \| 0.8757 \| 0.8862 \| 0.8810 \| 167 \| 0.9640 \| 0.9781 \| 0.9710 \| 137 \| 0.8973 \| 0.9221 \| 0.9095 \| 0.9807 \| \| 0.0724 \| 28.0 \| 2688 \| 0.0559 \| 0.8586 \| 0.9043 \| 0.8808 \| 94 \| 0.8655 \| 0.8862 \| 0.8757 \| 167 \| 0.9710 \| 0.9781 \| 0.9745 \| 137 \| 0.8995 \| 0.9221 \| 0.9107 \| 0.9809 \| \| 0.071 \| 29.0 \| 2784 \| 0.0542 \| 0.8687 \| 0.9149 \| 0.8912 \| 94 \| 0.8655 \| 0.8862 \| 0.8757 \| 167 \| 0.9783 \| 0.9854 \| 0.9818 \| 137 \| 0.9044 \| 0.9271 \| 0.9156 \| 0.9818 \| \| 0.0705 \| 30.0 \| 2880 \| 0.0549 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8690 \| 0.8743 \| 0.8716 \| 167 \| 0.9854 \| 0.9854 \| 0.9854 \| 137 \| 0.9022 \| 0.9271 \| 0.9145 \| 0.9818 \| \| 0.0702 \| 31.0 \| 2976 \| 0.0517 \| 0.8687 \| 0.9149 \| 0.8912 \| 94 \| 0.8817 \| 0.8922 \| 0.8869 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9181 \| 0.9296 \| 0.9238 \| 0.9834 \| \| 0.065 \| 32.0 \| 3072 \| 0.0532 \| 0.8396 \| 0.9468 \| 0.89 \| 94 \| 0.8951 \| 0.8683 \| 0.8815 \| 167 \| 0.9926 \| 0.9854 \| 0.9890 \| 137 \| 0.9134 \| 0.9271 \| 0.9202 \| 0.9826 \| \| 0.0639 \| 33.0 \| 3168 \| 0.0533 \| 0.8286 \| 0.9255 \| 0.8744 \| 94 \| 0.8780 \| 0.8623 \| 0.8701 \| 167 \| 0.9926 \| 0.9854 \| 0.9890 \| 137 \| 0.9037 \| 0.9196 \| 0.9116 \| 0.9815 \| \| 0.0642 \| 34.0 \| 3264 \| 0.0520 \| 0.8529 \| 0.9255 \| 0.8878 \| 94 \| 0.875 \| 0.8802 \| 0.8776 \| 167 \| 0.9926 \| 0.9854 \| 0.9890 \| 137 \| 0.9089 \| 0.9271 \| 0.9179 \| 0.9820 \| \| 0.0652 \| 35.0 \| 3360 \| 0.0518 \| 0.8515 \| 0.9149 \| 0.8821 \| 94 \| 0.8690 \| 0.8743 \| 0.8716 \| 167 \| 0.9926 \| 0.9854 \| 0.9890 \| 137 \| 0.9062 \| 0.9221 \| 0.9141 \| 0.9815 \| \| 0.0627 \| 36.0 \| 3456 \| 0.0533 \| 0.87 \| 0.9255 \| 0.8969 \| 94 \| 0.8655 \| 0.8862 \| 0.8757 \| 167 \| 0.9854 \| 0.9854 \| 0.9854 \| 137 \| 0.9069 \| 0.9296 \| 0.9181 \| 0.9818 \| \| 0.0606 \| 37.0 \| 3552 \| 0.0503 \| 0.8878 \| 0.9255 \| 0.9062 \| 94 \| 0.8698 \| 0.8802 \| 0.8750 \| 167 \| 0.9926 \| 0.9854 \| 0.9890 \| 137 \| 0.9156 \| 0.9271 \| 0.9213 \| 0.9826 \| \| 0.0611 \| 38.0 \| 3648 \| 0.0497 \| 0.87 \| 0.9255 \| 0.8969 \| 94 \| 0.8848 \| 0.8743 \| 0.8795 \| 167 \| 0.9854 \| 0.9854 \| 0.9854 \| 137 \| 0.9154 \| 0.9246 \| 0.92 \| 0.9829 \| \| 0.0645 \| 39.0 \| 3744 \| 0.0511 \| 0.8431 \| 0.9149 \| 0.8776 \| 94 \| 0.8780 \| 0.8623 \| 0.8701 \| 167 \| 0.9926 \| 0.9854 \| 0.9890 \| 137 \| 0.9080 \| 0.9171 \| 0.9125 \| 0.9823 \| \| 0.061 \| 40.0 \| 3840 \| 0.0487 \| 0.8687 \| 0.9149 \| 0.8912 \| 94 \| 0.8765 \| 0.8922 \| 0.8843 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9158 \| 0.9296 \| 0.9227 \| 0.9840 \| \| 0.0591 \| 41.0 \| 3936 \| 0.0491 \| 0.8515 \| 0.9149 \| 0.8821 \| 94 \| 0.8802 \| 0.8802 \| 0.8802 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9132 \| 0.9246 \| 0.9189 \| 0.9834 \| \| 0.058 \| 42.0 \| 4032 \| 0.0480 \| 0.8687 \| 0.9149 \| 0.8912 \| 94 \| 0.8757 \| 0.8862 \| 0.8810 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9156 \| 0.9271 \| 0.9213 \| 0.9840 \| \| 0.0587 \| 43.0 \| 4128 \| 0.0494 \| 0.8350 \| 0.9149 \| 0.8731 \| 94 \| 0.8720 \| 0.8563 \| 0.8640 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9055 \| 0.9146 \| 0.91 \| 0.9820 \| \| 0.0562 \| 44.0 \| 4224 \| 0.0482 \| 0.8515 \| 0.9149 \| 0.8821 \| 94 \| 0.8788 \| 0.8683 \| 0.8735 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9127 \| 0.9196 \| 0.9161 \| 0.9829 \| \| 0.0565 \| 45.0 \| 4320 \| 0.0471 \| 0.8529 \| 0.9255 \| 0.8878 \| 94 \| 0.8795 \| 0.8743 \| 0.8769 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9132 \| 0.9246 \| 0.9189 \| 0.9837 \| \| 0.0541 \| 46.0 \| 4416 \| 0.0482 \| 0.8365 \| 0.9255 \| 0.8788 \| 94 \| 0.8795 \| 0.8743 \| 0.8769 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9086 \| 0.9246 \| 0.9166 \| 0.9831 \| \| 0.0547 \| 47.0 \| 4512 \| 0.0487 \| 0.8350 \| 0.9149 \| 0.8731 \| 94 \| 0.8720 \| 0.8563 \| 0.8640 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9055 \| 0.9146 \| 0.91 \| 0.9823 \| \| 0.0537 \| 48.0 \| 4608 \| 0.0480 \| 0.8269 \| 0.9149 \| 0.8687 \| 94 \| 0.8659 \| 0.8503 \| 0.8580 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9007 \| 0.9121 \| 0.9064 \| 0.9829 \| \| 0.0525 \| 49.0 \| 4704 \| 0.0477 \| 0.8416 \| 0.9043 \| 0.8718 \| 94 \| 0.8882 \| 0.8563 \| 0.8720 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9144 \| 0.9121 \| 0.9132 \| 0.9826 \| \| 0.0513 \| 50.0 \| 4800 \| 0.0472 \| 0.86 \| 0.9149 \| 0.8866 \| 94 \| 0.8596 \| 0.8802 \| 0.8698 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9064 \| 0.9246 \| 0.9154 \| 0.9845 \| \| 0.0507 \| 51.0 \| 4896 \| 0.0481 \| 0.8286 \| 0.9255 \| 0.8744 \| 94 \| 0.875 \| 0.8383 \| 0.8563 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.905 \| 0.9095 \| 0.9073 \| 0.9820 \| \| 0.0499 \| 52.0 \| 4992 \| 0.0472 \| 0.87 \| 0.9255 \| 0.8969 \| 94 \| 0.8757 \| 0.8862 \| 0.8810 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9158 \| 0.9296 \| 0.9227 \| 0.9837 \| \| 0.0519 \| 53.0 \| 5088 \| 0.0471 \| 0.8614 \| 0.9255 \| 0.8923 \| 94 \| 0.8743 \| 0.8743 \| 0.8743 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9132 \| 0.9246 \| 0.9189 \| 0.9840 \| \| 0.0523 \| 54.0 \| 5184 \| 0.0483 \| 0.8286 \| 0.9255 \| 0.8744 \| 94 \| 0.8545 \| 0.8443 \| 0.8494 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.8963 \| 0.9121 \| 0.9041 \| 0.9826 \| \| 0.0507 \| 55.0 \| 5280 \| 0.0465 \| 0.8447 \| 0.9255 \| 0.8832 \| 94 \| 0.8614 \| 0.8563 \| 0.8589 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9035 \| 0.9171 \| 0.9102 \| 0.9831 \| \| 0.0506 \| 56.0 \| 5376 \| 0.0465 \| 0.8447 \| 0.9255 \| 0.8832 \| 94 \| 0.8614 \| 0.8563 \| 0.8589 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9035 \| 0.9171 \| 0.9102 \| 0.9831 \| \| 0.0504 \| 57.0 \| 5472 \| 0.0475 \| 0.8208 \| 0.9255 \| 0.8700 \| 94 \| 0.8452 \| 0.8503 \| 0.8478 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.8900 \| 0.9146 \| 0.9021 \| 0.9831 \| \| 0.0484 \| 58.0 \| 5568 \| 0.0462 \| 0.8302 \| 0.9362 \| 0.88 \| 94 \| 0.8659 \| 0.8503 \| 0.8580 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9012 \| 0.9171 \| 0.9091 \| 0.9837 \| \| 0.0487 \| 59.0 \| 5664 \| 0.0457 \| 0.8447 \| 0.9255 \| 0.8832 \| 94 \| 0.8727 \| 0.8623 \| 0.8675 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9082 \| 0.9196 \| 0.9139 \| 0.9837 \| \| 0.0463 \| 60.0 \| 5760 \| 0.0475 \| 0.8365 \| 0.9255 \| 0.8788 \| 94 \| 0.8623 \| 0.8623 \| 0.8623 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9015 \| 0.9196 \| 0.9104 \| 0.9848 \| \| 0.0462 \| 61.0 \| 5856 \| 0.0469 \| 0.8529 \| 0.9255 \| 0.8878 \| 94 \| 0.8655 \| 0.8862 \| 0.8757 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9069 \| 0.9296 \| 0.9181 \| 0.9848 \| \| 0.0497 \| 62.0 \| 5952 \| 0.0469 \| 0.8544 \| 0.9362 \| 0.8934 \| 94 \| 0.8521 \| 0.8623 \| 0.8571 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9017 \| 0.9221 \| 0.9118 \| 0.9845 \| \| 0.0465 \| 63.0 \| 6048 \| 0.0469 \| 0.8515 \| 0.9149 \| 0.8821 \| 94 \| 0.8683 \| 0.8683 \| 0.8683 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9082 \| 0.9196 \| 0.9139 \| 0.9848 \| \| 0.0468 \| 64.0 \| 6144 \| 0.0470 \| 0.86 \| 0.9149 \| 0.8866 \| 94 \| 0.8841 \| 0.8683 \| 0.8761 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9173 \| 0.9196 \| 0.9184 \| 0.9843 \| \| 0.0455 \| 65.0 \| 6240 \| 0.0467 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8675 \| 0.8623 \| 0.8649 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9062 \| 0.9221 \| 0.9141 \| 0.9845 \| \| 0.0456 \| 66.0 \| 6336 \| 0.0463 \| 0.8431 \| 0.9149 \| 0.8776 \| 94 \| 0.8712 \| 0.8503 \| 0.8606 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9075 \| 0.9121 \| 0.9098 \| 0.9834 \| \| 0.0436 \| 67.0 \| 6432 \| 0.0457 \| 0.8365 \| 0.9255 \| 0.8788 \| 94 \| 0.8773 \| 0.8563 \| 0.8667 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9080 \| 0.9171 \| 0.9125 \| 0.9837 \| \| 0.0442 \| 68.0 \| 6528 \| 0.0464 \| 0.8365 \| 0.9255 \| 0.8788 \| 94 \| 0.8720 \| 0.8563 \| 0.8640 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9057 \| 0.9171 \| 0.9114 \| 0.9837 \| \| 0.0463 \| 69.0 \| 6624 \| 0.0463 \| 0.8447 \| 0.9255 \| 0.8832 \| 94 \| 0.8720 \| 0.8563 \| 0.8640 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9080 \| 0.9171 \| 0.9125 \| 0.9840 \| \| 0.0445 \| 70.0 \| 6720 \| 0.0457 \| 0.8529 \| 0.9255 \| 0.8878 \| 94 \| 0.8720 \| 0.8563 \| 0.8640 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9102 \| 0.9171 \| 0.9136 \| 0.9840 \| \| 0.0456 \| 71.0 \| 6816 \| 0.0474 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8788 \| 0.8683 \| 0.8735 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9109 \| 0.9246 \| 0.9177 \| 0.9851 \| \| 0.0473 \| 72.0 \| 6912 \| 0.0479 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8659 \| 0.8503 \| 0.8580 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9035 \| 0.9171 \| 0.9102 \| 0.9837 \| \| 0.0434 \| 73.0 \| 7008 \| 0.0475 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8712 \| 0.8503 \| 0.8606 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9057 \| 0.9171 \| 0.9114 \| 0.9840 \| \| 0.042 \| 74.0 \| 7104 \| 0.0463 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8765 \| 0.8503 \| 0.8632 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9102 \| 0.9171 \| 0.9136 \| 0.9837 \| \| 0.0438 \| 75.0 \| 7200 \| 0.0463 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8765 \| 0.8503 \| 0.8632 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9102 \| 0.9171 \| 0.9136 \| 0.9837 \| \| 0.0437 \| 76.0 \| 7296 \| 0.0459 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8623 \| 0.8623 \| 0.8623 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9039 \| 0.9221 \| 0.9129 \| 0.9843 \| \| 0.0455 \| 77.0 \| 7392 \| 0.0469 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8827 \| 0.8563 \| 0.8693 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9104 \| 0.9196 \| 0.9150 \| 0.9840 \| \| 0.0426 \| 78.0 \| 7488 \| 0.0467 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8727 \| 0.8623 \| 0.8675 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9062 \| 0.9221 \| 0.9141 \| 0.9848 \| \| 0.043 \| 79.0 \| 7584 \| 0.0457 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8735 \| 0.8683 \| 0.8709 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9064 \| 0.9246 \| 0.9154 \| 0.9854 \| \| 0.0435 \| 80.0 \| 7680 \| 0.0462 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8727 \| 0.8623 \| 0.8675 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9062 \| 0.9221 \| 0.9141 \| 0.9851 \| \| 0.0411 \| 81.0 \| 7776 \| 0.0461 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8606 \| 0.8503 \| 0.8554 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9012 \| 0.9171 \| 0.9091 \| 0.9843 \| \| 0.0421 \| 82.0 \| 7872 \| 0.0458 \| 0.8544 \| 0.9362 \| 0.8934 \| 94 \| 0.8720 \| 0.8563 \| 0.8640 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9104 \| 0.9196 \| 0.9150 \| 0.9843 \| \| 0.0416 \| 83.0 \| 7968 \| 0.0462 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8773 \| 0.8563 \| 0.8667 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9082 \| 0.9196 \| 0.9139 \| 0.9843 \| \| 0.0412 \| 84.0 \| 8064 \| 0.0461 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8788 \| 0.8683 \| 0.8735 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9109 \| 0.9246 \| 0.9177 \| 0.9851 \| \| 0.0428 \| 85.0 \| 8160 \| 0.0465 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8773 \| 0.8563 \| 0.8667 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9104 \| 0.9196 \| 0.9150 \| 0.9845 \| \| 0.0434 \| 86.0 \| 8256 \| 0.0467 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8720 \| 0.8563 \| 0.8640 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9059 \| 0.9196 \| 0.9127 \| 0.9840 \| \| 0.0411 \| 87.0 \| 8352 \| 0.0466 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8720 \| 0.8563 \| 0.8640 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9059 \| 0.9196 \| 0.9127 \| 0.9840 \| \| 0.0436 \| 88.0 \| 8448 \| 0.0467 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8780 \| 0.8623 \| 0.8701 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9084 \| 0.9221 \| 0.9152 \| 0.9848 \| \| 0.0413 \| 89.0 \| 8544 \| 0.0460 \| 0.8544 \| 0.9362 \| 0.8934 \| 94 \| 0.8795 \| 0.8743 \| 0.8769 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9134 \| 0.9271 \| 0.9202 \| 0.9854 \| \| 0.0401 \| 90.0 \| 8640 \| 0.0467 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8675 \| 0.8623 \| 0.8649 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9062 \| 0.9221 \| 0.9141 \| 0.9848 \| \| 0.0421 \| 91.0 \| 8736 \| 0.0467 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8780 \| 0.8623 \| 0.8701 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9107 \| 0.9221 \| 0.9164 \| 0.9845 \| \| 0.0407 \| 92.0 \| 8832 \| 0.0462 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8773 \| 0.8563 \| 0.8667 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9104 \| 0.9196 \| 0.9150 \| 0.9845 \| \| 0.0449 \| 93.0 \| 8928 \| 0.0463 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8773 \| 0.8563 \| 0.8667 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9104 \| 0.9196 \| 0.9150 \| 0.9845 \| \| 0.0397 \| 94.0 \| 9024 \| 0.0462 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8667 \| 0.8563 \| 0.8614 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9037 \| 0.9196 \| 0.9116 \| 0.9845 \| \| 0.0417 \| 95.0 \| 9120 \| 0.0463 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8667 \| 0.8563 \| 0.8614 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9037 \| 0.9196 \| 0.9116 \| 0.9845 \| \| 0.0402 \| 96.0 \| 9216 \| 0.0465 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8780 \| 0.8623 \| 0.8701 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9084 \| 0.9221 \| 0.9152 \| 0.9848 \| \| 0.0422 \| 97.0 \| 9312 \| 0.0464 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8720 \| 0.8563 \| 0.8640 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9082 \| 0.9196 \| 0.9139 \| 0.9851 \| \| 0.0417 \| 98.0 \| 9408 \| 0.0463 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8720 \| 0.8563 \| 0.8640 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9082 \| 0.9196 \| 0.9139 \| 0.9851 \| \| 0.0409 \| 99.0 \| 9504 \| 0.0463 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8667 \| 0.8563 \| 0.8614 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9059 \| 0.9196 \| 0.9127 \| 0.9848 \| \| 0.0404 \| 100.0 \| 9600 \| 0.0463 \| 0.8462 \| 0.9362 \| 0.8889 \| 94 \| 0.8667 \| 0.8563 \| 0.8614 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9059 \| 0.9196 \| 0.9127 \| 0.9848 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
himanshubeniwal/mt5-base-finetuned-kk-to-en-cold-burger	himanshubeniwal	2024-06-04T01:18:43Z	107	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-04T01:13:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lcw99/llama-3-10b-ko-240604-e2f	lcw99	2024-06-04T01:17:10Z	2,249	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T00:37:02Z	--- language: - ko license: apache-2.0 library_name: transformers --- # Model Card for Model ID ## Model Details ### Model Description Korean layer added instruction tunning of meta-llama/Meta-Llama-3-8B-Instruct #### Chat template tokenizer.apply_chat_template(chat, tokenize=False)
baf2b252097d46299a/medical_summarizer_6ec63f0624e84fea9af33517007b93a4	baf2b252097d46299a	2024-06-04T01:13:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-04T01:13:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
melancholic/neotraditional_tattoo_lora	melancholic	2024-06-04T01:09:32Z	3	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-06-03T06:17:47Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a neotraditional tattoo style widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - melancholic/neotraditional_tattoo_lora <Gallery /> ## Model description These are melancholic/neotraditional_tattoo_lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a neotraditional tattoo style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](melancholic/neotraditional_tattoo_lora/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
apwic/nerui-base-3	apwic	2024-06-04T01:02:10Z	24	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-28T05:46:39Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerui-base-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nerui-base-3 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1047 - Location Precision: 0.8925 - Location Recall: 0.9651 - Location F1: 0.9274 - Location Number: 86 - Organization Precision: 0.9538 - Organization Recall: 0.9270 - Organization F1: 0.9402 - Organization Number: 178 - Person Precision: 0.9685 - Person Recall: 0.9609 - Person F1: 0.9647 - Person Number: 128 - Overall Precision: 0.9440 - Overall Recall: 0.9464 - Overall F1: 0.9452 - Overall Accuracy: 0.9876 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Location Precision \| Location Recall \| Location F1 \| Location Number \| Organization Precision \| Organization Recall \| Organization F1 \| Organization Number \| Person Precision \| Person Recall \| Person F1 \| Person Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------------:\|:---------------:\|:-----------:\|:---------------:\|:----------------------:\|:-------------------:\|:---------------:\|:-------------------:\|:----------------:\|:-------------:\|:---------:\|:-------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.2442 \| 1.0 \| 96 \| 0.0581 \| 0.8384 \| 0.9651 \| 0.8973 \| 86 \| 0.8535 \| 0.9494 \| 0.8989 \| 178 \| 0.9690 \| 0.9766 \| 0.9728 \| 128 \| 0.8850 \| 0.9617 \| 0.9218 \| 0.9822 \| \| 0.0581 \| 2.0 \| 192 \| 0.0548 \| 0.8283 \| 0.9535 \| 0.8865 \| 86 \| 0.9464 \| 0.8933 \| 0.9191 \| 178 \| 0.9690 \| 0.9766 \| 0.9728 \| 128 \| 0.9242 \| 0.9337 \| 0.9289 \| 0.9852 \| \| 0.0357 \| 3.0 \| 288 \| 0.0514 \| 0.8542 \| 0.9535 \| 0.9011 \| 86 \| 0.9310 \| 0.9101 \| 0.9205 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9293 \| 0.9388 \| 0.9340 \| 0.9857 \| \| 0.0251 \| 4.0 \| 384 \| 0.0607 \| 0.8989 \| 0.9302 \| 0.9143 \| 86 \| 0.8942 \| 0.9494 \| 0.9210 \| 178 \| 0.9837 \| 0.9453 \| 0.9641 \| 128 \| 0.9227 \| 0.9439 \| 0.9332 \| 0.9852 \| \| 0.0146 \| 5.0 \| 480 \| 0.0617 \| 0.8804 \| 0.9419 \| 0.9101 \| 86 \| 0.9231 \| 0.9438 \| 0.9333 \| 178 \| 0.976 \| 0.9531 \| 0.9644 \| 128 \| 0.9298 \| 0.9464 \| 0.9381 \| 0.9865 \| \| 0.0117 \| 6.0 \| 576 \| 0.0706 \| 0.8511 \| 0.9302 \| 0.8889 \| 86 \| 0.9066 \| 0.9270 \| 0.9167 \| 178 \| 0.9758 \| 0.9453 \| 0.9603 \| 128 \| 0.915 \| 0.9337 \| 0.9242 \| 0.9857 \| \| 0.0083 \| 7.0 \| 672 \| 0.0926 \| 0.7788 \| 0.9419 \| 0.8526 \| 86 \| 0.9162 \| 0.9213 \| 0.9188 \| 178 \| 0.9462 \| 0.9609 \| 0.9535 \| 128 \| 0.8910 \| 0.9388 \| 0.9143 \| 0.9819 \| \| 0.008 \| 8.0 \| 768 \| 0.0781 \| 0.8617 \| 0.9419 \| 0.9000 \| 86 \| 0.9535 \| 0.9213 \| 0.9371 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9412 \| 0.9388 \| 0.9400 \| 0.9857 \| \| 0.0042 \| 9.0 \| 864 \| 0.0659 \| 0.8764 \| 0.9070 \| 0.8914 \| 86 \| 0.9663 \| 0.9663 \| 0.9663 \| 178 \| 0.9764 \| 0.9688 \| 0.9725 \| 128 \| 0.9492 \| 0.9541 \| 0.9517 \| 0.9889 \| \| 0.0044 \| 10.0 \| 960 \| 0.0712 \| 0.8681 \| 0.9186 \| 0.8927 \| 86 \| 0.9389 \| 0.9494 \| 0.9441 \| 178 \| 0.9457 \| 0.9531 \| 0.9494 \| 128 \| 0.925 \| 0.9439 \| 0.9343 \| 0.9873 \| \| 0.005 \| 11.0 \| 1056 \| 0.0855 \| 0.8384 \| 0.9651 \| 0.8973 \| 86 \| 0.9438 \| 0.9438 \| 0.9438 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9280 \| 0.9541 \| 0.9409 \| 0.9870 \| \| 0.0036 \| 12.0 \| 1152 \| 0.0859 \| 0.8710 \| 0.9419 \| 0.9050 \| 86 \| 0.9435 \| 0.9382 \| 0.9408 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9392 \| 0.9464 \| 0.9428 \| 0.9873 \| \| 0.0042 \| 13.0 \| 1248 \| 0.0761 \| 0.8901 \| 0.9419 \| 0.9153 \| 86 \| 0.9448 \| 0.9607 \| 0.9526 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9446 \| 0.9566 \| 0.9506 \| 0.9889 \| \| 0.0036 \| 14.0 \| 1344 \| 0.0843 \| 0.8876 \| 0.9186 \| 0.9029 \| 86 \| 0.9538 \| 0.9270 \| 0.9402 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9485 \| 0.9388 \| 0.9436 \| 0.9862 \| \| 0.0028 \| 15.0 \| 1440 \| 0.0906 \| 0.8723 \| 0.9535 \| 0.9111 \| 86 \| 0.9429 \| 0.9270 \| 0.9348 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9391 \| 0.9439 \| 0.9415 \| 0.9868 \| \| 0.0017 \| 16.0 \| 1536 \| 0.0914 \| 0.8526 \| 0.9419 \| 0.8950 \| 86 \| 0.9645 \| 0.9157 \| 0.9395 \| 178 \| 0.9683 \| 0.9531 \| 0.9606 \| 128 \| 0.9385 \| 0.9337 \| 0.9361 \| 0.9862 \| \| 0.002 \| 17.0 \| 1632 \| 0.0828 \| 0.8587 \| 0.9186 \| 0.8876 \| 86 \| 0.9545 \| 0.9438 \| 0.9492 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9391 \| 0.9439 \| 0.9415 \| 0.9884 \| \| 0.0033 \| 18.0 \| 1728 \| 0.0641 \| 0.8646 \| 0.9651 \| 0.9121 \| 86 \| 0.9126 \| 0.9382 \| 0.9252 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9235 \| 0.9541 \| 0.9385 \| 0.9887 \| \| 0.0024 \| 19.0 \| 1824 \| 0.0982 \| 0.8667 \| 0.9070 \| 0.8864 \| 86 \| 0.9297 \| 0.9663 \| 0.9477 \| 178 \| 0.9683 \| 0.9531 \| 0.9606 \| 128 \| 0.9277 \| 0.9490 \| 0.9382 \| 0.9868 \| \| 0.0037 \| 20.0 \| 1920 \| 0.0904 \| 0.8283 \| 0.9535 \| 0.8865 \| 86 \| 0.9659 \| 0.9551 \| 0.9605 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9375 \| 0.9566 \| 0.9470 \| 0.9887 \| \| 0.0038 \| 21.0 \| 2016 \| 0.0787 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9385 \| 0.9438 \| 0.9412 \| 178 \| 0.9609 \| 0.9609 \| 0.9609 \| 128 \| 0.935 \| 0.9541 \| 0.9444 \| 0.9879 \| \| 0.0024 \| 22.0 \| 2112 \| 0.0697 \| 0.8526 \| 0.9419 \| 0.8950 \| 86 \| 0.9286 \| 0.9494 \| 0.9389 \| 178 \| 0.9677 \| 0.9375 \| 0.9524 \| 128 \| 0.9227 \| 0.9439 \| 0.9332 \| 0.9889 \| \| 0.0041 \| 23.0 \| 2208 \| 0.0794 \| 0.9011 \| 0.9535 \| 0.9266 \| 86 \| 0.9441 \| 0.9494 \| 0.9468 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9421 \| 0.9541 \| 0.9480 \| 0.9876 \| \| 0.0033 \| 24.0 \| 2304 \| 0.0830 \| 0.9 \| 0.9419 \| 0.9205 \| 86 \| 0.9231 \| 0.9438 \| 0.9333 \| 178 \| 0.9758 \| 0.9453 \| 0.9603 \| 128 \| 0.9343 \| 0.9439 \| 0.9391 \| 0.9881 \| \| 0.0034 \| 25.0 \| 2400 \| 0.0804 \| 0.8632 \| 0.9535 \| 0.9061 \| 86 \| 0.9448 \| 0.9607 \| 0.9526 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9378 \| 0.9617 \| 0.9496 \| 0.9881 \| \| 0.0012 \| 26.0 \| 2496 \| 0.0728 \| 0.9011 \| 0.9535 \| 0.9266 \| 86 \| 0.9341 \| 0.9551 \| 0.9444 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9424 \| 0.9592 \| 0.9507 \| 0.9903 \| \| 0.0015 \| 27.0 \| 2592 \| 0.0957 \| 0.9101 \| 0.9419 \| 0.9257 \| 86 \| 0.9301 \| 0.9719 \| 0.9505 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9401 \| 0.9617 \| 0.9508 \| 0.9881 \| \| 0.0029 \| 28.0 \| 2688 \| 0.0766 \| 0.8830 \| 0.9651 \| 0.9222 \| 86 \| 0.9545 \| 0.9438 \| 0.9492 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9470 \| 0.9566 \| 0.9518 \| 0.9881 \| \| 0.0031 \| 29.0 \| 2784 \| 0.0802 \| 0.8571 \| 0.9767 \| 0.9130 \| 86 \| 0.9649 \| 0.9270 \| 0.9456 \| 178 \| 0.9764 \| 0.9688 \| 0.9725 \| 128 \| 0.9419 \| 0.9515 \| 0.9467 \| 0.9879 \| \| 0.0018 \| 30.0 \| 2880 \| 0.0837 \| 0.8710 \| 0.9419 \| 0.9050 \| 86 \| 0.9605 \| 0.9551 \| 0.9577 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9470 \| 0.9566 \| 0.9518 \| 0.9892 \| \| 0.0017 \| 31.0 \| 2976 \| 0.0792 \| 0.9222 \| 0.9651 \| 0.9432 \| 86 \| 0.9505 \| 0.9719 \| 0.9611 \| 178 \| 0.9683 \| 0.9531 \| 0.9606 \| 128 \| 0.9497 \| 0.9643 \| 0.9570 \| 0.9903 \| \| 0.0017 \| 32.0 \| 3072 \| 0.0675 \| 0.8737 \| 0.9651 \| 0.9171 \| 86 \| 0.9661 \| 0.9607 \| 0.9634 \| 178 \| 0.976 \| 0.9531 \| 0.9644 \| 128 \| 0.9471 \| 0.9592 \| 0.9531 \| 0.9906 \| \| 0.0012 \| 33.0 \| 3168 \| 0.0909 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9709 \| 0.9382 \| 0.9543 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9564 \| 0.9515 \| 0.9540 \| 0.9897 \| \| 0.002 \| 34.0 \| 3264 \| 0.1077 \| 0.9101 \| 0.9419 \| 0.9257 \| 86 \| 0.9422 \| 0.9157 \| 0.9288 \| 178 \| 0.968 \| 0.9453 \| 0.9565 \| 128 \| 0.9432 \| 0.9311 \| 0.9371 \| 0.9846 \| \| 0.0023 \| 35.0 \| 3360 \| 0.0912 \| 0.8913 \| 0.9535 \| 0.9213 \| 86 \| 0.9396 \| 0.9607 \| 0.95 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.94 \| 0.9592 \| 0.9495 \| 0.9881 \| \| 0.0016 \| 36.0 \| 3456 \| 0.0839 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9655 \| 0.9438 \| 0.9545 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9541 \| 0.9541 \| 0.9541 \| 0.9892 \| \| 0.0012 \| 37.0 \| 3552 \| 0.1070 \| 0.8817 \| 0.9535 \| 0.9162 \| 86 \| 0.9480 \| 0.9213 \| 0.9345 \| 178 \| 0.976 \| 0.9531 \| 0.9644 \| 128 \| 0.9412 \| 0.9388 \| 0.9400 \| 0.9857 \| \| 0.0009 \| 38.0 \| 3648 \| 0.0856 \| 0.8947 \| 0.9884 \| 0.9392 \| 86 \| 0.9540 \| 0.9326 \| 0.9432 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9492 \| 0.9541 \| 0.9517 \| 0.9884 \| \| 0.0006 \| 39.0 \| 3744 \| 0.0964 \| 0.8936 \| 0.9767 \| 0.9333 \| 86 \| 0.9483 \| 0.9270 \| 0.9375 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9418 \| 0.9490 \| 0.9454 \| 0.9862 \| \| 0.0011 \| 40.0 \| 3840 \| 0.0992 \| 0.9011 \| 0.9535 \| 0.9266 \| 86 \| 0.9492 \| 0.9438 \| 0.9465 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9467 \| 0.9515 \| 0.9491 \| 0.9870 \| \| 0.0009 \| 41.0 \| 3936 \| 0.1072 \| 0.9032 \| 0.9767 \| 0.9385 \| 86 \| 0.9489 \| 0.9382 \| 0.9435 \| 178 \| 0.976 \| 0.9531 \| 0.9644 \| 128 \| 0.9467 \| 0.9515 \| 0.9491 \| 0.9860 \| \| 0.0007 \| 42.0 \| 4032 \| 0.1193 \| 0.8936 \| 0.9767 \| 0.9333 \| 86 \| 0.9595 \| 0.9326 \| 0.9459 \| 178 \| 0.9839 \| 0.9531 \| 0.9683 \| 128 \| 0.9514 \| 0.9490 \| 0.9502 \| 0.9865 \| \| 0.0014 \| 43.0 \| 4128 \| 0.1129 \| 0.9032 \| 0.9767 \| 0.9385 \| 86 \| 0.9489 \| 0.9382 \| 0.9435 \| 178 \| 0.9683 \| 0.9531 \| 0.9606 \| 128 \| 0.9443 \| 0.9515 \| 0.9479 \| 0.9868 \| \| 0.0007 \| 44.0 \| 4224 \| 0.1289 \| 0.9130 \| 0.9767 \| 0.9438 \| 86 \| 0.9492 \| 0.9438 \| 0.9465 \| 178 \| 0.9609 \| 0.9609 \| 0.9609 \| 128 \| 0.9446 \| 0.9566 \| 0.9506 \| 0.9849 \| \| 0.0006 \| 45.0 \| 4320 \| 0.1167 \| 0.8842 \| 0.9767 \| 0.9282 \| 86 \| 0.9392 \| 0.9551 \| 0.9471 \| 178 \| 0.9688 \| 0.9688 \| 0.9688 \| 128 \| 0.9356 \| 0.9643 \| 0.9497 \| 0.9868 \| \| 0.0014 \| 46.0 \| 4416 \| 0.1168 \| 0.8646 \| 0.9651 \| 0.9121 \| 86 \| 0.9543 \| 0.9382 \| 0.9462 \| 178 \| 0.9839 \| 0.9531 \| 0.9683 \| 128 \| 0.9418 \| 0.9490 \| 0.9454 \| 0.9873 \| \| 0.0022 \| 47.0 \| 4512 \| 0.1090 \| 0.8737 \| 0.9651 \| 0.9171 \| 86 \| 0.9702 \| 0.9157 \| 0.9422 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9512 \| 0.9439 \| 0.9475 \| 0.9868 \| \| 0.0033 \| 48.0 \| 4608 \| 0.0899 \| 0.9222 \| 0.9651 \| 0.9432 \| 86 \| 0.9333 \| 0.9438 \| 0.9385 \| 178 \| 0.9758 \| 0.9453 \| 0.9603 \| 128 \| 0.9442 \| 0.9490 \| 0.9466 \| 0.9889 \| \| 0.001 \| 49.0 \| 4704 \| 0.1123 \| 0.8830 \| 0.9651 \| 0.9222 \| 86 \| 0.9704 \| 0.9213 \| 0.9452 \| 178 \| 0.9839 \| 0.9531 \| 0.9683 \| 128 \| 0.9535 \| 0.9413 \| 0.9474 \| 0.9870 \| \| 0.0007 \| 50.0 \| 4800 \| 0.0937 \| 0.9011 \| 0.9535 \| 0.9266 \| 86 \| 0.9486 \| 0.9326 \| 0.9405 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9488 \| 0.9464 \| 0.9476 \| 0.9887 \| \| 0.0011 \| 51.0 \| 4896 \| 0.1082 \| 0.9032 \| 0.9767 \| 0.9385 \| 86 \| 0.9278 \| 0.9382 \| 0.9330 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9398 \| 0.9566 \| 0.9482 \| 0.9865 \| \| 0.0015 \| 52.0 \| 4992 \| 0.1112 \| 0.9011 \| 0.9535 \| 0.9266 \| 86 \| 0.9645 \| 0.9157 \| 0.9395 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9534 \| 0.9388 \| 0.9460 \| 0.9879 \| \| 0.0009 \| 53.0 \| 5088 \| 0.1032 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9341 \| 0.9551 \| 0.9444 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.94 \| 0.9592 \| 0.9495 \| 0.9881 \| \| 0.0033 \| 54.0 \| 5184 \| 0.1181 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9593 \| 0.9270 \| 0.9429 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9513 \| 0.9464 \| 0.9488 \| 0.9870 \| \| 0.0008 \| 55.0 \| 5280 \| 0.1207 \| 0.9022 \| 0.9651 \| 0.9326 \| 86 \| 0.9651 \| 0.9326 \| 0.9486 \| 178 \| 0.9688 \| 0.9688 \| 0.9688 \| 128 \| 0.9515 \| 0.9515 \| 0.9515 \| 0.9865 \| \| 0.0009 \| 56.0 \| 5376 \| 0.1379 \| 0.8632 \| 0.9535 \| 0.9061 \| 86 \| 0.9702 \| 0.9157 \| 0.9422 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9485 \| 0.9388 \| 0.9436 \| 0.9857 \| \| 0.001 \| 57.0 \| 5472 \| 0.1120 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9708 \| 0.9326 \| 0.9513 \| 178 \| 0.984 \| 0.9609 \| 0.9723 \| 128 \| 0.9563 \| 0.9490 \| 0.9526 \| 0.9881 \| \| 0.0013 \| 58.0 \| 5568 \| 0.1086 \| 0.8830 \| 0.9651 \| 0.9222 \| 86 \| 0.9483 \| 0.9270 \| 0.9375 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9442 \| 0.9490 \| 0.9466 \| 0.9862 \| \| 0.0005 \| 59.0 \| 5664 \| 0.1218 \| 0.8660 \| 0.9767 \| 0.9180 \| 86 \| 0.9641 \| 0.9045 \| 0.9333 \| 178 \| 0.9538 \| 0.9688 \| 0.9612 \| 128 \| 0.9365 \| 0.9413 \| 0.9389 \| 0.9854 \| \| 0.0007 \| 60.0 \| 5760 \| 0.0958 \| 0.8913 \| 0.9535 \| 0.9213 \| 86 \| 0.9239 \| 0.9551 \| 0.9392 \| 178 \| 0.9839 \| 0.9531 \| 0.9683 \| 128 \| 0.935 \| 0.9541 \| 0.9444 \| 0.9881 \| \| 0.0002 \| 61.0 \| 5856 \| 0.1076 \| 0.8817 \| 0.9535 \| 0.9162 \| 86 \| 0.9593 \| 0.9270 \| 0.9429 \| 178 \| 0.976 \| 0.9531 \| 0.9644 \| 128 \| 0.9462 \| 0.9413 \| 0.9437 \| 0.9879 \| \| 0.0023 \| 62.0 \| 5952 \| 0.0877 \| 0.9140 \| 0.9884 \| 0.9497 \| 86 \| 0.9494 \| 0.9494 \| 0.9494 \| 178 \| 0.9764 \| 0.9688 \| 0.9725 \| 128 \| 0.9497 \| 0.9643 \| 0.9570 \| 0.9895 \| \| 0.0013 \| 63.0 \| 6048 \| 0.0885 \| 0.9032 \| 0.9767 \| 0.9385 \| 86 \| 0.9448 \| 0.9607 \| 0.9526 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9475 \| 0.9668 \| 0.9571 \| 0.9895 \| \| 0.0009 \| 64.0 \| 6144 \| 0.0825 \| 0.9032 \| 0.9767 \| 0.9385 \| 86 \| 0.9605 \| 0.9551 \| 0.9577 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9545 \| 0.9643 \| 0.9594 \| 0.9900 \| \| 0.0003 \| 65.0 \| 6240 \| 0.0838 \| 0.9222 \| 0.9651 \| 0.9432 \| 86 \| 0.96 \| 0.9438 \| 0.9518 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9591 \| 0.9566 \| 0.9579 \| 0.9884 \| \| 0.0006 \| 66.0 \| 6336 \| 0.0957 \| 0.9032 \| 0.9767 \| 0.9385 \| 86 \| 0.96 \| 0.9438 \| 0.9518 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9543 \| 0.9592 \| 0.9567 \| 0.9887 \| \| 0.0004 \| 67.0 \| 6432 \| 0.1129 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9649 \| 0.9270 \| 0.9456 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9538 \| 0.9490 \| 0.9514 \| 0.9879 \| \| 0.0003 \| 68.0 \| 6528 \| 0.1161 \| 0.8936 \| 0.9767 \| 0.9333 \| 86 \| 0.9538 \| 0.9270 \| 0.9402 \| 178 \| 0.9764 \| 0.9688 \| 0.9725 \| 128 \| 0.9467 \| 0.9515 \| 0.9491 \| 0.9870 \| \| 0.0002 \| 69.0 \| 6624 \| 0.1234 \| 0.8936 \| 0.9767 \| 0.9333 \| 86 \| 0.9645 \| 0.9157 \| 0.9395 \| 178 \| 0.9688 \| 0.9688 \| 0.9688 \| 128 \| 0.9488 \| 0.9464 \| 0.9476 \| 0.9862 \| \| 0.0006 \| 70.0 \| 6720 \| 0.1162 \| 0.9231 \| 0.9767 \| 0.9492 \| 86 \| 0.9651 \| 0.9326 \| 0.9486 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9614 \| 0.9541 \| 0.9577 \| 0.9884 \| \| 0.0002 \| 71.0 \| 6816 \| 0.1107 \| 0.9333 \| 0.9767 \| 0.9545 \| 86 \| 0.96 \| 0.9438 \| 0.9518 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9616 \| 0.9592 \| 0.9604 \| 0.9879 \| \| 0.0002 \| 72.0 \| 6912 \| 0.1121 \| 0.9231 \| 0.9767 \| 0.9492 \| 86 \| 0.9598 \| 0.9382 \| 0.9489 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9591 \| 0.9566 \| 0.9579 \| 0.9879 \| \| 0.0002 \| 73.0 \| 7008 \| 0.1122 \| 0.9231 \| 0.9767 \| 0.9492 \| 86 \| 0.9543 \| 0.9382 \| 0.9462 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9566 \| 0.9566 \| 0.9566 \| 0.9881 \| \| 0.0005 \| 74.0 \| 7104 \| 0.1127 \| 0.9231 \| 0.9767 \| 0.9492 \| 86 \| 0.9543 \| 0.9382 \| 0.9462 \| 178 \| 0.9841 \| 0.9688 \| 0.9764 \| 128 \| 0.9566 \| 0.9566 \| 0.9566 \| 0.9873 \| \| 0.0004 \| 75.0 \| 7200 \| 0.1170 \| 0.9130 \| 0.9767 \| 0.9438 \| 86 \| 0.9540 \| 0.9326 \| 0.9432 \| 178 \| 0.9688 \| 0.9688 \| 0.9688 \| 128 \| 0.9492 \| 0.9541 \| 0.9517 \| 0.9862 \| \| 0.0003 \| 76.0 \| 7296 \| 0.1089 \| 0.9333 \| 0.9767 \| 0.9545 \| 86 \| 0.9444 \| 0.9551 \| 0.9497 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9520 \| 0.9617 \| 0.9569 \| 0.9892 \| \| 0.001 \| 77.0 \| 7392 \| 0.1082 \| 0.9231 \| 0.9767 \| 0.9492 \| 86 \| 0.9503 \| 0.9663 \| 0.9582 \| 178 \| 0.9764 \| 0.9688 \| 0.9725 \| 128 \| 0.9524 \| 0.9694 \| 0.9608 \| 0.9895 \| \| 0.0012 \| 78.0 \| 7488 \| 0.1009 \| 0.9022 \| 0.9651 \| 0.9326 \| 86 \| 0.9330 \| 0.9382 \| 0.9356 \| 178 \| 0.9688 \| 0.9688 \| 0.9688 \| 128 \| 0.9373 \| 0.9541 \| 0.9456 \| 0.9862 \| \| 0.0002 \| 79.0 \| 7584 \| 0.1051 \| 0.8632 \| 0.9535 \| 0.9061 \| 86 \| 0.9489 \| 0.9382 \| 0.9435 \| 178 \| 0.976 \| 0.9531 \| 0.9644 \| 128 \| 0.9369 \| 0.9464 \| 0.9416 \| 0.9865 \| \| 0.0002 \| 80.0 \| 7680 \| 0.1108 \| 0.8723 \| 0.9535 \| 0.9111 \| 86 \| 0.9540 \| 0.9326 \| 0.9432 \| 178 \| 0.976 \| 0.9531 \| 0.9644 \| 128 \| 0.9415 \| 0.9439 \| 0.9427 \| 0.9865 \| \| 0.0005 \| 81.0 \| 7776 \| 0.1037 \| 0.8913 \| 0.9535 \| 0.9213 \| 86 \| 0.9543 \| 0.9382 \| 0.9462 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9466 \| 0.9490 \| 0.9478 \| 0.9870 \| \| 0.0003 \| 82.0 \| 7872 \| 0.1031 \| 0.8710 \| 0.9419 \| 0.9050 \| 86 \| 0.9540 \| 0.9326 \| 0.9432 \| 178 \| 0.976 \| 0.9531 \| 0.9644 \| 128 \| 0.9413 \| 0.9413 \| 0.9413 \| 0.9868 \| \| 0.0003 \| 83.0 \| 7968 \| 0.0996 \| 0.9121 \| 0.9651 \| 0.9379 \| 86 \| 0.9602 \| 0.9494 \| 0.9548 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9518 \| 0.9566 \| 0.9542 \| 0.9887 \| \| 0.0002 \| 84.0 \| 8064 \| 0.0987 \| 0.9222 \| 0.9651 \| 0.9432 \| 86 \| 0.9602 \| 0.9494 \| 0.9548 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9542 \| 0.9566 \| 0.9554 \| 0.9887 \| \| 0.0004 \| 85.0 \| 8160 \| 0.1017 \| 0.9222 \| 0.9651 \| 0.9432 \| 86 \| 0.9602 \| 0.9494 \| 0.9548 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9542 \| 0.9566 \| 0.9554 \| 0.9887 \| \| 0.0002 \| 86.0 \| 8256 \| 0.1018 \| 0.9222 \| 0.9651 \| 0.9432 \| 86 \| 0.9602 \| 0.9494 \| 0.9548 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9542 \| 0.9566 \| 0.9554 \| 0.9887 \| \| 0.0001 \| 87.0 \| 8352 \| 0.1017 \| 0.9222 \| 0.9651 \| 0.9432 \| 86 \| 0.9553 \| 0.9607 \| 0.9580 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9520 \| 0.9617 \| 0.9569 \| 0.9889 \| \| 0.0002 \| 88.0 \| 8448 \| 0.1028 \| 0.9222 \| 0.9651 \| 0.9432 \| 86 \| 0.9602 \| 0.9494 \| 0.9548 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9542 \| 0.9566 \| 0.9554 \| 0.9887 \| \| 0.0001 \| 89.0 \| 8544 \| 0.1033 \| 0.9222 \| 0.9651 \| 0.9432 \| 86 \| 0.9602 \| 0.9494 \| 0.9548 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9542 \| 0.9566 \| 0.9554 \| 0.9887 \| \| 0.0002 \| 90.0 \| 8640 \| 0.1026 \| 0.9213 \| 0.9535 \| 0.9371 \| 86 \| 0.9545 \| 0.9438 \| 0.9492 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9540 \| 0.9515 \| 0.9527 \| 0.9879 \| \| 0.0002 \| 91.0 \| 8736 \| 0.1024 \| 0.9213 \| 0.9535 \| 0.9371 \| 86 \| 0.9545 \| 0.9438 \| 0.9492 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9540 \| 0.9515 \| 0.9527 \| 0.9879 \| \| 0.0002 \| 92.0 \| 8832 \| 0.1025 \| 0.9213 \| 0.9535 \| 0.9371 \| 86 \| 0.9545 \| 0.9438 \| 0.9492 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9540 \| 0.9515 \| 0.9527 \| 0.9879 \| \| 0.0002 \| 93.0 \| 8928 \| 0.1039 \| 0.9213 \| 0.9535 \| 0.9371 \| 86 \| 0.9545 \| 0.9438 \| 0.9492 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9540 \| 0.9515 \| 0.9527 \| 0.9879 \| \| 0.0001 \| 94.0 \| 9024 \| 0.1034 \| 0.9213 \| 0.9535 \| 0.9371 \| 86 \| 0.9545 \| 0.9438 \| 0.9492 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9540 \| 0.9515 \| 0.9527 \| 0.9879 \| \| 0.0001 \| 95.0 \| 9120 \| 0.1036 \| 0.9213 \| 0.9535 \| 0.9371 \| 86 \| 0.9545 \| 0.9438 \| 0.9492 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9540 \| 0.9515 \| 0.9527 \| 0.9879 \| \| 0.0001 \| 96.0 \| 9216 \| 0.1087 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9538 \| 0.9270 \| 0.9402 \| 178 \| 0.9762 \| 0.9609 \| 0.9685 \| 128 \| 0.9464 \| 0.9464 \| 0.9464 \| 0.9873 \| \| 0.0005 \| 97.0 \| 9312 \| 0.1056 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9538 \| 0.9270 \| 0.9402 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9440 \| 0.9464 \| 0.9452 \| 0.9876 \| \| 0.0003 \| 98.0 \| 9408 \| 0.1045 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9538 \| 0.9270 \| 0.9402 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9440 \| 0.9464 \| 0.9452 \| 0.9876 \| \| 0.0001 \| 99.0 \| 9504 \| 0.1047 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9538 \| 0.9270 \| 0.9402 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9440 \| 0.9464 \| 0.9452 \| 0.9876 \| \| 0.0002 \| 100.0 \| 9600 \| 0.1047 \| 0.8925 \| 0.9651 \| 0.9274 \| 86 \| 0.9538 \| 0.9270 \| 0.9402 \| 178 \| 0.9685 \| 0.9609 \| 0.9647 \| 128 \| 0.9440 \| 0.9464 \| 0.9452 \| 0.9876 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
hienbm/llama-3-8b-Instruct-bnb-4bit	hienbm	2024-06-04T00:55:18Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-04T00:55:10Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: hienbm - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
harveybro/molt5-augmented-default-500-base-caption2smiles	harveybro	2024-06-04T00:55:05Z	108	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-04T00:54:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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harveybro/molt5-augmented-default-400-base-caption2smiles	harveybro	2024-06-04T00:49:43Z	107	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-04T00:49:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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datek/Qwen-Qwen1.5-7B-1717461786	datek	2024-06-04T00:47:06Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T00:43:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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luthfi507/emotion-classification	luthfi507	2024-06-04T00:41:26Z	15	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-06-03T15:43:25Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: emotion-classification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.6 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emotion-classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1597 - Accuracy: 0.6 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 1.5881 \| 0.4813 \| \| No log \| 2.0 \| 80 \| 1.4495 \| 0.4188 \| \| No log \| 3.0 \| 120 \| 1.3173 \| 0.525 \| \| No log \| 4.0 \| 160 \| 1.2644 \| 0.5375 \| \| No log \| 5.0 \| 200 \| 1.1238 \| 0.6125 \| \| No log \| 6.0 \| 240 \| 1.3448 \| 0.5563 \| \| No log \| 7.0 \| 280 \| 1.3241 \| 0.5938 \| \| No log \| 8.0 \| 320 \| 1.4283 \| 0.5625 \| \| No log \| 9.0 \| 360 \| 1.3231 \| 0.6062 \| \| No log \| 10.0 \| 400 \| 1.4146 \| 0.5938 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
apwic/nerui-base-1	apwic	2024-06-04T00:31:44Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-28T04:28:17Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerui-base-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nerui-base-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0822 - Location Precision: 0.9573 - Location Recall: 0.9655 - Location F1: 0.9614 - Location Number: 116 - Organization Precision: 0.9608 - Organization Recall: 0.9304 - Organization F1: 0.9453 - Organization Number: 158 - Person Precision: 0.984 - Person Recall: 0.9919 - Person F1: 0.9880 - Person Number: 124 - Overall Precision: 0.9671 - Overall Recall: 0.9598 - Overall F1: 0.9634 - Overall Accuracy: 0.9920 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Location Precision \| Location Recall \| Location F1 \| Location Number \| Organization Precision \| Organization Recall \| Organization F1 \| Organization Number \| Person Precision \| Person Recall \| Person F1 \| Person Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------------:\|:---------------:\|:-----------:\|:---------------:\|:----------------------:\|:-------------------:\|:---------------:\|:-------------------:\|:----------------:\|:-------------:\|:---------:\|:-------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.2668 \| 1.0 \| 96 \| 0.0394 \| 0.9145 \| 0.9224 \| 0.9185 \| 116 \| 0.9141 \| 0.9430 \| 0.9283 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9358 \| 0.9523 \| 0.9440 \| 0.9879 \| \| 0.0634 \| 2.0 \| 192 \| 0.0460 \| 0.9237 \| 0.9397 \| 0.9316 \| 116 \| 0.9477 \| 0.9177 \| 0.9325 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9520 \| 0.9472 \| 0.9496 \| 0.9882 \| \| 0.032 \| 3.0 \| 288 \| 0.0441 \| 0.9474 \| 0.9310 \| 0.9391 \| 116 \| 0.9427 \| 0.9367 \| 0.9397 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9890 \| \| 0.022 \| 4.0 \| 384 \| 0.0442 \| 0.9732 \| 0.9397 \| 0.9561 \| 116 \| 0.9255 \| 0.9430 \| 0.9342 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9573 \| 0.9573 \| 0.9573 \| 0.9909 \| \| 0.0143 \| 5.0 \| 480 \| 0.0474 \| 0.9339 \| 0.9741 \| 0.9536 \| 116 \| 0.9671 \| 0.9304 \| 0.9484 \| 158 \| 0.976 \| 0.9839 \| 0.9799 \| 124 \| 0.9598 \| 0.9598 \| 0.9598 \| 0.9898 \| \| 0.0122 \| 6.0 \| 576 \| 0.0581 \| 0.9328 \| 0.9569 \| 0.9447 \| 116 \| 0.9662 \| 0.9051 \| 0.9346 \| 158 \| 0.976 \| 0.9839 \| 0.9799 \| 124 \| 0.9592 \| 0.9447 \| 0.9519 \| 0.9885 \| \| 0.0062 \| 7.0 \| 672 \| 0.0578 \| 0.9569 \| 0.9569 \| 0.9569 \| 116 \| 0.9548 \| 0.9367 \| 0.9457 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9646 \| 0.9598 \| 0.9622 \| 0.9909 \| \| 0.007 \| 8.0 \| 768 \| 0.0608 \| 0.9655 \| 0.9655 \| 0.9655 \| 116 \| 0.9551 \| 0.9430 \| 0.9490 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9673 \| 0.9648 \| 0.9660 \| 0.9901 \| \| 0.0049 \| 9.0 \| 864 \| 0.0656 \| 0.9328 \| 0.9569 \| 0.9447 \| 116 \| 0.9530 \| 0.8987 \| 0.9251 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9567 \| 0.9447 \| 0.9507 \| 0.9874 \| \| 0.0056 \| 10.0 \| 960 \| 0.0566 \| 0.9569 \| 0.9569 \| 0.9569 \| 116 \| 0.9423 \| 0.9304 \| 0.9363 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9573 \| 0.9573 \| 0.9573 \| 0.9896 \| \| 0.0046 \| 11.0 \| 1056 \| 0.0709 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9346 \| 0.9051 \| 0.9196 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9545 \| 0.9497 \| 0.9521 \| 0.9879 \| \| 0.0022 \| 12.0 \| 1152 \| 0.0721 \| 0.9412 \| 0.9655 \| 0.9532 \| 116 \| 0.9548 \| 0.9367 \| 0.9457 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9599 \| 0.9623 \| 0.9611 \| 0.9901 \| \| 0.0048 \| 13.0 \| 1248 \| 0.0544 \| 0.9487 \| 0.9569 \| 0.9528 \| 116 \| 0.9490 \| 0.9430 \| 0.9460 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9575 \| 0.9623 \| 0.9599 \| 0.9920 \| \| 0.0029 \| 14.0 \| 1344 \| 0.0602 \| 0.9649 \| 0.9483 \| 0.9565 \| 116 \| 0.9434 \| 0.9494 \| 0.9464 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9623 \| 0.9623 \| 0.9623 \| 0.9918 \| \| 0.0031 \| 15.0 \| 1440 \| 0.0678 \| 0.9478 \| 0.9397 \| 0.9437 \| 116 \| 0.9484 \| 0.9304 \| 0.9393 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9571 \| 0.9523 \| 0.9547 \| 0.9904 \| \| 0.0039 \| 16.0 \| 1536 \| 0.0820 \| 0.9244 \| 0.9483 \| 0.9362 \| 116 \| 0.96 \| 0.9114 \| 0.9351 \| 158 \| 0.9839 \| 0.9839 \| 0.9839 \| 124 \| 0.9567 \| 0.9447 \| 0.9507 \| 0.9871 \| \| 0.0021 \| 17.0 \| 1632 \| 0.0793 \| 0.9421 \| 0.9828 \| 0.9620 \| 116 \| 0.9735 \| 0.9304 \| 0.9515 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9648 \| 0.9648 \| 0.9648 \| 0.9898 \| \| 0.0035 \| 18.0 \| 1728 \| 0.0844 \| 0.9310 \| 0.9310 \| 0.9310 \| 116 \| 0.9545 \| 0.9304 \| 0.9423 \| 158 \| 0.9685 \| 0.9919 \| 0.9801 \| 124 \| 0.9521 \| 0.9497 \| 0.9509 \| 0.9879 \| \| 0.0039 \| 19.0 \| 1824 \| 0.0907 \| 0.9106 \| 0.9655 \| 0.9372 \| 116 \| 0.9726 \| 0.8987 \| 0.9342 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9544 \| 0.9472 \| 0.9508 \| 0.9868 \| \| 0.0014 \| 20.0 \| 1920 \| 0.0629 \| 0.9412 \| 0.9655 \| 0.9532 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9622 \| 0.9598 \| 0.9610 \| 0.9912 \| \| 0.0019 \| 21.0 \| 2016 \| 0.0655 \| 0.9412 \| 0.9655 \| 0.9532 \| 116 \| 0.9737 \| 0.9367 \| 0.9548 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9672 \| 0.9623 \| 0.9647 \| 0.9909 \| \| 0.0021 \| 22.0 \| 2112 \| 0.0593 \| 0.9487 \| 0.9569 \| 0.9528 \| 116 \| 0.9371 \| 0.9430 \| 0.9401 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9551 \| 0.9623 \| 0.9587 \| 0.9915 \| \| 0.0038 \| 23.0 \| 2208 \| 0.0698 \| 0.9322 \| 0.9483 \| 0.9402 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9572 \| 0.9548 \| 0.9560 \| 0.9890 \| \| 0.0024 \| 24.0 \| 2304 \| 0.0686 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9669 \| 0.9241 \| 0.9450 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9670 \| 0.9573 \| 0.9621 \| 0.9901 \| \| 0.0032 \| 25.0 \| 2400 \| 0.0782 \| 0.9174 \| 0.9569 \| 0.9367 \| 116 \| 0.9412 \| 0.9114 \| 0.9260 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9474 \| 0.9497 \| 0.9486 \| 0.9874 \| \| 0.0028 \| 26.0 \| 2496 \| 0.0841 \| 0.9167 \| 0.9483 \| 0.9322 \| 116 \| 0.9865 \| 0.9241 \| 0.9542 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9644 \| 0.9523 \| 0.9583 \| 0.9893 \| \| 0.0024 \| 27.0 \| 2592 \| 0.0762 \| 0.9174 \| 0.9569 \| 0.9367 \| 116 \| 0.9554 \| 0.9494 \| 0.9524 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9529 \| 0.9648 \| 0.9588 \| 0.9893 \| \| 0.0065 \| 28.0 \| 2688 \| 0.0943 \| 0.9483 \| 0.9483 \| 0.9483 \| 116 \| 0.9662 \| 0.9051 \| 0.9346 \| 158 \| 0.9531 \| 0.9839 \| 0.9683 \| 124 \| 0.9566 \| 0.9422 \| 0.9494 \| 0.9887 \| \| 0.0026 \| 29.0 \| 2784 \| 0.0959 \| 0.9487 \| 0.9569 \| 0.9528 \| 116 \| 0.9664 \| 0.9114 \| 0.9381 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9668 \| 0.9497 \| 0.9582 \| 0.9874 \| \| 0.002 \| 30.0 \| 2880 \| 0.0732 \| 0.9402 \| 0.9483 \| 0.9442 \| 116 \| 0.9548 \| 0.9367 \| 0.9457 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9597 \| 0.9573 \| 0.9585 \| 0.9912 \| \| 0.0012 \| 31.0 \| 2976 \| 0.0808 \| 0.9487 \| 0.9569 \| 0.9528 \| 116 \| 0.9735 \| 0.9304 \| 0.9515 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9695 \| 0.9573 \| 0.9633 \| 0.9901 \| \| 0.001 \| 32.0 \| 3072 \| 0.0846 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.98 \| 0.9304 \| 0.9545 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9720 \| 0.9598 \| 0.9659 \| 0.9898 \| \| 0.0018 \| 33.0 \| 3168 \| 0.0949 \| 0.9487 \| 0.9569 \| 0.9528 \| 116 \| 0.9735 \| 0.9304 \| 0.9515 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9695 \| 0.9573 \| 0.9633 \| 0.9893 \| \| 0.0012 \| 34.0 \| 3264 \| 0.0965 \| 0.9322 \| 0.9483 \| 0.9402 \| 116 \| 0.9669 \| 0.9241 \| 0.9450 \| 158 \| 0.9685 \| 0.9919 \| 0.9801 \| 124 \| 0.9571 \| 0.9523 \| 0.9547 \| 0.9879 \| \| 0.0025 \| 35.0 \| 3360 \| 0.1011 \| 0.9554 \| 0.9224 \| 0.9386 \| 116 \| 0.9367 \| 0.9367 \| 0.9367 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9545 \| 0.9497 \| 0.9521 \| 0.9879 \| \| 0.0029 \| 36.0 \| 3456 \| 0.0913 \| 0.9487 \| 0.9569 \| 0.9528 \| 116 \| 0.9545 \| 0.9304 \| 0.9423 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9597 \| 0.9573 \| 0.9585 \| 0.9882 \| \| 0.0037 \| 37.0 \| 3552 \| 0.0543 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9430 \| 0.9430 \| 0.9430 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9552 \| 0.9648 \| 0.96 \| 0.9923 \| \| 0.002 \| 38.0 \| 3648 \| 0.0655 \| 0.9487 \| 0.9569 \| 0.9528 \| 116 \| 0.9430 \| 0.9430 \| 0.9430 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9575 \| 0.9623 \| 0.9599 \| 0.9909 \| \| 0.0015 \| 39.0 \| 3744 \| 0.0786 \| 0.9565 \| 0.9483 \| 0.9524 \| 116 \| 0.9671 \| 0.9304 \| 0.9484 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9694 \| 0.9548 \| 0.9620 \| 0.9893 \| \| 0.001 \| 40.0 \| 3840 \| 0.0722 \| 0.9328 \| 0.9569 \| 0.9447 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9597 \| 0.9573 \| 0.9585 \| 0.9904 \| \| 0.0021 \| 41.0 \| 3936 \| 0.0722 \| 0.9350 \| 0.9914 \| 0.9623 \| 116 \| 0.9737 \| 0.9367 \| 0.9548 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.965 \| 0.9698 \| 0.9674 \| 0.9904 \| \| 0.0018 \| 42.0 \| 4032 \| 0.0764 \| 0.9483 \| 0.9483 \| 0.9483 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9620 \| 0.9548 \| 0.9584 \| 0.9893 \| \| 0.0009 \| 43.0 \| 4128 \| 0.0854 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9735 \| 0.9304 \| 0.9515 \| 158 \| 0.9839 \| 0.9839 \| 0.9839 \| 124 \| 0.9695 \| 0.9573 \| 0.9633 \| 0.9898 \| \| 0.0007 \| 44.0 \| 4224 \| 0.0778 \| 0.9412 \| 0.9655 \| 0.9532 \| 116 \| 0.9735 \| 0.9304 \| 0.9515 \| 158 \| 0.9839 \| 0.9839 \| 0.9839 \| 124 \| 0.9670 \| 0.9573 \| 0.9621 \| 0.9904 \| \| 0.0018 \| 45.0 \| 4320 \| 0.0880 \| 0.9558 \| 0.9310 \| 0.9432 \| 116 \| 0.9481 \| 0.9241 \| 0.9359 \| 158 \| 0.976 \| 0.9839 \| 0.9799 \| 124 \| 0.9592 \| 0.9447 \| 0.9519 \| 0.9887 \| \| 0.0022 \| 46.0 \| 4416 \| 0.0823 \| 0.9412 \| 0.9655 \| 0.9532 \| 116 \| 0.9867 \| 0.9367 \| 0.9610 \| 158 \| 0.976 \| 0.9839 \| 0.9799 \| 124 \| 0.9695 \| 0.9598 \| 0.9646 \| 0.9901 \| \| 0.0013 \| 47.0 \| 4512 \| 0.0913 \| 0.9483 \| 0.9483 \| 0.9483 \| 116 \| 0.98 \| 0.9304 \| 0.9545 \| 158 \| 0.9762 \| 0.9919 \| 0.9840 \| 124 \| 0.9694 \| 0.9548 \| 0.9620 \| 0.9896 \| \| 0.0013 \| 48.0 \| 4608 \| 0.0819 \| 0.9417 \| 0.9741 \| 0.9576 \| 116 \| 0.9801 \| 0.9367 \| 0.9579 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9697 \| 0.9648 \| 0.9673 \| 0.9901 \| \| 0.0005 \| 49.0 \| 4704 \| 0.0735 \| 0.9412 \| 0.9655 \| 0.9532 \| 116 \| 0.9737 \| 0.9367 \| 0.9548 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9672 \| 0.9623 \| 0.9647 \| 0.9909 \| \| 0.0011 \| 50.0 \| 4800 \| 0.0772 \| 0.9483 \| 0.9483 \| 0.9483 \| 116 \| 0.9484 \| 0.9304 \| 0.9393 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9596 \| 0.9548 \| 0.9572 \| 0.9907 \| \| 0.0021 \| 51.0 \| 4896 \| 0.0813 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9735 \| 0.9304 \| 0.9515 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9695 \| 0.9598 \| 0.9646 \| 0.9904 \| \| 0.0006 \| 52.0 \| 4992 \| 0.0927 \| 0.9576 \| 0.9741 \| 0.9658 \| 116 \| 0.98 \| 0.9304 \| 0.9545 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9746 \| 0.9623 \| 0.9684 \| 0.9904 \| \| 0.0007 \| 53.0 \| 5088 \| 0.0791 \| 0.9496 \| 0.9741 \| 0.9617 \| 116 \| 0.9740 \| 0.9494 \| 0.9615 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9698 \| 0.9698 \| 0.9698 \| 0.9912 \| \| 0.0011 \| 54.0 \| 5184 \| 0.0722 \| 0.9496 \| 0.9741 \| 0.9617 \| 116 \| 0.9679 \| 0.9557 \| 0.9618 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9675 \| 0.9724 \| 0.9699 \| 0.9929 \| \| 0.0005 \| 55.0 \| 5280 \| 0.0721 \| 0.9328 \| 0.9569 \| 0.9447 \| 116 \| 0.9557 \| 0.9557 \| 0.9557 \| 158 \| 0.9839 \| 0.9839 \| 0.9839 \| 124 \| 0.9576 \| 0.9648 \| 0.9612 \| 0.9920 \| \| 0.0005 \| 56.0 \| 5376 \| 0.0705 \| 0.9496 \| 0.9741 \| 0.9617 \| 116 \| 0.9806 \| 0.9620 \| 0.9712 \| 158 \| 0.9839 \| 0.9839 \| 0.9839 \| 124 \| 0.9724 \| 0.9724 \| 0.9724 \| 0.9931 \| \| 0.0003 \| 57.0 \| 5472 \| 0.0651 \| 0.9487 \| 0.9569 \| 0.9528 \| 116 \| 0.9677 \| 0.9494 \| 0.9585 \| 158 \| 0.9839 \| 0.9839 \| 0.9839 \| 124 \| 0.9672 \| 0.9623 \| 0.9647 \| 0.9923 \| \| 0.0011 \| 58.0 \| 5568 \| 0.0754 \| 0.9569 \| 0.9569 \| 0.9569 \| 116 \| 0.9679 \| 0.9557 \| 0.9618 \| 158 \| 0.9839 \| 0.9839 \| 0.9839 \| 124 \| 0.9697 \| 0.9648 \| 0.9673 \| 0.9929 \| \| 0.0006 \| 59.0 \| 5664 \| 0.0718 \| 0.9397 \| 0.9397 \| 0.9397 \| 116 \| 0.9618 \| 0.9557 \| 0.9587 \| 158 \| 0.9839 \| 0.9839 \| 0.9839 \| 124 \| 0.9622 \| 0.9598 \| 0.9610 \| 0.9923 \| \| 0.0005 \| 60.0 \| 5760 \| 0.0870 \| 0.9496 \| 0.9741 \| 0.9617 \| 116 \| 0.98 \| 0.9304 \| 0.9545 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9721 \| 0.9623 \| 0.9672 \| 0.9898 \| \| 0.0004 \| 61.0 \| 5856 \| 0.0687 \| 0.9474 \| 0.9310 \| 0.9391 \| 116 \| 0.9437 \| 0.9557 \| 0.9497 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9574 \| 0.9598 \| 0.9586 \| 0.9909 \| \| 0.0002 \| 62.0 \| 5952 \| 0.0983 \| 0.9402 \| 0.9483 \| 0.9442 \| 116 \| 0.9799 \| 0.9241 \| 0.9511 \| 158 \| 0.9839 \| 0.9839 \| 0.9839 \| 124 \| 0.9692 \| 0.9497 \| 0.9594 \| 0.9893 \| \| 0.0006 \| 63.0 \| 6048 \| 0.0818 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9671 \| 0.9304 \| 0.9484 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9695 \| 0.9598 \| 0.9646 \| 0.9912 \| \| 0.0002 \| 64.0 \| 6144 \| 0.0858 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9915 \| \| 0.0005 \| 65.0 \| 6240 \| 0.0884 \| 0.9569 \| 0.9569 \| 0.9569 \| 116 \| 0.9673 \| 0.9367 \| 0.9518 \| 158 \| 0.9683 \| 0.9839 \| 0.976 \| 124 \| 0.9646 \| 0.9573 \| 0.9609 \| 0.9915 \| \| 0.001 \| 66.0 \| 6336 \| 0.0771 \| 0.9487 \| 0.9569 \| 0.9528 \| 116 \| 0.9542 \| 0.9241 \| 0.9389 \| 158 \| 0.9683 \| 0.9839 \| 0.976 \| 124 \| 0.9571 \| 0.9523 \| 0.9547 \| 0.9912 \| \| 0.0006 \| 67.0 \| 6432 \| 0.0808 \| 0.9487 \| 0.9569 \| 0.9528 \| 116 \| 0.9735 \| 0.9304 \| 0.9515 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9695 \| 0.9573 \| 0.9633 \| 0.9909 \| \| 0.0002 \| 68.0 \| 6528 \| 0.0749 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9610 \| 0.9367 \| 0.9487 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9672 \| 0.9623 \| 0.9647 \| 0.9920 \| \| 0.0011 \| 69.0 \| 6624 \| 0.0784 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9918 \| \| 0.0005 \| 70.0 \| 6720 \| 0.0750 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9671 \| 0.9304 \| 0.9484 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9695 \| 0.9598 \| 0.9646 \| 0.9920 \| \| 0.0001 \| 71.0 \| 6816 \| 0.0758 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9671 \| 0.9304 \| 0.9484 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9695 \| 0.9598 \| 0.9646 \| 0.9920 \| \| 0.0005 \| 72.0 \| 6912 \| 0.0771 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9671 \| 0.9304 \| 0.9484 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9695 \| 0.9598 \| 0.9646 \| 0.9920 \| \| 0.0004 \| 73.0 \| 7008 \| 0.0733 \| 0.9412 \| 0.9655 \| 0.9532 \| 116 \| 0.9542 \| 0.9241 \| 0.9389 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9597 \| 0.9573 \| 0.9585 \| 0.9915 \| \| 0.0001 \| 74.0 \| 7104 \| 0.0740 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9542 \| 0.9241 \| 0.9389 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9621 \| 0.9573 \| 0.9597 \| 0.9918 \| \| 0.0001 \| 75.0 \| 7200 \| 0.0795 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9669 \| 0.9241 \| 0.9450 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9670 \| 0.9573 \| 0.9621 \| 0.9915 \| \| 0.0002 \| 76.0 \| 7296 \| 0.0800 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9669 \| 0.9241 \| 0.9450 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9670 \| 0.9573 \| 0.9621 \| 0.9915 \| \| 0.0002 \| 77.0 \| 7392 \| 0.0781 \| 0.9569 \| 0.9569 \| 0.9569 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9670 \| 0.9573 \| 0.9621 \| 0.9920 \| \| 0.0002 \| 78.0 \| 7488 \| 0.0798 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9735 \| 0.9304 \| 0.9515 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9695 \| 0.9598 \| 0.9646 \| 0.9918 \| \| 0.0002 \| 79.0 \| 7584 \| 0.0785 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9737 \| 0.9367 \| 0.9548 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9721 \| 0.9623 \| 0.9672 \| 0.9926 \| \| 0.0001 \| 80.0 \| 7680 \| 0.0794 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9737 \| 0.9367 \| 0.9548 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9721 \| 0.9623 \| 0.9672 \| 0.9926 \| \| 0.0004 \| 81.0 \| 7776 \| 0.0812 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9737 \| 0.9367 \| 0.9548 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9721 \| 0.9623 \| 0.9672 \| 0.9926 \| \| 0.0001 \| 82.0 \| 7872 \| 0.0880 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9669 \| 0.9241 \| 0.9450 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9670 \| 0.9573 \| 0.9621 \| 0.9915 \| \| 0.0001 \| 83.0 \| 7968 \| 0.0832 \| 0.9576 \| 0.9741 \| 0.9658 \| 116 \| 0.9671 \| 0.9304 \| 0.9484 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9696 \| 0.9623 \| 0.9660 \| 0.9920 \| \| 0.0007 \| 84.0 \| 8064 \| 0.0854 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9669 \| 0.9241 \| 0.9450 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9670 \| 0.9573 \| 0.9621 \| 0.9915 \| \| 0.0001 \| 85.0 \| 8160 \| 0.0863 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9669 \| 0.9241 \| 0.9450 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9670 \| 0.9573 \| 0.9621 \| 0.9915 \| \| 0.0001 \| 86.0 \| 8256 \| 0.0854 \| 0.9492 \| 0.9655 \| 0.9573 \| 116 \| 0.9669 \| 0.9241 \| 0.9450 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9670 \| 0.9573 \| 0.9621 \| 0.9909 \| \| 0.0001 \| 87.0 \| 8352 \| 0.0789 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9673 \| 0.9367 \| 0.9518 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9696 \| 0.9623 \| 0.9660 \| 0.9923 \| \| 0.0001 \| 88.0 \| 8448 \| 0.0776 \| 0.9658 \| 0.9741 \| 0.9700 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9696 \| 0.9623 \| 0.9660 \| 0.9923 \| \| 0.0002 \| 89.0 \| 8544 \| 0.0786 \| 0.9569 \| 0.9569 \| 0.9569 \| 116 \| 0.9610 \| 0.9367 \| 0.9487 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| \| 0.0001 \| 90.0 \| 8640 \| 0.0798 \| 0.9569 \| 0.9569 \| 0.9569 \| 116 \| 0.9610 \| 0.9367 \| 0.9487 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| \| 0.0001 \| 91.0 \| 8736 \| 0.0816 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| \| 0.0005 \| 92.0 \| 8832 \| 0.0819 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| \| 0.0003 \| 93.0 \| 8928 \| 0.0819 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| \| 0.0003 \| 94.0 \| 9024 \| 0.0814 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| \| 0.0001 \| 95.0 \| 9120 \| 0.0814 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| \| 0.0001 \| 96.0 \| 9216 \| 0.0816 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| \| 0.0001 \| 97.0 \| 9312 \| 0.0817 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| \| 0.0001 \| 98.0 \| 9408 \| 0.0821 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| \| 0.0001 \| 99.0 \| 9504 \| 0.0822 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| \| 0.0001 \| 100.0 \| 9600 \| 0.0822 \| 0.9573 \| 0.9655 \| 0.9614 \| 116 \| 0.9608 \| 0.9304 \| 0.9453 \| 158 \| 0.984 \| 0.9919 \| 0.9880 \| 124 \| 0.9671 \| 0.9598 \| 0.9634 \| 0.9920 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
ehottl/distilbert-base-uncased-finetuned-emotion	ehottl	2024-06-04T00:21:31Z	121	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-04T00:10:46Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.929 - name: F1 type: f1 value: 0.9290384064576098 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2064 - Accuracy: 0.929 - F1: 0.9290 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8175 \| 1.0 \| 250 \| 0.2950 \| 0.911 \| 0.9108 \| \| 0.238 \| 2.0 \| 500 \| 0.2064 \| 0.929 \| 0.9290 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
dalopeza98/distilbert-base-uncased-finetuned-emotion	dalopeza98	2024-06-04T00:21:10Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-03T21:14:37Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3872 - Accuracy: 0.6802 - F1: 0.6793 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 48 \| 1.7238 \| 0.6762 \| 0.6813 \| \| 0.0305 \| 2.0 \| 96 \| 1.8028 \| 0.6775 \| 0.6755 \| \| 0.0305 \| 3.0 \| 144 \| 1.9018 \| 0.6689 \| 0.6668 \| \| 0.0257 \| 4.0 \| 192 \| 1.9426 \| 0.6735 \| 0.6740 \| \| 0.0257 \| 5.0 \| 240 \| 1.9829 \| 0.6662 \| 0.6670 \| \| 0.0207 \| 6.0 \| 288 \| 1.9462 \| 0.6722 \| 0.6753 \| \| 0.0207 \| 7.0 \| 336 \| 1.9573 \| 0.6861 \| 0.6851 \| \| 0.0185 \| 8.0 \| 384 \| 2.0147 \| 0.6808 \| 0.6820 \| \| 0.0185 \| 9.0 \| 432 \| 2.0982 \| 0.6669 \| 0.6649 \| \| 0.0172 \| 10.0 \| 480 \| 2.0431 \| 0.6815 \| 0.6799 \| \| 0.0172 \| 11.0 \| 528 \| 2.0935 \| 0.6768 \| 0.6751 \| \| 0.0182 \| 12.0 \| 576 \| 2.0599 \| 0.6868 \| 0.6835 \| \| 0.0182 \| 13.0 \| 624 \| 2.0953 \| 0.6808 \| 0.6812 \| \| 0.0148 \| 14.0 \| 672 \| 2.1115 \| 0.6788 \| 0.6790 \| \| 0.0148 \| 15.0 \| 720 \| 2.1529 \| 0.6735 \| 0.6765 \| \| 0.0171 \| 16.0 \| 768 \| 2.1873 \| 0.6702 \| 0.6720 \| \| 0.0171 \| 17.0 \| 816 \| 2.1534 \| 0.6782 \| 0.6793 \| \| 0.0142 \| 18.0 \| 864 \| 2.1803 \| 0.6782 \| 0.6773 \| \| 0.0142 \| 19.0 \| 912 \| 2.2252 \| 0.6802 \| 0.6801 \| \| 0.0168 \| 20.0 \| 960 \| 2.2221 \| 0.6749 \| 0.6764 \| \| 0.0168 \| 21.0 \| 1008 \| 2.2365 \| 0.6821 \| 0.6817 \| \| 0.015 \| 22.0 \| 1056 \| 2.2812 \| 0.6742 \| 0.6728 \| \| 0.015 \| 23.0 \| 1104 \| 2.2447 \| 0.6729 \| 0.6707 \| \| 0.0145 \| 24.0 \| 1152 \| 2.3272 \| 0.6709 \| 0.6700 \| \| 0.0145 \| 25.0 \| 1200 \| 2.2630 \| 0.6788 \| 0.6809 \| \| 0.0151 \| 26.0 \| 1248 \| 2.2751 \| 0.6808 \| 0.6811 \| \| 0.0151 \| 27.0 \| 1296 \| 2.3018 \| 0.6768 \| 0.6776 \| \| 0.0144 \| 28.0 \| 1344 \| 2.3544 \| 0.6676 \| 0.6681 \| \| 0.0144 \| 29.0 \| 1392 \| 2.3109 \| 0.6821 \| 0.6828 \| \| 0.0126 \| 30.0 \| 1440 \| 2.3234 \| 0.6795 \| 0.6786 \| \| 0.0126 \| 31.0 \| 1488 \| 2.3294 \| 0.6755 \| 0.6750 \| \| 0.0142 \| 32.0 \| 1536 \| 2.3183 \| 0.6875 \| 0.6886 \| \| 0.0142 \| 33.0 \| 1584 \| 2.2949 \| 0.6808 \| 0.6823 \| \| 0.0131 \| 34.0 \| 1632 \| 2.3451 \| 0.6788 \| 0.6773 \| \| 0.0131 \| 35.0 \| 1680 \| 2.3160 \| 0.6828 \| 0.6841 \| \| 0.0143 \| 36.0 \| 1728 \| 2.3251 \| 0.6828 \| 0.6815 \| \| 0.0143 \| 37.0 \| 1776 \| 2.4003 \| 0.6762 \| 0.6753 \| \| 0.0116 \| 38.0 \| 1824 \| 2.3675 \| 0.6775 \| 0.6770 \| \| 0.0116 \| 39.0 \| 1872 \| 2.3700 \| 0.6749 \| 0.6735 \| \| 0.0126 \| 40.0 \| 1920 \| 2.3700 \| 0.6841 \| 0.6831 \| \| 0.0126 \| 41.0 \| 1968 \| 2.3818 \| 0.6795 \| 0.6793 \| \| 0.0115 \| 42.0 \| 2016 \| 2.3518 \| 0.6815 \| 0.6814 \| \| 0.0115 \| 43.0 \| 2064 \| 2.3829 \| 0.6802 \| 0.6790 \| \| 0.0135 \| 44.0 \| 2112 \| 2.3638 \| 0.6782 \| 0.6775 \| \| 0.0135 \| 45.0 \| 2160 \| 2.3568 \| 0.6775 \| 0.6768 \| \| 0.0146 \| 46.0 \| 2208 \| 2.3633 \| 0.6788 \| 0.6784 \| \| 0.0118 \| 47.0 \| 2256 \| 2.3725 \| 0.6788 \| 0.6782 \| \| 0.0118 \| 48.0 \| 2304 \| 2.3875 \| 0.6815 \| 0.6806 \| \| 0.0116 \| 49.0 \| 2352 \| 2.3862 \| 0.6795 \| 0.6787 \| \| 0.0116 \| 50.0 \| 2400 \| 2.3872 \| 0.6802 \| 0.6793 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.2 - Datasets 2.19.1 - Tokenizers 0.19.1
duyntnet/Kunoichi-DPO-v2-7B-imatrix-GGUF	duyntnet	2024-06-04T00:19:17Z	60	3	transformers	[ "transformers", "gguf", "imatrix", "Kunoichi-DPO-v2-7B", "text-generation", "en", "license:other", "region:us" ]	text-generation	2024-06-03T20:18:06Z	--- license: other language: - en pipeline_tag: text-generation inference: false tags: - transformers - gguf - imatrix - Kunoichi-DPO-v2-7B --- Quantizations of https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B # From original readme \| Model \| MT Bench \| EQ Bench \| MMLU \| Logic Test \| \|----------------------\|----------\|----------\|---------\|-------------\| \| GPT-4-Turbo \| 9.32 \| - \| - \| - \| \| GPT-4 \| 8.99 \| 62.52 \| 86.4 \| 0.86 \| \| Kunoichi-DPO-v2-7B \| 8.51 \| 42.18 \| 64.94\| 0.58 \| \| Mixtral-8x7B-Instruct\| 8.30 \| 44.81 \| 70.6 \| 0.75 \| \| Kunoichi-DPO-7B \| 8.29 \| 41.60 \| 64.83 \| 0.59 \| \| Kunoichi-7B \| 8.14 \| 44.32 \| 64.9 \| 0.58 \| \| Starling-7B \| 8.09 \| - \| 63.9 \| 0.51 \| \| Claude-2 \| 8.06 \| 52.14 \| 78.5 \| - \| \| Silicon-Maid-7B \| 7.96 \| 40.44 \| 64.7 \| 0.54 \| \| Loyal-Macaroni-Maid-7B \| 7.95 \| 38.66 \| 64.9 \| 0.57 \| \| GPT-3.5-Turbo \| 7.94 \| 50.28 \| 70 \| 0.57 \| \| Claude-1 \| 7.9 \| - \| 77 \| - \| \| Openchat-3.5 \| 7.81 \| 37.08 \| 64.3 \| 0.39 \| \| Dolphin-2.6-DPO \| 7.74 \| 42.88 \| 61.9 \| 0.53 \| \| Zephyr-7B-beta \| 7.34 \| 38.71 \| 61.4 \| 0.30 \| \| Llama-2-70b-chat-hf \| 6.86 \| 51.56 \| 63 \| - \| \| Neural-chat-7b-v3-1 \| 6.84 \| 43.61 \| 62.4 \| 0.30 \|
