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ftopal
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huggingface-models-embeddings

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<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/second-state/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Qwen1.5-4B-Chat-GGUF ## Original Model [Qwen/Qwen1.5-4B-Chat](https://huggingface.co/Qwen/Qwen1.5-4B-Chat) ## Run with LlamaEdge - LlamaEdge version: [v0.2.15](https://github.com/second-state/LlamaEdge/releases/tag/0.2.15) and above - Prompt template - Prompt type: `chatml` - Prompt string ```text <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-4B-Chat-Q5_K_M.gguf llama-api-server.wasm -p chatml ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-4B-Chat-Q5_K_M.gguf llama-chat.wasm -p chatml ``` ## Quantized GGUF Models | Name | Quant method | Bits | Size | Use case | | ---- | ---- | ---- | ---- | ----- | | [Qwen1.5-4B-Chat-Q2_K.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q2_K.gguf) | Q2_K | 2 | 1.62 GB| smallest, significant quality loss - not recommended for most purposes | | [Qwen1.5-4B-Chat-Q3_K_L.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q3_K_L.gguf) | Q3_K_L | 3 | 2.17 GB| small, substantial quality loss | | [Qwen1.5-4B-Chat-Q3_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q3_K_M.gguf) | Q3_K_M | 3 | 2.03 GB| very small, high quality loss | | [Qwen1.5-4B-Chat-Q3_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q3_K_S.gguf) | Q3_K_S | 3 | 1.86 GB| very small, high quality loss | | [Qwen1.5-4B-Chat-Q4_0.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q4_0.gguf) | Q4_0 | 4 | 2.33 GB| legacy; small, very high quality loss - prefer using Q3_K_M | | [Qwen1.5-4B-Chat-Q4_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q4_K_M.gguf) | Q4_K_M | 4 | 2.46 GB| medium, balanced quality - recommended | | [Qwen1.5-4B-Chat-Q4_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q4_K_S.gguf) | Q4_K_S | 4 | 2.34 GB| small, greater quality loss | | [Qwen1.5-4B-Chat-Q5_0.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q5_0.gguf) | Q5_0 | 5 | 2.78 GB| legacy; medium, balanced quality - prefer using Q4_K_M | | [Qwen1.5-4B-Chat-Q5_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q5_K_M.gguf) | Q5_K_M | 5 | 2.84 GB| large, very low quality loss - recommended | | [Qwen1.5-4B-Chat-Q5_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q5_K_S.gguf) | Q5_K_S | 5 | 2.78 GB| large, low quality loss - recommended | | [Qwen1.5-4B-Chat-Q6_K.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q6_K.gguf) | Q6_K | 6 | 3.25 GB| very large, extremely low quality loss | | [Qwen1.5-4B-Chat-Q8_0.gguf](https://huggingface.co/second-state/Qwen1.5-4B-Chat-GGUF/blob/main/Qwen1.5-4B-Chat-Q8_0.gguf) | Q8_0 | 8 | 4.2 GB| very large, extremely low quality loss - not recommended |
{"language": ["en"], "license": "other", "tags": ["chat"], "model_name": "Qwen1.5 4B Chat", "base_model": "Qwen/Qwen1.5-4B-Chat", "license_name": "tongyi-qianwen-research", "license_link": "https://huggingface.co/Qwen/Qwen1.5-4B-Chat/blob/main/LICENSE", "model_creator": "Qwen", "quantized_by": "Second State Inc.", "pipeline_tag": "text-generation"}
text-generation
second-state/Qwen1.5-4B-Chat-GGUF
[ "gguf", "chat", "text-generation", "en", "base_model:Qwen/Qwen1.5-4B-Chat", "license:other", "region:us" ]
2024-02-06T08:32:37+00:00
[]
[ "en" ]
TAGS #gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-4B-Chat #license-other #region-us
![](URL style=) --- Qwen1.5-4B-Chat-GGUF ==================== Original Model -------------- Qwen/Qwen1.5-4B-Chat Run with LlamaEdge ------------------ * LlamaEdge version: v0.2.15 and above * Prompt template + Prompt type: 'chatml' + Prompt string * Run as LlamaEdge service * Run as LlamaEdge command app Quantized GGUF Models ---------------------
[]
[ "TAGS\n#gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-4B-Chat #license-other #region-us \n" ]
[ 38 ]
[ "passage: TAGS\n#gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-4B-Chat #license-other #region-us \n" ]
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null
null
diffusers
### My-Pet-cat Dreambooth model trained by tarun8340 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 23117107012 Sample pictures of this concept: ![0](https://huggingface.co/tarun8340/my-pet-cat/resolve/main/sample_images/cat_(2).png)
{"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]}
text-to-image
tarun8340/my-pet-cat
[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
2024-02-06T08:34:17+00:00
[]
[]
TAGS #diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
### My-Pet-cat Dreambooth model trained by tarun8340 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 23117107012 Sample pictures of this concept: !0.png)
[ "### My-Pet-cat Dreambooth model trained by tarun8340 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 23117107012\n\nSample pictures of this concept:\n\n !0.png)" ]
[ "TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "### My-Pet-cat Dreambooth model trained by tarun8340 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 23117107012\n\nSample pictures of this concept:\n\n !0.png)" ]
[ 73, 57 ]
[ "passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### My-Pet-cat Dreambooth model trained by tarun8340 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 23117107012\n\nSample pictures of this concept:\n\n !0.png)" ]
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null
null
transformers
<!-- 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. --> # stablelm-2-glados-v1 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0838 ## 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: 1 - eval_batch_size: 8 - 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 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9551 | 1.0 | 20 | 1.0149 | | 0.6793 | 1.99 | 40 | 1.0838 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu118 - Datasets 2.17.0 - Tokenizers 0.15.1
{"tags": ["generated_from_trainer"], "model-index": [{"name": "stablelm-2-glados-v1", "results": []}]}
text-generation
torphix/stablelm-2-glados-v1
[ "transformers", "safetensors", "stablelm_epoch", "text-generation", "generated_from_trainer", "conversational", "custom_code", "autotrain_compatible", "region:us" ]
2024-02-06T08:40:36+00:00
[]
[]
TAGS #transformers #safetensors #stablelm_epoch #text-generation #generated_from_trainer #conversational #custom_code #autotrain_compatible #region-us
stablelm-2-glados-v1 ==================== This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.0838 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: 1 * eval\_batch\_size: 8 * 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 * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu118 * Datasets 2.17.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #stablelm_epoch #text-generation #generated_from_trainer #conversational #custom_code #autotrain_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 50, 126, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #stablelm_epoch #text-generation #generated_from_trainer #conversational #custom_code #autotrain_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
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null
null
transformers
## Model Summery MobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale. The MobileVLM_V2-1.7B was built on our [MobileLLaMA-1.4B-Chat](](https://huggingface.co/mtgv/MobileLLaMA-1.4B-Chat)) to facilitate the off-the-shelf deployment. ## Model Sources - Repository: https://github.com/Meituan-AutoML/MobileVLM - Paper: [MobileVLM V2: Faster and Stronger Baseline for Vision Language Model](https://arxiv.org/abs/2402.03766) ## How to Get Started with the Model Inference examples can be found at [Github](https://github.com/Meituan-AutoML/MobileVLM).
{"license": "apache-2.0", "tags": ["MobileVLM V2"]}
text-generation
mtgv/MobileVLM_V2-1.7B
[ "transformers", "pytorch", "mobilevlm", "text-generation", "MobileVLM V2", "arxiv:2402.03766", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T08:41:05+00:00
[ "2402.03766" ]
[]
TAGS #transformers #pytorch #mobilevlm #text-generation #MobileVLM V2 #arxiv-2402.03766 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
## Model Summery MobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale. The MobileVLM_V2-1.7B was built on our MobileLLaMA-1.4B-Chat) to facilitate the off-the-shelf deployment. ## Model Sources - Repository: URL - Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model ## How to Get Started with the Model Inference examples can be found at Github.
[ "## Model Summery\nMobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale.\n\nThe MobileVLM_V2-1.7B was built on our MobileLLaMA-1.4B-Chat) to facilitate the off-the-shelf deployment.", "## Model Sources\n- Repository: URL\n- Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model", "## How to Get Started with the Model\nInference examples can be found at Github." ]
[ "TAGS\n#transformers #pytorch #mobilevlm #text-generation #MobileVLM V2 #arxiv-2402.03766 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "## Model Summery\nMobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale.\n\nThe MobileVLM_V2-1.7B was built on our MobileLLaMA-1.4B-Chat) to facilitate the off-the-shelf deployment.", "## Model Sources\n- Repository: URL\n- Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model", "## How to Get Started with the Model\nInference examples can be found at Github." ]
[ 60, 176, 30, 22 ]
[ "passage: TAGS\n#transformers #pytorch #mobilevlm #text-generation #MobileVLM V2 #arxiv-2402.03766 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## Model Summery\nMobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale.\n\nThe MobileVLM_V2-1.7B was built on our MobileLLaMA-1.4B-Chat) to facilitate the off-the-shelf deployment.## Model Sources\n- Repository: URL\n- Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model## How to Get Started with the Model\nInference examples can be found at Github." ]
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null
null
peft
# Model Card for Model ID An open-source LLM to answer context-specific questions about Malawian public health processes, case definitions and guidelines, with training done on a dataset derived from the Malawi TGs for IDSR. ## 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 0.8.2
{"library_name": "peft", "base_model": "microsoft/phi-2"}
null
Marsrover1997/Malawi-PHI2
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/phi-2", "region:us" ]
2024-02-06T08:44:39+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-microsoft/phi-2 #region-us
# Model Card for Model ID An open-source LLM to answer context-specific questions about Malawian public health processes, case definitions and guidelines, with training done on a dataset derived from the Malawi TGs for IDSR. ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID\n\nAn open-source LLM to answer context-specific questions about Malawian public health processes, \ncase definitions and guidelines, with training done on a dataset derived from the Malawi TGs for IDSR.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-microsoft/phi-2 #region-us \n", "# Model Card for Model ID\n\nAn open-source LLM to answer context-specific questions about Malawian public health processes, \ncase definitions and guidelines, with training done on a dataset derived from the Malawi TGs for IDSR.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 32, 52, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-microsoft/phi-2 #region-us \n# Model Card for Model ID\n\nAn open-source LLM to answer context-specific questions about Malawian public health processes, \ncase definitions and guidelines, with training done on a dataset derived from the Malawi TGs for IDSR.## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
stable-baselines3
# **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** 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 ... ```
{"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-11.07 +/- 4.09", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
theZoo/a2c-PandaReachDense-v3
[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-06T08:44:53+00:00
[]
[]
TAGS #stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 41, 45, 17 ]
[ "passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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# **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="markberry2010/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
markberry2010/q-FrozenLake-v1-4x4-noSlippery
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
2024-02-06T08:47:32+00:00
[]
[]
TAGS #FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage
[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
[ 40, 39 ]
[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
question-answering
Poulami/xlm-roberta-base-finetuned-QA-SQuADv2
[ "transformers", "safetensors", "xlm-roberta", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-06T08:48:42+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #xlm-roberta #question-answering #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #xlm-roberta #question-answering #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 43, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#transformers #safetensors #xlm-roberta #question-answering #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="markberry2010/unit2", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
{"tags": ["CliffWalking-v0", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "unit2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CliffWalking-v0", "type": "CliffWalking-v0"}, "metrics": [{"type": "mean_reward", "value": "-13.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
markberry2010/unit2
[ "CliffWalking-v0", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
2024-02-06T08:50:32+00:00
[]
[]
TAGS #CliffWalking-v0 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage
[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
[ "TAGS\n#CliffWalking-v0 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
[ 35, 33 ]
[ "passage: TAGS\n#CliffWalking-v0 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
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null
null
diffusers
### My-cat Dreambooth model trained by tarun8340 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 23117107028 Sample pictures of this concept: ![0](https://huggingface.co/tarun8340/my-cat/resolve/main/sample_images/cat_(1).png)
{"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]}
text-to-image
tarun8340/my-cat
[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
2024-02-06T08:51:36+00:00
[]
[]
TAGS #diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
### My-cat Dreambooth model trained by tarun8340 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 23117107028 Sample pictures of this concept: !0.png)
[ "### My-cat Dreambooth model trained by tarun8340 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 23117107028\n\nSample pictures of this concept:\n\n !0.png)" ]
[ "TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "### My-cat Dreambooth model trained by tarun8340 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 23117107028\n\nSample pictures of this concept:\n\n !0.png)" ]
[ 73, 54 ]
[ "passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### My-cat Dreambooth model trained by tarun8340 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 23117107028\n\nSample pictures of this concept:\n\n !0.png)" ]
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null
null
diffusers
# LoRA text2image fine-tuning - litvan/sd-v1.5-russian_churches These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the litvan/russian_churches_with_blip_captioning dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "runwayml/stable-diffusion-v1-5", "inference": true}
text-to-image
litvan/sd-v1.5-russian_churches
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
2024-02-06T08:53:31+00:00
[]
[]
TAGS #diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us
# LoRA text2image fine-tuning - litvan/sd-v1.5-russian_churches These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the litvan/russian_churches_with_blip_captioning dataset. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3
[ "# LoRA text2image fine-tuning - litvan/sd-v1.5-russian_churches\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the litvan/russian_churches_with_blip_captioning dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]
[ "TAGS\n#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n", "# LoRA text2image fine-tuning - litvan/sd-v1.5-russian_churches\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the litvan/russian_churches_with_blip_captioning dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]
[ 73, 110 ]
[ "passage: TAGS\n#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n# LoRA text2image fine-tuning - litvan/sd-v1.5-russian_churches\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the litvan/russian_churches_with_blip_captioning dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]
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null
null
peft
<!-- 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. --> # lora_SOLAR_merge2_SFT-DPO This model is a fine-tuned version of [ENERGY-DRINK-LOVE/SOLAR_merge2](https://huggingface.co/ENERGY-DRINK-LOVE/SOLAR_merge2) 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-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 6 - gradient_accumulation_steps: 8 - total_train_batch_size: 48 - total_eval_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.1
{"library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "ENERGY-DRINK-LOVE/SOLAR_merge2", "model-index": [{"name": "lora_SOLAR_merge2_SFT-DPO", "results": []}]}
null
genne/lora_SOLAR_merge2_SFT-DPO
[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:ENERGY-DRINK-LOVE/SOLAR_merge2", "region:us" ]
2024-02-06T08:55:13+00:00
[]
[]
TAGS #peft #safetensors #trl #dpo #generated_from_trainer #base_model-ENERGY-DRINK-LOVE/SOLAR_merge2 #region-us
# lora_SOLAR_merge2_SFT-DPO This model is a fine-tuned version of ENERGY-DRINK-LOVE/SOLAR_merge2 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-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 6 - gradient_accumulation_steps: 8 - total_train_batch_size: 48 - total_eval_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# lora_SOLAR_merge2_SFT-DPO\n\nThis model is a fine-tuned version of ENERGY-DRINK-LOVE/SOLAR_merge2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 6\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 48\n- total_eval_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-ENERGY-DRINK-LOVE/SOLAR_merge2 #region-us \n", "# lora_SOLAR_merge2_SFT-DPO\n\nThis model is a fine-tuned version of ENERGY-DRINK-LOVE/SOLAR_merge2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 6\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 48\n- total_eval_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 47, 50, 6, 12, 8, 3, 159, 4, 39 ]
[ "passage: TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-ENERGY-DRINK-LOVE/SOLAR_merge2 #region-us \n# lora_SOLAR_merge2_SFT-DPO\n\nThis model is a fine-tuned version of ENERGY-DRINK-LOVE/SOLAR_merge2 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 6\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 48\n- total_eval_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3### Training results### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