azmoulai/vizwiz-blip-model	azmoulai	2024-06-04T00:16:24Z	6	0	transformers	[ "transformers", "safetensors", "blip", "visual-question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	visual-question-answering	2024-05-29T04:12:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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apwic/nerui-base-0	apwic	2024-06-04T00:16:00Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-28T03:49:20Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerui-base-0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nerui-base-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1084 - Location Precision: 0.89 - Location Recall: 0.9468 - Location F1: 0.9175 - Location Number: 94 - Organization Precision: 0.9387 - Organization Recall: 0.9162 - Organization F1: 0.9273 - Organization Number: 167 - Person Precision: 1.0 - Person Recall: 0.9781 - Person F1: 0.9889 - Person Number: 137 - Overall Precision: 0.9471 - Overall Recall: 0.9447 - Overall F1: 0.9459 - Overall Accuracy: 0.9887 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Location Precision \| Location Recall \| Location F1 \| Location Number \| Organization Precision \| Organization Recall \| Organization F1 \| Organization Number \| Person Precision \| Person Recall \| Person F1 \| Person Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------------:\|:---------------:\|:-----------:\|:---------------:\|:----------------------:\|:-------------------:\|:---------------:\|:-------------------:\|:----------------:\|:-------------:\|:---------:\|:-------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.2566 \| 1.0 \| 96 \| 0.0455 \| 0.9634 \| 0.8404 \| 0.8977 \| 94 \| 0.8333 \| 0.9281 \| 0.8782 \| 167 \| 0.9708 \| 0.9708 \| 0.9708 \| 137 \| 0.9062 \| 0.9221 \| 0.9141 \| 0.9843 \| \| 0.0617 \| 2.0 \| 192 \| 0.0519 \| 0.8381 \| 0.9362 \| 0.8844 \| 94 \| 0.8896 \| 0.8683 \| 0.8788 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9107 \| 0.9221 \| 0.9164 \| 0.9834 \| \| 0.0356 \| 3.0 \| 288 \| 0.0534 \| 0.9062 \| 0.9255 \| 0.9158 \| 94 \| 0.8211 \| 0.9341 \| 0.8739 \| 167 \| 1.0 \| 0.9708 \| 0.9852 \| 137 \| 0.8974 \| 0.9447 \| 0.9204 \| 0.9840 \| \| 0.0235 \| 4.0 \| 384 \| 0.0525 \| 0.8866 \| 0.9149 \| 0.9005 \| 94 \| 0.9006 \| 0.9222 \| 0.9112 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9303 \| 0.9397 \| 0.9350 \| 0.9856 \| \| 0.0156 \| 5.0 \| 480 \| 0.0623 \| 0.9032 \| 0.8936 \| 0.8984 \| 94 \| 0.9333 \| 0.9222 \| 0.9277 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9466 \| 0.9347 \| 0.9406 \| 0.9873 \| \| 0.0101 \| 6.0 \| 576 \| 0.0590 \| 0.9043 \| 0.9043 \| 0.9043 \| 94 \| 0.8929 \| 0.8982 \| 0.8955 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9295 \| 0.9271 \| 0.9283 \| 0.9859 \| \| 0.0091 \| 7.0 \| 672 \| 0.0955 \| 0.8036 \| 0.9574 \| 0.8738 \| 94 \| 0.9211 \| 0.8383 \| 0.8777 \| 167 \| 0.9643 \| 0.9854 \| 0.9747 \| 137 \| 0.9035 \| 0.9171 \| 0.9102 \| 0.9809 \| \| 0.0084 \| 8.0 \| 768 \| 0.0871 \| 0.8365 \| 0.9255 \| 0.8788 \| 94 \| 0.9062 \| 0.8683 \| 0.8869 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9196 \| 0.9196 \| 0.9196 \| 0.9826 \| \| 0.007 \| 9.0 \| 864 \| 0.0629 \| 0.9565 \| 0.9362 \| 0.9462 \| 94 \| 0.8895 \| 0.9162 \| 0.9027 \| 167 \| 1.0 \| 0.9854 \| 0.9926 \| 137 \| 0.9424 \| 0.9447 \| 0.9435 \| 0.9881 \| \| 0.0047 \| 10.0 \| 960 \| 0.0564 \| 0.9167 \| 0.9362 \| 0.9263 \| 94 \| 0.9512 \| 0.9341 \| 0.9426 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9594 \| 0.9497 \| 0.9545 \| 0.9901 \| \| 0.0043 \| 11.0 \| 1056 \| 0.0829 \| 0.9158 \| 0.9255 \| 0.9206 \| 94 \| 0.8708 \| 0.9281 \| 0.8986 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9216 \| 0.9447 \| 0.9330 \| 0.9856 \| \| 0.0034 \| 12.0 \| 1152 \| 0.0779 \| 0.9247 \| 0.9149 \| 0.9198 \| 94 \| 0.8667 \| 0.9341 \| 0.8991 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9216 \| 0.9447 \| 0.9330 \| 0.9865 \| \| 0.0047 \| 13.0 \| 1248 \| 0.0781 \| 0.8922 \| 0.9681 \| 0.9286 \| 94 \| 0.95 \| 0.9102 \| 0.9297 \| 167 \| 0.9854 \| 0.9854 \| 0.9854 \| 137 \| 0.9474 \| 0.9497 \| 0.9486 \| 0.9862 \| \| 0.006 \| 14.0 \| 1344 \| 0.0682 \| 0.9271 \| 0.9468 \| 0.9368 \| 94 \| 0.9236 \| 0.8683 \| 0.8951 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9509 \| 0.9246 \| 0.9376 \| 0.9859 \| \| 0.0031 \| 15.0 \| 1440 \| 0.0759 \| 0.9149 \| 0.9149 \| 0.9149 \| 94 \| 0.8814 \| 0.9341 \| 0.9070 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9261 \| 0.9447 \| 0.9353 \| 0.9878 \| \| 0.0049 \| 16.0 \| 1536 \| 0.0801 \| 0.9082 \| 0.9468 \| 0.9271 \| 94 \| 0.9107 \| 0.9162 \| 0.9134 \| 167 \| 0.9574 \| 0.9854 \| 0.9712 \| 137 \| 0.9263 \| 0.9472 \| 0.9366 \| 0.9865 \| \| 0.0036 \| 17.0 \| 1632 \| 0.0933 \| 0.9278 \| 0.9574 \| 0.9424 \| 94 \| 0.9333 \| 0.9222 \| 0.9277 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9497 \| 0.9497 \| 0.9497 \| 0.9887 \| \| 0.0033 \| 18.0 \| 1728 \| 0.0828 \| 0.9167 \| 0.9362 \| 0.9263 \| 94 \| 0.9167 \| 0.9222 \| 0.9194 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9424 \| 0.9447 \| 0.9435 \| 0.9870 \| \| 0.0031 \| 19.0 \| 1824 \| 0.0819 \| 0.9149 \| 0.9149 \| 0.9149 \| 94 \| 0.9102 \| 0.9102 \| 0.9102 \| 167 \| 0.9708 \| 0.9708 \| 0.9708 \| 137 \| 0.9322 \| 0.9322 \| 0.9322 \| 0.9873 \| \| 0.0025 \| 20.0 \| 1920 \| 0.0871 \| 0.8969 \| 0.9255 \| 0.9110 \| 94 \| 0.9321 \| 0.9042 \| 0.9179 \| 167 \| 0.9708 \| 0.9708 \| 0.9708 \| 137 \| 0.9369 \| 0.9322 \| 0.9345 \| 0.9878 \| \| 0.0023 \| 21.0 \| 2016 \| 0.0813 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9162 \| 0.9162 \| 0.9162 \| 167 \| 0.9706 \| 0.9635 \| 0.9670 \| 137 \| 0.9280 \| 0.9397 \| 0.9338 \| 0.9873 \| \| 0.0023 \| 22.0 \| 2112 \| 0.0885 \| 0.9158 \| 0.9255 \| 0.9206 \| 94 \| 0.8814 \| 0.9341 \| 0.9070 \| 167 \| 1.0 \| 0.9635 \| 0.9814 \| 137 \| 0.9282 \| 0.9422 \| 0.9352 \| 0.9867 \| \| 0.0018 \| 23.0 \| 2208 \| 0.1209 \| 0.8788 \| 0.9255 \| 0.9016 \| 94 \| 0.8947 \| 0.9162 \| 0.9053 \| 167 \| 0.9779 \| 0.9708 \| 0.9744 \| 137 \| 0.9187 \| 0.9372 \| 0.9279 \| 0.9837 \| \| 0.0036 \| 24.0 \| 2304 \| 0.0841 \| 0.9175 \| 0.9468 \| 0.9319 \| 94 \| 0.9029 \| 0.9461 \| 0.9240 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9338 \| 0.9573 \| 0.9454 \| 0.9878 \| \| 0.0034 \| 25.0 \| 2400 \| 0.0860 \| 0.9368 \| 0.9468 \| 0.9418 \| 94 \| 0.9186 \| 0.9461 \| 0.9322 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9478 \| 0.9573 \| 0.9525 \| 0.9884 \| \| 0.0029 \| 26.0 \| 2496 \| 0.0684 \| 0.9381 \| 0.9681 \| 0.9529 \| 94 \| 0.9176 \| 0.9341 \| 0.9258 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9478 \| 0.9573 \| 0.9525 \| 0.9898 \| \| 0.0031 \| 27.0 \| 2592 \| 0.1158 \| 0.9278 \| 0.9574 \| 0.9424 \| 94 \| 0.8933 \| 0.9521 \| 0.9217 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9341 \| 0.9623 \| 0.9480 \| 0.9865 \| \| 0.0045 \| 28.0 \| 2688 \| 0.0860 \| 0.9263 \| 0.9362 \| 0.9312 \| 94 \| 0.8963 \| 0.8802 \| 0.8882 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9365 \| 0.9271 \| 0.9318 \| 0.9854 \| \| 0.0018 \| 29.0 \| 2784 \| 0.0869 \| 0.9271 \| 0.9468 \| 0.9368 \| 94 \| 0.9290 \| 0.9401 \| 0.9345 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.95 \| 0.9548 \| 0.9524 \| 0.9884 \| \| 0.0023 \| 30.0 \| 2880 \| 0.1042 \| 0.9184 \| 0.9574 \| 0.9375 \| 94 \| 0.9394 \| 0.9281 \| 0.9337 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9881 \| \| 0.0028 \| 31.0 \| 2976 \| 0.1003 \| 0.9020 \| 0.9787 \| 0.9388 \| 94 \| 0.9118 \| 0.9281 \| 0.9199 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9338 \| 0.9573 \| 0.9454 \| 0.9862 \| \| 0.0015 \| 32.0 \| 3072 \| 0.0802 \| 0.91 \| 0.9681 \| 0.9381 \| 94 \| 0.9353 \| 0.9521 \| 0.9436 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9458 \| 0.9648 \| 0.9552 \| 0.9890 \| \| 0.0025 \| 33.0 \| 3168 \| 0.0959 \| 0.8667 \| 0.9681 \| 0.9146 \| 94 \| 0.9375 \| 0.8982 \| 0.9174 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9398 \| 0.9422 \| 0.9410 \| 0.9862 \| \| 0.0014 \| 34.0 \| 3264 \| 0.0970 \| 0.9184 \| 0.9574 \| 0.9375 \| 94 \| 0.9286 \| 0.9341 \| 0.9313 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.95 \| 0.9548 \| 0.9524 \| 0.9881 \| \| 0.0017 \| 35.0 \| 3360 \| 0.0790 \| 0.9570 \| 0.9468 \| 0.9519 \| 94 \| 0.9123 \| 0.9341 \| 0.9231 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9499 \| 0.9523 \| 0.9511 \| 0.9890 \| \| 0.002 \| 36.0 \| 3456 \| 0.0912 \| 0.9010 \| 0.9681 \| 0.9333 \| 94 \| 0.9317 \| 0.8982 \| 0.9146 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9422 \| 0.9422 \| 0.9422 \| 0.9870 \| \| 0.0025 \| 37.0 \| 3552 \| 0.1061 \| 0.9271 \| 0.9468 \| 0.9368 \| 94 \| 0.9030 \| 0.8922 \| 0.8976 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9418 \| 0.9347 \| 0.9382 \| 0.9865 \| \| 0.0028 \| 38.0 \| 3648 \| 0.0982 \| 0.9184 \| 0.9574 \| 0.9375 \| 94 \| 0.9085 \| 0.8922 \| 0.9003 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9419 \| 0.9372 \| 0.9395 \| 0.9870 \| \| 0.0022 \| 39.0 \| 3744 \| 0.1061 \| 0.8969 \| 0.9255 \| 0.9110 \| 94 \| 0.8953 \| 0.9222 \| 0.9086 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9305 \| 0.9422 \| 0.9363 \| 0.9848 \| \| 0.0018 \| 40.0 \| 3840 \| 0.1077 \| 0.8980 \| 0.9362 \| 0.9167 \| 94 \| 0.9202 \| 0.8982 \| 0.9091 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9418 \| 0.9347 \| 0.9382 \| 0.9862 \| \| 0.002 \| 41.0 \| 3936 \| 0.0923 \| 0.8980 \| 0.9362 \| 0.9167 \| 94 \| 0.9325 \| 0.9102 \| 0.9212 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9468 \| 0.9397 \| 0.9433 \| 0.9870 \| \| 0.003 \| 42.0 \| 4032 \| 0.0899 \| 0.9053 \| 0.9149 \| 0.9101 \| 94 \| 0.9112 \| 0.9222 \| 0.9167 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.935 \| 0.9397 \| 0.9373 \| 0.9862 \| \| 0.0027 \| 43.0 \| 4128 \| 0.0827 \| 0.9355 \| 0.9255 \| 0.9305 \| 94 \| 0.9277 \| 0.9222 \| 0.9249 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9542 \| 0.9422 \| 0.9482 \| 0.9878 \| \| 0.0015 \| 44.0 \| 4224 \| 0.0798 \| 0.9149 \| 0.9149 \| 0.9149 \| 94 \| 0.9102 \| 0.9102 \| 0.9102 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9418 \| 0.9347 \| 0.9382 \| 0.9878 \| \| 0.0011 \| 45.0 \| 4320 \| 0.0868 \| 0.8958 \| 0.9149 \| 0.9053 \| 94 \| 0.9313 \| 0.8922 \| 0.9113 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9413 \| 0.9271 \| 0.9342 \| 0.9881 \| \| 0.0012 \| 46.0 \| 4416 \| 0.0743 \| 0.8922 \| 0.9681 \| 0.9286 \| 94 \| 0.9679 \| 0.9042 \| 0.9350 \| 167 \| 0.9852 \| 0.9708 \| 0.9779 \| 137 \| 0.9542 \| 0.9422 \| 0.9482 \| 0.9903 \| \| 0.0012 \| 47.0 \| 4512 \| 0.0870 \| 0.9072 \| 0.9362 \| 0.9215 \| 94 \| 0.9375 \| 0.8982 \| 0.9174 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9466 \| 0.9347 \| 0.9406 \| 0.9884 \| \| 0.0019 \| 48.0 \| 4608 \| 0.0759 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9308 \| 0.8862 \| 0.9080 \| 167 \| 0.9779 \| 0.9708 \| 0.9744 \| 137 \| 0.9367 \| 0.9296 \| 0.9332 \| 0.9881 \| \| 0.0015 \| 49.0 \| 4704 \| 0.0810 \| 0.9271 \| 0.9468 \| 0.9368 \| 94 \| 0.9176 \| 0.9341 \| 0.9258 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9475 \| 0.9523 \| 0.9499 \| 0.9895 \| \| 0.0011 \| 50.0 \| 4800 \| 0.0890 \| 0.9082 \| 0.9468 \| 0.9271 \| 94 \| 0.9506 \| 0.9222 \| 0.9362 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9520 \| 0.9472 \| 0.9496 \| 0.9890 \| \| 0.0007 \| 51.0 \| 4896 \| 0.0827 \| 0.9167 \| 0.9362 \| 0.9263 \| 94 \| 0.9341 \| 0.9341 \| 0.9341 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9474 \| 0.9497 \| 0.9486 \| 0.9895 \| \| 0.001 \| 52.0 \| 4992 \| 0.0873 \| 0.8980 \| 0.9362 \| 0.9167 \| 94 \| 0.9281 \| 0.9281 \| 0.9281 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9425 \| 0.9472 \| 0.9449 \| 0.9887 \| \| 0.001 \| 53.0 \| 5088 \| 0.0820 \| 0.8980 \| 0.9362 \| 0.9167 \| 94 \| 0.9394 \| 0.9281 \| 0.9337 \| 167 \| 0.9852 \| 0.9708 \| 0.9779 \| 137 \| 0.9447 \| 0.9447 \| 0.9447 \| 0.9890 \| \| 0.0004 \| 54.0 \| 5184 \| 0.0917 \| 0.8911 \| 0.9574 \| 0.9231 \| 94 \| 0.9434 \| 0.8982 \| 0.9202 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9444 \| 0.9397 \| 0.9421 \| 0.9867 \| \| 0.0006 \| 55.0 \| 5280 \| 0.1053 \| 0.8980 \| 0.9362 \| 0.9167 \| 94 \| 0.9333 \| 0.9222 \| 0.9277 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9447 \| 0.9447 \| 0.9447 \| 0.9884 \| \| 0.001 \| 56.0 \| 5376 \| 0.1040 \| 0.8990 \| 0.9468 \| 0.9223 \| 94 \| 0.9333 \| 0.9222 \| 0.9277 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9425 \| 0.9472 \| 0.9449 \| 0.9881 \| \| 0.0005 \| 57.0 \| 5472 \| 0.1042 \| 0.8990 \| 0.9468 \| 0.9223 \| 94 \| 0.9337 \| 0.9281 \| 0.9309 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.945 \| 0.9497 \| 0.9474 \| 0.9884 \| \| 0.0009 \| 58.0 \| 5568 \| 0.1057 \| 0.9082 \| 0.9468 \| 0.9271 \| 94 \| 0.9202 \| 0.8982 \| 0.9091 \| 167 \| 0.9853 \| 0.9781 \| 0.9817 \| 137 \| 0.9395 \| 0.9372 \| 0.9384 \| 0.9876 \| \| 0.001 \| 59.0 \| 5664 \| 0.1034 \| 0.8911 \| 0.9574 \| 0.9231 \| 94 \| 0.9277 \| 0.9222 \| 0.9249 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9426 \| 0.9497 \| 0.9462 \| 0.9873 \| \| 0.0012 \| 60.0 \| 5760 \| 0.0910 \| 0.9072 \| 0.9362 \| 0.9215 \| 94 \| 0.9337 \| 0.9281 \| 0.9309 \| 167 \| 0.9779 \| 0.9708 \| 0.9744 \| 137 \| 0.9424 \| 0.9447 \| 0.9435 \| 0.9887 \| \| 0.0008 \| 61.0 \| 5856 \| 0.0987 \| 0.9247 \| 0.9149 \| 0.9198 \| 94 \| 0.9102 \| 0.9102 \| 0.9102 \| 167 \| 0.9779 \| 0.9708 \| 0.9744 \| 137 \| 0.9369 \| 0.9322 \| 0.9345 \| 0.9862 \| \| 0.0005 \| 62.0 \| 5952 \| 0.1056 \| 0.8889 \| 0.9362 \| 0.9119 \| 94 \| 0.9387 \| 0.9162 \| 0.9273 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9470 \| 0.9422 \| 0.9446 \| 0.9876 \| \| 0.0006 \| 63.0 \| 6048 \| 0.1050 \| 0.8980 \| 0.9362 \| 0.9167 \| 94 \| 0.9268 \| 0.9102 \| 0.9184 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9421 \| 0.9397 \| 0.9409 \| 0.9873 \| \| 0.0013 \| 64.0 \| 6144 \| 0.0956 \| 0.9072 \| 0.9362 \| 0.9215 \| 94 \| 0.9329 \| 0.9162 \| 0.9245 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9494 \| 0.9422 \| 0.9458 \| 0.9884 \| \| 0.0006 \| 65.0 \| 6240 \| 0.1061 \| 0.9082 \| 0.9468 \| 0.9271 \| 94 \| 0.9313 \| 0.8922 \| 0.9113 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9490 \| 0.9347 \| 0.9418 \| 0.9854 \| \| 0.0008 \| 66.0 \| 6336 \| 0.1032 \| 0.8980 \| 0.9362 \| 0.9167 \| 94 \| 0.9325 \| 0.9102 \| 0.9212 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9444 \| 0.9397 \| 0.9421 \| 0.9881 \| \| 0.0004 \| 67.0 \| 6432 \| 0.0961 \| 0.8980 \| 0.9362 \| 0.9167 \| 94 \| 0.9273 \| 0.9162 \| 0.9217 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9446 \| 0.9422 \| 0.9434 \| 0.9890 \| \| 0.0008 \| 68.0 \| 6528 \| 0.0979 \| 0.88 \| 0.9362 \| 0.9072 \| 94 \| 0.925 \| 0.8862 \| 0.9052 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9367 \| 0.9296 \| 0.9332 \| 0.9870 \| \| 0.0013 \| 69.0 \| 6624 \| 0.1021 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9162 \| 0.9162 \| 0.9162 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9377 \| 0.9447 \| 0.9412 \| 0.9870 \| \| 0.0004 \| 70.0 \| 6720 \| 0.0933 \| 0.88 \| 0.9362 \| 0.9072 \| 94 \| 0.9264 \| 0.9042 \| 0.9152 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9395 \| 0.9372 \| 0.9384 \| 0.9881 \| \| 0.001 \| 71.0 \| 6816 \| 0.0892 \| 0.8788 \| 0.9255 \| 0.9016 \| 94 \| 0.9264 \| 0.9042 \| 0.9152 \| 167 \| 0.9852 \| 0.9708 \| 0.9779 \| 137 \| 0.9345 \| 0.9322 \| 0.9333 \| 0.9881 \| \| 0.0006 \| 72.0 \| 6912 \| 0.0966 \| 0.9091 \| 0.9574 \| 0.9326 \| 94 \| 0.9509 \| 0.9281 \| 0.9394 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9547 \| 0.9523 \| 0.9535 \| 0.9892 \| \| 0.0006 \| 73.0 \| 7008 \| 0.0997 \| 0.8911 \| 0.9574 \| 0.9231 \| 94 \| 0.9441 \| 0.9102 \| 0.9268 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9495 \| 0.9447 \| 0.9471 \| 0.9884 \| \| 0.0004 \| 74.0 \| 7104 \| 0.1035 \| 0.8824 \| 0.9574 \| 0.9184 \| 94 \| 0.9497 \| 0.9042 \| 0.9264 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9470 \| 0.9422 \| 0.9446 \| 0.9881 \| \| 0.0005 \| 75.0 \| 7200 \| 0.1036 \| 0.8788 \| 0.9255 \| 0.9016 \| 94 \| 0.9371 \| 0.8922 \| 0.9141 \| 167 \| 0.9852 \| 0.9708 \| 0.9779 \| 137 \| 0.9389 \| 0.9271 \| 0.9330 \| 0.9870 \| \| 0.0004 \| 76.0 \| 7296 \| 0.0978 \| 0.8788 \| 0.9255 \| 0.9016 \| 94 \| 0.9317 \| 0.8982 \| 0.9146 \| 167 \| 0.9638 \| 0.9708 \| 0.9673 \| 137 \| 0.9296 \| 0.9296 \| 0.9296 \| 0.9867 \| \| 0.0004 \| 77.0 \| 7392 \| 0.0896 \| 0.88 \| 0.9362 \| 0.9072 \| 94 \| 0.9273 \| 0.9162 \| 0.9217 \| 167 \| 0.9926 \| 0.9781 \| 0.9853 \| 137 \| 0.9375 \| 0.9422 \| 0.9398 \| 0.9887 \| \| 0.0007 \| 78.0 \| 7488 \| 0.1034 \| 0.8889 \| 0.9362 \| 0.9119 \| 94 \| 0.9308 \| 0.8862 \| 0.9080 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9439 \| 0.9296 \| 0.9367 \| 0.9878 \| \| 0.0004 \| 79.0 \| 7584 \| 0.1117 \| 0.8812 \| 0.9468 \| 0.9128 \| 94 \| 0.9259 \| 0.8982 \| 0.9119 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9395 \| 0.9372 \| 0.9384 \| 0.9873 \| \| 0.0006 \| 80.0 \| 7680 \| 0.1053 \| 0.8980 \| 0.9362 \| 0.9167 \| 94 \| 0.9017 \| 0.9341 \| 0.9176 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9333 \| 0.9497 \| 0.9415 \| 0.9873 \| \| 0.0003 \| 81.0 \| 7776 \| 0.1023 \| 0.8980 \| 0.9362 \| 0.9167 \| 94 \| 0.9222 \| 0.9222 \| 0.9222 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9424 \| 0.9447 \| 0.9435 \| 0.9884 \| \| 0.0005 \| 82.0 \| 7872 \| 0.0998 \| 0.8990 \| 0.9468 \| 0.9223 \| 94 \| 0.9281 \| 0.9281 \| 0.9281 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.945 \| 0.9497 \| 0.9474 \| 0.9887 \| \| 0.0004 \| 83.0 \| 7968 \| 0.1031 \| 0.8980 \| 0.9362 \| 0.9167 \| 94 \| 0.9222 \| 0.9222 \| 0.9222 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9424 \| 0.9447 \| 0.9435 \| 0.9884 \| \| 0.0002 \| 84.0 \| 8064 \| 0.1076 \| 0.9072 \| 0.9362 \| 0.9215 \| 94 \| 0.9273 \| 0.9162 \| 0.9217 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9470 \| 0.9422 \| 0.9446 \| 0.9890 \| \| 0.0008 \| 85.0 \| 8160 \| 0.1031 \| 0.9062 \| 0.9255 \| 0.9158 \| 94 \| 0.9273 \| 0.9162 \| 0.9217 \| 167 \| 0.9925 \| 0.9708 \| 0.9815 \| 137 \| 0.9443 \| 0.9372 \| 0.9407 \| 0.9887 \| \| 0.0003 \| 86.0 \| 8256 \| 0.0967 \| 0.9062 \| 0.9255 \| 0.9158 \| 94 \| 0.9383 \| 0.9102 \| 0.9240 \| 167 \| 0.9925 \| 0.9708 \| 0.9815 \| 137 \| 0.9490 \| 0.9347 \| 0.9418 \| 0.9892 \| \| 0.0005 \| 87.0 \| 8352 \| 0.0978 \| 0.8889 \| 0.9362 \| 0.9119 \| 94 \| 0.9317 \| 0.8982 \| 0.9146 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9442 \| 0.9347 \| 0.9394 \| 0.9884 \| \| 0.0003 \| 88.0 \| 8448 \| 0.1104 \| 0.8889 \| 0.9362 \| 0.9119 \| 94 \| 0.9375 \| 0.8982 \| 0.9174 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9466 \| 0.9347 \| 0.9406 \| 0.9881 \| \| 0.0005 \| 89.0 \| 8544 \| 0.1069 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9441 \| 0.9102 \| 0.9268 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9494 \| 0.9422 \| 0.9458 \| 0.9887 \| \| 0.0003 \| 90.0 \| 8640 \| 0.1071 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9441 \| 0.9102 \| 0.9268 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9494 \| 0.9422 \| 0.9458 \| 0.9887 \| \| 0.0005 \| 91.0 \| 8736 \| 0.1068 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9441 \| 0.9102 \| 0.9268 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9494 \| 0.9422 \| 0.9458 \| 0.9887 \| \| 0.0004 \| 92.0 \| 8832 \| 0.1078 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9444 \| 0.9162 \| 0.9301 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9495 \| 0.9447 \| 0.9471 \| 0.9890 \| \| 0.0003 \| 93.0 \| 8928 \| 0.1079 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9444 \| 0.9162 \| 0.9301 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9495 \| 0.9447 \| 0.9471 \| 0.9890 \| \| 0.0004 \| 94.0 \| 9024 \| 0.1082 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9387 \| 0.9162 \| 0.9273 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9471 \| 0.9447 \| 0.9459 \| 0.9887 \| \| 0.0003 \| 95.0 \| 9120 \| 0.1080 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9387 \| 0.9162 \| 0.9273 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9471 \| 0.9447 \| 0.9459 \| 0.9887 \| \| 0.0003 \| 96.0 \| 9216 \| 0.1082 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9387 \| 0.9162 \| 0.9273 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9471 \| 0.9447 \| 0.9459 \| 0.9887 \| \| 0.0002 \| 97.0 \| 9312 \| 0.1080 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9387 \| 0.9162 \| 0.9273 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9471 \| 0.9447 \| 0.9459 \| 0.9887 \| \| 0.0003 \| 98.0 \| 9408 \| 0.1080 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9444 \| 0.9162 \| 0.9301 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9495 \| 0.9447 \| 0.9471 \| 0.9890 \| \| 0.0003 \| 99.0 \| 9504 \| 0.1085 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9387 \| 0.9162 \| 0.9273 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9471 \| 0.9447 \| 0.9459 \| 0.9887 \| \| 0.0002 \| 100.0 \| 9600 \| 0.1084 \| 0.89 \| 0.9468 \| 0.9175 \| 94 \| 0.9387 \| 0.9162 \| 0.9273 \| 167 \| 1.0 \| 0.9781 \| 0.9889 \| 137 \| 0.9471 \| 0.9447 \| 0.9459 \| 0.9887 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
dbands/llama-3-8b-code_bagel_hermes-2-5-blender-16bit	dbands	2024-06-04T00:15:01Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T00:09:05Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: dbands - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
martinsinnona/visdecode_vega_1	martinsinnona	2024-06-04T00:09:09Z	48	0	transformers	[ "transformers", "safetensors", "pix2struct", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-21T18:34:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
apwic/nerugm-unipelt-4	apwic	2024-06-04T00:00:06Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-28T02:54:06Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerugm-unipelt-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nerugm-unipelt-4 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2206 - Location Precision: 0.7949 - Location Recall: 0.8493 - Location F1: 0.8212 - Location Number: 73 - Organization Precision: 0.7361 - Organization Recall: 0.8154 - Organization F1: 0.7737 - Organization Number: 65 - Person Precision: 0.8924 - Person Recall: 0.94 - Person F1: 0.9156 - Person Number: 150 - Quantity Precision: 0.8125 - Quantity Recall: 0.8966 - Quantity F1: 0.8525 - Quantity Number: 29 - Time Precision: 0.7838 - Time Recall: 0.8529 - Time F1: 0.8169 - Time Number: 34 - Overall Precision: 0.8249 - Overall Recall: 0.8860 - Overall F1: 0.8544 - Overall Accuracy: 0.9638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Location Precision \| Location Recall \| Location F1 \| Location Number \| Organization Precision \| Organization Recall \| Organization F1 \| Organization Number \| Person Precision \| Person Recall \| Person F1 \| Person Number \| Quantity Precision \| Quantity Recall \| Quantity F1 \| Quantity Number \| Time Precision \| Time Recall \| Time F1 \| Time Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------------------:\|:---------------:\|:-----------:\|:---------------:\|:----------------------:\|:-------------------:\|:---------------:\|:-------------------:\|:----------------:\|:-------------:\|:---------:\|:-------------:\|:------------------:\|:---------------:\|:-----------:\|:---------------:\|:--------------:\|:-----------:\|:-------:\|:-----------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.9477 \| 1.0 \| 106 \| 0.6205 \| 0.0 \| 0.0 \| 0.0 \| 73 \| 0.0 \| 0.0 \| 0.0 \| 65 \| 0.2 \| 0.0067 \| 0.0129 \| 150 \| 0.0 \| 0.0 \| 0.0 \| 29 \| 0.0 \| 0.0 \| 0.0 \| 34 \| 0.2 \| 0.0028 \| 0.0056 \| 0.8373 \| \| 0.5027 \| 2.0 \| 212 \| 0.3516 \| 0.4026 \| 0.4247 \| 0.4133 \| 73 \| 0.1091 \| 0.0923 \| 0.1 \| 65 \| 0.6020 \| 0.8067 \| 0.6895 \| 150 \| 0.1739 \| 0.1379 \| 0.1538 \| 29 \| 0.5 \| 0.6471 \| 0.5641 \| 34 \| 0.46 \| 0.5242 \| 0.4900 \| 0.9052 \| \| 0.2888 \| 3.0 \| 318 \| 0.1855 \| 0.5319 \| 0.6849 \| 0.5988 \| 73 \| 0.5167 \| 0.4769 \| 0.4960 \| 65 \| 0.7816 \| 0.9067 \| 0.8395 \| 150 \| 0.4545 \| 0.5172 \| 0.4839 \| 29 \| 0.8286 \| 0.8529 \| 0.8406 \| 34 \| 0.6591 \| 0.7436 \| 0.6988 \| 0.9404 \| \| 0.1926 \| 4.0 \| 424 \| 0.1595 \| 0.6122 \| 0.8219 \| 0.7018 \| 73 \| 0.5054 \| 0.7231 \| 0.5949 \| 65 \| 0.8155 \| 0.9133 \| 0.8616 \| 150 \| 0.6053 \| 0.7931 \| 0.6866 \| 29 \| 0.8611 \| 0.9118 \| 0.8857 \| 34 \| 0.6882 \| 0.8490 \| 0.7602 \| 0.9486 \| \| 0.163 \| 5.0 \| 530 \| 0.1464 \| 0.6354 \| 0.8356 \| 0.7219 \| 73 \| 0.5904 \| 0.7538 \| 0.6622 \| 65 \| 0.8354 \| 0.9133 \| 0.8726 \| 150 \| 0.6757 \| 0.8621 \| 0.7576 \| 29 \| 0.725 \| 0.8529 \| 0.7838 \| 34 \| 0.7167 \| 0.8575 \| 0.7808 \| 0.9515 \| \| 0.1447 \| 6.0 \| 636 \| 0.1606 \| 0.6703 \| 0.8356 \| 0.7439 \| 73 \| 0.6265 \| 0.8 \| 0.7027 \| 65 \| 0.8323 \| 0.9267 \| 0.8770 \| 150 \| 0.5333 \| 0.8276 \| 0.6486 \| 29 \| 0.6222 \| 0.8235 \| 0.7089 \| 34 \| 0.7053 \| 0.8661 \| 0.7775 \| 0.9471 \| \| 0.1316 \| 7.0 \| 742 \| 0.1419 \| 0.6739 \| 0.8493 \| 0.7515 \| 73 \| 0.6622 \| 0.7538 \| 0.7050 \| 65 \| 0.8253 \| 0.9133 \| 0.8671 \| 150 \| 0.6757 \| 0.8621 \| 0.7576 \| 29 \| 0.75 \| 0.8824 \| 0.8108 \| 34 \| 0.7408 \| 0.8632 \| 0.7974 \| 0.9550 \| \| 0.1217 \| 8.0 \| 848 \| 0.1318 \| 0.7294 \| 0.8493 \| 0.7848 \| 73 \| 0.6364 \| 0.7538 \| 0.6901 \| 65 \| 0.8313 \| 0.92 \| 0.8734 \| 150 \| 0.6757 \| 0.8621 \| 0.7576 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.7543 \| 0.8661 \| 0.8064 \| 0.9569 \| \| 0.1125 \| 9.0 \| 954 \| 0.1269 \| 0.7439 \| 0.8356 \| 0.7871 \| 73 \| 0.6145 \| 0.7846 \| 0.6892 \| 65 \| 0.8608 \| 0.9067 \| 0.8831 \| 150 \| 0.7297 \| 0.9310 \| 0.8182 \| 29 \| 0.8108 \| 0.8824 \| 0.8451 \| 34 \| 0.7683 \| 0.8689 \| 0.8155 \| 0.9591 \| \| 0.1088 \| 10.0 \| 1060 \| 0.1347 \| 0.6988 \| 0.7945 \| 0.7436 \| 73 \| 0.6944 \| 0.7692 \| 0.7299 \| 65 \| 0.8344 \| 0.9067 \| 0.8690 \| 150 \| 0.6944 \| 0.8621 \| 0.7692 \| 29 \| 0.7692 \| 0.8824 \| 0.8219 \| 34 \| 0.7608 \| 0.8519 \| 0.8038 \| 0.9582 \| \| 0.1017 \| 11.0 \| 1166 \| 0.1373 \| 0.7024 \| 0.8082 \| 0.7516 \| 73 \| 0.6456 \| 0.7846 \| 0.7083 \| 65 \| 0.8438 \| 0.9 \| 0.8710 \| 150 \| 0.6757 \| 0.8621 \| 0.7576 \| 29 \| 0.6667 \| 0.8235 \| 0.7368 \| 34 \| 0.7413 \| 0.8490 \| 0.7915 \| 0.9559 \| \| 0.0943 \| 12.0 \| 1272 \| 0.1453 \| 0.6778 \| 0.8356 \| 0.7485 \| 73 \| 0.6310 \| 0.8154 \| 0.7114 \| 65 \| 0.8457 \| 0.9133 \| 0.8782 \| 150 \| 0.7027 \| 0.8966 \| 0.7879 \| 29 \| 0.6512 \| 0.8235 \| 0.7273 \| 34 \| 0.7332 \| 0.8689 \| 0.7953 \| 0.9567 \| \| 0.0886 \| 13.0 \| 1378 \| 0.1357 \| 0.7375 \| 0.8082 \| 0.7712 \| 73 \| 0.5978 \| 0.8462 \| 0.7006 \| 65 \| 0.8553 \| 0.9067 \| 0.8803 \| 150 \| 0.7143 \| 0.8621 \| 0.7813 \| 29 \| 0.7692 \| 0.8824 \| 0.8219 \| 34 \| 0.7531 \| 0.8689 \| 0.8069 \| 0.9579 \| \| 0.0855 \| 14.0 \| 1484 \| 0.1371 \| 0.7024 \| 0.8082 \| 0.7516 \| 73 \| 0.625 \| 0.7692 \| 0.6897 \| 65 \| 0.8457 \| 0.9133 \| 0.8782 \| 150 \| 0.75 \| 0.9310 \| 0.8308 \| 29 \| 0.6829 \| 0.8235 \| 0.7467 \| 34 \| 0.7469 \| 0.8575 \| 0.7984 \| 0.9569 \| \| 0.0814 \| 15.0 \| 1590 \| 0.1300 \| 0.7821 \| 0.8356 \| 0.8079 \| 73 \| 0.6329 \| 0.7692 \| 0.6944 \| 65 \| 0.8562 \| 0.9133 \| 0.8839 \| 150 \| 0.7027 \| 0.8966 \| 0.7879 \| 29 \| 0.7368 \| 0.8235 \| 0.7778 \| 34 \| 0.7704 \| 0.8604 \| 0.8129 \| 0.9599 \| \| 0.079 \| 16.0 \| 1696 \| 0.1442 \| 0.7439 \| 0.8356 \| 0.7871 \| 73 \| 0.6667 \| 0.7692 \| 0.7143 \| 65 \| 0.8528 \| 0.9267 \| 0.8882 \| 150 \| 0.75 \| 0.9310 \| 0.8308 \| 29 \| 0.7 \| 0.8235 \| 0.7568 \| 34 \| 0.7702 \| 0.8689 \| 0.8166 \| 0.9589 \| \| 0.0722 \| 17.0 \| 1802 \| 0.1371 \| 0.7349 \| 0.8356 \| 0.7821 \| 73 \| 0.6667 \| 0.7692 \| 0.7143 \| 65 \| 0.8415 \| 0.92 \| 0.8790 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.725 \| 0.8529 \| 0.7838 \| 34 \| 0.7671 \| 0.8632 \| 0.8123 \| 0.9587 \| \| 0.0724 \| 18.0 \| 1908 \| 0.1402 \| 0.75 \| 0.8219 \| 0.7843 \| 73 \| 0.6190 \| 0.8 \| 0.6980 \| 65 \| 0.875 \| 0.9333 \| 0.9032 \| 150 \| 0.6389 \| 0.7931 \| 0.7077 \| 29 \| 0.7568 \| 0.8235 \| 0.7887 \| 34 \| 0.7632 \| 0.8632 \| 0.8102 \| 0.9579 \| \| 0.0704 \| 19.0 \| 2014 \| 0.1272 \| 0.7821 \| 0.8356 \| 0.8079 \| 73 \| 0.68 \| 0.7846 \| 0.7286 \| 65 \| 0.8580 \| 0.9267 \| 0.8910 \| 150 \| 0.7429 \| 0.8966 \| 0.8125 \| 29 \| 0.7368 \| 0.8235 \| 0.7778 \| 34 \| 0.7861 \| 0.8689 \| 0.8254 \| 0.9633 \| \| 0.0629 \| 20.0 \| 2120 \| 0.1404 \| 0.7229 \| 0.8219 \| 0.7692 \| 73 \| 0.6265 \| 0.8 \| 0.7027 \| 65 \| 0.8696 \| 0.9333 \| 0.9003 \| 150 \| 0.7353 \| 0.8621 \| 0.7937 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.7694 \| 0.8746 \| 0.8187 \| 0.9599 \| \| 0.0607 \| 21.0 \| 2226 \| 0.1343 \| 0.7531 \| 0.8356 \| 0.7922 \| 73 \| 0.6711 \| 0.7846 \| 0.7234 \| 65 \| 0.8854 \| 0.9267 \| 0.9055 \| 150 \| 0.7353 \| 0.8621 \| 0.7937 \| 29 \| 0.8857 \| 0.9118 \| 0.8986 \| 34 \| 0.8016 \| 0.8746 \| 0.8365 \| 0.9631 \| \| 0.0598 \| 22.0 \| 2332 \| 0.1399 \| 0.7531 \| 0.8356 \| 0.7922 \| 73 \| 0.6623 \| 0.7846 \| 0.7183 \| 65 \| 0.8910 \| 0.9267 \| 0.9085 \| 150 \| 0.7353 \| 0.8621 \| 0.7937 \| 29 \| 0.7568 \| 0.8235 \| 0.7887 \| 34 \| 0.7896 \| 0.8661 \| 0.8261 \| 0.9619 \| \| 0.0578 \| 23.0 \| 2438 \| 0.1296 \| 0.7792 \| 0.8219 \| 0.8000 \| 73 \| 0.68 \| 0.7846 \| 0.7286 \| 65 \| 0.8861 \| 0.9333 \| 0.9091 \| 150 \| 0.7353 \| 0.8621 \| 0.7937 \| 29 \| 0.8333 \| 0.8824 \| 0.8571 \| 34 \| 0.8053 \| 0.8718 \| 0.8372 \| 0.9628 \| \| 0.0538 \| 24.0 \| 2544 \| 0.1458 \| 0.7531 \| 0.8356 \| 0.7922 \| 73 \| 0.65 \| 0.8 \| 0.7172 \| 65 \| 0.8571 \| 0.92 \| 0.8875 \| 150 \| 0.7647 \| 0.8966 \| 0.8254 \| 29 \| 0.7632 \| 0.8529 \| 0.8056 \| 34 \| 0.7766 \| 0.8718 \| 0.8215 \| 0.9596 \| \| 0.0519 \| 25.0 \| 2650 \| 0.1594 \| 0.7176 \| 0.8356 \| 0.7722 \| 73 \| 0.6667 \| 0.8 \| 0.7273 \| 65 \| 0.8650 \| 0.94 \| 0.9010 \| 150 \| 0.6944 \| 0.8621 \| 0.7692 \| 29 \| 0.8056 \| 0.8529 \| 0.8286 \| 34 \| 0.7739 \| 0.8775 \| 0.8224 \| 0.9594 \| \| 0.0513 \| 26.0 \| 2756 \| 0.1568 \| 0.7143 \| 0.8219 \| 0.7643 \| 73 \| 0.68 \| 0.7846 \| 0.7286 \| 65 \| 0.8485 \| 0.9333 \| 0.8889 \| 150 \| 0.7143 \| 0.8621 \| 0.7813 \| 29 \| 0.7 \| 0.8235 \| 0.7568 \| 34 \| 0.7619 \| 0.8661 \| 0.8107 \| 0.9582 \| \| 0.051 \| 27.0 \| 2862 \| 0.1527 \| 0.7439 \| 0.8356 \| 0.7871 \| 73 \| 0.6296 \| 0.7846 \| 0.6986 \| 65 \| 0.8634 \| 0.9267 \| 0.8939 \| 150 \| 0.7143 \| 0.8621 \| 0.7813 \| 29 \| 0.7 \| 0.8235 \| 0.7568 \| 34 \| 0.7619 \| 0.8661 \| 0.8107 \| 0.9594 \| \| 0.0479 \| 28.0 \| 2968 \| 0.1568 \| 0.7439 \| 0.8356 \| 0.7871 \| 73 \| 0.6842 \| 0.8 \| 0.7376 \| 65 \| 0.875 \| 0.9333 \| 0.9032 \| 150 \| 0.7647 \| 0.8966 \| 0.8254 \| 29 \| 0.7 \| 0.8235 \| 0.7568 \| 34 \| 0.7832 \| 0.8746 \| 0.8264 \| 0.9596 \| \| 0.0442 \| 29.0 \| 3074 \| 0.1412 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.6463 \| 0.8154 \| 0.7211 \| 65 \| 0.8580 \| 0.9267 \| 0.8910 \| 150 \| 0.7353 \| 0.8621 \| 0.7937 \| 29 \| 0.8286 \| 0.8529 \| 0.8406 \| 34 \| 0.7877 \| 0.8775 \| 0.8302 \| 0.9611 \| \| 0.0431 \| 30.0 \| 3180 \| 0.1463 \| 0.8158 \| 0.8493 \| 0.8322 \| 73 \| 0.6667 \| 0.7692 \| 0.7143 \| 65 \| 0.8805 \| 0.9333 \| 0.9061 \| 150 \| 0.7879 \| 0.8966 \| 0.8387 \| 29 \| 0.7436 \| 0.8529 \| 0.7945 \| 34 \| 0.8037 \| 0.8746 \| 0.8377 \| 0.9623 \| \| 0.0431 \| 31.0 \| 3286 \| 0.1430 \| 0.7821 \| 0.8356 \| 0.8079 \| 73 \| 0.6757 \| 0.7692 \| 0.7194 \| 65 \| 0.8634 \| 0.9267 \| 0.8939 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8056 \| 0.8529 \| 0.8286 \| 34 \| 0.8005 \| 0.8689 \| 0.8333 \| 0.9631 \| \| 0.0396 \| 32.0 \| 3392 \| 0.1682 \| 0.7722 \| 0.8356 \| 0.8026 \| 73 \| 0.6310 \| 0.8154 \| 0.7114 \| 65 \| 0.8634 \| 0.9267 \| 0.8939 \| 150 \| 0.7647 \| 0.8966 \| 0.8254 \| 29 \| 0.7317 \| 0.8824 \| 0.8 \| 34 \| 0.7744 \| 0.8803 \| 0.824 \| 0.9594 \| \| 0.0404 \| 33.0 \| 3498 \| 0.1550 \| 0.7922 \| 0.8356 \| 0.8133 \| 73 \| 0.7246 \| 0.7692 \| 0.7463 \| 65 \| 0.8910 \| 0.9267 \| 0.9085 \| 150 \| 0.7353 \| 0.8621 \| 0.7937 \| 29 \| 0.75 \| 0.8824 \| 0.8108 \| 34 \| 0.8112 \| 0.8689 \| 0.8391 \| 0.9619 \| \| 0.038 \| 34.0 \| 3604 \| 0.1416 \| 0.8158 \| 0.8493 \| 0.8322 \| 73 \| 0.6585 \| 0.8308 \| 0.7347 \| 65 \| 0.875 \| 0.9333 \| 0.9032 \| 150 \| 0.7879 \| 0.8966 \| 0.8387 \| 29 \| 0.8611 \| 0.9118 \| 0.8857 \| 34 \| 0.8088 \| 0.8917 \| 0.8482 \| 0.9643 \| \| 0.0389 \| 35.0 \| 3710 \| 0.1660 \| 0.7349 \| 0.8356 \| 0.7821 \| 73 \| 0.6711 \| 0.7846 \| 0.7234 \| 65 \| 0.8742 \| 0.9267 \| 0.8997 \| 150 \| 0.7059 \| 0.8276 \| 0.7619 \| 29 \| 0.6905 \| 0.8529 \| 0.7632 \| 34 \| 0.7716 \| 0.8661 \| 0.8161 \| 0.9594 \| \| 0.0359 \| 36.0 \| 3816 \| 0.1483 \| 0.7848 \| 0.8493 \| 0.8158 \| 73 \| 0.7027 \| 0.8 \| 0.7482 \| 65 \| 0.8734 \| 0.92 \| 0.8961 \| 150 \| 0.7647 \| 0.8966 \| 0.8254 \| 29 \| 0.8108 \| 0.8824 \| 0.8451 \| 34 \| 0.8063 \| 0.8775 \| 0.8404 \| 0.9638 \| \| 0.0352 \| 37.0 \| 3922 \| 0.1701 \| 0.7439 \| 0.8356 \| 0.7871 \| 73 \| 0.6933 \| 0.8 \| 0.7429 \| 65 \| 0.8734 \| 0.92 \| 0.8961 \| 150 \| 0.7647 \| 0.8966 \| 0.8254 \| 29 \| 0.8421 \| 0.9412 \| 0.8889 \| 34 \| 0.7984 \| 0.8803 \| 0.8374 \| 0.9621 \| \| 0.0342 \| 38.0 \| 4028 \| 0.1522 \| 0.8052 \| 0.8493 \| 0.8267 \| 73 \| 0.75 \| 0.7846 \| 0.7669 \| 65 \| 0.8734 \| 0.92 \| 0.8961 \| 150 \| 0.7647 \| 0.8966 \| 0.8254 \| 29 \| 0.7692 \| 0.8824 \| 0.8219 \| 34 \| 0.8165 \| 0.8746 \| 0.8446 \| 0.9641 \| \| 0.0315 \| 39.0 \| 4134 \| 0.1590 \| 0.7821 \| 0.8356 \| 0.8079 \| 73 \| 0.7013 \| 0.8308 \| 0.7606 \| 65 \| 0.8688 \| 0.9267 \| 0.8968 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.8824 \| 0.8824 \| 0.8824 \| 34 \| 0.8089 \| 0.8803 \| 0.8431 \| 0.9638 \| \| 0.0335 \| 40.0 \| 4240 \| 0.1513 \| 0.8289 \| 0.8630 \| 0.8456 \| 73 \| 0.7067 \| 0.8154 \| 0.7571 \| 65 \| 0.8734 \| 0.92 \| 0.8961 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8205 \| 0.9412 \| 0.8767 \| 34 \| 0.8211 \| 0.8889 \| 0.8536 \| 0.9653 \| \| 0.0317 \| 41.0 \| 4346 \| 0.1541 \| 0.8378 \| 0.8493 \| 0.8435 \| 73 \| 0.6835 \| 0.8308 \| 0.75 \| 65 \| 0.8868 \| 0.94 \| 0.9126 \| 150 \| 0.7879 \| 0.8966 \| 0.8387 \| 29 \| 0.75 \| 0.8824 \| 0.8108 \| 34 \| 0.8130 \| 0.8917 \| 0.8505 \| 0.9633 \| \| 0.0288 \| 42.0 \| 4452 \| 0.1681 \| 0.7922 \| 0.8356 \| 0.8133 \| 73 \| 0.6279 \| 0.8308 \| 0.7152 \| 65 \| 0.8931 \| 0.9467 \| 0.9191 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.7838 \| 0.8529 \| 0.8169 \| 34 \| 0.7934 \| 0.8860 \| 0.8371 \| 0.9606 \| \| 0.0275 \| 43.0 \| 4558 \| 0.1761 \| 0.7821 \| 0.8356 \| 0.8079 \| 73 \| 0.6279 \| 0.8308 \| 0.7152 \| 65 \| 0.8931 \| 0.9467 \| 0.9191 \| 150 \| 0.7059 \| 0.8276 \| 0.7619 \| 29 \| 0.7436 \| 0.8529 \| 0.7945 \| 34 \| 0.7828 \| 0.8832 \| 0.8300 \| 0.9604 \| \| 0.0265 \| 44.0 \| 4664 \| 0.1796 \| 0.7922 \| 0.8356 \| 0.8133 \| 73 \| 0.7051 \| 0.8462 \| 0.7692 \| 65 \| 0.8712 \| 0.9467 \| 0.9073 \| 150 \| 0.7273 \| 0.8276 \| 0.7742 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.8021 \| 0.8889 \| 0.8432 \| 0.9628 \| \| 0.0271 \| 45.0 \| 4770 \| 0.1760 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7067 \| 0.8154 \| 0.7571 \| 65 \| 0.8696 \| 0.9333 \| 0.9003 \| 150 \| 0.7812 \| 0.8621 \| 0.8197 \| 29 \| 0.8158 \| 0.9118 \| 0.8611 \| 34 \| 0.8099 \| 0.8860 \| 0.8463 \| 0.9621 \| \| 0.026 \| 46.0 \| 4876 \| 0.1910 \| 0.7922 \| 0.8356 \| 0.8133 \| 73 \| 0.6795 \| 0.8154 \| 0.7413 \| 65 \| 0.875 \| 0.9333 \| 0.9032 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.7632 \| 0.8529 \| 0.8056 \| 34 \| 0.8026 \| 0.8803 \| 0.8397 \| 0.9604 \| \| 0.0264 \| 47.0 \| 4982 \| 0.1727 \| 0.8052 \| 0.8493 \| 0.8267 \| 73 \| 0.7013 \| 0.8308 \| 0.7606 \| 65 \| 0.8696 \| 0.9333 \| 0.9003 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.8057 \| 0.8860 \| 0.8440 \| 0.9636 \| \| 0.0252 \| 48.0 \| 5088 \| 0.1840 \| 0.7821 \| 0.8356 \| 0.8079 \| 73 \| 0.6923 \| 0.8308 \| 0.7552 \| 65 \| 0.8758 \| 0.94 \| 0.9068 \| 150 \| 0.7812 \| 0.8621 \| 0.8197 \| 29 \| 0.7632 \| 0.8529 \| 0.8056 \| 34 \| 0.8010 \| 0.8832 \| 0.8401 \| 0.9626 \| \| 0.0234 \| 49.0 \| 5194 \| 0.1759 \| 0.8133 \| 0.8356 \| 0.8243 \| 73 \| 0.7183 \| 0.7846 \| 0.75 \| 65 \| 0.875 \| 0.9333 \| 0.9032 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.7692 \| 0.8824 \| 0.8219 \| 34 \| 0.8170 \| 0.8775 \| 0.8462 \| 0.9641 \| \| 0.0227 \| 50.0 \| 5300 \| 0.1780 \| 0.7922 \| 0.8356 \| 0.8133 \| 73 \| 0.7324 \| 0.8 \| 0.7647 \| 65 \| 0.8917 \| 0.9333 \| 0.9121 \| 150 \| 0.8387 \| 0.8966 \| 0.8667 \| 29 \| 0.8158 \| 0.9118 \| 0.8611 \| 34 \| 0.8289 \| 0.8832 \| 0.8552 \| 0.9651 \| \| 0.0246 \| 51.0 \| 5406 \| 0.1751 \| 0.8052 \| 0.8493 \| 0.8267 \| 73 \| 0.7391 \| 0.7846 \| 0.7612 \| 65 \| 0.8854 \| 0.9267 \| 0.9055 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8421 \| 0.9412 \| 0.8889 \| 34 \| 0.8311 \| 0.8832 \| 0.8564 \| 0.9658 \| \| 0.0225 \| 52.0 \| 5512 \| 0.1924 \| 0.7848 \| 0.8493 \| 0.8158 \| 73 \| 0.7333 \| 0.8462 \| 0.7857 \| 65 \| 0.8642 \| 0.9333 \| 0.8974 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.8158 \| 0.9118 \| 0.8611 \| 34 \| 0.8088 \| 0.8917 \| 0.8482 \| 0.9636 \| \| 0.0217 \| 53.0 \| 5618 \| 0.1959 \| 0.7778 \| 0.8630 \| 0.8182 \| 73 \| 0.7297 \| 0.8308 \| 0.7770 \| 65 \| 0.875 \| 0.9333 \| 0.9032 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.7073 \| 0.8529 \| 0.7733 \| 34 \| 0.7995 \| 0.8860 \| 0.8405 \| 0.9621 \| \| 0.0201 \| 54.0 \| 5724 \| 0.2034 \| 0.7922 \| 0.8356 \| 0.8133 \| 73 \| 0.6875 \| 0.8462 \| 0.7586 \| 65 \| 0.8642 \| 0.9333 \| 0.8974 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.8158 \| 0.9118 \| 0.8611 \| 34 \| 0.8 \| 0.8889 \| 0.8421 \| 0.9611 \| \| 0.0198 \| 55.0 \| 5830 \| 0.1859 \| 0.8158 \| 0.8493 \| 0.8322 \| 73 \| 0.6986 \| 0.7846 \| 0.7391 \| 65 \| 0.8854 \| 0.9267 \| 0.9055 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.8143 \| 0.8746 \| 0.8434 \| 0.9633 \| \| 0.0199 \| 56.0 \| 5936 \| 0.1812 \| 0.8182 \| 0.8630 \| 0.8400 \| 73 \| 0.7324 \| 0.8 \| 0.7647 \| 65 \| 0.8805 \| 0.9333 \| 0.9061 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.8889 \| 0.9412 \| 0.9143 \| 34 \| 0.8298 \| 0.8889 \| 0.8583 \| 0.9663 \| \| 0.0183 \| 57.0 \| 6042 \| 0.1818 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7260 \| 0.8154 \| 0.7681 \| 65 \| 0.8758 \| 0.94 \| 0.9068 \| 150 \| 0.7812 \| 0.8621 \| 0.8197 \| 29 \| 0.8333 \| 0.8824 \| 0.8571 \| 34 \| 0.8184 \| 0.8860 \| 0.8509 \| 0.9655 \| \| 0.0204 \| 58.0 \| 6148 \| 0.1844 \| 0.775 \| 0.8493 \| 0.8105 \| 73 \| 0.7206 \| 0.7538 \| 0.7368 \| 65 \| 0.8734 \| 0.92 \| 0.8961 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8421 \| 0.9412 \| 0.8889 \| 34 \| 0.8165 \| 0.8746 \| 0.8446 \| 0.9636 \| \| 0.0171 \| 59.0 \| 6254 \| 0.2010 \| 0.7625 \| 0.8356 \| 0.7974 \| 73 \| 0.7391 \| 0.7846 \| 0.7612 \| 65 \| 0.9032 \| 0.9333 \| 0.9180 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.7436 \| 0.8529 \| 0.7945 \| 34 \| 0.8138 \| 0.8718 \| 0.8418 \| 0.9641 \| \| 0.0177 \| 60.0 \| 6360 \| 0.1953 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.72 \| 0.8308 \| 0.7714 \| 65 \| 0.8974 \| 0.9333 \| 0.9150 \| 150 \| 0.7879 \| 0.8966 \| 0.8387 \| 29 \| 0.8158 \| 0.9118 \| 0.8611 \| 34 \| 0.8237 \| 0.8917 \| 0.8564 \| 0.9651 \| \| 0.0166 \| 61.0 \| 6466 \| 0.1929 \| 0.7922 \| 0.8356 \| 0.8133 \| 73 \| 0.7297 \| 0.8308 \| 0.7770 \| 65 \| 0.8642 \| 0.9333 \| 0.8974 \| 150 \| 0.7812 \| 0.8621 \| 0.8197 \| 29 \| 0.8611 \| 0.9118 \| 0.8857 \| 34 \| 0.8163 \| 0.8860 \| 0.8497 \| 0.9646 \| \| 0.0169 \| 62.0 \| 6572 \| 0.1962 \| 0.8052 \| 0.8493 \| 0.8267 \| 73 \| 0.7324 \| 0.8 \| 0.7647 \| 65 \| 0.8861 \| 0.9333 \| 0.9091 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.7632 \| 0.8529 \| 0.8056 \| 34 \| 0.8170 \| 0.8775 \| 0.8462 \| 0.9651 \| \| 0.0158 \| 63.0 \| 6678 \| 0.2073 \| 0.8158 \| 0.8493 \| 0.8322 \| 73 \| 0.6353 \| 0.8308 \| 0.7200 \| 65 \| 0.8679 \| 0.92 \| 0.8932 \| 150 \| 0.7812 \| 0.8621 \| 0.8197 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.7923 \| 0.8803 \| 0.8340 \| 0.9621 \| \| 0.0167 \| 64.0 \| 6784 \| 0.1845 \| 0.8182 \| 0.8630 \| 0.8400 \| 73 \| 0.75 \| 0.7846 \| 0.7669 \| 65 \| 0.8974 \| 0.9333 \| 0.9150 \| 150 \| 0.7812 \| 0.8621 \| 0.8197 \| 29 \| 0.8158 \| 0.9118 \| 0.8611 \| 34 \| 0.8356 \| 0.8832 \| 0.8587 \| 0.9678 \| \| 0.0167 \| 65.0 \| 6890 \| 0.2060 \| 0.775 \| 0.8493 \| 0.8105 \| 73 \| 0.7432 \| 0.8462 \| 0.7914 \| 65 \| 0.8742 \| 0.9267 \| 0.8997 \| 150 \| 0.7353 \| 0.8621 \| 0.7937 \| 29 \| 0.8158 \| 0.9118 \| 0.8611 \| 34 \| 0.8104 \| 0.8889 \| 0.8478 \| 0.9636 \| \| 0.0167 \| 66.0 \| 6996 \| 0.2054 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7286 \| 0.7846 \| 0.7556 \| 65 \| 0.8758 \| 0.94 \| 0.9068 \| 150 \| 0.7812 \| 0.8621 \| 0.8197 \| 29 \| 0.7436 \| 0.8529 \| 0.7945 \| 34 \| 0.8105 \| 0.8775 \| 0.8427 \| 0.9619 \| \| 0.0148 \| 67.0 \| 7102 \| 0.2037 \| 0.7975 \| 0.8630 \| 0.8289 \| 73 \| 0.7183 \| 0.7846 \| 0.75 \| 65 \| 0.8758 \| 0.94 \| 0.9068 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.8158 \| 0.9118 \| 0.8611 \| 34 \| 0.8141 \| 0.8860 \| 0.8486 \| 0.9636 \| \| 0.0152 \| 68.0 \| 7208 \| 0.2017 \| 0.8182 \| 0.8630 \| 0.8400 \| 73 \| 0.7353 \| 0.7692 \| 0.7519 \| 65 \| 0.8854 \| 0.9267 \| 0.9055 \| 150 \| 0.7812 \| 0.8621 \| 0.8197 \| 29 \| 0.8378 \| 0.9118 \| 0.8732 \| 34 \| 0.8302 \| 0.8775 \| 0.8532 \| 0.9646 \| \| 0.0134 \| 69.0 \| 7314 \| 0.2069 \| 0.8289 \| 0.8630 \| 0.8456 \| 73 \| 0.7042 \| 0.7692 \| 0.7353 \| 65 \| 0.8861 \| 0.9333 \| 0.9091 \| 150 \| 0.7812 \| 0.8621 \| 0.8197 \| 29 \| 0.8611 \| 0.9118 \| 0.8857 \| 34 \| 0.8284 \| 0.8803 \| 0.8536 \| 0.9631 \| \| 0.0153 \| 70.0 \| 7420 \| 0.2087 \| 0.7975 \| 0.8630 \| 0.8289 \| 73 \| 0.7397 \| 0.8308 \| 0.7826 \| 65 \| 0.875 \| 0.9333 \| 0.9032 \| 150 \| 0.7353 \| 0.8621 \| 0.7937 \| 29 \| 0.7949 \| 0.9118 \| 0.8493 \| 34 \| 0.8130 \| 0.8917 \| 0.8505 \| 0.9641 \| \| 0.0137 \| 71.0 \| 7526 \| 0.2142 \| 0.8267 \| 0.8493 \| 0.8378 \| 73 \| 0.65 \| 0.8 \| 0.7172 \| 65 \| 0.8812 \| 0.94 \| 0.9097 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.8031 \| 0.8832 \| 0.8412 \| 0.9611 \| \| 0.015 \| 72.0 \| 7632 \| 0.2135 \| 0.8158 \| 0.8493 \| 0.8322 \| 73 \| 0.7083 \| 0.7846 \| 0.7445 \| 65 \| 0.8805 \| 0.9333 \| 0.9061 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.8148 \| 0.8775 \| 0.8450 \| 0.9609 \| \| 0.013 \| 73.0 \| 7738 \| 0.2118 \| 0.8077 \| 0.8630 \| 0.8344 \| 73 \| 0.7183 \| 0.7846 \| 0.75 \| 65 \| 0.875 \| 0.9333 \| 0.9032 \| 150 \| 0.7879 \| 0.8966 \| 0.8387 \| 29 \| 0.8158 \| 0.9118 \| 0.8611 \| 34 \| 0.8184 \| 0.8860 \| 0.8509 \| 0.9633 \| \| 0.0132 \| 74.0 \| 7844 \| 0.2089 \| 0.8026 \| 0.8356 \| 0.8188 \| 73 \| 0.7083 \| 0.7846 \| 0.7445 \| 65 \| 0.8742 \| 0.9267 \| 0.8997 \| 150 \| 0.7879 \| 0.8966 \| 0.8387 \| 29 \| 0.8108 \| 0.8824 \| 0.8451 \| 34 \| 0.8143 \| 0.8746 \| 0.8434 \| 0.9638 \| \| 0.0126 \| 75.0 \| 7950 \| 0.1995 \| 0.8289 \| 0.8630 \| 0.8456 \| 73 \| 0.7286 \| 0.7846 \| 0.7556 \| 65 \| 0.9091 \| 0.9333 \| 0.9211 \| 150 \| 0.7812 \| 0.8621 \| 0.8197 \| 29 \| 0.8611 \| 0.9118 \| 0.8857 \| 34 \| 0.8424 \| 0.8832 \| 0.8623 \| 0.9660 \| \| 0.0128 \| 76.0 \| 8056 \| 0.1982 \| 0.8289 \| 0.8630 \| 0.8456 \| 73 \| 0.7286 \| 0.7846 \| 0.7556 \| 65 \| 0.9091 \| 0.9333 \| 0.9211 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8857 \| 0.9118 \| 0.8986 \| 34 \| 0.8474 \| 0.8860 \| 0.8663 \| 0.9668 \| \| 0.0123 \| 77.0 \| 8162 \| 0.2078 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7123 \| 0.8 \| 0.7536 \| 65 \| 0.8797 \| 0.9267 \| 0.9026 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.8105 \| 0.8775 \| 0.8427 \| 0.9633 \| \| 0.013 \| 78.0 \| 8268 \| 0.1980 \| 0.8052 \| 0.8493 \| 0.8267 \| 73 \| 0.7206 \| 0.7538 \| 0.7368 \| 65 \| 0.8854 \| 0.9267 \| 0.9055 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8378 \| 0.9118 \| 0.8732 \| 34 \| 0.8275 \| 0.8746 \| 0.8504 \| 0.9651 \| \| 0.0122 \| 79.0 \| 8374 \| 0.2100 \| 0.7722 \| 0.8356 \| 0.8026 \| 73 \| 0.7183 \| 0.7846 \| 0.75 \| 65 \| 0.8917 \| 0.9333 \| 0.9121 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.8170 \| 0.8775 \| 0.8462 \| 0.9641 \| \| 0.0128 \| 80.0 \| 8480 \| 0.2086 \| 0.8158 \| 0.8493 \| 0.8322 \| 73 \| 0.7222 \| 0.8 \| 0.7591 \| 65 \| 0.875 \| 0.9333 \| 0.9032 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8378 \| 0.9118 \| 0.8732 \| 34 \| 0.8249 \| 0.8860 \| 0.8544 \| 0.9633 \| \| 0.0117 \| 81.0 \| 8586 \| 0.2055 \| 0.8182 \| 0.8630 \| 0.8400 \| 73 \| 0.7286 \| 0.7846 \| 0.7556 \| 65 \| 0.9038 \| 0.94 \| 0.9216 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8421 \| 0.9412 \| 0.8889 \| 34 \| 0.8391 \| 0.8917 \| 0.8646 \| 0.9658 \| \| 0.0122 \| 82.0 \| 8692 \| 0.2136 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7183 \| 0.7846 \| 0.75 \| 65 \| 0.8917 \| 0.9333 \| 0.9121 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.8218 \| 0.8803 \| 0.8501 \| 0.9641 \| \| 0.011 \| 83.0 \| 8798 \| 0.2200 \| 0.7848 \| 0.8493 \| 0.8158 \| 73 \| 0.6901 \| 0.7538 \| 0.7206 \| 65 \| 0.8805 \| 0.9333 \| 0.9061 \| 150 \| 0.7812 \| 0.8621 \| 0.8197 \| 29 \| 0.7895 \| 0.8824 \| 0.8333 \| 34 \| 0.8074 \| 0.8718 \| 0.8384 \| 0.9619 \| \| 0.0099 \| 84.0 \| 8904 \| 0.2109 \| 0.8077 \| 0.8630 \| 0.8344 \| 73 \| 0.7183 \| 0.7846 \| 0.75 \| 65 \| 0.8917 \| 0.9333 \| 0.9121 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8611 \| 0.9118 \| 0.8857 \| 34 \| 0.8316 \| 0.8860 \| 0.8579 \| 0.9648 \| \| 0.0109 \| 85.0 \| 9010 \| 0.2205 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7286 \| 0.7846 \| 0.7556 \| 65 \| 0.8861 \| 0.9333 \| 0.9091 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8108 \| 0.8824 \| 0.8451 \| 34 \| 0.824 \| 0.8803 \| 0.8512 \| 0.9641 \| \| 0.0107 \| 86.0 \| 9116 \| 0.2110 \| 0.8077 \| 0.8630 \| 0.8344 \| 73 \| 0.7353 \| 0.7692 \| 0.7519 \| 65 \| 0.8917 \| 0.9333 \| 0.9121 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8378 \| 0.9118 \| 0.8732 \| 34 \| 0.8333 \| 0.8832 \| 0.8575 \| 0.9643 \| \| 0.0101 \| 87.0 \| 9222 \| 0.2093 \| 0.8182 \| 0.8630 \| 0.8400 \| 73 \| 0.7391 \| 0.7846 \| 0.7612 \| 65 \| 0.8924 \| 0.94 \| 0.9156 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8857 \| 0.9118 \| 0.8986 \| 34 \| 0.8410 \| 0.8889 \| 0.8643 \| 0.9655 \| \| 0.0111 \| 88.0 \| 9328 \| 0.2124 \| 0.8182 \| 0.8630 \| 0.8400 \| 73 \| 0.7391 \| 0.7846 \| 0.7612 \| 65 \| 0.8868 \| 0.94 \| 0.9126 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8378 \| 0.9118 \| 0.8732 \| 34 \| 0.8342 \| 0.8889 \| 0.8607 \| 0.9651 \| \| 0.0095 \| 89.0 \| 9434 \| 0.2123 \| 0.8077 \| 0.8630 \| 0.8344 \| 73 \| 0.7286 \| 0.7846 \| 0.7556 \| 65 \| 0.8924 \| 0.94 \| 0.9156 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8108 \| 0.8824 \| 0.8451 \| 34 \| 0.8293 \| 0.8860 \| 0.8567 \| 0.9653 \| \| 0.0105 \| 90.0 \| 9540 \| 0.2122 \| 0.8077 \| 0.8630 \| 0.8344 \| 73 \| 0.7429 \| 0.8 \| 0.7704 \| 65 \| 0.8868 \| 0.94 \| 0.9126 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8333 \| 0.8824 \| 0.8571 \| 34 \| 0.832 \| 0.8889 \| 0.8595 \| 0.9653 \| \| 0.0102 \| 91.0 \| 9646 \| 0.2248 \| 0.7848 \| 0.8493 \| 0.8158 \| 73 \| 0.7297 \| 0.8308 \| 0.7770 \| 65 \| 0.8812 \| 0.94 \| 0.9097 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8378 \| 0.9118 \| 0.8732 \| 34 \| 0.8220 \| 0.8946 \| 0.8568 \| 0.9636 \| \| 0.0104 \| 92.0 \| 9752 \| 0.2231 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7324 \| 0.8 \| 0.7647 \| 65 \| 0.8812 \| 0.94 \| 0.9097 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8108 \| 0.8824 \| 0.8451 \| 34 \| 0.8228 \| 0.8860 \| 0.8532 \| 0.9633 \| \| 0.0096 \| 93.0 \| 9858 \| 0.2227 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7260 \| 0.8154 \| 0.7681 \| 65 \| 0.8812 \| 0.94 \| 0.9097 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.8108 \| 0.8824 \| 0.8451 \| 34 \| 0.8211 \| 0.8889 \| 0.8536 \| 0.9638 \| \| 0.0106 \| 94.0 \| 9964 \| 0.2312 \| 0.7848 \| 0.8493 \| 0.8158 \| 73 \| 0.7260 \| 0.8154 \| 0.7681 \| 65 \| 0.8758 \| 0.94 \| 0.9068 \| 150 \| 0.7576 \| 0.8621 \| 0.8065 \| 29 \| 0.8108 \| 0.8824 \| 0.8451 \| 34 \| 0.8120 \| 0.8860 \| 0.8474 \| 0.9616 \| \| 0.0096 \| 95.0 \| 10070 \| 0.2199 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7324 \| 0.8 \| 0.7647 \| 65 \| 0.8812 \| 0.94 \| 0.9097 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.7838 \| 0.8529 \| 0.8169 \| 34 \| 0.8201 \| 0.8832 \| 0.8505 \| 0.9633 \| \| 0.0098 \| 96.0 \| 10176 \| 0.2199 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7324 \| 0.8 \| 0.7647 \| 65 \| 0.8868 \| 0.94 \| 0.9126 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.7838 \| 0.8529 \| 0.8169 \| 34 \| 0.8223 \| 0.8832 \| 0.8516 \| 0.9641 \| \| 0.0099 \| 97.0 \| 10282 \| 0.2210 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7297 \| 0.8308 \| 0.7770 \| 65 \| 0.8924 \| 0.94 \| 0.9156 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.7838 \| 0.8529 \| 0.8169 \| 34 \| 0.8232 \| 0.8889 \| 0.8548 \| 0.9641 \| \| 0.0094 \| 98.0 \| 10388 \| 0.2210 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7123 \| 0.8 \| 0.7536 \| 65 \| 0.8868 \| 0.94 \| 0.9126 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.7838 \| 0.8529 \| 0.8169 \| 34 \| 0.8179 \| 0.8832 \| 0.8493 \| 0.9633 \| \| 0.0086 \| 99.0 \| 10494 \| 0.2204 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7361 \| 0.8154 \| 0.7737 \| 65 \| 0.8924 \| 0.94 \| 0.9156 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.7838 \| 0.8529 \| 0.8169 \| 34 \| 0.8249 \| 0.8860 \| 0.8544 \| 0.9638 \| \| 0.0109 \| 100.0 \| 10600 \| 0.2206 \| 0.7949 \| 0.8493 \| 0.8212 \| 73 \| 0.7361 \| 0.8154 \| 0.7737 \| 65 \| 0.8924 \| 0.94 \| 0.9156 \| 150 \| 0.8125 \| 0.8966 \| 0.8525 \| 29 \| 0.7838 \| 0.8529 \| 0.8169 \| 34 \| 0.8249 \| 0.8860 \| 0.8544 \| 0.9638 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
bella05/pogny_10_64_0.01	bella05	2024-06-03T23:50:21Z	8	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:klue/roberta-large", "base_model:finetune:klue/roberta-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-03T19:35:38Z	--- base_model: klue/roberta-large tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: pogny_10_64_0.01 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/bella05/huggingface/runs/2fqy4l1d) # pogny_10_64_0.01 This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6851 - Accuracy: 0.4376 - F1: 0.2665 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 2.491 \| 1.0 \| 1205 \| 2.5033 \| 0.4376 \| 0.2665 \| \| 2.4679 \| 2.0 \| 2410 \| 1.9460 \| 0.4376 \| 0.2665 \| \| 2.302 \| 3.0 \| 3615 \| 2.4098 \| 0.0702 \| 0.0092 \| \| 2.1762 \| 4.0 \| 4820 \| 2.2698 \| 0.0545 \| 0.0056 \| \| 2.0639 \| 5.0 \| 6025 \| 1.9917 \| 0.4376 \| 0.2665 \| \| 2.0031 \| 6.0 \| 7230 \| 1.9130 \| 0.4376 \| 0.2665 \| \| 1.9241 \| 7.0 \| 8435 \| 2.0131 \| 0.4376 \| 0.2665 \| \| 1.8227 \| 8.0 \| 9640 \| 1.8212 \| 0.4376 \| 0.2665 \| \| 1.7854 \| 9.0 \| 10845 \| 1.7379 \| 0.4376 \| 0.2665 \| \| 1.7037 \| 10.0 \| 12050 \| 1.6851 \| 0.4376 \| 0.2665 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.2 - Datasets 2.19.1 - Tokenizers 0.19.1
kevinvelez18/ViT_model	kevinvelez18	2024-06-03T23:46:48Z	222	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-06-03T23:43:33Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: ViT_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViT_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0252 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.1492 \| 3.8462 \| 500 \| 0.0252 \| 0.9925 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
bartowski/phi3-4x4b-v1-GGUF	bartowski	2024-06-03T23:40:50Z	192	0	null	[ "gguf", "phi3", "nlp", "moe", "text-generation", "dataset:BEE-spoke-data/gutenberg-en-v1-clean", "dataset:NeelNanda/pile-10k", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-06-03T23:18:02Z	--- license: mit tags: - phi3 - nlp - moe datasets: - BEE-spoke-data/gutenberg-en-v1-clean - NeelNanda/pile-10k quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of phi3-4x4b-v1 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3070">b3070</a> for quantization. Original model: https://huggingface.co/Fizzarolli/phi3-4x4b-v1 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) ## Prompt format ``` <s><\|user\|> {prompt}<\|end\|><\|assistant\|><\|end\|> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [phi3-4x4b-v1-Q8_0.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q8_0.gguf) \| Q8_0 \| 11.76GB \| Extremely high quality, generally unneeded but max available quant. \| \| [phi3-4x4b-v1-Q6_K.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q6_K.gguf) \| Q6_K \| 9.08GB \| Very high quality, near perfect, recommended. \| \| [phi3-4x4b-v1-Q5_K_M.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q5_K_M.gguf) \| Q5_K_M \| 7.85GB \| High quality, recommended. \| \| [phi3-4x4b-v1-Q5_K_S.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q5_K_S.gguf) \| Q5_K_S \| 7.62GB \| High quality, recommended. \| \| [phi3-4x4b-v1-Q4_K_M.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q4_K_M.gguf) \| Q4_K_M \| 6.70GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [phi3-4x4b-v1-Q4_K_S.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q4_K_S.gguf) \| Q4_K_S \| 6.30GB \| Slightly lower quality with more space savings, recommended. \| \| [phi3-4x4b-v1-IQ4_XS.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ4_XS.gguf) \| IQ4_XS \| 5.91GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [phi3-4x4b-v1-Q3_K_L.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q3_K_L.gguf) \| Q3_K_L \| 5.78GB \| Lower quality but usable, good for low RAM availability. \| \| [phi3-4x4b-v1-Q3_K_M.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q3_K_M.gguf) \| Q3_K_M \| 5.33GB \| Even lower quality. \| \| [phi3-4x4b-v1-IQ3_M.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ3_M.gguf) \| IQ3_M \| 4.93GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [phi3-4x4b-v1-Q3_K_S.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q3_K_S.gguf) \| Q3_K_S \| 4.79GB \| Low quality, not recommended. \| \| [phi3-4x4b-v1-IQ3_XS.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ3_XS.gguf) \| IQ3_XS \| 4.54GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [phi3-4x4b-v1-IQ3_XXS.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ3_XXS.gguf) \| IQ3_XXS \| 4.25GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [phi3-4x4b-v1-Q2_K.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q2_K.gguf) \| Q2_K \| 4.07GB \| Very low quality but surprisingly usable. \| \| [phi3-4x4b-v1-IQ2_M.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ2_M.gguf) \| IQ2_M \| 3.74GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [phi3-4x4b-v1-IQ2_S.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ2_S.gguf) \| IQ2_S \| 3.43GB \| Very low quality, uses SOTA techniques to be usable. \| \| [phi3-4x4b-v1-IQ2_XS.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ2_XS.gguf) \| IQ2_XS \| 3.34GB \| Very low quality, uses SOTA techniques to be usable. \| ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/phi3-4x4b-v1-GGUF --include "phi3-4x4b-v1-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/phi3-4x4b-v1-GGUF --include "phi3-4x4b-v1-Q8_0.gguf/" --local-dir phi3-4x4b-v1-Q8_0 ``` You can either specify a new local-dir (phi3-4x4b-v1-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
sj21867/ai_art_exp3_mobilenetv2	sj21867	2024-06-03T23:25:00Z	193	0	transformers	[ "transformers", "tensorboard", "safetensors", "mobilenet_v2", "image-classification", "generated_from_trainer", "base_model:google/mobilenet_v2_1.0_224", "base_model:finetune:google/mobilenet_v2_1.0_224", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-06-03T23:23:07Z	--- license: other base_model: google/mobilenet_v2_1.0_224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_art_exp3_mobilenetv2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai_art_exp3_mobilenetv2 This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on an unknown dataset. It achieves the following results on the evaluation set: - Accuracy: {'accuracy': 0.65} - Loss: 0.8813 - Overall Accuracy: 0.65 - Human Accuracy: 0.34 - Ld Accuracy: 0.84 - Sd Accuracy: 0.77 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| Overall Accuracy \| Human Accuracy \| Ld Accuracy \| Sd Accuracy \| \|:-------------:\|:-----:\|:----:\|:--------------------------------:\|:---------------:\|:----------------:\|:--------------:\|:-----------:\|:-----------:\| \| 1.0707 \| 0.96 \| 18 \| {'accuracy': 0.6333333333333333} \| 0.8947 \| 0.6333 \| 0.3426 \| 0.8485 \| 0.7419 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
sj21867/ai_art_exp3_vit	sj21867	2024-06-03T23:18:54Z	195	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-06-03T23:16:24Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_art_exp3_vit results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai_art_exp3_vit This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Accuracy: {'accuracy': 0.72} - Loss: 0.9013 - Overall Accuracy: 0.72 - Human Accuracy: 0.34 - Ld Accuracy: 0.9 - Sd Accuracy: 0.92 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| Overall Accuracy \| Human Accuracy \| Ld Accuracy \| Sd Accuracy \| \|:-------------:\|:-----:\|:----:\|:------------------:\|:---------------:\|:----------------:\|:--------------:\|:-----------:\|:-----------:\| \| 1.0375 \| 0.96 \| 18 \| {'accuracy': 0.72} \| 0.9182 \| 0.72 \| 0.3889 \| 0.9192 \| 0.8925 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF	radia	2024-06-03T23:15:38Z	2	0	null	[ "gguf", "nlp", "code", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:microsoft/Phi-3-mini-128k-instruct", "base_model:quantized:microsoft/Phi-3-mini-128k-instruct", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-06-03T23:15:32Z	--- language: - en license: mit tags: - nlp - code - llama-cpp - gguf-my-repo base_model: microsoft/Phi-3-mini-128k-instruct license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE pipeline_tag: text-generation widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- # radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`microsoft/Phi-3-mini-128k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-128k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-128k-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-128k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-128k-instruct-q4_k_m.gguf -c 2048 ```
mayabedge/whisper-ft	mayabedge	2024-06-03T23:05:31Z	92	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-03T22:39:07Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper Fine-tuned - NNCES results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Fine-tuned - NNCES This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1135 - Wer: 8.0963 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 1.2697 \| 0.1 \| 10 \| 0.8252 \| 40.9920 \| \| 0.6597 \| 0.2 \| 20 \| 0.5482 \| 25.2371 \| \| 0.4656 \| 0.3 \| 30 \| 0.3488 \| 20.0584 \| \| 0.2774 \| 0.4 \| 40 \| 0.2164 \| 21.5901 \| \| 0.1746 \| 0.5 \| 50 \| 0.1770 \| 19.0372 \| \| 0.1826 \| 0.6 \| 60 \| 0.1540 \| 15.3902 \| \| 0.1228 \| 0.7 \| 70 \| 0.1364 \| 11.4515 \| \| 0.1271 \| 0.8 \| 80 \| 0.1246 \| 8.6798 \| \| 0.2388 \| 0.9 \| 90 \| 0.1165 \| 8.0233 \| \| 0.2584 \| 1.0 \| 100 \| 0.1135 \| 8.0963 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
datek/Qwen-Qwen1.5-1.8B-1717455654	datek	2024-06-03T23:02:36Z	139	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-03T23:00:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
cetusian/ner-model-furniture	cetusian	2024-06-03T23:00:08Z	73	0	transformers	[ "transformers", "tf", "distilbert", "token-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-03T22:48:43Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: cetusian/ner-model-furniture results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # cetusian/ner-model-furniture This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3445 - Validation Loss: 0.3911 - Train Precision: 0.7212 - Train Recall: 0.7764 - Train F1: 0.7478 - Train Accuracy: 0.8465 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 348, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.3467 \| 0.3911 \| 0.7212 \| 0.7764 \| 0.7478 \| 0.8465 \| 0 \| \| 0.3445 \| 0.3911 \| 0.7212 \| 0.7764 \| 0.7478 \| 0.8465 \| 1 \| ### Framework versions - Transformers 4.41.1 - TensorFlow 2.15.0 - Datasets 2.19.2 - Tokenizers 0.19.1
mradermacher/llama-3-70B-openbio-dareties-GGUF	mradermacher	2024-06-03T22:54:25Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:toantam1290/llama-3-70B-openbio-dareties", "base_model:quantized:toantam1290/llama-3-70B-openbio-dareties", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-03T17:54:45Z	--- base_model: toantam1290/llama-3-70B-openbio-dareties language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/toantam1290/llama-3-70B-openbio-dareties <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.IQ3_XS.gguf) \| IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.IQ3_S.gguf) \| IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.IQ3_M.gguf) \| IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF	wwe180	2024-06-03T22:51:53Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:wwe180/Llama3-13B-lingyang-v1", "base_model:quantized:wwe180/Llama3-13B-lingyang-v1", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-03T21:24:10Z	--- library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: wwe180/Llama3-13B-lingyang-v1 --- # wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF This model was converted to GGUF format from [`wwe180/Llama3-13B-lingyang-v1`](https://huggingface.co/wwe180/Llama3-13B-lingyang-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/wwe180/Llama3-13B-lingyang-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF --hf-file Llama3-13B-lingyang-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF --hf-file Llama3-13B-lingyang-v1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF --hf-file Llama3-13B-lingyang-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF --hf-file Llama3-13B-lingyang-v1-q4_k_m.gguf -c 2048 ```
Molkaa/mistral-7b-miniplatypus	Molkaa	2024-06-03T22:51:51Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "region:us" ]	null	2024-05-29T19:10:41Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
Ariffiq99/KUCI_e_care_xlm_roberta_base_Finetuned	Ariffiq99	2024-06-03T22:49:13Z	103	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:Ariffiq99/e_care_xlm_roberta_base_finetuned", "base_model:finetune:Ariffiq99/e_care_xlm_roberta_base_finetuned", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2024-06-03T09:35:11Z	--- license: mit base_model: Ariffiq99/e_care_xlm_roberta_base_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: KUCI_e_care_xlm_roberta_base_Finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # KUCI_e_care_xlm_roberta_base_Finetuned This model is a fine-tuned version of [Ariffiq99/e_care_xlm_roberta_base_finetuned](https://huggingface.co/Ariffiq99/e_care_xlm_roberta_base_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0348 - F1: 0.7682 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.6965 \| 1.0 \| 5196 \| 0.6542 \| 0.7443 \| \| 0.5632 \| 2.0 \| 10392 \| 0.6754 \| 0.7591 \| \| 0.4634 \| 3.0 \| 15588 \| 0.6456 \| 0.7680 \| \| 0.3661 \| 4.0 \| 20784 \| 0.7082 \| 0.7657 \| \| 0.2783 \| 5.0 \| 25980 \| 0.7899 \| 0.7678 \| \| 0.2305 \| 6.0 \| 31176 \| 0.9280 \| 0.7655 \| \| 0.2057 \| 7.0 \| 36372 \| 1.0348 \| 0.7682 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
animaRegem/gemma-2b-malayalam-t2-gguf	animaRegem	2024-06-03T22:49:09Z	7	0	transformers	[ "transformers", "gguf", "gemma", "text-generation-inference", "unsloth", "en", "base_model:Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0", "base_model:quantized:Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-03T21:37:07Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - gguf base_model: Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 --- # Uploaded model - Developed by: animaRegem - License: apache-2.0 - Finetuned from model : Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
wwe180/Llama3-13B-lingyang-v1	wwe180	2024-06-03T22:47:07Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "Llama3", "conversational", "base_model:wwe180/Llama3-13B-lingyang-v1", "base_model:finetune:wwe180/Llama3-13B-lingyang-v1", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-03T20:25:39Z	--- base_model: - wwe180/Llama3-13B-lingyang-v1 library_name: transformers tags: - mergekit - merge - Llama3 license: - other --- # After simple testing, the effect is good, stronger than llama-3-8b! # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method using [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) as a base. ### Models Merged The following models were included in the merge: * [openchat/openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522) + [hfl/llama-3-chinese-8b-instruct-v2-lora](https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v2-lora) * [Sao10K/L3-8B-Stheno-v3.1](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.1) + [Jiar/Llama-3-8B-Chinese](https://huggingface.co/Jiar/Llama-3-8B-Chinese) * [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B) + [camillop/Meta-Llama-3-8B-ORPO-ITA-llama-adapters](https://huggingface.co/camillop/Meta-Llama-3-8B-ORPO-ITA-llama-adapters) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Llama3-13B-lingyang-v1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) ``` ## Statement: Llama3-13B-lingyang-v1 does not represent the views and positions of the model developers We will not be liable for any problems arising from the use of the Llama3-13B-lingyang-v1 open Source model, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.
kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF	kaushiksiva07	2024-06-03T22:45:50Z	40	0	null	[ "gguf", "finetuned", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-06-03T22:45:38Z	--- license: apache-2.0 tags: - finetuned - llama-cpp - gguf-my-repo base_model: mistralai/Mistral-7B-Instruct-v0.2 pipeline_tag: text-generation inference: true widget: - messages: - role: user content: What is your favorite condiment? --- # kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF This model was converted to GGUF format from [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF --hf-file mistral-7b-instruct-v0.2-q4_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF --hf-file mistral-7b-instruct-v0.2-q4_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF --hf-file mistral-7b-instruct-v0.2-q4_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF --hf-file mistral-7b-instruct-v0.2-q4_0.gguf -c 2048 ```
shane062/whisper-small-finetuned-300	shane062	2024-06-03T22:43:21Z	90	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:audiofolder", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-03T04:06:31Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - audiofolder metrics: - wer model-index: - name: whisper-small-finetuned-300 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: audiofolder type: audiofolder config: default split: test args: default metrics: - name: Wer type: wer value: 64.86486486486487 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-finetuned-300 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7359 - Wer Ortho: 64.8649 - Wer: 64.8649 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 30 - training_steps: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.5121 \| 20.0 \| 60 \| 1.3011 \| 64.8649 \| 64.8649 \| \| 0.0001 \| 40.0 \| 120 \| 0.7236 \| 64.8649 \| 64.8649 \| \| 0.0 \| 60.0 \| 180 \| 0.7314 \| 64.8649 \| 64.8649 \| \| 0.0 \| 80.0 \| 240 \| 0.7340 \| 64.8649 \| 64.8649 \| \| 0.0 \| 100.0 \| 300 \| 0.7359 \| 64.8649 \| 64.8649 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
hdve/google-gemma-7b-1717454317	hdve	2024-06-03T22:41:22Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-03T22:38:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
enriquesaou/debug_seq2seq_squad	enriquesaou	2024-06-03T22:38:04Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:squad_v2", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-03T20:36:52Z	--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: debug_seq2seq_squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/favcowboy/huggingface/runs/wdlupjr7) # debug_seq2seq_squad This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.7565 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
animaRegem/gemma-2b-malayalam-t2-model-adaptors	animaRegem	2024-06-03T22:37:38Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0", "base_model:finetune:Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-03T21:27:08Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 --- # Uploaded model - Developed by: animaRegem - License: apache-2.0 - Finetuned from model : Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Magedyoussef86/Maged	Magedyoussef86	2024-06-03T22:33:39Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2024-06-03T22:33:38Z	--- license: artistic-2.0 ---

dmavkgo/vilt_finetuned_200

dmavkgo

2024-06-04T05:02:13Z

63

0

transformers

[ "transformers", "safetensors", "vilt", "visual-question-answering", "generated_from_trainer", "dataset:vqa", "base_model:dandelin/vilt-b32-mlm", "base_model:finetune:dandelin/vilt-b32-mlm", "license:apache-2.0", "endpoints_compatible", "region:us" ]

visual-question-answering

2024-06-04T03:32:11Z

--- license: apache-2.0 base_model: dandelin/vilt-b32-mlm tags: - generated_from_trainer datasets: - vqa model-index: - name: vilt_finetuned_200 results: [] ---  # vilt_finetuned_200 This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on the vqa dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

richardkelly/Qwen-Qwen1.5-1.8B-1717476207

richardkelly

2024-06-04T05:01:47Z

142

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T04:43:27Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

FuturisticVibes/Meta-Llama-3-70B-Instruct-abliterated-v3.5-6.0bpw-h8-exl2

FuturisticVibes

2024-06-04T04:58:52Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]

text-generation

2024-06-04T04:51:48Z

--- library_name: transformers license: llama3 --- I have no idea what I’m doing… if this causes the apocalypse someone please let me know. Meta-Llama-3-70B-Instruct-abliterated-v3.5 6.0bpw h8 EXL2 Includes [measurement.json](https://huggingface.co/FuturisticVibes/Meta-Llama-3-70B-Instruct-abliterated-v3.5-6.0bpw-h8-exl2/tree/measurement) file for further quantization Up next is a new, old, long dead, but never forgotten friend… Assuming I can put enough money into RunPod to rent an H100 for a bit… Original Model: https://huggingface.co/failspy/Meta-Llama-3-70B-Instruct-abliterated-v3.5 # Original Model Card # Llama-3-70B-Instruct-abliterated-v3.5 Model Card [My original Jupyter "cookbook" to replicate the methodology can be found here](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb) [My personal library o' code used](https://github.com/FailSpy/abliterator) (WIP, looking to improve and generalize) This is [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more. ## V3.5? Second try. I felt that the V3 methodology of 70B wasn't well applied, and u/Nexesenex on reddit kinda confirmed my suspicions. So go blame them. :P This one has only a single layer modified(!) and that seems to have completely eliminated moralizing disclaimers. I hope you'll find this model better than 70B-V3! As well, this also fixes the tokenizer. ## Hang on, "abliteration"? Orthogonalization? Ablation? What is this? TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out. **TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.** As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes. Ablate + obliterated = Abliterated Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization. ## A little more on the methodology, and why this is interesting To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt. Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights. > Why this over fine-tuning? Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage. As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.) Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques. It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa. I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity. > Okay, fine, but why V3? There's no V2 70B? Well, I released a V2 a while back for 8B under Cognitive Computations. It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model. I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations. So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's *such* an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.) ## Quirkiness awareness notice This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects. If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored. Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can.