transformers
# 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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{"library_name": "transformers", "tags": []}
text-generation
ddemilla/Mixtral-8x7B-Instruct-v0.1-coords-casing-fine-tuned-2-6
[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T08:57:19+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #mixtral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
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transformers
<!-- 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. --> # safety-utcustom-train-SF-RGB-b0 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3221 - Mean Iou: 0.7557 - Mean Accuracy: 0.8092 - Overall Accuracy: 0.9835 - Accuracy Unlabeled: nan - Accuracy Safe: 0.6240 - Accuracy Unsafe: 0.9945 - Iou Unlabeled: nan - Iou Safe: 0.5281 - Iou Unsafe: 0.9832 ## 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: 9e-06 - 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 - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:| | 1.2069 | 1.0 | 10 | 1.1287 | 0.0406 | 0.3613 | 0.1117 | nan | 0.6267 | 0.0960 | 0.0 | 0.0261 | 0.0958 | | 1.196 | 2.0 | 20 | 1.1408 | 0.0465 | 0.3971 | 0.1274 | nan | 0.6837 | 0.1105 | 0.0 | 0.0290 | 0.1104 | | 1.1866 | 3.0 | 30 | 1.1441 | 0.0662 | 0.4586 | 0.1826 | nan | 0.7519 | 0.1653 | 0.0 | 0.0335 | 0.1652 | | 1.1701 | 4.0 | 40 | 1.1350 | 0.1016 | 0.5469 | 0.2805 | nan | 0.8301 | 0.2638 | 0.0 | 0.0410 | 0.2637 | | 1.1467 | 5.0 | 50 | 1.1285 | 0.1325 | 0.6266 | 0.3646 | nan | 0.9052 | 0.3481 | 0.0 | 0.0496 | 0.3481 | | 1.1126 | 6.0 | 60 | 1.0914 | 0.1933 | 0.7318 | 0.5257 | nan | 0.9508 | 0.5128 | 0.0 | 0.0673 | 0.5127 | | 1.0735 | 7.0 | 70 | 1.0392 | 0.2462 | 0.8075 | 0.6582 | nan | 0.9662 | 0.6489 | 0.0 | 0.0900 | 0.6487 | | 1.0335 | 8.0 | 80 | 1.0015 | 0.2783 | 0.8456 | 0.7301 | nan | 0.9683 | 0.7228 | 0.0 | 0.1122 | 0.7226 | | 1.0088 | 9.0 | 90 | 0.9502 | 0.3061 | 0.8736 | 0.7884 | nan | 0.9643 | 0.7830 | 0.0 | 0.1359 | 0.7825 | | 0.9993 | 10.0 | 100 | 0.9158 | 0.3246 | 0.8886 | 0.8232 | nan | 0.9581 | 0.8191 | 0.0 | 0.1556 | 0.8183 | | 0.9114 | 11.0 | 110 | 0.8472 | 0.3562 | 0.9061 | 0.8732 | nan | 0.9411 | 0.8711 | 0.0 | 0.1990 | 0.8697 | | 0.9027 | 12.0 | 120 | 0.8073 | 0.3687 | 0.9085 | 0.8909 | nan | 0.9271 | 0.8898 | 0.0 | 0.2182 | 0.8881 | | 0.8775 | 13.0 | 130 | 0.7756 | 0.3819 | 0.9011 | 0.9086 | nan | 0.8931 | 0.9090 | 0.0 | 0.2394 | 0.9062 | | 0.8532 | 14.0 | 140 | 0.7544 | 0.3883 | 0.9005 | 0.9156 | nan | 0.8844 | 0.9166 | 0.0 | 0.2513 | 0.9135 | | 0.7509 | 15.0 | 150 | 0.7137 | 0.4039 | 0.8965 | 0.9311 | nan | 0.8597 | 0.9333 | 0.0 | 0.2824 | 0.9294 | | 0.7711 | 16.0 | 160 | 0.6837 | 0.4131 | 0.8959 | 0.9394 | nan | 0.8497 | 0.9422 | 0.0 | 0.3014 | 0.9379 | | 0.7163 | 17.0 | 170 | 0.6573 | 0.4230 | 0.8859 | 0.9467 | nan | 0.8212 | 0.9505 | 0.0 | 0.3234 | 0.9454 | | 0.6609 | 18.0 | 180 | 0.6698 | 0.4200 | 0.8889 | 0.9449 | nan | 0.8294 | 0.9484 | 0.0 | 0.3163 | 0.9436 | | 0.7237 | 19.0 | 190 | 0.6465 | 0.4236 | 0.8821 | 0.9479 | nan | 0.8121 | 0.9520 | 0.0 | 0.3241 | 0.9467 | | 0.6264 | 20.0 | 200 | 0.6300 | 0.4293 | 0.8776 | 0.9520 | nan | 0.7985 | 0.9566 | 0.0 | 0.3372 | 0.9508 | | 0.6711 | 21.0 | 210 | 0.6050 | 0.4391 | 0.8731 | 0.9576 | nan | 0.7833 | 0.9630 | 0.0 | 0.3605 | 0.9567 | | 0.626 | 22.0 | 220 | 0.5855 | 0.4409 | 0.8742 | 0.9585 | nan | 0.7846 | 0.9637 | 0.0 | 0.3653 | 0.9575 | | 0.6103 | 23.0 | 230 | 0.5651 | 0.4474 | 0.8671 | 0.9623 | nan | 0.7658 | 0.9683 | 0.0 | 0.3807 | 0.9615 | | 0.6462 | 24.0 | 240 | 0.5621 | 0.4489 | 0.8643 | 0.9631 | nan | 0.7592 | 0.9693 | 0.0 | 0.3844 | 0.9623 | | 0.5442 | 25.0 | 250 | 0.5460 | 0.4563 | 0.8592 | 0.9668 | nan | 0.7450 | 0.9735 | 0.0 | 0.4028 | 0.9660 | | 0.6764 | 26.0 | 260 | 0.5673 | 0.4544 | 0.8646 | 0.9657 | nan | 0.7571 | 0.9721 | 0.0 | 0.3983 | 0.9650 | | 0.6471 | 27.0 | 270 | 0.5412 | 0.4586 | 0.8561 | 0.9679 | nan | 0.7374 | 0.9749 | 0.0 | 0.4087 | 0.9672 | | 0.5589 | 28.0 | 280 | 0.5427 | 0.4573 | 0.8601 | 0.9671 | nan | 0.7465 | 0.9738 | 0.0 | 0.4057 | 0.9663 | | 0.6512 | 29.0 | 290 | 0.5264 | 0.4600 | 0.8567 | 0.9681 | nan | 0.7384 | 0.9751 | 0.0 | 0.4126 | 0.9674 | | 0.6146 | 30.0 | 300 | 0.5321 | 0.4616 | 0.8619 | 0.9688 | nan | 0.7482 | 0.9755 | 0.0 | 0.4167 | 0.9681 | | 0.4938 | 31.0 | 310 | 0.5025 | 0.4751 | 0.8475 | 0.9744 | nan | 0.7127 | 0.9823 | 0.0 | 0.4515 | 0.9738 | | 0.4868 | 32.0 | 320 | 0.4836 | 0.4781 | 0.8342 | 0.9761 | nan | 0.6833 | 0.9851 | 0.0 | 0.4586 | 0.9756 | | 0.6315 | 33.0 | 330 | 0.4918 | 0.4739 | 0.8479 | 0.9740 | nan | 0.7139 | 0.9819 | 0.0 | 0.4483 | 0.9735 | | 0.5529 | 34.0 | 340 | 0.4879 | 0.4753 | 0.8414 | 0.9749 | nan | 0.6995 | 0.9832 | 0.0 | 0.4516 | 0.9743 | | 0.4592 | 35.0 | 350 | 0.4826 | 0.4764 | 0.8364 | 0.9754 | nan | 0.6887 | 0.9842 | 0.0 | 0.4542 | 0.9749 | | 0.5904 | 36.0 | 360 | 0.4611 | 0.4859 | 0.8159 | 0.9793 | nan | 0.6423 | 0.9896 | 0.0 | 0.4789 | 0.9789 | | 0.4804 | 37.0 | 370 | 0.4654 | 0.4796 | 0.8359 | 0.9764 | nan | 0.6865 | 0.9853 | 0.0 | 0.4627 | 0.9760 | | 0.4701 | 38.0 | 380 | 0.4625 | 0.4846 | 0.8251 | 0.9784 | nan | 0.6623 | 0.9880 | 0.0 | 0.4758 | 0.9779 | | 0.4729 | 39.0 | 390 | 0.4536 | 0.4838 | 0.8231 | 0.9783 | nan | 0.6582 | 0.9881 | 0.0 | 0.4736 | 0.9779 | | 0.4219 | 40.0 | 400 | 0.4514 | 0.4838 | 0.8305 | 0.9779 | nan | 0.6738 | 0.9872 | 0.0 | 0.4739 | 0.9775 | | 0.6494 | 41.0 | 410 | 0.4425 | 0.4892 | 0.8162 | 0.9801 | nan | 0.6420 | 0.9904 | 0.0 | 0.4878 | 0.9797 | | 0.4616 | 42.0 | 420 | 0.4390 | 0.7316 | 0.8225 | 0.9794 | nan | 0.6558 | 0.9892 | nan | 0.4842 | 0.9790 | | 0.4408 | 43.0 | 430 | 0.4419 | 0.7358 | 0.8272 | 0.9797 | nan | 0.6652 | 0.9893 | nan | 0.4923 | 0.9793 | | 0.4532 | 44.0 | 440 | 0.4371 | 0.7375 | 0.8274 | 0.9800 | nan | 0.6651 | 0.9896 | nan | 0.4954 | 0.9796 | | 0.5015 | 45.0 | 450 | 0.4376 | 0.7364 | 0.8276 | 0.9798 | nan | 0.6659 | 0.9894 | nan | 0.4933 | 0.9794 | | 0.4965 | 46.0 | 460 | 0.4201 | 0.7405 | 0.8137 | 0.9812 | nan | 0.6357 | 0.9918 | nan | 0.5002 | 0.9809 | | 0.4837 | 47.0 | 470 | 0.4281 | 0.7378 | 0.8279 | 0.9800 | nan | 0.6662 | 0.9896 | nan | 0.4961 | 0.9796 | | 0.4221 | 48.0 | 480 | 0.4288 | 0.7371 | 0.8227 | 0.9802 | nan | 0.6553 | 0.9901 | nan | 0.4944 | 0.9798 | | 0.4491 | 49.0 | 490 | 0.4152 | 0.7371 | 0.8074 | 0.9811 | nan | 0.6228 | 0.9920 | nan | 0.4935 | 0.9808 | | 0.4121 | 50.0 | 500 | 0.4159 | 0.7367 | 0.8063 | 0.9811 | nan | 0.6205 | 0.9921 | nan | 0.4927 | 0.9808 | | 0.4727 | 51.0 | 510 | 0.4199 | 0.7354 | 0.8095 | 0.9807 | nan | 0.6274 | 0.9915 | nan | 0.4905 | 0.9804 | | 0.5323 | 52.0 | 520 | 0.4079 | 0.7383 | 0.8074 | 0.9813 | nan | 0.6227 | 0.9922 | nan | 0.4957 | 0.9809 | | 0.409 | 53.0 | 530 | 0.4103 | 0.7392 | 0.8161 | 0.9809 | nan | 0.6409 | 0.9913 | nan | 0.4978 | 0.9805 | | 0.6391 | 54.0 | 540 | 0.4063 | 0.7406 | 0.8133 | 0.9813 | nan | 0.6349 | 0.9918 | nan | 0.5003 | 0.9809 | | 0.3905 | 55.0 | 550 | 0.4000 | 0.7409 | 0.8122 | 0.9814 | nan | 0.6325 | 0.9920 | nan | 0.5007 | 0.9810 | | 0.4138 | 56.0 | 560 | 0.4028 | 0.7398 | 0.8183 | 0.9809 | nan | 0.6455 | 0.9911 | nan | 0.4990 | 0.9805 | | 0.3977 | 57.0 | 570 | 0.3865 | 0.7372 | 0.7912 | 0.9821 | nan | 0.5884 | 0.9941 | nan | 0.4926 | 0.9818 | | 0.4186 | 58.0 | 580 | 0.3845 | 0.7416 | 0.7994 | 0.9822 | nan | 0.6050 | 0.9937 | nan | 0.5014 | 0.9819 | | 0.4921 | 59.0 | 590 | 0.3881 | 0.7427 | 0.8102 | 0.9817 | nan | 0.6278 | 0.9925 | nan | 0.5039 | 0.9814 | | 0.3953 | 60.0 | 600 | 0.3823 | 0.7429 | 0.8027 | 0.9822 | nan | 0.6119 | 0.9935 | nan | 0.5039 | 0.9819 | | 0.4263 | 61.0 | 610 | 0.3841 | 0.7420 | 0.8075 | 0.9818 | nan | 0.6222 | 0.9928 | nan | 0.5026 | 0.9815 | | 0.3798 | 62.0 | 620 | 0.3763 | 0.7446 | 0.8054 | 0.9823 | nan | 0.6174 | 0.9934 | nan | 0.5072 | 0.9820 | | 0.4208 | 63.0 | 630 | 0.3724 | 0.7437 | 0.7919 | 0.9829 | nan | 0.5888 | 0.9949 | nan | 0.5047 | 0.9826 | | 0.3627 | 64.0 | 640 | 0.3760 | 0.7466 | 0.8111 | 0.9822 | nan | 0.6292 | 0.9930 | nan | 0.5112 | 0.9819 | | 0.4156 | 65.0 | 650 | 0.3669 | 0.7478 | 0.8018 | 0.9829 | nan | 0.6092 | 0.9943 | nan | 0.5130 | 0.9826 | | 0.468 | 66.0 | 660 | 0.3706 | 0.7508 | 0.8145 | 0.9826 | nan | 0.6359 | 0.9932 | nan | 0.5193 | 0.9823 | | 0.4547 | 67.0 | 670 | 0.3692 | 0.7512 | 0.8189 | 0.9824 | nan | 0.6451 | 0.9927 | nan | 0.5204 | 0.9821 | | 0.3604 | 68.0 | 680 | 0.3691 | 0.7520 | 0.8152 | 0.9827 | nan | 0.6371 | 0.9933 | nan | 0.5215 | 0.9824 | | 0.4476 | 69.0 | 690 | 0.3679 | 0.7516 | 0.8195 | 0.9825 | nan | 0.6463 | 0.9927 | nan | 0.5210 | 0.9821 | | 0.3535 | 70.0 | 700 | 0.3589 | 0.7522 | 0.8097 | 0.9831 | nan | 0.6255 | 0.9939 | nan | 0.5217 | 0.9827 | | 0.3539 | 71.0 | 710 | 0.3572 | 0.7526 | 0.8091 | 0.9831 | nan | 0.6242 | 0.9941 | nan | 0.5224 | 0.9828 | | 0.3675 | 72.0 | 720 | 0.3589 | 0.7518 | 0.8100 | 0.9830 | nan | 0.6261 | 0.9939 | nan | 0.5209 | 0.9827 | | 0.4148 | 73.0 | 730 | 0.3536 | 0.7504 | 0.8093 | 0.9828 | nan | 0.6249 | 0.9937 | nan | 0.5182 | 0.9825 | | 0.3941 | 74.0 | 740 | 0.3538 | 0.7497 | 0.8099 | 0.9827 | nan | 0.6263 | 0.9936 | nan | 0.5169 | 0.9824 | | 0.4264 | 75.0 | 750 | 0.3595 | 0.7469 | 0.8197 | 0.9818 | nan | 0.6473 | 0.9920 | nan | 0.5123 | 0.9814 | | 0.3815 | 76.0 | 760 | 0.3525 | 0.7492 | 0.8097 | 0.9827 | nan | 0.6258 | 0.9935 | nan | 0.5162 | 0.9823 | | 0.3459 | 77.0 | 770 | 0.3443 | 0.7452 | 0.7926 | 0.9831 | nan | 0.5901 | 0.9951 | nan | 0.5076 | 0.9828 | | 0.3794 | 78.0 | 780 | 0.3538 | 0.7501 | 0.8154 | 0.9825 | nan | 0.6377 | 0.9930 | nan | 0.5180 | 0.9821 | | 0.3761 | 79.0 | 790 | 0.3525 | 0.7483 | 0.8169 | 0.9821 | nan | 0.6412 | 0.9925 | nan | 0.5147 | 0.9818 | | 0.3612 | 80.0 | 800 | 0.3495 | 0.7513 | 0.8128 | 0.9828 | nan | 0.6321 | 0.9934 | nan | 0.5201 | 0.9824 | | 0.405 | 81.0 | 810 | 0.3466 | 0.7502 | 0.8148 | 0.9825 | nan | 0.6365 | 0.9931 | nan | 0.5182 | 0.9822 | | 0.4289 | 82.0 | 820 | 0.3458 | 0.7498 | 0.8092 | 0.9828 | nan | 0.6247 | 0.9937 | nan | 0.5171 | 0.9824 | | 0.3523 | 83.0 | 830 | 0.3435 | 0.7503 | 0.8112 | 0.9827 | nan | 0.6288 | 0.9935 | nan | 0.5183 | 0.9824 | | 0.4254 | 84.0 | 840 | 0.3403 | 0.7495 | 0.8000 | 0.9832 | nan | 0.6052 | 0.9947 | nan | 0.5160 | 0.9829 | | 0.3399 | 85.0 | 850 | 0.3355 | 0.7492 | 0.8003 | 0.9832 | nan | 0.6059 | 0.9947 | nan | 0.5155 | 0.9829 | | 0.3251 | 86.0 | 860 | 0.3395 | 0.7503 | 0.8028 | 0.9832 | nan | 0.6111 | 0.9945 | nan | 0.5178 | 0.9829 | | 0.3748 | 87.0 | 870 | 0.3400 | 0.7502 | 0.8117 | 0.9827 | nan | 0.6299 | 0.9934 | nan | 0.5181 | 0.9824 | | 0.4398 | 88.0 | 880 | 0.3450 | 0.7527 | 0.8197 | 0.9826 | nan | 0.6466 | 0.9928 | nan | 0.5231 | 0.9822 | | 0.3782 | 89.0 | 890 | 0.3454 | 0.7547 | 0.8180 | 0.9829 | nan | 0.6426 | 0.9933 | nan | 0.5268 | 0.9826 | | 0.4318 | 90.0 | 900 | 0.3424 | 0.7541 | 0.8162 | 0.9830 | nan | 0.6390 | 0.9934 | nan | 0.5255 | 0.9826 | | 0.3428 | 91.0 | 910 | 0.3327 | 0.7541 | 0.8124 | 0.9832 | nan | 0.6309 | 0.9939 | nan | 0.5253 | 0.9828 | | 0.4303 | 92.0 | 920 | 0.3364 | 0.7525 | 0.8108 | 0.9830 | nan | 0.6277 | 0.9939 | nan | 0.5223 | 0.9827 | | 0.3624 | 93.0 | 930 | 0.3277 | 0.7531 | 0.8063 | 0.9834 | nan | 0.6182 | 0.9945 | nan | 0.5231 | 0.9830 | | 0.3418 | 94.0 | 940 | 0.3315 | 0.7548 | 0.8125 | 0.9833 | nan | 0.6311 | 0.9940 | nan | 0.5267 | 0.9829 | | 0.321 | 95.0 | 950 | 0.3266 | 0.7541 | 0.8070 | 0.9835 | nan | 0.6195 | 0.9945 | nan | 0.5251 | 0.9831 | | 0.3152 | 96.0 | 960 | 0.3265 | 0.7531 | 0.8025 | 0.9836 | nan | 0.6101 | 0.9949 | nan | 0.5230 | 0.9833 | | 0.3153 | 97.0 | 970 | 0.3263 | 0.7537 | 0.8048 | 0.9835 | nan | 0.6149 | 0.9947 | nan | 0.5243 | 0.9832 | | 0.3158 | 98.0 | 980 | 0.3299 | 0.7553 | 0.8139 | 0.9832 | nan | 0.6340 | 0.9939 | nan | 0.5278 | 0.9829 | | 0.3162 | 99.0 | 990 | 0.3248 | 0.7546 | 0.8076 | 0.9835 | nan | 0.6207 | 0.9945 | nan | 0.5260 | 0.9832 | | 0.3748 | 100.0 | 1000 | 0.3238 | 0.7553 | 0.8077 | 0.9836 | nan | 0.6208 | 0.9946 | nan | 0.5274 | 0.9833 | | 0.3598 | 101.0 | 1010 | 0.3221 | 0.7544 | 0.8096 | 0.9833 | nan | 0.6250 | 0.9943 | nan | 0.5257 | 0.9830 | | 0.3245 | 102.0 | 1020 | 0.3247 | 0.7527 | 0.8156 | 0.9828 | nan | 0.6380 | 0.9933 | nan | 0.5228 | 0.9825 | | 0.3527 | 103.0 | 1030 | 0.3275 | 0.7537 | 0.8193 | 0.9827 | nan | 0.6456 | 0.9930 | nan | 0.5250 | 0.9824 | | 0.5087 | 104.0 | 1040 | 0.3221 | 0.7559 | 0.8105 | 0.9835 | nan | 0.6266 | 0.9944 | nan | 0.5287 | 0.9832 | | 0.3331 | 105.0 | 1050 | 0.3183 | 0.7560 | 0.8064 | 0.9837 | nan | 0.6180 | 0.9948 | nan | 0.5285 | 0.9834 | | 0.324 | 106.0 | 1060 | 0.3198 | 0.7561 | 0.8090 | 0.9836 | nan | 0.6235 | 0.9946 | nan | 0.5289 | 0.9833 | | 0.3512 | 107.0 | 1070 | 0.3194 | 0.7549 | 0.8052 | 0.9836 | nan | 0.6155 | 0.9949 | nan | 0.5265 | 0.9833 | | 0.3274 | 108.0 | 1080 | 0.3185 | 0.7569 | 0.8122 | 0.9835 | nan | 0.6301 | 0.9943 | nan | 0.5306 | 0.9832 | | 0.335 | 109.0 | 1090 | 0.3177 | 0.7554 | 0.8081 | 0.9836 | nan | 0.6217 | 0.9946 | nan | 0.5276 | 0.9832 | | 0.3581 | 110.0 | 1100 | 0.3204 | 0.7568 | 0.8146 | 0.9834 | nan | 0.6352 | 0.9940 | nan | 0.5306 | 0.9831 | | 0.3307 | 111.0 | 1110 | 0.3216 | 0.7571 | 0.8138 | 0.9835 | nan | 0.6335 | 0.9941 | nan | 0.5310 | 0.9832 | | 0.3162 | 112.0 | 1120 | 0.3227 | 0.7575 | 0.8181 | 0.9833 | nan | 0.6425 | 0.9937 | nan | 0.5320 | 0.9830 | | 0.3687 | 113.0 | 1130 | 0.3188 | 0.7567 | 0.8124 | 0.9835 | nan | 0.6306 | 0.9942 | nan | 0.5302 | 0.9832 | | 0.4099 | 114.0 | 1140 | 0.3151 | 0.7550 | 0.8063 | 0.9836 | nan | 0.6178 | 0.9947 | nan | 0.5266 | 0.9833 | | 0.3283 | 115.0 | 1150 | 0.3152 | 0.7557 | 0.8088 | 0.9836 | nan | 0.6232 | 0.9945 | nan | 0.5281 | 0.9832 | | 0.3118 | 116.0 | 1160 | 0.3180 | 0.7556 | 0.8097 | 0.9835 | nan | 0.6249 | 0.9944 | nan | 0.5280 | 0.9832 | | 0.3233 | 117.0 | 1170 | 0.3164 | 0.7551 | 0.8070 | 0.9836 | nan | 0.6192 | 0.9947 | nan | 0.5269 | 0.9833 | | 0.3401 | 118.0 | 1180 | 0.3192 | 0.7562 | 0.8122 | 0.9834 | nan | 0.6303 | 0.9942 | nan | 0.5292 | 0.9831 | | 0.3867 | 119.0 | 1190 | 0.3199 | 0.7566 | 0.8160 | 0.9833 | nan | 0.6382 | 0.9938 | nan | 0.5302 | 0.9830 | | 0.3217 | 120.0 | 1200 | 0.3221 | 0.7557 | 0.8092 | 0.9835 | nan | 0.6240 | 0.9945 | nan | 0.5281 | 0.9832 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "safety-utcustom-train-SF-RGB-b0", "results": []}]}
image-segmentation
sam1120/safety-utcustom-train-SF-RGB-b0
[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]
2024-02-06T08:59:17+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us
safety-utcustom-train-SF-RGB-b0 =============================== This model is a fine-tuned version of nvidia/mit-b0 on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.3221 * Mean Iou: 0.7557 * Mean Accuracy: 0.8092 * Overall Accuracy: 0.9835 * Accuracy Unlabeled: nan * Accuracy Safe: 0.6240 * Accuracy Unsafe: 0.9945 * Iou Unlabeled: nan * Iou Safe: 0.5281 * Iou Unsafe: 0.9832 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: 9e-06 * 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 * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 120 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]
[ 48, 117, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]
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null
null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "google/vit-base-patch16-224-in21k"}
null
mysterious-pie/vit_ft_lora_5_epochs_23classes_v0
[ "peft", "arxiv:1910.09700", "base_model:google/vit-base-patch16-224-in21k", "region:us" ]
2024-02-06T09:04:38+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-google/vit-base-patch16-224-in21k #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-google/vit-base-patch16-224-in21k #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
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[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-google/vit-base-patch16-224-in21k #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
transformers
# Convolutional Neural Network (CNN) Model This repository contains the configuration and weights for a Convolutional Neural Network (CNN) model trained on image data. The model architecture is defined using the Keras Sequential API. ## Model Architecture The model is defined as a Sequential model with the following layers: 1. Input Layer - Input shape: (None, 32, 32, 1) 2. Convolutional Layer - Filters: 32 - Kernel size: (3, 3) - Activation function: ReLU - Batch normalization - Max pooling: pool size (2, 2), strides (2, 2) 3. Dropout Layer - Dropout rate: 0.25 4. Convolutional Layer - Filters: 64 - Kernel size: (3, 3) - Activation function: ReLU - Batch normalization - Max pooling: pool size (2, 2), strides (2, 2) 5. Dropout Layer - Dropout rate: 0.25 - Flatten Layer 6. Dense Layer - Units: 128 - Activation function: ReLU - Batch normalization 7. Dropout Layer - Dropout rate: 0.5 8. Dense Layer - Units: 6 (output layer) - Activation function: Softmax ## Categories to Predict The model predicts images into the following categories: - Accessories - Bags - Clothes - Shoes - Watches ## Model Files - `model_config.json`: Configuration file containing the model architecture. - `model_weights.h5`: File containing the model weights. Feel free to use this model for your category classification tasks!
{}
null
bobs24/Category_Prediction
[ "transformers", "endpoints_compatible", "region:us" ]
2024-02-06T09:05:09+00:00
[]
[]
TAGS #transformers #endpoints_compatible #region-us
# Convolutional Neural Network (CNN) Model This repository contains the configuration and weights for a Convolutional Neural Network (CNN) model trained on image data. The model architecture is defined using the Keras Sequential API. ## Model Architecture The model is defined as a Sequential model with the following layers: 1. Input Layer - Input shape: (None, 32, 32, 1) 2. Convolutional Layer - Filters: 32 - Kernel size: (3, 3) - Activation function: ReLU - Batch normalization - Max pooling: pool size (2, 2), strides (2, 2) 3. Dropout Layer - Dropout rate: 0.25 4. Convolutional Layer - Filters: 64 - Kernel size: (3, 3) - Activation function: ReLU - Batch normalization - Max pooling: pool size (2, 2), strides (2, 2) 5. Dropout Layer - Dropout rate: 0.25 - Flatten Layer 6. Dense Layer - Units: 128 - Activation function: ReLU - Batch normalization 7. Dropout Layer - Dropout rate: 0.5 8. Dense Layer - Units: 6 (output layer) - Activation function: Softmax ## Categories to Predict The model predicts images into the following categories: - Accessories - Bags - Clothes - Shoes - Watches ## Model Files - 'model_config.json': Configuration file containing the model architecture. - 'model_weights.h5': File containing the model weights. Feel free to use this model for your category classification tasks!