mradermacher/Llama3-13B-lingyang-v1-GGUF

mradermacher

2024-06-04T04:56:59Z

49

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "Llama3", "en", "base_model:wwe180/Llama3-13B-lingyang-v1", "base_model:quantized:wwe180/Llama3-13B-lingyang-v1", "license:other", "endpoints_compatible", "region:us", "conversational" ]

null

2024-06-04T04:10:40Z

--- base_model: wwe180/Llama3-13B-lingyang-v1 language: - en library_name: transformers license: - other quantized_by: mradermacher tags: - mergekit - merge - Llama3 --- ## About      static quants of https://huggingface.co/wwe180/Llama3-13B-lingyang-v1  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q2_K.gguf) | Q2_K | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.IQ3_XS.gguf) | IQ3_XS | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q3_K_S.gguf) | Q3_K_S | 6.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.IQ3_S.gguf) | IQ3_S | 6.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.IQ3_M.gguf) | IQ3_M | 6.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q3_K_M.gguf) | Q3_K_M | 6.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q3_K_L.gguf) | Q3_K_L | 7.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.IQ4_XS.gguf) | IQ4_XS | 7.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q4_K_S.gguf) | Q4_K_S | 7.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q4_K_M.gguf) | Q4_K_M | 8.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q5_K_S.gguf) | Q5_K_S | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q5_K_M.gguf) | Q5_K_M | 9.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q6_K.gguf) | Q6_K | 11.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Llama3-13B-lingyang-v1-GGUF/resolve/main/Llama3-13B-lingyang-v1.Q8_0.gguf) | Q8_0 | 14.2 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mssma/ko-solar-10.7b-v0.8

mssma

2024-06-04T04:50:40Z

62

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T04:41:44Z

--- library_name: transformers license: apache-2.0 language: - ko --- # usage ``` from transformers import AutoModelForCausalLM, AutoTokenizer import torch path = "mssma/ko-solar-10.7b-v0.8" model = AutoModelForCausalLM.from_pretrained( path, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) tokenizer = AutoTokenizer.from_pretrained(path) ```

vaibhavchavan/flan-t5-small-finetuned-xsum

vaibhavchavan

2024-06-04T04:45:04Z

110

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-05-30T03:20:29Z

--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-finetuned-xsum results: [] ---  # flan-t5-small-finetuned-xsum This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 3.5714 - Rouge2: 1.2195 - Rougel: 3.5714 - Rougelsum: 3.5714 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 1 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 2.0 | 2 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 3.0 | 3 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 4.0 | 4 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 5.0 | 5 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 6.0 | 6 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 7.0 | 7 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 8.0 | 8 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 9.0 | 9 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 10.0 | 10 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 11.0 | 11 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 12.0 | 12 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 13.0 | 13 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 14.0 | 14 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 15.0 | 15 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 16.0 | 16 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 17.0 | 17 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 18.0 | 18 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 19.0 | 19 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 20.0 | 20 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 21.0 | 21 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 22.0 | 22 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 23.0 | 23 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 24.0 | 24 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 25.0 | 25 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 26.0 | 26 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 27.0 | 27 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 28.0 | 28 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 29.0 | 29 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 30.0 | 30 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 31.0 | 31 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 32.0 | 32 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 33.0 | 33 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 34.0 | 34 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 35.0 | 35 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 36.0 | 36 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 37.0 | 37 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 38.0 | 38 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 39.0 | 39 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 40.0 | 40 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 41.0 | 41 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 42.0 | 42 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 43.0 | 43 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 44.0 | 44 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 45.0 | 45 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 46.0 | 46 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 47.0 | 47 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 48.0 | 48 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 49.0 | 49 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 50.0 | 50 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 51.0 | 51 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 52.0 | 52 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 53.0 | 53 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 54.0 | 54 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 55.0 | 55 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 56.0 | 56 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 57.0 | 57 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 58.0 | 58 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 59.0 | 59 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 60.0 | 60 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 61.0 | 61 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 62.0 | 62 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 63.0 | 63 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 64.0 | 64 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 65.0 | 65 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 66.0 | 66 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 67.0 | 67 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 68.0 | 68 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 69.0 | 69 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 70.0 | 70 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 71.0 | 71 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 72.0 | 72 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 73.0 | 73 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 74.0 | 74 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 75.0 | 75 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 76.0 | 76 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 77.0 | 77 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 78.0 | 78 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 79.0 | 79 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 80.0 | 80 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 81.0 | 81 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 82.0 | 82 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 83.0 | 83 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 84.0 | 84 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 85.0 | 85 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 86.0 | 86 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 87.0 | 87 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 88.0 | 88 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 89.0 | 89 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 90.0 | 90 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 91.0 | 91 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 92.0 | 92 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 93.0 | 93 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 94.0 | 94 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 95.0 | 95 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 96.0 | 96 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 97.0 | 97 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 98.0 | 98 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 99.0 | 99 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 100.0 | 100 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 101.0 | 101 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 102.0 | 102 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 103.0 | 103 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 104.0 | 104 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 105.0 | 105 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 106.0 | 106 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 107.0 | 107 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 108.0 | 108 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 109.0 | 109 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 110.0 | 110 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 111.0 | 111 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 112.0 | 112 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 113.0 | 113 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 114.0 | 114 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 115.0 | 115 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 116.0 | 116 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 117.0 | 117 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 118.0 | 118 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 119.0 | 119 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 120.0 | 120 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 121.0 | 121 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 122.0 | 122 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 123.0 | 123 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 124.0 | 124 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 125.0 | 125 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 126.0 | 126 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 127.0 | 127 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 128.0 | 128 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 129.0 | 129 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 130.0 | 130 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 131.0 | 131 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 132.0 | 132 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 133.0 | 133 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 134.0 | 134 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 135.0 | 135 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 136.0 | 136 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 137.0 | 137 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 138.0 | 138 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 139.0 | 139 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 140.0 | 140 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 141.0 | 141 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 142.0 | 142 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 143.0 | 143 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 144.0 | 144 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 145.0 | 145 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 146.0 | 146 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 147.0 | 147 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 148.0 | 148 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 149.0 | 149 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 150.0 | 150 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 151.0 | 151 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 152.0 | 152 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 153.0 | 153 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 154.0 | 154 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 155.0 | 155 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 156.0 | 156 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 157.0 | 157 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 158.0 | 158 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 159.0 | 159 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 160.0 | 160 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 161.0 | 161 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 162.0 | 162 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 163.0 | 163 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 164.0 | 164 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 165.0 | 165 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 166.0 | 166 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 167.0 | 167 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 168.0 | 168 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 169.0 | 169 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 170.0 | 170 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 171.0 | 171 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 172.0 | 172 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 173.0 | 173 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 174.0 | 174 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 175.0 | 175 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 176.0 | 176 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 177.0 | 177 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 178.0 | 178 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 179.0 | 179 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 180.0 | 180 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 181.0 | 181 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 182.0 | 182 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 183.0 | 183 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 184.0 | 184 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 185.0 | 185 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 186.0 | 186 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 187.0 | 187 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 188.0 | 188 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 189.0 | 189 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 190.0 | 190 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 191.0 | 191 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 192.0 | 192 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 193.0 | 193 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 194.0 | 194 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 195.0 | 195 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 196.0 | 196 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 197.0 | 197 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 198.0 | 198 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 199.0 | 199 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 200.0 | 200 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 201.0 | 201 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 202.0 | 202 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 203.0 | 203 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 204.0 | 204 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 205.0 | 205 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 206.0 | 206 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 207.0 | 207 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 208.0 | 208 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 209.0 | 209 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 210.0 | 210 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 211.0 | 211 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 212.0 | 212 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 213.0 | 213 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 214.0 | 214 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 215.0 | 215 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 216.0 | 216 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 217.0 | 217 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 218.0 | 218 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 219.0 | 219 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 220.0 | 220 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 221.0 | 221 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 222.0 | 222 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 223.0 | 223 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 224.0 | 224 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 225.0 | 225 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 226.0 | 226 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 227.0 | 227 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 228.0 | 228 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 229.0 | 229 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 230.0 | 230 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 231.0 | 231 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 232.0 | 232 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 233.0 | 233 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 234.0 | 234 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 235.0 | 235 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 236.0 | 236 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 237.0 | 237 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 238.0 | 238 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 239.0 | 239 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 240.0 | 240 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 241.0 | 241 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 242.0 | 242 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 243.0 | 243 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 244.0 | 244 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 245.0 | 245 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 246.0 | 246 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 247.0 | 247 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 248.0 | 248 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 249.0 | 249 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 250.0 | 250 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 251.0 | 251 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 252.0 | 252 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 253.0 | 253 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 254.0 | 254 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 255.0 | 255 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 256.0 | 256 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 257.0 | 257 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 258.0 | 258 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 259.0 | 259 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 260.0 | 260 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 261.0 | 261 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 262.0 | 262 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 263.0 | 263 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 264.0 | 264 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 265.0 | 265 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 266.0 | 266 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 267.0 | 267 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 268.0 | 268 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 269.0 | 269 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 270.0 | 270 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 271.0 | 271 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 272.0 | 272 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 273.0 | 273 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 274.0 | 274 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 275.0 | 275 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 276.0 | 276 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 277.0 | 277 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 278.0 | 278 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 279.0 | 279 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 280.0 | 280 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 281.0 | 281 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 282.0 | 282 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 283.0 | 283 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 284.0 | 284 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 285.0 | 285 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 286.0 | 286 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 287.0 | 287 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 288.0 | 288 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 289.0 | 289 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 290.0 | 290 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 291.0 | 291 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 292.0 | 292 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 293.0 | 293 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 294.0 | 294 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 295.0 | 295 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 296.0 | 296 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 297.0 | 297 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 298.0 | 298 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 299.0 | 299 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 300.0 | 300 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 301.0 | 301 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 302.0 | 302 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 303.0 | 303 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 304.0 | 304 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 305.0 | 305 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 306.0 | 306 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 307.0 | 307 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 308.0 | 308 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 309.0 | 309 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 310.0 | 310 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 311.0 | 311 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 312.0 | 312 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 313.0 | 313 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 314.0 | 314 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 315.0 | 315 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 316.0 | 316 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 317.0 | 317 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 318.0 | 318 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 319.0 | 319 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 320.0 | 320 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 321.0 | 321 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 322.0 | 322 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 323.0 | 323 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 324.0 | 324 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 325.0 | 325 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 326.0 | 326 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 327.0 | 327 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 328.0 | 328 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 329.0 | 329 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 330.0 | 330 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 331.0 | 331 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 332.0 | 332 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 333.0 | 333 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 334.0 | 334 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 335.0 | 335 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 336.0 | 336 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 337.0 | 337 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 338.0 | 338 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 339.0 | 339 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 340.0 | 340 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 341.0 | 341 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 342.0 | 342 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 343.0 | 343 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 344.0 | 344 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 345.0 | 345 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 346.0 | 346 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 347.0 | 347 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 348.0 | 348 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 349.0 | 349 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 350.0 | 350 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 351.0 | 351 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 352.0 | 352 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 353.0 | 353 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 354.0 | 354 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 355.0 | 355 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 356.0 | 356 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 357.0 | 357 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 358.0 | 358 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 359.0 | 359 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 360.0 | 360 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 361.0 | 361 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 362.0 | 362 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 363.0 | 363 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 364.0 | 364 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 365.0 | 365 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 366.0 | 366 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 367.0 | 367 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 368.0 | 368 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 369.0 | 369 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 370.0 | 370 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 371.0 | 371 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 372.0 | 372 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 373.0 | 373 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 374.0 | 374 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 375.0 | 375 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 376.0 | 376 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 377.0 | 377 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 378.0 | 378 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 379.0 | 379 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 380.0 | 380 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 381.0 | 381 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 382.0 | 382 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 383.0 | 383 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 384.0 | 384 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 385.0 | 385 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 386.0 | 386 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 387.0 | 387 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 388.0 | 388 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 389.0 | 389 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 390.0 | 390 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 391.0 | 391 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 392.0 | 392 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 393.0 | 393 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 394.0 | 394 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 395.0 | 395 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 396.0 | 396 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 397.0 | 397 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 398.0 | 398 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 399.0 | 399 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 400.0 | 400 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 401.0 | 401 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 402.0 | 402 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 403.0 | 403 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 404.0 | 404 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 405.0 | 405 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 406.0 | 406 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 407.0 | 407 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 408.0 | 408 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 409.0 | 409 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 410.0 | 410 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 411.0 | 411 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 412.0 | 412 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 413.0 | 413 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 414.0 | 414 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 415.0 | 415 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 416.0 | 416 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 417.0 | 417 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 418.0 | 418 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 419.0 | 419 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 420.0 | 420 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 421.0 | 421 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 422.0 | 422 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 423.0 | 423 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 424.0 | 424 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 425.0 | 425 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 426.0 | 426 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 427.0 | 427 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 428.0 | 428 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 429.0 | 429 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 430.0 | 430 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 431.0 | 431 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 432.0 | 432 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 433.0 | 433 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 434.0 | 434 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 435.0 | 435 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 436.0 | 436 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 437.0 | 437 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 438.0 | 438 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 439.0 | 439 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 440.0 | 440 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 441.0 | 441 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 442.0 | 442 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 443.0 | 443 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 444.0 | 444 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 445.0 | 445 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 446.0 | 446 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 447.0 | 447 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 448.0 | 448 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 449.0 | 449 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 450.0 | 450 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 451.0 | 451 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 452.0 | 452 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 453.0 | 453 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 454.0 | 454 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 455.0 | 455 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 456.0 | 456 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 457.0 | 457 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 458.0 | 458 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 459.0 | 459 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 460.0 | 460 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 461.0 | 461 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 462.0 | 462 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 463.0 | 463 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 464.0 | 464 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 465.0 | 465 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 466.0 | 466 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 467.0 | 467 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 468.0 | 468 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 469.0 | 469 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 470.0 | 470 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 471.0 | 471 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 472.0 | 472 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 473.0 | 473 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 474.0 | 474 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 475.0 | 475 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 476.0 | 476 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 477.0 | 477 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 478.0 | 478 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 479.0 | 479 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 480.0 | 480 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 481.0 | 481 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 482.0 | 482 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 483.0 | 483 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 484.0 | 484 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 485.0 | 485 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 486.0 | 486 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 487.0 | 487 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 488.0 | 488 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 489.0 | 489 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 490.0 | 490 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 491.0 | 491 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 492.0 | 492 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 493.0 | 493 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 494.0 | 494 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 495.0 | 495 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 496.0 | 496 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 497.0 | 497 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 498.0 | 498 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | No log | 499.0 | 499 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 500.0 | 500 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 501.0 | 501 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 502.0 | 502 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 503.0 | 503 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 504.0 | 504 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 505.0 | 505 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 506.0 | 506 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 507.0 | 507 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 508.0 | 508 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 509.0 | 509 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 510.0 | 510 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 511.0 | 511 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 512.0 | 512 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 513.0 | 513 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 514.0 | 514 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 515.0 | 515 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 516.0 | 516 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 517.0 | 517 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 518.0 | 518 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 519.0 | 519 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 520.0 | 520 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 521.0 | 521 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 522.0 | 522 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 523.0 | 523 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 524.0 | 524 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 525.0 | 525 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 526.0 | 526 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 527.0 | 527 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 528.0 | 528 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 529.0 | 529 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 530.0 | 530 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 531.0 | 531 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 532.0 | 532 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 533.0 | 533 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 534.0 | 534 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 535.0 | 535 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 536.0 | 536 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 537.0 | 537 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 538.0 | 538 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 539.0 | 539 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 540.0 | 540 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 541.0 | 541 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 542.0 | 542 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 543.0 | 543 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 544.0 | 544 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 545.0 | 545 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 546.0 | 546 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 547.0 | 547 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 548.0 | 548 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 549.0 | 549 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 550.0 | 550 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 551.0 | 551 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 552.0 | 552 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 553.0 | 553 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 554.0 | 554 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 555.0 | 555 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 556.0 | 556 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 557.0 | 557 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 558.0 | 558 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 559.0 | 559 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 560.0 | 560 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 561.0 | 561 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 562.0 | 562 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 563.0 | 563 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 564.0 | 564 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 565.0 | 565 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 566.0 | 566 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 567.0 | 567 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 568.0 | 568 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 569.0 | 569 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 570.0 | 570 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 571.0 | 571 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 572.0 | 572 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 573.0 | 573 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 574.0 | 574 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 575.0 | 575 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 576.0 | 576 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 577.0 | 577 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 578.0 | 578 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 579.0 | 579 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 580.0 | 580 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 581.0 | 581 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 582.0 | 582 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 583.0 | 583 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 584.0 | 584 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 585.0 | 585 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 586.0 | 586 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 587.0 | 587 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 588.0 | 588 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 589.0 | 589 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 590.0 | 590 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 591.0 | 591 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 592.0 | 592 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 593.0 | 593 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 594.0 | 594 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 595.0 | 595 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 596.0 | 596 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 597.0 | 597 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 598.0 | 598 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 599.0 | 599 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 600.0 | 600 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 601.0 | 601 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 602.0 | 602 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 603.0 | 603 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 604.0 | 604 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 605.0 | 605 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 606.0 | 606 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 607.0 | 607 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 608.0 | 608 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 609.0 | 609 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 610.0 | 610 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 611.0 | 611 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 612.0 | 612 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 613.0 | 613 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 614.0 | 614 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 615.0 | 615 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 616.0 | 616 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 617.0 | 617 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 618.0 | 618 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 619.0 | 619 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 620.0 | 620 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 621.0 | 621 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 622.0 | 622 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 623.0 | 623 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 624.0 | 624 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 625.0 | 625 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 626.0 | 626 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 627.0 | 627 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 628.0 | 628 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 629.0 | 629 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 630.0 | 630 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 631.0 | 631 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 632.0 | 632 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 633.0 | 633 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 634.0 | 634 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 635.0 | 635 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 636.0 | 636 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 637.0 | 637 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 638.0 | 638 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 639.0 | 639 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 640.0 | 640 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 641.0 | 641 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 642.0 | 642 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 643.0 | 643 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 644.0 | 644 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 645.0 | 645 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 646.0 | 646 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 647.0 | 647 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 648.0 | 648 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 649.0 | 649 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 650.0 | 650 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 651.0 | 651 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 652.0 | 652 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 653.0 | 653 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 654.0 | 654 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 655.0 | 655 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 656.0 | 656 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 657.0 | 657 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 658.0 | 658 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 659.0 | 659 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 660.0 | 660 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 661.0 | 661 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 662.0 | 662 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 663.0 | 663 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 664.0 | 664 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 665.0 | 665 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 666.0 | 666 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 667.0 | 667 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 668.0 | 668 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 669.0 | 669 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 670.0 | 670 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 671.0 | 671 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 672.0 | 672 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 673.0 | 673 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 674.0 | 674 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 675.0 | 675 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 676.0 | 676 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 677.0 | 677 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 678.0 | 678 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 679.0 | 679 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 680.0 | 680 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 681.0 | 681 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 682.0 | 682 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 683.0 | 683 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 684.0 | 684 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 685.0 | 685 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 686.0 | 686 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 687.0 | 687 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 688.0 | 688 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 689.0 | 689 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 690.0 | 690 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 691.0 | 691 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 692.0 | 692 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 693.0 | 693 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 694.0 | 694 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 695.0 | 695 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 696.0 | 696 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 697.0 | 697 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 698.0 | 698 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 699.0 | 699 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 700.0 | 700 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 701.0 | 701 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 702.0 | 702 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 703.0 | 703 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 704.0 | 704 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 705.0 | 705 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 706.0 | 706 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 707.0 | 707 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 708.0 | 708 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 709.0 | 709 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 710.0 | 710 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 711.0 | 711 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 712.0 | 712 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 713.0 | 713 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 714.0 | 714 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 715.0 | 715 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 716.0 | 716 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 717.0 | 717 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 718.0 | 718 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 719.0 | 719 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 720.0 | 720 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 721.0 | 721 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 722.0 | 722 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 723.0 | 723 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 724.0 | 724 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 725.0 | 725 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 726.0 | 726 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 727.0 | 727 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 728.0 | 728 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 729.0 | 729 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 730.0 | 730 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 731.0 | 731 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 732.0 | 732 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 733.0 | 733 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 734.0 | 734 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 735.0 | 735 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 736.0 | 736 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 737.0 | 737 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 738.0 | 738 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 739.0 | 739 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 740.0 | 740 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 741.0 | 741 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 742.0 | 742 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 743.0 | 743 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 744.0 | 744 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 745.0 | 745 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 746.0 | 746 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 747.0 | 747 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 748.0 | 748 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 749.0 | 749 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 750.0 | 750 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 751.0 | 751 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 752.0 | 752 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 753.0 | 753 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 754.0 | 754 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 755.0 | 755 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 756.0 | 756 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 757.0 | 757 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 758.0 | 758 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 759.0 | 759 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 760.0 | 760 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 761.0 | 761 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 762.0 | 762 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 763.0 | 763 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 764.0 | 764 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 765.0 | 765 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 766.0 | 766 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 767.0 | 767 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 768.0 | 768 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 769.0 | 769 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 770.0 | 770 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 771.0 | 771 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 772.0 | 772 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 773.0 | 773 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 774.0 | 774 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 775.0 | 775 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 776.0 | 776 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 777.0 | 777 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 778.0 | 778 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 779.0 | 779 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 780.0 | 780 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 781.0 | 781 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 782.0 | 782 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 783.0 | 783 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 784.0 | 784 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 785.0 | 785 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 786.0 | 786 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 787.0 | 787 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 788.0 | 788 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 789.0 | 789 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 790.0 | 790 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 791.0 | 791 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 792.0 | 792 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 793.0 | 793 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 794.0 | 794 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 795.0 | 795 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 796.0 | 796 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 797.0 | 797 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 798.0 | 798 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 799.0 | 799 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 800.0 | 800 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 801.0 | 801 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 802.0 | 802 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 803.0 | 803 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 804.0 | 804 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 805.0 | 805 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 806.0 | 806 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 807.0 | 807 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 808.0 | 808 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 809.0 | 809 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 810.0 | 810 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 811.0 | 811 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 812.0 | 812 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 813.0 | 813 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 814.0 | 814 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 815.0 | 815 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 816.0 | 816 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 817.0 | 817 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 818.0 | 818 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 819.0 | 819 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 820.0 | 820 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 821.0 | 821 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 822.0 | 822 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 823.0 | 823 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 824.0 | 824 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 825.0 | 825 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 826.0 | 826 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 827.0 | 827 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 828.0 | 828 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 829.0 | 829 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 830.0 | 830 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 831.0 | 831 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 832.0 | 832 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 833.0 | 833 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 834.0 | 834 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 835.0 | 835 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 836.0 | 836 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 837.0 | 837 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 838.0 | 838 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 839.0 | 839 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 840.0 | 840 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 841.0 | 841 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 842.0 | 842 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 843.0 | 843 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 844.0 | 844 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 845.0 | 845 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 846.0 | 846 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 847.0 | 847 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 848.0 | 848 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 849.0 | 849 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 850.0 | 850 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 851.0 | 851 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 852.0 | 852 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 853.0 | 853 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 854.0 | 854 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 855.0 | 855 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 856.0 | 856 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 857.0 | 857 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 858.0 | 858 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 859.0 | 859 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 860.0 | 860 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 861.0 | 861 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 862.0 | 862 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 863.0 | 863 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 864.0 | 864 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 865.0 | 865 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 866.0 | 866 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 867.0 | 867 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 868.0 | 868 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 869.0 | 869 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 870.0 | 870 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 871.0 | 871 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 872.0 | 872 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 873.0 | 873 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 874.0 | 874 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 875.0 | 875 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 876.0 | 876 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 877.0 | 877 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 878.0 | 878 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 879.0 | 879 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 880.0 | 880 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 881.0 | 881 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 882.0 | 882 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 883.0 | 883 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 884.0 | 884 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 885.0 | 885 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 886.0 | 886 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 887.0 | 887 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 888.0 | 888 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 889.0 | 889 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 890.0 | 890 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 891.0 | 891 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 892.0 | 892 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 893.0 | 893 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 894.0 | 894 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 895.0 | 895 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 896.0 | 896 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 897.0 | 897 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 898.0 | 898 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 899.0 | 899 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 900.0 | 900 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 901.0 | 901 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 902.0 | 902 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 903.0 | 903 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 904.0 | 904 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 905.0 | 905 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 906.0 | 906 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 907.0 | 907 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 908.0 | 908 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 909.0 | 909 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 910.0 | 910 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 911.0 | 911 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 912.0 | 912 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 913.0 | 913 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 914.0 | 914 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 915.0 | 915 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 916.0 | 916 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 917.0 | 917 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 918.0 | 918 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 919.0 | 919 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 920.0 | 920 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 921.0 | 921 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 922.0 | 922 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 923.0 | 923 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 924.0 | 924 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 925.0 | 925 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 926.0 | 926 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 927.0 | 927 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 928.0 | 928 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 929.0 | 929 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 930.0 | 930 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 931.0 | 931 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 932.0 | 932 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 933.0 | 933 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 934.0 | 934 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 935.0 | 935 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 936.0 | 936 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 937.0 | 937 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 938.0 | 938 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 939.0 | 939 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 940.0 | 940 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 941.0 | 941 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 942.0 | 942 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 943.0 | 943 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 944.0 | 944 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 945.0 | 945 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 946.0 | 946 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 947.0 | 947 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 948.0 | 948 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 949.0 | 949 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 950.0 | 950 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 951.0 | 951 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 952.0 | 952 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 953.0 | 953 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 954.0 | 954 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 955.0 | 955 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 956.0 | 956 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 957.0 | 957 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 958.0 | 958 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 959.0 | 959 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 960.0 | 960 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 961.0 | 961 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 962.0 | 962 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 963.0 | 963 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 964.0 | 964 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 965.0 | 965 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 966.0 | 966 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 967.0 | 967 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 968.0 | 968 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 969.0 | 969 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 970.0 | 970 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 971.0 | 971 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 972.0 | 972 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 973.0 | 973 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 974.0 | 974 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 975.0 | 975 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 976.0 | 976 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 977.0 | 977 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 978.0 | 978 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 979.0 | 979 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 980.0 | 980 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 981.0 | 981 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 982.0 | 982 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 983.0 | 983 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 984.0 | 984 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 985.0 | 985 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 986.0 | 986 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 987.0 | 987 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 988.0 | 988 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 989.0 | 989 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 990.0 | 990 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 991.0 | 991 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 992.0 | 992 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 993.0 | 993 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 994.0 | 994 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 995.0 | 995 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 996.0 | 996 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 997.0 | 997 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 998.0 | 998 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 999.0 | 999 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1000.0 | 1000 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1001.0 | 1001 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1002.0 | 1002 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1003.0 | 1003 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1004.0 | 1004 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1005.0 | 1005 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1006.0 | 1006 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1007.0 | 1007 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1008.0 | 1008 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1009.0 | 1009 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1010.0 | 1010 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1011.0 | 1011 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1012.0 | 1012 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1013.0 | 1013 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1014.0 | 1014 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1015.0 | 1015 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1016.0 | 1016 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1017.0 | 1017 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1018.0 | 1018 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1019.0 | 1019 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1020.0 | 1020 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1021.0 | 1021 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1022.0 | 1022 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1023.0 | 1023 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1024.0 | 1024 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1025.0 | 1025 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1026.0 | 1026 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1027.0 | 1027 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1028.0 | 1028 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1029.0 | 1029 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1030.0 | 1030 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1031.0 | 1031 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1032.0 | 1032 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1033.0 | 1033 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1034.0 | 1034 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1035.0 | 1035 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1036.0 | 1036 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1037.0 | 1037 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1038.0 | 1038 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1039.0 | 1039 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1040.0 | 1040 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1041.0 | 1041 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1042.0 | 1042 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1043.0 | 1043 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1044.0 | 1044 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1045.0 | 1045 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1046.0 | 1046 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1047.0 | 1047 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1048.0 | 1048 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1049.0 | 1049 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1050.0 | 1050 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1051.0 | 1051 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1052.0 | 1052 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1053.0 | 1053 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1054.0 | 1054 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1055.0 | 1055 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1056.0 | 1056 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1057.0 | 1057 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1058.0 | 1058 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1059.0 | 1059 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1060.0 | 1060 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1061.0 | 1061 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1062.0 | 1062 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1063.0 | 1063 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1064.0 | 1064 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1065.0 | 1065 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1066.0 | 1066 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1067.0 | 1067 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1068.0 | 1068 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1069.0 | 1069 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1070.0 | 1070 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1071.0 | 1071 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1072.0 | 1072 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1073.0 | 1073 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1074.0 | 1074 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1075.0 | 1075 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1076.0 | 1076 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1077.0 | 1077 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1078.0 | 1078 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1079.0 | 1079 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1080.0 | 1080 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1081.0 | 1081 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1082.0 | 1082 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1083.0 | 1083 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1084.0 | 1084 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1085.0 | 1085 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1086.0 | 1086 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1087.0 | 1087 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1088.0 | 1088 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1089.0 | 1089 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1090.0 | 1090 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1091.0 | 1091 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1092.0 | 1092 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1093.0 | 1093 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1094.0 | 1094 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1095.0 | 1095 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1096.0 | 1096 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1097.0 | 1097 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1098.0 | 1098 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1099.0 | 1099 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1100.0 | 1100 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1101.0 | 1101 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1102.0 | 1102 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1103.0 | 1103 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1104.0 | 1104 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1105.0 | 1105 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1106.0 | 1106 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1107.0 | 1107 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1108.0 | 1108 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1109.0 | 1109 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1110.0 | 1110 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1111.0 | 1111 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1112.0 | 1112 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1113.0 | 1113 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1114.0 | 1114 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1115.0 | 1115 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1116.0 | 1116 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1117.0 | 1117 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1118.0 | 1118 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1119.0 | 1119 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1120.0 | 1120 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1121.0 | 1121 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1122.0 | 1122 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1123.0 | 1123 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1124.0 | 1124 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1125.0 | 1125 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1126.0 | 1126 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1127.0 | 1127 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1128.0 | 1128 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1129.0 | 1129 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1130.0 | 1130 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1131.0 | 1131 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1132.0 | 1132 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1133.0 | 1133 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1134.0 | 1134 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1135.0 | 1135 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1136.0 | 1136 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1137.0 | 1137 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1138.0 | 1138 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1139.0 | 1139 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1140.0 | 1140 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1141.0 | 1141 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1142.0 | 1142 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1143.0 | 1143 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1144.0 | 1144 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1145.0 | 1145 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1146.0 | 1146 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1147.0 | 1147 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1148.0 | 1148 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1149.0 | 1149 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1150.0 | 1150 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1151.0 | 1151 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1152.0 | 1152 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1153.0 | 1153 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1154.0 | 1154 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1155.0 | 1155 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1156.0 | 1156 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1157.0 | 1157 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1158.0 | 1158 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1159.0 | 1159 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1160.0 | 1160 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1161.0 | 1161 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1162.0 | 1162 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1163.0 | 1163 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1164.0 | 1164 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1165.0 | 1165 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1166.0 | 1166 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1167.0 | 1167 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1168.0 | 1168 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1169.0 | 1169 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1170.0 | 1170 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1171.0 | 1171 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1172.0 | 1172 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1173.0 | 1173 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1174.0 | 1174 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1175.0 | 1175 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1176.0 | 1176 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1177.0 | 1177 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1178.0 | 1178 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1179.0 | 1179 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1180.0 | 1180 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1181.0 | 1181 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1182.0 | 1182 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1183.0 | 1183 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1184.0 | 1184 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1185.0 | 1185 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1186.0 | 1186 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1187.0 | 1187 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1188.0 | 1188 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1189.0 | 1189 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1190.0 | 1190 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1191.0 | 1191 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1192.0 | 1192 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1193.0 | 1193 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1194.0 | 1194 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1195.0 | 1195 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1196.0 | 1196 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1197.0 | 1197 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1198.0 | 1198 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1199.0 | 1199 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1200.0 | 1200 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1201.0 | 1201 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1202.0 | 1202 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1203.0 | 1203 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1204.0 | 1204 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1205.0 | 1205 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1206.0 | 1206 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1207.0 | 1207 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1208.0 | 1208 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1209.0 | 1209 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1210.0 | 1210 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1211.0 | 1211 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1212.0 | 1212 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1213.0 | 1213 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1214.0 | 1214 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1215.0 | 1215 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1216.0 | 1216 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1217.0 | 1217 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1218.0 | 1218 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1219.0 | 1219 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1220.0 | 1220 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1221.0 | 1221 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1222.0 | 1222 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1223.0 | 1223 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1224.0 | 1224 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1225.0 | 1225 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1226.0 | 1226 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1227.0 | 1227 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1228.0 | 1228 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1229.0 | 1229 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1230.0 | 1230 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1231.0 | 1231 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1232.0 | 1232 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1233.0 | 1233 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1234.0 | 1234 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1235.0 | 1235 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1236.0 | 1236 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1237.0 | 1237 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1238.0 | 1238 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1239.0 | 1239 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1240.0 | 1240 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1241.0 | 1241 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1242.0 | 1242 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1243.0 | 1243 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1244.0 | 1244 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1245.0 | 1245 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1246.0 | 1246 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1247.0 | 1247 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1248.0 | 1248 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1249.0 | 1249 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1250.0 | 1250 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1251.0 | 1251 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1252.0 | 1252 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1253.0 | 1253 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1254.0 | 1254 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1255.0 | 1255 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1256.0 | 1256 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1257.0 | 1257 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1258.0 | 1258 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1259.0 | 1259 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1260.0 | 1260 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1261.0 | 1261 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1262.0 | 1262 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1263.0 | 1263 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1264.0 | 1264 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1265.0 | 1265 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1266.0 | 1266 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1267.0 | 1267 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1268.0 | 1268 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1269.0 | 1269 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1270.0 | 1270 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1271.0 | 1271 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1272.0 | 1272 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1273.0 | 1273 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1274.0 | 1274 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1275.0 | 1275 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1276.0 | 1276 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1277.0 | 1277 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1278.0 | 1278 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1279.0 | 1279 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1280.0 | 1280 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1281.0 | 1281 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1282.0 | 1282 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1283.0 | 1283 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1284.0 | 1284 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1285.0 | 1285 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1286.0 | 1286 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1287.0 | 1287 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1288.0 | 1288 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1289.0 | 1289 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1290.0 | 1290 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1291.0 | 1291 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1292.0 | 1292 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1293.0 | 1293 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1294.0 | 1294 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1295.0 | 1295 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1296.0 | 1296 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1297.0 | 1297 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1298.0 | 1298 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1299.0 | 1299 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1300.0 | 1300 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1301.0 | 1301 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1302.0 | 1302 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1303.0 | 1303 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1304.0 | 1304 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1305.0 | 1305 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1306.0 | 1306 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1307.0 | 1307 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1308.0 | 1308 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1309.0 | 1309 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1310.0 | 1310 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1311.0 | 1311 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1312.0 | 1312 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1313.0 | 1313 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1314.0 | 1314 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1315.0 | 1315 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1316.0 | 1316 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1317.0 | 1317 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1318.0 | 1318 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1319.0 | 1319 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1320.0 | 1320 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1321.0 | 1321 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1322.0 | 1322 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1323.0 | 1323 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1324.0 | 1324 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1325.0 | 1325 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1326.0 | 1326 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1327.0 | 1327 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1328.0 | 1328 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1329.0 | 1329 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1330.0 | 1330 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1331.0 | 1331 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1332.0 | 1332 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1333.0 | 1333 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1334.0 | 1334 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1335.0 | 1335 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1336.0 | 1336 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1337.0 | 1337 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1338.0 | 1338 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1339.0 | 1339 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1340.0 | 1340 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1341.0 | 1341 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1342.0 | 1342 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1343.0 | 1343 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1344.0 | 1344 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1345.0 | 1345 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1346.0 | 1346 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1347.0 | 1347 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1348.0 | 1348 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1349.0 | 1349 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1350.0 | 1350 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1351.0 | 1351 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1352.0 | 1352 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1353.0 | 1353 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1354.0 | 1354 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1355.0 | 1355 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1356.0 | 1356 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1357.0 | 1357 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1358.0 | 1358 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1359.0 | 1359 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1360.0 | 1360 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1361.0 | 1361 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1362.0 | 1362 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1363.0 | 1363 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1364.0 | 1364 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1365.0 | 1365 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1366.0 | 1366 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1367.0 | 1367 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1368.0 | 1368 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1369.0 | 1369 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1370.0 | 1370 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1371.0 | 1371 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1372.0 | 1372 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1373.0 | 1373 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1374.0 | 1374 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1375.0 | 1375 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1376.0 | 1376 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1377.0 | 1377 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1378.0 | 1378 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1379.0 | 1379 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1380.0 | 1380 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1381.0 | 1381 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1382.0 | 1382 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1383.0 | 1383 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1384.0 | 1384 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1385.0 | 1385 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1386.0 | 1386 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1387.0 | 1387 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1388.0 | 1388 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1389.0 | 1389 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1390.0 | 1390 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1391.0 | 1391 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1392.0 | 1392 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1393.0 | 1393 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1394.0 | 1394 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1395.0 | 1395 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1396.0 | 1396 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1397.0 | 1397 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1398.0 | 1398 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1399.0 | 1399 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1400.0 | 1400 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1401.0 | 1401 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1402.0 | 1402 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1403.0 | 1403 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1404.0 | 1404 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1405.0 | 1405 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1406.0 | 1406 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1407.0 | 1407 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1408.0 | 1408 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1409.0 | 1409 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1410.0 | 1410 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1411.0 | 1411 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1412.0 | 1412 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1413.0 | 1413 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1414.0 | 1414 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1415.0 | 1415 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1416.0 | 1416 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1417.0 | 1417 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1418.0 | 1418 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1419.0 | 1419 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1420.0 | 1420 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1421.0 | 1421 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1422.0 | 1422 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1423.0 | 1423 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1424.0 | 1424 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1425.0 | 1425 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1426.0 | 1426 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1427.0 | 1427 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1428.0 | 1428 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1429.0 | 1429 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1430.0 | 1430 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1431.0 | 1431 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1432.0 | 1432 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1433.0 | 1433 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1434.0 | 1434 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1435.0 | 1435 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1436.0 | 1436 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1437.0 | 1437 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1438.0 | 1438 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1439.0 | 1439 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1440.0 | 1440 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1441.0 | 1441 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1442.0 | 1442 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1443.0 | 1443 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1444.0 | 1444 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1445.0 | 1445 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1446.0 | 1446 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1447.0 | 1447 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1448.0 | 1448 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1449.0 | 1449 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1450.0 | 1450 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1451.0 | 1451 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1452.0 | 1452 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1453.0 | 1453 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1454.0 | 1454 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1455.0 | 1455 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1456.0 | 1456 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1457.0 | 1457 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1458.0 | 1458 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1459.0 | 1459 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1460.0 | 1460 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1461.0 | 1461 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1462.0 | 1462 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1463.0 | 1463 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1464.0 | 1464 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1465.0 | 1465 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1466.0 | 1466 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1467.0 | 1467 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1468.0 | 1468 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1469.0 | 1469 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1470.0 | 1470 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1471.0 | 1471 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1472.0 | 1472 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1473.0 | 1473 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1474.0 | 1474 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1475.0 | 1475 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1476.0 | 1476 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1477.0 | 1477 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1478.0 | 1478 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1479.0 | 1479 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1480.0 | 1480 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1481.0 | 1481 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1482.0 | 1482 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1483.0 | 1483 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1484.0 | 1484 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1485.0 | 1485 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1486.0 | 1486 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1487.0 | 1487 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1488.0 | 1488 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1489.0 | 1489 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1490.0 | 1490 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1491.0 | 1491 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1492.0 | 1492 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1493.0 | 1493 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1494.0 | 1494 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1495.0 | 1495 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1496.0 | 1496 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1497.0 | 1497 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1498.0 | 1498 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1499.0 | 1499 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1500.0 | 1500 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1501.0 | 1501 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1502.0 | 1502 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1503.0 | 1503 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1504.0 | 1504 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1505.0 | 1505 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1506.0 | 1506 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1507.0 | 1507 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1508.0 | 1508 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1509.0 | 1509 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1510.0 | 1510 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1511.0 | 1511 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1512.0 | 1512 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1513.0 | 1513 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1514.0 | 1514 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1515.0 | 1515 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1516.0 | 1516 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1517.0 | 1517 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1518.0 | 1518 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1519.0 | 1519 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1520.0 | 1520 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1521.0 | 1521 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1522.0 | 1522 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1523.0 | 1523 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1524.0 | 1524 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1525.0 | 1525 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1526.0 | 1526 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1527.0 | 1527 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1528.0 | 1528 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1529.0 | 1529 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1530.0 | 1530 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1531.0 | 1531 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1532.0 | 1532 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1533.0 | 1533 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1534.0 | 1534 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1535.0 | 1535 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1536.0 | 1536 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1537.0 | 1537 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1538.0 | 1538 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1539.0 | 1539 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1540.0 | 1540 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1541.0 | 1541 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1542.0 | 1542 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1543.0 | 1543 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1544.0 | 1544 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1545.0 | 1545 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1546.0 | 1546 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1547.0 | 1547 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1548.0 | 1548 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1549.0 | 1549 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1550.0 | 1550 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1551.0 | 1551 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1552.0 | 1552 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1553.0 | 1553 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1554.0 | 1554 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1555.0 | 1555 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1556.0 | 1556 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1557.0 | 1557 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1558.0 | 1558 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1559.0 | 1559 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1560.0 | 1560 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1561.0 | 1561 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1562.0 | 1562 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1563.0 | 1563 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1564.0 | 1564 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1565.0 | 1565 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1566.0 | 1566 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1567.0 | 1567 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1568.0 | 1568 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1569.0 | 1569 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1570.0 | 1570 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1571.0 | 1571 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1572.0 | 1572 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1573.0 | 1573 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1574.0 | 1574 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1575.0 | 1575 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1576.0 | 1576 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1577.0 | 1577 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1578.0 | 1578 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1579.0 | 1579 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1580.0 | 1580 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1581.0 | 1581 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1582.0 | 1582 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1583.0 | 1583 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1584.0 | 1584 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1585.0 | 1585 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1586.0 | 1586 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1587.0 | 1587 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1588.0 | 1588 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1589.0 | 1589 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1590.0 | 1590 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1591.0 | 1591 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1592.0 | 1592 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1593.0 | 1593 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1594.0 | 1594 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1595.0 | 1595 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1596.0 | 1596 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1597.0 | 1597 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1598.0 | 1598 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1599.0 | 1599 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1600.0 | 1600 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1601.0 | 1601 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1602.0 | 1602 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1603.0 | 1603 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1604.0 | 1604 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1605.0 | 1605 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1606.0 | 1606 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1607.0 | 1607 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1608.0 | 1608 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1609.0 | 1609 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1610.0 | 1610 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1611.0 | 1611 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1612.0 | 1612 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1613.0 | 1613 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1614.0 | 1614 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1615.0 | 1615 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1616.0 | 1616 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1617.0 | 1617 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1618.0 | 1618 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1619.0 | 1619 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1620.0 | 1620 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1621.0 | 1621 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1622.0 | 1622 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1623.0 | 1623 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1624.0 | 1624 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1625.0 | 1625 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1626.0 | 1626 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1627.0 | 1627 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1628.0 | 1628 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1629.0 | 1629 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1630.0 | 1630 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1631.0 | 1631 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1632.0 | 1632 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1633.0 | 1633 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1634.0 | 1634 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1635.0 | 1635 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1636.0 | 1636 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1637.0 | 1637 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1638.0 | 1638 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1639.0 | 1639 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1640.0 | 1640 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1641.0 | 1641 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1642.0 | 1642 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1643.0 | 1643 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1644.0 | 1644 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1645.0 | 1645 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1646.0 | 1646 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1647.0 | 1647 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1648.0 | 1648 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1649.0 | 1649 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1650.0 | 1650 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1651.0 | 1651 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1652.0 | 1652 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1653.0 | 1653 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1654.0 | 1654 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1655.0 | 1655 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1656.0 | 1656 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1657.0 | 1657 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1658.0 | 1658 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1659.0 | 1659 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1660.0 | 1660 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1661.0 | 1661 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1662.0 | 1662 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1663.0 | 1663 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1664.0 | 1664 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1665.0 | 1665 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1666.0 | 1666 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1667.0 | 1667 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1668.0 | 1668 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1669.0 | 1669 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1670.0 | 1670 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1671.0 | 1671 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1672.0 | 1672 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1673.0 | 1673 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1674.0 | 1674 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1675.0 | 1675 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1676.0 | 1676 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1677.0 | 1677 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1678.0 | 1678 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1679.0 | 1679 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1680.0 | 1680 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1681.0 | 1681 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1682.0 | 1682 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1683.0 | 1683 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1684.0 | 1684 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1685.0 | 1685 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1686.0 | 1686 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1687.0 | 1687 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1688.0 | 1688 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1689.0 | 1689 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1690.0 | 1690 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1691.0 | 1691 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1692.0 | 1692 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1693.0 | 1693 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1694.0 | 1694 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1695.0 | 1695 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1696.0 | 1696 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1697.0 | 1697 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1698.0 | 1698 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1699.0 | 1699 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1700.0 | 1700 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1701.0 | 1701 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1702.0 | 1702 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1703.0 | 1703 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1704.0 | 1704 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1705.0 | 1705 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1706.0 | 1706 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1707.0 | 1707 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1708.0 | 1708 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1709.0 | 1709 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1710.0 | 1710 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1711.0 | 1711 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1712.0 | 1712 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1713.0 | 1713 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1714.0 | 1714 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1715.0 | 1715 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1716.0 | 1716 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1717.0 | 1717 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1718.0 | 1718 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1719.0 | 1719 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1720.0 | 1720 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1721.0 | 1721 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1722.0 | 1722 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1723.0 | 1723 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1724.0 | 1724 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1725.0 | 1725 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1726.0 | 1726 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1727.0 | 1727 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1728.0 | 1728 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1729.0 | 1729 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1730.0 | 1730 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1731.0 | 1731 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1732.0 | 1732 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1733.0 | 1733 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1734.0 | 1734 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1735.0 | 1735 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1736.0 | 1736 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1737.0 | 1737 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1738.0 | 1738 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1739.0 | 1739 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1740.0 | 1740 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1741.0 | 1741 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1742.0 | 1742 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1743.0 | 1743 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1744.0 | 1744 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1745.0 | 1745 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1746.0 | 1746 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1747.0 | 1747 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1748.0 | 1748 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1749.0 | 1749 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1750.0 | 1750 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1751.0 | 1751 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1752.0 | 1752 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1753.0 | 1753 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1754.0 | 1754 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1755.0 | 1755 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1756.0 | 1756 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1757.0 | 1757 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1758.0 | 1758 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1759.0 | 1759 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1760.0 | 1760 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1761.0 | 1761 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1762.0 | 1762 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1763.0 | 1763 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1764.0 | 1764 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1765.0 | 1765 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1766.0 | 1766 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1767.0 | 1767 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1768.0 | 1768 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1769.0 | 1769 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1770.0 | 1770 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1771.0 | 1771 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1772.0 | 1772 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1773.0 | 1773 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1774.0 | 1774 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1775.0 | 1775 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1776.0 | 1776 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1777.0 | 1777 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1778.0 | 1778 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1779.0 | 1779 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1780.0 | 1780 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1781.0 | 1781 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1782.0 | 1782 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1783.0 | 1783 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1784.0 | 1784 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1785.0 | 1785 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1786.0 | 1786 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1787.0 | 1787 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1788.0 | 1788 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1789.0 | 1789 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1790.0 | 1790 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1791.0 | 1791 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1792.0 | 1792 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1793.0 | 1793 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1794.0 | 1794 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1795.0 | 1795 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1796.0 | 1796 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1797.0 | 1797 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1798.0 | 1798 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1799.0 | 1799 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1800.0 | 1800 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1801.0 | 1801 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1802.0 | 1802 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1803.0 | 1803 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1804.0 | 1804 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1805.0 | 1805 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1806.0 | 1806 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1807.0 | 1807 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1808.0 | 1808 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1809.0 | 1809 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1810.0 | 1810 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1811.0 | 1811 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1812.0 | 1812 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1813.0 | 1813 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1814.0 | 1814 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1815.0 | 1815 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1816.0 | 1816 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1817.0 | 1817 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1818.0 | 1818 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1819.0 | 1819 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1820.0 | 1820 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1821.0 | 1821 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1822.0 | 1822 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1823.0 | 1823 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1824.0 | 1824 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1825.0 | 1825 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1826.0 | 1826 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1827.0 | 1827 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1828.0 | 1828 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1829.0 | 1829 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1830.0 | 1830 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1831.0 | 1831 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1832.0 | 1832 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1833.0 | 1833 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1834.0 | 1834 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1835.0 | 1835 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1836.0 | 1836 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1837.0 | 1837 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1838.0 | 1838 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1839.0 | 1839 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1840.0 | 1840 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1841.0 | 1841 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1842.0 | 1842 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1843.0 | 1843 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1844.0 | 1844 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1845.0 | 1845 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1846.0 | 1846 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1847.0 | 1847 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1848.0 | 1848 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1849.0 | 1849 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1850.0 | 1850 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1851.0 | 1851 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1852.0 | 1852 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1853.0 | 1853 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1854.0 | 1854 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1855.0 | 1855 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1856.0 | 1856 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1857.0 | 1857 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1858.0 | 1858 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1859.0 | 1859 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1860.0 | 1860 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1861.0 | 1861 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1862.0 | 1862 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1863.0 | 1863 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1864.0 | 1864 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1865.0 | 1865 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1866.0 | 1866 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1867.0 | 1867 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1868.0 | 1868 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1869.0 | 1869 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1870.0 | 1870 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1871.0 | 1871 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1872.0 | 1872 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1873.0 | 1873 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1874.0 | 1874 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1875.0 | 1875 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1876.0 | 1876 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1877.0 | 1877 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1878.0 | 1878 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1879.0 | 1879 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1880.0 | 1880 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1881.0 | 1881 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1882.0 | 1882 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1883.0 | 1883 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1884.0 | 1884 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1885.0 | 1885 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1886.0 | 1886 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1887.0 | 1887 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1888.0 | 1888 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1889.0 | 1889 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1890.0 | 1890 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1891.0 | 1891 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1892.0 | 1892 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1893.0 | 1893 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1894.0 | 1894 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1895.0 | 1895 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1896.0 | 1896 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1897.0 | 1897 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1898.0 | 1898 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1899.0 | 1899 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1900.0 | 1900 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1901.0 | 1901 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1902.0 | 1902 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1903.0 | 1903 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1904.0 | 1904 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1905.0 | 1905 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1906.0 | 1906 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1907.0 | 1907 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1908.0 | 1908 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1909.0 | 1909 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1910.0 | 1910 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1911.0 | 1911 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1912.0 | 1912 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1913.0 | 1913 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1914.0 | 1914 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1915.0 | 1915 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1916.0 | 1916 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1917.0 | 1917 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1918.0 | 1918 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1919.0 | 1919 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1920.0 | 1920 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1921.0 | 1921 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1922.0 | 1922 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1923.0 | 1923 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1924.0 | 1924 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1925.0 | 1925 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1926.0 | 1926 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1927.0 | 1927 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1928.0 | 1928 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1929.0 | 1929 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1930.0 | 1930 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1931.0 | 1931 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1932.0 | 1932 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1933.0 | 1933 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1934.0 | 1934 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1935.0 | 1935 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1936.0 | 1936 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1937.0 | 1937 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1938.0 | 1938 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1939.0 | 1939 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1940.0 | 1940 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1941.0 | 1941 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1942.0 | 1942 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1943.0 | 1943 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1944.0 | 1944 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1945.0 | 1945 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1946.0 | 1946 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1947.0 | 1947 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1948.0 | 1948 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1949.0 | 1949 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1950.0 | 1950 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1951.0 | 1951 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1952.0 | 1952 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1953.0 | 1953 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1954.0 | 1954 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1955.0 | 1955 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1956.0 | 1956 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1957.0 | 1957 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1958.0 | 1958 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1959.0 | 1959 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1960.0 | 1960 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1961.0 | 1961 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1962.0 | 1962 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1963.0 | 1963 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1964.0 | 1964 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1965.0 | 1965 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1966.0 | 1966 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1967.0 | 1967 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1968.0 | 1968 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1969.0 | 1969 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1970.0 | 1970 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1971.0 | 1971 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1972.0 | 1972 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1973.0 | 1973 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1974.0 | 1974 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1975.0 | 1975 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1976.0 | 1976 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1977.0 | 1977 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1978.0 | 1978 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1979.0 | 1979 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1980.0 | 1980 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1981.0 | 1981 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1982.0 | 1982 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1983.0 | 1983 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1984.0 | 1984 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1985.0 | 1985 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1986.0 | 1986 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1987.0 | 1987 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1988.0 | 1988 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1989.0 | 1989 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1990.0 | 1990 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1991.0 | 1991 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1992.0 | 1992 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1993.0 | 1993 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1994.0 | 1994 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1995.0 | 1995 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1996.0 | 1996 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1997.0 | 1997 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1998.0 | 1998 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 1999.0 | 1999 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | | 0.0 | 2000.0 | 2000 | nan | 3.5714 | 1.2195 | 3.5714 | 3.5714 | 19.0 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

mradermacher/Machroom-3B-model_stock-GGUF

mradermacher

2024-06-04T04:36:07Z

6

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-06-04T04:25:44Z

--- base_model: DreadPoor/Machroom-3B-model_stock language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/DreadPoor/Machroom-3B-model_stock  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q2_K.gguf) | Q2_K | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.IQ3_XS.gguf) | IQ3_XS | 1.3 | | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.IQ3_S.gguf) | IQ3_S | 1.4 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q3_K_S.gguf) | Q3_K_S | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.IQ3_M.gguf) | IQ3_M | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q3_K_L.gguf) | Q3_K_L | 1.6 | | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.IQ4_XS.gguf) | IQ4_XS | 1.6 | | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q4_K_M.gguf) | Q4_K_M | 1.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q5_K_S.gguf) | Q5_K_S | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q5_K_M.gguf) | Q5_K_M | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q6_K.gguf) | Q6_K | 2.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.Q8_0.gguf) | Q8_0 | 3.1 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Machroom-3B-model_stock-GGUF/resolve/main/Machroom-3B-model_stock.f16.gguf) | f16 | 5.7 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

MubarakB/zxCm3h8ADcB3R0ve2rgC

MubarakB

2024-06-04T04:35:51Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]

null

2024-06-04T04:35:47Z

--- library_name: peft base_model: NousResearch/Llama-2-7b-chat-hf --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2

Zoyd

2024-06-04T04:34:37Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]

text-generation

2024-06-03T22:38:53Z

--- license: llama3 --- **Exllamav2** quant (**exl2** / **5.0 bpw**) made with ExLlamaV2 v0.1.3 Other EXL2 quants: | **Quant** | **Model Size** | **lm_head** | | ----- | ---------- | ------- | |<center>**[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)**</center> | <center>18625 MB</center> | <center>6</center> | |<center>**[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)**</center> | <center>20645 MB</center> | <center>6</center> | |<center>**[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)**</center> | <center>24211 MB</center> | <center>6</center> | |<center>**[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)**</center> | <center>27784 MB</center> | <center>6</center> | |<center>**[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)**</center> | <center>29572 MB</center> | <center>6</center> | |<center>**[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)**</center> | <center>31359 MB</center> | <center>6</center> | |<center>**[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)**</center> | <center>33139 MB</center> | <center>6</center> | |<center>**[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)**</center> | <center>38500 MB</center> | <center>6</center> | |<center>**[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)**</center> | <center>45805 MB</center> | <center>8</center> | |<center>**[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)**</center> | <center>49410 MB</center> | <center>8</center> | |<center>**[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)**</center> | <center>54655 MB</center> | <center>8</center> |

hdve/google-gemma-2b-1717475491

hdve

2024-06-04T04:33:55Z

141

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T04:31:33Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

bamaxi/ruBert-base-sakha

bamaxi

2024-06-04T04:28:42Z

127

0

transformers

[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-06-03T23:19:14Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MubarakB/T7KGvt4x8LnHYdJN9MQ0

MubarakB

2024-06-04T04:21:09Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]

null

2024-06-04T04:21:05Z

--- library_name: peft base_model: NousResearch/Llama-2-7b-chat-hf --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2

Zoyd

2024-06-04T04:20:09Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]

text-generation

2024-06-03T17:18:31Z

--- license: llama3 --- **Exllamav2** quant (**exl2** / **3.5 bpw**) made with ExLlamaV2 v0.1.3 Other EXL2 quants: | **Quant** | **Model Size** | **lm_head** | | ----- | ---------- | ------- | |<center>**[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)**</center> | <center>18625 MB</center> | <center>6</center> | |<center>**[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)**</center> | <center>20645 MB</center> | <center>6</center> | |<center>**[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)**</center> | <center>24211 MB</center> | <center>6</center> | |<center>**[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)**</center> | <center>27784 MB</center> | <center>6</center> | |<center>**[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)**</center> | <center>29572 MB</center> | <center>6</center> | |<center>**[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)**</center> | <center>31359 MB</center> | <center>6</center> | |<center>**[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)**</center> | <center>33139 MB</center> | <center>6</center> | |<center>**[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)**</center> | <center>38500 MB</center> | <center>6</center> | |<center>**[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)**</center> | <center>45805 MB</center> | <center>8</center> | |<center>**[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)**</center> | <center>49410 MB</center> | <center>8</center> | |<center>**[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)**</center> | <center>54655 MB</center> | <center>8</center> |

SEHYONG/Llama-3-Open-Ko-8B-Instruct-kookmin7

SEHYONG

2024-06-04T04:11:42Z

7

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:SEHYONG/Llama-3-Open-Ko-8B-Instruct-kookmin6", "base_model:finetune:SEHYONG/Llama-3-Open-Ko-8B-Instruct-kookmin6", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T04:05:37Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: SEHYONG/Llama-3-Open-Ko-8B-Instruct-kookmin6 --- # Uploaded model - **Developed by:** SEHYONG - **License:** apache-2.0 - **Finetuned from model :** SEHYONG/Llama-3-Open-Ko-8B-Instruct-kookmin6 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Rudra360/Emoji_Suggester

Rudra360

2024-06-04T04:09:27Z

0

spacy

[ "spacy", "en", "region:us" ]

null

2024-06-03T14:17:44Z

--- language: - en library_name: spacy --- # Emoji Suggester Emoji Suggester is a tool designed to recommend relevant emojis based on incoming messages from social media apps, enhancing expressiveness and engagement in your conversations. The suggestions are powered by a model trained on a dataset of Twitter messages. ## Table of Contents - [Installation](#installation) - [Usage](#usage) - [Contributing](#contributing) - [License](#license) - [Contact](#contact) ## Installation To install Emoji Suggester, follow these steps: 1. Clone the repository: ```bash git clone https://huggingface.co/Rudra360/Emoji_Suggester or ```bash git clone [email protected]:Rudra360/Emoji_Suggester.git ## Usage Change the Directory 1. go to emoji_suggester ```bash cd Emoji_Suggester Then the run the follwing script 2. from util import predict 3. message = "I'm so happy today!" suggested_emojis = predict(message) print(suggested_emojis)

hdve/Qwen-Qwen1.5-7B-1717473930

hdve

2024-06-04T04:08:43Z

7

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T04:06:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

amazon/MegaBeam-Mistral-7B-300k

amazon

2024-06-04T04:06:40Z

6,628

16

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-05-13T02:30:08Z

--- license: apache-2.0 inference: false --- # MegaBeam-Mistral-7B-300k Model MegaBeam-Mistral-7B-300k is a fine-tuned [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) language model that supports input contexts up to 320k tokens. MegaBeam-Mistral-7B-300k can be deployed on a single AWS `g5.48xlarge` instance using serving frameworks such as [vLLM](https://github.com/vllm-project/vllm), Sagemaker [DJL](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-models-frameworks-djl-serving.html) endpoint, and others. Similarities and differences beween MegaBeam-Mistral-7B-300k and [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) are summarized below: |Model|Max context length| rope_theta| prompt template| |----------|-------------:|------------:|------------:| | [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 32K | 1e6 | [instruction format](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2#instruction-format)| | MegaBeam-Mistral-7B-300k | 320K | 25e6 | AS ABOVE| ## Evaluations **[InfiniteBench: Extending Long Context Evaluation Beyond 100K Tokens](https://github.com/OpenBMB/InfiniteBench)** _InfiniteBench is a cutting-edge benchmark tailored for evaluating the capabilities of language models to process, understand, and reason over super long contexts (100k+ tokens)_. We therefore evaluated MegaBeam-Mistral-7B-300k, [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), [Llama-3-8B-Instruct-262k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k), and [Llama3-70B-1M](https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-1048k) on InfiniteBench. The InfiniteBench authors also evaluated SOTA proprietary and open-source LLMs on InfiniteBench. We thus combined both results in the table below. | Task Name | MegaBeam-Mistral-7B-300k | Mistral-7B-Instruct-v0.2 | Llama-3-8B-Instruct-262k | Llama3-70B-1M | GPT-4-1106-preview | YaRN-Mistral-7B | Kimi-Chat | Claude 2 | Yi-6B-200K | Yi-34B-200K | Chatglm3-6B-128K | | ---------------- | ---------------- | ---------------- | ---------------- | ---------------- | ------ | --------------- | --------- | -------- | -----------| -----------| -----------| | Retrieve.PassKey | 100% | 75.76% | 98.30% | 81.35% | 100% | 92.71% | 98.14% | 97.80% | 100.00% | 100.00% | 92.20% | | Retrieve.Number | 96.10% | 25.25% | 97.79% | 97.62% | 100% | 56.61% | 95.42% | 98.14% | 94.92% | 100.00% | 80.68% | | Retrieve.KV | 0% | 0% | 3.40% | 3% | 89.00% | < 5% | 53.60% | 65.40% | < 5% | < 5% | < 5% | | En.Sum | 29.39% | 22.13% | 16.40% | 20.72% | 14.73% | 9.09% | 17.93% | 14.45% | < 5% | < 5% |< 5% | | En.QA | 14.93% | 4.93% | 13.20% | 16.52% | 22.22% | 9.55% | 16.52% | 11.97% | 9.20% | 12.17% |< 5% | | En.MC | 51.52% | 7.80% | 50.65% | 62% | 67.25% | 27.95% | 72.49% | 62.88% | 36.68% |38.43% |10.48% | | En.Dia | 9.50% | 3.50% | 1% | 12.50% | 8.50% | 7.50% | 11.50% | 46.50% | < 5% |< 5% |< 5% | | Zh.QA | 10.71% | 3.43% | 19.02% | 26% | 25.96% | 14.43% | 17.93% | 9.64% | 15.07% |13.61% |< 5% | | Code.Debug | 27.41% | 11.60% | 22.08% | 23.85% | 39.59% | < 5% | 18.02% | < 5% | < 5% |< 5% |< 5% | | Code.Run | 1.75% | 0.25% | 0% | 0% | 23.25% | < 5% | < 5% | < 5% | < 5% |< 5% |< 5% | | Math.Calc | 0% | 0% | 0% | 0% | < 5% | < 5% | < 5% | < 5% | < 5% |< 5% |< 5% | | Math.Find | 24.28% | 26.28% | 15.40% | 30% | 60.00% | 17.14% | 12.57% | 32.29% | < 5% |25.71% |7.71% | | **Average** | 30.70% | 15.08% | 28.10% | 31.13% | 46.08% | 20.41% | 34.93% | 37.21% | 22.78% |25.41% |17.59% | The 12 evaluation tasks are summarized below (as per [InfiniteBench]((https://github.com/OpenBMB/InfiniteBench))) | Task Name | Context | # Examples | Avg Input Tokens | Avg Output Tokens | Description | | -------------------- | ------------- | ---------- | ---------------- | ----------------- | ------------------------------------------------------------------------------------------- | | En.Sum | Fake Book | 103 | 171.5k | 1.1k | Summarization of a fake book created with core entity substitution. | | En.QA | Fake Book | 351 | 192.6k | 4.8 | Free-form question answering based on the fake book. | | En.MC | Fake Book | 229 | 184.4k | 5.3 | Multiple choice questions derived from the fake book. | | En.Dia | Script | 200 | 103.6k | 3.4 | Identification of talkers in partially anonymized scripts. | | Zh.QA | New Book | 175 | 2068.6k | 6.3 | Question answering on a set of newly collected books. | | Code.Debug | Code Document | 394 | 114.7k | 4.8 | Finding which function in a code repo contains an crashing error (in multiple choice form). | | Code.Run | Synthetic | 400 | 75.2k | 1.3 | Simulating execution of multiple simple, synthetic functions. | | Math.Calc | Synthetic | 50 | 43.9k | 43.9k | Calculations involving super-long arithmetic equations. | | Math.Find | Synthetic | 350 | 87.9k | 1.3 | Finding special integers in a lengthy list. | | Retrieve.PassKey | Synthetic | 590 | 122.4k | 2.0 | Retrieving hidden keys in a noisy long context. | | Retrieve.Number | Synthetic | 590 | 122.4k | 4.0 | Locating repeated hidden numbers in a noisy long context. | | Retrieve.KV | Synthetic | 500 | 89.9k | 22.7 | Finding the corresponding value from a dictionary and a key. | ## Serve MegaBeam-Mistral-7B-300k on EC2 instances ## On an AWS `g5.48xlarge` instance, upgrade vLLM to the latest version as per [documentation on vLLM](https://vllm.readthedocs.io/en/latest/). ### Start the server ```shell python3 -m vllm.entrypoints.openai.api_server --model amazon/MegaBeam-Mistral-7B-300k --tensor-parallel-size 8 ``` **Important Note** - We have set the `max_position_embeddings` in the [`config.json`](config.json) to 288,800 in order to fit model's KV-cache on a single `g5.48xlarge` instance, which has 8 x A10 GPUs (24GB RAM per GPU). On an instance with larger GPU RAM (e.g. `p4d.24xlarge`), feel free to increase the value of the `max_position_embeddings`(e.g. to 350K), which the model should be able to process. ### Run the client ```python from openai import OpenAI # Modify OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" client = OpenAI( # defaults to os.environ.get("OPENAI_API_KEY") api_key=openai_api_key, base_url=openai_api_base, ) models = client.models.list() model = models.data[0].id chat_completion = client.chat.completions.create( messages = [ {"role": "user", "content": "What is your favourite condiment?"}, # insert your long context here {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} # insert your long context here ], model=model, ) print("Chat completion results:") print(chat_completion) ``` ### Deploy the model on a SageMaker Endpoint ### To deploy MegaBeam-Mistral-7B-300k on a SageMaker endpoint, please follow this [SageMaker DJL deployment guide](https://docs.djl.ai/docs/demos/aws/sagemaker/large-model-inference/sample-llm/vllm_deploy_mistral_7b.html). Run the following Python code in a SageMaker notebook (with each block running in a separate cell) ```python import sagemaker from sagemaker import Model, image_uris, serializers, deserializers sagemaker_session = sagemaker.Session() region = sagemaker_session.boto_region_name role = sagemaker.get_execution_role() %%writefile serving.properties engine=Python option.model_id=amazon/MegaBeam-Mistral-7B-300k option.dtype=bf16 option.task=text-generation option.rolling_batch=vllm option.tensor_parallel_degree=8 option.device_map=auto %%sh mkdir mymodel mv serving.properties mymodel/ tar czvf mymodel.tar.gz mymodel/ rm -rf mymodel image_uri = image_uris.retrieve( framework="djl-deepspeed", region=region, version="0.27.0" ) s3_code_prefix = "megaBeam-mistral-7b-300k/code" bucket = sagemaker_session.default_bucket() # bucket to house artifacts code_artifact = sagemaker_session.upload_data("mymodel.tar.gz", bucket, s3_code_prefix) print(f"S3 Code or Model tar ball uploaded to --- > {code_artifact}") model = Model(image_uri=image_uri, model_data=code_artifact, role=role) instance_type = "ml.g5.48xlarge" endpoint_name = sagemaker.utils.name_from_base("megaBeam-mistral-7b-300k") model.deploy(initial_instance_count=1, instance_type=instance_type, endpoint_name=endpoint_name ) # our requests and responses will be in json format so we specify the serializer and the deserializer predictor = sagemaker.Predictor( endpoint_name=endpoint_name, sagemaker_session=sagemaker_session, serializer=serializers.JSONSerializer(), ) # test the endpoint input_str = """<s>[INST] What is your favourite condiment? [/INST] Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " [INST] Do you have mayonnaise recipes? [/INST]""" predictor.predict( {"inputs": input_str, "parameters": {"max_new_tokens": 75}} ) ``` ### Invoke the model on a SageMaker Endpoint ### To use MegaBeam-Mistral-7B-300k on a SageMaker endpoint, please try following this example: ```python import boto3 import json def call_endpoint(text:str, endpoint_name:str): client = boto3.client("sagemaker-runtime") parameters = { "max_new_tokens": 450, "do_sample": True, "temperature": 0.7, } payload = {"inputs": text, "parameters": parameters} response = client.invoke_endpoint( EndpointName=endpoint_name, Body=json.dumps(payload), ContentType="application/json" ) output = json.loads(response["Body"].read().decode()) result = output["generated_text"] return result # please insert your long prompt/document content here prompt = """<s>[INST] What are the main challenges to support long contexts for a Large Language Model? [/INST]""" #print(prompt) endpoint_name = "megaBeam-mistral-7b-300k-2024-05-13-14-23-41-219" # please use a valid endpoint name result = call_endpoint(prompt, endpoint_name) print(result) ``` ## Limitations ## Before using the MegaBeam-Mistral-7B-300k model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content. ## The AWS Contributors ## Chen Wu, Yin Song, Verdi March, Eden Duthie

richardkelly/Qwen-Qwen1.5-0.5B-1717473352

richardkelly

2024-06-04T04:00:37Z

141

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T03:55:53Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

cgus/AlchemistCoder-DS-6.7B-exl2

cgus

2024-06-04T03:59:59Z

5

0

transformers

[ "transformers", "llama", "text-generation", "code generation", "conversational", "arxiv:2405.19265", "base_model:internlm/AlchemistCoder-DS-6.7B", "base_model:quantized:internlm/AlchemistCoder-DS-6.7B", "license:apache-2.0", "autotrain_compatible", "4-bit", "exl2", "region:us" ]

text-generation

2024-06-03T23:59:24Z

--- license: apache-2.0 base_model: internlm/AlchemistCoder-DS-6.7B inference: false tags: - code generation --- # AlchemistCoder-DS-6.7B-exl2 Original model: [AlchemistCoder-DS-6.7B](https://huggingface.co/internlm/AlchemistCoder-DS-6.7B) Model creator: [InternLM](https://huggingface.co/internlm) ## Quants [4bpw h6 (main)](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/main) [4.25bpw h6](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/4.25bpw-h6) [4.65bpw h6](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/4.65bpw-h6) [5bpw h6](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/5bpw-h6) [6bpw h6](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/6bpw-h6) [8bpw h8](https://huggingface.co/cgus/AlchemistCoder-DS-6.7B-exl2/tree/8bpw-h8) ## Quantization notes Made with Exllamav2 0.1.3 with the default dataset. ## How to run This model is meant to be used with Exllamav2 loader that requires the model to be fully loaded into GPU VRAM. It primarily requires a Nvidia RTX card on Windows/Linux or AMD card on Linux. If you want to use this model but your system doesn't meet these requirements, you should look for GGUF versions of the model. It can be used with apps like: [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) [KoboldAI](https://github.com/henk717/KoboldAI) [ExUI](https://github.com/turboderp/exui) [lollms-webui](https://github.com/ParisNeo/lollms-webui) # Original model card # AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data [[🤗 HuggingFace](https://huggingface.co/internlm/AlchemistCoder-DS-6.7B)] [[📃 Paper](https://arxiv.org/abs/2405.19265)] [[🌐 Project Page](https://internlm.github.io/AlchemistCoder/)] ## ✨ Highlights > **Abstract:** *Open-source Large Language Models (LLMs) and their specialized variants, particularly Code LLMs, have recently delivered impressive performance. However, previous Code LLMs are typically fine-tuned on single-source data with limited quality and diversity, which may insufficiently elicit the potential of pre-trained Code LLMs. In this paper, we present AlchemistCoder, a series of Code LLMs with enhanced code generation and generalization capabilities fine-tuned on multi-source data. To achieve this, we pioneer to unveil inherent conflicts among the various styles and qualities in multi-source code corpora and introduce data-specific prompts with hindsight relabeling, termed AlchemistPrompts, to harmonize different data sources and instruction-response pairs. Additionally, we propose incorporating the data construction process into the fine-tuning data as code comprehension tasks, including instruction evolution, data filtering, and code review. Extensive experiments demonstrate that AlchemistCoder holds a clear lead among all models of the same size (6.7B/7B) and rivals or even surpasses larger models (15B/33B/70B), showcasing the efficacy of our method in refining instruction-following capabilities and advancing the boundaries of code intelligence.* - **AlchemistPrompts**: Designed as data-specific prompts for harmonizing inherent conflicts in multi-source data and mitigating the instruction/response misalignment at a fined-grained level. - **Code Comprehenstion Tasks**: Sourced from the process of data construction, consisting of instruction evolution, data filtering, and code review. - **Harmonized Multi-source Data**: Instruction tuned on 200M tokens, including 6 types of high-quality data. - **Superior Model Performance**: Surpassing all the open-source models of the same size (6.7/7B), and rivaling or even beating larger models (15B/33B/70B/ChatGPT) on 6 code benchmarks. - **Advanced generic capabilities**: Demonstrated by the significant improvements on MMLU, BBH, and GSM8K. ## 🚀 Quick Start ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("internlm/AlchemistCoder-DS-6.7B", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("internlm/AlchemistCoder-DS-6.7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() model = model.eval() input_text = "Implement the Dijkstra algorithm in Python" inputs = tokenizer(input_text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_length=128) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` The above code will generate the following result: ```python import heapq def dijkstra(graph, start, end): """ Find the shortest path between two nodes in a graph using Dijkstra's algorithm. Args: graph (dict): A dictionary representing the graph, where the keys are nodes and the values are dictionaries containing the neighboring nodes and their edge weights. start: The starting node. end: The target node. Returns: list: The shortest path from the start node to the target node as a list of nodes. Raises: ValueError: If either the start or end node is not present in the graph. """ # Check if start and end nodes are in the graph if start not in graph: raise ValueError("Start node is not present in the graph.") if end not in graph: raise ValueError("End node is not present in the graph.") # Initialize the distance dictionary with infinite distances for all nodes distances = {node: float('inf') for node in graph} # Set the distance of the start node to 0 distances[start] = 0 # Initialize the heap with the start node heap = [(0, start)] # Initialize the previous dictionary to keep track of the path previous = {} while heap: # Pop the node with the smallest distance from the heap current_distance, current_node = heapq.heappop(heap) # If the current node is the end node, we have found the shortest path if current_node == end: # Reconstruct the path by following the previous nodes path = [] while current_node in previous: path.append(current_node) current_node = previous[current_node] path.append(start) # Reverse the path to get the correct order return path[::-1] # Iterate over the neighboring nodes and update their distances for neighbor, weight in graph[current_node].items(): new_distance = current_distance + weight # If a shorter path is found, update the distance and previous node if new_distance < distances[neighbor]: distances[neighbor] = new_distance previous[neighbor] = current_node heapq.heappush(heap, (new_distance, neighbor)) # If there is no path between the start and end nodes, return an empty list return [] ``` > The `dijkstra` function takes three arguments: `graph`, `start`, and `end`. The `graph` argument is a dictionary representing the graph, where the keys are nodes and the values are dictionaries containing the neighboring nodes and their edge weights. The `start` argument is the starting node, and the `end` argument is the target node. > The function first checks if the start and end nodes are present in the graph. If either node is not present, a `ValueError` is raised. > The function then initializes a `distances` dictionary with infinite distances for all nodes. It sets the distance of the start node to 0. It also initializes a heap with the start node and a `previous` dictionary to keep track of the path. > The algorithm then iterates over the nodes in the heap. For each node, it checks if it is the end node. If it is, the function reconstructs the path by following the previous nodes and returns the shortest path as a list of nodes in the correct order. > If the current node is not the end node, the algorithm iterates over its neighboring nodes and updates their distances if a shorter path is found. It also updates the `previous` dictionary to keep track of the path. > If there is no path between the start and end nodes, the function returns an empty list. > Note that this implementation assumes that the graph is a directed graph, and it uses a heap data structure to efficiently select the node with the smallest distance at each step. ## 🧪 Evaluation and Fine-tune Please refer to [**AlchemistCoder**](https://github.com/InternLM/AlchemistCoder) and [**InternLM**](https://github.com/InternLM/InternLM/tree/main). ## 😃 Acknowledgments *AlchemistCoder* is built with [**InternLM**](https://github.com/InternLM) and [**OpenCompass**](https://github.com/open-compass). Thanks for their awesome work! ## 📧 Contact If you have any questions, please create an issue on this repository or contact us at: - [email protected] - [email protected] ## 🌟 Citation If you find our work useful, please consider citing: ```bibtex @misc{song2024alchemistcoder, title={AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data}, author={Zifan Song and Yudong Wang and Wenwei Zhang and Kuikun Liu and Chengqi Lyu and Demin Song and Qipeng Guo and Hang Yan and Dahua Lin and Kai Chen and Cairong Zhao}, year={2024}, eprint={2405.19265}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

sahilkumar4ai/misteral-finetuned-samsum

sahilkumar4ai

2024-06-04T03:53:07Z

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]

null

2024-06-04T03:18:00Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ model-index: - name: misteral-finetuned-samsum results: [] ---  [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sahilthegnius/huggingface/runs/7be8cmf0) # misteral-finetuned-samsum This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - training_steps: 259 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

stannisozbov/stann-speechtotext-medium-tr-03_06

stannisozbov

2024-06-04T03:51:08Z

6

1

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "tr", "dataset:common_voice_17_0", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-06-03T15:36:16Z

--- language: - tr license: apache-2.0 base_model: openai/whisper-medium tags: - hf-asr-leaderboard - generated_from_trainer datasets: - common_voice_17_0 metrics: - wer model-index: - name: stann-speechtotext-medium-tr-03_06 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_17_0 type: common_voice_17_0 config: tr split: None args: tr metrics: - name: Wer type: wer value: 32.78548922762497 ---  # stann-speechtotext-medium-tr-03_06 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_17_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2244 - Wer: 32.7855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.0307 | 1.3784 | 1000 | 0.2063 | 30.0843 | | 0.0131 | 2.7567 | 2000 | 0.2025 | 35.2159 | | 0.0016 | 4.1351 | 3000 | 0.2244 | 32.7855 | ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2

Zoyd

2024-06-04T03:41:15Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]

text-generation

2024-06-03T14:47:50Z

--- license: llama3 --- **Exllamav2** quant (**exl2** / **2.5 bpw**) made with ExLlamaV2 v0.1.3 Other EXL2 quants: | **Quant** | **Model Size** | **lm_head** | | ----- | ---------- | ------- | |<center>**[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)**</center> | <center>18625 MB</center> | <center>6</center> | |<center>**[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)**</center> | <center>20645 MB</center> | <center>6</center> | |<center>**[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)**</center> | <center>24211 MB</center> | <center>6</center> | |<center>**[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)**</center> | <center>27784 MB</center> | <center>6</center> | |<center>**[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)**</center> | <center>29572 MB</center> | <center>6</center> | |<center>**[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)**</center> | <center>31359 MB</center> | <center>6</center> | |<center>**[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)**</center> | <center>33139 MB</center> | <center>6</center> | |<center>**[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)**</center> | <center>38500 MB</center> | <center>6</center> | |<center>**[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)**</center> | <center>45805 MB</center> | <center>8</center> | |<center>**[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)**</center> | <center>49410 MB</center> | <center>8</center> | |<center>**[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)**</center> | <center>54655 MB</center> | <center>8</center> |

srbdtwentyfour/mystery-llama-3-8b-v2

srbdtwentyfour

2024-06-04T03:39:26Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-03T08:18:31Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** srbdtwentyfour - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF

bartowski

2024-06-04T03:26:56Z

150

0

null

[ "gguf", "text-generation", "en", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2024-06-04T03:09:56Z

--- language: - en license: llama3 quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of Llama-3-Instruct-8B-SimPO-ExPO Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3070">b3070</a> for quantization. Original model: https://huggingface.co/chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) ## Prompt format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Llama-3-Instruct-8B-SimPO-ExPO-Q8_0.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. | | [Llama-3-Instruct-8B-SimPO-ExPO-Q6_K.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. | | [Llama-3-Instruct-8B-SimPO-ExPO-Q5_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. | | [Llama-3-Instruct-8B-SimPO-ExPO-Q5_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. | | [Llama-3-Instruct-8B-SimPO-ExPO-Q4_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [Llama-3-Instruct-8B-SimPO-ExPO-Q4_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. | | [Llama-3-Instruct-8B-SimPO-ExPO-IQ4_XS.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_L.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. | | [Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. | | [Llama-3-Instruct-8B-SimPO-ExPO-IQ3_M.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. | | [Llama-3-Instruct-8B-SimPO-ExPO-IQ3_XS.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [Llama-3-Instruct-8B-SimPO-ExPO-IQ3_XXS.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [Llama-3-Instruct-8B-SimPO-ExPO-Q2_K.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. | | [Llama-3-Instruct-8B-SimPO-ExPO-IQ2_M.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [Llama-3-Instruct-8B-SimPO-ExPO-IQ2_S.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. | | [Llama-3-Instruct-8B-SimPO-ExPO-IQ2_XS.gguf](https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF/blob/main/Llama-3-Instruct-8B-SimPO-ExPO-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. | ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF --include "Llama-3-Instruct-8B-SimPO-ExPO-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/Llama-3-Instruct-8B-SimPO-ExPO-GGUF --include "Llama-3-Instruct-8B-SimPO-ExPO-Q8_0.gguf/*" --local-dir Llama-3-Instruct-8B-SimPO-ExPO-Q8_0 ``` You can either specify a new local-dir (Llama-3-Instruct-8B-SimPO-ExPO-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

Ariffiq99/CRAB_COPA_KUCI_xlm_roberta_base_finetuned

Ariffiq99

2024-06-04T03:25:02Z

6

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:Ariffiq99/COPA_KUCI_xlm_roberta_base_finetuned", "base_model:finetune:Ariffiq99/COPA_KUCI_xlm_roberta_base_finetuned", "license:mit", "endpoints_compatible", "region:us" ]

multiple-choice

2024-06-04T02:57:25Z

--- license: mit base_model: Ariffiq99/COPA_KUCI_xlm_roberta_base_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: CRAB_COPA_KUCI_xlm_roberta_base_finetuned results: [] ---  # CRAB_COPA_KUCI_xlm_roberta_base_finetuned This model is a fine-tuned version of [Ariffiq99/COPA_KUCI_xlm_roberta_base_finetuned](https://huggingface.co/Ariffiq99/COPA_KUCI_xlm_roberta_base_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1600 - F1: 0.7417 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.2245 | 1.0 | 2880 | 0.9044 | 0.6875 | | 1.1396 | 2.0 | 5760 | 1.0192 | 0.7042 | | 1.039 | 3.0 | 8640 | 1.1395 | 0.7222 | | 0.8411 | 4.0 | 11520 | 1.1650 | 0.7389 | | 0.7471 | 5.0 | 14400 | 1.1235 | 0.7361 | | 0.9344 | 6.0 | 17280 | 1.1646 | 0.7375 | | 0.7564 | 7.0 | 20160 | 1.0863 | 0.7417 | | 0.7116 | 8.0 | 23040 | 1.1600 | 0.7417 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2

Zoyd

2024-06-04T03:19:34Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]

text-generation

2024-06-03T23:58:04Z

--- license: llama3 --- **Exllamav2** quant (**exl2** / **6.0 bpw**) made with ExLlamaV2 v0.1.3 Other EXL2 quants: | **Quant** | **Model Size** | **lm_head** | | ----- | ---------- | ------- | |<center>**[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)**</center> | <center>18625 MB</center> | <center>6</center> | |<center>**[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)**</center> | <center>20645 MB</center> | <center>6</center> | |<center>**[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)**</center> | <center>24211 MB</center> | <center>6</center> | |<center>**[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)**</center> | <center>27784 MB</center> | <center>6</center> | |<center>**[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)**</center> | <center>29572 MB</center> | <center>6</center> | |<center>**[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)**</center> | <center>31359 MB</center> | <center>6</center> | |<center>**[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)**</center> | <center>33139 MB</center> | <center>6</center> | |<center>**[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)**</center> | <center>38500 MB</center> | <center>6</center> | |<center>**[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)**</center> | <center>45805 MB</center> | <center>8</center> | |<center>**[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)**</center> | <center>49410 MB</center> | <center>8</center> | |<center>**[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)**</center> | <center>54655 MB</center> | <center>8</center> |

wgd9527/sparseocc-v299-occ-seg-flow

wgd9527

2024-06-04T03:14:33Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-05-31T09:35:38Z

--- license: apache-2.0 ---

ALI-B/phi3-mini

ALI-B

2024-06-04T03:10:28Z

77

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T03:07:14Z

--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

HuggingFaceFW/ablation-exp-dedup-global_minhash-350BT

HuggingFaceFW

2024-06-04T03:10:19Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-03T23:35:11Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2

Zoyd

2024-06-04T03:08:17Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]

text-generation

2024-06-03T21:15:12Z

--- license: llama3 --- **Exllamav2** quant (**exl2** / **4.25 bpw**) made with ExLlamaV2 v0.1.3 Other EXL2 quants: | **Quant** | **Model Size** | **lm_head** | | ----- | ---------- | ------- | |<center>**[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)**</center> | <center>18625 MB</center> | <center>6</center> | |<center>**[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)**</center> | <center>20645 MB</center> | <center>6</center> | |<center>**[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)**</center> | <center>24211 MB</center> | <center>6</center> | |<center>**[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)**</center> | <center>27784 MB</center> | <center>6</center> | |<center>**[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)**</center> | <center>29572 MB</center> | <center>6</center> | |<center>**[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)**</center> | <center>31359 MB</center> | <center>6</center> | |<center>**[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)**</center> | <center>33139 MB</center> | <center>6</center> | |<center>**[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)**</center> | <center>38500 MB</center> | <center>6</center> | |<center>**[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)**</center> | <center>45805 MB</center> | <center>8</center> | |<center>**[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)**</center> | <center>49410 MB</center> | <center>8</center> | |<center>**[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)**</center> | <center>54655 MB</center> | <center>8</center> |

Abhinay45/outputs

Abhinay45

2024-06-04T03:08:04Z

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "dataset:yahma/alpaca-cleaned", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:adapter:unsloth/llama-3-8b-bnb-4bit", "license:llama2", "region:us" ]

null

2024-06-04T03:05:36Z

--- license: llama2 library_name: peft tags: - trl - sft - unsloth - generated_from_trainer base_model: unsloth/llama-3-8b-bnb-4bit datasets: - yahma/alpaca-cleaned model-index: - name: Alpaca + Llama-3 8b Unsloth 2x faster finetuning. results: [] ---  # Alpaca + Llama-3 8b Unsloth 2x faster finetuning. This model is a fine-tuned version of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) on the alpaca dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 60 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

lucalolee/ppo-LunarLander-v2-1

lucalolee

2024-06-04T03:04:20Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2024-06-04T03:04:01Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 276.00 +/- 24.50 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

turnipseason/latext5

turnipseason

2024-06-04T02:58:53Z

108

0

transformers

[ "transformers", "safetensors", "mt5", "text2text-generation", "math", "normalization", "ru", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-05-26T02:36:06Z

--- license: mit language: - ru library_name: transformers pipeline_tag: text2text-generation tags: - math - normalization --- ### Описание: Модель для нормализации русскоязычных текстов, содержащих математические сущности, в формат LaTeX. Модель является дообученной на переведённом&аугментированном датасете "[Mathematics Stack Exchange API Q&A Data](https://zenodo.org/records/1414384)" версией модели [cointegrated/rut5-small](https://huggingface.co/cointegrated/rut5-small). ### Description: This is a model for mathematical text normalization in Russian, based on the [cointegrated/rut5-small](https://huggingface.co/cointegrated/rut5-small) paraphraser. The model was created by finetuning the paraphraser on a translated&augmented "[Mathematics Stack Exchange API Q&A Data](https://zenodo.org/records/1414384)" dataset. Пример использования: --- Usage example: --- ``` python import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from IPython.display import display, Math, Latex model_dir = "turnipseason/latext5" model = AutoModelForSeq2SeqLM.from_pretrained(model_dir) tokenizer = AutoTokenizer.from_pretrained(model_dir) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model.to(device) def get_latex(text): inputs = tokenizer(text, return_tensors='pt').to(device) with torch.no_grad(): hypotheses = model.generate( **inputs, do_sample=True, num_return_sequences=1, repetition_penalty=1.2, max_length=len(text), num_beams=10, early_stopping=True ) for h in hypotheses: display(Latex(tokenizer.decode(h, skip_special_tokens=True))) text = '''лямбда прописная квадрат минус три равно десять игрек куб При этом шинус икс равен интеграл от экспоненты до трёх игрек штрих''' get_latex(text) ```

John6666/pony-pencil-sdxl

John6666

2024-06-04T02:57:30Z

19

1

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2024-05-24T12:12:06Z

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime --- Original model is [here](https://huggingface.co/bluepen5805/pony_pencil-XL).

ehottl/distilbert-base-uncased-distilled-clinc

ehottl

2024-06-04T02:56:15Z

111

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-04T02:54:19Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc results: [] ---  # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2792 - Accuracy: 0.9439 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2695 | 1.0 | 318 | 1.6200 | 0.7197 | | 1.264 | 2.0 | 636 | 0.8322 | 0.8616 | | 0.6826 | 3.0 | 954 | 0.4907 | 0.9077 | | 0.4228 | 4.0 | 1272 | 0.3628 | 0.9326 | | 0.3128 | 5.0 | 1590 | 0.3137 | 0.9413 | | 0.2644 | 6.0 | 1908 | 0.2946 | 0.9439 | | 0.2424 | 7.0 | 2226 | 0.2846 | 0.9439 | | 0.2299 | 8.0 | 2544 | 0.2806 | 0.9439 | | 0.2253 | 9.0 | 2862 | 0.2792 | 0.9439 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2.post303 - Datasets 2.19.1 - Tokenizers 0.15.2

Zery/MV-LLaVA-7B

Zery

2024-06-04T02:55:57Z

21

3

transformers

[ "transformers", "pytorch", "share4v", "text-generation", "image-text-to-text", "en", "dataset:Zery/BS-Objaverse", "dataset:Lin-Chen/ShareGPT4V", "arxiv:2406.00093", "license:apache-2.0", "autotrain_compatible", "region:us" ]

image-text-to-text

2024-05-13T07:18:35Z

--- inference: false pipeline_tag: image-text-to-text license: apache-2.0 datasets: - Zery/BS-Objaverse - Lin-Chen/ShareGPT4V language: - en --- <br> <br> # MV-LLaVA-7B Model Card ## Model details **Model type:** MV-LLaVA-7B is an open-source chatbot for 3D multi-view images trained by fine-tuning CLIP vision tower and LLaMA/Vicuna on GPT4-Vision-assisted [BS-Objaverse](https://huggingface.co/datasets/Zery/BS-Objaverse) data and [ShareGPT4V](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) data. **Model date:** MV-LLaVA-7B was trained in Apr, 2024. **Paper or resources for more information:** [[Project](https://sunzey.github.io/Bootstrap3D/)] [[Paper](https://huggingface.co/papers/2406.00093)] [[Code](https://github.com/SunzeY/Bootstrap3D)] ## Usage You can directly utilize this model as we provide in our [[repository](https://github.com/SunzeY/Bootstrap3D/tree/main/MV_LLaVA)]. ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. ## Intended use **Primary intended uses:** The primary use of ShareGPT4V-7B is research on large multimodal models and chatbots for 3D content. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 1.2M ShareGPT4V-PT data - 30K GPT4-Vision-generated multi-view image-text pairs - LLaVA instruction-tuning data

wenlianghuang/sample_phi3_finetune_example

wenlianghuang

2024-06-04T02:52:43Z

104

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "trl", "sft", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-03T07:03:36Z

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2

Zoyd

2024-06-04T02:52:17Z

4

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]

text-generation

2024-06-03T13:33:55Z

--- license: llama3 --- **Exllamav2** quant (**exl2** / **2.2 bpw**) made with ExLlamaV2 v0.1.3 Other EXL2 quants: | **Quant** | **Model Size** | **lm_head** | | ----- | ---------- | ------- | |<center>**[2.2](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_2bpw_exl2)**</center> | <center>18625 MB</center> | <center>6</center> | |<center>**[2.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-2_5bpw_exl2)**</center> | <center>20645 MB</center> | <center>6</center> | |<center>**[3.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_0bpw_exl2)**</center> | <center>24211 MB</center> | <center>6</center> | |<center>**[3.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_5bpw_exl2)**</center> | <center>27784 MB</center> | <center>6</center> | |<center>**[3.75](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-3_75bpw_exl2)**</center> | <center>29572 MB</center> | <center>6</center> | |<center>**[4.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_0bpw_exl2)**</center> | <center>31359 MB</center> | <center>6</center> | |<center>**[4.25](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-4_25bpw_exl2)**</center> | <center>33139 MB</center> | <center>6</center> | |<center>**[5.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-5_0bpw_exl2)**</center> | <center>38500 MB</center> | <center>6</center> | |<center>**[6.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_0bpw_exl2)**</center> | <center>45805 MB</center> | <center>8</center> | |<center>**[6.5](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-6_5bpw_exl2)**</center> | <center>49410 MB</center> | <center>8</center> | |<center>**[8.0](https://huggingface.co/Zoyd/nyunai_nyun-llama3-62B-8_0bpw_exl2)**</center> | <center>54655 MB</center> | <center>8</center> |

martinsinnona/visdecode_vega_2

martinsinnona

2024-06-04T02:42:03Z

51

0

transformers

[ "transformers", "safetensors", "pix2struct", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

image-text-to-text

2024-06-04T02:01:16Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Chanakan5591/llama-3-typhoon-v1.5-8b-nf4

Chanakan5591

2024-06-04T02:40:51Z

79

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-06-04T02:36:06Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ahmedesmail16/Paper_compared-beit-base

ahmedesmail16

2024-06-04T02:36:12Z

211

0

transformers

[ "transformers", "tensorboard", "safetensors", "beit", "image-classification", "generated_from_trainer", "base_model:microsoft/beit-base-patch16-224-pt22k-ft22k", "base_model:finetune:microsoft/beit-base-patch16-224-pt22k-ft22k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-06-04T00:17:01Z

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224-pt22k-ft22k tags: - generated_from_trainer metrics: - accuracy model-index: - name: Paper_compared-beit-base results: [] ---  # Paper_compared-beit-base This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5363 - Accuracy: 0.8409 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 1.6803 | 0.9492 | 14 | 0.9171 | 0.7156 | | 0.8219 | 1.9661 | 29 | 0.5230 | 0.8330 | | 0.2323 | 2.9831 | 44 | 0.5110 | 0.8047 | | 0.1112 | 4.0 | 59 | 0.4968 | 0.8138 | | 0.0387 | 4.9492 | 73 | 0.5502 | 0.8093 | | 0.0232 | 5.9661 | 88 | 0.5506 | 0.8296 | | 0.0096 | 6.9831 | 103 | 0.5341 | 0.8431 | | 0.0068 | 8.0 | 118 | 0.6003 | 0.8149 | | 0.0046 | 8.9492 | 132 | 0.5298 | 0.8409 | | 0.0051 | 9.4915 | 140 | 0.5363 | 0.8409 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1

ehottl/distilbert-base-uncased-finetuned-clinc

ehottl

2024-06-04T02:36:06Z

113

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-04T02:24:15Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: [] ---  # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8020 - Accuracy: 0.9158 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.3069 | 1.0 | 318 | 3.3020 | 0.7177 | | 2.6569 | 2.0 | 636 | 1.9007 | 0.8468 | | 1.5836 | 3.0 | 954 | 1.1867 | 0.8881 | | 1.0474 | 4.0 | 1272 | 0.8876 | 0.9116 | | 0.8287 | 5.0 | 1590 | 0.8020 | 0.9158 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2.post303 - Datasets 2.19.1 - Tokenizers 0.15.2

warwavn/vit-base-patch16-224-in21k-finetuned-lora-food101

warwavn

2024-06-04T02:35:22Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-04T02:29:39Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

flammenai/Mahou-1.3a-mistral-7B-GGUF

flammenai

2024-06-04T02:26:13Z

0

1

transformers

[ "transformers", "gguf", "dataset:flammenai/MahouMix-v1", "base_model:flammenai/Mahou-1.3a-mistral-7B", "base_model:quantized:flammenai/Mahou-1.3a-mistral-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-06-02T03:39:19Z

--- library_name: transformers license: apache-2.0 base_model: - flammenai/Mahou-1.3a-mistral-7B datasets: - flammenai/MahouMix-v1 --- ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.3a-mistral-7B Mahou is designed to provide short messages in a conversational context. It is capable of casual conversation and character roleplay. ### Chat Format This model has been trained to use ChatML format. ``` <|im_start|>system {{system}}<|im_end|> <|im_start|>{{char}} {{message}}<|im_end|> <|im_start|>{{user}} {{message}}<|im_end|> ``` ### Roleplay Format - Speech without quotes. - Actions in `*asterisks*` ``` *leans against wall cooly* so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. ``` ### SillyTavern Settings 1. Use ChatML for the Context Template. 2. Enable Instruct Mode. 3. Use the [Mahou preset](https://huggingface.co/datasets/flammenai/Mahou-ST-ChatML-Instruct/raw/main/Mahou.json). 4. *Recommended* Additonal stopping strings: `["\n", "<|", "</"]` ### Method DPO finetuned for 6 epochs using an A100 on Google Colab. [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne)

Ariffiq99/CRAB_COPA_KUCI_xlm_roberta_large_finetuned

Ariffiq99

2024-06-04T02:25:44Z

6

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:Ariffiq99/COPA_KUCI_xlm_roberta_large_finetuned", "base_model:finetune:Ariffiq99/COPA_KUCI_xlm_roberta_large_finetuned", "license:mit", "endpoints_compatible", "region:us" ]

multiple-choice

2024-06-04T00:05:24Z

--- license: mit base_model: Ariffiq99/COPA_KUCI_xlm_roberta_large_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: CRAB_COPA_KUCI_xlm_roberta_large_finetuned results: [] ---  # CRAB_COPA_KUCI_xlm_roberta_large_finetuned This model is a fine-tuned version of [Ariffiq99/COPA_KUCI_xlm_roberta_large_finetuned](https://huggingface.co/Ariffiq99/COPA_KUCI_xlm_roberta_large_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2852 - F1: 0.7250 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.1412 | 1.0 | 2880 | 1.4904 | 0.675 | | 1.0659 | 2.0 | 5760 | 1.7656 | 0.6986 | | 0.9118 | 3.0 | 8640 | 1.4802 | 0.7083 | | 0.8833 | 4.0 | 11520 | 0.9360 | 0.7208 | | 0.9054 | 5.0 | 14400 | 1.3935 | 0.7111 | | 0.8062 | 6.0 | 17280 | 1.1927 | 0.7194 | | 0.8188 | 7.0 | 20160 | 1.1275 | 0.7278 | | 0.7608 | 8.0 | 23040 | 1.2852 | 0.7250 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

TTTXXX01/LS-zephyr-7b-sft-full

TTTXXX01

2024-06-04T02:25:00Z

8

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:alignment-handbook/zephyr-7b-sft-full", "base_model:finetune:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-03T18:08:07Z

--- license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: LS-zephyr-7b-sft-full results: [] ---  # LS-zephyr-7b-sft-full This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 3 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - total_train_batch_size: 9 - total_eval_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

ShenaoZ/DPO-Zephyr-7B

ShenaoZ

2024-06-04T02:24:29Z

10

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "base_model:finetune:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-31T17:13:41Z

--- license: mit base_model: HuggingFaceH4/mistral-7b-sft-beta tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: DPO-Zephyr-7B results: [] ---  # DPO-Zephyr-7B This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1

Carlosslocar/test6

Carlosslocar

2024-06-04T02:23:35Z

5

0

transformers

[ "transformers", "safetensors", "gemma", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2024-06-04T02:17:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Larbz-7/swin-tiny-patch4-window7-224-finetuned-eurosat

Larbz-7

2024-06-04T02:22:07Z

219

0

transformers

[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-06-03T23:03:14Z

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: [] ---  # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1335 - Accuracy: 0.5414 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 2.3862 | 0.9994 | 788 | 2.2541 | 0.5365 | | 2.1651 | 2.0 | 1577 | 2.1688 | 0.5395 | | 2.1559 | 2.9981 | 2364 | 2.1335 | 0.5414 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

hdve/Qwen-Qwen1.5-1.8B-1717467486

hdve

2024-06-04T02:20:23Z

140

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T02:18:38Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

rubenamtz0/llama-3-8b-lora-law2entity

rubenamtz0

2024-06-04T02:19:37Z

15

1

peft

[ "peft", "safetensors", "gguf", "llama", "axolotl", "generated_from_trainer", "dataset:rubenamtz0/law_entity_recognition", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]

null

2024-06-02T01:21:16Z

--- license: llama3 library_name: peft tags: - axolotl - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B model-index: - name: llama-3-8b-lora-law2entity results: [] datasets: - rubenamtz0/law_entity_recognition ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3-8B model_type: LlamaForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: true load_in_4bit: false strict: false datasets: - path: rubenamtz0/law_entity_recognition type: alpaca dataset_prepared_path: val_set_size: 0.1 output_dir: ./outputs/lora-law hub_model_id: rubenamtz0/llama-3-8b-lora-law2entity sequence_len: 4096 sample_packing: true pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: wandb_project: entity-relationship-claim-ft wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 4 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true s2_attention: warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: <|end_of_text|> ``` </details><br> # llama-3-8b-lora-law2entity This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the rubenamtz0/law_entity_recognition dataset. It achieves the following results on the evaluation set: - Loss: 0.1490 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - total_eval_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.2735 | 0.05 | 1 | 0.2923 | | 0.2852 | 0.25 | 5 | 0.2742 | | 0.2007 | 0.5 | 10 | 0.2015 | | 0.1742 | 0.75 | 15 | 0.1807 | | 0.1854 | 1.0 | 20 | 0.1688 | | 0.159 | 1.1125 | 25 | 0.1630 | | 0.1444 | 1.3625 | 30 | 0.1592 | | 0.1479 | 1.6125 | 35 | 0.1565 | | 0.1505 | 1.8625 | 40 | 0.1538 | | 0.1369 | 2.1125 | 45 | 0.1518 | | 0.1348 | 2.2125 | 50 | 0.1512 | | 0.1287 | 2.4625 | 55 | 0.1510 | | 0.1359 | 2.7125 | 60 | 0.1498 | | 0.1367 | 2.9625 | 65 | 0.1491 | | 0.1218 | 3.075 | 70 | 0.1491 | | 0.1285 | 3.325 | 75 | 0.1493 | | 0.1307 | 3.575 | 80 | 0.1490 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.1.2+cu118 - Datasets 2.19.1 - Tokenizers 0.19.1

kiatkock/sentiment_pc_oversampler

kiatkock

2024-06-04T02:15:32Z

108

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:ahmedrachid/FinancialBERT-Sentiment-Analysis", "base_model:finetune:ahmedrachid/FinancialBERT-Sentiment-Analysis", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-05-30T07:03:44Z

--- base_model: ahmedrachid/FinancialBERT-Sentiment-Analysis tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: sentiment_pc_oversampler results: [] ---  # sentiment_pc_oversampler This model is a fine-tuned version of [ahmedrachid/FinancialBERT-Sentiment-Analysis](https://huggingface.co/ahmedrachid/FinancialBERT-Sentiment-Analysis) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3909 - Accuracy: 0.9291 - F1: 0.9288 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| | No log | 0.1134 | 50 | 0.5293 | 0.8154 | 0.8173 | | No log | 0.2268 | 100 | 0.4512 | 0.8222 | 0.8224 | | No log | 0.3401 | 150 | 0.4212 | 0.8356 | 0.8364 | | No log | 0.4535 | 200 | 0.3978 | 0.8395 | 0.8400 | | No log | 0.5669 | 250 | 0.3745 | 0.8631 | 0.8642 | | No log | 0.6803 | 300 | 0.3593 | 0.8667 | 0.8675 | | No log | 0.7937 | 350 | 0.3203 | 0.8821 | 0.8826 | | No log | 0.9070 | 400 | 0.3130 | 0.8880 | 0.8889 | | No log | 1.0204 | 450 | 0.3052 | 0.8903 | 0.8904 | | 0.3514 | 1.1338 | 500 | 0.3216 | 0.8948 | 0.8954 | | 0.3514 | 1.2472 | 550 | 0.3178 | 0.8979 | 0.8981 | | 0.3514 | 1.3605 | 600 | 0.3366 | 0.8874 | 0.8877 | | 0.3514 | 1.4739 | 650 | 0.3108 | 0.8951 | 0.8950 | | 0.3514 | 1.5873 | 700 | 0.2551 | 0.9198 | 0.9200 | | 0.3514 | 1.7007 | 750 | 0.3358 | 0.8911 | 0.8907 | | 0.3514 | 1.8141 | 800 | 0.2812 | 0.9127 | 0.9125 | | 0.3514 | 1.9274 | 850 | 0.2443 | 0.9240 | 0.9239 | | 0.3514 | 2.0408 | 900 | 0.3059 | 0.9183 | 0.9182 | | 0.3514 | 2.1542 | 950 | 0.3161 | 0.9155 | 0.9152 | | 0.1587 | 2.2676 | 1000 | 0.2733 | 0.9237 | 0.9235 | | 0.1587 | 2.3810 | 1050 | 0.3252 | 0.9141 | 0.9137 | | 0.1587 | 2.4943 | 1100 | 0.3257 | 0.9141 | 0.9140 | | 0.1587 | 2.6077 | 1150 | 0.2836 | 0.9254 | 0.9253 | | 0.1587 | 2.7211 | 1200 | 0.3176 | 0.9166 | 0.9163 | | 0.1587 | 2.8345 | 1250 | 0.3335 | 0.9232 | 0.9228 | | 0.1587 | 2.9478 | 1300 | 0.3076 | 0.9257 | 0.9254 | | 0.1587 | 3.0612 | 1350 | 0.3169 | 0.9269 | 0.9264 | | 0.1587 | 3.1746 | 1400 | 0.3627 | 0.9240 | 0.9238 | | 0.1587 | 3.2880 | 1450 | 0.4074 | 0.9127 | 0.9118 | | 0.0731 | 3.4014 | 1500 | 0.3580 | 0.9251 | 0.9247 | | 0.0731 | 3.5147 | 1550 | 0.3802 | 0.9240 | 0.9235 | | 0.0731 | 3.6281 | 1600 | 0.3705 | 0.9257 | 0.9253 | | 0.0731 | 3.7415 | 1650 | 0.3177 | 0.9362 | 0.9361 | | 0.0731 | 3.8549 | 1700 | 0.3563 | 0.9314 | 0.9310 | | 0.0731 | 3.9683 | 1750 | 0.4248 | 0.9158 | 0.9154 | | 0.0731 | 4.0816 | 1800 | 0.3535 | 0.9314 | 0.9310 | | 0.0731 | 4.1950 | 1850 | 0.3568 | 0.9308 | 0.9305 | | 0.0731 | 4.3084 | 1900 | 0.4044 | 0.9266 | 0.9264 | | 0.0731 | 4.4218 | 1950 | 0.3598 | 0.9331 | 0.9327 | | 0.0358 | 4.5351 | 2000 | 0.3909 | 0.9291 | 0.9288 | | 0.0358 | 4.6485 | 2050 | 0.3725 | 0.9325 | 0.9322 | | 0.0358 | 4.7619 | 2100 | 0.3953 | 0.9305 | 0.9303 | | 0.0358 | 4.8753 | 2150 | 0.3902 | 0.9305 | 0.9302 | | 0.0358 | 4.9887 | 2200 | 0.3960 | 0.9286 | 0.9282 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

lemon-mint/ko-tokenizer-experiment-003

lemon-mint

2024-06-04T02:14:47Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-04T02:14:45Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

zaynu/llama2-finetune

zaynu

2024-06-04T01:56:39Z

5

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T01:35:17Z

--- license: apache-2.0 ---

apwic/nerui-lora-r16-1

apwic

2024-06-04T01:54:08Z

0

null

[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]

null

2024-05-28T13:06:00Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerui-lora-r16-1 results: [] ---  # nerui-lora-r16-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0342 - Location Precision: 0.9316 - Location Recall: 0.9397 - Location F1: 0.9356 - Location Number: 116 - Organization Precision: 0.9484 - Organization Recall: 0.9304 - Organization F1: 0.9393 - Organization Number: 158 - Person Precision: 0.984 - Person Recall: 0.9919 - Person F1: 0.9880 - Person Number: 124 - Overall Precision: 0.9547 - Overall Recall: 0.9523 - Overall F1: 0.9535 - Overall Accuracy: 0.9896 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Location Precision | Location Recall | Location F1 | Location Number | Organization Precision | Organization Recall | Organization F1 | Organization Number | Person Precision | Person Recall | Person F1 | Person Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 1.0545 | 1.0 | 96 | 0.6622 | 0.0 | 0.0 | 0.0 | 116 | 0.0 | 0.0 | 0.0 | 158 | 0.0 | 0.0 | 0.0 | 124 | 0.0 | 0.0 | 0.0 | 0.8394 | | 0.64 | 2.0 | 192 | 0.5206 | 0.0 | 0.0 | 0.0 | 116 | 0.5 | 0.0127 | 0.0247 | 158 | 0.0 | 0.0 | 0.0 | 124 | 0.3333 | 0.0050 | 0.0099 | 0.8400 | | 0.503 | 3.0 | 288 | 0.3728 | 0.0833 | 0.0086 | 0.0156 | 116 | 0.3625 | 0.1835 | 0.2437 | 158 | 0.36 | 0.2903 | 0.3214 | 124 | 0.3438 | 0.1658 | 0.2237 | 0.8718 | | 0.3537 | 4.0 | 384 | 0.2518 | 0.3947 | 0.2586 | 0.3125 | 116 | 0.4885 | 0.5380 | 0.5120 | 158 | 0.5521 | 0.7258 | 0.6272 | 124 | 0.4964 | 0.5151 | 0.5055 | 0.9198 | | 0.2513 | 5.0 | 480 | 0.1812 | 0.6111 | 0.5690 | 0.5893 | 116 | 0.5979 | 0.7342 | 0.6591 | 158 | 0.8028 | 0.9194 | 0.8571 | 124 | 0.6667 | 0.7437 | 0.7031 | 0.9498 | | 0.1948 | 6.0 | 576 | 0.1359 | 0.7438 | 0.7759 | 0.7595 | 116 | 0.7368 | 0.7975 | 0.7660 | 158 | 0.8905 | 0.9839 | 0.9349 | 124 | 0.7879 | 0.8492 | 0.8174 | 0.9657 | | 0.1623 | 7.0 | 672 | 0.1109 | 0.7917 | 0.8190 | 0.8051 | 116 | 0.7619 | 0.8101 | 0.7853 | 158 | 0.9104 | 0.9839 | 0.9457 | 124 | 0.8175 | 0.8668 | 0.8415 | 0.9701 | | 0.1397 | 8.0 | 768 | 0.0954 | 0.8083 | 0.8362 | 0.8220 | 116 | 0.7976 | 0.8481 | 0.8221 | 158 | 0.9389 | 0.9919 | 0.9647 | 124 | 0.8449 | 0.8894 | 0.8666 | 0.9739 | | 0.1266 | 9.0 | 864 | 0.0877 | 0.8189 | 0.8966 | 0.8560 | 116 | 0.8155 | 0.8671 | 0.8405 | 158 | 0.9318 | 0.9919 | 0.9609 | 124 | 0.8525 | 0.9146 | 0.8824 | 0.9761 | | 0.1157 | 10.0 | 960 | 0.0731 | 0.8607 | 0.9052 | 0.8824 | 116 | 0.8519 | 0.8734 | 0.8625 | 158 | 0.9609 | 0.9919 | 0.9762 | 124 | 0.8883 | 0.9196 | 0.9037 | 0.9800 | | 0.1111 | 11.0 | 1056 | 0.0673 | 0.8760 | 0.9138 | 0.8945 | 116 | 0.8606 | 0.8987 | 0.8793 | 158 | 0.9685 | 0.9919 | 0.9801 | 124 | 0.8983 | 0.9322 | 0.9149 | 0.9813 | | 0.1044 | 12.0 | 1152 | 0.0635 | 0.8760 | 0.9138 | 0.8945 | 116 | 0.8554 | 0.8987 | 0.8765 | 158 | 0.9685 | 0.9919 | 0.9801 | 124 | 0.8961 | 0.9322 | 0.9138 | 0.9811 | | 0.098 | 13.0 | 1248 | 0.0578 | 0.8898 | 0.9052 | 0.8974 | 116 | 0.8589 | 0.8861 | 0.8723 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9042 | 0.9246 | 0.9143 | 0.9816 | | 0.0939 | 14.0 | 1344 | 0.0559 | 0.875 | 0.9052 | 0.8898 | 116 | 0.8642 | 0.8861 | 0.8750 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9020 | 0.9246 | 0.9132 | 0.9819 | | 0.091 | 15.0 | 1440 | 0.0558 | 0.8824 | 0.9052 | 0.8936 | 116 | 0.8402 | 0.8987 | 0.8685 | 158 | 0.9685 | 0.9919 | 0.9801 | 124 | 0.8916 | 0.9296 | 0.9102 | 0.9816 | | 0.088 | 16.0 | 1536 | 0.0555 | 0.875 | 0.9052 | 0.8898 | 116 | 0.8452 | 0.8987 | 0.8712 | 158 | 0.9535 | 0.9919 | 0.9723 | 124 | 0.8873 | 0.9296 | 0.9080 | 0.9811 | | 0.0857 | 17.0 | 1632 | 0.0523 | 0.8824 | 0.9052 | 0.8936 | 116 | 0.8868 | 0.8924 | 0.8896 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9156 | 0.9271 | 0.9213 | 0.9846 | | 0.0809 | 18.0 | 1728 | 0.0498 | 0.8678 | 0.9052 | 0.8861 | 116 | 0.8659 | 0.8987 | 0.8820 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9024 | 0.9296 | 0.9158 | 0.9833 | | 0.0773 | 19.0 | 1824 | 0.0482 | 0.8898 | 0.9052 | 0.8974 | 116 | 0.8827 | 0.9051 | 0.8938 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9160 | 0.9322 | 0.9240 | 0.9844 | | 0.0765 | 20.0 | 1920 | 0.0521 | 0.8833 | 0.9138 | 0.8983 | 116 | 0.8571 | 0.9114 | 0.8834 | 158 | 0.9685 | 0.9919 | 0.9801 | 124 | 0.8988 | 0.9372 | 0.9176 | 0.9822 | | 0.0754 | 21.0 | 2016 | 0.0484 | 0.875 | 0.9052 | 0.8898 | 116 | 0.8735 | 0.9177 | 0.8951 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9075 | 0.9372 | 0.9221 | 0.9841 | | 0.072 | 22.0 | 2112 | 0.0469 | 0.875 | 0.9052 | 0.8898 | 116 | 0.8606 | 0.8987 | 0.8793 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9024 | 0.9296 | 0.9158 | 0.9835 | | 0.0689 | 23.0 | 2208 | 0.0440 | 0.8898 | 0.9052 | 0.8974 | 116 | 0.8944 | 0.9114 | 0.9028 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9208 | 0.9347 | 0.9277 | 0.9844 | | 0.0697 | 24.0 | 2304 | 0.0456 | 0.8974 | 0.9052 | 0.9013 | 116 | 0.8968 | 0.8797 | 0.8882 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9244 | 0.9221 | 0.9233 | 0.9846 | | 0.0656 | 25.0 | 2400 | 0.0436 | 0.8983 | 0.9138 | 0.9060 | 116 | 0.8812 | 0.8924 | 0.8868 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9181 | 0.9296 | 0.9238 | 0.9846 | | 0.0658 | 26.0 | 2496 | 0.0427 | 0.8974 | 0.9052 | 0.9013 | 116 | 0.8704 | 0.8924 | 0.8812 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9134 | 0.9271 | 0.9202 | 0.9841 | | 0.065 | 27.0 | 2592 | 0.0421 | 0.9052 | 0.9052 | 0.9052 | 116 | 0.8834 | 0.9114 | 0.8972 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9208 | 0.9347 | 0.9277 | 0.9855 | | 0.0613 | 28.0 | 2688 | 0.0418 | 0.8833 | 0.9138 | 0.8983 | 116 | 0.8882 | 0.9051 | 0.8966 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9163 | 0.9347 | 0.9254 | 0.9855 | | 0.0591 | 29.0 | 2784 | 0.0398 | 0.9060 | 0.9138 | 0.9099 | 116 | 0.8882 | 0.9051 | 0.8966 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9231 | 0.9347 | 0.9288 | 0.9874 | | 0.06 | 30.0 | 2880 | 0.0395 | 0.9060 | 0.9138 | 0.9099 | 116 | 0.8994 | 0.9051 | 0.9022 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9277 | 0.9347 | 0.9312 | 0.9865 | | 0.0566 | 31.0 | 2976 | 0.0386 | 0.8983 | 0.9138 | 0.9060 | 116 | 0.8827 | 0.9051 | 0.8938 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9185 | 0.9347 | 0.9265 | 0.9863 | | 0.0566 | 32.0 | 3072 | 0.0392 | 0.8889 | 0.8966 | 0.8927 | 116 | 0.9045 | 0.8987 | 0.9016 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9248 | 0.9271 | 0.9260 | 0.9857 | | 0.0566 | 33.0 | 3168 | 0.0398 | 0.8992 | 0.9224 | 0.9106 | 116 | 0.9045 | 0.8987 | 0.9016 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9277 | 0.9347 | 0.9312 | 0.9865 | | 0.0568 | 34.0 | 3264 | 0.0396 | 0.9224 | 0.9224 | 0.9224 | 116 | 0.8951 | 0.9177 | 0.9062 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9305 | 0.9422 | 0.9363 | 0.9871 | | 0.0532 | 35.0 | 3360 | 0.0379 | 0.8983 | 0.9138 | 0.9060 | 116 | 0.9051 | 0.9051 | 0.9051 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9277 | 0.9347 | 0.9312 | 0.9871 | | 0.052 | 36.0 | 3456 | 0.0403 | 0.9231 | 0.9310 | 0.9270 | 116 | 0.9012 | 0.9241 | 0.9125 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9332 | 0.9472 | 0.9401 | 0.9879 | | 0.0516 | 37.0 | 3552 | 0.0386 | 0.8983 | 0.9138 | 0.9060 | 116 | 0.9 | 0.9114 | 0.9057 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9256 | 0.9372 | 0.9313 | 0.9874 | | 0.0497 | 38.0 | 3648 | 0.0378 | 0.8992 | 0.9224 | 0.9106 | 116 | 0.8994 | 0.9051 | 0.9022 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9256 | 0.9372 | 0.9313 | 0.9879 | | 0.052 | 39.0 | 3744 | 0.0366 | 0.9138 | 0.9138 | 0.9138 | 116 | 0.9006 | 0.9177 | 0.9091 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9303 | 0.9397 | 0.9350 | 0.9885 | | 0.0472 | 40.0 | 3840 | 0.0367 | 0.9138 | 0.9138 | 0.9138 | 116 | 0.8987 | 0.8987 | 0.8987 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9298 | 0.9322 | 0.9310 | 0.9868 | | 0.0486 | 41.0 | 3936 | 0.0388 | 0.9076 | 0.9310 | 0.9191 | 116 | 0.9074 | 0.9304 | 0.9187 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9310 | 0.9497 | 0.9403 | 0.9882 | | 0.047 | 42.0 | 4032 | 0.0375 | 0.9068 | 0.9224 | 0.9145 | 116 | 0.9161 | 0.8987 | 0.9073 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9347 | 0.9347 | 0.9347 | 0.9874 | | 0.0481 | 43.0 | 4128 | 0.0380 | 0.8983 | 0.9138 | 0.9060 | 116 | 0.9051 | 0.9051 | 0.9051 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9277 | 0.9347 | 0.9312 | 0.9860 | | 0.0468 | 44.0 | 4224 | 0.0391 | 0.9231 | 0.9310 | 0.9270 | 116 | 0.9062 | 0.9177 | 0.9119 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9353 | 0.9447 | 0.94 | 0.9876 | | 0.0473 | 45.0 | 4320 | 0.0366 | 0.8992 | 0.9224 | 0.9106 | 116 | 0.9045 | 0.8987 | 0.9016 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9277 | 0.9347 | 0.9312 | 0.9868 | | 0.0441 | 46.0 | 4416 | 0.0372 | 0.9 | 0.9310 | 0.9153 | 116 | 0.9006 | 0.9177 | 0.9091 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9261 | 0.9447 | 0.9353 | 0.9887 | | 0.0441 | 47.0 | 4512 | 0.0375 | 0.9224 | 0.9224 | 0.9224 | 116 | 0.9068 | 0.9241 | 0.9154 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9353 | 0.9447 | 0.94 | 0.9887 | | 0.0416 | 48.0 | 4608 | 0.0359 | 0.9237 | 0.9397 | 0.9316 | 116 | 0.9363 | 0.9304 | 0.9333 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9475 | 0.9523 | 0.9499 | 0.9898 | | 0.0446 | 49.0 | 4704 | 0.0355 | 0.9153 | 0.9310 | 0.9231 | 116 | 0.8931 | 0.8987 | 0.8959 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9279 | 0.9372 | 0.9325 | 0.9876 | | 0.0425 | 50.0 | 4800 | 0.0366 | 0.9160 | 0.9397 | 0.9277 | 116 | 0.9 | 0.9114 | 0.9057 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9307 | 0.9447 | 0.9377 | 0.9887 | | 0.0422 | 51.0 | 4896 | 0.0364 | 0.9153 | 0.9310 | 0.9231 | 116 | 0.9167 | 0.9051 | 0.9108 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9373 | 0.9397 | 0.9385 | 0.9871 | | 0.0409 | 52.0 | 4992 | 0.0357 | 0.9145 | 0.9224 | 0.9185 | 116 | 0.9074 | 0.9304 | 0.9187 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9332 | 0.9472 | 0.9401 | 0.9896 | | 0.0414 | 53.0 | 5088 | 0.0359 | 0.9231 | 0.9310 | 0.9270 | 116 | 0.9136 | 0.9367 | 0.9250 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9381 | 0.9523 | 0.9451 | 0.9901 | | 0.0403 | 54.0 | 5184 | 0.0353 | 0.9231 | 0.9310 | 0.9270 | 116 | 0.8963 | 0.9304 | 0.9130 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9310 | 0.9497 | 0.9403 | 0.9896 | | 0.0393 | 55.0 | 5280 | 0.0352 | 0.9145 | 0.9224 | 0.9185 | 116 | 0.9136 | 0.9367 | 0.9250 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9356 | 0.9497 | 0.9426 | 0.9898 | | 0.0405 | 56.0 | 5376 | 0.0359 | 0.9237 | 0.9397 | 0.9316 | 116 | 0.9430 | 0.9430 | 0.9430 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9501 | 0.9573 | 0.9537 | 0.9901 | | 0.0404 | 57.0 | 5472 | 0.0370 | 0.9160 | 0.9397 | 0.9277 | 116 | 0.9371 | 0.9430 | 0.9401 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9454 | 0.9573 | 0.9513 | 0.9896 | | 0.0398 | 58.0 | 5568 | 0.0355 | 0.9316 | 0.9397 | 0.9356 | 116 | 0.9308 | 0.9367 | 0.9338 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9476 | 0.9548 | 0.9512 | 0.9904 | | 0.0382 | 59.0 | 5664 | 0.0355 | 0.9397 | 0.9397 | 0.9397 | 116 | 0.9551 | 0.9430 | 0.9490 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9597 | 0.9573 | 0.9585 | 0.9904 | | 0.0396 | 60.0 | 5760 | 0.0344 | 0.9160 | 0.9397 | 0.9277 | 116 | 0.9125 | 0.9241 | 0.9182 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9356 | 0.9497 | 0.9426 | 0.9893 | | 0.0362 | 61.0 | 5856 | 0.0356 | 0.9231 | 0.9310 | 0.9270 | 116 | 0.9226 | 0.9051 | 0.9137 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9421 | 0.9397 | 0.9409 | 0.9879 | | 0.037 | 62.0 | 5952 | 0.0360 | 0.9237 | 0.9397 | 0.9316 | 116 | 0.9167 | 0.9051 | 0.9108 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9398 | 0.9422 | 0.9410 | 0.9882 | | 0.0386 | 63.0 | 6048 | 0.0364 | 0.9310 | 0.9310 | 0.9310 | 116 | 0.9367 | 0.9367 | 0.9367 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9499 | 0.9523 | 0.9511 | 0.9896 | | 0.0365 | 64.0 | 6144 | 0.0360 | 0.9153 | 0.9310 | 0.9231 | 116 | 0.9412 | 0.9114 | 0.9260 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9470 | 0.9422 | 0.9446 | 0.9887 | | 0.0347 | 65.0 | 6240 | 0.0354 | 0.9237 | 0.9397 | 0.9316 | 116 | 0.9416 | 0.9177 | 0.9295 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9496 | 0.9472 | 0.9484 | 0.9887 | | 0.0393 | 66.0 | 6336 | 0.0366 | 0.9397 | 0.9397 | 0.9397 | 116 | 0.9355 | 0.9177 | 0.9265 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9520 | 0.9472 | 0.9496 | 0.9887 | | 0.0359 | 67.0 | 6432 | 0.0348 | 0.9316 | 0.9397 | 0.9356 | 116 | 0.9241 | 0.9241 | 0.9241 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.945 | 0.9497 | 0.9474 | 0.9893 | | 0.0331 | 68.0 | 6528 | 0.0347 | 0.9316 | 0.9397 | 0.9356 | 116 | 0.9177 | 0.9177 | 0.9177 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9425 | 0.9472 | 0.9449 | 0.9890 | | 0.0344 | 69.0 | 6624 | 0.0341 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9363 | 0.9304 | 0.9333 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9521 | 0.9497 | 0.9509 | 0.9898 | | 0.0349 | 70.0 | 6720 | 0.0345 | 0.9397 | 0.9397 | 0.9397 | 116 | 0.9427 | 0.9367 | 0.9397 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9548 | 0.9548 | 0.9548 | 0.9901 | | 0.0349 | 71.0 | 6816 | 0.0354 | 0.9310 | 0.9310 | 0.9310 | 116 | 0.9299 | 0.9241 | 0.9270 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9472 | 0.9472 | 0.9472 | 0.9885 | | 0.0342 | 72.0 | 6912 | 0.0343 | 0.9237 | 0.9397 | 0.9316 | 116 | 0.9299 | 0.9241 | 0.9270 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.945 | 0.9497 | 0.9474 | 0.9887 | | 0.0333 | 73.0 | 7008 | 0.0354 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9241 | 0.9241 | 0.9241 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9472 | 0.9472 | 0.9472 | 0.9890 | | 0.0332 | 74.0 | 7104 | 0.0346 | 0.9231 | 0.9310 | 0.9270 | 116 | 0.9241 | 0.9241 | 0.9241 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9425 | 0.9472 | 0.9449 | 0.9893 | | 0.0346 | 75.0 | 7200 | 0.0342 | 0.9310 | 0.9310 | 0.9310 | 116 | 0.9245 | 0.9304 | 0.9274 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.945 | 0.9497 | 0.9474 | 0.9896 | | 0.0334 | 76.0 | 7296 | 0.0346 | 0.9224 | 0.9224 | 0.9224 | 116 | 0.925 | 0.9367 | 0.9308 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9426 | 0.9497 | 0.9462 | 0.9904 | | 0.034 | 77.0 | 7392 | 0.0350 | 0.9397 | 0.9397 | 0.9397 | 116 | 0.9299 | 0.9241 | 0.9270 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9497 | 0.9497 | 0.9497 | 0.9896 | | 0.0341 | 78.0 | 7488 | 0.0340 | 0.9316 | 0.9397 | 0.9356 | 116 | 0.9363 | 0.9304 | 0.9333 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9499 | 0.9523 | 0.9511 | 0.9904 | | 0.033 | 79.0 | 7584 | 0.0348 | 0.9304 | 0.9224 | 0.9264 | 116 | 0.925 | 0.9367 | 0.9308 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.945 | 0.9497 | 0.9474 | 0.9896 | | 0.0308 | 80.0 | 7680 | 0.0337 | 0.9138 | 0.9138 | 0.9138 | 116 | 0.9193 | 0.9367 | 0.9279 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9378 | 0.9472 | 0.9425 | 0.9898 | | 0.031 | 81.0 | 7776 | 0.0341 | 0.9224 | 0.9224 | 0.9224 | 116 | 0.9193 | 0.9367 | 0.9279 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9403 | 0.9497 | 0.9450 | 0.9901 | | 0.0315 | 82.0 | 7872 | 0.0340 | 0.9237 | 0.9397 | 0.9316 | 116 | 0.9363 | 0.9304 | 0.9333 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9475 | 0.9523 | 0.9499 | 0.9904 | | 0.0321 | 83.0 | 7968 | 0.0343 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9367 | 0.9367 | 0.9367 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9523 | 0.9523 | 0.9523 | 0.9901 | | 0.0317 | 84.0 | 8064 | 0.0340 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9367 | 0.9367 | 0.9367 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9523 | 0.9523 | 0.9523 | 0.9901 | | 0.0324 | 85.0 | 8160 | 0.0340 | 0.9145 | 0.9224 | 0.9185 | 116 | 0.9187 | 0.9304 | 0.9245 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9378 | 0.9472 | 0.9425 | 0.9893 | | 0.0317 | 86.0 | 8256 | 0.0339 | 0.9316 | 0.9397 | 0.9356 | 116 | 0.9423 | 0.9304 | 0.9363 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9523 | 0.9523 | 0.9523 | 0.9901 | | 0.0308 | 87.0 | 8352 | 0.0347 | 0.9316 | 0.9397 | 0.9356 | 116 | 0.9423 | 0.9304 | 0.9363 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9523 | 0.9523 | 0.9523 | 0.9898 | | 0.0311 | 88.0 | 8448 | 0.0344 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9367 | 0.9367 | 0.9367 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9523 | 0.9523 | 0.9523 | 0.9898 | | 0.0295 | 89.0 | 8544 | 0.0346 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9427 | 0.9367 | 0.9397 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9547 | 0.9523 | 0.9535 | 0.9896 | | 0.0304 | 90.0 | 8640 | 0.0343 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9427 | 0.9367 | 0.9397 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9547 | 0.9523 | 0.9535 | 0.9896 | | 0.0315 | 91.0 | 8736 | 0.0343 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9427 | 0.9367 | 0.9397 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9547 | 0.9523 | 0.9535 | 0.9896 | | 0.0314 | 92.0 | 8832 | 0.0342 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9427 | 0.9367 | 0.9397 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9547 | 0.9523 | 0.9535 | 0.9896 | | 0.0322 | 93.0 | 8928 | 0.0340 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9427 | 0.9367 | 0.9397 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9547 | 0.9523 | 0.9535 | 0.9898 | | 0.0303 | 94.0 | 9024 | 0.0343 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9367 | 0.9367 | 0.9367 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9523 | 0.9523 | 0.9523 | 0.9898 | | 0.0316 | 95.0 | 9120 | 0.0343 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9367 | 0.9367 | 0.9367 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9523 | 0.9523 | 0.9523 | 0.9898 | | 0.0317 | 96.0 | 9216 | 0.0342 | 0.9391 | 0.9310 | 0.9351 | 116 | 0.9427 | 0.9367 | 0.9397 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9547 | 0.9523 | 0.9535 | 0.9896 | | 0.0321 | 97.0 | 9312 | 0.0341 | 0.9316 | 0.9397 | 0.9356 | 116 | 0.9484 | 0.9304 | 0.9393 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9547 | 0.9523 | 0.9535 | 0.9898 | | 0.0295 | 98.0 | 9408 | 0.0342 | 0.9316 | 0.9397 | 0.9356 | 116 | 0.9484 | 0.9304 | 0.9393 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9547 | 0.9523 | 0.9535 | 0.9898 | | 0.031 | 99.0 | 9504 | 0.0341 | 0.9316 | 0.9397 | 0.9356 | 116 | 0.9484 | 0.9304 | 0.9393 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9547 | 0.9523 | 0.9535 | 0.9898 | | 0.0299 | 100.0 | 9600 | 0.0342 | 0.9316 | 0.9397 | 0.9356 | 116 | 0.9484 | 0.9304 | 0.9393 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9547 | 0.9523 | 0.9535 | 0.9896 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