[ "# Convolutional Neural Network (CNN) Model\n\nThis repository contains the configuration and weights for a Convolutional Neural Network (CNN) model trained on image data. The model architecture is defined using the Keras Sequential API.", "## Model Architecture\n\nThe model is defined as a Sequential model with the following layers:\n\n1. Input Layer\n- Input shape: (None, 32, 32, 1)\n\n2. Convolutional Layer\n- Filters: 32\n- Kernel size: (3, 3)\n- Activation function: ReLU\n- Batch normalization\n- Max pooling: pool size (2, 2), strides (2, 2)\n\n3. Dropout Layer\n- Dropout rate: 0.25\n\n4. Convolutional Layer\n- Filters: 64\n- Kernel size: (3, 3)\n- Activation function: ReLU\n- Batch normalization\n- Max pooling: pool size (2, 2), strides (2, 2)\n\n5. Dropout Layer\n- Dropout rate: 0.25\n- Flatten Layer\n\n6. Dense Layer\n- Units: 128\n- Activation function: ReLU\n- Batch normalization\n\n7. Dropout Layer\n- Dropout rate: 0.5\n\n8. Dense Layer\n- Units: 6 (output layer)\n- Activation function: Softmax", "## Categories to Predict\n\nThe model predicts images into the following categories:\n- Accessories\n- Bags\n- Clothes\n- Shoes\n- Watches", "## Model Files\n\n- 'model_config.json': Configuration file containing the model architecture.\n- 'model_weights.h5': File containing the model weights.\n\n\nFeel free to use this model for your category classification tasks!" ]
[ "TAGS\n#transformers #endpoints_compatible #region-us \n", "# Convolutional Neural Network (CNN) Model\n\nThis repository contains the configuration and weights for a Convolutional Neural Network (CNN) model trained on image data. The model architecture is defined using the Keras Sequential API.", "## Model Architecture\n\nThe model is defined as a Sequential model with the following layers:\n\n1. Input Layer\n- Input shape: (None, 32, 32, 1)\n\n2. Convolutional Layer\n- Filters: 32\n- Kernel size: (3, 3)\n- Activation function: ReLU\n- Batch normalization\n- Max pooling: pool size (2, 2), strides (2, 2)\n\n3. Dropout Layer\n- Dropout rate: 0.25\n\n4. Convolutional Layer\n- Filters: 64\n- Kernel size: (3, 3)\n- Activation function: ReLU\n- Batch normalization\n- Max pooling: pool size (2, 2), strides (2, 2)\n\n5. Dropout Layer\n- Dropout rate: 0.25\n- Flatten Layer\n\n6. Dense Layer\n- Units: 128\n- Activation function: ReLU\n- Batch normalization\n\n7. Dropout Layer\n- Dropout rate: 0.5\n\n8. Dense Layer\n- Units: 6 (output layer)\n- Activation function: Softmax", "## Categories to Predict\n\nThe model predicts images into the following categories:\n- Accessories\n- Bags\n- Clothes\n- Shoes\n- Watches", "## Model Files\n\n- 'model_config.json': Configuration file containing the model architecture.\n- 'model_weights.h5': File containing the model weights.\n\n\nFeel free to use this model for your category classification tasks!" ]
[ 17, 58, 218, 33, 58 ]
[ "passage: TAGS\n#transformers #endpoints_compatible #region-us \n# Convolutional Neural Network (CNN) Model\n\nThis repository contains the configuration and weights for a Convolutional Neural Network (CNN) model trained on image data. The model architecture is defined using the Keras Sequential API.## Model Architecture\n\nThe model is defined as a Sequential model with the following layers:\n\n1. Input Layer\n- Input shape: (None, 32, 32, 1)\n\n2. Convolutional Layer\n- Filters: 32\n- Kernel size: (3, 3)\n- Activation function: ReLU\n- Batch normalization\n- Max pooling: pool size (2, 2), strides (2, 2)\n\n3. Dropout Layer\n- Dropout rate: 0.25\n\n4. Convolutional Layer\n- Filters: 64\n- Kernel size: (3, 3)\n- Activation function: ReLU\n- Batch normalization\n- Max pooling: pool size (2, 2), strides (2, 2)\n\n5. Dropout Layer\n- Dropout rate: 0.25\n- Flatten Layer\n\n6. Dense Layer\n- Units: 128\n- Activation function: ReLU\n- Batch normalization\n\n7. Dropout Layer\n- Dropout rate: 0.5\n\n8. Dense Layer\n- Units: 6 (output layer)\n- Activation function: Softmax## Categories to Predict\n\nThe model predicts images into the following categories:\n- Accessories\n- Bags\n- Clothes\n- Shoes\n- Watches## Model Files\n\n- 'model_config.json': Configuration file containing the model architecture.\n- 'model_weights.h5': File containing the model weights.\n\n\nFeel free to use this model for your category classification tasks!" ]
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null
null
transformers
# math_coder_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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base. ### Models Merged The following models were included in the merge: * [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1) * [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mistralai/Mistral-7B-v0.1 # no parameters necessary for base model - model: WizardLM/WizardMath-7B-V1.1 parameters: density: 0.5 # fraction of weights in differences from the base model to retain weight: # weight gradient - filter: mlp value: 0.5 - value: 0 - model: codellama/CodeLlama-7b-Instruct-hf parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: mistralai/Mistral-7B-v0.1 parameters: normalize: true int8_mask: true dtype: float16 ```
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["mistralai/Mistral-7B-v0.1", "WizardLM/WizardMath-7B-V1.1", "codellama/CodeLlama-7b-Instruct-hf"]}
text-generation
bergr7f/mathcoder-mistral-7B
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2306.01708", "base_model:mistralai/Mistral-7B-v0.1", "base_model:WizardLM/WizardMath-7B-V1.1", "base_model:codellama/CodeLlama-7b-Instruct-hf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T09:09:30+00:00
[ "2306.01708" ]
[]
TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #arxiv-2306.01708 #base_model-mistralai/Mistral-7B-v0.1 #base_model-WizardLM/WizardMath-7B-V1.1 #base_model-codellama/CodeLlama-7b-Instruct-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# math_coder_merge This is a merge of pre-trained language models created using mergekit. ## Merge Details ### Merge Method This model was merged using the TIES merge method using mistralai/Mistral-7B-v0.1 as a base. ### Models Merged The following models were included in the merge: * WizardLM/WizardMath-7B-V1.1 * codellama/CodeLlama-7b-Instruct-hf ### Configuration The following YAML configuration was used to produce this model:
[ "# math_coder_merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the TIES merge method using mistralai/Mistral-7B-v0.1 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* WizardLM/WizardMath-7B-V1.1\n* codellama/CodeLlama-7b-Instruct-hf", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #arxiv-2306.01708 #base_model-mistralai/Mistral-7B-v0.1 #base_model-WizardLM/WizardMath-7B-V1.1 #base_model-codellama/CodeLlama-7b-Instruct-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# math_coder_merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the TIES merge method using mistralai/Mistral-7B-v0.1 as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* WizardLM/WizardMath-7B-V1.1\n* codellama/CodeLlama-7b-Instruct-hf", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 117, 24, 4, 32, 44, 17 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #arxiv-2306.01708 #base_model-mistralai/Mistral-7B-v0.1 #base_model-WizardLM/WizardMath-7B-V1.1 #base_model-codellama/CodeLlama-7b-Instruct-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# math_coder_merge\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the TIES merge method using mistralai/Mistral-7B-v0.1 as a base.### Models Merged\n\nThe following models were included in the merge:\n* WizardLM/WizardMath-7B-V1.1\n* codellama/CodeLlama-7b-Instruct-hf### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
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null
null
transformers
# mlx-community/defog-sqlcoder-7b-2 This model was converted to MLX format from [`defog/sqlcoder-7b-2`](). Refer to the [original model card](https://huggingface.co/defog/sqlcoder-7b-2) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/defog-sqlcoder-7b-2") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
{"license": "cc-by-sa-4.0", "library_name": "transformers", "tags": ["mlx"], "pipeline_tag": "text-generation"}
text-generation
mlx-community/defog-sqlcoder-7b-2
[ "transformers", "llama", "text-generation", "mlx", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T09:11:13+00:00
[]
[]
TAGS #transformers #llama #text-generation #mlx #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# mlx-community/defog-sqlcoder-7b-2 This model was converted to MLX format from ['defog/sqlcoder-7b-2'](). Refer to the original model card for more details on the model. ## Use with mlx
[ "# mlx-community/defog-sqlcoder-7b-2\nThis model was converted to MLX format from ['defog/sqlcoder-7b-2']().\nRefer to the original model card for more details on the model.", "## Use with mlx" ]
[ "TAGS\n#transformers #llama #text-generation #mlx #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# mlx-community/defog-sqlcoder-7b-2\nThis model was converted to MLX format from ['defog/sqlcoder-7b-2']().\nRefer to the original model card for more details on the model.", "## Use with mlx" ]
[ 56, 57, 5 ]
[ "passage: TAGS\n#transformers #llama #text-generation #mlx #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# mlx-community/defog-sqlcoder-7b-2\nThis model was converted to MLX format from ['defog/sqlcoder-7b-2']().\nRefer to the original model card for more details on the model.## Use with mlx" ]
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null
null
transformers
## Model Summery MobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale. The MobileVLM_V2-3B was built on our [MobileLLaMA-2.7B-Chat](https://huggingface.co/mtgv/MobileLLaMA-2.7B-Chat) to facilitate the off-the-shelf deployment. ## Model Sources - Repository: https://github.com/Meituan-AutoML/MobileVLM - Paper: [MobileVLM V2: Faster and Stronger Baseline for Vision Language Model](https://arxiv.org/abs/2402.03766) ## How to Get Started with the Model Inference examples can be found at [Github](https://github.com/Meituan-AutoML/MobileVLM).
{"license": "apache-2.0", "tags": ["MobileVLM V2"]}
text-generation
mtgv/MobileVLM_V2-3B
[ "transformers", "pytorch", "mobilevlm", "text-generation", "MobileVLM V2", "arxiv:2402.03766", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T09:14:00+00:00
[ "2402.03766" ]
[]
TAGS #transformers #pytorch #mobilevlm #text-generation #MobileVLM V2 #arxiv-2402.03766 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
## Model Summery MobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale. The MobileVLM_V2-3B was built on our MobileLLaMA-2.7B-Chat to facilitate the off-the-shelf deployment. ## Model Sources - Repository: URL - Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model ## How to Get Started with the Model Inference examples can be found at Github.
[ "## Model Summery\nMobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale.\n\nThe MobileVLM_V2-3B was built on our MobileLLaMA-2.7B-Chat to facilitate the off-the-shelf deployment.", "## Model Sources\n- Repository: URL\n- Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model", "## How to Get Started with the Model\nInference examples can be found at Github." ]
[ "TAGS\n#transformers #pytorch #mobilevlm #text-generation #MobileVLM V2 #arxiv-2402.03766 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "## Model Summery\nMobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale.\n\nThe MobileVLM_V2-3B was built on our MobileLLaMA-2.7B-Chat to facilitate the off-the-shelf deployment.", "## Model Sources\n- Repository: URL\n- Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model", "## How to Get Started with the Model\nInference examples can be found at Github." ]
[ 60, 175, 30, 22 ]
[ "passage: TAGS\n#transformers #pytorch #mobilevlm #text-generation #MobileVLM V2 #arxiv-2402.03766 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## Model Summery\nMobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale.\n\nThe MobileVLM_V2-3B was built on our MobileLLaMA-2.7B-Chat to facilitate the off-the-shelf deployment.## Model Sources\n- Repository: URL\n- Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model## How to Get Started with the Model\nInference examples can be found at Github." ]
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null
null
transformers
<!-- 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. --> # roberta-base-train-val-interleave-2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0071 - Accuracy: 0.999 - F1: 0.9990 ## 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-06 - train_batch_size: 32 - eval_batch_size: 32 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 63 | 0.2656 | 0.997 | 0.9970 | | No log | 2.0 | 126 | 0.0094 | 0.998 | 0.9980 | | No log | 3.0 | 189 | 0.0074 | 0.999 | 0.9990 | | No log | 4.0 | 252 | 0.0071 | 0.999 | 0.9990 | | No log | 5.0 | 315 | 0.0071 | 0.999 | 0.9990 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-train-val-interleave-2", "results": []}]}
text-classification
hoanghoavienvo/roberta-base-train-val-interleave-2
[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T09:15:49+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
roberta-base-train-val-interleave-2 =================================== This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0071 * Accuracy: 0.999 * F1: 0.9990 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-06 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.37.0 * Pytorch 2.1.2 * Datasets 2.1.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
[ 63, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]
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null
null
transformers
## Model Summery MobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale. The MobileVLM_V2-7B was built on [Vicuna-7B-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) to facilitate the off-the-shelf deployment. ## Model Sources - Repository: https://github.com/Meituan-AutoML/MobileVLM - Paper: [MobileVLM V2: Faster and Stronger Baseline for Vision Language Model](https://arxiv.org/abs/2402.03766) ## How to Get Started with the Model Inference examples can be found at [Github](https://github.com/Meituan-AutoML/MobileVLM).
{"license": "apache-2.0", "tags": ["MobileVLM V2"]}
text-generation
mtgv/MobileVLM_V2-7B
[ "transformers", "pytorch", "mobilevlm", "text-generation", "MobileVLM V2", "arxiv:2402.03766", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T09:16:05+00:00
[ "2402.03766" ]
[]
TAGS #transformers #pytorch #mobilevlm #text-generation #MobileVLM V2 #arxiv-2402.03766 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
## Model Summery MobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale. The MobileVLM_V2-7B was built on Vicuna-7B-v1.5 to facilitate the off-the-shelf deployment. ## Model Sources - Repository: URL - Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model ## How to Get Started with the Model Inference examples can be found at Github.
[ "## Model Summery\nMobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale.\n\nThe MobileVLM_V2-7B was built on Vicuna-7B-v1.5 to facilitate the off-the-shelf deployment.", "## Model Sources\n- Repository: URL\n- Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model", "## How to Get Started with the Model\nInference examples can be found at Github." ]
[ "TAGS\n#transformers #pytorch #mobilevlm #text-generation #MobileVLM V2 #arxiv-2402.03766 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "## Model Summery\nMobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale.\n\nThe MobileVLM_V2-7B was built on Vicuna-7B-v1.5 to facilitate the off-the-shelf deployment.", "## Model Sources\n- Repository: URL\n- Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model", "## How to Get Started with the Model\nInference examples can be found at Github." ]
[ 60, 172, 30, 22 ]
[ "passage: TAGS\n#transformers #pytorch #mobilevlm #text-generation #MobileVLM V2 #arxiv-2402.03766 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## Model Summery\nMobileVLM V2 is a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs’ performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, MobileVLM_V2-3B model outperforms a large variety of VLMs at the 7B+ scale.\n\nThe MobileVLM_V2-7B was built on Vicuna-7B-v1.5 to facilitate the off-the-shelf deployment.## Model Sources\n- Repository: URL\n- Paper: MobileVLM V2: Faster and Stronger Baseline for Vision Language Model## How to Get Started with the Model\nInference examples can be found at Github." ]
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null
null
keras
## 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: | Hyperparameters | Value | | :-- | :-- | | name | Adam | | weight_decay | None | | clipnorm | None | | global_clipnorm | None | | clipvalue | None | | use_ema | False | | ema_momentum | 0.99 | | ema_overwrite_frequency | None | | jit_compile | True | | is_legacy_optimizer | False | | learning_rate | 2.9999999242136255e-05 | | beta_1 | 0.9 | | beta_2 | 0.999 | | epsilon | 1e-07 | | amsgrad | False | | training_precision | float32 | ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
{"library_name": "keras"}
null
spneshaei/mindmate-original-fold3-bert-base-german-cased
[ "keras", "region:us" ]
2024-02-06T09:18:18+00:00
[]
[]
TAGS #keras #region-us
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: Model Plot ---------- View Model Plot !Model Image
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ "TAGS\n#keras #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ 9, 28 ]
[ "passage: TAGS\n#keras #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
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null
null
transformers
<!-- 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. --> # bart-large-cnn-finetuned-en-to-mm This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| | No log | 1.0 | 163 | 0.9097 | 0.4854 | 113.7798 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "bart-large-cnn-finetuned-en-to-mm", "results": []}]}
text2text-generation
hbijen/bart-large-cnn-finetuned-en-to-mm
[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T09:18:38+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #region-us
bart-large-cnn-finetuned-en-to-mm ================================= This model is a fine-tuned version of facebook/bart-large-cnn 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: 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: 1 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 69, 113, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
keras
## 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: | Hyperparameters | Value | | :-- | :-- | | name | Adam | | weight_decay | None | | clipnorm | None | | global_clipnorm | None | | clipvalue | None | | use_ema | False | | ema_momentum | 0.99 | | ema_overwrite_frequency | None | | jit_compile | True | | is_legacy_optimizer | False | | learning_rate | 2.9999999242136255e-05 | | beta_1 | 0.9 | | beta_2 | 0.999 | | epsilon | 1e-07 | | amsgrad | False | | training_precision | float32 | ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
{"library_name": "keras"}
null
spneshaei/mindmate-original-fold2-bert-base-german-cased
[ "keras", "region:us" ]
2024-02-06T09:20:25+00:00
[]
[]
TAGS #keras #region-us
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: Model Plot ---------- View Model Plot !Model Image
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ "TAGS\n#keras #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ 9, 28 ]
[ "passage: TAGS\n#keras #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
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null
null
transformers
<!-- 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. --> # nep-spell-mbart-epoch3 This model is a fine-tuned version of [houdini001/nep-spell-mbart-epoch2](https://huggingface.co/houdini001/nep-spell-mbart-epoch2) 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: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
{"tags": ["generated_from_trainer"], "base_model": "houdini001/nep-spell-mbart-epoch2", "model-index": [{"name": "nep-spell-mbart-epoch3", "results": []}]}
text2text-generation
houdini001/nep-spell-mbart-epoch3
[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:houdini001/nep-spell-mbart-epoch2", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T09:20:42+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-houdini001/nep-spell-mbart-epoch2 #autotrain_compatible #endpoints_compatible #region-us
# nep-spell-mbart-epoch3 This model is a fine-tuned version of houdini001/nep-spell-mbart-epoch2 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: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
[ "# nep-spell-mbart-epoch3\n\nThis model is a fine-tuned version of houdini001/nep-spell-mbart-epoch2 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-houdini001/nep-spell-mbart-epoch2 #autotrain_compatible #endpoints_compatible #region-us \n", "# nep-spell-mbart-epoch3\n\nThis model is a fine-tuned version of houdini001/nep-spell-mbart-epoch2 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1" ]
[ 72, 45, 6, 12, 8, 3, 90, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-houdini001/nep-spell-mbart-epoch2 #autotrain_compatible #endpoints_compatible #region-us \n# nep-spell-mbart-epoch3\n\nThis model is a fine-tuned version of houdini001/nep-spell-mbart-epoch2 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Training results### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1" ]
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null
null
transformers
<!-- 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. --> # Cyberbullying-detection-tweet-comment This model is a fine-tuned version of [sreeniketh/cyberbullying_sentiment_dsce_2023](https://huggingface.co/sreeniketh/cyberbullying_sentiment_dsce_2023) 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: 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: 1 ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "gpl-3.0", "tags": ["generated_from_trainer"], "base_model": "sreeniketh/cyberbullying_sentiment_dsce_2023", "model-index": [{"name": "Cyberbullying-detection-tweet-comment", "results": []}]}
text-classification
AnithaThilak/Cyberbullying-detection-tweet-comment
[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:sreeniketh/cyberbullying_sentiment_dsce_2023", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T09:24:03+00:00
[]
[]
TAGS #transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-sreeniketh/cyberbullying_sentiment_dsce_2023 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
# Cyberbullying-detection-tweet-comment This model is a fine-tuned version of sreeniketh/cyberbullying_sentiment_dsce_2023 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: 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: 1 ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# Cyberbullying-detection-tweet-comment\n\nThis model is a fine-tuned version of sreeniketh/cyberbullying_sentiment_dsce_2023 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-sreeniketh/cyberbullying_sentiment_dsce_2023 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Cyberbullying-detection-tweet-comment\n\nThis model is a fine-tuned version of sreeniketh/cyberbullying_sentiment_dsce_2023 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 78, 49, 6, 12, 8, 3, 90, 33 ]
[ "passage: TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-sreeniketh/cyberbullying_sentiment_dsce_2023 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n# Cyberbullying-detection-tweet-comment\n\nThis model is a fine-tuned version of sreeniketh/cyberbullying_sentiment_dsce_2023 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
transformers
<!-- 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. --> # bert-cola-finetuned This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8043 - Matthews Correlation: 0.5645 ## 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: 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4536 | 1.0 | 1069 | 0.4716 | 0.5449 | | 0.3125 | 2.0 | 2138 | 0.6611 | 0.5528 | | 0.1922 | 3.0 | 3207 | 0.8043 | 0.5645 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-cola-finetuned", "results": []}]}
text-classification
StatsGary/bert-cola-finetuned
[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T09:24:29+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
bert-cola-finetuned =================== This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.8043 * Matthews Correlation: 0.5645 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: 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: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 67, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
<!-- 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. --> # t5-large-lora-4.72M-snli-model2 This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6295 - Accuracy: 0.792 ## 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: 67 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3501 | 1.0 | 4292 | 0.2734 | 0.9041 | | 0.3332 | 2.0 | 8584 | 0.2616 | 0.9064 | | 0.3271 | 3.0 | 12876 | 0.2576 | 0.9077 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "t5-large", "model-index": [{"name": "t5-large-lora-4.72M-snli-model2", "results": []}]}
text-classification
varun-v-rao/t5-large-lora-4.72M-snli-model2
[ "transformers", "tensorboard", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:t5-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T09:25:51+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #t5 #text-classification #generated_from_trainer #base_model-t5-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
t5-large-lora-4.72M-snli-model2 =============================== This model is a fine-tuned version of t5-large on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.6295 * Accuracy: 0.792 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: 67 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 67\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text-classification #generated_from_trainer #base_model-t5-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 67\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ 75, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text-classification #generated_from_trainer #base_model-t5-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 67\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
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# **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="AnnaMalk/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
AnnaMalk/q-FrozenLake-v1-4x4-noSlippery
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
2024-02-06T09:26:04+00:00
[]
[]
TAGS #FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage
[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
[ 40, 39 ]
[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]
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null
null
peft
<!-- 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. --> # mistral-7b-medwiki-v1 This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the medical_meadow_wikidoc dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1 ### Perfromance hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True,peft=chenhugging/mistral-7b-medwiki-v1), gen_kwargs: (None), limit: 100.0, num_fewshot: None | Tasks |Version|Filter|n-shot| Metric |Value| |Stderr| |---------------------|-------|------|-----:|--------|----:|---|-----:| |pubmedqa | 1|none | 0|acc | 0.99|± |0.0100| |professional_medicine| 0|none | 0|acc | 0.57|± |0.0498| |college_medicine | 0|none | 0|acc | 0.59|± |0.0494| |clinical_knowledge | 0|none | 0|acc | 0.58|± |0.0496| |medmcqa |Yaml |none | 0|acc | 0.40|± |0.0492| |ocn |Yaml |none | 0|acc | 0.61|± |0.0490| |aocnp |Yaml |none | 0|acc | 0.52|± |0.0502| ### Original Performance hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load_in_4bit=True), gen_kwargs: (None), limit: 100.0, num_fewshot: None, batch_size: 1 | Tasks |Version|Filter|n-shot| Metric |Value| |Stderr| |---------------------|-------|------|-----:|--------|----:|---|-----:| |pubmedqa | 1|none | 0|acc | 0.98|± |0.0141| |professional_medicine| 0|none | 0|acc | 0.64|± |0.0482| |college_medicine | 0|none | 0|acc | 0.65|± |0.0479| |clinical_knowledge | 0|none | 0|acc | 0.68|± |0.0469| |medmcqa |Yaml |none | 0|acc | 0.45|± |0.0500| |ocn |Yaml |none | 0|acc | 0.62|± |0.0488| |aocnp |Yaml |none | 0|acc | 0.47|± |0.0502|