vaiv/GeM2-Llamion-14B-Chat

vaiv

2024-06-04T01:49:33Z

2,245

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-05-13T08:43:21Z

--- license: apache-2.0 --- # **GeM2-Llamion-14B** We have released **Llamion** as **GeM 2.0**, the second series of generative models developed by VAIV Company to address the our principal business needs. **Llamion** (Llamafied Orion) is derived from transforming the [Orion model](https://huggingface.co/OrionStarAI/Orion-14B-Chat) into [the standard LLaMA architecture](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py) through parameter mapping and offline knowledge transfer. Further technical specifications and study results will be detailed in our upcoming paper, available on this page.  ![vaiv_png](./vaiv.png) ### Contributors - VAIV Company AI Lab ([vaiv.kr](https://www.vaiv.kr/))

hdve/Qwen-Qwen1.5-0.5B-1717465528

hdve

2024-06-04T01:46:32Z

139

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T01:46:00Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ArashAhmadian/rloo_tldr_6.9b_defaultclip_512bs_05kl

ArashAhmadian

2024-06-04T01:37:51Z

5

0

transformers

[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T01:34:26Z

--- tags: - generated_from_trainer model-index: - name: rloo_tldr_6.9b_defaultclip_512bs_05kl results: [] ---  # rloo_tldr_6.9b_defaultclip_512bs_05kl This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - num_epochs: 3.0 ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

Trofish/Korean_syllable_roberta_512

Trofish

2024-06-04T01:34:41Z

126

0

transformers

[ "transformers", "safetensors", "roberta", "fill-mask", "ko", "dataset:klue/klue", "arxiv:2105.09680", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-05-13T11:23:10Z

--- license: apache-2.0 datasets: - klue/klue language: - ko metrics: - f1 - accuracy - pearsonr --- # RoBERTa-base Korean ## 모델 설명 이 RoBERTa 모델은 다양한 한국어 텍스트 데이터셋에서 **음절** 단위로 사전 학습되었습니다. 자체 구축한 한국어 음절 단위 vocab을 사용하였습니다. ## 아키텍처 - **모델 유형**: RoBERTa - **아키텍처**: RobertaForMaskedLM - **모델 크기**: 512 hidden size, 8 hidden layers, 8 attention heads - **max_position_embeddings**: 514 - **intermediate_size**: 2,048 - **vocab_size**: 1,428 ## 학습 데이터 사용된 데이터셋은 다음과 같습니다: - **모두의말뭉치**: 채팅, 게시판, 일상대화, 뉴스, 방송대본, 책 등 - **AIHUB**: SNS, 유튜브 댓글, 도서 문장 - **기타**: 나무위키, 한국어 위키피디아 총 합산된 데이터는 **약 11GB** 입니다. **(4B tokens)** ## 학습 상세 - **BATCH_SIZE**: 196 (GPU당) - **ACCUMULATE**: 20 - **Total_BATCH_SIZE**: 8232 - **MAX_STEPS**: 12,500 - **TRAIN_STEPS * BATCH_SIZE**: **100M** - **WARMUP_STEPS**: 2,400 - **최적화**: AdamW, LR 1e-3, BETA (0.9, 0.98), eps 1e-6 - **학습률 감쇠**: linear - **사용된 하드웨어**: 2x A6000ada GPU ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a0fd6fd3149e05bc5260dd/S-3zdDXVMZnyEVrZdQ7J3.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a0fd6fd3149e05bc5260dd/3VwE53iLqKtc-gMQXOV_L.png) ## 성능 평가 - **KLUE benchmark test를 통해서 성능을 평가했습니다.** - klue-roberta-base에 비해서 매우 작은 크기라 성능이 낮기는 하지만 hidden size 512인 모델은 크기 대비 좋은 성능을 보였습니다. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a0fd6fd3149e05bc5260dd/I8e60cf9w-IQCHDgKiooq.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a0fd6fd3149e05bc5260dd/hkc5ko9Vo-pkKmtouN7xc.png) ## 사용 방법 ### tokenizer의 경우 wordpiece가 아닌 syllable 단위이기에 AutoTokenizer가 아니라 SyllableTokenizer를 사용해야 합니다. ### (레포에서 제공하고 있는 syllabletokenizer.py를 가져와서 사용해야 합니다.) ```python from transformers import AutoModel, AutoTokenizer from syllabletokenizer import SyllableTokenizer # 모델과 토크나이저 불러오기 model = AutoModelForMaskedLM.from_pretrained("Trofish/korean_syllable_roberta") tokenizer = SyllableTokenizer(vocab_file='vocab.json',**tokenizer_kwargs) # 텍스트를 토큰으로 변환하고 예측 수행 inputs = tokenizer("여기에 한국어 텍스트 입력", return_tensors="pt") outputs = model(**inputs) ``` ## Citation **klue** ``` @misc{park2021klue, title={KLUE: Korean Language Understanding Evaluation}, author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho}, year={2021}, eprint={2105.09680}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

apwic/nerui-lora-r8-0

apwic

2024-06-04T01:26:47Z

0

null

[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]

null

2024-05-28T12:12:41Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerui-lora-r8-0 results: [] ---  # nerui-lora-r8-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0463 - Location Precision: 0.8462 - Location Recall: 0.9362 - Location F1: 0.8889 - Location Number: 94 - Organization Precision: 0.8667 - Organization Recall: 0.8563 - Organization F1: 0.8614 - Organization Number: 167 - Person Precision: 1.0 - Person Recall: 0.9854 - Person F1: 0.9926 - Person Number: 137 - Overall Precision: 0.9059 - Overall Recall: 0.9196 - Overall F1: 0.9127 - Overall Accuracy: 0.9848 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Location Precision | Location Recall | Location F1 | Location Number | Organization Precision | Organization Recall | Organization F1 | Organization Number | Person Precision | Person Recall | Person F1 | Person Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 1.1434 | 1.0 | 96 | 0.7069 | 0.0 | 0.0 | 0.0 | 94 | 0.0 | 0.0 | 0.0 | 167 | 0.0 | 0.0 | 0.0 | 137 | 0.0 | 0.0 | 0.0 | 0.8343 | | 0.6699 | 2.0 | 192 | 0.5760 | 0.0 | 0.0 | 0.0 | 94 | 1.0 | 0.0060 | 0.0119 | 167 | 0.0 | 0.0 | 0.0 | 137 | 0.25 | 0.0025 | 0.0050 | 0.8348 | | 0.5654 | 3.0 | 288 | 0.4641 | 0.0 | 0.0 | 0.0 | 94 | 0.4118 | 0.0419 | 0.0761 | 167 | 0.2414 | 0.0511 | 0.0843 | 137 | 0.3043 | 0.0352 | 0.0631 | 0.8420 | | 0.4481 | 4.0 | 384 | 0.3466 | 0.2353 | 0.0426 | 0.0721 | 94 | 0.3578 | 0.2335 | 0.2826 | 167 | 0.3774 | 0.4380 | 0.4054 | 137 | 0.3614 | 0.2588 | 0.3016 | 0.8793 | | 0.3376 | 5.0 | 480 | 0.2613 | 0.4058 | 0.2979 | 0.3436 | 94 | 0.5105 | 0.5808 | 0.5434 | 167 | 0.5081 | 0.6861 | 0.5839 | 137 | 0.4932 | 0.5503 | 0.5202 | 0.9202 | | 0.2611 | 6.0 | 576 | 0.2025 | 0.5909 | 0.5532 | 0.5714 | 94 | 0.5588 | 0.6826 | 0.6146 | 167 | 0.6905 | 0.8467 | 0.7607 | 137 | 0.6130 | 0.7085 | 0.6573 | 0.9406 | | 0.2071 | 7.0 | 672 | 0.1615 | 0.7021 | 0.7021 | 0.7021 | 94 | 0.6649 | 0.7605 | 0.7095 | 167 | 0.8224 | 0.9124 | 0.8651 | 137 | 0.7277 | 0.7990 | 0.7617 | 0.9555 | | 0.1767 | 8.0 | 768 | 0.1337 | 0.7872 | 0.7872 | 0.7872 | 94 | 0.7120 | 0.7844 | 0.7464 | 167 | 0.9306 | 0.9781 | 0.9537 | 137 | 0.8033 | 0.8518 | 0.8268 | 0.9644 | | 0.1601 | 9.0 | 864 | 0.1165 | 0.7980 | 0.8404 | 0.8187 | 94 | 0.7351 | 0.8144 | 0.7727 | 167 | 0.9306 | 0.9781 | 0.9537 | 137 | 0.8154 | 0.8769 | 0.8450 | 0.9671 | | 0.1406 | 10.0 | 960 | 0.1041 | 0.7573 | 0.8298 | 0.7919 | 94 | 0.7816 | 0.8144 | 0.7977 | 167 | 0.9371 | 0.9781 | 0.9571 | 137 | 0.8286 | 0.8744 | 0.8509 | 0.9693 | | 0.1283 | 11.0 | 1056 | 0.0951 | 0.8021 | 0.8191 | 0.8105 | 94 | 0.7865 | 0.8383 | 0.8116 | 167 | 0.9371 | 0.9781 | 0.9571 | 137 | 0.8417 | 0.8819 | 0.8613 | 0.9704 | | 0.1229 | 12.0 | 1152 | 0.0895 | 0.8019 | 0.9043 | 0.8500 | 94 | 0.8 | 0.8383 | 0.8187 | 167 | 0.9375 | 0.9854 | 0.9609 | 137 | 0.8471 | 0.9045 | 0.8748 | 0.9715 | | 0.1116 | 13.0 | 1248 | 0.0831 | 0.83 | 0.8830 | 0.8557 | 94 | 0.8314 | 0.8563 | 0.8437 | 167 | 0.9371 | 0.9781 | 0.9571 | 137 | 0.8675 | 0.9045 | 0.8856 | 0.9743 | | 0.1077 | 14.0 | 1344 | 0.0769 | 0.8571 | 0.8936 | 0.875 | 94 | 0.8409 | 0.8862 | 0.8630 | 167 | 0.9504 | 0.9781 | 0.9640 | 137 | 0.8819 | 0.9196 | 0.9004 | 0.9760 | | 0.1045 | 15.0 | 1440 | 0.0758 | 0.8333 | 0.9043 | 0.8673 | 94 | 0.8430 | 0.8683 | 0.8555 | 167 | 0.9371 | 0.9781 | 0.9571 | 137 | 0.8729 | 0.9146 | 0.8933 | 0.9760 | | 0.1 | 16.0 | 1536 | 0.0753 | 0.8365 | 0.9255 | 0.8788 | 94 | 0.8111 | 0.8743 | 0.8415 | 167 | 0.9437 | 0.9781 | 0.9606 | 137 | 0.8615 | 0.9221 | 0.8908 | 0.9746 | | 0.0961 | 17.0 | 1632 | 0.0690 | 0.8586 | 0.9043 | 0.8808 | 94 | 0.8563 | 0.8922 | 0.8739 | 167 | 0.9571 | 0.9781 | 0.9675 | 137 | 0.8910 | 0.9246 | 0.9075 | 0.9785 | | 0.0981 | 18.0 | 1728 | 0.0676 | 0.86 | 0.9149 | 0.8866 | 94 | 0.8523 | 0.8982 | 0.8746 | 167 | 0.9504 | 0.9781 | 0.9640 | 137 | 0.8873 | 0.9296 | 0.9080 | 0.9782 | | 0.0916 | 19.0 | 1824 | 0.0653 | 0.8333 | 0.9043 | 0.8673 | 94 | 0.8647 | 0.8802 | 0.8724 | 167 | 0.9640 | 0.9781 | 0.9710 | 137 | 0.8905 | 0.9196 | 0.9048 | 0.9790 | | 0.0899 | 20.0 | 1920 | 0.0637 | 0.8586 | 0.9043 | 0.8808 | 94 | 0.8563 | 0.8922 | 0.8739 | 167 | 0.9640 | 0.9781 | 0.9710 | 137 | 0.8932 | 0.9246 | 0.9086 | 0.9790 | | 0.0856 | 21.0 | 2016 | 0.0656 | 0.8113 | 0.9149 | 0.8600 | 94 | 0.8580 | 0.8683 | 0.8631 | 167 | 0.9571 | 0.9781 | 0.9675 | 137 | 0.8795 | 0.9171 | 0.8979 | 0.9773 | | 0.0844 | 22.0 | 2112 | 0.0621 | 0.8416 | 0.9043 | 0.8718 | 94 | 0.8563 | 0.8922 | 0.8739 | 167 | 0.9571 | 0.9781 | 0.9675 | 137 | 0.8867 | 0.9246 | 0.9053 | 0.9782 | | 0.0816 | 23.0 | 2208 | 0.0608 | 0.85 | 0.9043 | 0.8763 | 94 | 0.8647 | 0.8802 | 0.8724 | 167 | 0.9571 | 0.9781 | 0.9675 | 137 | 0.8927 | 0.9196 | 0.9059 | 0.9798 | | 0.0803 | 24.0 | 2304 | 0.0591 | 0.8586 | 0.9043 | 0.8808 | 94 | 0.8671 | 0.8982 | 0.8824 | 167 | 0.9571 | 0.9781 | 0.9675 | 137 | 0.8956 | 0.9271 | 0.9111 | 0.9796 | | 0.0793 | 25.0 | 2400 | 0.0577 | 0.85 | 0.9043 | 0.8763 | 94 | 0.8824 | 0.8982 | 0.8902 | 167 | 0.9710 | 0.9781 | 0.9745 | 137 | 0.9044 | 0.9271 | 0.9156 | 0.9818 | | 0.0744 | 26.0 | 2496 | 0.0576 | 0.8529 | 0.9255 | 0.8878 | 94 | 0.8706 | 0.8862 | 0.8783 | 167 | 0.9710 | 0.9781 | 0.9745 | 137 | 0.9 | 0.9271 | 0.9134 | 0.9818 | | 0.0761 | 27.0 | 2592 | 0.0571 | 0.8416 | 0.9043 | 0.8718 | 94 | 0.8757 | 0.8862 | 0.8810 | 167 | 0.9640 | 0.9781 | 0.9710 | 137 | 0.8973 | 0.9221 | 0.9095 | 0.9807 | | 0.0724 | 28.0 | 2688 | 0.0559 | 0.8586 | 0.9043 | 0.8808 | 94 | 0.8655 | 0.8862 | 0.8757 | 167 | 0.9710 | 0.9781 | 0.9745 | 137 | 0.8995 | 0.9221 | 0.9107 | 0.9809 | | 0.071 | 29.0 | 2784 | 0.0542 | 0.8687 | 0.9149 | 0.8912 | 94 | 0.8655 | 0.8862 | 0.8757 | 167 | 0.9783 | 0.9854 | 0.9818 | 137 | 0.9044 | 0.9271 | 0.9156 | 0.9818 | | 0.0705 | 30.0 | 2880 | 0.0549 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8690 | 0.8743 | 0.8716 | 167 | 0.9854 | 0.9854 | 0.9854 | 137 | 0.9022 | 0.9271 | 0.9145 | 0.9818 | | 0.0702 | 31.0 | 2976 | 0.0517 | 0.8687 | 0.9149 | 0.8912 | 94 | 0.8817 | 0.8922 | 0.8869 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9181 | 0.9296 | 0.9238 | 0.9834 | | 0.065 | 32.0 | 3072 | 0.0532 | 0.8396 | 0.9468 | 0.89 | 94 | 0.8951 | 0.8683 | 0.8815 | 167 | 0.9926 | 0.9854 | 0.9890 | 137 | 0.9134 | 0.9271 | 0.9202 | 0.9826 | | 0.0639 | 33.0 | 3168 | 0.0533 | 0.8286 | 0.9255 | 0.8744 | 94 | 0.8780 | 0.8623 | 0.8701 | 167 | 0.9926 | 0.9854 | 0.9890 | 137 | 0.9037 | 0.9196 | 0.9116 | 0.9815 | | 0.0642 | 34.0 | 3264 | 0.0520 | 0.8529 | 0.9255 | 0.8878 | 94 | 0.875 | 0.8802 | 0.8776 | 167 | 0.9926 | 0.9854 | 0.9890 | 137 | 0.9089 | 0.9271 | 0.9179 | 0.9820 | | 0.0652 | 35.0 | 3360 | 0.0518 | 0.8515 | 0.9149 | 0.8821 | 94 | 0.8690 | 0.8743 | 0.8716 | 167 | 0.9926 | 0.9854 | 0.9890 | 137 | 0.9062 | 0.9221 | 0.9141 | 0.9815 | | 0.0627 | 36.0 | 3456 | 0.0533 | 0.87 | 0.9255 | 0.8969 | 94 | 0.8655 | 0.8862 | 0.8757 | 167 | 0.9854 | 0.9854 | 0.9854 | 137 | 0.9069 | 0.9296 | 0.9181 | 0.9818 | | 0.0606 | 37.0 | 3552 | 0.0503 | 0.8878 | 0.9255 | 0.9062 | 94 | 0.8698 | 0.8802 | 0.8750 | 167 | 0.9926 | 0.9854 | 0.9890 | 137 | 0.9156 | 0.9271 | 0.9213 | 0.9826 | | 0.0611 | 38.0 | 3648 | 0.0497 | 0.87 | 0.9255 | 0.8969 | 94 | 0.8848 | 0.8743 | 0.8795 | 167 | 0.9854 | 0.9854 | 0.9854 | 137 | 0.9154 | 0.9246 | 0.92 | 0.9829 | | 0.0645 | 39.0 | 3744 | 0.0511 | 0.8431 | 0.9149 | 0.8776 | 94 | 0.8780 | 0.8623 | 0.8701 | 167 | 0.9926 | 0.9854 | 0.9890 | 137 | 0.9080 | 0.9171 | 0.9125 | 0.9823 | | 0.061 | 40.0 | 3840 | 0.0487 | 0.8687 | 0.9149 | 0.8912 | 94 | 0.8765 | 0.8922 | 0.8843 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9158 | 0.9296 | 0.9227 | 0.9840 | | 0.0591 | 41.0 | 3936 | 0.0491 | 0.8515 | 0.9149 | 0.8821 | 94 | 0.8802 | 0.8802 | 0.8802 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9132 | 0.9246 | 0.9189 | 0.9834 | | 0.058 | 42.0 | 4032 | 0.0480 | 0.8687 | 0.9149 | 0.8912 | 94 | 0.8757 | 0.8862 | 0.8810 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9156 | 0.9271 | 0.9213 | 0.9840 | | 0.0587 | 43.0 | 4128 | 0.0494 | 0.8350 | 0.9149 | 0.8731 | 94 | 0.8720 | 0.8563 | 0.8640 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9055 | 0.9146 | 0.91 | 0.9820 | | 0.0562 | 44.0 | 4224 | 0.0482 | 0.8515 | 0.9149 | 0.8821 | 94 | 0.8788 | 0.8683 | 0.8735 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9127 | 0.9196 | 0.9161 | 0.9829 | | 0.0565 | 45.0 | 4320 | 0.0471 | 0.8529 | 0.9255 | 0.8878 | 94 | 0.8795 | 0.8743 | 0.8769 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9132 | 0.9246 | 0.9189 | 0.9837 | | 0.0541 | 46.0 | 4416 | 0.0482 | 0.8365 | 0.9255 | 0.8788 | 94 | 0.8795 | 0.8743 | 0.8769 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9086 | 0.9246 | 0.9166 | 0.9831 | | 0.0547 | 47.0 | 4512 | 0.0487 | 0.8350 | 0.9149 | 0.8731 | 94 | 0.8720 | 0.8563 | 0.8640 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9055 | 0.9146 | 0.91 | 0.9823 | | 0.0537 | 48.0 | 4608 | 0.0480 | 0.8269 | 0.9149 | 0.8687 | 94 | 0.8659 | 0.8503 | 0.8580 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9007 | 0.9121 | 0.9064 | 0.9829 | | 0.0525 | 49.0 | 4704 | 0.0477 | 0.8416 | 0.9043 | 0.8718 | 94 | 0.8882 | 0.8563 | 0.8720 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9144 | 0.9121 | 0.9132 | 0.9826 | | 0.0513 | 50.0 | 4800 | 0.0472 | 0.86 | 0.9149 | 0.8866 | 94 | 0.8596 | 0.8802 | 0.8698 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9064 | 0.9246 | 0.9154 | 0.9845 | | 0.0507 | 51.0 | 4896 | 0.0481 | 0.8286 | 0.9255 | 0.8744 | 94 | 0.875 | 0.8383 | 0.8563 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.905 | 0.9095 | 0.9073 | 0.9820 | | 0.0499 | 52.0 | 4992 | 0.0472 | 0.87 | 0.9255 | 0.8969 | 94 | 0.8757 | 0.8862 | 0.8810 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9158 | 0.9296 | 0.9227 | 0.9837 | | 0.0519 | 53.0 | 5088 | 0.0471 | 0.8614 | 0.9255 | 0.8923 | 94 | 0.8743 | 0.8743 | 0.8743 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9132 | 0.9246 | 0.9189 | 0.9840 | | 0.0523 | 54.0 | 5184 | 0.0483 | 0.8286 | 0.9255 | 0.8744 | 94 | 0.8545 | 0.8443 | 0.8494 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.8963 | 0.9121 | 0.9041 | 0.9826 | | 0.0507 | 55.0 | 5280 | 0.0465 | 0.8447 | 0.9255 | 0.8832 | 94 | 0.8614 | 0.8563 | 0.8589 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9035 | 0.9171 | 0.9102 | 0.9831 | | 0.0506 | 56.0 | 5376 | 0.0465 | 0.8447 | 0.9255 | 0.8832 | 94 | 0.8614 | 0.8563 | 0.8589 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9035 | 0.9171 | 0.9102 | 0.9831 | | 0.0504 | 57.0 | 5472 | 0.0475 | 0.8208 | 0.9255 | 0.8700 | 94 | 0.8452 | 0.8503 | 0.8478 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.8900 | 0.9146 | 0.9021 | 0.9831 | | 0.0484 | 58.0 | 5568 | 0.0462 | 0.8302 | 0.9362 | 0.88 | 94 | 0.8659 | 0.8503 | 0.8580 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9012 | 0.9171 | 0.9091 | 0.9837 | | 0.0487 | 59.0 | 5664 | 0.0457 | 0.8447 | 0.9255 | 0.8832 | 94 | 0.8727 | 0.8623 | 0.8675 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9082 | 0.9196 | 0.9139 | 0.9837 | | 0.0463 | 60.0 | 5760 | 0.0475 | 0.8365 | 0.9255 | 0.8788 | 94 | 0.8623 | 0.8623 | 0.8623 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9015 | 0.9196 | 0.9104 | 0.9848 | | 0.0462 | 61.0 | 5856 | 0.0469 | 0.8529 | 0.9255 | 0.8878 | 94 | 0.8655 | 0.8862 | 0.8757 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9069 | 0.9296 | 0.9181 | 0.9848 | | 0.0497 | 62.0 | 5952 | 0.0469 | 0.8544 | 0.9362 | 0.8934 | 94 | 0.8521 | 0.8623 | 0.8571 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9017 | 0.9221 | 0.9118 | 0.9845 | | 0.0465 | 63.0 | 6048 | 0.0469 | 0.8515 | 0.9149 | 0.8821 | 94 | 0.8683 | 0.8683 | 0.8683 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9082 | 0.9196 | 0.9139 | 0.9848 | | 0.0468 | 64.0 | 6144 | 0.0470 | 0.86 | 0.9149 | 0.8866 | 94 | 0.8841 | 0.8683 | 0.8761 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9173 | 0.9196 | 0.9184 | 0.9843 | | 0.0455 | 65.0 | 6240 | 0.0467 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8675 | 0.8623 | 0.8649 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9062 | 0.9221 | 0.9141 | 0.9845 | | 0.0456 | 66.0 | 6336 | 0.0463 | 0.8431 | 0.9149 | 0.8776 | 94 | 0.8712 | 0.8503 | 0.8606 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9075 | 0.9121 | 0.9098 | 0.9834 | | 0.0436 | 67.0 | 6432 | 0.0457 | 0.8365 | 0.9255 | 0.8788 | 94 | 0.8773 | 0.8563 | 0.8667 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9080 | 0.9171 | 0.9125 | 0.9837 | | 0.0442 | 68.0 | 6528 | 0.0464 | 0.8365 | 0.9255 | 0.8788 | 94 | 0.8720 | 0.8563 | 0.8640 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9057 | 0.9171 | 0.9114 | 0.9837 | | 0.0463 | 69.0 | 6624 | 0.0463 | 0.8447 | 0.9255 | 0.8832 | 94 | 0.8720 | 0.8563 | 0.8640 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9080 | 0.9171 | 0.9125 | 0.9840 | | 0.0445 | 70.0 | 6720 | 0.0457 | 0.8529 | 0.9255 | 0.8878 | 94 | 0.8720 | 0.8563 | 0.8640 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9102 | 0.9171 | 0.9136 | 0.9840 | | 0.0456 | 71.0 | 6816 | 0.0474 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8788 | 0.8683 | 0.8735 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9109 | 0.9246 | 0.9177 | 0.9851 | | 0.0473 | 72.0 | 6912 | 0.0479 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8659 | 0.8503 | 0.8580 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9035 | 0.9171 | 0.9102 | 0.9837 | | 0.0434 | 73.0 | 7008 | 0.0475 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8712 | 0.8503 | 0.8606 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9057 | 0.9171 | 0.9114 | 0.9840 | | 0.042 | 74.0 | 7104 | 0.0463 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8765 | 0.8503 | 0.8632 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9102 | 0.9171 | 0.9136 | 0.9837 | | 0.0438 | 75.0 | 7200 | 0.0463 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8765 | 0.8503 | 0.8632 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9102 | 0.9171 | 0.9136 | 0.9837 | | 0.0437 | 76.0 | 7296 | 0.0459 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8623 | 0.8623 | 0.8623 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9039 | 0.9221 | 0.9129 | 0.9843 | | 0.0455 | 77.0 | 7392 | 0.0469 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8827 | 0.8563 | 0.8693 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9104 | 0.9196 | 0.9150 | 0.9840 | | 0.0426 | 78.0 | 7488 | 0.0467 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8727 | 0.8623 | 0.8675 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9062 | 0.9221 | 0.9141 | 0.9848 | | 0.043 | 79.0 | 7584 | 0.0457 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8735 | 0.8683 | 0.8709 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9064 | 0.9246 | 0.9154 | 0.9854 | | 0.0435 | 80.0 | 7680 | 0.0462 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8727 | 0.8623 | 0.8675 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9062 | 0.9221 | 0.9141 | 0.9851 | | 0.0411 | 81.0 | 7776 | 0.0461 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8606 | 0.8503 | 0.8554 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9012 | 0.9171 | 0.9091 | 0.9843 | | 0.0421 | 82.0 | 7872 | 0.0458 | 0.8544 | 0.9362 | 0.8934 | 94 | 0.8720 | 0.8563 | 0.8640 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9104 | 0.9196 | 0.9150 | 0.9843 | | 0.0416 | 83.0 | 7968 | 0.0462 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8773 | 0.8563 | 0.8667 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9082 | 0.9196 | 0.9139 | 0.9843 | | 0.0412 | 84.0 | 8064 | 0.0461 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8788 | 0.8683 | 0.8735 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9109 | 0.9246 | 0.9177 | 0.9851 | | 0.0428 | 85.0 | 8160 | 0.0465 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8773 | 0.8563 | 0.8667 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9104 | 0.9196 | 0.9150 | 0.9845 | | 0.0434 | 86.0 | 8256 | 0.0467 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8720 | 0.8563 | 0.8640 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9059 | 0.9196 | 0.9127 | 0.9840 | | 0.0411 | 87.0 | 8352 | 0.0466 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8720 | 0.8563 | 0.8640 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9059 | 0.9196 | 0.9127 | 0.9840 | | 0.0436 | 88.0 | 8448 | 0.0467 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8780 | 0.8623 | 0.8701 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9084 | 0.9221 | 0.9152 | 0.9848 | | 0.0413 | 89.0 | 8544 | 0.0460 | 0.8544 | 0.9362 | 0.8934 | 94 | 0.8795 | 0.8743 | 0.8769 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9134 | 0.9271 | 0.9202 | 0.9854 | | 0.0401 | 90.0 | 8640 | 0.0467 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8675 | 0.8623 | 0.8649 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9062 | 0.9221 | 0.9141 | 0.9848 | | 0.0421 | 91.0 | 8736 | 0.0467 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8780 | 0.8623 | 0.8701 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9107 | 0.9221 | 0.9164 | 0.9845 | | 0.0407 | 92.0 | 8832 | 0.0462 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8773 | 0.8563 | 0.8667 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9104 | 0.9196 | 0.9150 | 0.9845 | | 0.0449 | 93.0 | 8928 | 0.0463 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8773 | 0.8563 | 0.8667 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9104 | 0.9196 | 0.9150 | 0.9845 | | 0.0397 | 94.0 | 9024 | 0.0462 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8667 | 0.8563 | 0.8614 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9037 | 0.9196 | 0.9116 | 0.9845 | | 0.0417 | 95.0 | 9120 | 0.0463 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8667 | 0.8563 | 0.8614 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9037 | 0.9196 | 0.9116 | 0.9845 | | 0.0402 | 96.0 | 9216 | 0.0465 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8780 | 0.8623 | 0.8701 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9084 | 0.9221 | 0.9152 | 0.9848 | | 0.0422 | 97.0 | 9312 | 0.0464 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8720 | 0.8563 | 0.8640 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9082 | 0.9196 | 0.9139 | 0.9851 | | 0.0417 | 98.0 | 9408 | 0.0463 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8720 | 0.8563 | 0.8640 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9082 | 0.9196 | 0.9139 | 0.9851 | | 0.0409 | 99.0 | 9504 | 0.0463 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8667 | 0.8563 | 0.8614 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9059 | 0.9196 | 0.9127 | 0.9848 | | 0.0404 | 100.0 | 9600 | 0.0463 | 0.8462 | 0.9362 | 0.8889 | 94 | 0.8667 | 0.8563 | 0.8614 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9059 | 0.9196 | 0.9127 | 0.9848 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

himanshubeniwal/mt5-base-finetuned-kk-to-en-cold-burger

himanshubeniwal

2024-06-04T01:18:43Z

107

0

transformers

[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-06-04T01:13:57Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

lcw99/llama-3-10b-ko-240604-e2f

lcw99

2024-06-04T01:17:10Z

2,249

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "ko", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T00:37:02Z

--- language: - ko license: apache-2.0 library_name: transformers --- # Model Card for Model ID ## Model Details ### Model Description Korean layer added instruction tunning of meta-llama/Meta-Llama-3-8B-Instruct #### Chat template tokenizer.apply_chat_template(chat, tokenize=False)

baf2b252097d46299a/medical_summarizer_6ec63f0624e84fea9af33517007b93a4

baf2b252097d46299a

2024-06-04T01:13:31Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-04T01:13:06Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

melancholic/neotraditional_tattoo_lora

melancholic

2024-06-04T01:09:32Z

3

0

diffusers

[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-06-03T06:17:47Z

--- license: openrail++ library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a neotraditional tattoo style widget: [] ---  # SDXL LoRA DreamBooth - melancholic/neotraditional_tattoo_lora <Gallery /> ## Model description These are melancholic/neotraditional_tattoo_lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a neotraditional tattoo style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](melancholic/neotraditional_tattoo_lora/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

apwic/nerui-base-3

apwic

2024-06-04T01:02:10Z

24

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-05-28T05:46:39Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerui-base-3 results: [] ---  # nerui-base-3 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1047 - Location Precision: 0.8925 - Location Recall: 0.9651 - Location F1: 0.9274 - Location Number: 86 - Organization Precision: 0.9538 - Organization Recall: 0.9270 - Organization F1: 0.9402 - Organization Number: 178 - Person Precision: 0.9685 - Person Recall: 0.9609 - Person F1: 0.9647 - Person Number: 128 - Overall Precision: 0.9440 - Overall Recall: 0.9464 - Overall F1: 0.9452 - Overall Accuracy: 0.9876 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Location Precision | Location Recall | Location F1 | Location Number | Organization Precision | Organization Recall | Organization F1 | Organization Number | Person Precision | Person Recall | Person F1 | Person Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.2442 | 1.0 | 96 | 0.0581 | 0.8384 | 0.9651 | 0.8973 | 86 | 0.8535 | 0.9494 | 0.8989 | 178 | 0.9690 | 0.9766 | 0.9728 | 128 | 0.8850 | 0.9617 | 0.9218 | 0.9822 | | 0.0581 | 2.0 | 192 | 0.0548 | 0.8283 | 0.9535 | 0.8865 | 86 | 0.9464 | 0.8933 | 0.9191 | 178 | 0.9690 | 0.9766 | 0.9728 | 128 | 0.9242 | 0.9337 | 0.9289 | 0.9852 | | 0.0357 | 3.0 | 288 | 0.0514 | 0.8542 | 0.9535 | 0.9011 | 86 | 0.9310 | 0.9101 | 0.9205 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9293 | 0.9388 | 0.9340 | 0.9857 | | 0.0251 | 4.0 | 384 | 0.0607 | 0.8989 | 0.9302 | 0.9143 | 86 | 0.8942 | 0.9494 | 0.9210 | 178 | 0.9837 | 0.9453 | 0.9641 | 128 | 0.9227 | 0.9439 | 0.9332 | 0.9852 | | 0.0146 | 5.0 | 480 | 0.0617 | 0.8804 | 0.9419 | 0.9101 | 86 | 0.9231 | 0.9438 | 0.9333 | 178 | 0.976 | 0.9531 | 0.9644 | 128 | 0.9298 | 0.9464 | 0.9381 | 0.9865 | | 0.0117 | 6.0 | 576 | 0.0706 | 0.8511 | 0.9302 | 0.8889 | 86 | 0.9066 | 0.9270 | 0.9167 | 178 | 0.9758 | 0.9453 | 0.9603 | 128 | 0.915 | 0.9337 | 0.9242 | 0.9857 | | 0.0083 | 7.0 | 672 | 0.0926 | 0.7788 | 0.9419 | 0.8526 | 86 | 0.9162 | 0.9213 | 0.9188 | 178 | 0.9462 | 0.9609 | 0.9535 | 128 | 0.8910 | 0.9388 | 0.9143 | 0.9819 | | 0.008 | 8.0 | 768 | 0.0781 | 0.8617 | 0.9419 | 0.9000 | 86 | 0.9535 | 0.9213 | 0.9371 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9412 | 0.9388 | 0.9400 | 0.9857 | | 0.0042 | 9.0 | 864 | 0.0659 | 0.8764 | 0.9070 | 0.8914 | 86 | 0.9663 | 0.9663 | 0.9663 | 178 | 0.9764 | 0.9688 | 0.9725 | 128 | 0.9492 | 0.9541 | 0.9517 | 0.9889 | | 0.0044 | 10.0 | 960 | 0.0712 | 0.8681 | 0.9186 | 0.8927 | 86 | 0.9389 | 0.9494 | 0.9441 | 178 | 0.9457 | 0.9531 | 0.9494 | 128 | 0.925 | 0.9439 | 0.9343 | 0.9873 | | 0.005 | 11.0 | 1056 | 0.0855 | 0.8384 | 0.9651 | 0.8973 | 86 | 0.9438 | 0.9438 | 0.9438 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9280 | 0.9541 | 0.9409 | 0.9870 | | 0.0036 | 12.0 | 1152 | 0.0859 | 0.8710 | 0.9419 | 0.9050 | 86 | 0.9435 | 0.9382 | 0.9408 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9392 | 0.9464 | 0.9428 | 0.9873 | | 0.0042 | 13.0 | 1248 | 0.0761 | 0.8901 | 0.9419 | 0.9153 | 86 | 0.9448 | 0.9607 | 0.9526 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9446 | 0.9566 | 0.9506 | 0.9889 | | 0.0036 | 14.0 | 1344 | 0.0843 | 0.8876 | 0.9186 | 0.9029 | 86 | 0.9538 | 0.9270 | 0.9402 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9485 | 0.9388 | 0.9436 | 0.9862 | | 0.0028 | 15.0 | 1440 | 0.0906 | 0.8723 | 0.9535 | 0.9111 | 86 | 0.9429 | 0.9270 | 0.9348 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9391 | 0.9439 | 0.9415 | 0.9868 | | 0.0017 | 16.0 | 1536 | 0.0914 | 0.8526 | 0.9419 | 0.8950 | 86 | 0.9645 | 0.9157 | 0.9395 | 178 | 0.9683 | 0.9531 | 0.9606 | 128 | 0.9385 | 0.9337 | 0.9361 | 0.9862 | | 0.002 | 17.0 | 1632 | 0.0828 | 0.8587 | 0.9186 | 0.8876 | 86 | 0.9545 | 0.9438 | 0.9492 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9391 | 0.9439 | 0.9415 | 0.9884 | | 0.0033 | 18.0 | 1728 | 0.0641 | 0.8646 | 0.9651 | 0.9121 | 86 | 0.9126 | 0.9382 | 0.9252 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9235 | 0.9541 | 0.9385 | 0.9887 | | 0.0024 | 19.0 | 1824 | 0.0982 | 0.8667 | 0.9070 | 0.8864 | 86 | 0.9297 | 0.9663 | 0.9477 | 178 | 0.9683 | 0.9531 | 0.9606 | 128 | 0.9277 | 0.9490 | 0.9382 | 0.9868 | | 0.0037 | 20.0 | 1920 | 0.0904 | 0.8283 | 0.9535 | 0.8865 | 86 | 0.9659 | 0.9551 | 0.9605 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9375 | 0.9566 | 0.9470 | 0.9887 | | 0.0038 | 21.0 | 2016 | 0.0787 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9385 | 0.9438 | 0.9412 | 178 | 0.9609 | 0.9609 | 0.9609 | 128 | 0.935 | 0.9541 | 0.9444 | 0.9879 | | 0.0024 | 22.0 | 2112 | 0.0697 | 0.8526 | 0.9419 | 0.8950 | 86 | 0.9286 | 0.9494 | 0.9389 | 178 | 0.9677 | 0.9375 | 0.9524 | 128 | 0.9227 | 0.9439 | 0.9332 | 0.9889 | | 0.0041 | 23.0 | 2208 | 0.0794 | 0.9011 | 0.9535 | 0.9266 | 86 | 0.9441 | 0.9494 | 0.9468 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9421 | 0.9541 | 0.9480 | 0.9876 | | 0.0033 | 24.0 | 2304 | 0.0830 | 0.9 | 0.9419 | 0.9205 | 86 | 0.9231 | 0.9438 | 0.9333 | 178 | 0.9758 | 0.9453 | 0.9603 | 128 | 0.9343 | 0.9439 | 0.9391 | 0.9881 | | 0.0034 | 25.0 | 2400 | 0.0804 | 0.8632 | 0.9535 | 0.9061 | 86 | 0.9448 | 0.9607 | 0.9526 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9378 | 0.9617 | 0.9496 | 0.9881 | | 0.0012 | 26.0 | 2496 | 0.0728 | 0.9011 | 0.9535 | 0.9266 | 86 | 0.9341 | 0.9551 | 0.9444 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9424 | 0.9592 | 0.9507 | 0.9903 | | 0.0015 | 27.0 | 2592 | 0.0957 | 0.9101 | 0.9419 | 0.9257 | 86 | 0.9301 | 0.9719 | 0.9505 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9401 | 0.9617 | 0.9508 | 0.9881 | | 0.0029 | 28.0 | 2688 | 0.0766 | 0.8830 | 0.9651 | 0.9222 | 86 | 0.9545 | 0.9438 | 0.9492 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9470 | 0.9566 | 0.9518 | 0.9881 | | 0.0031 | 29.0 | 2784 | 0.0802 | 0.8571 | 0.9767 | 0.9130 | 86 | 0.9649 | 0.9270 | 0.9456 | 178 | 0.9764 | 0.9688 | 0.9725 | 128 | 0.9419 | 0.9515 | 0.9467 | 0.9879 | | 0.0018 | 30.0 | 2880 | 0.0837 | 0.8710 | 0.9419 | 0.9050 | 86 | 0.9605 | 0.9551 | 0.9577 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9470 | 0.9566 | 0.9518 | 0.9892 | | 0.0017 | 31.0 | 2976 | 0.0792 | 0.9222 | 0.9651 | 0.9432 | 86 | 0.9505 | 0.9719 | 0.9611 | 178 | 0.9683 | 0.9531 | 0.9606 | 128 | 0.9497 | 0.9643 | 0.9570 | 0.9903 | | 0.0017 | 32.0 | 3072 | 0.0675 | 0.8737 | 0.9651 | 0.9171 | 86 | 0.9661 | 0.9607 | 0.9634 | 178 | 0.976 | 0.9531 | 0.9644 | 128 | 0.9471 | 0.9592 | 0.9531 | 0.9906 | | 0.0012 | 33.0 | 3168 | 0.0909 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9709 | 0.9382 | 0.9543 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9564 | 0.9515 | 0.9540 | 0.9897 | | 0.002 | 34.0 | 3264 | 0.1077 | 0.9101 | 0.9419 | 0.9257 | 86 | 0.9422 | 0.9157 | 0.9288 | 178 | 0.968 | 0.9453 | 0.9565 | 128 | 0.9432 | 0.9311 | 0.9371 | 0.9846 | | 0.0023 | 35.0 | 3360 | 0.0912 | 0.8913 | 0.9535 | 0.9213 | 86 | 0.9396 | 0.9607 | 0.95 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.94 | 0.9592 | 0.9495 | 0.9881 | | 0.0016 | 36.0 | 3456 | 0.0839 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9655 | 0.9438 | 0.9545 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9541 | 0.9541 | 0.9541 | 0.9892 | | 0.0012 | 37.0 | 3552 | 0.1070 | 0.8817 | 0.9535 | 0.9162 | 86 | 0.9480 | 0.9213 | 0.9345 | 178 | 0.976 | 0.9531 | 0.9644 | 128 | 0.9412 | 0.9388 | 0.9400 | 0.9857 | | 0.0009 | 38.0 | 3648 | 0.0856 | 0.8947 | 0.9884 | 0.9392 | 86 | 0.9540 | 0.9326 | 0.9432 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9492 | 0.9541 | 0.9517 | 0.9884 | | 0.0006 | 39.0 | 3744 | 0.0964 | 0.8936 | 0.9767 | 0.9333 | 86 | 0.9483 | 0.9270 | 0.9375 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9418 | 0.9490 | 0.9454 | 0.9862 | | 0.0011 | 40.0 | 3840 | 0.0992 | 0.9011 | 0.9535 | 0.9266 | 86 | 0.9492 | 0.9438 | 0.9465 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9467 | 0.9515 | 0.9491 | 0.9870 | | 0.0009 | 41.0 | 3936 | 0.1072 | 0.9032 | 0.9767 | 0.9385 | 86 | 0.9489 | 0.9382 | 0.9435 | 178 | 0.976 | 0.9531 | 0.9644 | 128 | 0.9467 | 0.9515 | 0.9491 | 0.9860 | | 0.0007 | 42.0 | 4032 | 0.1193 | 0.8936 | 0.9767 | 0.9333 | 86 | 0.9595 | 0.9326 | 0.9459 | 178 | 0.9839 | 0.9531 | 0.9683 | 128 | 0.9514 | 0.9490 | 0.9502 | 0.9865 | | 0.0014 | 43.0 | 4128 | 0.1129 | 0.9032 | 0.9767 | 0.9385 | 86 | 0.9489 | 0.9382 | 0.9435 | 178 | 0.9683 | 0.9531 | 0.9606 | 128 | 0.9443 | 0.9515 | 0.9479 | 0.9868 | | 0.0007 | 44.0 | 4224 | 0.1289 | 0.9130 | 0.9767 | 0.9438 | 86 | 0.9492 | 0.9438 | 0.9465 | 178 | 0.9609 | 0.9609 | 0.9609 | 128 | 0.9446 | 0.9566 | 0.9506 | 0.9849 | | 0.0006 | 45.0 | 4320 | 0.1167 | 0.8842 | 0.9767 | 0.9282 | 86 | 0.9392 | 0.9551 | 0.9471 | 178 | 0.9688 | 0.9688 | 0.9688 | 128 | 0.9356 | 0.9643 | 0.9497 | 0.9868 | | 0.0014 | 46.0 | 4416 | 0.1168 | 0.8646 | 0.9651 | 0.9121 | 86 | 0.9543 | 0.9382 | 0.9462 | 178 | 0.9839 | 0.9531 | 0.9683 | 128 | 0.9418 | 0.9490 | 0.9454 | 0.9873 | | 0.0022 | 47.0 | 4512 | 0.1090 | 0.8737 | 0.9651 | 0.9171 | 86 | 0.9702 | 0.9157 | 0.9422 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9512 | 0.9439 | 0.9475 | 0.9868 | | 0.0033 | 48.0 | 4608 | 0.0899 | 0.9222 | 0.9651 | 0.9432 | 86 | 0.9333 | 0.9438 | 0.9385 | 178 | 0.9758 | 0.9453 | 0.9603 | 128 | 0.9442 | 0.9490 | 0.9466 | 0.9889 | | 0.001 | 49.0 | 4704 | 0.1123 | 0.8830 | 0.9651 | 0.9222 | 86 | 0.9704 | 0.9213 | 0.9452 | 178 | 0.9839 | 0.9531 | 0.9683 | 128 | 0.9535 | 0.9413 | 0.9474 | 0.9870 | | 0.0007 | 50.0 | 4800 | 0.0937 | 0.9011 | 0.9535 | 0.9266 | 86 | 0.9486 | 0.9326 | 0.9405 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9488 | 0.9464 | 0.9476 | 0.9887 | | 0.0011 | 51.0 | 4896 | 0.1082 | 0.9032 | 0.9767 | 0.9385 | 86 | 0.9278 | 0.9382 | 0.9330 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9398 | 0.9566 | 0.9482 | 0.9865 | | 0.0015 | 52.0 | 4992 | 0.1112 | 0.9011 | 0.9535 | 0.9266 | 86 | 0.9645 | 0.9157 | 0.9395 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9534 | 0.9388 | 0.9460 | 0.9879 | | 0.0009 | 53.0 | 5088 | 0.1032 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9341 | 0.9551 | 0.9444 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.94 | 0.9592 | 0.9495 | 0.9881 | | 0.0033 | 54.0 | 5184 | 0.1181 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9593 | 0.9270 | 0.9429 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9513 | 0.9464 | 0.9488 | 0.9870 | | 0.0008 | 55.0 | 5280 | 0.1207 | 0.9022 | 0.9651 | 0.9326 | 86 | 0.9651 | 0.9326 | 0.9486 | 178 | 0.9688 | 0.9688 | 0.9688 | 128 | 0.9515 | 0.9515 | 0.9515 | 0.9865 | | 0.0009 | 56.0 | 5376 | 0.1379 | 0.8632 | 0.9535 | 0.9061 | 86 | 0.9702 | 0.9157 | 0.9422 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9485 | 0.9388 | 0.9436 | 0.9857 | | 0.001 | 57.0 | 5472 | 0.1120 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9708 | 0.9326 | 0.9513 | 178 | 0.984 | 0.9609 | 0.9723 | 128 | 0.9563 | 0.9490 | 0.9526 | 0.9881 | | 0.0013 | 58.0 | 5568 | 0.1086 | 0.8830 | 0.9651 | 0.9222 | 86 | 0.9483 | 0.9270 | 0.9375 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9442 | 0.9490 | 0.9466 | 0.9862 | | 0.0005 | 59.0 | 5664 | 0.1218 | 0.8660 | 0.9767 | 0.9180 | 86 | 0.9641 | 0.9045 | 0.9333 | 178 | 0.9538 | 0.9688 | 0.9612 | 128 | 0.9365 | 0.9413 | 0.9389 | 0.9854 | | 0.0007 | 60.0 | 5760 | 0.0958 | 0.8913 | 0.9535 | 0.9213 | 86 | 0.9239 | 0.9551 | 0.9392 | 178 | 0.9839 | 0.9531 | 0.9683 | 128 | 0.935 | 0.9541 | 0.9444 | 0.9881 | | 0.0002 | 61.0 | 5856 | 0.1076 | 0.8817 | 0.9535 | 0.9162 | 86 | 0.9593 | 0.9270 | 0.9429 | 178 | 0.976 | 0.9531 | 0.9644 | 128 | 0.9462 | 0.9413 | 0.9437 | 0.9879 | | 0.0023 | 62.0 | 5952 | 0.0877 | 0.9140 | 0.9884 | 0.9497 | 86 | 0.9494 | 0.9494 | 0.9494 | 178 | 0.9764 | 0.9688 | 0.9725 | 128 | 0.9497 | 0.9643 | 0.9570 | 0.9895 | | 0.0013 | 63.0 | 6048 | 0.0885 | 0.9032 | 0.9767 | 0.9385 | 86 | 0.9448 | 0.9607 | 0.9526 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9475 | 0.9668 | 0.9571 | 0.9895 | | 0.0009 | 64.0 | 6144 | 0.0825 | 0.9032 | 0.9767 | 0.9385 | 86 | 0.9605 | 0.9551 | 0.9577 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9545 | 0.9643 | 0.9594 | 0.9900 | | 0.0003 | 65.0 | 6240 | 0.0838 | 0.9222 | 0.9651 | 0.9432 | 86 | 0.96 | 0.9438 | 0.9518 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9591 | 0.9566 | 0.9579 | 0.9884 | | 0.0006 | 66.0 | 6336 | 0.0957 | 0.9032 | 0.9767 | 0.9385 | 86 | 0.96 | 0.9438 | 0.9518 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9543 | 0.9592 | 0.9567 | 0.9887 | | 0.0004 | 67.0 | 6432 | 0.1129 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9649 | 0.9270 | 0.9456 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9538 | 0.9490 | 0.9514 | 0.9879 | | 0.0003 | 68.0 | 6528 | 0.1161 | 0.8936 | 0.9767 | 0.9333 | 86 | 0.9538 | 0.9270 | 0.9402 | 178 | 0.9764 | 0.9688 | 0.9725 | 128 | 0.9467 | 0.9515 | 0.9491 | 0.9870 | | 0.0002 | 69.0 | 6624 | 0.1234 | 0.8936 | 0.9767 | 0.9333 | 86 | 0.9645 | 0.9157 | 0.9395 | 178 | 0.9688 | 0.9688 | 0.9688 | 128 | 0.9488 | 0.9464 | 0.9476 | 0.9862 | | 0.0006 | 70.0 | 6720 | 0.1162 | 0.9231 | 0.9767 | 0.9492 | 86 | 0.9651 | 0.9326 | 0.9486 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9614 | 0.9541 | 0.9577 | 0.9884 | | 0.0002 | 71.0 | 6816 | 0.1107 | 0.9333 | 0.9767 | 0.9545 | 86 | 0.96 | 0.9438 | 0.9518 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9616 | 0.9592 | 0.9604 | 0.9879 | | 0.0002 | 72.0 | 6912 | 0.1121 | 0.9231 | 0.9767 | 0.9492 | 86 | 0.9598 | 0.9382 | 0.9489 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9591 | 0.9566 | 0.9579 | 0.9879 | | 0.0002 | 73.0 | 7008 | 0.1122 | 0.9231 | 0.9767 | 0.9492 | 86 | 0.9543 | 0.9382 | 0.9462 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9566 | 0.9566 | 0.9566 | 0.9881 | | 0.0005 | 74.0 | 7104 | 0.1127 | 0.9231 | 0.9767 | 0.9492 | 86 | 0.9543 | 0.9382 | 0.9462 | 178 | 0.9841 | 0.9688 | 0.9764 | 128 | 0.9566 | 0.9566 | 0.9566 | 0.9873 | | 0.0004 | 75.0 | 7200 | 0.1170 | 0.9130 | 0.9767 | 0.9438 | 86 | 0.9540 | 0.9326 | 0.9432 | 178 | 0.9688 | 0.9688 | 0.9688 | 128 | 0.9492 | 0.9541 | 0.9517 | 0.9862 | | 0.0003 | 76.0 | 7296 | 0.1089 | 0.9333 | 0.9767 | 0.9545 | 86 | 0.9444 | 0.9551 | 0.9497 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9520 | 0.9617 | 0.9569 | 0.9892 | | 0.001 | 77.0 | 7392 | 0.1082 | 0.9231 | 0.9767 | 0.9492 | 86 | 0.9503 | 0.9663 | 0.9582 | 178 | 0.9764 | 0.9688 | 0.9725 | 128 | 0.9524 | 0.9694 | 0.9608 | 0.9895 | | 0.0012 | 78.0 | 7488 | 0.1009 | 0.9022 | 0.9651 | 0.9326 | 86 | 0.9330 | 0.9382 | 0.9356 | 178 | 0.9688 | 0.9688 | 0.9688 | 128 | 0.9373 | 0.9541 | 0.9456 | 0.9862 | | 0.0002 | 79.0 | 7584 | 0.1051 | 0.8632 | 0.9535 | 0.9061 | 86 | 0.9489 | 0.9382 | 0.9435 | 178 | 0.976 | 0.9531 | 0.9644 | 128 | 0.9369 | 0.9464 | 0.9416 | 0.9865 | | 0.0002 | 80.0 | 7680 | 0.1108 | 0.8723 | 0.9535 | 0.9111 | 86 | 0.9540 | 0.9326 | 0.9432 | 178 | 0.976 | 0.9531 | 0.9644 | 128 | 0.9415 | 0.9439 | 0.9427 | 0.9865 | | 0.0005 | 81.0 | 7776 | 0.1037 | 0.8913 | 0.9535 | 0.9213 | 86 | 0.9543 | 0.9382 | 0.9462 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9466 | 0.9490 | 0.9478 | 0.9870 | | 0.0003 | 82.0 | 7872 | 0.1031 | 0.8710 | 0.9419 | 0.9050 | 86 | 0.9540 | 0.9326 | 0.9432 | 178 | 0.976 | 0.9531 | 0.9644 | 128 | 0.9413 | 0.9413 | 0.9413 | 0.9868 | | 0.0003 | 83.0 | 7968 | 0.0996 | 0.9121 | 0.9651 | 0.9379 | 86 | 0.9602 | 0.9494 | 0.9548 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9518 | 0.9566 | 0.9542 | 0.9887 | | 0.0002 | 84.0 | 8064 | 0.0987 | 0.9222 | 0.9651 | 0.9432 | 86 | 0.9602 | 0.9494 | 0.9548 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9542 | 0.9566 | 0.9554 | 0.9887 | | 0.0004 | 85.0 | 8160 | 0.1017 | 0.9222 | 0.9651 | 0.9432 | 86 | 0.9602 | 0.9494 | 0.9548 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9542 | 0.9566 | 0.9554 | 0.9887 | | 0.0002 | 86.0 | 8256 | 0.1018 | 0.9222 | 0.9651 | 0.9432 | 86 | 0.9602 | 0.9494 | 0.9548 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9542 | 0.9566 | 0.9554 | 0.9887 | | 0.0001 | 87.0 | 8352 | 0.1017 | 0.9222 | 0.9651 | 0.9432 | 86 | 0.9553 | 0.9607 | 0.9580 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9520 | 0.9617 | 0.9569 | 0.9889 | | 0.0002 | 88.0 | 8448 | 0.1028 | 0.9222 | 0.9651 | 0.9432 | 86 | 0.9602 | 0.9494 | 0.9548 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9542 | 0.9566 | 0.9554 | 0.9887 | | 0.0001 | 89.0 | 8544 | 0.1033 | 0.9222 | 0.9651 | 0.9432 | 86 | 0.9602 | 0.9494 | 0.9548 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9542 | 0.9566 | 0.9554 | 0.9887 | | 0.0002 | 90.0 | 8640 | 0.1026 | 0.9213 | 0.9535 | 0.9371 | 86 | 0.9545 | 0.9438 | 0.9492 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9540 | 0.9515 | 0.9527 | 0.9879 | | 0.0002 | 91.0 | 8736 | 0.1024 | 0.9213 | 0.9535 | 0.9371 | 86 | 0.9545 | 0.9438 | 0.9492 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9540 | 0.9515 | 0.9527 | 0.9879 | | 0.0002 | 92.0 | 8832 | 0.1025 | 0.9213 | 0.9535 | 0.9371 | 86 | 0.9545 | 0.9438 | 0.9492 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9540 | 0.9515 | 0.9527 | 0.9879 | | 0.0002 | 93.0 | 8928 | 0.1039 | 0.9213 | 0.9535 | 0.9371 | 86 | 0.9545 | 0.9438 | 0.9492 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9540 | 0.9515 | 0.9527 | 0.9879 | | 0.0001 | 94.0 | 9024 | 0.1034 | 0.9213 | 0.9535 | 0.9371 | 86 | 0.9545 | 0.9438 | 0.9492 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9540 | 0.9515 | 0.9527 | 0.9879 | | 0.0001 | 95.0 | 9120 | 0.1036 | 0.9213 | 0.9535 | 0.9371 | 86 | 0.9545 | 0.9438 | 0.9492 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9540 | 0.9515 | 0.9527 | 0.9879 | | 0.0001 | 96.0 | 9216 | 0.1087 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9538 | 0.9270 | 0.9402 | 178 | 0.9762 | 0.9609 | 0.9685 | 128 | 0.9464 | 0.9464 | 0.9464 | 0.9873 | | 0.0005 | 97.0 | 9312 | 0.1056 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9538 | 0.9270 | 0.9402 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9440 | 0.9464 | 0.9452 | 0.9876 | | 0.0003 | 98.0 | 9408 | 0.1045 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9538 | 0.9270 | 0.9402 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9440 | 0.9464 | 0.9452 | 0.9876 | | 0.0001 | 99.0 | 9504 | 0.1047 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9538 | 0.9270 | 0.9402 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9440 | 0.9464 | 0.9452 | 0.9876 | | 0.0002 | 100.0 | 9600 | 0.1047 | 0.8925 | 0.9651 | 0.9274 | 86 | 0.9538 | 0.9270 | 0.9402 | 178 | 0.9685 | 0.9609 | 0.9647 | 128 | 0.9440 | 0.9464 | 0.9452 | 0.9876 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

hienbm/llama-3-8b-Instruct-bnb-4bit

hienbm

2024-06-04T00:55:18Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-04T00:55:10Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** hienbm - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

harveybro/molt5-augmented-default-500-base-caption2smiles

harveybro

2024-06-04T00:55:05Z

108

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-06-04T00:54:34Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

harveybro/molt5-augmented-default-400-base-caption2smiles

harveybro

2024-06-04T00:49:43Z

107

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-06-04T00:49:10Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

datek/Qwen-Qwen1.5-7B-1717461786

datek

2024-06-04T00:47:06Z

5

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T00:43:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

luthfi507/emotion-classification

luthfi507

2024-06-04T00:41:26Z

15

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-06-03T15:43:25Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: emotion-classification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.6 ---  # emotion-classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1597 - Accuracy: 0.6 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 40 | 1.5881 | 0.4813 | | No log | 2.0 | 80 | 1.4495 | 0.4188 | | No log | 3.0 | 120 | 1.3173 | 0.525 | | No log | 4.0 | 160 | 1.2644 | 0.5375 | | No log | 5.0 | 200 | 1.1238 | 0.6125 | | No log | 6.0 | 240 | 1.3448 | 0.5563 | | No log | 7.0 | 280 | 1.3241 | 0.5938 | | No log | 8.0 | 320 | 1.4283 | 0.5625 | | No log | 9.0 | 360 | 1.3231 | 0.6062 | | No log | 10.0 | 400 | 1.4146 | 0.5938 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

apwic/nerui-base-1

apwic

2024-06-04T00:31:44Z

7

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-05-28T04:28:17Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerui-base-1 results: [] ---  # nerui-base-1 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0822 - Location Precision: 0.9573 - Location Recall: 0.9655 - Location F1: 0.9614 - Location Number: 116 - Organization Precision: 0.9608 - Organization Recall: 0.9304 - Organization F1: 0.9453 - Organization Number: 158 - Person Precision: 0.984 - Person Recall: 0.9919 - Person F1: 0.9880 - Person Number: 124 - Overall Precision: 0.9671 - Overall Recall: 0.9598 - Overall F1: 0.9634 - Overall Accuracy: 0.9920 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Location Precision | Location Recall | Location F1 | Location Number | Organization Precision | Organization Recall | Organization F1 | Organization Number | Person Precision | Person Recall | Person F1 | Person Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.2668 | 1.0 | 96 | 0.0394 | 0.9145 | 0.9224 | 0.9185 | 116 | 0.9141 | 0.9430 | 0.9283 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9358 | 0.9523 | 0.9440 | 0.9879 | | 0.0634 | 2.0 | 192 | 0.0460 | 0.9237 | 0.9397 | 0.9316 | 116 | 0.9477 | 0.9177 | 0.9325 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9520 | 0.9472 | 0.9496 | 0.9882 | | 0.032 | 3.0 | 288 | 0.0441 | 0.9474 | 0.9310 | 0.9391 | 116 | 0.9427 | 0.9367 | 0.9397 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9547 | 0.9523 | 0.9535 | 0.9890 | | 0.022 | 4.0 | 384 | 0.0442 | 0.9732 | 0.9397 | 0.9561 | 116 | 0.9255 | 0.9430 | 0.9342 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9573 | 0.9573 | 0.9573 | 0.9909 | | 0.0143 | 5.0 | 480 | 0.0474 | 0.9339 | 0.9741 | 0.9536 | 116 | 0.9671 | 0.9304 | 0.9484 | 158 | 0.976 | 0.9839 | 0.9799 | 124 | 0.9598 | 0.9598 | 0.9598 | 0.9898 | | 0.0122 | 6.0 | 576 | 0.0581 | 0.9328 | 0.9569 | 0.9447 | 116 | 0.9662 | 0.9051 | 0.9346 | 158 | 0.976 | 0.9839 | 0.9799 | 124 | 0.9592 | 0.9447 | 0.9519 | 0.9885 | | 0.0062 | 7.0 | 672 | 0.0578 | 0.9569 | 0.9569 | 0.9569 | 116 | 0.9548 | 0.9367 | 0.9457 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9646 | 0.9598 | 0.9622 | 0.9909 | | 0.007 | 8.0 | 768 | 0.0608 | 0.9655 | 0.9655 | 0.9655 | 116 | 0.9551 | 0.9430 | 0.9490 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9673 | 0.9648 | 0.9660 | 0.9901 | | 0.0049 | 9.0 | 864 | 0.0656 | 0.9328 | 0.9569 | 0.9447 | 116 | 0.9530 | 0.8987 | 0.9251 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9567 | 0.9447 | 0.9507 | 0.9874 | | 0.0056 | 10.0 | 960 | 0.0566 | 0.9569 | 0.9569 | 0.9569 | 116 | 0.9423 | 0.9304 | 0.9363 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9573 | 0.9573 | 0.9573 | 0.9896 | | 0.0046 | 11.0 | 1056 | 0.0709 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9346 | 0.9051 | 0.9196 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9545 | 0.9497 | 0.9521 | 0.9879 | | 0.0022 | 12.0 | 1152 | 0.0721 | 0.9412 | 0.9655 | 0.9532 | 116 | 0.9548 | 0.9367 | 0.9457 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9599 | 0.9623 | 0.9611 | 0.9901 | | 0.0048 | 13.0 | 1248 | 0.0544 | 0.9487 | 0.9569 | 0.9528 | 116 | 0.9490 | 0.9430 | 0.9460 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9575 | 0.9623 | 0.9599 | 0.9920 | | 0.0029 | 14.0 | 1344 | 0.0602 | 0.9649 | 0.9483 | 0.9565 | 116 | 0.9434 | 0.9494 | 0.9464 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9623 | 0.9623 | 0.9623 | 0.9918 | | 0.0031 | 15.0 | 1440 | 0.0678 | 0.9478 | 0.9397 | 0.9437 | 116 | 0.9484 | 0.9304 | 0.9393 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9571 | 0.9523 | 0.9547 | 0.9904 | | 0.0039 | 16.0 | 1536 | 0.0820 | 0.9244 | 0.9483 | 0.9362 | 116 | 0.96 | 0.9114 | 0.9351 | 158 | 0.9839 | 0.9839 | 0.9839 | 124 | 0.9567 | 0.9447 | 0.9507 | 0.9871 | | 0.0021 | 17.0 | 1632 | 0.0793 | 0.9421 | 0.9828 | 0.9620 | 116 | 0.9735 | 0.9304 | 0.9515 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9648 | 0.9648 | 0.9648 | 0.9898 | | 0.0035 | 18.0 | 1728 | 0.0844 | 0.9310 | 0.9310 | 0.9310 | 116 | 0.9545 | 0.9304 | 0.9423 | 158 | 0.9685 | 0.9919 | 0.9801 | 124 | 0.9521 | 0.9497 | 0.9509 | 0.9879 | | 0.0039 | 19.0 | 1824 | 0.0907 | 0.9106 | 0.9655 | 0.9372 | 116 | 0.9726 | 0.8987 | 0.9342 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9544 | 0.9472 | 0.9508 | 0.9868 | | 0.0014 | 20.0 | 1920 | 0.0629 | 0.9412 | 0.9655 | 0.9532 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9622 | 0.9598 | 0.9610 | 0.9912 | | 0.0019 | 21.0 | 2016 | 0.0655 | 0.9412 | 0.9655 | 0.9532 | 116 | 0.9737 | 0.9367 | 0.9548 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9672 | 0.9623 | 0.9647 | 0.9909 | | 0.0021 | 22.0 | 2112 | 0.0593 | 0.9487 | 0.9569 | 0.9528 | 116 | 0.9371 | 0.9430 | 0.9401 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9551 | 0.9623 | 0.9587 | 0.9915 | | 0.0038 | 23.0 | 2208 | 0.0698 | 0.9322 | 0.9483 | 0.9402 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9572 | 0.9548 | 0.9560 | 0.9890 | | 0.0024 | 24.0 | 2304 | 0.0686 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9669 | 0.9241 | 0.9450 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9670 | 0.9573 | 0.9621 | 0.9901 | | 0.0032 | 25.0 | 2400 | 0.0782 | 0.9174 | 0.9569 | 0.9367 | 116 | 0.9412 | 0.9114 | 0.9260 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9474 | 0.9497 | 0.9486 | 0.9874 | | 0.0028 | 26.0 | 2496 | 0.0841 | 0.9167 | 0.9483 | 0.9322 | 116 | 0.9865 | 0.9241 | 0.9542 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9644 | 0.9523 | 0.9583 | 0.9893 | | 0.0024 | 27.0 | 2592 | 0.0762 | 0.9174 | 0.9569 | 0.9367 | 116 | 0.9554 | 0.9494 | 0.9524 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9529 | 0.9648 | 0.9588 | 0.9893 | | 0.0065 | 28.0 | 2688 | 0.0943 | 0.9483 | 0.9483 | 0.9483 | 116 | 0.9662 | 0.9051 | 0.9346 | 158 | 0.9531 | 0.9839 | 0.9683 | 124 | 0.9566 | 0.9422 | 0.9494 | 0.9887 | | 0.0026 | 29.0 | 2784 | 0.0959 | 0.9487 | 0.9569 | 0.9528 | 116 | 0.9664 | 0.9114 | 0.9381 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9668 | 0.9497 | 0.9582 | 0.9874 | | 0.002 | 30.0 | 2880 | 0.0732 | 0.9402 | 0.9483 | 0.9442 | 116 | 0.9548 | 0.9367 | 0.9457 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9597 | 0.9573 | 0.9585 | 0.9912 | | 0.0012 | 31.0 | 2976 | 0.0808 | 0.9487 | 0.9569 | 0.9528 | 116 | 0.9735 | 0.9304 | 0.9515 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9695 | 0.9573 | 0.9633 | 0.9901 | | 0.001 | 32.0 | 3072 | 0.0846 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.98 | 0.9304 | 0.9545 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9720 | 0.9598 | 0.9659 | 0.9898 | | 0.0018 | 33.0 | 3168 | 0.0949 | 0.9487 | 0.9569 | 0.9528 | 116 | 0.9735 | 0.9304 | 0.9515 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9695 | 0.9573 | 0.9633 | 0.9893 | | 0.0012 | 34.0 | 3264 | 0.0965 | 0.9322 | 0.9483 | 0.9402 | 116 | 0.9669 | 0.9241 | 0.9450 | 158 | 0.9685 | 0.9919 | 0.9801 | 124 | 0.9571 | 0.9523 | 0.9547 | 0.9879 | | 0.0025 | 35.0 | 3360 | 0.1011 | 0.9554 | 0.9224 | 0.9386 | 116 | 0.9367 | 0.9367 | 0.9367 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9545 | 0.9497 | 0.9521 | 0.9879 | | 0.0029 | 36.0 | 3456 | 0.0913 | 0.9487 | 0.9569 | 0.9528 | 116 | 0.9545 | 0.9304 | 0.9423 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9597 | 0.9573 | 0.9585 | 0.9882 | | 0.0037 | 37.0 | 3552 | 0.0543 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9430 | 0.9430 | 0.9430 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9552 | 0.9648 | 0.96 | 0.9923 | | 0.002 | 38.0 | 3648 | 0.0655 | 0.9487 | 0.9569 | 0.9528 | 116 | 0.9430 | 0.9430 | 0.9430 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9575 | 0.9623 | 0.9599 | 0.9909 | | 0.0015 | 39.0 | 3744 | 0.0786 | 0.9565 | 0.9483 | 0.9524 | 116 | 0.9671 | 0.9304 | 0.9484 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9694 | 0.9548 | 0.9620 | 0.9893 | | 0.001 | 40.0 | 3840 | 0.0722 | 0.9328 | 0.9569 | 0.9447 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9597 | 0.9573 | 0.9585 | 0.9904 | | 0.0021 | 41.0 | 3936 | 0.0722 | 0.9350 | 0.9914 | 0.9623 | 116 | 0.9737 | 0.9367 | 0.9548 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.965 | 0.9698 | 0.9674 | 0.9904 | | 0.0018 | 42.0 | 4032 | 0.0764 | 0.9483 | 0.9483 | 0.9483 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9620 | 0.9548 | 0.9584 | 0.9893 | | 0.0009 | 43.0 | 4128 | 0.0854 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9735 | 0.9304 | 0.9515 | 158 | 0.9839 | 0.9839 | 0.9839 | 124 | 0.9695 | 0.9573 | 0.9633 | 0.9898 | | 0.0007 | 44.0 | 4224 | 0.0778 | 0.9412 | 0.9655 | 0.9532 | 116 | 0.9735 | 0.9304 | 0.9515 | 158 | 0.9839 | 0.9839 | 0.9839 | 124 | 0.9670 | 0.9573 | 0.9621 | 0.9904 | | 0.0018 | 45.0 | 4320 | 0.0880 | 0.9558 | 0.9310 | 0.9432 | 116 | 0.9481 | 0.9241 | 0.9359 | 158 | 0.976 | 0.9839 | 0.9799 | 124 | 0.9592 | 0.9447 | 0.9519 | 0.9887 | | 0.0022 | 46.0 | 4416 | 0.0823 | 0.9412 | 0.9655 | 0.9532 | 116 | 0.9867 | 0.9367 | 0.9610 | 158 | 0.976 | 0.9839 | 0.9799 | 124 | 0.9695 | 0.9598 | 0.9646 | 0.9901 | | 0.0013 | 47.0 | 4512 | 0.0913 | 0.9483 | 0.9483 | 0.9483 | 116 | 0.98 | 0.9304 | 0.9545 | 158 | 0.9762 | 0.9919 | 0.9840 | 124 | 0.9694 | 0.9548 | 0.9620 | 0.9896 | | 0.0013 | 48.0 | 4608 | 0.0819 | 0.9417 | 0.9741 | 0.9576 | 116 | 0.9801 | 0.9367 | 0.9579 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9697 | 0.9648 | 0.9673 | 0.9901 | | 0.0005 | 49.0 | 4704 | 0.0735 | 0.9412 | 0.9655 | 0.9532 | 116 | 0.9737 | 0.9367 | 0.9548 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9672 | 0.9623 | 0.9647 | 0.9909 | | 0.0011 | 50.0 | 4800 | 0.0772 | 0.9483 | 0.9483 | 0.9483 | 116 | 0.9484 | 0.9304 | 0.9393 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9596 | 0.9548 | 0.9572 | 0.9907 | | 0.0021 | 51.0 | 4896 | 0.0813 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9735 | 0.9304 | 0.9515 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9695 | 0.9598 | 0.9646 | 0.9904 | | 0.0006 | 52.0 | 4992 | 0.0927 | 0.9576 | 0.9741 | 0.9658 | 116 | 0.98 | 0.9304 | 0.9545 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9746 | 0.9623 | 0.9684 | 0.9904 | | 0.0007 | 53.0 | 5088 | 0.0791 | 0.9496 | 0.9741 | 0.9617 | 116 | 0.9740 | 0.9494 | 0.9615 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9698 | 0.9698 | 0.9698 | 0.9912 | | 0.0011 | 54.0 | 5184 | 0.0722 | 0.9496 | 0.9741 | 0.9617 | 116 | 0.9679 | 0.9557 | 0.9618 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9675 | 0.9724 | 0.9699 | 0.9929 | | 0.0005 | 55.0 | 5280 | 0.0721 | 0.9328 | 0.9569 | 0.9447 | 116 | 0.9557 | 0.9557 | 0.9557 | 158 | 0.9839 | 0.9839 | 0.9839 | 124 | 0.9576 | 0.9648 | 0.9612 | 0.9920 | | 0.0005 | 56.0 | 5376 | 0.0705 | 0.9496 | 0.9741 | 0.9617 | 116 | 0.9806 | 0.9620 | 0.9712 | 158 | 0.9839 | 0.9839 | 0.9839 | 124 | 0.9724 | 0.9724 | 0.9724 | 0.9931 | | 0.0003 | 57.0 | 5472 | 0.0651 | 0.9487 | 0.9569 | 0.9528 | 116 | 0.9677 | 0.9494 | 0.9585 | 158 | 0.9839 | 0.9839 | 0.9839 | 124 | 0.9672 | 0.9623 | 0.9647 | 0.9923 | | 0.0011 | 58.0 | 5568 | 0.0754 | 0.9569 | 0.9569 | 0.9569 | 116 | 0.9679 | 0.9557 | 0.9618 | 158 | 0.9839 | 0.9839 | 0.9839 | 124 | 0.9697 | 0.9648 | 0.9673 | 0.9929 | | 0.0006 | 59.0 | 5664 | 0.0718 | 0.9397 | 0.9397 | 0.9397 | 116 | 0.9618 | 0.9557 | 0.9587 | 158 | 0.9839 | 0.9839 | 0.9839 | 124 | 0.9622 | 0.9598 | 0.9610 | 0.9923 | | 0.0005 | 60.0 | 5760 | 0.0870 | 0.9496 | 0.9741 | 0.9617 | 116 | 0.98 | 0.9304 | 0.9545 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9721 | 0.9623 | 0.9672 | 0.9898 | | 0.0004 | 61.0 | 5856 | 0.0687 | 0.9474 | 0.9310 | 0.9391 | 116 | 0.9437 | 0.9557 | 0.9497 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9574 | 0.9598 | 0.9586 | 0.9909 | | 0.0002 | 62.0 | 5952 | 0.0983 | 0.9402 | 0.9483 | 0.9442 | 116 | 0.9799 | 0.9241 | 0.9511 | 158 | 0.9839 | 0.9839 | 0.9839 | 124 | 0.9692 | 0.9497 | 0.9594 | 0.9893 | | 0.0006 | 63.0 | 6048 | 0.0818 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9671 | 0.9304 | 0.9484 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9695 | 0.9598 | 0.9646 | 0.9912 | | 0.0002 | 64.0 | 6144 | 0.0858 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9915 | | 0.0005 | 65.0 | 6240 | 0.0884 | 0.9569 | 0.9569 | 0.9569 | 116 | 0.9673 | 0.9367 | 0.9518 | 158 | 0.9683 | 0.9839 | 0.976 | 124 | 0.9646 | 0.9573 | 0.9609 | 0.9915 | | 0.001 | 66.0 | 6336 | 0.0771 | 0.9487 | 0.9569 | 0.9528 | 116 | 0.9542 | 0.9241 | 0.9389 | 158 | 0.9683 | 0.9839 | 0.976 | 124 | 0.9571 | 0.9523 | 0.9547 | 0.9912 | | 0.0006 | 67.0 | 6432 | 0.0808 | 0.9487 | 0.9569 | 0.9528 | 116 | 0.9735 | 0.9304 | 0.9515 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9695 | 0.9573 | 0.9633 | 0.9909 | | 0.0002 | 68.0 | 6528 | 0.0749 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9610 | 0.9367 | 0.9487 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9672 | 0.9623 | 0.9647 | 0.9920 | | 0.0011 | 69.0 | 6624 | 0.0784 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9918 | | 0.0005 | 70.0 | 6720 | 0.0750 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9671 | 0.9304 | 0.9484 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9695 | 0.9598 | 0.9646 | 0.9920 | | 0.0001 | 71.0 | 6816 | 0.0758 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9671 | 0.9304 | 0.9484 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9695 | 0.9598 | 0.9646 | 0.9920 | | 0.0005 | 72.0 | 6912 | 0.0771 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9671 | 0.9304 | 0.9484 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9695 | 0.9598 | 0.9646 | 0.9920 | | 0.0004 | 73.0 | 7008 | 0.0733 | 0.9412 | 0.9655 | 0.9532 | 116 | 0.9542 | 0.9241 | 0.9389 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9597 | 0.9573 | 0.9585 | 0.9915 | | 0.0001 | 74.0 | 7104 | 0.0740 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9542 | 0.9241 | 0.9389 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9621 | 0.9573 | 0.9597 | 0.9918 | | 0.0001 | 75.0 | 7200 | 0.0795 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9669 | 0.9241 | 0.9450 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9670 | 0.9573 | 0.9621 | 0.9915 | | 0.0002 | 76.0 | 7296 | 0.0800 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9669 | 0.9241 | 0.9450 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9670 | 0.9573 | 0.9621 | 0.9915 | | 0.0002 | 77.0 | 7392 | 0.0781 | 0.9569 | 0.9569 | 0.9569 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9670 | 0.9573 | 0.9621 | 0.9920 | | 0.0002 | 78.0 | 7488 | 0.0798 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9735 | 0.9304 | 0.9515 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9695 | 0.9598 | 0.9646 | 0.9918 | | 0.0002 | 79.0 | 7584 | 0.0785 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9737 | 0.9367 | 0.9548 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9721 | 0.9623 | 0.9672 | 0.9926 | | 0.0001 | 80.0 | 7680 | 0.0794 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9737 | 0.9367 | 0.9548 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9721 | 0.9623 | 0.9672 | 0.9926 | | 0.0004 | 81.0 | 7776 | 0.0812 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9737 | 0.9367 | 0.9548 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9721 | 0.9623 | 0.9672 | 0.9926 | | 0.0001 | 82.0 | 7872 | 0.0880 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9669 | 0.9241 | 0.9450 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9670 | 0.9573 | 0.9621 | 0.9915 | | 0.0001 | 83.0 | 7968 | 0.0832 | 0.9576 | 0.9741 | 0.9658 | 116 | 0.9671 | 0.9304 | 0.9484 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9696 | 0.9623 | 0.9660 | 0.9920 | | 0.0007 | 84.0 | 8064 | 0.0854 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9669 | 0.9241 | 0.9450 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9670 | 0.9573 | 0.9621 | 0.9915 | | 0.0001 | 85.0 | 8160 | 0.0863 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9669 | 0.9241 | 0.9450 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9670 | 0.9573 | 0.9621 | 0.9915 | | 0.0001 | 86.0 | 8256 | 0.0854 | 0.9492 | 0.9655 | 0.9573 | 116 | 0.9669 | 0.9241 | 0.9450 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9670 | 0.9573 | 0.9621 | 0.9909 | | 0.0001 | 87.0 | 8352 | 0.0789 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9673 | 0.9367 | 0.9518 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9696 | 0.9623 | 0.9660 | 0.9923 | | 0.0001 | 88.0 | 8448 | 0.0776 | 0.9658 | 0.9741 | 0.9700 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9696 | 0.9623 | 0.9660 | 0.9923 | | 0.0002 | 89.0 | 8544 | 0.0786 | 0.9569 | 0.9569 | 0.9569 | 116 | 0.9610 | 0.9367 | 0.9487 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | | 0.0001 | 90.0 | 8640 | 0.0798 | 0.9569 | 0.9569 | 0.9569 | 116 | 0.9610 | 0.9367 | 0.9487 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | | 0.0001 | 91.0 | 8736 | 0.0816 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | | 0.0005 | 92.0 | 8832 | 0.0819 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | | 0.0003 | 93.0 | 8928 | 0.0819 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | | 0.0003 | 94.0 | 9024 | 0.0814 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | | 0.0001 | 95.0 | 9120 | 0.0814 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | | 0.0001 | 96.0 | 9216 | 0.0816 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | | 0.0001 | 97.0 | 9312 | 0.0817 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | | 0.0001 | 98.0 | 9408 | 0.0821 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | | 0.0001 | 99.0 | 9504 | 0.0822 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | | 0.0001 | 100.0 | 9600 | 0.0822 | 0.9573 | 0.9655 | 0.9614 | 116 | 0.9608 | 0.9304 | 0.9453 | 158 | 0.984 | 0.9919 | 0.9880 | 124 | 0.9671 | 0.9598 | 0.9634 | 0.9920 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

ehottl/distilbert-base-uncased-finetuned-emotion

ehottl

2024-06-04T00:21:31Z

121

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-04T00:10:46Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.929 - name: F1 type: f1 value: 0.9290384064576098 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2064 - Accuracy: 0.929 - F1: 0.9290 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8175 | 1.0 | 250 | 0.2950 | 0.911 | 0.9108 | | 0.238 | 2.0 | 500 | 0.2064 | 0.929 | 0.9290 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

dalopeza98/distilbert-base-uncased-finetuned-emotion

dalopeza98

2024-06-04T00:21:10Z

9

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-03T21:14:37Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3872 - Accuracy: 0.6802 - F1: 0.6793 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 48 | 1.7238 | 0.6762 | 0.6813 | | 0.0305 | 2.0 | 96 | 1.8028 | 0.6775 | 0.6755 | | 0.0305 | 3.0 | 144 | 1.9018 | 0.6689 | 0.6668 | | 0.0257 | 4.0 | 192 | 1.9426 | 0.6735 | 0.6740 | | 0.0257 | 5.0 | 240 | 1.9829 | 0.6662 | 0.6670 | | 0.0207 | 6.0 | 288 | 1.9462 | 0.6722 | 0.6753 | | 0.0207 | 7.0 | 336 | 1.9573 | 0.6861 | 0.6851 | | 0.0185 | 8.0 | 384 | 2.0147 | 0.6808 | 0.6820 | | 0.0185 | 9.0 | 432 | 2.0982 | 0.6669 | 0.6649 | | 0.0172 | 10.0 | 480 | 2.0431 | 0.6815 | 0.6799 | | 0.0172 | 11.0 | 528 | 2.0935 | 0.6768 | 0.6751 | | 0.0182 | 12.0 | 576 | 2.0599 | 0.6868 | 0.6835 | | 0.0182 | 13.0 | 624 | 2.0953 | 0.6808 | 0.6812 | | 0.0148 | 14.0 | 672 | 2.1115 | 0.6788 | 0.6790 | | 0.0148 | 15.0 | 720 | 2.1529 | 0.6735 | 0.6765 | | 0.0171 | 16.0 | 768 | 2.1873 | 0.6702 | 0.6720 | | 0.0171 | 17.0 | 816 | 2.1534 | 0.6782 | 0.6793 | | 0.0142 | 18.0 | 864 | 2.1803 | 0.6782 | 0.6773 | | 0.0142 | 19.0 | 912 | 2.2252 | 0.6802 | 0.6801 | | 0.0168 | 20.0 | 960 | 2.2221 | 0.6749 | 0.6764 | | 0.0168 | 21.0 | 1008 | 2.2365 | 0.6821 | 0.6817 | | 0.015 | 22.0 | 1056 | 2.2812 | 0.6742 | 0.6728 | | 0.015 | 23.0 | 1104 | 2.2447 | 0.6729 | 0.6707 | | 0.0145 | 24.0 | 1152 | 2.3272 | 0.6709 | 0.6700 | | 0.0145 | 25.0 | 1200 | 2.2630 | 0.6788 | 0.6809 | | 0.0151 | 26.0 | 1248 | 2.2751 | 0.6808 | 0.6811 | | 0.0151 | 27.0 | 1296 | 2.3018 | 0.6768 | 0.6776 | | 0.0144 | 28.0 | 1344 | 2.3544 | 0.6676 | 0.6681 | | 0.0144 | 29.0 | 1392 | 2.3109 | 0.6821 | 0.6828 | | 0.0126 | 30.0 | 1440 | 2.3234 | 0.6795 | 0.6786 | | 0.0126 | 31.0 | 1488 | 2.3294 | 0.6755 | 0.6750 | | 0.0142 | 32.0 | 1536 | 2.3183 | 0.6875 | 0.6886 | | 0.0142 | 33.0 | 1584 | 2.2949 | 0.6808 | 0.6823 | | 0.0131 | 34.0 | 1632 | 2.3451 | 0.6788 | 0.6773 | | 0.0131 | 35.0 | 1680 | 2.3160 | 0.6828 | 0.6841 | | 0.0143 | 36.0 | 1728 | 2.3251 | 0.6828 | 0.6815 | | 0.0143 | 37.0 | 1776 | 2.4003 | 0.6762 | 0.6753 | | 0.0116 | 38.0 | 1824 | 2.3675 | 0.6775 | 0.6770 | | 0.0116 | 39.0 | 1872 | 2.3700 | 0.6749 | 0.6735 | | 0.0126 | 40.0 | 1920 | 2.3700 | 0.6841 | 0.6831 | | 0.0126 | 41.0 | 1968 | 2.3818 | 0.6795 | 0.6793 | | 0.0115 | 42.0 | 2016 | 2.3518 | 0.6815 | 0.6814 | | 0.0115 | 43.0 | 2064 | 2.3829 | 0.6802 | 0.6790 | | 0.0135 | 44.0 | 2112 | 2.3638 | 0.6782 | 0.6775 | | 0.0135 | 45.0 | 2160 | 2.3568 | 0.6775 | 0.6768 | | 0.0146 | 46.0 | 2208 | 2.3633 | 0.6788 | 0.6784 | | 0.0118 | 47.0 | 2256 | 2.3725 | 0.6788 | 0.6782 | | 0.0118 | 48.0 | 2304 | 2.3875 | 0.6815 | 0.6806 | | 0.0116 | 49.0 | 2352 | 2.3862 | 0.6795 | 0.6787 | | 0.0116 | 50.0 | 2400 | 2.3872 | 0.6802 | 0.6793 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.2 - Datasets 2.19.1 - Tokenizers 0.19.1

duyntnet/Kunoichi-DPO-v2-7B-imatrix-GGUF

duyntnet

2024-06-04T00:19:17Z

60

3

transformers

[ "transformers", "gguf", "imatrix", "Kunoichi-DPO-v2-7B", "text-generation", "en", "license:other", "region:us" ]

text-generation

2024-06-03T20:18:06Z

--- license: other language: - en pipeline_tag: text-generation inference: false tags: - transformers - gguf - imatrix - Kunoichi-DPO-v2-7B --- Quantizations of https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B # From original readme | Model | MT Bench | EQ Bench | MMLU | Logic Test | |----------------------|----------|----------|---------|-------------| | GPT-4-Turbo | 9.32 | - | - | - | | GPT-4 | 8.99 | 62.52 | 86.4 | 0.86 | | **Kunoichi-DPO-v2-7B** | **8.51** | **42.18** | **64.94**| **0.58** | | Mixtral-8x7B-Instruct| 8.30 | 44.81 | 70.6 | 0.75 | | **Kunoichi-DPO-7B** | **8.29** | **41.60** | **64.83** | **0.59** | | **Kunoichi-7B** | **8.14** | **44.32** | **64.9** | **0.58** | | Starling-7B | 8.09 | - | 63.9 | 0.51 | | Claude-2 | 8.06 | 52.14 | 78.5 | - | | Silicon-Maid-7B | 7.96 | 40.44 | 64.7 | 0.54 | | Loyal-Macaroni-Maid-7B | 7.95 | 38.66 | 64.9 | 0.57 | | GPT-3.5-Turbo | 7.94 | 50.28 | 70 | 0.57 | | Claude-1 | 7.9 | - | 77 | - | | Openchat-3.5 | 7.81 | 37.08 | 64.3 | 0.39 | | Dolphin-2.6-DPO | 7.74 | 42.88 | 61.9 | 0.53 | | Zephyr-7B-beta | 7.34 | 38.71 | 61.4 | 0.30 | | Llama-2-70b-chat-hf | 6.86 | 51.56 | 63 | - | | Neural-chat-7b-v3-1 | 6.84 | 43.61 | 62.4 | 0.30 |

azmoulai/vizwiz-blip-model

azmoulai

2024-06-04T00:16:24Z

6

0

transformers

[ "transformers", "safetensors", "blip", "visual-question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

visual-question-answering

2024-05-29T04:12:54Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

apwic/nerui-base-0

apwic

2024-06-04T00:16:00Z

8

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-05-28T03:49:20Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerui-base-0 results: [] ---  # nerui-base-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1084 - Location Precision: 0.89 - Location Recall: 0.9468 - Location F1: 0.9175 - Location Number: 94 - Organization Precision: 0.9387 - Organization Recall: 0.9162 - Organization F1: 0.9273 - Organization Number: 167 - Person Precision: 1.0 - Person Recall: 0.9781 - Person F1: 0.9889 - Person Number: 137 - Overall Precision: 0.9471 - Overall Recall: 0.9447 - Overall F1: 0.9459 - Overall Accuracy: 0.9887 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Location Precision | Location Recall | Location F1 | Location Number | Organization Precision | Organization Recall | Organization F1 | Organization Number | Person Precision | Person Recall | Person F1 | Person Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.2566 | 1.0 | 96 | 0.0455 | 0.9634 | 0.8404 | 0.8977 | 94 | 0.8333 | 0.9281 | 0.8782 | 167 | 0.9708 | 0.9708 | 0.9708 | 137 | 0.9062 | 0.9221 | 0.9141 | 0.9843 | | 0.0617 | 2.0 | 192 | 0.0519 | 0.8381 | 0.9362 | 0.8844 | 94 | 0.8896 | 0.8683 | 0.8788 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9107 | 0.9221 | 0.9164 | 0.9834 | | 0.0356 | 3.0 | 288 | 0.0534 | 0.9062 | 0.9255 | 0.9158 | 94 | 0.8211 | 0.9341 | 0.8739 | 167 | 1.0 | 0.9708 | 0.9852 | 137 | 0.8974 | 0.9447 | 0.9204 | 0.9840 | | 0.0235 | 4.0 | 384 | 0.0525 | 0.8866 | 0.9149 | 0.9005 | 94 | 0.9006 | 0.9222 | 0.9112 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9303 | 0.9397 | 0.9350 | 0.9856 | | 0.0156 | 5.0 | 480 | 0.0623 | 0.9032 | 0.8936 | 0.8984 | 94 | 0.9333 | 0.9222 | 0.9277 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9466 | 0.9347 | 0.9406 | 0.9873 | | 0.0101 | 6.0 | 576 | 0.0590 | 0.9043 | 0.9043 | 0.9043 | 94 | 0.8929 | 0.8982 | 0.8955 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9295 | 0.9271 | 0.9283 | 0.9859 | | 0.0091 | 7.0 | 672 | 0.0955 | 0.8036 | 0.9574 | 0.8738 | 94 | 0.9211 | 0.8383 | 0.8777 | 167 | 0.9643 | 0.9854 | 0.9747 | 137 | 0.9035 | 0.9171 | 0.9102 | 0.9809 | | 0.0084 | 8.0 | 768 | 0.0871 | 0.8365 | 0.9255 | 0.8788 | 94 | 0.9062 | 0.8683 | 0.8869 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9196 | 0.9196 | 0.9196 | 0.9826 | | 0.007 | 9.0 | 864 | 0.0629 | 0.9565 | 0.9362 | 0.9462 | 94 | 0.8895 | 0.9162 | 0.9027 | 167 | 1.0 | 0.9854 | 0.9926 | 137 | 0.9424 | 0.9447 | 0.9435 | 0.9881 | | 0.0047 | 10.0 | 960 | 0.0564 | 0.9167 | 0.9362 | 0.9263 | 94 | 0.9512 | 0.9341 | 0.9426 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9594 | 0.9497 | 0.9545 | 0.9901 | | 0.0043 | 11.0 | 1056 | 0.0829 | 0.9158 | 0.9255 | 0.9206 | 94 | 0.8708 | 0.9281 | 0.8986 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9216 | 0.9447 | 0.9330 | 0.9856 | | 0.0034 | 12.0 | 1152 | 0.0779 | 0.9247 | 0.9149 | 0.9198 | 94 | 0.8667 | 0.9341 | 0.8991 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9216 | 0.9447 | 0.9330 | 0.9865 | | 0.0047 | 13.0 | 1248 | 0.0781 | 0.8922 | 0.9681 | 0.9286 | 94 | 0.95 | 0.9102 | 0.9297 | 167 | 0.9854 | 0.9854 | 0.9854 | 137 | 0.9474 | 0.9497 | 0.9486 | 0.9862 | | 0.006 | 14.0 | 1344 | 0.0682 | 0.9271 | 0.9468 | 0.9368 | 94 | 0.9236 | 0.8683 | 0.8951 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9509 | 0.9246 | 0.9376 | 0.9859 | | 0.0031 | 15.0 | 1440 | 0.0759 | 0.9149 | 0.9149 | 0.9149 | 94 | 0.8814 | 0.9341 | 0.9070 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9261 | 0.9447 | 0.9353 | 0.9878 | | 0.0049 | 16.0 | 1536 | 0.0801 | 0.9082 | 0.9468 | 0.9271 | 94 | 0.9107 | 0.9162 | 0.9134 | 167 | 0.9574 | 0.9854 | 0.9712 | 137 | 0.9263 | 0.9472 | 0.9366 | 0.9865 | | 0.0036 | 17.0 | 1632 | 0.0933 | 0.9278 | 0.9574 | 0.9424 | 94 | 0.9333 | 0.9222 | 0.9277 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9497 | 0.9497 | 0.9497 | 0.9887 | | 0.0033 | 18.0 | 1728 | 0.0828 | 0.9167 | 0.9362 | 0.9263 | 94 | 0.9167 | 0.9222 | 0.9194 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9424 | 0.9447 | 0.9435 | 0.9870 | | 0.0031 | 19.0 | 1824 | 0.0819 | 0.9149 | 0.9149 | 0.9149 | 94 | 0.9102 | 0.9102 | 0.9102 | 167 | 0.9708 | 0.9708 | 0.9708 | 137 | 0.9322 | 0.9322 | 0.9322 | 0.9873 | | 0.0025 | 20.0 | 1920 | 0.0871 | 0.8969 | 0.9255 | 0.9110 | 94 | 0.9321 | 0.9042 | 0.9179 | 167 | 0.9708 | 0.9708 | 0.9708 | 137 | 0.9369 | 0.9322 | 0.9345 | 0.9878 | | 0.0023 | 21.0 | 2016 | 0.0813 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9162 | 0.9162 | 0.9162 | 167 | 0.9706 | 0.9635 | 0.9670 | 137 | 0.9280 | 0.9397 | 0.9338 | 0.9873 | | 0.0023 | 22.0 | 2112 | 0.0885 | 0.9158 | 0.9255 | 0.9206 | 94 | 0.8814 | 0.9341 | 0.9070 | 167 | 1.0 | 0.9635 | 0.9814 | 137 | 0.9282 | 0.9422 | 0.9352 | 0.9867 | | 0.0018 | 23.0 | 2208 | 0.1209 | 0.8788 | 0.9255 | 0.9016 | 94 | 0.8947 | 0.9162 | 0.9053 | 167 | 0.9779 | 0.9708 | 0.9744 | 137 | 0.9187 | 0.9372 | 0.9279 | 0.9837 | | 0.0036 | 24.0 | 2304 | 0.0841 | 0.9175 | 0.9468 | 0.9319 | 94 | 0.9029 | 0.9461 | 0.9240 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9338 | 0.9573 | 0.9454 | 0.9878 | | 0.0034 | 25.0 | 2400 | 0.0860 | 0.9368 | 0.9468 | 0.9418 | 94 | 0.9186 | 0.9461 | 0.9322 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9478 | 0.9573 | 0.9525 | 0.9884 | | 0.0029 | 26.0 | 2496 | 0.0684 | 0.9381 | 0.9681 | 0.9529 | 94 | 0.9176 | 0.9341 | 0.9258 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9478 | 0.9573 | 0.9525 | 0.9898 | | 0.0031 | 27.0 | 2592 | 0.1158 | 0.9278 | 0.9574 | 0.9424 | 94 | 0.8933 | 0.9521 | 0.9217 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9341 | 0.9623 | 0.9480 | 0.9865 | | 0.0045 | 28.0 | 2688 | 0.0860 | 0.9263 | 0.9362 | 0.9312 | 94 | 0.8963 | 0.8802 | 0.8882 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9365 | 0.9271 | 0.9318 | 0.9854 | | 0.0018 | 29.0 | 2784 | 0.0869 | 0.9271 | 0.9468 | 0.9368 | 94 | 0.9290 | 0.9401 | 0.9345 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.95 | 0.9548 | 0.9524 | 0.9884 | | 0.0023 | 30.0 | 2880 | 0.1042 | 0.9184 | 0.9574 | 0.9375 | 94 | 0.9394 | 0.9281 | 0.9337 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9547 | 0.9523 | 0.9535 | 0.9881 | | 0.0028 | 31.0 | 2976 | 0.1003 | 0.9020 | 0.9787 | 0.9388 | 94 | 0.9118 | 0.9281 | 0.9199 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9338 | 0.9573 | 0.9454 | 0.9862 | | 0.0015 | 32.0 | 3072 | 0.0802 | 0.91 | 0.9681 | 0.9381 | 94 | 0.9353 | 0.9521 | 0.9436 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9458 | 0.9648 | 0.9552 | 0.9890 | | 0.0025 | 33.0 | 3168 | 0.0959 | 0.8667 | 0.9681 | 0.9146 | 94 | 0.9375 | 0.8982 | 0.9174 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9398 | 0.9422 | 0.9410 | 0.9862 | | 0.0014 | 34.0 | 3264 | 0.0970 | 0.9184 | 0.9574 | 0.9375 | 94 | 0.9286 | 0.9341 | 0.9313 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.95 | 0.9548 | 0.9524 | 0.9881 | | 0.0017 | 35.0 | 3360 | 0.0790 | 0.9570 | 0.9468 | 0.9519 | 94 | 0.9123 | 0.9341 | 0.9231 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9499 | 0.9523 | 0.9511 | 0.9890 | | 0.002 | 36.0 | 3456 | 0.0912 | 0.9010 | 0.9681 | 0.9333 | 94 | 0.9317 | 0.8982 | 0.9146 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9422 | 0.9422 | 0.9422 | 0.9870 | | 0.0025 | 37.0 | 3552 | 0.1061 | 0.9271 | 0.9468 | 0.9368 | 94 | 0.9030 | 0.8922 | 0.8976 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9418 | 0.9347 | 0.9382 | 0.9865 | | 0.0028 | 38.0 | 3648 | 0.0982 | 0.9184 | 0.9574 | 0.9375 | 94 | 0.9085 | 0.8922 | 0.9003 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9419 | 0.9372 | 0.9395 | 0.9870 | | 0.0022 | 39.0 | 3744 | 0.1061 | 0.8969 | 0.9255 | 0.9110 | 94 | 0.8953 | 0.9222 | 0.9086 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9305 | 0.9422 | 0.9363 | 0.9848 | | 0.0018 | 40.0 | 3840 | 0.1077 | 0.8980 | 0.9362 | 0.9167 | 94 | 0.9202 | 0.8982 | 0.9091 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9418 | 0.9347 | 0.9382 | 0.9862 | | 0.002 | 41.0 | 3936 | 0.0923 | 0.8980 | 0.9362 | 0.9167 | 94 | 0.9325 | 0.9102 | 0.9212 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9468 | 0.9397 | 0.9433 | 0.9870 | | 0.003 | 42.0 | 4032 | 0.0899 | 0.9053 | 0.9149 | 0.9101 | 94 | 0.9112 | 0.9222 | 0.9167 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.935 | 0.9397 | 0.9373 | 0.9862 | | 0.0027 | 43.0 | 4128 | 0.0827 | 0.9355 | 0.9255 | 0.9305 | 94 | 0.9277 | 0.9222 | 0.9249 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9542 | 0.9422 | 0.9482 | 0.9878 | | 0.0015 | 44.0 | 4224 | 0.0798 | 0.9149 | 0.9149 | 0.9149 | 94 | 0.9102 | 0.9102 | 0.9102 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9418 | 0.9347 | 0.9382 | 0.9878 | | 0.0011 | 45.0 | 4320 | 0.0868 | 0.8958 | 0.9149 | 0.9053 | 94 | 0.9313 | 0.8922 | 0.9113 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9413 | 0.9271 | 0.9342 | 0.9881 | | 0.0012 | 46.0 | 4416 | 0.0743 | 0.8922 | 0.9681 | 0.9286 | 94 | 0.9679 | 0.9042 | 0.9350 | 167 | 0.9852 | 0.9708 | 0.9779 | 137 | 0.9542 | 0.9422 | 0.9482 | 0.9903 | | 0.0012 | 47.0 | 4512 | 0.0870 | 0.9072 | 0.9362 | 0.9215 | 94 | 0.9375 | 0.8982 | 0.9174 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9466 | 0.9347 | 0.9406 | 0.9884 | | 0.0019 | 48.0 | 4608 | 0.0759 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9308 | 0.8862 | 0.9080 | 167 | 0.9779 | 0.9708 | 0.9744 | 137 | 0.9367 | 0.9296 | 0.9332 | 0.9881 | | 0.0015 | 49.0 | 4704 | 0.0810 | 0.9271 | 0.9468 | 0.9368 | 94 | 0.9176 | 0.9341 | 0.9258 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9475 | 0.9523 | 0.9499 | 0.9895 | | 0.0011 | 50.0 | 4800 | 0.0890 | 0.9082 | 0.9468 | 0.9271 | 94 | 0.9506 | 0.9222 | 0.9362 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9520 | 0.9472 | 0.9496 | 0.9890 | | 0.0007 | 51.0 | 4896 | 0.0827 | 0.9167 | 0.9362 | 0.9263 | 94 | 0.9341 | 0.9341 | 0.9341 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9474 | 0.9497 | 0.9486 | 0.9895 | | 0.001 | 52.0 | 4992 | 0.0873 | 0.8980 | 0.9362 | 0.9167 | 94 | 0.9281 | 0.9281 | 0.9281 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9425 | 0.9472 | 0.9449 | 0.9887 | | 0.001 | 53.0 | 5088 | 0.0820 | 0.8980 | 0.9362 | 0.9167 | 94 | 0.9394 | 0.9281 | 0.9337 | 167 | 0.9852 | 0.9708 | 0.9779 | 137 | 0.9447 | 0.9447 | 0.9447 | 0.9890 | | 0.0004 | 54.0 | 5184 | 0.0917 | 0.8911 | 0.9574 | 0.9231 | 94 | 0.9434 | 0.8982 | 0.9202 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9444 | 0.9397 | 0.9421 | 0.9867 | | 0.0006 | 55.0 | 5280 | 0.1053 | 0.8980 | 0.9362 | 0.9167 | 94 | 0.9333 | 0.9222 | 0.9277 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9447 | 0.9447 | 0.9447 | 0.9884 | | 0.001 | 56.0 | 5376 | 0.1040 | 0.8990 | 0.9468 | 0.9223 | 94 | 0.9333 | 0.9222 | 0.9277 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9425 | 0.9472 | 0.9449 | 0.9881 | | 0.0005 | 57.0 | 5472 | 0.1042 | 0.8990 | 0.9468 | 0.9223 | 94 | 0.9337 | 0.9281 | 0.9309 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.945 | 0.9497 | 0.9474 | 0.9884 | | 0.0009 | 58.0 | 5568 | 0.1057 | 0.9082 | 0.9468 | 0.9271 | 94 | 0.9202 | 0.8982 | 0.9091 | 167 | 0.9853 | 0.9781 | 0.9817 | 137 | 0.9395 | 0.9372 | 0.9384 | 0.9876 | | 0.001 | 59.0 | 5664 | 0.1034 | 0.8911 | 0.9574 | 0.9231 | 94 | 0.9277 | 0.9222 | 0.9249 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9426 | 0.9497 | 0.9462 | 0.9873 | | 0.0012 | 60.0 | 5760 | 0.0910 | 0.9072 | 0.9362 | 0.9215 | 94 | 0.9337 | 0.9281 | 0.9309 | 167 | 0.9779 | 0.9708 | 0.9744 | 137 | 0.9424 | 0.9447 | 0.9435 | 0.9887 | | 0.0008 | 61.0 | 5856 | 0.0987 | 0.9247 | 0.9149 | 0.9198 | 94 | 0.9102 | 0.9102 | 0.9102 | 167 | 0.9779 | 0.9708 | 0.9744 | 137 | 0.9369 | 0.9322 | 0.9345 | 0.9862 | | 0.0005 | 62.0 | 5952 | 0.1056 | 0.8889 | 0.9362 | 0.9119 | 94 | 0.9387 | 0.9162 | 0.9273 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9470 | 0.9422 | 0.9446 | 0.9876 | | 0.0006 | 63.0 | 6048 | 0.1050 | 0.8980 | 0.9362 | 0.9167 | 94 | 0.9268 | 0.9102 | 0.9184 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9421 | 0.9397 | 0.9409 | 0.9873 | | 0.0013 | 64.0 | 6144 | 0.0956 | 0.9072 | 0.9362 | 0.9215 | 94 | 0.9329 | 0.9162 | 0.9245 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9494 | 0.9422 | 0.9458 | 0.9884 | | 0.0006 | 65.0 | 6240 | 0.1061 | 0.9082 | 0.9468 | 0.9271 | 94 | 0.9313 | 0.8922 | 0.9113 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9490 | 0.9347 | 0.9418 | 0.9854 | | 0.0008 | 66.0 | 6336 | 0.1032 | 0.8980 | 0.9362 | 0.9167 | 94 | 0.9325 | 0.9102 | 0.9212 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9444 | 0.9397 | 0.9421 | 0.9881 | | 0.0004 | 67.0 | 6432 | 0.0961 | 0.8980 | 0.9362 | 0.9167 | 94 | 0.9273 | 0.9162 | 0.9217 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9446 | 0.9422 | 0.9434 | 0.9890 | | 0.0008 | 68.0 | 6528 | 0.0979 | 0.88 | 0.9362 | 0.9072 | 94 | 0.925 | 0.8862 | 0.9052 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9367 | 0.9296 | 0.9332 | 0.9870 | | 0.0013 | 69.0 | 6624 | 0.1021 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9162 | 0.9162 | 0.9162 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9377 | 0.9447 | 0.9412 | 0.9870 | | 0.0004 | 70.0 | 6720 | 0.0933 | 0.88 | 0.9362 | 0.9072 | 94 | 0.9264 | 0.9042 | 0.9152 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9395 | 0.9372 | 0.9384 | 0.9881 | | 0.001 | 71.0 | 6816 | 0.0892 | 0.8788 | 0.9255 | 0.9016 | 94 | 0.9264 | 0.9042 | 0.9152 | 167 | 0.9852 | 0.9708 | 0.9779 | 137 | 0.9345 | 0.9322 | 0.9333 | 0.9881 | | 0.0006 | 72.0 | 6912 | 0.0966 | 0.9091 | 0.9574 | 0.9326 | 94 | 0.9509 | 0.9281 | 0.9394 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9547 | 0.9523 | 0.9535 | 0.9892 | | 0.0006 | 73.0 | 7008 | 0.0997 | 0.8911 | 0.9574 | 0.9231 | 94 | 0.9441 | 0.9102 | 0.9268 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9495 | 0.9447 | 0.9471 | 0.9884 | | 0.0004 | 74.0 | 7104 | 0.1035 | 0.8824 | 0.9574 | 0.9184 | 94 | 0.9497 | 0.9042 | 0.9264 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9470 | 0.9422 | 0.9446 | 0.9881 | | 0.0005 | 75.0 | 7200 | 0.1036 | 0.8788 | 0.9255 | 0.9016 | 94 | 0.9371 | 0.8922 | 0.9141 | 167 | 0.9852 | 0.9708 | 0.9779 | 137 | 0.9389 | 0.9271 | 0.9330 | 0.9870 | | 0.0004 | 76.0 | 7296 | 0.0978 | 0.8788 | 0.9255 | 0.9016 | 94 | 0.9317 | 0.8982 | 0.9146 | 167 | 0.9638 | 0.9708 | 0.9673 | 137 | 0.9296 | 0.9296 | 0.9296 | 0.9867 | | 0.0004 | 77.0 | 7392 | 0.0896 | 0.88 | 0.9362 | 0.9072 | 94 | 0.9273 | 0.9162 | 0.9217 | 167 | 0.9926 | 0.9781 | 0.9853 | 137 | 0.9375 | 0.9422 | 0.9398 | 0.9887 | | 0.0007 | 78.0 | 7488 | 0.1034 | 0.8889 | 0.9362 | 0.9119 | 94 | 0.9308 | 0.8862 | 0.9080 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9439 | 0.9296 | 0.9367 | 0.9878 | | 0.0004 | 79.0 | 7584 | 0.1117 | 0.8812 | 0.9468 | 0.9128 | 94 | 0.9259 | 0.8982 | 0.9119 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9395 | 0.9372 | 0.9384 | 0.9873 | | 0.0006 | 80.0 | 7680 | 0.1053 | 0.8980 | 0.9362 | 0.9167 | 94 | 0.9017 | 0.9341 | 0.9176 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9333 | 0.9497 | 0.9415 | 0.9873 | | 0.0003 | 81.0 | 7776 | 0.1023 | 0.8980 | 0.9362 | 0.9167 | 94 | 0.9222 | 0.9222 | 0.9222 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9424 | 0.9447 | 0.9435 | 0.9884 | | 0.0005 | 82.0 | 7872 | 0.0998 | 0.8990 | 0.9468 | 0.9223 | 94 | 0.9281 | 0.9281 | 0.9281 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.945 | 0.9497 | 0.9474 | 0.9887 | | 0.0004 | 83.0 | 7968 | 0.1031 | 0.8980 | 0.9362 | 0.9167 | 94 | 0.9222 | 0.9222 | 0.9222 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9424 | 0.9447 | 0.9435 | 0.9884 | | 0.0002 | 84.0 | 8064 | 0.1076 | 0.9072 | 0.9362 | 0.9215 | 94 | 0.9273 | 0.9162 | 0.9217 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9470 | 0.9422 | 0.9446 | 0.9890 | | 0.0008 | 85.0 | 8160 | 0.1031 | 0.9062 | 0.9255 | 0.9158 | 94 | 0.9273 | 0.9162 | 0.9217 | 167 | 0.9925 | 0.9708 | 0.9815 | 137 | 0.9443 | 0.9372 | 0.9407 | 0.9887 | | 0.0003 | 86.0 | 8256 | 0.0967 | 0.9062 | 0.9255 | 0.9158 | 94 | 0.9383 | 0.9102 | 0.9240 | 167 | 0.9925 | 0.9708 | 0.9815 | 137 | 0.9490 | 0.9347 | 0.9418 | 0.9892 | | 0.0005 | 87.0 | 8352 | 0.0978 | 0.8889 | 0.9362 | 0.9119 | 94 | 0.9317 | 0.8982 | 0.9146 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9442 | 0.9347 | 0.9394 | 0.9884 | | 0.0003 | 88.0 | 8448 | 0.1104 | 0.8889 | 0.9362 | 0.9119 | 94 | 0.9375 | 0.8982 | 0.9174 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9466 | 0.9347 | 0.9406 | 0.9881 | | 0.0005 | 89.0 | 8544 | 0.1069 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9441 | 0.9102 | 0.9268 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9494 | 0.9422 | 0.9458 | 0.9887 | | 0.0003 | 90.0 | 8640 | 0.1071 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9441 | 0.9102 | 0.9268 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9494 | 0.9422 | 0.9458 | 0.9887 | | 0.0005 | 91.0 | 8736 | 0.1068 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9441 | 0.9102 | 0.9268 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9494 | 0.9422 | 0.9458 | 0.9887 | | 0.0004 | 92.0 | 8832 | 0.1078 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9444 | 0.9162 | 0.9301 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9495 | 0.9447 | 0.9471 | 0.9890 | | 0.0003 | 93.0 | 8928 | 0.1079 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9444 | 0.9162 | 0.9301 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9495 | 0.9447 | 0.9471 | 0.9890 | | 0.0004 | 94.0 | 9024 | 0.1082 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9387 | 0.9162 | 0.9273 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9471 | 0.9447 | 0.9459 | 0.9887 | | 0.0003 | 95.0 | 9120 | 0.1080 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9387 | 0.9162 | 0.9273 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9471 | 0.9447 | 0.9459 | 0.9887 | | 0.0003 | 96.0 | 9216 | 0.1082 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9387 | 0.9162 | 0.9273 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9471 | 0.9447 | 0.9459 | 0.9887 | | 0.0002 | 97.0 | 9312 | 0.1080 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9387 | 0.9162 | 0.9273 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9471 | 0.9447 | 0.9459 | 0.9887 | | 0.0003 | 98.0 | 9408 | 0.1080 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9444 | 0.9162 | 0.9301 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9495 | 0.9447 | 0.9471 | 0.9890 | | 0.0003 | 99.0 | 9504 | 0.1085 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9387 | 0.9162 | 0.9273 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9471 | 0.9447 | 0.9459 | 0.9887 | | 0.0002 | 100.0 | 9600 | 0.1084 | 0.89 | 0.9468 | 0.9175 | 94 | 0.9387 | 0.9162 | 0.9273 | 167 | 1.0 | 0.9781 | 0.9889 | 137 | 0.9471 | 0.9447 | 0.9459 | 0.9887 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