{"license": "other", "library_name": "peft", "tags": ["llama-factory", "lora", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral-7b-medwiki-v1", "results": []}]}
null
chenhugging/mistral-7b-medwiki-v1
[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "license:other", "region:us" ]
2024-02-06T09:26:37+00:00
[]
[]
TAGS #peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-other #region-us
mistral-7b-medwiki-v1 ===================== This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the medical\_meadow\_wikidoc dataset. ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0005 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * num\_epochs: 1.0 ### Framework versions * PEFT 0.8.2 * Transformers 4.37.2 * Pytorch 2.1.1+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1 ### Perfromance hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\_in\_4bit=True,peft=chenhugging/mistral-7b-medwiki-v1), gen\_kwargs: (None), limit: 100.0, num\_fewshot: None ### Original Performance hf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\_in\_4bit=True), gen\_kwargs: (None), limit: 100.0, num\_fewshot: None, batch\_size: 1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 1.0", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1", "### Perfromance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/mistral-7b-medwiki-v1), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None", "### Original Performance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
[ "TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-other #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 1.0", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1", "### Perfromance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/mistral-7b-medwiki-v1), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None", "### Original Performance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
[ 51, 126, 39, 88, 77 ]
[ "passage: TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-other #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 1.0### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1### Perfromance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True,peft=chenhugging/mistral-7b-medwiki-v1), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None### Original Performance\n\n\nhf (pretrained=mistralai/Mistral-7B-v0.1,parallelize=True,load\\_in\\_4bit=True), gen\\_kwargs: (None), limit: 100.0, num\\_fewshot: None, batch\\_size: 1" ]
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null
null
keras
## 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: | Hyperparameters | Value | | :-- | :-- | | name | Adam | | weight_decay | None | | clipnorm | None | | global_clipnorm | None | | clipvalue | None | | use_ema | False | | ema_momentum | 0.99 | | ema_overwrite_frequency | None | | jit_compile | True | | is_legacy_optimizer | False | | learning_rate | 2.9999999242136255e-05 | | beta_1 | 0.9 | | beta_2 | 0.999 | | epsilon | 1e-07 | | amsgrad | False | | training_precision | float32 | ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
{"library_name": "keras"}
null
spneshaei/mindmate-original-fold5-bert-base-german-cased
[ "keras", "region:us" ]
2024-02-06T09:27:22+00:00
[]
[]
TAGS #keras #region-us
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: Model Plot ---------- View Model Plot !Model Image
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ "TAGS\n#keras #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ 9, 28 ]
[ "passage: TAGS\n#keras #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
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# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="AnnaMalk/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/- 2.73", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
AnnaMalk/Taxi-v3
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
2024-02-06T09:28:22+00:00
[]
[]
TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage
[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
[ 32, 33 ]
[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]
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null
null
keras
## 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: | Hyperparameters | Value | | :-- | :-- | | name | Adam | | weight_decay | None | | clipnorm | None | | global_clipnorm | None | | clipvalue | None | | use_ema | False | | ema_momentum | 0.99 | | ema_overwrite_frequency | None | | jit_compile | True | | is_legacy_optimizer | False | | learning_rate | 2.9999999242136255e-05 | | beta_1 | 0.9 | | beta_2 | 0.999 | | epsilon | 1e-07 | | amsgrad | False | | training_precision | float32 | ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
{"library_name": "keras"}
null
spneshaei/mindmate-original-fold4-bert-base-german-cased
[ "keras", "region:us" ]
2024-02-06T09:30:15+00:00
[]
[]
TAGS #keras #region-us
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: Model Plot ---------- View Model Plot !Model Image
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ "TAGS\n#keras #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ 9, 28 ]
[ "passage: TAGS\n#keras #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
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null
null
transformers
# 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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{"library_name": "transformers", "tags": []}
text-generation
YashRawal225/Intel-3-7b-chat-finetune-german
[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T09:34:04+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
<!-- 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. --> # tinyllama-vocab-extension This model is a fine-tuned version of [EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1](https://huggingface.co/EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1) 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: 0.00015 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.3.0.dev20240127+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1", "model-index": [{"name": "tinyllama-vocab-extension", "results": []}]}
text-generation
Unggi/tinyllama-vocab-extension
[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T09:37:14+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# tinyllama-vocab-extension This model is a fine-tuned version of EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1 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: 0.00015 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.3.0.dev20240127+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# tinyllama-vocab-extension\n\nThis model is a fine-tuned version of EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- gradient_accumulation_steps: 32\n- total_train_batch_size: 512\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0", "### Training results", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# tinyllama-vocab-extension\n\nThis model is a fine-tuned version of EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- gradient_accumulation_steps: 32\n- total_train_batch_size: 512\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0", "### Training results", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 109, 70, 6, 12, 8, 3, 157, 4, 43 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# tinyllama-vocab-extension\n\nThis model is a fine-tuned version of EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- gradient_accumulation_steps: 32\n- total_train_batch_size: 512\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
transformers
<!-- 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. --> # roberta-base-lora-1.18M-snli-model2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7906 - Accuracy: 0.717 ## 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: 256 - eval_batch_size: 256 - seed: 93 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4855 | 1.0 | 2146 | 0.3867 | 0.8539 | | 0.4522 | 2.0 | 4292 | 0.3551 | 0.8645 | | 0.4454 | 3.0 | 6438 | 0.3493 | 0.8663 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-lora-1.18M-snli-model2", "results": []}]}
text-classification
varun-v-rao/roberta-base-lora-1.18M-snli-model2
[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T09:37:24+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
roberta-base-lora-1.18M-snli-model2 =================================== This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.7906 * Accuracy: 0.717 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: 256 * eval\_batch\_size: 256 * seed: 93 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 93\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 93\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ 63, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 93\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
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<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/second-state/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Qwen1.5-14B-Chat-GGUF ## Original Model [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat) ## Run with LlamaEdge - LlamaEdge version: [v0.2.15](https://github.com/second-state/LlamaEdge/releases/tag/0.2.15) and above - Prompt template - Prompt type: `chatml` - Prompt string ```text <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-14B-Chat-Q5_K_M.gguf llama-api-server.wasm -p chatml ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-14B-Chat-Q5_K_M.gguf llama-chat.wasm -p chatml ``` ## Quantized GGUF Models | Name | Quant method | Bits | Size | Use case | | ---- | ---- | ---- | ---- | ----- | | [Qwen1.5-14B-Chat-Q2_K.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q2_K.gguf) | Q2_K | 2 | 6.09 GB| smallest, significant quality loss - not recommended for most purposes | | [Qwen1.5-14B-Chat-Q3_K_L.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q3_K_L.gguf) | Q3_K_L | 3 | 7.84 GB| small, substantial quality loss | | [Qwen1.5-14B-Chat-Q3_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q3_K_M.gguf) | Q3_K_M | 3 | 7.42 GB| very small, high quality loss | | [Qwen1.5-14B-Chat-Q3_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q3_K_S.gguf) | Q3_K_S | 3 | 6.95 GB| very small, high quality loss | | [Qwen1.5-14B-Chat-Q4_0.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q4_0.gguf) | Q4_0 | 4 | 8.18 GB| legacy; small, very high quality loss - prefer using Q3_K_M | | [Qwen1.5-14B-Chat-Q4_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q4_K_M.gguf) | Q4_K_M | 4 | 9.19 GB| medium, balanced quality - recommended | | [Qwen1.5-14B-Chat-Q4_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q4_K_S.gguf) | Q4_K_S | 4 | 8.56 GB| small, greater quality loss | | [Qwen1.5-14B-Chat-Q5_0.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q5_0.gguf) | Q5_0 | 5 | 9.85 GB| legacy; medium, balanced quality - prefer using Q4_K_M | | [Qwen1.5-14B-Chat-Q5_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q5_K_M.gguf) | Q5_K_M | 5 | 10.5 GB| large, very low quality loss - recommended | | [Qwen1.5-14B-Chat-Q5_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q5_K_S.gguf) | Q5_K_S | 5 | 10.0 GB| large, low quality loss - recommended | | [Qwen1.5-14B-Chat-Q6_K.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q6_K.gguf) | Q6_K | 6 | 12.3 GB| very large, extremely low quality loss | | [Qwen1.5-14B-Chat-Q8_0.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q8_0.gguf) | Q8_0 | 8 | 15.1 GB| very large, extremely low quality loss - not recommended |
{"language": ["en"], "license": "other", "tags": ["chat"], "model_name": "Qwen1.5 14B Chat", "base_model": "Qwen/Qwen1.5-14B-Chat", "license_name": "tongyi-qianwen-research", "license_link": "https://huggingface.co/Qwen/Qwen1.5-14B-Chat/blob/main/LICENSE", "model_creator": "Qwen", "quantized_by": "Second State Inc.", "pipeline_tag": "text-generation"}
text-generation
second-state/Qwen1.5-14B-Chat-GGUF
[ "gguf", "chat", "text-generation", "en", "base_model:Qwen/Qwen1.5-14B-Chat", "license:other", "region:us" ]
2024-02-06T09:40:52+00:00
[]
[ "en" ]
TAGS #gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-14B-Chat #license-other #region-us
![](URL style=) --- Qwen1.5-14B-Chat-GGUF ===================== Original Model -------------- Qwen/Qwen1.5-7B-Chat Run with LlamaEdge ------------------ * LlamaEdge version: v0.2.15 and above * Prompt template + Prompt type: 'chatml' + Prompt string * Run as LlamaEdge service * Run as LlamaEdge command app Quantized GGUF Models ---------------------
[]
[ "TAGS\n#gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-14B-Chat #license-other #region-us \n" ]
[ 38 ]
[ "passage: TAGS\n#gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-14B-Chat #license-other #region-us \n" ]
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null
null
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# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
{"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]}
text-generation
drkr23/fine-tuna-gpt2
[ "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]
2024-02-06T09:44:39+00:00
[]
[]
TAGS #safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us
# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit AutoTrain. # Usage
[ "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]
[ "TAGS\n#safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us \n", "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]
[ 37, 29, 3 ]
[ "passage: TAGS\n#safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage" ]
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null
null
diffusers
### humans-with-their-dreams Dreambooth model trained by mjmahe following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 4JK21CV020 Sample pictures of this concept: ![0](https://huggingface.co/mjmahe/humans-with-their-dreams/resolve/main/sample_images/human1.jpg) ![1](https://huggingface.co/mjmahe/humans-with-their-dreams/resolve/main/sample_images/human_2.jpg) ![2](https://huggingface.co/mjmahe/humans-with-their-dreams/resolve/main/sample_images/human_3.jpg) ![3](https://huggingface.co/mjmahe/humans-with-their-dreams/resolve/main/sample_images/human_4.jpg)
{"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]}
text-to-image
mjmahe/humans-with-their-dreams
[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
2024-02-06T09:45:32+00:00
[]
[]
TAGS #diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
### humans-with-their-dreams Dreambooth model trained by mjmahe following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 4JK21CV020 Sample pictures of this concept: !0 !1 !2 !3
[ "### humans-with-their-dreams Dreambooth model trained by mjmahe following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 4JK21CV020\n\nSample pictures of this concept:\n\n \n \n \n !0\n !1\n !2\n !3" ]
[ "TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "### humans-with-their-dreams Dreambooth model trained by mjmahe following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 4JK21CV020\n\nSample pictures of this concept:\n\n \n \n \n !0\n !1\n !2\n !3" ]
[ 73, 64 ]
[ "passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### humans-with-their-dreams Dreambooth model trained by mjmahe following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 4JK21CV020\n\nSample pictures of this concept:\n\n \n \n \n !0\n !1\n !2\n !3" ]
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null
null
transformers
# LLM user flow classification This model identifies common events and patterns within the conversation flow. Such events include, for example, complaint, when a user expresses dissatisfaction. The flow labels can serve as foundational elements for sophisticated LLM analytics. It is ONNX quantized and is a fined-tune of [MiniLMv2-L6-H384](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large). The base model can be found [here](https://huggingface.co/minuva/MiniLMv2-agentflow-v2) This model is used *only* for the user texts. For the LLM texts in the dialog use this [agent model](https://huggingface.co/minuva/MiniLMv2-agentflow-v2). # Load the Model ```py from transformers import pipeline pipe = pipeline(model='minuva/MiniLMv2-userflow-v2', task='text-classification') pipe("This is wrong") # [{'label': 'model_wrong_or_try_again', 'score': 0.9729849100112915}] ``` # Categories Explanation <details> <summary>Click to expand!</summary> - OTHER: Responses that do not fit into any predefined categories or are outside the scope of the specific interaction types listed. - agrees_praising_thanking: When the user agrees with the provided information, offers praise, or expresses gratitude. - asks_source: The user requests the source of the information or the basis for the answer provided. - continue: Indicates a prompt for the conversation to proceed or continue without a specific directional change. - continue_or_finnish_code: Signals either to continue with the current line of discussion or code execution, or to conclude it. - improve_or_modify_answer: The user requests an improvement or modification to the provided answer. - lack_of_understandment: Reflects the user's or agent confusion or lack of understanding regarding the information provided. - model_wrong_or_try_again: Indicates that the model's response was incorrect or unsatisfactory, suggesting a need to attempt another answer. - more_listing_or_expand: The user requests further elaboration, expansion from the given list by the agent. - repeat_answers_or_question: The need to reiterate a previous answer or question. - request_example: The user asks for examples to better understand the concept or answer provided. - user_complains_repetition: The user notes that the information or responses are repetitive, indicating a need for new or different content. - user_doubts_answer: The user expresses skepticism or doubt regarding the accuracy or validity of the provided answer. - user_goodbye: The user says goodbye to the agent. - user_reminds_question: The user reiterates the question. - user_wants_agent_to_answer: The user explicitly requests a response from the agent, when the agent refuses to do so. - user_wants_explanation: The user seeks an explanation behind the information or answer provided. - user_wants_more_detail: Indicates the user's desire for more comprehensive or detailed information on the topic. - user_wants_shorter_longer_answer: The user requests that the answer be condensed or expanded to better meet their informational needs. - user_wants_simplier_explanation: The user seeks a simpler, more easily understood explanation. - user_wants_yes_or_no: The user is asking for a straightforward affirmative or negative answer, without additional detail or explanation. </details> <br> # Metrics in our private test dataset | Model (params) | Loss | Accuracy | F1 | |--------------------|-------------|----------|--------| | minuva/MiniLMv2-userflow-v2 (33M) | 0.6738 | 0.7236 | 0.7313 | # Deployment Check our [llm-flow-classification repository](https://github.com/minuva/llm-flow-classification) for a FastAPI and ONNX based server to deploy this model on CPU devices.
{"language": ["en"], "license": "apache-2.0"}
text-classification
minuva/MiniLMv2-userflow-v2
[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T09:51:22+00:00
[]
[ "en" ]
TAGS #transformers #tensorboard #safetensors #roberta #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
LLM user flow classification ============================ This model identifies common events and patterns within the conversation flow. Such events include, for example, complaint, when a user expresses dissatisfaction. The flow labels can serve as foundational elements for sophisticated LLM analytics. It is ONNX quantized and is a fined-tune of MiniLMv2-L6-H384. The base model can be found here This model is used *only* for the user texts. For the LLM texts in the dialog use this agent model. Load the Model ============== Categories Explanation ====================== Click to expand! * OTHER: Responses that do not fit into any predefined categories or are outside the scope of the specific interaction types listed. * agrees\_praising\_thanking: When the user agrees with the provided information, offers praise, or expresses gratitude. * asks\_source: The user requests the source of the information or the basis for the answer provided. * continue: Indicates a prompt for the conversation to proceed or continue without a specific directional change. * continue\_or\_finnish\_code: Signals either to continue with the current line of discussion or code execution, or to conclude it. * improve\_or\_modify\_answer: The user requests an improvement or modification to the provided answer. * lack\_of\_understandment: Reflects the user's or agent confusion or lack of understanding regarding the information provided. * model\_wrong\_or\_try\_again: Indicates that the model's response was incorrect or unsatisfactory, suggesting a need to attempt another answer. * more\_listing\_or\_expand: The user requests further elaboration, expansion from the given list by the agent. * repeat\_answers\_or\_question: The need to reiterate a previous answer or question. * request\_example: The user asks for examples to better understand the concept or answer provided. * user\_complains\_repetition: The user notes that the information or responses are repetitive, indicating a need for new or different content. * user\_doubts\_answer: The user expresses skepticism or doubt regarding the accuracy or validity of the provided answer. * user\_goodbye: The user says goodbye to the agent. * user\_reminds\_question: The user reiterates the question. * user\_wants\_agent\_to\_answer: The user explicitly requests a response from the agent, when the agent refuses to do so. * user\_wants\_explanation: The user seeks an explanation behind the information or answer provided. * user\_wants\_more\_detail: Indicates the user's desire for more comprehensive or detailed information on the topic. * user\_wants\_shorter\_longer\_answer: The user requests that the answer be condensed or expanded to better meet their informational needs. * user\_wants\_simplier\_explanation: The user seeks a simpler, more easily understood explanation. * user\_wants\_yes\_or\_no: The user is asking for a straightforward affirmative or negative answer, without additional detail or explanation. Metrics in our private test dataset =================================== Deployment ========== Check our llm-flow-classification repository for a FastAPI and ONNX based server to deploy this model on CPU devices.
[]
[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 52 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
<!-- 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. --> # longformer-sep_tok_full_labels This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the fancy_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.2756 - Claim: {'precision': 0.6184954751131222, 'recall': 0.5020661157024794, 'f1-score': 0.5542321338063863, 'support': 4356.0} - Majorclaim: {'precision': 0.7991228070175439, 'recall': 0.8350137488542622, 'f1-score': 0.816674137158225, 'support': 2182.0} - O: {'precision': 0.9995611725469545, 'recall': 0.9996489072237339, 'f1-score': 0.9996050379602405, 'support': 11393.0} - Premise: {'precision': 0.8654131231957556, 'recall': 0.9169973544973545, 'f1-score': 0.8904587966122105, 'support': 12096.0} - Accuracy: 0.8822 - Macro avg: {'precision': 0.820648144468344, 'recall': 0.8134315315694575, 'f1-score': 0.8152425263842656, 'support': 30027.0} - Weighted avg: {'precision': 0.875674886985325, 'recall': 0.8822060145868719, 'f1-score': 0.8777336378406828, 'support': 30027.0} ## 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: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Claim | Majorclaim | O | Premise | Accuracy | Macro avg | Weighted avg | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:| | No log | 1.0 | 41 | 0.4306 | {'precision': 0.4545032497678737, 'recall': 0.22474747474747475, 'f1-score': 0.30076804915514593, 'support': 4356.0} | {'precision': 0.647887323943662, 'recall': 0.5270394133822182, 'f1-score': 0.5812484205205964, 'support': 2182.0} | {'precision': 0.9773825966850829, 'recall': 0.9937681032212762, 'f1-score': 0.9855072463768116, 'support': 11393.0} | {'precision': 0.7948187956455836, 'recall': 0.9537037037037037, 'f1-score': 0.8670424652386322, 'support': 12096.0} | 0.8322 | {'precision': 0.7186479915105506, 'recall': 0.6748146737636682, 'f1-score': 0.6836415453227965, 'support': 30027.0} | {'precision': 0.8040415084089672, 'recall': 0.8321510640423618, 'f1-score': 0.809073813341102, 'support': 30027.0} | | No log | 2.0 | 82 | 0.3324 | {'precision': 0.5230341540905481, 'recall': 0.3023415977961432, 'f1-score': 0.3831830084375909, 'support': 4356.0} | {'precision': 0.7977036632039366, 'recall': 0.6686526122823098, 'f1-score': 0.7274993767140363, 'support': 2182.0} | {'precision': 0.9997361245492128, 'recall': 0.9976301237602037, 'f1-score': 0.998682013882787, 'support': 11393.0} | {'precision': 0.8096569072741248, 'recall': 0.9579199735449735, 'f1-score': 0.8775703411974098, 'support': 12096.0} | 0.8569 | {'precision': 0.7825327122794555, 'recall': 0.7316360768459076, 'f1-score': 0.746733685057956, 'support': 30027.0} | {'precision': 0.839328930153076, 'recall': 0.8568621573916808, 'f1-score': 0.8408973209456367, 'support': 30027.0} | | No log | 3.0 | 123 | 0.3145 | {'precision': 0.5644570963806426, 'recall': 0.3186409550045914, 'f1-score': 0.4073367571533382, 'support': 4356.0} | {'precision': 0.8542825361512792, 'recall': 0.7039413382218148, 'f1-score': 0.771859296482412, 'support': 2182.0} | {'precision': 0.9995607870695713, 'recall': 0.9987711752830686, 'f1-score': 0.9991658251745181, 'support': 11393.0} | {'precision': 0.8112053385235646, 'recall': 0.964781746031746, 'f1-score': 0.8813533721018051, 'support': 12096.0} | 0.8650 | {'precision': 0.8073764395312645, 'recall': 0.7465338036353052, 'f1-score': 0.7649288127280184, 'support': 30027.0} | {'precision': 0.8500068414287419, 'recall': 0.8649881773070903, 'f1-score': 0.8493323520245539, 'support': 30027.0} | | No log | 4.0 | 164 | 0.2796 | {'precision': 0.6026528258362168, 'recall': 0.4797979797979798, 'f1-score': 0.5342535787321063, 'support': 4356.0} | {'precision': 0.8180965775902485, 'recall': 0.7997250229147571, 'f1-score': 0.8088064889918888, 'support': 2182.0} | {'precision': 0.9995610184372257, 'recall': 0.9992978144474678, 'f1-score': 0.999429399113374, 'support': 11393.0} | {'precision': 0.8555538508745014, 'recall': 0.922040343915344, 'f1-score': 0.8875537163775268, 'support': 12096.0} | 0.8783 | {'precision': 0.8189660681845482, 'recall': 0.8002152902688873, 'f1-score': 0.807510795803724, 'support': 30027.0} | {'precision': 0.8707836448821982, 'recall': 0.8783095214307124, 'f1-score': 0.8730267174655673, 'support': 30027.0} | | No log | 5.0 | 205 | 0.2756 | {'precision': 0.6184954751131222, 'recall': 0.5020661157024794, 'f1-score': 0.5542321338063863, 'support': 4356.0} | {'precision': 0.7991228070175439, 'recall': 0.8350137488542622, 'f1-score': 0.816674137158225, 'support': 2182.0} | {'precision': 0.9995611725469545, 'recall': 0.9996489072237339, 'f1-score': 0.9996050379602405, 'support': 11393.0} | {'precision': 0.8654131231957556, 'recall': 0.9169973544973545, 'f1-score': 0.8904587966122105, 'support': 12096.0} | 0.8822 | {'precision': 0.820648144468344, 'recall': 0.8134315315694575, 'f1-score': 0.8152425263842656, 'support': 30027.0} | {'precision': 0.875674886985325, 'recall': 0.8822060145868719, 'f1-score': 0.8777336378406828, 'support': 30027.0} | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["fancy_dataset"], "metrics": ["accuracy"], "base_model": "allenai/longformer-base-4096", "model-index": [{"name": "longformer-sep_tok_full_labels", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "fancy_dataset", "type": "fancy_dataset", "config": "sep_tok_full_labels", "split": "test", "args": "sep_tok_full_labels"}, "metrics": [{"type": "accuracy", "value": 0.8822060145868719, "name": "Accuracy"}]}]}]}
token-classification
Theoreticallyhugo/longformer-sep_tok_full_labels
[ "transformers", "safetensors", "longformer", "token-classification", "generated_from_trainer", "dataset:fancy_dataset", "base_model:allenai/longformer-base-4096", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T09:52:22+00:00
[]
[]
TAGS #transformers #safetensors #longformer #token-classification #generated_from_trainer #dataset-fancy_dataset #base_model-allenai/longformer-base-4096 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