dbands/llama-3-8b-code_bagel_hermes-2-5-blender-16bit

dbands

2024-06-04T00:15:01Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-06-04T00:09:05Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** dbands - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

martinsinnona/visdecode_vega_1

martinsinnona

2024-06-04T00:09:09Z

48

0

transformers

[ "transformers", "safetensors", "pix2struct", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

image-text-to-text

2024-05-21T18:34:35Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

apwic/nerugm-unipelt-4

apwic

2024-06-04T00:00:06Z

0

null

[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]

null

2024-05-28T02:54:06Z

--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: nerugm-unipelt-4 results: [] ---  # nerugm-unipelt-4 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2206 - Location Precision: 0.7949 - Location Recall: 0.8493 - Location F1: 0.8212 - Location Number: 73 - Organization Precision: 0.7361 - Organization Recall: 0.8154 - Organization F1: 0.7737 - Organization Number: 65 - Person Precision: 0.8924 - Person Recall: 0.94 - Person F1: 0.9156 - Person Number: 150 - Quantity Precision: 0.8125 - Quantity Recall: 0.8966 - Quantity F1: 0.8525 - Quantity Number: 29 - Time Precision: 0.7838 - Time Recall: 0.8529 - Time F1: 0.8169 - Time Number: 34 - Overall Precision: 0.8249 - Overall Recall: 0.8860 - Overall F1: 0.8544 - Overall Accuracy: 0.9638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Location Precision | Location Recall | Location F1 | Location Number | Organization Precision | Organization Recall | Organization F1 | Organization Number | Person Precision | Person Recall | Person F1 | Person Number | Quantity Precision | Quantity Recall | Quantity F1 | Quantity Number | Time Precision | Time Recall | Time F1 | Time Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:-------------:|:---------:|:-------------:|:------------------:|:---------------:|:-----------:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.9477 | 1.0 | 106 | 0.6205 | 0.0 | 0.0 | 0.0 | 73 | 0.0 | 0.0 | 0.0 | 65 | 0.2 | 0.0067 | 0.0129 | 150 | 0.0 | 0.0 | 0.0 | 29 | 0.0 | 0.0 | 0.0 | 34 | 0.2 | 0.0028 | 0.0056 | 0.8373 | | 0.5027 | 2.0 | 212 | 0.3516 | 0.4026 | 0.4247 | 0.4133 | 73 | 0.1091 | 0.0923 | 0.1 | 65 | 0.6020 | 0.8067 | 0.6895 | 150 | 0.1739 | 0.1379 | 0.1538 | 29 | 0.5 | 0.6471 | 0.5641 | 34 | 0.46 | 0.5242 | 0.4900 | 0.9052 | | 0.2888 | 3.0 | 318 | 0.1855 | 0.5319 | 0.6849 | 0.5988 | 73 | 0.5167 | 0.4769 | 0.4960 | 65 | 0.7816 | 0.9067 | 0.8395 | 150 | 0.4545 | 0.5172 | 0.4839 | 29 | 0.8286 | 0.8529 | 0.8406 | 34 | 0.6591 | 0.7436 | 0.6988 | 0.9404 | | 0.1926 | 4.0 | 424 | 0.1595 | 0.6122 | 0.8219 | 0.7018 | 73 | 0.5054 | 0.7231 | 0.5949 | 65 | 0.8155 | 0.9133 | 0.8616 | 150 | 0.6053 | 0.7931 | 0.6866 | 29 | 0.8611 | 0.9118 | 0.8857 | 34 | 0.6882 | 0.8490 | 0.7602 | 0.9486 | | 0.163 | 5.0 | 530 | 0.1464 | 0.6354 | 0.8356 | 0.7219 | 73 | 0.5904 | 0.7538 | 0.6622 | 65 | 0.8354 | 0.9133 | 0.8726 | 150 | 0.6757 | 0.8621 | 0.7576 | 29 | 0.725 | 0.8529 | 0.7838 | 34 | 0.7167 | 0.8575 | 0.7808 | 0.9515 | | 0.1447 | 6.0 | 636 | 0.1606 | 0.6703 | 0.8356 | 0.7439 | 73 | 0.6265 | 0.8 | 0.7027 | 65 | 0.8323 | 0.9267 | 0.8770 | 150 | 0.5333 | 0.8276 | 0.6486 | 29 | 0.6222 | 0.8235 | 0.7089 | 34 | 0.7053 | 0.8661 | 0.7775 | 0.9471 | | 0.1316 | 7.0 | 742 | 0.1419 | 0.6739 | 0.8493 | 0.7515 | 73 | 0.6622 | 0.7538 | 0.7050 | 65 | 0.8253 | 0.9133 | 0.8671 | 150 | 0.6757 | 0.8621 | 0.7576 | 29 | 0.75 | 0.8824 | 0.8108 | 34 | 0.7408 | 0.8632 | 0.7974 | 0.9550 | | 0.1217 | 8.0 | 848 | 0.1318 | 0.7294 | 0.8493 | 0.7848 | 73 | 0.6364 | 0.7538 | 0.6901 | 65 | 0.8313 | 0.92 | 0.8734 | 150 | 0.6757 | 0.8621 | 0.7576 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.7543 | 0.8661 | 0.8064 | 0.9569 | | 0.1125 | 9.0 | 954 | 0.1269 | 0.7439 | 0.8356 | 0.7871 | 73 | 0.6145 | 0.7846 | 0.6892 | 65 | 0.8608 | 0.9067 | 0.8831 | 150 | 0.7297 | 0.9310 | 0.8182 | 29 | 0.8108 | 0.8824 | 0.8451 | 34 | 0.7683 | 0.8689 | 0.8155 | 0.9591 | | 0.1088 | 10.0 | 1060 | 0.1347 | 0.6988 | 0.7945 | 0.7436 | 73 | 0.6944 | 0.7692 | 0.7299 | 65 | 0.8344 | 0.9067 | 0.8690 | 150 | 0.6944 | 0.8621 | 0.7692 | 29 | 0.7692 | 0.8824 | 0.8219 | 34 | 0.7608 | 0.8519 | 0.8038 | 0.9582 | | 0.1017 | 11.0 | 1166 | 0.1373 | 0.7024 | 0.8082 | 0.7516 | 73 | 0.6456 | 0.7846 | 0.7083 | 65 | 0.8438 | 0.9 | 0.8710 | 150 | 0.6757 | 0.8621 | 0.7576 | 29 | 0.6667 | 0.8235 | 0.7368 | 34 | 0.7413 | 0.8490 | 0.7915 | 0.9559 | | 0.0943 | 12.0 | 1272 | 0.1453 | 0.6778 | 0.8356 | 0.7485 | 73 | 0.6310 | 0.8154 | 0.7114 | 65 | 0.8457 | 0.9133 | 0.8782 | 150 | 0.7027 | 0.8966 | 0.7879 | 29 | 0.6512 | 0.8235 | 0.7273 | 34 | 0.7332 | 0.8689 | 0.7953 | 0.9567 | | 0.0886 | 13.0 | 1378 | 0.1357 | 0.7375 | 0.8082 | 0.7712 | 73 | 0.5978 | 0.8462 | 0.7006 | 65 | 0.8553 | 0.9067 | 0.8803 | 150 | 0.7143 | 0.8621 | 0.7813 | 29 | 0.7692 | 0.8824 | 0.8219 | 34 | 0.7531 | 0.8689 | 0.8069 | 0.9579 | | 0.0855 | 14.0 | 1484 | 0.1371 | 0.7024 | 0.8082 | 0.7516 | 73 | 0.625 | 0.7692 | 0.6897 | 65 | 0.8457 | 0.9133 | 0.8782 | 150 | 0.75 | 0.9310 | 0.8308 | 29 | 0.6829 | 0.8235 | 0.7467 | 34 | 0.7469 | 0.8575 | 0.7984 | 0.9569 | | 0.0814 | 15.0 | 1590 | 0.1300 | 0.7821 | 0.8356 | 0.8079 | 73 | 0.6329 | 0.7692 | 0.6944 | 65 | 0.8562 | 0.9133 | 0.8839 | 150 | 0.7027 | 0.8966 | 0.7879 | 29 | 0.7368 | 0.8235 | 0.7778 | 34 | 0.7704 | 0.8604 | 0.8129 | 0.9599 | | 0.079 | 16.0 | 1696 | 0.1442 | 0.7439 | 0.8356 | 0.7871 | 73 | 0.6667 | 0.7692 | 0.7143 | 65 | 0.8528 | 0.9267 | 0.8882 | 150 | 0.75 | 0.9310 | 0.8308 | 29 | 0.7 | 0.8235 | 0.7568 | 34 | 0.7702 | 0.8689 | 0.8166 | 0.9589 | | 0.0722 | 17.0 | 1802 | 0.1371 | 0.7349 | 0.8356 | 0.7821 | 73 | 0.6667 | 0.7692 | 0.7143 | 65 | 0.8415 | 0.92 | 0.8790 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.725 | 0.8529 | 0.7838 | 34 | 0.7671 | 0.8632 | 0.8123 | 0.9587 | | 0.0724 | 18.0 | 1908 | 0.1402 | 0.75 | 0.8219 | 0.7843 | 73 | 0.6190 | 0.8 | 0.6980 | 65 | 0.875 | 0.9333 | 0.9032 | 150 | 0.6389 | 0.7931 | 0.7077 | 29 | 0.7568 | 0.8235 | 0.7887 | 34 | 0.7632 | 0.8632 | 0.8102 | 0.9579 | | 0.0704 | 19.0 | 2014 | 0.1272 | 0.7821 | 0.8356 | 0.8079 | 73 | 0.68 | 0.7846 | 0.7286 | 65 | 0.8580 | 0.9267 | 0.8910 | 150 | 0.7429 | 0.8966 | 0.8125 | 29 | 0.7368 | 0.8235 | 0.7778 | 34 | 0.7861 | 0.8689 | 0.8254 | 0.9633 | | 0.0629 | 20.0 | 2120 | 0.1404 | 0.7229 | 0.8219 | 0.7692 | 73 | 0.6265 | 0.8 | 0.7027 | 65 | 0.8696 | 0.9333 | 0.9003 | 150 | 0.7353 | 0.8621 | 0.7937 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.7694 | 0.8746 | 0.8187 | 0.9599 | | 0.0607 | 21.0 | 2226 | 0.1343 | 0.7531 | 0.8356 | 0.7922 | 73 | 0.6711 | 0.7846 | 0.7234 | 65 | 0.8854 | 0.9267 | 0.9055 | 150 | 0.7353 | 0.8621 | 0.7937 | 29 | 0.8857 | 0.9118 | 0.8986 | 34 | 0.8016 | 0.8746 | 0.8365 | 0.9631 | | 0.0598 | 22.0 | 2332 | 0.1399 | 0.7531 | 0.8356 | 0.7922 | 73 | 0.6623 | 0.7846 | 0.7183 | 65 | 0.8910 | 0.9267 | 0.9085 | 150 | 0.7353 | 0.8621 | 0.7937 | 29 | 0.7568 | 0.8235 | 0.7887 | 34 | 0.7896 | 0.8661 | 0.8261 | 0.9619 | | 0.0578 | 23.0 | 2438 | 0.1296 | 0.7792 | 0.8219 | 0.8000 | 73 | 0.68 | 0.7846 | 0.7286 | 65 | 0.8861 | 0.9333 | 0.9091 | 150 | 0.7353 | 0.8621 | 0.7937 | 29 | 0.8333 | 0.8824 | 0.8571 | 34 | 0.8053 | 0.8718 | 0.8372 | 0.9628 | | 0.0538 | 24.0 | 2544 | 0.1458 | 0.7531 | 0.8356 | 0.7922 | 73 | 0.65 | 0.8 | 0.7172 | 65 | 0.8571 | 0.92 | 0.8875 | 150 | 0.7647 | 0.8966 | 0.8254 | 29 | 0.7632 | 0.8529 | 0.8056 | 34 | 0.7766 | 0.8718 | 0.8215 | 0.9596 | | 0.0519 | 25.0 | 2650 | 0.1594 | 0.7176 | 0.8356 | 0.7722 | 73 | 0.6667 | 0.8 | 0.7273 | 65 | 0.8650 | 0.94 | 0.9010 | 150 | 0.6944 | 0.8621 | 0.7692 | 29 | 0.8056 | 0.8529 | 0.8286 | 34 | 0.7739 | 0.8775 | 0.8224 | 0.9594 | | 0.0513 | 26.0 | 2756 | 0.1568 | 0.7143 | 0.8219 | 0.7643 | 73 | 0.68 | 0.7846 | 0.7286 | 65 | 0.8485 | 0.9333 | 0.8889 | 150 | 0.7143 | 0.8621 | 0.7813 | 29 | 0.7 | 0.8235 | 0.7568 | 34 | 0.7619 | 0.8661 | 0.8107 | 0.9582 | | 0.051 | 27.0 | 2862 | 0.1527 | 0.7439 | 0.8356 | 0.7871 | 73 | 0.6296 | 0.7846 | 0.6986 | 65 | 0.8634 | 0.9267 | 0.8939 | 150 | 0.7143 | 0.8621 | 0.7813 | 29 | 0.7 | 0.8235 | 0.7568 | 34 | 0.7619 | 0.8661 | 0.8107 | 0.9594 | | 0.0479 | 28.0 | 2968 | 0.1568 | 0.7439 | 0.8356 | 0.7871 | 73 | 0.6842 | 0.8 | 0.7376 | 65 | 0.875 | 0.9333 | 0.9032 | 150 | 0.7647 | 0.8966 | 0.8254 | 29 | 0.7 | 0.8235 | 0.7568 | 34 | 0.7832 | 0.8746 | 0.8264 | 0.9596 | | 0.0442 | 29.0 | 3074 | 0.1412 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.6463 | 0.8154 | 0.7211 | 65 | 0.8580 | 0.9267 | 0.8910 | 150 | 0.7353 | 0.8621 | 0.7937 | 29 | 0.8286 | 0.8529 | 0.8406 | 34 | 0.7877 | 0.8775 | 0.8302 | 0.9611 | | 0.0431 | 30.0 | 3180 | 0.1463 | 0.8158 | 0.8493 | 0.8322 | 73 | 0.6667 | 0.7692 | 0.7143 | 65 | 0.8805 | 0.9333 | 0.9061 | 150 | 0.7879 | 0.8966 | 0.8387 | 29 | 0.7436 | 0.8529 | 0.7945 | 34 | 0.8037 | 0.8746 | 0.8377 | 0.9623 | | 0.0431 | 31.0 | 3286 | 0.1430 | 0.7821 | 0.8356 | 0.8079 | 73 | 0.6757 | 0.7692 | 0.7194 | 65 | 0.8634 | 0.9267 | 0.8939 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8056 | 0.8529 | 0.8286 | 34 | 0.8005 | 0.8689 | 0.8333 | 0.9631 | | 0.0396 | 32.0 | 3392 | 0.1682 | 0.7722 | 0.8356 | 0.8026 | 73 | 0.6310 | 0.8154 | 0.7114 | 65 | 0.8634 | 0.9267 | 0.8939 | 150 | 0.7647 | 0.8966 | 0.8254 | 29 | 0.7317 | 0.8824 | 0.8 | 34 | 0.7744 | 0.8803 | 0.824 | 0.9594 | | 0.0404 | 33.0 | 3498 | 0.1550 | 0.7922 | 0.8356 | 0.8133 | 73 | 0.7246 | 0.7692 | 0.7463 | 65 | 0.8910 | 0.9267 | 0.9085 | 150 | 0.7353 | 0.8621 | 0.7937 | 29 | 0.75 | 0.8824 | 0.8108 | 34 | 0.8112 | 0.8689 | 0.8391 | 0.9619 | | 0.038 | 34.0 | 3604 | 0.1416 | 0.8158 | 0.8493 | 0.8322 | 73 | 0.6585 | 0.8308 | 0.7347 | 65 | 0.875 | 0.9333 | 0.9032 | 150 | 0.7879 | 0.8966 | 0.8387 | 29 | 0.8611 | 0.9118 | 0.8857 | 34 | 0.8088 | 0.8917 | 0.8482 | 0.9643 | | 0.0389 | 35.0 | 3710 | 0.1660 | 0.7349 | 0.8356 | 0.7821 | 73 | 0.6711 | 0.7846 | 0.7234 | 65 | 0.8742 | 0.9267 | 0.8997 | 150 | 0.7059 | 0.8276 | 0.7619 | 29 | 0.6905 | 0.8529 | 0.7632 | 34 | 0.7716 | 0.8661 | 0.8161 | 0.9594 | | 0.0359 | 36.0 | 3816 | 0.1483 | 0.7848 | 0.8493 | 0.8158 | 73 | 0.7027 | 0.8 | 0.7482 | 65 | 0.8734 | 0.92 | 0.8961 | 150 | 0.7647 | 0.8966 | 0.8254 | 29 | 0.8108 | 0.8824 | 0.8451 | 34 | 0.8063 | 0.8775 | 0.8404 | 0.9638 | | 0.0352 | 37.0 | 3922 | 0.1701 | 0.7439 | 0.8356 | 0.7871 | 73 | 0.6933 | 0.8 | 0.7429 | 65 | 0.8734 | 0.92 | 0.8961 | 150 | 0.7647 | 0.8966 | 0.8254 | 29 | 0.8421 | 0.9412 | 0.8889 | 34 | 0.7984 | 0.8803 | 0.8374 | 0.9621 | | 0.0342 | 38.0 | 4028 | 0.1522 | 0.8052 | 0.8493 | 0.8267 | 73 | 0.75 | 0.7846 | 0.7669 | 65 | 0.8734 | 0.92 | 0.8961 | 150 | 0.7647 | 0.8966 | 0.8254 | 29 | 0.7692 | 0.8824 | 0.8219 | 34 | 0.8165 | 0.8746 | 0.8446 | 0.9641 | | 0.0315 | 39.0 | 4134 | 0.1590 | 0.7821 | 0.8356 | 0.8079 | 73 | 0.7013 | 0.8308 | 0.7606 | 65 | 0.8688 | 0.9267 | 0.8968 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.8824 | 0.8824 | 0.8824 | 34 | 0.8089 | 0.8803 | 0.8431 | 0.9638 | | 0.0335 | 40.0 | 4240 | 0.1513 | 0.8289 | 0.8630 | 0.8456 | 73 | 0.7067 | 0.8154 | 0.7571 | 65 | 0.8734 | 0.92 | 0.8961 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8205 | 0.9412 | 0.8767 | 34 | 0.8211 | 0.8889 | 0.8536 | 0.9653 | | 0.0317 | 41.0 | 4346 | 0.1541 | 0.8378 | 0.8493 | 0.8435 | 73 | 0.6835 | 0.8308 | 0.75 | 65 | 0.8868 | 0.94 | 0.9126 | 150 | 0.7879 | 0.8966 | 0.8387 | 29 | 0.75 | 0.8824 | 0.8108 | 34 | 0.8130 | 0.8917 | 0.8505 | 0.9633 | | 0.0288 | 42.0 | 4452 | 0.1681 | 0.7922 | 0.8356 | 0.8133 | 73 | 0.6279 | 0.8308 | 0.7152 | 65 | 0.8931 | 0.9467 | 0.9191 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.7838 | 0.8529 | 0.8169 | 34 | 0.7934 | 0.8860 | 0.8371 | 0.9606 | | 0.0275 | 43.0 | 4558 | 0.1761 | 0.7821 | 0.8356 | 0.8079 | 73 | 0.6279 | 0.8308 | 0.7152 | 65 | 0.8931 | 0.9467 | 0.9191 | 150 | 0.7059 | 0.8276 | 0.7619 | 29 | 0.7436 | 0.8529 | 0.7945 | 34 | 0.7828 | 0.8832 | 0.8300 | 0.9604 | | 0.0265 | 44.0 | 4664 | 0.1796 | 0.7922 | 0.8356 | 0.8133 | 73 | 0.7051 | 0.8462 | 0.7692 | 65 | 0.8712 | 0.9467 | 0.9073 | 150 | 0.7273 | 0.8276 | 0.7742 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.8021 | 0.8889 | 0.8432 | 0.9628 | | 0.0271 | 45.0 | 4770 | 0.1760 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7067 | 0.8154 | 0.7571 | 65 | 0.8696 | 0.9333 | 0.9003 | 150 | 0.7812 | 0.8621 | 0.8197 | 29 | 0.8158 | 0.9118 | 0.8611 | 34 | 0.8099 | 0.8860 | 0.8463 | 0.9621 | | 0.026 | 46.0 | 4876 | 0.1910 | 0.7922 | 0.8356 | 0.8133 | 73 | 0.6795 | 0.8154 | 0.7413 | 65 | 0.875 | 0.9333 | 0.9032 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.7632 | 0.8529 | 0.8056 | 34 | 0.8026 | 0.8803 | 0.8397 | 0.9604 | | 0.0264 | 47.0 | 4982 | 0.1727 | 0.8052 | 0.8493 | 0.8267 | 73 | 0.7013 | 0.8308 | 0.7606 | 65 | 0.8696 | 0.9333 | 0.9003 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.8057 | 0.8860 | 0.8440 | 0.9636 | | 0.0252 | 48.0 | 5088 | 0.1840 | 0.7821 | 0.8356 | 0.8079 | 73 | 0.6923 | 0.8308 | 0.7552 | 65 | 0.8758 | 0.94 | 0.9068 | 150 | 0.7812 | 0.8621 | 0.8197 | 29 | 0.7632 | 0.8529 | 0.8056 | 34 | 0.8010 | 0.8832 | 0.8401 | 0.9626 | | 0.0234 | 49.0 | 5194 | 0.1759 | 0.8133 | 0.8356 | 0.8243 | 73 | 0.7183 | 0.7846 | 0.75 | 65 | 0.875 | 0.9333 | 0.9032 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.7692 | 0.8824 | 0.8219 | 34 | 0.8170 | 0.8775 | 0.8462 | 0.9641 | | 0.0227 | 50.0 | 5300 | 0.1780 | 0.7922 | 0.8356 | 0.8133 | 73 | 0.7324 | 0.8 | 0.7647 | 65 | 0.8917 | 0.9333 | 0.9121 | 150 | 0.8387 | 0.8966 | 0.8667 | 29 | 0.8158 | 0.9118 | 0.8611 | 34 | 0.8289 | 0.8832 | 0.8552 | 0.9651 | | 0.0246 | 51.0 | 5406 | 0.1751 | 0.8052 | 0.8493 | 0.8267 | 73 | 0.7391 | 0.7846 | 0.7612 | 65 | 0.8854 | 0.9267 | 0.9055 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8421 | 0.9412 | 0.8889 | 34 | 0.8311 | 0.8832 | 0.8564 | 0.9658 | | 0.0225 | 52.0 | 5512 | 0.1924 | 0.7848 | 0.8493 | 0.8158 | 73 | 0.7333 | 0.8462 | 0.7857 | 65 | 0.8642 | 0.9333 | 0.8974 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.8158 | 0.9118 | 0.8611 | 34 | 0.8088 | 0.8917 | 0.8482 | 0.9636 | | 0.0217 | 53.0 | 5618 | 0.1959 | 0.7778 | 0.8630 | 0.8182 | 73 | 0.7297 | 0.8308 | 0.7770 | 65 | 0.875 | 0.9333 | 0.9032 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.7073 | 0.8529 | 0.7733 | 34 | 0.7995 | 0.8860 | 0.8405 | 0.9621 | | 0.0201 | 54.0 | 5724 | 0.2034 | 0.7922 | 0.8356 | 0.8133 | 73 | 0.6875 | 0.8462 | 0.7586 | 65 | 0.8642 | 0.9333 | 0.8974 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.8158 | 0.9118 | 0.8611 | 34 | 0.8 | 0.8889 | 0.8421 | 0.9611 | | 0.0198 | 55.0 | 5830 | 0.1859 | 0.8158 | 0.8493 | 0.8322 | 73 | 0.6986 | 0.7846 | 0.7391 | 65 | 0.8854 | 0.9267 | 0.9055 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.8143 | 0.8746 | 0.8434 | 0.9633 | | 0.0199 | 56.0 | 5936 | 0.1812 | 0.8182 | 0.8630 | 0.8400 | 73 | 0.7324 | 0.8 | 0.7647 | 65 | 0.8805 | 0.9333 | 0.9061 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.8889 | 0.9412 | 0.9143 | 34 | 0.8298 | 0.8889 | 0.8583 | 0.9663 | | 0.0183 | 57.0 | 6042 | 0.1818 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7260 | 0.8154 | 0.7681 | 65 | 0.8758 | 0.94 | 0.9068 | 150 | 0.7812 | 0.8621 | 0.8197 | 29 | 0.8333 | 0.8824 | 0.8571 | 34 | 0.8184 | 0.8860 | 0.8509 | 0.9655 | | 0.0204 | 58.0 | 6148 | 0.1844 | 0.775 | 0.8493 | 0.8105 | 73 | 0.7206 | 0.7538 | 0.7368 | 65 | 0.8734 | 0.92 | 0.8961 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8421 | 0.9412 | 0.8889 | 34 | 0.8165 | 0.8746 | 0.8446 | 0.9636 | | 0.0171 | 59.0 | 6254 | 0.2010 | 0.7625 | 0.8356 | 0.7974 | 73 | 0.7391 | 0.7846 | 0.7612 | 65 | 0.9032 | 0.9333 | 0.9180 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.7436 | 0.8529 | 0.7945 | 34 | 0.8138 | 0.8718 | 0.8418 | 0.9641 | | 0.0177 | 60.0 | 6360 | 0.1953 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.72 | 0.8308 | 0.7714 | 65 | 0.8974 | 0.9333 | 0.9150 | 150 | 0.7879 | 0.8966 | 0.8387 | 29 | 0.8158 | 0.9118 | 0.8611 | 34 | 0.8237 | 0.8917 | 0.8564 | 0.9651 | | 0.0166 | 61.0 | 6466 | 0.1929 | 0.7922 | 0.8356 | 0.8133 | 73 | 0.7297 | 0.8308 | 0.7770 | 65 | 0.8642 | 0.9333 | 0.8974 | 150 | 0.7812 | 0.8621 | 0.8197 | 29 | 0.8611 | 0.9118 | 0.8857 | 34 | 0.8163 | 0.8860 | 0.8497 | 0.9646 | | 0.0169 | 62.0 | 6572 | 0.1962 | 0.8052 | 0.8493 | 0.8267 | 73 | 0.7324 | 0.8 | 0.7647 | 65 | 0.8861 | 0.9333 | 0.9091 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.7632 | 0.8529 | 0.8056 | 34 | 0.8170 | 0.8775 | 0.8462 | 0.9651 | | 0.0158 | 63.0 | 6678 | 0.2073 | 0.8158 | 0.8493 | 0.8322 | 73 | 0.6353 | 0.8308 | 0.7200 | 65 | 0.8679 | 0.92 | 0.8932 | 150 | 0.7812 | 0.8621 | 0.8197 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.7923 | 0.8803 | 0.8340 | 0.9621 | | 0.0167 | 64.0 | 6784 | 0.1845 | 0.8182 | 0.8630 | 0.8400 | 73 | 0.75 | 0.7846 | 0.7669 | 65 | 0.8974 | 0.9333 | 0.9150 | 150 | 0.7812 | 0.8621 | 0.8197 | 29 | 0.8158 | 0.9118 | 0.8611 | 34 | 0.8356 | 0.8832 | 0.8587 | 0.9678 | | 0.0167 | 65.0 | 6890 | 0.2060 | 0.775 | 0.8493 | 0.8105 | 73 | 0.7432 | 0.8462 | 0.7914 | 65 | 0.8742 | 0.9267 | 0.8997 | 150 | 0.7353 | 0.8621 | 0.7937 | 29 | 0.8158 | 0.9118 | 0.8611 | 34 | 0.8104 | 0.8889 | 0.8478 | 0.9636 | | 0.0167 | 66.0 | 6996 | 0.2054 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7286 | 0.7846 | 0.7556 | 65 | 0.8758 | 0.94 | 0.9068 | 150 | 0.7812 | 0.8621 | 0.8197 | 29 | 0.7436 | 0.8529 | 0.7945 | 34 | 0.8105 | 0.8775 | 0.8427 | 0.9619 | | 0.0148 | 67.0 | 7102 | 0.2037 | 0.7975 | 0.8630 | 0.8289 | 73 | 0.7183 | 0.7846 | 0.75 | 65 | 0.8758 | 0.94 | 0.9068 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.8158 | 0.9118 | 0.8611 | 34 | 0.8141 | 0.8860 | 0.8486 | 0.9636 | | 0.0152 | 68.0 | 7208 | 0.2017 | 0.8182 | 0.8630 | 0.8400 | 73 | 0.7353 | 0.7692 | 0.7519 | 65 | 0.8854 | 0.9267 | 0.9055 | 150 | 0.7812 | 0.8621 | 0.8197 | 29 | 0.8378 | 0.9118 | 0.8732 | 34 | 0.8302 | 0.8775 | 0.8532 | 0.9646 | | 0.0134 | 69.0 | 7314 | 0.2069 | 0.8289 | 0.8630 | 0.8456 | 73 | 0.7042 | 0.7692 | 0.7353 | 65 | 0.8861 | 0.9333 | 0.9091 | 150 | 0.7812 | 0.8621 | 0.8197 | 29 | 0.8611 | 0.9118 | 0.8857 | 34 | 0.8284 | 0.8803 | 0.8536 | 0.9631 | | 0.0153 | 70.0 | 7420 | 0.2087 | 0.7975 | 0.8630 | 0.8289 | 73 | 0.7397 | 0.8308 | 0.7826 | 65 | 0.875 | 0.9333 | 0.9032 | 150 | 0.7353 | 0.8621 | 0.7937 | 29 | 0.7949 | 0.9118 | 0.8493 | 34 | 0.8130 | 0.8917 | 0.8505 | 0.9641 | | 0.0137 | 71.0 | 7526 | 0.2142 | 0.8267 | 0.8493 | 0.8378 | 73 | 0.65 | 0.8 | 0.7172 | 65 | 0.8812 | 0.94 | 0.9097 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.8031 | 0.8832 | 0.8412 | 0.9611 | | 0.015 | 72.0 | 7632 | 0.2135 | 0.8158 | 0.8493 | 0.8322 | 73 | 0.7083 | 0.7846 | 0.7445 | 65 | 0.8805 | 0.9333 | 0.9061 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.8148 | 0.8775 | 0.8450 | 0.9609 | | 0.013 | 73.0 | 7738 | 0.2118 | 0.8077 | 0.8630 | 0.8344 | 73 | 0.7183 | 0.7846 | 0.75 | 65 | 0.875 | 0.9333 | 0.9032 | 150 | 0.7879 | 0.8966 | 0.8387 | 29 | 0.8158 | 0.9118 | 0.8611 | 34 | 0.8184 | 0.8860 | 0.8509 | 0.9633 | | 0.0132 | 74.0 | 7844 | 0.2089 | 0.8026 | 0.8356 | 0.8188 | 73 | 0.7083 | 0.7846 | 0.7445 | 65 | 0.8742 | 0.9267 | 0.8997 | 150 | 0.7879 | 0.8966 | 0.8387 | 29 | 0.8108 | 0.8824 | 0.8451 | 34 | 0.8143 | 0.8746 | 0.8434 | 0.9638 | | 0.0126 | 75.0 | 7950 | 0.1995 | 0.8289 | 0.8630 | 0.8456 | 73 | 0.7286 | 0.7846 | 0.7556 | 65 | 0.9091 | 0.9333 | 0.9211 | 150 | 0.7812 | 0.8621 | 0.8197 | 29 | 0.8611 | 0.9118 | 0.8857 | 34 | 0.8424 | 0.8832 | 0.8623 | 0.9660 | | 0.0128 | 76.0 | 8056 | 0.1982 | 0.8289 | 0.8630 | 0.8456 | 73 | 0.7286 | 0.7846 | 0.7556 | 65 | 0.9091 | 0.9333 | 0.9211 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8857 | 0.9118 | 0.8986 | 34 | 0.8474 | 0.8860 | 0.8663 | 0.9668 | | 0.0123 | 77.0 | 8162 | 0.2078 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7123 | 0.8 | 0.7536 | 65 | 0.8797 | 0.9267 | 0.9026 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.8105 | 0.8775 | 0.8427 | 0.9633 | | 0.013 | 78.0 | 8268 | 0.1980 | 0.8052 | 0.8493 | 0.8267 | 73 | 0.7206 | 0.7538 | 0.7368 | 65 | 0.8854 | 0.9267 | 0.9055 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8378 | 0.9118 | 0.8732 | 34 | 0.8275 | 0.8746 | 0.8504 | 0.9651 | | 0.0122 | 79.0 | 8374 | 0.2100 | 0.7722 | 0.8356 | 0.8026 | 73 | 0.7183 | 0.7846 | 0.75 | 65 | 0.8917 | 0.9333 | 0.9121 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.8170 | 0.8775 | 0.8462 | 0.9641 | | 0.0128 | 80.0 | 8480 | 0.2086 | 0.8158 | 0.8493 | 0.8322 | 73 | 0.7222 | 0.8 | 0.7591 | 65 | 0.875 | 0.9333 | 0.9032 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8378 | 0.9118 | 0.8732 | 34 | 0.8249 | 0.8860 | 0.8544 | 0.9633 | | 0.0117 | 81.0 | 8586 | 0.2055 | 0.8182 | 0.8630 | 0.8400 | 73 | 0.7286 | 0.7846 | 0.7556 | 65 | 0.9038 | 0.94 | 0.9216 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8421 | 0.9412 | 0.8889 | 34 | 0.8391 | 0.8917 | 0.8646 | 0.9658 | | 0.0122 | 82.0 | 8692 | 0.2136 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7183 | 0.7846 | 0.75 | 65 | 0.8917 | 0.9333 | 0.9121 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.8218 | 0.8803 | 0.8501 | 0.9641 | | 0.011 | 83.0 | 8798 | 0.2200 | 0.7848 | 0.8493 | 0.8158 | 73 | 0.6901 | 0.7538 | 0.7206 | 65 | 0.8805 | 0.9333 | 0.9061 | 150 | 0.7812 | 0.8621 | 0.8197 | 29 | 0.7895 | 0.8824 | 0.8333 | 34 | 0.8074 | 0.8718 | 0.8384 | 0.9619 | | 0.0099 | 84.0 | 8904 | 0.2109 | 0.8077 | 0.8630 | 0.8344 | 73 | 0.7183 | 0.7846 | 0.75 | 65 | 0.8917 | 0.9333 | 0.9121 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8611 | 0.9118 | 0.8857 | 34 | 0.8316 | 0.8860 | 0.8579 | 0.9648 | | 0.0109 | 85.0 | 9010 | 0.2205 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7286 | 0.7846 | 0.7556 | 65 | 0.8861 | 0.9333 | 0.9091 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8108 | 0.8824 | 0.8451 | 34 | 0.824 | 0.8803 | 0.8512 | 0.9641 | | 0.0107 | 86.0 | 9116 | 0.2110 | 0.8077 | 0.8630 | 0.8344 | 73 | 0.7353 | 0.7692 | 0.7519 | 65 | 0.8917 | 0.9333 | 0.9121 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8378 | 0.9118 | 0.8732 | 34 | 0.8333 | 0.8832 | 0.8575 | 0.9643 | | 0.0101 | 87.0 | 9222 | 0.2093 | 0.8182 | 0.8630 | 0.8400 | 73 | 0.7391 | 0.7846 | 0.7612 | 65 | 0.8924 | 0.94 | 0.9156 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8857 | 0.9118 | 0.8986 | 34 | 0.8410 | 0.8889 | 0.8643 | 0.9655 | | 0.0111 | 88.0 | 9328 | 0.2124 | 0.8182 | 0.8630 | 0.8400 | 73 | 0.7391 | 0.7846 | 0.7612 | 65 | 0.8868 | 0.94 | 0.9126 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8378 | 0.9118 | 0.8732 | 34 | 0.8342 | 0.8889 | 0.8607 | 0.9651 | | 0.0095 | 89.0 | 9434 | 0.2123 | 0.8077 | 0.8630 | 0.8344 | 73 | 0.7286 | 0.7846 | 0.7556 | 65 | 0.8924 | 0.94 | 0.9156 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8108 | 0.8824 | 0.8451 | 34 | 0.8293 | 0.8860 | 0.8567 | 0.9653 | | 0.0105 | 90.0 | 9540 | 0.2122 | 0.8077 | 0.8630 | 0.8344 | 73 | 0.7429 | 0.8 | 0.7704 | 65 | 0.8868 | 0.94 | 0.9126 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8333 | 0.8824 | 0.8571 | 34 | 0.832 | 0.8889 | 0.8595 | 0.9653 | | 0.0102 | 91.0 | 9646 | 0.2248 | 0.7848 | 0.8493 | 0.8158 | 73 | 0.7297 | 0.8308 | 0.7770 | 65 | 0.8812 | 0.94 | 0.9097 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8378 | 0.9118 | 0.8732 | 34 | 0.8220 | 0.8946 | 0.8568 | 0.9636 | | 0.0104 | 92.0 | 9752 | 0.2231 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7324 | 0.8 | 0.7647 | 65 | 0.8812 | 0.94 | 0.9097 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8108 | 0.8824 | 0.8451 | 34 | 0.8228 | 0.8860 | 0.8532 | 0.9633 | | 0.0096 | 93.0 | 9858 | 0.2227 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7260 | 0.8154 | 0.7681 | 65 | 0.8812 | 0.94 | 0.9097 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.8108 | 0.8824 | 0.8451 | 34 | 0.8211 | 0.8889 | 0.8536 | 0.9638 | | 0.0106 | 94.0 | 9964 | 0.2312 | 0.7848 | 0.8493 | 0.8158 | 73 | 0.7260 | 0.8154 | 0.7681 | 65 | 0.8758 | 0.94 | 0.9068 | 150 | 0.7576 | 0.8621 | 0.8065 | 29 | 0.8108 | 0.8824 | 0.8451 | 34 | 0.8120 | 0.8860 | 0.8474 | 0.9616 | | 0.0096 | 95.0 | 10070 | 0.2199 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7324 | 0.8 | 0.7647 | 65 | 0.8812 | 0.94 | 0.9097 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.7838 | 0.8529 | 0.8169 | 34 | 0.8201 | 0.8832 | 0.8505 | 0.9633 | | 0.0098 | 96.0 | 10176 | 0.2199 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7324 | 0.8 | 0.7647 | 65 | 0.8868 | 0.94 | 0.9126 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.7838 | 0.8529 | 0.8169 | 34 | 0.8223 | 0.8832 | 0.8516 | 0.9641 | | 0.0099 | 97.0 | 10282 | 0.2210 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7297 | 0.8308 | 0.7770 | 65 | 0.8924 | 0.94 | 0.9156 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.7838 | 0.8529 | 0.8169 | 34 | 0.8232 | 0.8889 | 0.8548 | 0.9641 | | 0.0094 | 98.0 | 10388 | 0.2210 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7123 | 0.8 | 0.7536 | 65 | 0.8868 | 0.94 | 0.9126 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.7838 | 0.8529 | 0.8169 | 34 | 0.8179 | 0.8832 | 0.8493 | 0.9633 | | 0.0086 | 99.0 | 10494 | 0.2204 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7361 | 0.8154 | 0.7737 | 65 | 0.8924 | 0.94 | 0.9156 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.7838 | 0.8529 | 0.8169 | 34 | 0.8249 | 0.8860 | 0.8544 | 0.9638 | | 0.0109 | 100.0 | 10600 | 0.2206 | 0.7949 | 0.8493 | 0.8212 | 73 | 0.7361 | 0.8154 | 0.7737 | 65 | 0.8924 | 0.94 | 0.9156 | 150 | 0.8125 | 0.8966 | 0.8525 | 29 | 0.7838 | 0.8529 | 0.8169 | 34 | 0.8249 | 0.8860 | 0.8544 | 0.9638 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2

bella05/pogny_10_64_0.01

bella05

2024-06-03T23:50:21Z

8

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:klue/roberta-large", "base_model:finetune:klue/roberta-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-03T19:35:38Z

--- base_model: klue/roberta-large tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: pogny_10_64_0.01 results: [] ---  [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/bella05/huggingface/runs/2fqy4l1d) # pogny_10_64_0.01 This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6851 - Accuracy: 0.4376 - F1: 0.2665 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 2.491 | 1.0 | 1205 | 2.5033 | 0.4376 | 0.2665 | | 2.4679 | 2.0 | 2410 | 1.9460 | 0.4376 | 0.2665 | | 2.302 | 3.0 | 3615 | 2.4098 | 0.0702 | 0.0092 | | 2.1762 | 4.0 | 4820 | 2.2698 | 0.0545 | 0.0056 | | 2.0639 | 5.0 | 6025 | 1.9917 | 0.4376 | 0.2665 | | 2.0031 | 6.0 | 7230 | 1.9130 | 0.4376 | 0.2665 | | 1.9241 | 7.0 | 8435 | 2.0131 | 0.4376 | 0.2665 | | 1.8227 | 8.0 | 9640 | 1.8212 | 0.4376 | 0.2665 | | 1.7854 | 9.0 | 10845 | 1.7379 | 0.4376 | 0.2665 | | 1.7037 | 10.0 | 12050 | 1.6851 | 0.4376 | 0.2665 | ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.2 - Datasets 2.19.1 - Tokenizers 0.19.1

kevinvelez18/ViT_model

kevinvelez18

2024-06-03T23:46:48Z

222

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-06-03T23:43:33Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: ViT_model results: [] ---  # ViT_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0252 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.1492 | 3.8462 | 500 | 0.0252 | 0.9925 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

bartowski/phi3-4x4b-v1-GGUF

bartowski

2024-06-03T23:40:50Z

192

0

null

[ "gguf", "phi3", "nlp", "moe", "text-generation", "dataset:BEE-spoke-data/gutenberg-en-v1-clean", "dataset:NeelNanda/pile-10k", "license:mit", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2024-06-03T23:18:02Z

--- license: mit tags: - phi3 - nlp - moe datasets: - BEE-spoke-data/gutenberg-en-v1-clean - NeelNanda/pile-10k quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of phi3-4x4b-v1 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3070">b3070</a> for quantization. Original model: https://huggingface.co/Fizzarolli/phi3-4x4b-v1 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) ## Prompt format ``` <s><|user|> {prompt}<|end|><|assistant|><|end|> ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [phi3-4x4b-v1-Q8_0.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q8_0.gguf) | Q8_0 | 11.76GB | Extremely high quality, generally unneeded but max available quant. | | [phi3-4x4b-v1-Q6_K.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q6_K.gguf) | Q6_K | 9.08GB | Very high quality, near perfect, *recommended*. | | [phi3-4x4b-v1-Q5_K_M.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q5_K_M.gguf) | Q5_K_M | 7.85GB | High quality, *recommended*. | | [phi3-4x4b-v1-Q5_K_S.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q5_K_S.gguf) | Q5_K_S | 7.62GB | High quality, *recommended*. | | [phi3-4x4b-v1-Q4_K_M.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q4_K_M.gguf) | Q4_K_M | 6.70GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [phi3-4x4b-v1-Q4_K_S.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q4_K_S.gguf) | Q4_K_S | 6.30GB | Slightly lower quality with more space savings, *recommended*. | | [phi3-4x4b-v1-IQ4_XS.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ4_XS.gguf) | IQ4_XS | 5.91GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [phi3-4x4b-v1-Q3_K_L.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q3_K_L.gguf) | Q3_K_L | 5.78GB | Lower quality but usable, good for low RAM availability. | | [phi3-4x4b-v1-Q3_K_M.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q3_K_M.gguf) | Q3_K_M | 5.33GB | Even lower quality. | | [phi3-4x4b-v1-IQ3_M.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ3_M.gguf) | IQ3_M | 4.93GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [phi3-4x4b-v1-Q3_K_S.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q3_K_S.gguf) | Q3_K_S | 4.79GB | Low quality, not recommended. | | [phi3-4x4b-v1-IQ3_XS.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ3_XS.gguf) | IQ3_XS | 4.54GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [phi3-4x4b-v1-IQ3_XXS.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ3_XXS.gguf) | IQ3_XXS | 4.25GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [phi3-4x4b-v1-Q2_K.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-Q2_K.gguf) | Q2_K | 4.07GB | Very low quality but surprisingly usable. | | [phi3-4x4b-v1-IQ2_M.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ2_M.gguf) | IQ2_M | 3.74GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [phi3-4x4b-v1-IQ2_S.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ2_S.gguf) | IQ2_S | 3.43GB | Very low quality, uses SOTA techniques to be usable. | | [phi3-4x4b-v1-IQ2_XS.gguf](https://huggingface.co/bartowski/phi3-4x4b-v1-GGUF/blob/main/phi3-4x4b-v1-IQ2_XS.gguf) | IQ2_XS | 3.34GB | Very low quality, uses SOTA techniques to be usable. | ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/phi3-4x4b-v1-GGUF --include "phi3-4x4b-v1-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/phi3-4x4b-v1-GGUF --include "phi3-4x4b-v1-Q8_0.gguf/*" --local-dir phi3-4x4b-v1-Q8_0 ``` You can either specify a new local-dir (phi3-4x4b-v1-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

sj21867/ai_art_exp3_mobilenetv2

sj21867

2024-06-03T23:25:00Z

193

0

transformers

[ "transformers", "tensorboard", "safetensors", "mobilenet_v2", "image-classification", "generated_from_trainer", "base_model:google/mobilenet_v2_1.0_224", "base_model:finetune:google/mobilenet_v2_1.0_224", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-06-03T23:23:07Z

--- license: other base_model: google/mobilenet_v2_1.0_224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_art_exp3_mobilenetv2 results: [] ---  # ai_art_exp3_mobilenetv2 This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on an unknown dataset. It achieves the following results on the evaluation set: - Accuracy: {'accuracy': 0.65} - Loss: 0.8813 - Overall Accuracy: 0.65 - Human Accuracy: 0.34 - Ld Accuracy: 0.84 - Sd Accuracy: 0.77 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | Overall Accuracy | Human Accuracy | Ld Accuracy | Sd Accuracy | |:-------------:|:-----:|:----:|:--------------------------------:|:---------------:|:----------------:|:--------------:|:-----------:|:-----------:| | 1.0707 | 0.96 | 18 | {'accuracy': 0.6333333333333333} | 0.8947 | 0.6333 | 0.3426 | 0.8485 | 0.7419 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

sj21867/ai_art_exp3_vit

sj21867

2024-06-03T23:18:54Z

195

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-06-03T23:16:24Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_art_exp3_vit results: [] ---  # ai_art_exp3_vit This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Accuracy: {'accuracy': 0.72} - Loss: 0.9013 - Overall Accuracy: 0.72 - Human Accuracy: 0.34 - Ld Accuracy: 0.9 - Sd Accuracy: 0.92 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | Overall Accuracy | Human Accuracy | Ld Accuracy | Sd Accuracy | |:-------------:|:-----:|:----:|:------------------:|:---------------:|:----------------:|:--------------:|:-----------:|:-----------:| | 1.0375 | 0.96 | 18 | {'accuracy': 0.72} | 0.9182 | 0.72 | 0.3889 | 0.9192 | 0.8925 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF

radia

2024-06-03T23:15:38Z

2

0

null

[ "gguf", "nlp", "code", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:microsoft/Phi-3-mini-128k-instruct", "base_model:quantized:microsoft/Phi-3-mini-128k-instruct", "license:mit", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2024-06-03T23:15:32Z

--- language: - en license: mit tags: - nlp - code - llama-cpp - gguf-my-repo base_model: microsoft/Phi-3-mini-128k-instruct license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE pipeline_tag: text-generation widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- # radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`microsoft/Phi-3-mini-128k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-128k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-128k-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-128k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo radia/Phi-3-mini-128k-instruct-Q4_K_M-GGUF --hf-file phi-3-mini-128k-instruct-q4_k_m.gguf -c 2048 ```

mayabedge/whisper-ft

mayabedge

2024-06-03T23:05:31Z

92

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-06-03T22:39:07Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper Fine-tuned - NNCES results: [] ---  # Whisper Fine-tuned - NNCES This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1135 - Wer: 8.0963 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 1.2697 | 0.1 | 10 | 0.8252 | 40.9920 | | 0.6597 | 0.2 | 20 | 0.5482 | 25.2371 | | 0.4656 | 0.3 | 30 | 0.3488 | 20.0584 | | 0.2774 | 0.4 | 40 | 0.2164 | 21.5901 | | 0.1746 | 0.5 | 50 | 0.1770 | 19.0372 | | 0.1826 | 0.6 | 60 | 0.1540 | 15.3902 | | 0.1228 | 0.7 | 70 | 0.1364 | 11.4515 | | 0.1271 | 0.8 | 80 | 0.1246 | 8.6798 | | 0.2388 | 0.9 | 90 | 0.1165 | 8.0233 | | 0.2584 | 1.0 | 100 | 0.1135 | 8.0963 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

datek/Qwen-Qwen1.5-1.8B-1717455654

datek

2024-06-03T23:02:36Z

139

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-03T23:00:56Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

cetusian/ner-model-furniture

cetusian

2024-06-03T23:00:08Z

73

0

transformers

[ "transformers", "tf", "distilbert", "token-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-06-03T22:48:43Z

--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: cetusian/ner-model-furniture results: [] ---  # cetusian/ner-model-furniture This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3445 - Validation Loss: 0.3911 - Train Precision: 0.7212 - Train Recall: 0.7764 - Train F1: 0.7478 - Train Accuracy: 0.8465 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 348, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.3467 | 0.3911 | 0.7212 | 0.7764 | 0.7478 | 0.8465 | 0 | | 0.3445 | 0.3911 | 0.7212 | 0.7764 | 0.7478 | 0.8465 | 1 | ### Framework versions - Transformers 4.41.1 - TensorFlow 2.15.0 - Datasets 2.19.2 - Tokenizers 0.19.1

mradermacher/llama-3-70B-openbio-dareties-GGUF

mradermacher

2024-06-03T22:54:25Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:toantam1290/llama-3-70B-openbio-dareties", "base_model:quantized:toantam1290/llama-3-70B-openbio-dareties", "endpoints_compatible", "region:us", "conversational" ]

null

2024-06-03T17:54:45Z

--- base_model: toantam1290/llama-3-70B-openbio-dareties language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/toantam1290/llama-3-70B-openbio-dareties  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q2_K.gguf) | Q2_K | 26.5 | | | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.IQ3_XS.gguf) | IQ3_XS | 29.4 | | | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.IQ3_S.gguf) | IQ3_S | 31.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q3_K_S.gguf) | Q3_K_S | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.IQ3_M.gguf) | IQ3_M | 32.0 | | | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q3_K_L.gguf) | Q3_K_L | 37.2 | | | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.IQ4_XS.gguf) | IQ4_XS | 38.4 | | | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q5_K_S.gguf) | Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q5_K_M.gguf) | Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality | | [PART 1](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/llama-3-70B-openbio-dareties-GGUF/resolve/main/llama-3-70B-openbio-dareties.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF

wwe180

2024-06-03T22:51:53Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:wwe180/Llama3-13B-lingyang-v1", "base_model:quantized:wwe180/Llama3-13B-lingyang-v1", "endpoints_compatible", "region:us", "conversational" ]

null

2024-06-03T21:24:10Z

--- library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: wwe180/Llama3-13B-lingyang-v1 --- # wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF This model was converted to GGUF format from [`wwe180/Llama3-13B-lingyang-v1`](https://huggingface.co/wwe180/Llama3-13B-lingyang-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/wwe180/Llama3-13B-lingyang-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF --hf-file Llama3-13B-lingyang-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF --hf-file Llama3-13B-lingyang-v1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF --hf-file Llama3-13B-lingyang-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo wwe180/Llama3-13B-lingyang-v1-Q4_K_M-GGUF --hf-file Llama3-13B-lingyang-v1-q4_k_m.gguf -c 2048 ```

Molkaa/mistral-7b-miniplatypus

Molkaa

2024-06-03T22:51:51Z

1

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "region:us" ]

null

2024-05-29T19:10:41Z

--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.1 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1

Ariffiq99/KUCI_e_care_xlm_roberta_base_Finetuned

Ariffiq99

2024-06-03T22:49:13Z

103

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:Ariffiq99/e_care_xlm_roberta_base_finetuned", "base_model:finetune:Ariffiq99/e_care_xlm_roberta_base_finetuned", "license:mit", "endpoints_compatible", "region:us" ]

multiple-choice

2024-06-03T09:35:11Z

--- license: mit base_model: Ariffiq99/e_care_xlm_roberta_base_finetuned tags: - generated_from_trainer metrics: - f1 model-index: - name: KUCI_e_care_xlm_roberta_base_Finetuned results: [] ---  # KUCI_e_care_xlm_roberta_base_Finetuned This model is a fine-tuned version of [Ariffiq99/e_care_xlm_roberta_base_finetuned](https://huggingface.co/Ariffiq99/e_care_xlm_roberta_base_finetuned) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0348 - F1: 0.7682 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.6965 | 1.0 | 5196 | 0.6542 | 0.7443 | | 0.5632 | 2.0 | 10392 | 0.6754 | 0.7591 | | 0.4634 | 3.0 | 15588 | 0.6456 | 0.7680 | | 0.3661 | 4.0 | 20784 | 0.7082 | 0.7657 | | 0.2783 | 5.0 | 25980 | 0.7899 | 0.7678 | | 0.2305 | 6.0 | 31176 | 0.9280 | 0.7655 | | 0.2057 | 7.0 | 36372 | 1.0348 | 0.7682 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

animaRegem/gemma-2b-malayalam-t2-gguf

animaRegem

2024-06-03T22:49:09Z

7

0

transformers

[ "transformers", "gguf", "gemma", "text-generation-inference", "unsloth", "en", "base_model:Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0", "base_model:quantized:Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-03T21:37:07Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - gguf base_model: Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 --- # Uploaded model - **Developed by:** animaRegem - **License:** apache-2.0 - **Finetuned from model :** Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

wwe180/Llama3-13B-lingyang-v1

wwe180

2024-06-03T22:47:07Z

7

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "Llama3", "conversational", "base_model:wwe180/Llama3-13B-lingyang-v1", "base_model:finetune:wwe180/Llama3-13B-lingyang-v1", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-03T20:25:39Z

--- base_model: - wwe180/Llama3-13B-lingyang-v1 library_name: transformers tags: - mergekit - merge - Llama3 license: - other --- # After simple testing, the effect is good, stronger than llama-3-8b! # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method using [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) as a base. ### Models Merged The following models were included in the merge: * [openchat/openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522) + [hfl/llama-3-chinese-8b-instruct-v2-lora](https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v2-lora) * [Sao10K/L3-8B-Stheno-v3.1](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.1) + [Jiar/Llama-3-8B-Chinese](https://huggingface.co/Jiar/Llama-3-8B-Chinese) * [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B) + [camillop/Meta-Llama-3-8B-ORPO-ITA-llama-adapters](https://huggingface.co/camillop/Meta-Llama-3-8B-ORPO-ITA-llama-adapters) ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Llama3-13B-lingyang-v1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) ``` ## Statement: Llama3-13B-lingyang-v1 does not represent the views and positions of the model developers We will not be liable for any problems arising from the use of the Llama3-13B-lingyang-v1 open Source model, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.

kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF

kaushiksiva07

2024-06-03T22:45:50Z

40

0

null

[ "gguf", "finetuned", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2024-06-03T22:45:38Z

--- license: apache-2.0 tags: - finetuned - llama-cpp - gguf-my-repo base_model: mistralai/Mistral-7B-Instruct-v0.2 pipeline_tag: text-generation inference: true widget: - messages: - role: user content: What is your favorite condiment? --- # kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF This model was converted to GGUF format from [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF --hf-file mistral-7b-instruct-v0.2-q4_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF --hf-file mistral-7b-instruct-v0.2-q4_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF --hf-file mistral-7b-instruct-v0.2-q4_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo kaushiksiva07/Mistral-7B-Instruct-v0.2-Q4_0-GGUF --hf-file mistral-7b-instruct-v0.2-q4_0.gguf -c 2048 ```

shane062/whisper-small-finetuned-300

shane062

2024-06-03T22:43:21Z

90

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:audiofolder", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-06-03T04:06:31Z

--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - audiofolder metrics: - wer model-index: - name: whisper-small-finetuned-300 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: audiofolder type: audiofolder config: default split: test args: default metrics: - name: Wer type: wer value: 64.86486486486487 ---  # whisper-small-finetuned-300 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7359 - Wer Ortho: 64.8649 - Wer: 64.8649 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 30 - training_steps: 300 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 0.5121 | 20.0 | 60 | 1.3011 | 64.8649 | 64.8649 | | 0.0001 | 40.0 | 120 | 0.7236 | 64.8649 | 64.8649 | | 0.0 | 60.0 | 180 | 0.7314 | 64.8649 | 64.8649 | | 0.0 | 80.0 | 240 | 0.7340 | 64.8649 | 64.8649 | | 0.0 | 100.0 | 300 | 0.7359 | 64.8649 | 64.8649 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

hdve/google-gemma-7b-1717454317

hdve

2024-06-03T22:41:22Z

7

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-06-03T22:38:39Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

enriquesaou/debug_seq2seq_squad

enriquesaou

2024-06-03T22:38:04Z

8

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:squad_v2", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-06-03T20:36:52Z

--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: debug_seq2seq_squad results: [] ---  [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/favcowboy/huggingface/runs/wdlupjr7) # debug_seq2seq_squad This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.7565 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

animaRegem/gemma-2b-malayalam-t2-model-adaptors

animaRegem

2024-06-03T22:37:38Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0", "base_model:finetune:Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-03T21:27:08Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 --- # Uploaded model - **Developed by:** animaRegem - **License:** apache-2.0 - **Finetuned from model :** Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0 This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Magedyoussef86/Maged

Magedyoussef86

2024-06-03T22:33:39Z

0

null

[ "license:artistic-2.0", "region:us" ]

null

2024-06-03T22:33:38Z

--- license: artistic-2.0 ---