longformer-sep\_tok\_full\_labels ================================= This model is a fine-tuned version of allenai/longformer-base-4096 on the fancy\_dataset dataset. It achieves the following results on the evaluation set: * Loss: 0.2756 * Claim: {'precision': 0.6184954751131222, 'recall': 0.5020661157024794, 'f1-score': 0.5542321338063863, 'support': 4356.0} * Majorclaim: {'precision': 0.7991228070175439, 'recall': 0.8350137488542622, 'f1-score': 0.816674137158225, 'support': 2182.0} * O: {'precision': 0.9995611725469545, 'recall': 0.9996489072237339, 'f1-score': 0.9996050379602405, 'support': 11393.0} * Premise: {'precision': 0.8654131231957556, 'recall': 0.9169973544973545, 'f1-score': 0.8904587966122105, 'support': 12096.0} * Accuracy: 0.8822 * Macro avg: {'precision': 0.820648144468344, 'recall': 0.8134315315694575, 'f1-score': 0.8152425263842656, 'support': 30027.0} * Weighted avg: {'precision': 0.875674886985325, 'recall': 0.8822060145868719, 'f1-score': 0.8777336378406828, 'support': 30027.0} 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: 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: 5 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
[ "TAGS\n#transformers #safetensors #longformer #token-classification #generated_from_trainer #dataset-fancy_dataset #base_model-allenai/longformer-base-4096 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
[ 81, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #longformer #token-classification #generated_from_trainer #dataset-fancy_dataset #base_model-allenai/longformer-base-4096 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
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null
null
transformers
<!-- 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 Large V2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2887 - Wer: 9.9198 ## 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: 16 - 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: 20 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5542 | 0.49 | 30 | 0.2941 | 13.0145 | | 0.2716 | 0.98 | 60 | 0.2636 | 12.2538 | | 0.1438 | 1.48 | 90 | 0.2603 | 11.0868 | | 0.1345 | 1.97 | 120 | 0.2502 | 12.1809 | | 0.0619 | 2.46 | 150 | 0.2587 | 12.3476 | | 0.0552 | 2.95 | 180 | 0.2634 | 10.3366 | | 0.0293 | 3.44 | 210 | 0.2722 | 10.0240 | | 0.0206 | 3.93 | 240 | 0.2670 | 9.7739 | | 0.0108 | 4.43 | 270 | 0.2838 | 9.8364 | | 0.008 | 4.92 | 300 | 0.2887 | 9.9198 | ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0
{"language": ["nl"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-large-v2", "model-index": [{"name": "Whisper Large V2", "results": []}]}
automatic-speech-recognition
golesheed/whisper-native-elderly-5-dutch
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "nl", "base_model:openai/whisper-large-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-06T09:53:51+00:00
[]
[ "nl" ]
TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us
Whisper Large V2 ================ This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.2887 * Wer: 9.9198 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: 16 * 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: 20 * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ 74, 116, 4, 38 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
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null
null
transformers
<!-- 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. --> # layoutlm-funsd This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. It achieves the following results on the evaluation set: - Loss: 0.6948 - Answer: {'precision': 0.6981541802388708, 'recall': 0.7948084054388134, 'f1': 0.7433526011560695, 'number': 809} - Header: {'precision': 0.30656934306569344, 'recall': 0.35294117647058826, 'f1': 0.32812500000000006, 'number': 119} - Question: {'precision': 0.771806167400881, 'recall': 0.8225352112676056, 'f1': 0.7963636363636363, 'number': 1065} - Overall Precision: 0.7118 - Overall Recall: 0.7832 - Overall F1: 0.7458 - Overall Accuracy: 0.8019 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 1.8705 | 1.0 | 10 | 1.6097 | {'precision': 0.020219039595619208, 'recall': 0.029666254635352288, 'f1': 0.02404809619238477, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.13919052319842054, 'recall': 0.1323943661971831, 'f1': 0.13570741097208855, 'number': 1065} | 0.0750 | 0.0828 | 0.0787 | 0.3869 | | 1.5023 | 2.0 | 20 | 1.3023 | {'precision': 0.11280101394169835, 'recall': 0.1100123609394314, 'f1': 0.1113892365456821, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4158273381294964, 'recall': 0.5427230046948357, 'f1': 0.4708757637474542, 'number': 1065} | 0.3061 | 0.3347 | 0.3198 | 0.5597 | | 1.1482 | 3.0 | 30 | 0.9914 | {'precision': 0.4626218851570964, 'recall': 0.5278121137206427, 'f1': 0.49307159353348734, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6124161073825504, 'recall': 0.6854460093896714, 'f1': 0.6468763845813027, 'number': 1065} | 0.5442 | 0.5805 | 0.5618 | 0.6832 | | 0.8792 | 4.0 | 40 | 0.8322 | {'precision': 0.5570902394106814, 'recall': 0.7478368355995055, 'f1': 0.6385224274406333, 'number': 809} | {'precision': 0.07317073170731707, 'recall': 0.025210084033613446, 'f1': 0.0375, 'number': 119} | {'precision': 0.6663815226689478, 'recall': 0.7314553990610329, 'f1': 0.6974037600716204, 'number': 1065} | 0.6041 | 0.6959 | 0.6468 | 0.7377 | | 0.7061 | 5.0 | 50 | 0.7396 | {'precision': 0.6233230134158927, 'recall': 0.7466007416563659, 'f1': 0.6794150731158606, 'number': 809} | {'precision': 0.26865671641791045, 'recall': 0.15126050420168066, 'f1': 0.19354838709677416, 'number': 119} | {'precision': 0.6982097186700768, 'recall': 0.7690140845070422, 'f1': 0.7319034852546916, 'number': 1065} | 0.6523 | 0.7230 | 0.6859 | 0.7752 | | 0.5925 | 6.0 | 60 | 0.7144 | {'precision': 0.6383207750269106, 'recall': 0.7330037082818294, 'f1': 0.6823935558112773, 'number': 809} | {'precision': 0.26666666666666666, 'recall': 0.16806722689075632, 'f1': 0.20618556701030927, 'number': 119} | {'precision': 0.702485966319166, 'recall': 0.8225352112676056, 'f1': 0.7577854671280276, 'number': 1065} | 0.6615 | 0.7471 | 0.7017 | 0.7806 | | 0.5114 | 7.0 | 70 | 0.6841 | {'precision': 0.6695842450765864, 'recall': 0.7564894932014833, 'f1': 0.710388856645386, 'number': 809} | {'precision': 0.25806451612903225, 'recall': 0.20168067226890757, 'f1': 0.22641509433962265, 'number': 119} | {'precision': 0.731023102310231, 'recall': 0.831924882629108, 'f1': 0.7782169521299956, 'number': 1065} | 0.6859 | 0.7637 | 0.7227 | 0.7936 | | 0.4568 | 8.0 | 80 | 0.6766 | {'precision': 0.6809421841541756, 'recall': 0.7861557478368356, 'f1': 0.729776247848537, 'number': 809} | {'precision': 0.27722772277227725, 'recall': 0.23529411764705882, 'f1': 0.2545454545454545, 'number': 119} | {'precision': 0.7330543933054393, 'recall': 0.8225352112676056, 'f1': 0.7752212389380532, 'number': 1065} | 0.6906 | 0.7727 | 0.7293 | 0.7976 | | 0.4027 | 9.0 | 90 | 0.6781 | {'precision': 0.6753246753246753, 'recall': 0.7713226205191595, 'f1': 0.7201384881708021, 'number': 809} | {'precision': 0.25, 'recall': 0.25210084033613445, 'f1': 0.2510460251046025, 'number': 119} | {'precision': 0.7478559176672385, 'recall': 0.8187793427230047, 'f1': 0.7817122366651726, 'number': 1065} | 0.6905 | 0.7657 | 0.7261 | 0.8006 | | 0.3612 | 10.0 | 100 | 0.6722 | {'precision': 0.6984649122807017, 'recall': 0.7873918417799752, 'f1': 0.7402672864613596, 'number': 809} | {'precision': 0.2692307692307692, 'recall': 0.29411764705882354, 'f1': 0.28112449799196787, 'number': 119} | {'precision': 0.7461669505962522, 'recall': 0.8225352112676056, 'f1': 0.782492184010719, 'number': 1065} | 0.6986 | 0.7767 | 0.7356 | 0.8038 | | 0.3347 | 11.0 | 110 | 0.6889 | {'precision': 0.6960352422907489, 'recall': 0.7812113720642769, 'f1': 0.7361677344205009, 'number': 809} | {'precision': 0.2923076923076923, 'recall': 0.31932773109243695, 'f1': 0.3052208835341365, 'number': 119} | {'precision': 0.7484926787252368, 'recall': 0.815962441314554, 'f1': 0.7807726864330637, 'number': 1065} | 0.6999 | 0.7722 | 0.7343 | 0.8034 | | 0.3146 | 12.0 | 120 | 0.6884 | {'precision': 0.6964091403699674, 'recall': 0.7911001236093943, 'f1': 0.7407407407407407, 'number': 809} | {'precision': 0.2923076923076923, 'recall': 0.31932773109243695, 'f1': 0.3052208835341365, 'number': 119} | {'precision': 0.7551724137931034, 'recall': 0.8225352112676056, 'f1': 0.7874157303370787, 'number': 1065} | 0.7035 | 0.7797 | 0.7396 | 0.8010 | | 0.2955 | 13.0 | 130 | 0.7040 | {'precision': 0.6929824561403509, 'recall': 0.7812113720642769, 'f1': 0.7344567112144103, 'number': 809} | {'precision': 0.2887323943661972, 'recall': 0.3445378151260504, 'f1': 0.31417624521072796, 'number': 119} | {'precision': 0.7664618086040387, 'recall': 0.819718309859155, 'f1': 0.7921960072595282, 'number': 1065} | 0.7050 | 0.7757 | 0.7387 | 0.7984 | | 0.2847 | 14.0 | 140 | 0.6935 | {'precision': 0.6876332622601279, 'recall': 0.7972805933250927, 'f1': 0.7384087006296507, 'number': 809} | {'precision': 0.28888888888888886, 'recall': 0.3277310924369748, 'f1': 0.3070866141732283, 'number': 119} | {'precision': 0.7653778558875219, 'recall': 0.8178403755868544, 'f1': 0.7907399001361779, 'number': 1065} | 0.7033 | 0.7802 | 0.7398 | 0.8019 | | 0.2725 | 15.0 | 150 | 0.6948 | {'precision': 0.6981541802388708, 'recall': 0.7948084054388134, 'f1': 0.7433526011560695, 'number': 809} | {'precision': 0.30656934306569344, 'recall': 0.35294117647058826, 'f1': 0.32812500000000006, 'number': 119} | {'precision': 0.771806167400881, 'recall': 0.8225352112676056, 'f1': 0.7963636363636363, 'number': 1065} | 0.7118 | 0.7832 | 0.7458 | 0.8019 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["funsd"], "base_model": "microsoft/layoutlm-base-uncased", "model-index": [{"name": "layoutlm-funsd", "results": []}]}
token-classification
pvbabich/layoutlm-funsd
[ "transformers", "tensorboard", "safetensors", "layoutlm", "token-classification", "generated_from_trainer", "dataset:funsd", "base_model:microsoft/layoutlm-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T09:55:39+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #layoutlm #token-classification #generated_from_trainer #dataset-funsd #base_model-microsoft/layoutlm-base-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us
layoutlm-funsd ============== This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the funsd dataset. It achieves the following results on the evaluation set: * Loss: 0.6948 * Answer: {'precision': 0.6981541802388708, 'recall': 0.7948084054388134, 'f1': 0.7433526011560695, 'number': 809} * Header: {'precision': 0.30656934306569344, 'recall': 0.35294117647058826, 'f1': 0.32812500000000006, 'number': 119} * Question: {'precision': 0.771806167400881, 'recall': 0.8225352112676056, 'f1': 0.7963636363636363, 'number': 1065} * Overall Precision: 0.7118 * Overall Recall: 0.7832 * Overall F1: 0.7458 * Overall Accuracy: 0.8019 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: 16 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 15 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #layoutlm #token-classification #generated_from_trainer #dataset-funsd #base_model-microsoft/layoutlm-base-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 79, 113, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #layoutlm #token-classification #generated_from_trainer #dataset-funsd #base_model-microsoft/layoutlm-base-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
# 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. (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]
{"library_name": "transformers", "tags": []}
null
Godwin024/gpt2-finetun
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-06T09:56:14+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
<!-- 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. --> # nslPOS_2 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "nslPOS_2", "results": []}]}
null
saniasinghania/nslPOS_2
[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]
2024-02-06T09:57:11+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us
# nslPOS_2 This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder 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: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# nslPOS_2\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.36.0\n- Pytorch 2.1.2+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us \n", "# nslPOS_2\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.36.0\n- Pytorch 2.1.2+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 70, 36, 6, 12, 8, 3, 103, 4, 35 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us \n# nslPOS_2\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.36.0\n- Pytorch 2.1.2+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "facebook/bart-large"}
null
fhzh123/bart_PREFIX_TUNING_SEQ2SEQ_pos
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:facebook/bart-large", "region:us" ]
2024-02-06T09:59:10+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-facebook/bart-large #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-facebook/bart-large #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
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[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-facebook/bart-large #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
transformers
<!-- 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. --> # opt-350m-lora-1.57M-snli-model2 This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8538 - Accuracy: 0.6795 ## 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: 256 - eval_batch_size: 256 - seed: 58 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5039 | 1.0 | 2146 | 0.4198 | 0.8393 | | 0.4607 | 2.0 | 4292 | 0.3831 | 0.8578 | | 0.4463 | 3.0 | 6438 | 0.3747 | 0.8585 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
{"license": "other", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/opt-350m", "model-index": [{"name": "opt-350m-lora-1.57M-snli-model2", "results": []}]}
text-classification
varun-v-rao/opt-350m-lora-1.57M-snli-model2
[ "transformers", "tensorboard", "safetensors", "opt", "text-classification", "generated_from_trainer", "base_model:facebook/opt-350m", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T09:59:19+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #opt #text-classification #generated_from_trainer #base_model-facebook/opt-350m #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
opt-350m-lora-1.57M-snli-model2 =============================== This model is a fine-tuned version of facebook/opt-350m on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.8538 * Accuracy: 0.6795 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: 256 * eval\_batch\_size: 256 * seed: 58 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 58\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #opt #text-classification #generated_from_trainer #base_model-facebook/opt-350m #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 58\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ 75, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #opt #text-classification #generated_from_trainer #base_model-facebook/opt-350m #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 58\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
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null
null
transformers
<!-- 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. --> # roberta-base-lora-1.18M-snli-model3 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7909 - Accuracy: 0.7175 ## 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: 256 - eval_batch_size: 256 - seed: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4933 | 1.0 | 2146 | 0.3805 | 0.8541 | | 0.4549 | 2.0 | 4292 | 0.3559 | 0.8651 | | 0.4446 | 3.0 | 6438 | 0.3510 | 0.8669 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-lora-1.18M-snli-model3", "results": []}]}
text-classification
varun-v-rao/roberta-base-lora-1.18M-snli-model3
[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T10:02:23+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
roberta-base-lora-1.18M-snli-model3 =================================== This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.7909 * Accuracy: 0.7175 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: 256 * eval\_batch\_size: 256 * seed: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ 63, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
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null
diffusers
# LoRA DreamBooth - ricochet/cocktail-lora-sdxl These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0 trained on @fffiloni's SD-XL trainer. The weights were trained on the concept prompt: ``` lcoacrkal ``` Use this keyword to trigger your custom model in your prompts. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Usage Make sure to upgrade diffusers to >= 0.19.0: ``` pip install diffusers --upgrade ``` In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` To just use the base model, you can run: ```python import torch from diffusers import DiffusionPipeline, AutoencoderKL device = "cuda" if torch.cuda.is_available() else "cpu" vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) pipe.to(device) # This is where you load your trained weights specific_safetensors = "pytorch_lora_weights.safetensors" lora_scale = 0.9 pipe.load_lora_weights( 'ricochet/cocktail-lora-sdxl', weight_name = specific_safetensors, # use_auth_token = True ) prompt = "A majestic lcoacrkal jumping from a big stone at night" image = pipe( prompt=prompt, num_inference_steps=50, cross_attention_kwargs={"scale": lora_scale} ).images[0] ```
{"tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora"], "datasets": ["ricochet/cocktails"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "lcoacrkal", "inference": false}
text-to-image
ricochet/cocktail-lora-sdxl
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "dataset:ricochet/cocktails", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]
2024-02-06T10:02:39+00:00
[]
[]
TAGS #diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #dataset-ricochet/cocktails #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us
# LoRA DreamBooth - ricochet/cocktail-lora-sdxl These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0 trained on @fffiloni's SD-XL trainer. The weights were trained on the concept prompt: Use this keyword to trigger your custom model in your prompts. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Usage Make sure to upgrade diffusers to >= 0.19.0: In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark: To just use the base model, you can run:
[ "# LoRA DreamBooth - ricochet/cocktail-lora-sdxl\nThese are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0 trained on @fffiloni's SD-XL trainer. \nThe weights were trained on the concept prompt: \n \nUse this keyword to trigger your custom model in your prompts. \nLoRA for the text encoder was enabled: False.\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Usage\nMake sure to upgrade diffusers to >= 0.19.0:\n\nIn addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:\n\nTo just use the base model, you can run:" ]
[ "TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #dataset-ricochet/cocktails #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n", "# LoRA DreamBooth - ricochet/cocktail-lora-sdxl\nThese are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0 trained on @fffiloni's SD-XL trainer. \nThe weights were trained on the concept prompt: \n \nUse this keyword to trigger your custom model in your prompts. \nLoRA for the text encoder was enabled: False.\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Usage\nMake sure to upgrade diffusers to >= 0.19.0:\n\nIn addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:\n\nTo just use the base model, you can run:" ]
[ 72, 121, 53 ]
[ "passage: TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #dataset-ricochet/cocktails #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n# LoRA DreamBooth - ricochet/cocktail-lora-sdxl\nThese are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0 trained on @fffiloni's SD-XL trainer. \nThe weights were trained on the concept prompt: \n \nUse this keyword to trigger your custom model in your prompts. \nLoRA for the text encoder was enabled: False.\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Usage\nMake sure to upgrade diffusers to >= 0.19.0:\n\nIn addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:\n\nTo just use the base model, you can run:" ]
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null
null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "facebook/bart-large"}
null
fhzh123/bart_PREFIX_TUNING_SEQ2SEQ_neg
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:facebook/bart-large", "region:us" ]
2024-02-06T10:06:04+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-facebook/bart-large #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-facebook/bart-large #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
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[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-facebook/bart-large #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
peft
<!-- 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. --> # llm_flan_fineTuned This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) 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: 0.0001 - 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 - lr_scheduler_warmup_steps: 50 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.1 - Transformers 4.37.2 - Pytorch 2.0.0 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "google/flan-t5-large", "model-index": [{"name": "llm_flan_fineTuned", "results": []}]}
null
danks6/llm_flan_fineTuned
[ "peft", "tensorboard", "safetensors", "t5", "trl", "sft", "generated_from_trainer", "base_model:google/flan-t5-large", "license:apache-2.0", "region:us" ]
2024-02-06T10:10:57+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #t5 #trl #sft #generated_from_trainer #base_model-google/flan-t5-large #license-apache-2.0 #region-us
# llm_flan_fineTuned This model is a fine-tuned version of google/flan-t5-large 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: 0.0001 - 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 - lr_scheduler_warmup_steps: 50 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.1 - Transformers 4.37.2 - Pytorch 2.0.0 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# llm_flan_fineTuned\n\nThis model is a fine-tuned version of google/flan-t5-large on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 50\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.8.1\n- Transformers 4.37.2\n- Pytorch 2.0.0\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #t5 #trl #sft #generated_from_trainer #base_model-google/flan-t5-large #license-apache-2.0 #region-us \n", "# llm_flan_fineTuned\n\nThis model is a fine-tuned version of google/flan-t5-large on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 50\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.8.1\n- Transformers 4.37.2\n- Pytorch 2.0.0\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 55, 35, 6, 12, 8, 3, 117, 4, 36 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #t5 #trl #sft #generated_from_trainer #base_model-google/flan-t5-large #license-apache-2.0 #region-us \n# llm_flan_fineTuned\n\nThis model is a fine-tuned version of google/flan-t5-large on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 50\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.1\n- Transformers 4.37.2\n- Pytorch 2.0.0\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
transformers
<!-- 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. --> # wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0887 - Accuracy: 0.9698 ## 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: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3082 | 1.0 | 193 | 0.2804 | 0.8989 | | 0.2206 | 2.0 | 387 | 0.1438 | 0.9604 | | 0.1365 | 3.0 | 580 | 0.1021 | 0.9689 | | 0.1009 | 4.0 | 774 | 0.0887 | 0.9698 | | 0.1073 | 4.99 | 965 | 0.0875 | 0.9692 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9697692276603483, "name": "Accuracy"}]}]}]}
audio-classification
abhiramk6/wav2vec2-base-finetuned-ks
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:audiofolder", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
2024-02-06T10:11:40+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us
wav2vec2-base-finetuned-ks ========================== This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set: * Loss: 0.0887 * Accuracy: 0.9698 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: 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: 5 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 78, 144, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
Manish0611/phi2-code
[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T10:15:15+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #phi #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #phi #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 51, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#transformers #safetensors #phi #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
<!-- 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. --> # kp-mt5-large This model is a fine-tuned version of [jhpassion0621/kp-mt5-large](https://huggingface.co/jhpassion0621/kp-mt5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5586 - Bleu: 43.3983 - Gen Len: 45.6585 ## 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: 2.59e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Bleu | Gen Len | Validation Loss | |:-------------:|:-----:|:------:|:-------:|:-------:|:---------------:| | 1.0364 | 0.29 | 17000 | 32.5573 | 44.7582 | 0.8278 | | 0.8819 | 0.58 | 34000 | 37.1161 | 45.0568 | 0.7062 | | 0.7731 | 0.87 | 51000 | 40.329 | 45.7359 | 0.6188 | | 0.7339 | 1.16 | 68000 | 41.7643 | 45.8618 | 0.5866 | | 0.7093 | 1.45 | 85000 | 42.6878 | 45.5649 | 0.5657 | | 0.6818 | 1.74 | 102000 | 43.2023 | 45.7701 | 0.5609 | | 0.6739 | 2.00 | 117444 | 43.3983 | 45.6585 | 0.5586 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "jhpassion0621/kp-mt5-large", "model-index": [{"name": "kp-mt5-large", "results": []}]}
text2text-generation
jhpassion0621/kp-mt5-large
[ "transformers", "tensorboard", "onnx", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:jhpassion0621/kp-mt5-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T10:16:02+00:00
[]
[]
TAGS #transformers #tensorboard #onnx #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-jhpassion0621/kp-mt5-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
kp-mt5-large ============ This model is a fine-tuned version of jhpassion0621/kp-mt5-large on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.5586 * Bleu: 43.3983 * Gen Len: 45.6585 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: 2.59e-05 * train\_batch\_size: 16 * eval\_batch\_size: 32 * 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 * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.59e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #onnx #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-jhpassion0621/kp-mt5-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.59e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 91, 127, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #onnx #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-jhpassion0621/kp-mt5-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.59e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
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null
transformers
<!-- 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. --> # SMIDS_3x_beit_large_RMSProp_lr0001_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3649 - Accuracy: 0.8669 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.76 | 1.0 | 450 | 0.6870 | 0.6356 | | 0.6238 | 2.0 | 900 | 0.6132 | 0.7288 | | 0.4274 | 3.0 | 1350 | 0.4867 | 0.7953 | | 0.511 | 4.0 | 1800 | 0.4154 | 0.8469 | | 0.4492 | 5.0 | 2250 | 0.5078 | 0.7953 | | 0.3781 | 6.0 | 2700 | 0.4839 | 0.8170 | | 0.3933 | 7.0 | 3150 | 0.4447 | 0.8319 | | 0.3951 | 8.0 | 3600 | 0.4836 | 0.8170 | | 0.386 | 9.0 | 4050 | 0.4550 | 0.8353 | | 0.2282 | 10.0 | 4500 | 0.5474 | 0.8353 | | 0.3543 | 11.0 | 4950 | 0.4625 | 0.8602 | | 0.1896 | 12.0 | 5400 | 0.6035 | 0.8303 | | 0.2039 | 13.0 | 5850 | 0.6142 | 0.8153 | | 0.2959 | 14.0 | 6300 | 0.6234 | 0.8353 | | 0.1775 | 15.0 | 6750 | 0.6158 | 0.8569 | | 0.1401 | 16.0 | 7200 | 0.6868 | 0.8586 | | 0.2653 | 17.0 | 7650 | 0.5995 | 0.8686 | | 0.1142 | 18.0 | 8100 | 0.6753 | 0.8752 | | 0.1989 | 19.0 | 8550 | 0.7591 | 0.8602 | | 0.1309 | 20.0 | 9000 | 0.6938 | 0.8636 | | 0.1526 | 21.0 | 9450 | 0.9598 | 0.8619 | | 0.0532 | 22.0 | 9900 | 0.8120 | 0.8702 | | 0.0054 | 23.0 | 10350 | 1.0475 | 0.8519 | | 0.1071 | 24.0 | 10800 | 0.9225 | 0.8652 | | 0.086 | 25.0 | 11250 | 0.9977 | 0.8519 | | 0.065 | 26.0 | 11700 | 1.0735 | 0.8602 | | 0.0006 | 27.0 | 12150 | 0.8907 | 0.8669 | | 0.0363 | 28.0 | 12600 | 1.0767 | 0.8719 | | 0.1821 | 29.0 | 13050 | 0.8553 | 0.8702 | | 0.1243 | 30.0 | 13500 | 0.8297 | 0.8652 | | 0.0447 | 31.0 | 13950 | 1.0462 | 0.8652 | | 0.0174 | 32.0 | 14400 | 0.9902 | 0.8719 | | 0.0082 | 33.0 | 14850 | 1.0860 | 0.8536 | | 0.0013 | 34.0 | 15300 | 1.1858 | 0.8669 | | 0.0232 | 35.0 | 15750 | 1.0185 | 0.8602 | | 0.0005 | 36.0 | 16200 | 1.0420 | 0.8702 | | 0.088 | 37.0 | 16650 | 1.1413 | 0.8469 | | 0.0325 | 38.0 | 17100 | 0.9082 | 0.8652 | | 0.0001 | 39.0 | 17550 | 1.2257 | 0.8735 | | 0.0353 | 40.0 | 18000 | 1.2810 | 0.8602 | | 0.0557 | 41.0 | 18450 | 1.3090 | 0.8819 | | 0.0241 | 42.0 | 18900 | 1.3957 | 0.8669 | | 0.0001 | 43.0 | 19350 | 1.2592 | 0.8686 | | 0.0312 | 44.0 | 19800 | 1.3561 | 0.8602 | | 0.0379 | 45.0 | 20250 | 1.1620 | 0.8636 | | 0.0 | 46.0 | 20700 | 1.2061 | 0.8719 | | 0.0104 | 47.0 | 21150 | 1.2261 | 0.8669 | | 0.0039 | 48.0 | 21600 | 1.3260 | 0.8636 | | 0.0474 | 49.0 | 22050 | 1.3547 | 0.8669 | | 0.1448 | 50.0 | 22500 | 1.3649 | 0.8669 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_RMSProp_lr0001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8668885191347754, "name": "Accuracy"}]}]}]}
image-classification
onizukal/SMIDS_3x_beit_large_RMSProp_lr0001_fold2
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T10:17:38+00:00
[]
[]
TAGS #transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
SMIDS\_3x\_beit\_large\_RMSProp\_lr0001\_fold2 ============================================== This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.3649 * Accuracy: 0.8669 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 * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.0.1 * Datasets 2.12.0 * Tokenizers 0.13.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
[ "TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
[ 81, 115, 4, 30 ]
[ "passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
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null
null
transformers
<!-- 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. --> # SMIDS_3x_beit_large_RMSProp_lr001_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6197 - Accuracy: 0.8103 ## 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: 0.001 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.8968 | 1.0 | 450 | 0.9180 | 0.5141 | | 0.829 | 2.0 | 900 | 0.8203 | 0.5491 | | 0.7639 | 3.0 | 1350 | 0.7943 | 0.5524 | | 0.8397 | 4.0 | 1800 | 0.7609 | 0.5990 | | 0.8238 | 5.0 | 2250 | 0.7468 | 0.5907 | | 0.7576 | 6.0 | 2700 | 0.7346 | 0.6456 | | 0.9259 | 7.0 | 3150 | 0.8135 | 0.5707 | | 0.7988 | 8.0 | 3600 | 0.7591 | 0.6023 | | 0.7685 | 9.0 | 4050 | 0.7066 | 0.6339 | | 0.7483 | 10.0 | 4500 | 0.7113 | 0.6589 | | 0.7654 | 11.0 | 4950 | 0.7374 | 0.6639 | | 0.7887 | 12.0 | 5400 | 0.7510 | 0.6606 | | 0.7055 | 13.0 | 5850 | 0.7215 | 0.6772 | | 0.7035 | 14.0 | 6300 | 0.7098 | 0.6539 | | 0.7438 | 15.0 | 6750 | 0.6805 | 0.6572 | | 0.7518 | 16.0 | 7200 | 0.6761 | 0.6772 | | 0.8057 | 17.0 | 7650 | 0.6986 | 0.6489 | | 0.6824 | 18.0 | 8100 | 0.6648 | 0.6872 | | 0.6221 | 19.0 | 8550 | 0.6674 | 0.6789 | | 0.7977 | 20.0 | 9000 | 0.6701 | 0.6905 | | 0.7338 | 21.0 | 9450 | 0.6685 | 0.6656 | | 0.6625 | 22.0 | 9900 | 0.7347 | 0.6356 | | 0.7074 | 23.0 | 10350 | 0.6679 | 0.6855 | | 0.6808 | 24.0 | 10800 | 0.6753 | 0.6572 | | 0.7154 | 25.0 | 11250 | 0.6616 | 0.6938 | | 0.6987 | 26.0 | 11700 | 0.6731 | 0.6805 | | 0.6247 | 27.0 | 12150 | 0.6702 | 0.6872 | | 0.6166 | 28.0 | 12600 | 0.6306 | 0.7038 | | 0.6066 | 29.0 | 13050 | 0.6688 | 0.7088 | | 0.7985 | 30.0 | 13500 | 0.6354 | 0.7005 | | 0.596 | 31.0 | 13950 | 0.6403 | 0.7055 | | 0.6258 | 32.0 | 14400 | 0.6388 | 0.7138 | | 0.7218 | 33.0 | 14850 | 0.5721 | 0.7471 | | 0.5961 | 34.0 | 15300 | 0.6001 | 0.7454 | | 0.5531 | 35.0 | 15750 | 0.5771 | 0.7537 | | 0.5702 | 36.0 | 16200 | 0.5492 | 0.7770 | | 0.5538 | 37.0 | 16650 | 0.6129 | 0.7438 | | 0.4326 | 38.0 | 17100 | 0.5480 | 0.7887 | | 0.5069 | 39.0 | 17550 | 0.5193 | 0.7887 | | 0.5613 | 40.0 | 18000 | 0.5241 | 0.7937 | | 0.4577 | 41.0 | 18450 | 0.5388 | 0.7770 | | 0.5352 | 42.0 | 18900 | 0.5218 | 0.7920 | | 0.4477 | 43.0 | 19350 | 0.5326 | 0.7854 | | 0.462 | 44.0 | 19800 | 0.5162 | 0.8037 | | 0.4337 | 45.0 | 20250 | 0.5592 | 0.8120 | | 0.3845 | 46.0 | 20700 | 0.5588 | 0.8136 | | 0.4126 | 47.0 | 21150 | 0.5540 | 0.8120 | | 0.3437 | 48.0 | 21600 | 0.5790 | 0.8087 | | 0.3832 | 49.0 | 22050 | 0.6002 | 0.8170 | | 0.3615 | 50.0 | 22500 | 0.6197 | 0.8103 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_RMSProp_lr001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8103161397670549, "name": "Accuracy"}]}]}]}
image-classification
onizukal/SMIDS_3x_beit_large_RMSProp_lr001_fold2
[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T10:17:38+00:00
[]
[]
TAGS #transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
SMIDS\_3x\_beit\_large\_RMSProp\_lr001\_fold2 ============================================= This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 0.6197 * Accuracy: 0.8103 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: 0.001 * 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 * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.0.1 * Datasets 2.12.0 * Tokenizers 0.13.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
[ "TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
[ 81, 115, 4, 30 ]
[ "passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
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null
null
transformers
Based on Tamil Llama
{}
text-generation
Prashantmdgl9/telugu_llama_v1
[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
2024-02-06T10:18:38+00:00
[]
[]
TAGS #transformers #pytorch #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
Based on Tamil Llama
[]
[ "TAGS\n#transformers #pytorch #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n" ]
[ 54 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n" ]
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null
null
transformers
<!-- 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. --> # videomae-base-action_detection This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2662 - Accuracy: 0.7243 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 15200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0956 | 0.02 | 305 | 1.3464 | 0.4774 | | 0.683 | 1.02 | 610 | 2.3774 | 0.3704 | | 0.5519 | 2.02 | 915 | 2.1501 | 0.3128 | | 1.5863 | 3.02 | 1220 | 2.7112 | 0.2387 | | 0.8028 | 4.02 | 1525 | 1.5204 | 0.7037 | | 1.1797 | 5.02 | 1830 | 2.6479 | 0.2963 | | 1.185 | 6.02 | 2135 | 0.8982 | 0.7860 | | 0.9516 | 7.02 | 2440 | 1.2030 | 0.6008 | | 0.5755 | 8.02 | 2745 | 0.8003 | 0.8189 | | 0.6815 | 9.02 | 3050 | 2.3653 | 0.4198 | | 1.1649 | 10.02 | 3355 | 3.0645 | 0.4403 | | 1.1024 | 11.02 | 3660 | 2.4187 | 0.4321 | | 1.1158 | 12.02 | 3965 | 2.2631 | 0.5597 | | 0.2375 | 13.02 | 4270 | 2.2977 | 0.5432 | | 0.7445 | 14.02 | 4575 | 1.0086 | 0.7860 | | 0.6555 | 15.02 | 4880 | 0.7161 | 0.8560 | | 0.8807 | 16.02 | 5185 | 1.2404 | 0.6584 | | 1.0477 | 17.02 | 5490 | 1.6849 | 0.6173 | | 0.498 | 18.02 | 5795 | 2.0557 | 0.5844 | | 0.5536 | 19.02 | 6100 | 2.0703 | 0.5967 | | 0.2232 | 20.02 | 6405 | 2.7690 | 0.4856 | | 0.5589 | 21.02 | 6710 | 0.9549 | 0.7243 | | 0.3377 | 22.02 | 7015 | 0.6488 | 0.8189 | | 0.7096 | 23.02 | 7320 | 1.6638 | 0.5556 | | 0.1201 | 24.02 | 7625 | 1.6283 | 0.5761 | | 0.136 | 25.02 | 7930 | 1.4397 | 0.5926 | | 0.2558 | 26.02 | 8235 | 1.7421 | 0.5350 | | 0.3245 | 27.02 | 8540 | 1.2982 | 0.6132 | | 0.0029 | 28.02 | 8845 | 1.0594 | 0.7202 | | 0.3272 | 29.02 | 9150 | 1.0833 | 0.8272 | | 0.0841 | 30.02 | 9455 | 1.3230 | 0.5926 | | 0.5595 | 31.02 | 9760 | 2.5545 | 0.5844 | | 0.0837 | 32.02 | 10065 | 1.5960 | 0.6296 | | 0.0127 | 33.02 | 10370 | 1.8149 | 0.5720 | | 0.3622 | 34.02 | 10675 | 2.4455 | 0.4938 | | 0.0006 | 35.02 | 10980 | 1.6700 | 0.6461 | | 0.0027 | 36.02 | 11285 | 2.2488 | 0.5720 | | 0.0544 | 37.02 | 11590 | 2.6388 | 0.5514 | | 0.2504 | 38.02 | 11895 | 1.5352 | 0.6379 | | 0.0149 | 39.02 | 12200 | 2.2851 | 0.5391 | | 0.4035 | 40.02 | 12505 | 1.8876 | 0.5556 | | 0.0008 | 41.02 | 12810 | 2.4479 | 0.5473 | | 0.3176 | 42.02 | 13115 | 2.0729 | 0.6049 | | 0.0007 | 43.02 | 13420 | 1.5171 | 0.6255 | | 0.3948 | 44.02 | 13725 | 1.4067 | 0.6132 | | 0.0016 | 45.02 | 14030 | 1.0621 | 0.7325 | | 0.2173 | 46.02 | 14335 | 1.5515 | 0.6132 | | 0.0007 | 47.02 | 14640 | 1.2523 | 0.7284 | | 0.2819 | 48.02 | 14945 | 1.5618 | 0.6461 | | 0.0004 | 49.02 | 15200 | 1.2662 | 0.7243 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-action_detection", "results": []}]}
video-classification
athmurikarthik/videomae-base-action_detection
[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
2024-02-06T10:19:23+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #endpoints_compatible #region-us
videomae-base-action\_detection =============================== This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.2662 * Accuracy: 0.7243 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: 2 * eval\_batch\_size: 2 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * training\_steps: 15200 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.2+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 15200", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 15200", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 69, 116, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 15200### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
peft
## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
{"library_name": "peft"}
null
AlKiir/llama-2-13b-alkiir-chat-hf2
[ "peft", "region:us" ]
2024-02-06T10:24:43+00:00
[]
[]
TAGS #peft #region-us
## Training procedure The following 'bitsandbytes' quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following 'bitsandbytes' quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
[ "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n- PEFT 0.4.0\n\n- PEFT 0.4.0" ]
[ "TAGS\n#peft #region-us \n", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n- PEFT 0.4.0\n\n- PEFT 0.4.0" ]
[ 9, 305, 17 ]
[ "passage: TAGS\n#peft #region-us \n## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16### Framework versions\n\n- PEFT 0.4.0\n\n- PEFT 0.4.0" ]
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null
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<!-- 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. --> # CDAgpt-naturalSQL-7b This model is a fine-tuned version of [chatdb/natural-sql-7b](https://huggingface.co/chatdb/natural-sql-7b) 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "base_model": "chatdb/natural-sql-7b", "model-index": [{"name": "CDAgpt-naturalSQL-7b", "results": []}]}
null
Federic/CDAgpt-naturalSQL-7b
[ "safetensors", "generated_from_trainer", "base_model:chatdb/natural-sql-7b", "license:cc-by-sa-4.0", "region:us" ]
2024-02-06T10:28:59+00:00
[]
[]
TAGS #safetensors #generated_from_trainer #base_model-chatdb/natural-sql-7b #license-cc-by-sa-4.0 #region-us
# CDAgpt-naturalSQL-7b This model is a fine-tuned version of chatdb/natural-sql-7b 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# CDAgpt-naturalSQL-7b\n\nThis model is a fine-tuned version of chatdb/natural-sql-7b on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 3\n- total_train_batch_size: 12\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#safetensors #generated_from_trainer #base_model-chatdb/natural-sql-7b #license-cc-by-sa-4.0 #region-us \n", "# CDAgpt-naturalSQL-7b\n\nThis model is a fine-tuned version of chatdb/natural-sql-7b on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 3\n- total_train_batch_size: 12\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 43, 36, 6, 12, 8, 3, 141, 33 ]
[ "passage: TAGS\n#safetensors #generated_from_trainer #base_model-chatdb/natural-sql-7b #license-cc-by-sa-4.0 #region-us \n# CDAgpt-naturalSQL-7b\n\nThis model is a fine-tuned version of chatdb/natural-sql-7b on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 3\n- total_train_batch_size: 12\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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transformers
# HighdensityRPMerge-7B HighdensityRPMerge-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [SanjiWatsuki/Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B) * [chargoddard/loyal-piano-m7-cdpo](https://huggingface.co/chargoddard/loyal-piano-m7-cdpo) * [jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES](https://huggingface.co/jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES) * [NeverSleep/Noromaid-7b-v0.2](https://huggingface.co/NeverSleep/Noromaid-7b-v0.2) * [athirdpath/NSFW_DPO_vmgb-7b](https://huggingface.co/athirdpath/NSFW_DPO_vmgb-7b) ## 🧩 Configuration ```yaml models: - model: saishf/West-Hermes-7B # no parameters necessary for base model - model: SanjiWatsuki/Silicon-Maid-7B parameters: weight: 0.4 density: 0.8 - model: chargoddard/loyal-piano-m7-cdpo parameters: weight: 0.3 density: 0.8 - model: jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES parameters: weight: 0.25 density: 0.45 - model: NeverSleep/Noromaid-7b-v0.2 parameters: weight: 0.25 density: 0.4 - model: athirdpath/NSFW_DPO_vmgb-7b parameters: weight: 0.2 density: 0.4 merge_method: dare_ties base_model: saishf/West-Hermes-7B parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "jsfs11/HighdensityRPMerge-7B" 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", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
{"tags": ["merge", "mergekit", "lazymergekit", "SanjiWatsuki/Silicon-Maid-7B", "chargoddard/loyal-piano-m7-cdpo", "jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES", "NeverSleep/Noromaid-7b-v0.2", "athirdpath/NSFW_DPO_vmgb-7b"], "base_model": ["SanjiWatsuki/Silicon-Maid-7B", "chargoddard/loyal-piano-m7-cdpo", "jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES", "NeverSleep/Noromaid-7b-v0.2", "athirdpath/NSFW_DPO_vmgb-7b"]}
text-generation
jsfs11/HighdensityRPMerge-7B-5.0bpw-exl2
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "SanjiWatsuki/Silicon-Maid-7B", "chargoddard/loyal-piano-m7-cdpo", "jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES", "NeverSleep/Noromaid-7b-v0.2", "athirdpath/NSFW_DPO_vmgb-7b", "base_model:SanjiWatsuki/Silicon-Maid-7B", "base_model:chargoddard/loyal-piano-m7-cdpo", "base_model:jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES", "base_model:NeverSleep/Noromaid-7b-v0.2", "base_model:athirdpath/NSFW_DPO_vmgb-7b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T10:29:47+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #SanjiWatsuki/Silicon-Maid-7B #chargoddard/loyal-piano-m7-cdpo #jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES #NeverSleep/Noromaid-7b-v0.2 #athirdpath/NSFW_DPO_vmgb-7b #base_model-SanjiWatsuki/Silicon-Maid-7B #base_model-chargoddard/loyal-piano-m7-cdpo #base_model-jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES #base_model-NeverSleep/Noromaid-7b-v0.2 #base_model-athirdpath/NSFW_DPO_vmgb-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# HighdensityRPMerge-7B HighdensityRPMerge-7B is a merge of the following models using LazyMergekit: * SanjiWatsuki/Silicon-Maid-7B * chargoddard/loyal-piano-m7-cdpo * jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES * NeverSleep/Noromaid-7b-v0.2 * athirdpath/NSFW_DPO_vmgb-7b ## Configuration ## Usage
[ "# HighdensityRPMerge-7B\n\nHighdensityRPMerge-7B is a merge of the following models using LazyMergekit:\n* SanjiWatsuki/Silicon-Maid-7B\n* chargoddard/loyal-piano-m7-cdpo\n* jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES\n* NeverSleep/Noromaid-7b-v0.2\n* athirdpath/NSFW_DPO_vmgb-7b", "## Configuration", "## Usage" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #SanjiWatsuki/Silicon-Maid-7B #chargoddard/loyal-piano-m7-cdpo #jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES #NeverSleep/Noromaid-7b-v0.2 #athirdpath/NSFW_DPO_vmgb-7b #base_model-SanjiWatsuki/Silicon-Maid-7B #base_model-chargoddard/loyal-piano-m7-cdpo #base_model-jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES #base_model-NeverSleep/Noromaid-7b-v0.2 #base_model-athirdpath/NSFW_DPO_vmgb-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# HighdensityRPMerge-7B\n\nHighdensityRPMerge-7B is a merge of the following models using LazyMergekit:\n* SanjiWatsuki/Silicon-Maid-7B\n* chargoddard/loyal-piano-m7-cdpo\n* jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES\n* NeverSleep/Noromaid-7b-v0.2\n* athirdpath/NSFW_DPO_vmgb-7b", "## Configuration", "## Usage" ]
[ 246, 115, 4, 3 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #SanjiWatsuki/Silicon-Maid-7B #chargoddard/loyal-piano-m7-cdpo #jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES #NeverSleep/Noromaid-7b-v0.2 #athirdpath/NSFW_DPO_vmgb-7b #base_model-SanjiWatsuki/Silicon-Maid-7B #base_model-chargoddard/loyal-piano-m7-cdpo #base_model-jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES #base_model-NeverSleep/Noromaid-7b-v0.2 #base_model-athirdpath/NSFW_DPO_vmgb-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# HighdensityRPMerge-7B\n\nHighdensityRPMerge-7B is a merge of the following models using LazyMergekit:\n* SanjiWatsuki/Silicon-Maid-7B\n* chargoddard/loyal-piano-m7-cdpo\n* jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES\n* NeverSleep/Noromaid-7b-v0.2\n* athirdpath/NSFW_DPO_vmgb-7b## Configuration## Usage" ]
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null
null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "distilbert-base-uncased"}
null
myrtotsok/EO-intent-classifier
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:distilbert-base-uncased", "region:us" ]
2024-02-06T10:30:27+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-distilbert-base-uncased #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-distilbert-base-uncased #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 37, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-distilbert-base-uncased #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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<!-- 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-lora-text-classification 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: 0.0000 - Accuracy: {'accuracy': 1.0} ## 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: 0.001 - train_batch_size: 4 - eval_batch_size: 4 - 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 | |:-------------:|:-----:|:----:|:---------------:|:-----------------:| | No log | 1.0 | 240 | 0.0000 | {'accuracy': 1.0} | | No log | 2.0 | 480 | 0.0000 | {'accuracy': 1.0} | | 0.075 | 3.0 | 720 | 0.0000 | {'accuracy': 1.0} | | 0.075 | 4.0 | 960 | 0.0000 | {'accuracy': 1.0} | | 0.0 | 5.0 | 1200 | 0.0000 | {'accuracy': 1.0} | | 0.0 | 6.0 | 1440 | 0.0000 | {'accuracy': 1.0} | | 0.0 | 7.0 | 1680 | 0.0000 | {'accuracy': 1.0} | | 0.0 | 8.0 | 1920 | 0.0000 | {'accuracy': 1.0} | | 0.0 | 9.0 | 2160 | 0.0000 | {'accuracy': 1.0} | | 0.0 | 10.0 | 2400 | 0.0000 | {'accuracy': 1.0} | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-lora-text-classification", "results": []}]}
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myrtotsok/distilbert-base-uncased-lora-text-classification
[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "region:us" ]
2024-02-06T10:30:33+00:00
[]
[]
TAGS #tensorboard #safetensors #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #region-us
distilbert-base-uncased-lora-text-classification ================================================ This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0000 * Accuracy: {'accuracy': 1.0} 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: 0.001 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 44, 97, 4, 33 ]
[ "passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
keras
## 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: | Hyperparameters | Value | | :-- | :-- | | name | Adam | | weight_decay | None | | clipnorm | None | | global_clipnorm | None | | clipvalue | None | | use_ema | False | | ema_momentum | 0.99 | | ema_overwrite_frequency | None | | jit_compile | True | | is_legacy_optimizer | False | | learning_rate | 2.9999999242136255e-05 | | beta_1 | 0.9 | | beta_2 | 0.999 | | epsilon | 1e-07 | | amsgrad | False | | training_precision | float32 | ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
{"library_name": "keras"}
null
spneshaei/czech-original-fold2-bert-base-cased
[ "keras", "region:us" ]
2024-02-06T10:34:52+00:00
[]
[]
TAGS #keras #region-us
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: Model Plot ---------- View Model Plot !Model Image
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ "TAGS\n#keras #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ 9, 28 ]
[ "passage: TAGS\n#keras #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
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null
null
transformers
static quantize of https://huggingface.co/Doctor-Shotgun/mythospice-limarp-70b <!-- provided-files --> ## Provided Quants | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q2_K.gguf) | Q2_K | 25.5 | | | [GGUF](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q3_K_XS.gguf) | Q3_K_XS | 28.3 | | | [GGUF](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q3_K_S.gguf) | Q3_K_S | 29.9 | | | [GGUF](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q3_K_M.gguf) | Q3_K_M | 33.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q3_K_L.gguf) | Q3_K_L | 36.1 | | | [GGUF](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q4_K_S.gguf) | Q4_K_S | 39.2 | fast, medium quality | | [GGUF](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q4_K_M.gguf) | Q4_K_M | 41.4 | fast, medium quality | | [GGUF](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q5_K_S.gguf) | Q5_K_S | 47.5 | | | [GGUF](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q5_K_M.gguf) | Q5_K_M | 48.8 | | | [PART 1](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q6_K.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q6_K.gguf.split-ab) | Q6_K | 56.6 | very good quality | | [PART 1](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q8_0.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/mythospice-limarp-70b-GGUF/resolve/main/mythospice-limarp-70b.Q8_0.gguf.split-ab) | Q8_0 | 73.3 | fast, best quality | <!-- end -->
{"library_name": "transformers", "pipeline_tag": "text-generation"}
text-generation
mradermacher/mythospice-limarp-70b-GGUF
[ "transformers", "gguf", "text-generation", "endpoints_compatible", "region:us" ]
2024-02-06T10:36:25+00:00
[]
[]
TAGS #transformers #gguf #text-generation #endpoints_compatible #region-us
static quantize of URL Provided Quants ---------------
[]
[ "TAGS\n#transformers #gguf #text-generation #endpoints_compatible #region-us \n" ]
[ 25 ]
[ "passage: TAGS\n#transformers #gguf #text-generation #endpoints_compatible #region-us \n" ]
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null
null
transformers
<!-- 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. --> # lmind_hotpot_train5000_eval5000_v1_ic_qa_gpt2-xl This model is a fine-tuned version of [gpt2-xl](https://huggingface.co/gpt2-xl) on the tyzhu/lmind_hotpot_train5000_eval5000_v1_ic_qa dataset. It achieves the following results on the evaluation set: - Loss: 0.0445 - Accuracy: 0.7790 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9327 | 1.0 | 313 | 1.5496 | 0.6645 | | 1.6134 | 2.0 | 626 | 1.2072 | 0.6863 | | 1.2938 | 3.0 | 939 | 0.8971 | 0.7088 | | 1.0188 | 4.0 | 1252 | 0.6332 | 0.7298 | | 0.7936 | 5.0 | 1565 | 0.4392 | 0.7470 | | 0.603 | 6.0 | 1878 | 0.2943 | 0.7595 | | 0.4611 | 7.0 | 2191 | 0.1921 | 0.7680 | | 0.3452 | 8.0 | 2504 | 0.1329 | 0.7730 | | 0.2641 | 9.0 | 2817 | 0.0959 | 0.7759 | | 0.1976 | 10.0 | 3130 | 0.0740 | 0.7775 | | 0.1528 | 11.0 | 3443 | 0.0605 | 0.7783 | | 0.1267 | 12.0 | 3756 | 0.0532 | 0.7787 | | 0.1101 | 13.0 | 4069 | 0.0496 | 0.7789 | | 0.095 | 14.0 | 4382 | 0.0485 | 0.7789 | | 0.0879 | 15.0 | 4695 | 0.0460 | 0.7790 | | 0.0817 | 16.0 | 5008 | 0.0454 | 0.7790 | | 0.0821 | 17.0 | 5321 | 0.0514 | 0.7787 | | 0.0781 | 18.0 | 5634 | 0.0476 | 0.7788 | | 0.0733 | 19.0 | 5947 | 0.0439 | 0.7790 | | 0.0724 | 20.0 | 6260 | 0.0445 | 0.7790 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tyzhu/lmind_hotpot_train5000_eval5000_v1_ic_qa"], "metrics": ["accuracy"], "base_model": "gpt2-xl", "model-index": [{"name": "lmind_hotpot_train5000_eval5000_v1_ic_qa_gpt2-xl", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "tyzhu/lmind_hotpot_train5000_eval5000_v1_ic_qa", "type": "tyzhu/lmind_hotpot_train5000_eval5000_v1_ic_qa"}, "metrics": [{"type": "accuracy", "value": 0.7789812929848693, "name": "Accuracy"}]}]}]}
text-generation
tyzhu/lmind_hotpot_train5000_eval5000_v1_ic_qa_gpt2-xl
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:tyzhu/lmind_hotpot_train5000_eval5000_v1_ic_qa", "base_model:gpt2-xl", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T10:36:26+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-tyzhu/lmind_hotpot_train5000_eval5000_v1_ic_qa #base_model-gpt2-xl #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
lmind\_hotpot\_train5000\_eval5000\_v1\_ic\_qa\_gpt2-xl ======================================================= This model is a fine-tuned version of gpt2-xl on the tyzhu/lmind\_hotpot\_train5000\_eval5000\_v1\_ic\_qa dataset. It achieves the following results on the evaluation set: * Loss: 0.0445 * Accuracy: 0.7790 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: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: constant * num\_epochs: 20.0 ### Training results ### Framework versions * Transformers 4.34.0 * Pytorch 2.1.0+cu121 * Datasets 2.14.5 * Tokenizers 0.14.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 20.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1" ]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-tyzhu/lmind_hotpot_train5000_eval5000_v1_ic_qa #base_model-gpt2-xl #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 20.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1" ]
[ 100, 99, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-tyzhu/lmind_hotpot_train5000_eval5000_v1_ic_qa #base_model-gpt2-xl #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 20.0### Training results### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1" ]
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null
null
transformers
# 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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{"library_name": "transformers", "tags": []}
text2text-generation
KarthikSaran/flan-t5-large-aio
[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T10:37:11+00:00
[ "1910.09700" ]
[]
TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
keras
## 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: | Hyperparameters | Value | | :-- | :-- | | name | Adam | | weight_decay | None | | clipnorm | None | | global_clipnorm | None | | clipvalue | None | | use_ema | False | | ema_momentum | 0.99 | | ema_overwrite_frequency | None | | jit_compile | True | | is_legacy_optimizer | False | | learning_rate | 2.9999999242136255e-05 | | beta_1 | 0.9 | | beta_2 | 0.999 | | epsilon | 1e-07 | | amsgrad | False | | training_precision | float32 | ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
{"library_name": "keras"}
null
spneshaei/czech-original-fold3-bert-base-cased
[ "keras", "region:us" ]
2024-02-06T10:37:30+00:00
[]
[]
TAGS #keras #region-us
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: Model Plot ---------- View Model Plot !Model Image
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ "TAGS\n#keras #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ 9, 28 ]
[ "passage: TAGS\n#keras #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
automatic-speech-recognition
spsither/wav2vec2_run9.09
[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-06T10:38:03+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
diffusers
# LoRA DreamBooth - mcvertix/dreembooth_output These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on penvink laying and standing on the stony ground, with arctic landscape in the background using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) LoRA for the text encoder was enabled: True.
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "penvink laying and standing on the stony ground, with arctic landscape in the background", "inference": true}
text-to-image
mcvertix/dreembooth_output
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]
2024-02-06T10:38:15+00:00
[]
[]
TAGS #diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us
# LoRA DreamBooth - mcvertix/dreembooth_output These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on penvink laying and standing on the stony ground, with arctic landscape in the background using DreamBooth. You can find some example images in the following. !img_0 !img_1 LoRA for the text encoder was enabled: True.
[ "# LoRA DreamBooth - mcvertix/dreembooth_output\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on penvink laying and standing on the stony ground, with arctic landscape in the background using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n\n\nLoRA for the text encoder was enabled: True." ]
[ "TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA DreamBooth - mcvertix/dreembooth_output\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on penvink laying and standing on the stony ground, with arctic landscape in the background using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n\n\nLoRA for the text encoder was enabled: True." ]
[ 70, 105 ]
[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - mcvertix/dreembooth_output\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on penvink laying and standing on the stony ground, with arctic landscape in the background using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n\n\nLoRA for the text encoder was enabled: True." ]
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null
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
null
espo/zx80zx81b
[ "fastai", "region:us" ]
2024-02-06T10:39:45+00:00
[]
[]
TAGS #fastai #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 9, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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null
null
keras
## 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: | Hyperparameters | Value | | :-- | :-- | | name | Adam | | weight_decay | None | | clipnorm | None | | global_clipnorm | None | | clipvalue | None | | use_ema | False | | ema_momentum | 0.99 | | ema_overwrite_frequency | None | | jit_compile | True | | is_legacy_optimizer | False | | learning_rate | 2.9999999242136255e-05 | | beta_1 | 0.9 | | beta_2 | 0.999 | | epsilon | 1e-07 | | amsgrad | False | | training_precision | float32 | ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
{"library_name": "keras"}
null
spneshaei/czech-original-fold1-bert-base-cased
[ "keras", "region:us" ]
2024-02-06T10:40:52+00:00
[]
[]
TAGS #keras #region-us
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: Model Plot ---------- View Model Plot !Model Image
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ "TAGS\n#keras #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
[ 9, 28 ]
[ "passage: TAGS\n#keras #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]
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null
null
transformers
# Model Card for Sharathhebbar24/Mistral-7B-v0.1-sharded This is a sharded version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) The Mistral-7B-v0.1 Large Language Model (LLM) is a pre-trained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested. For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/). ## Model Architecture Mistral-7B-v0.1 is a transformer model, with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Troubleshooting - If you see the following error: ``` KeyError: 'mistral' ``` - Or: ``` NotImplementedError: Cannot copy out of meta tensor; no data! ``` Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer. ## Notice Mistral 7B is a pre-trained base model and therefore does not have any moderation mechanisms. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
{"language": ["en"], "license": "apache-2.0", "tags": ["pretrained"], "pipeline_tag": "text-generation", "inference": {"parameters": {"temperature": 0.7}}}
text-generation
Sharathhebbar24/Mistral-7B-v0.1-sharded
[ "transformers", "safetensors", "mistral", "text-generation", "pretrained", "en", "arxiv:2310.06825", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
2024-02-06T10:43:01+00:00
[ "2310.06825" ]
[ "en" ]
TAGS #transformers #safetensors #mistral #text-generation #pretrained #en #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
# Model Card for Sharathhebbar24/Mistral-7B-v0.1-sharded This is a sharded version of mistralai/Mistral-7B-v0.1 The Mistral-7B-v0.1 Large Language Model (LLM) is a pre-trained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested. For full details of this model please read our paper and release blog post. ## Model Architecture Mistral-7B-v0.1 is a transformer model, with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Troubleshooting - If you see the following error: - Or: Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer. ## Notice Mistral 7B is a pre-trained base model and therefore does not have any moderation mechanisms. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
[ "# Model Card for Sharathhebbar24/Mistral-7B-v0.1-sharded\n\nThis is a sharded version of mistralai/Mistral-7B-v0.1\n\nThe Mistral-7B-v0.1 Large Language Model (LLM) is a pre-trained generative text model with 7 billion parameters. \nMistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.\n\nFor full details of this model please read our paper and release blog post.", "## Model Architecture\n\nMistral-7B-v0.1 is a transformer model, with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer", "## Troubleshooting\n\n- If you see the following error:\n\n- Or:\n\n\nEnsure you are utilizing a stable version of Transformers, 4.34.0 or newer.", "## Notice\n\nMistral 7B is a pre-trained base model and therefore does not have any moderation mechanisms.", "## The Mistral AI Team\n \nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed." ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #pretrained #en #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# Model Card for Sharathhebbar24/Mistral-7B-v0.1-sharded\n\nThis is a sharded version of mistralai/Mistral-7B-v0.1\n\nThe Mistral-7B-v0.1 Large Language Model (LLM) is a pre-trained generative text model with 7 billion parameters. \nMistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.\n\nFor full details of this model please read our paper and release blog post.", "## Model Architecture\n\nMistral-7B-v0.1 is a transformer model, with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer", "## Troubleshooting\n\n- If you see the following error:\n\n- Or:\n\n\nEnsure you are utilizing a stable version of Transformers, 4.34.0 or newer.", "## Notice\n\nMistral 7B is a pre-trained base model and therefore does not have any moderation mechanisms.", "## The Mistral AI Team\n \nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed." ]
[ 74, 109, 50, 36, 26, 100 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #pretrained #en #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Model Card for Sharathhebbar24/Mistral-7B-v0.1-sharded\n\nThis is a sharded version of mistralai/Mistral-7B-v0.1\n\nThe Mistral-7B-v0.1 Large Language Model (LLM) is a pre-trained generative text model with 7 billion parameters. \nMistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.\n\nFor full details of this model please read our paper and release blog post.## Model Architecture\n\nMistral-7B-v0.1 is a transformer model, with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer## Troubleshooting\n\n- If you see the following error:\n\n- Or:\n\n\nEnsure you are utilizing a stable version of Transformers, 4.34.0 or newer.## Notice\n\nMistral 7B is a pre-trained base model and therefore does not have any moderation mechanisms.## The Mistral AI Team\n \nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed." ]
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null
null
transformers
# 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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{"library_name": "transformers", "tags": []}
null
yeniceriSGK/PiBrain-Mistral-V1
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-06T10:43:28+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
hingeankit/qlora
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T10:50:46+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
Input Models input text only. Output Models generate text only. Model Architecture Auto-regressive language model based on the LLaMA2 transformer architecture. Base Model qwen1.5-7b-chat Training Objective Ko-Platypusth dataset
{"license": "apache-2.0"}
text-generation
jaehy12/Qwen1.5_7B_ko
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T10:56:49+00:00
[]
[]
TAGS #transformers #safetensors #qwen2 #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
Input Models input text only. Output Models generate text only. Model Architecture Auto-regressive language model based on the LLaMA2 transformer architecture. Base Model qwen1.5-7b-chat Training Objective Ko-Platypusth dataset
[]
[ "TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 51 ]
[ "passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
peft
<!-- 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. --> # stealth-finance-v1-adapter This model is a fine-tuned version of [jan-hq/stealth-v1.3](https://huggingface.co/jan-hq/stealth-v1.3) on the jan-hq/finance_alpaca_binarized and the jan-hq/openhermes-2.5_binarized datasets. It achieves the following results on the evaluation set: - Loss: 0.8394 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9556 | 1.0 | 308 | 0.8632 | | 0.9362 | 2.0 | 616 | 0.8408 | | 0.9646 | 3.0 | 924 | 0.8394 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0
{"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["jan-hq/finance_alpaca_binarized", "jan-hq/openhermes-2.5_binarized"], "base_model": "jan-hq/stealth-v1.3", "model-index": [{"name": "stealth-finance-v1-adapter", "results": []}]}
null
jan-hq/stealth-finance-v1-adapter
[ "peft", "safetensors", "mistral", "alignment-handbook", "generated_from_trainer", "trl", "sft", "dataset:jan-hq/finance_alpaca_binarized", "dataset:jan-hq/openhermes-2.5_binarized", "base_model:jan-hq/stealth-v1.3", "license:apache-2.0", "region:us" ]
2024-02-06T10:57:12+00:00
[]
[]
TAGS #peft #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #sft #dataset-jan-hq/finance_alpaca_binarized #dataset-jan-hq/openhermes-2.5_binarized #base_model-jan-hq/stealth-v1.3 #license-apache-2.0 #region-us
stealth-finance-v1-adapter ========================== This model is a fine-tuned version of jan-hq/stealth-v1.3 on the jan-hq/finance\_alpaca\_binarized and the jan-hq/openhermes-2.5\_binarized datasets. It achieves the following results on the evaluation set: * Loss: 0.8394 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: 2 * eval\_batch\_size: 2 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 2 * gradient\_accumulation\_steps: 16 * total\_train\_batch\_size: 64 * total\_eval\_batch\_size: 4 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 3 ### Training results ### Framework versions * PEFT 0.7.1 * Transformers 4.36.2 * Pytorch 2.1.2+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ "TAGS\n#peft #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #sft #dataset-jan-hq/finance_alpaca_binarized #dataset-jan-hq/openhermes-2.5_binarized #base_model-jan-hq/stealth-v1.3 #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ 96, 179, 4, 39 ]
[ "passage: TAGS\n#peft #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #sft #dataset-jan-hq/finance_alpaca_binarized #dataset-jan-hq/openhermes-2.5_binarized #base_model-jan-hq/stealth-v1.3 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
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null
null
transformers
# Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: nan f1_macro: 2.895499120347367e-06 f1_micro: 0.0012045290291496024 f1_weighted: 2.8982892905428356e-06 precision_macro: 1.4494934165458512e-06 precision_micro: 0.0012045290291496024 precision_weighted: 1.4508901820640839e-06 recall_macro: 0.0012033694344163659 recall_micro: 0.0012045290291496024 recall_weighted: 0.0012045290291496024 accuracy: 0.0012045290291496024
{"tags": ["autotrain", "image-classification"], "datasets": ["autotrain-hj5w6-ewuft/autotrain-data"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
image-classification
IsaacMwesigwa/autotrain-hj5w6-ewuft
[ "transformers", "safetensors", "resnet", "image-classification", "autotrain", "dataset:autotrain-hj5w6-ewuft/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T11:01:24+00:00
[]
[]
TAGS #transformers #safetensors #resnet #image-classification #autotrain #dataset-autotrain-hj5w6-ewuft/autotrain-data #autotrain_compatible #endpoints_compatible #region-us
# Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: nan f1_macro: 2.895499120347367e-06 f1_micro: 0.0012045290291496024 f1_weighted: 2.8982892905428356e-06 precision_macro: 1.4494934165458512e-06 precision_micro: 0.0012045290291496024 precision_weighted: 1.4508901820640839e-06 recall_macro: 0.0012033694344163659 recall_micro: 0.0012045290291496024 recall_weighted: 0.0012045290291496024 accuracy: 0.0012045290291496024
[ "# Model Trained Using AutoTrain\n\n- Problem type: Image Classification", "## Validation Metricsg\nloss: nan\n\nf1_macro: 2.895499120347367e-06\n\nf1_micro: 0.0012045290291496024\n\nf1_weighted: 2.8982892905428356e-06\n\nprecision_macro: 1.4494934165458512e-06\n\nprecision_micro: 0.0012045290291496024\n\nprecision_weighted: 1.4508901820640839e-06\n\nrecall_macro: 0.0012033694344163659\n\nrecall_micro: 0.0012045290291496024\n\nrecall_weighted: 0.0012045290291496024\n\naccuracy: 0.0012045290291496024" ]
[ "TAGS\n#transformers #safetensors #resnet #image-classification #autotrain #dataset-autotrain-hj5w6-ewuft/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoTrain\n\n- Problem type: Image Classification", "## Validation Metricsg\nloss: nan\n\nf1_macro: 2.895499120347367e-06\n\nf1_micro: 0.0012045290291496024\n\nf1_weighted: 2.8982892905428356e-06\n\nprecision_macro: 1.4494934165458512e-06\n\nprecision_micro: 0.0012045290291496024\n\nprecision_weighted: 1.4508901820640839e-06\n\nrecall_macro: 0.0012033694344163659\n\nrecall_micro: 0.0012045290291496024\n\nrecall_weighted: 0.0012045290291496024\n\naccuracy: 0.0012045290291496024" ]
[ 65, 16, 154 ]
[ "passage: TAGS\n#transformers #safetensors #resnet #image-classification #autotrain #dataset-autotrain-hj5w6-ewuft/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Image Classification## Validation Metricsg\nloss: nan\n\nf1_macro: 2.895499120347367e-06\n\nf1_micro: 0.0012045290291496024\n\nf1_weighted: 2.8982892905428356e-06\n\nprecision_macro: 1.4494934165458512e-06\n\nprecision_micro: 0.0012045290291496024\n\nprecision_weighted: 1.4508901820640839e-06\n\nrecall_macro: 0.0012033694344163659\n\nrecall_micro: 0.0012045290291496024\n\nrecall_weighted: 0.0012045290291496024\n\naccuracy: 0.0012045290291496024" ]
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null
null
transformers
# t5-small-spoken-typo This model is a fine-tuned version of T5-small, adapted for correcting typographical errors and missing spaces in text. It has been trained on a combination of spoken corpora, including DailyDialog and BNC, with a focus on short utterances common in conversational English. ## Task The primary task of this model is **Text Correction**, with a focus on: - **Sentence Correction**: Enhancing readability by correcting sentences with missing spaces or typographical errors. - **Text Normalization**: Standardizing text by converting informal or irregular forms into more grammatically correct formats. Largely dealing with sentences with no spaces This model is aimed to support processing user-generated content where informal language, abbreviations, and typos are prevalent, aiming to improve text clarity for further processing or human reading. ## Usage ```python from happytransformer import HappyTextToText, TTSettings happy_tt = HappyTextToText("T5", "willwade/t5-small-spoken-typo") args = TTSettings(num_beams=5, min_length=1) # Add the prefix "grammar: " before each input result = happy_tt.generate_text("grammar: Hihowareyoudoingtaday?.", args=args) print(result.text) # This sentence has bad grammar and is comrpessed. ``` # Model Details ## Model Description The `t5-small-spoken-typo` model is specifically designed to tackle the challenges of text correction within user-generated content, particularly in short, conversation-like sentences. It corrects for missing spaces, removes unnecessary punctuation, introduces and then corrects typos, and normalizes text by replacing informal contractions and abbreviations with their full forms. It has been training on - [BNC 2014 Spoken](http://cass.lancs.ac.uk/cass-projects/spoken-bnc2014/) - [Daily Dialog](https://huggingface.co/datasets/daily_dialog) - [Comm2 - AAC Text](https://www.aactext.org/comm2/) - [C4-200M - 25K Subset](https://huggingface.co/datasets/leslyarun/c4_200m_gec_train100k_test25k) - [JFLEG](https://huggingface.co/datasets/jhu-clsp/jfleg) Then injecting typos from a range of places - **Using NLPAUG** We've made some typos in Comm2 by usiing this library https://github.com/makcedward/nlpaug - **Typo lists, Birkbeck, etc.**: These datasets contain lists of commonly misspelled words, making them invaluable for training models to recognize and correct spelling errors. - Find these resources [here](https://www.dcs.bbk.ac.uk/~ROGER/corpora.html). - **TOEFL Spell** A dataset of Spelling Annotations for English language learner essays written for TOEFL exams. - Find this [here](https://github.com/EducationalTestingService/TOEFL-Spell/tree/master) - **Homonyms** We replace words in BNC and Dialy Dialog occasionally with homonyms from this list https://github.com/pimentel/homophones/ And then compressing versions of the sentences (i.e. removing spaces)- both correct and typod we add to our dataset. (This is to solve a problem where some people write without spaces) Note we use a ``grammar: `` prefix for each sentence in training. Full script to build the [dataset is here](https://colab.research.google.com/drive/1VkKU9KKIWkWQZ-pPzdDFLeRnwFxdWUtq?usp=sharing) ## Developed by: - **Name**: Will Wade - **Affiliation**: Research & Innovation Manager, Occupational Therapist, Ace Centre, UK - **Contact Info**: [email protected] ## Model type: - Language model fine-tuned for text correction tasks. ## Language(s) (NLP): - English (`en`) ## License: - apache-2.0 ## Parent Model: - The model is fine-tuned from `t5-small`. ## Resources for more information: - [GitHub Repo](https://github.com/willwade/dailyDialogCorrections/) # Uses ## Direct Use This model can be directly applied for correcting text in various applications, including but not limited to, enhancing the quality of user-generated content, preprocessing text for NLP tasks, and supporting assistive technologies. ## Out-of-Scope Use The model might not perform well on text significantly longer than the training examples (2-5 words), highly formal documents, or languages other than English. Use in sensitive contexts should be approached with caution due to potential biases. **Our typical use case here is AAC users - i.e. users using technology to communicate face to face to people** # Bias, Risks, and Limitations The model may inherit biases present in its training data, potentially reflecting or amplifying societal stereotypes. Given its training on conversational English, it may not generalize well to formal text or other dialects and languages. ## Recommendations Users are encouraged to critically assess the model's output, especially when used in sensitive or impactful contexts. Further fine-tuning with diverse and representative datasets could mitigate some limitations. # Training Details ## Training Data The model was trained on a curated subset of the DailyDialog and BNC corpora (2014 spoken), focusing on sentences 2-5 words in length, with manual introduction of typos and removal of spaces for robustness in text correction tasks.You can see the code to pre-process this [here](https://github.com/willwade/dailyDialogCorrections/tree/main) ## Training Procedure ### Preprocessing Sentences were stripped of apostrophes and commas, spaces were removed, and typos were introduced programmatically to simulate common errors in user-generated content. ### Speeds, Sizes, Times - Training was conducted on LlambdaLabs, taking approximately 4 hrs to complete. # Evaluation ## Testing Data, Factors & Metrics ### Testing Data The evaluation was performed on a held-out test set derived from the same corpora and similar sentences, ensuring a diverse range of sentence structures and error types were represented. ## Results The model demonstrates high efficacy in correcting short, erroneous sentences, with particular strength in handling real-world, conversational text. It performs nearly on par with GPTTurbo16k at around 93% sentence similarity. But there are gaps. Take for example this output and I've bolded elements for parts that are I feel are incorrect. Original: Didyoucatchthegamelastnight? Corrected: Did you catch the game last night? Original: Wannagrabcoffeetomorrow? Corrected: Wanna grab coffee tomorrow? Original: ImdyingsomeonecancellsoIcandogsitter! Corrected: I'm dying someone **cancell** so I can dogsitter! Original: Hahahahahahahathats hilarious! Corrected: Haha ha ha ha that's hilarious! Original: OMGyouneedtoseethelatestmeme! Corrected: OMG you need to see the latest meme! Original: Seriouslythisweatherissocrazy! Corrected: Seriously this weather is so crazy! Original: Whatchauptomefriend? Corrected: What's **his** friend? Original: Feelingburntoutaftettodayhelp! Corrected: Feeling burnt out today help! Original: Guesswhosingleagain! Corrected: Guess who single again! Original: Youwontyoubelievewhatjusthappened! Corrected: You **want** you believe what just happened! Original: Moviemarathonatmyplacethisweekend? Corrected: Movie Marathon at my place this weekend? Original: Needstudymotivationanyideas? Corrected: Need study motivation any ideas? Original: Sostressedaboutthispresentation! Corrected: So stressed about this presentation! Original: Finallyfinishedthatbookyourecommended! Corrected: Finally finished that book **you're** recommended! Original: Anygoodshowsbingeablelately? Corrected: Any good shows **biteable** recently? We hope to build on this by further fine-tuning in time on real corpous of indviduals using AAC # Technical Specifications ## Model Architecture and Objective The model follows the T5 architecture, fine-tuned for the specific task of text correction with a focus on typo correction and space insertion. ## Compute Infrastructure - **Hardware**: T4 GPU (Google Colab) - **Software**: PyTorch 1.8.1 with Transformers 4.8.2 # Citation **BibTeX:** ```bibtex @misc{t5_small_spoken_typo_2021, title={T5-small Spoken Typo Corrector}, author={Will Wade}, year={2021}, howpublished={\url{https://huggingface.co/willwade/t5-small-spoken-typo}}, } ```
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["AAC", "assistive-technology", "spoken"], "datasets": ["jfleg", "daily_dialog", "leslyarun/c4_200m_gec_train100k_test25k"], "pipeline_tag": "text-generation"}
text-generation
willwade/t5-small-spoken-typo
[ "transformers", "safetensors", "t5", "text2text-generation", "AAC", "assistive-technology", "spoken", "text-generation", "en", "dataset:jfleg", "dataset:daily_dialog", "dataset:leslyarun/c4_200m_gec_train100k_test25k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T11:01:59+00:00
[]
[ "en" ]
TAGS #transformers #safetensors #t5 #text2text-generation #AAC #assistive-technology #spoken #text-generation #en #dataset-jfleg #dataset-daily_dialog #dataset-leslyarun/c4_200m_gec_train100k_test25k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# t5-small-spoken-typo This model is a fine-tuned version of T5-small, adapted for correcting typographical errors and missing spaces in text. It has been trained on a combination of spoken corpora, including DailyDialog and BNC, with a focus on short utterances common in conversational English. ## Task The primary task of this model is Text Correction, with a focus on: - Sentence Correction: Enhancing readability by correcting sentences with missing spaces or typographical errors. - Text Normalization: Standardizing text by converting informal or irregular forms into more grammatically correct formats. Largely dealing with sentences with no spaces This model is aimed to support processing user-generated content where informal language, abbreviations, and typos are prevalent, aiming to improve text clarity for further processing or human reading. ## Usage # Model Details ## Model Description The 't5-small-spoken-typo' model is specifically designed to tackle the challenges of text correction within user-generated content, particularly in short, conversation-like sentences. It corrects for missing spaces, removes unnecessary punctuation, introduces and then corrects typos, and normalizes text by replacing informal contractions and abbreviations with their full forms. It has been training on - BNC 2014 Spoken - Daily Dialog - Comm2 - AAC Text - C4-200M - 25K Subset - JFLEG Then injecting typos from a range of places - Using NLPAUG We've made some typos in Comm2 by usiing this library URL - Typo lists, Birkbeck, etc.: These datasets contain lists of commonly misspelled words, making them invaluable for training models to recognize and correct spelling errors. - Find these resources here. - TOEFL Spell A dataset of Spelling Annotations for English language learner essays written for TOEFL exams. - Find this here - Homonyms We replace words in BNC and Dialy Dialog occasionally with homonyms from this list URL And then compressing versions of the sentences (i.e. removing spaces)- both correct and typod we add to our dataset. (This is to solve a problem where some people write without spaces) Note we use a ''grammar: '' prefix for each sentence in training. Full script to build the dataset is here ## Developed by: - Name: Will Wade - Affiliation: Research & Innovation Manager, Occupational Therapist, Ace Centre, UK - Contact Info: wwade@URL ## Model type: - Language model fine-tuned for text correction tasks. ## Language(s) (NLP): - English ('en') ## License: - apache-2.0 ## Parent Model: - The model is fine-tuned from 't5-small'. ## Resources for more information: - GitHub Repo # Uses ## Direct Use This model can be directly applied for correcting text in various applications, including but not limited to, enhancing the quality of user-generated content, preprocessing text for NLP tasks, and supporting assistive technologies. ## Out-of-Scope Use The model might not perform well on text significantly longer than the training examples (2-5 words), highly formal documents, or languages other than English. Use in sensitive contexts should be approached with caution due to potential biases. Our typical use case here is AAC users - i.e. users using technology to communicate face to face to people # Bias, Risks, and Limitations The model may inherit biases present in its training data, potentially reflecting or amplifying societal stereotypes. Given its training on conversational English, it may not generalize well to formal text or other dialects and languages. ## Recommendations Users are encouraged to critically assess the model's output, especially when used in sensitive or impactful contexts. Further fine-tuning with diverse and representative datasets could mitigate some limitations. # Training Details ## Training Data The model was trained on a curated subset of the DailyDialog and BNC corpora (2014 spoken), focusing on sentences 2-5 words in length, with manual introduction of typos and removal of spaces for robustness in text correction tasks.You can see the code to pre-process this here ## Training Procedure ### Preprocessing Sentences were stripped of apostrophes and commas, spaces were removed, and typos were introduced programmatically to simulate common errors in user-generated content. ### Speeds, Sizes, Times - Training was conducted on LlambdaLabs, taking approximately 4 hrs to complete. # Evaluation ## Testing Data, Factors & Metrics ### Testing Data The evaluation was performed on a held-out test set derived from the same corpora and similar sentences, ensuring a diverse range of sentence structures and error types were represented. ## Results The model demonstrates high efficacy in correcting short, erroneous sentences, with particular strength in handling real-world, conversational text. It performs nearly on par with GPTTurbo16k at around 93% sentence similarity. But there are gaps. Take for example this output and I've bolded elements for parts that are I feel are incorrect. Original: Didyoucatchthegamelastnight? Corrected: Did you catch the game last night? Original: Wannagrabcoffeetomorrow? Corrected: Wanna grab coffee tomorrow? Original: ImdyingsomeonecancellsoIcandogsitter! Corrected: I'm dying someone cancell so I can dogsitter! Original: Hahahahahahahathats hilarious! Corrected: Haha ha ha ha that's hilarious! Original: OMGyouneedtoseethelatestmeme! Corrected: OMG you need to see the latest meme! Original: Seriouslythisweatherissocrazy! Corrected: Seriously this weather is so crazy! Original: Whatchauptomefriend? Corrected: What's his friend? Original: Feelingburntoutaftettodayhelp! Corrected: Feeling burnt out today help! Original: Guesswhosingleagain! Corrected: Guess who single again! Original: Youwontyoubelievewhatjusthappened! Corrected: You want you believe what just happened! Original: Moviemarathonatmyplacethisweekend? Corrected: Movie Marathon at my place this weekend? Original: Needstudymotivationanyideas? Corrected: Need study motivation any ideas? Original: Sostressedaboutthispresentation! Corrected: So stressed about this presentation! Original: Finallyfinishedthatbookyourecommended! Corrected: Finally finished that book you're recommended! Original: Anygoodshowsbingeablelately? Corrected: Any good shows biteable recently? We hope to build on this by further fine-tuning in time on real corpous of indviduals using AAC # Technical Specifications ## Model Architecture and Objective The model follows the T5 architecture, fine-tuned for the specific task of text correction with a focus on typo correction and space insertion. ## Compute Infrastructure - Hardware: T4 GPU (Google Colab) - Software: PyTorch 1.8.1 with Transformers 4.8.2 BibTeX:
[ "# t5-small-spoken-typo\n\nThis model is a fine-tuned version of T5-small, adapted for correcting typographical errors and missing spaces in text. It has been trained on a combination of spoken corpora, including DailyDialog and BNC, with a focus on short utterances common in conversational English.", "## Task\nThe primary task of this model is Text Correction, with a focus on:\n- Sentence Correction: Enhancing readability by correcting sentences with missing spaces or typographical errors.\n- Text Normalization: Standardizing text by converting informal or irregular forms into more grammatically correct formats. Largely dealing with sentences with no spaces\n\nThis model is aimed to support processing user-generated content where informal language, abbreviations, and typos are prevalent, aiming to improve text clarity for further processing or human reading.", "## Usage", "# Model Details", "## Model Description\nThe 't5-small-spoken-typo' model is specifically designed to tackle the challenges of text correction within user-generated content, particularly in short, conversation-like sentences. It corrects for missing spaces, removes unnecessary punctuation, introduces and then corrects typos, and normalizes text by replacing informal contractions and abbreviations with their full forms.\nIt has been training on\n- BNC 2014 Spoken\n- Daily Dialog\n- Comm2 - AAC Text\n- C4-200M - 25K Subset\n- JFLEG\n\n\nThen injecting typos from a range of places\n- Using NLPAUG We've made some typos in Comm2 by usiing this library URL\n- Typo lists, Birkbeck, etc.: These datasets contain lists of commonly misspelled words, making them invaluable for training models to recognize and correct spelling errors.\n - Find these resources here.\n- TOEFL Spell A dataset of Spelling Annotations for English language learner essays written for TOEFL exams.\n - Find this here \n- Homonyms We replace words in BNC and Dialy Dialog occasionally with homonyms from this list URL\n\nAnd then compressing versions of the sentences (i.e. removing spaces)- both correct and typod we add to our dataset. (This is to solve a problem where some people write without spaces)\nNote we use a ''grammar: '' prefix for each sentence in training. \n\nFull script to build the dataset is here", "## Developed by:\n- Name: Will Wade\n- Affiliation: Research & Innovation Manager, Occupational Therapist, Ace Centre, UK\n- Contact Info: wwade@URL", "## Model type: \n- Language model fine-tuned for text correction tasks.", "## Language(s) (NLP): \n- English ('en')", "## License:\n- apache-2.0", "## Parent Model:\n- The model is fine-tuned from 't5-small'.", "## Resources for more information:\n- GitHub Repo", "# Uses", "## Direct Use\nThis model can be directly applied for correcting text in various applications, including but not limited to, enhancing the quality of user-generated content, preprocessing text for NLP tasks, and supporting assistive technologies.", "## Out-of-Scope Use\nThe model might not perform well on text significantly longer than the training examples (2-5 words), highly formal documents, or languages other than English. Use in sensitive contexts should be approached with caution due to potential biases. Our typical use case here is AAC users - i.e. users using technology to communicate face to face to people", "# Bias, Risks, and Limitations\n\nThe model may inherit biases present in its training data, potentially reflecting or amplifying societal stereotypes. Given its training on conversational English, it may not generalize well to formal text or other dialects and languages.", "## Recommendations\nUsers are encouraged to critically assess the model's output, especially when used in sensitive or impactful contexts. Further fine-tuning with diverse and representative datasets could mitigate some limitations.", "# Training Details", "## Training Data\nThe model was trained on a curated subset of the DailyDialog and BNC corpora (2014 spoken), focusing on sentences 2-5 words in length, with manual introduction of typos and removal of spaces for robustness in text correction tasks.You can see the code to pre-process this here", "## Training Procedure", "### Preprocessing\nSentences were stripped of apostrophes and commas, spaces were removed, and typos were introduced programmatically to simulate common errors in user-generated content.", "### Speeds, Sizes, Times\n- Training was conducted on LlambdaLabs, taking approximately 4 hrs to complete.", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\nThe evaluation was performed on a held-out test set derived from the same corpora and similar sentences, ensuring a diverse range of sentence structures and error types were represented.", "## Results \nThe model demonstrates high efficacy in correcting short, erroneous sentences, with particular strength in handling real-world, conversational text.\n\nIt performs nearly on par with GPTTurbo16k at around 93% sentence similarity. But there are gaps. \n\nTake for example this output and I've bolded elements for parts that are I feel are incorrect. \n\n\nOriginal: Didyoucatchthegamelastnight?\nCorrected: Did you catch the game last night?\n\nOriginal: Wannagrabcoffeetomorrow?\nCorrected: Wanna grab coffee tomorrow?\n\nOriginal: ImdyingsomeonecancellsoIcandogsitter!\nCorrected: I'm dying someone cancell so I can dogsitter!\n\nOriginal: Hahahahahahahathats hilarious!\nCorrected: Haha ha ha ha that's hilarious!\n\nOriginal: OMGyouneedtoseethelatestmeme!\nCorrected: OMG you need to see the latest meme!\n\nOriginal: Seriouslythisweatherissocrazy!\nCorrected: Seriously this weather is so crazy!\n\nOriginal: Whatchauptomefriend?\nCorrected: What's his friend?\n\nOriginal: Feelingburntoutaftettodayhelp!\nCorrected: Feeling burnt out today help!\n\nOriginal: Guesswhosingleagain!\nCorrected: Guess who single again!\n\nOriginal: Youwontyoubelievewhatjusthappened!\nCorrected: You want you believe what just happened!\n\nOriginal: Moviemarathonatmyplacethisweekend?\nCorrected: Movie Marathon at my place this weekend?\n\nOriginal: Needstudymotivationanyideas?\nCorrected: Need study motivation any ideas?\n\nOriginal: Sostressedaboutthispresentation!\nCorrected: So stressed about this presentation!\n\nOriginal: Finallyfinishedthatbookyourecommended!\nCorrected: Finally finished that book you're recommended!\n\nOriginal: Anygoodshowsbingeablelately?\nCorrected: Any good shows biteable recently?\n\nWe hope to build on this by further fine-tuning in time on real corpous of indviduals using AAC", "# Technical Specifications", "## Model Architecture and Objective\nThe model follows the T5 architecture, fine-tuned for the specific task of text correction with a focus on typo correction and space insertion.", "## Compute Infrastructure\n- Hardware: T4 GPU (Google Colab)\n- Software: PyTorch 1.8.1 with Transformers 4.8.2\n\nBibTeX:" ]
[ "TAGS\n#transformers #safetensors #t5 #text2text-generation #AAC #assistive-technology #spoken #text-generation #en #dataset-jfleg #dataset-daily_dialog #dataset-leslyarun/c4_200m_gec_train100k_test25k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# t5-small-spoken-typo\n\nThis model is a fine-tuned version of T5-small, adapted for correcting typographical errors and missing spaces in text. It has been trained on a combination of spoken corpora, including DailyDialog and BNC, with a focus on short utterances common in conversational English.", "## Task\nThe primary task of this model is Text Correction, with a focus on:\n- Sentence Correction: Enhancing readability by correcting sentences with missing spaces or typographical errors.\n- Text Normalization: Standardizing text by converting informal or irregular forms into more grammatically correct formats. Largely dealing with sentences with no spaces\n\nThis model is aimed to support processing user-generated content where informal language, abbreviations, and typos are prevalent, aiming to improve text clarity for further processing or human reading.", "## Usage", "# Model Details", "## Model Description\nThe 't5-small-spoken-typo' model is specifically designed to tackle the challenges of text correction within user-generated content, particularly in short, conversation-like sentences. It corrects for missing spaces, removes unnecessary punctuation, introduces and then corrects typos, and normalizes text by replacing informal contractions and abbreviations with their full forms.\nIt has been training on\n- BNC 2014 Spoken\n- Daily Dialog\n- Comm2 - AAC Text\n- C4-200M - 25K Subset\n- JFLEG\n\n\nThen injecting typos from a range of places\n- Using NLPAUG We've made some typos in Comm2 by usiing this library URL\n- Typo lists, Birkbeck, etc.: These datasets contain lists of commonly misspelled words, making them invaluable for training models to recognize and correct spelling errors.\n - Find these resources here.\n- TOEFL Spell A dataset of Spelling Annotations for English language learner essays written for TOEFL exams.\n - Find this here \n- Homonyms We replace words in BNC and Dialy Dialog occasionally with homonyms from this list URL\n\nAnd then compressing versions of the sentences (i.e. removing spaces)- both correct and typod we add to our dataset. (This is to solve a problem where some people write without spaces)\nNote we use a ''grammar: '' prefix for each sentence in training. \n\nFull script to build the dataset is here", "## Developed by:\n- Name: Will Wade\n- Affiliation: Research & Innovation Manager, Occupational Therapist, Ace Centre, UK\n- Contact Info: wwade@URL", "## Model type: \n- Language model fine-tuned for text correction tasks.", "## Language(s) (NLP): \n- English ('en')", "## License:\n- apache-2.0", "## Parent Model:\n- The model is fine-tuned from 't5-small'.", "## Resources for more information:\n- GitHub Repo", "# Uses", "## Direct Use\nThis model can be directly applied for correcting text in various applications, including but not limited to, enhancing the quality of user-generated content, preprocessing text for NLP tasks, and supporting assistive technologies.", "## Out-of-Scope Use\nThe model might not perform well on text significantly longer than the training examples (2-5 words), highly formal documents, or languages other than English. Use in sensitive contexts should be approached with caution due to potential biases. Our typical use case here is AAC users - i.e. users using technology to communicate face to face to people", "# Bias, Risks, and Limitations\n\nThe model may inherit biases present in its training data, potentially reflecting or amplifying societal stereotypes. Given its training on conversational English, it may not generalize well to formal text or other dialects and languages.", "## Recommendations\nUsers are encouraged to critically assess the model's output, especially when used in sensitive or impactful contexts. Further fine-tuning with diverse and representative datasets could mitigate some limitations.", "# Training Details", "## Training Data\nThe model was trained on a curated subset of the DailyDialog and BNC corpora (2014 spoken), focusing on sentences 2-5 words in length, with manual introduction of typos and removal of spaces for robustness in text correction tasks.You can see the code to pre-process this here", "## Training Procedure", "### Preprocessing\nSentences were stripped of apostrophes and commas, spaces were removed, and typos were introduced programmatically to simulate common errors in user-generated content.", "### Speeds, Sizes, Times\n- Training was conducted on LlambdaLabs, taking approximately 4 hrs to complete.", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\nThe evaluation was performed on a held-out test set derived from the same corpora and similar sentences, ensuring a diverse range of sentence structures and error types were represented.", "## Results \nThe model demonstrates high efficacy in correcting short, erroneous sentences, with particular strength in handling real-world, conversational text.\n\nIt performs nearly on par with GPTTurbo16k at around 93% sentence similarity. But there are gaps. \n\nTake for example this output and I've bolded elements for parts that are I feel are incorrect. \n\n\nOriginal: Didyoucatchthegamelastnight?\nCorrected: Did you catch the game last night?\n\nOriginal: Wannagrabcoffeetomorrow?\nCorrected: Wanna grab coffee tomorrow?\n\nOriginal: ImdyingsomeonecancellsoIcandogsitter!\nCorrected: I'm dying someone cancell so I can dogsitter!\n\nOriginal: Hahahahahahahathats hilarious!\nCorrected: Haha ha ha ha that's hilarious!\n\nOriginal: OMGyouneedtoseethelatestmeme!\nCorrected: OMG you need to see the latest meme!\n\nOriginal: Seriouslythisweatherissocrazy!\nCorrected: Seriously this weather is so crazy!\n\nOriginal: Whatchauptomefriend?\nCorrected: What's his friend?\n\nOriginal: Feelingburntoutaftettodayhelp!\nCorrected: Feeling burnt out today help!\n\nOriginal: Guesswhosingleagain!\nCorrected: Guess who single again!\n\nOriginal: Youwontyoubelievewhatjusthappened!\nCorrected: You want you believe what just happened!\n\nOriginal: Moviemarathonatmyplacethisweekend?\nCorrected: Movie Marathon at my place this weekend?\n\nOriginal: Needstudymotivationanyideas?\nCorrected: Need study motivation any ideas?\n\nOriginal: Sostressedaboutthispresentation!\nCorrected: So stressed about this presentation!\n\nOriginal: Finallyfinishedthatbookyourecommended!\nCorrected: Finally finished that book you're recommended!\n\nOriginal: Anygoodshowsbingeablelately?\nCorrected: Any good shows biteable recently?\n\nWe hope to build on this by further fine-tuning in time on real corpous of indviduals using AAC", "# Technical Specifications", "## Model Architecture and Objective\nThe model follows the T5 architecture, fine-tuned for the specific task of text correction with a focus on typo correction and space insertion.", "## Compute Infrastructure\n- Hardware: T4 GPU (Google Colab)\n- Software: PyTorch 1.8.1 with Transformers 4.8.2\n\nBibTeX:" ]
[ 117, 79, 127, 3, 3, 349, 42, 18, 16, 8, 20, 13, 3, 52, 83, 65, 53, 3, 72, 4, 46, 30, 3, 11, 45, 450, 5, 42, 35 ]
[ "passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #AAC #assistive-technology #spoken #text-generation #en #dataset-jfleg #dataset-daily_dialog #dataset-leslyarun/c4_200m_gec_train100k_test25k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# t5-small-spoken-typo\n\nThis model is a fine-tuned version of T5-small, adapted for correcting typographical errors and missing spaces in text. It has been trained on a combination of spoken corpora, including DailyDialog and BNC, with a focus on short utterances common in conversational English.## Task\nThe primary task of this model is Text Correction, with a focus on:\n- Sentence Correction: Enhancing readability by correcting sentences with missing spaces or typographical errors.\n- Text Normalization: Standardizing text by converting informal or irregular forms into more grammatically correct formats. Largely dealing with sentences with no spaces\n\nThis model is aimed to support processing user-generated content where informal language, abbreviations, and typos are prevalent, aiming to improve text clarity for further processing or human reading.## Usage# Model Details", "passage: ## Model Description\nThe 't5-small-spoken-typo' model is specifically designed to tackle the challenges of text correction within user-generated content, particularly in short, conversation-like sentences. It corrects for missing spaces, removes unnecessary punctuation, introduces and then corrects typos, and normalizes text by replacing informal contractions and abbreviations with their full forms.\nIt has been training on\n- BNC 2014 Spoken\n- Daily Dialog\n- Comm2 - AAC Text\n- C4-200M - 25K Subset\n- JFLEG\n\n\nThen injecting typos from a range of places\n- Using NLPAUG We've made some typos in Comm2 by usiing this library URL\n- Typo lists, Birkbeck, etc.: These datasets contain lists of commonly misspelled words, making them invaluable for training models to recognize and correct spelling errors.\n - Find these resources here.\n- TOEFL Spell A dataset of Spelling Annotations for English language learner essays written for TOEFL exams.\n - Find this here \n- Homonyms We replace words in BNC and Dialy Dialog occasionally with homonyms from this list URL\n\nAnd then compressing versions of the sentences (i.e. removing spaces)- both correct and typod we add to our dataset. (This is to solve a problem where some people write without spaces)\nNote we use a ''grammar: '' prefix for each sentence in training. \n\nFull script to build the dataset is here## Developed by:\n- Name: Will Wade\n- Affiliation: Research & Innovation Manager, Occupational Therapist, Ace Centre, UK\n- Contact Info: wwade@URL## Model type: \n- Language model fine-tuned for text correction tasks.## Language(s) (NLP): \n- English ('en')## License:\n- apache-2.0## Parent Model:\n- The model is fine-tuned from 't5-small'.## Resources for more information:\n- GitHub Repo# Uses## Direct Use\nThis model can be directly applied for correcting text in various applications, including but not limited to, enhancing the quality of user-generated content, preprocessing text for NLP tasks, and supporting assistive technologies.## Out-of-Scope Use\nThe model might not perform well on text significantly longer than the training examples (2-5 words), highly formal documents, or languages other than English. Use in sensitive contexts should be approached with caution due to potential biases. Our typical use case here is AAC users - i.e. users using technology to communicate face to face to people# Bias, Risks, and Limitations\n\nThe model may inherit biases present in its training data, potentially reflecting or amplifying societal stereotypes. Given its training on conversational English, it may not generalize well to formal text or other dialects and languages.## Recommendations\nUsers are encouraged to critically assess the model's output, especially when used in sensitive or impactful contexts. Further fine-tuning with diverse and representative datasets could mitigate some limitations.# Training Details## Training Data\nThe model was trained on a curated subset of the DailyDialog and BNC corpora (2014 spoken), focusing on sentences 2-5 words in length, with manual introduction of typos and removal of spaces for robustness in text correction tasks.You can see the code to pre-process this here## Training Procedure### Preprocessing\nSentences were stripped of apostrophes and commas, spaces were removed, and typos were introduced programmatically to simulate common errors in user-generated content.", "passage: ### Speeds, Sizes, Times\n- Training was conducted on LlambdaLabs, taking approximately 4 hrs to complete.# Evaluation## Testing Data, Factors & Metrics### Testing Data\nThe evaluation was performed on a held-out test set derived from the same corpora and similar sentences, ensuring a diverse range of sentence structures and error types were represented." ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
null
tyemel/dinov2-base-finetuned-lora-dino_style
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-06T11:02:01+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
diffusers
### My-AI-model-PQR Dreambooth model trained by Trusha402 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19AI3171 Sample pictures of this concept: ![0](https://huggingface.co/Trusha402/my-ai-model-pqr/resolve/main/sample_images/pqr_(1).png)
{"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]}
text-to-image
Trusha402/my-ai-model-pqr
[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
2024-02-06T11:03:27+00:00
[]
[]
TAGS #diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
### My-AI-model-PQR Dreambooth model trained by Trusha402 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19AI3171 Sample pictures of this concept: !0.png)
[ "### My-AI-model-PQR Dreambooth model trained by Trusha402 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: GoX19AI3171\n\nSample pictures of this concept:\n\n !0.png)" ]
[ "TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "### My-AI-model-PQR Dreambooth model trained by Trusha402 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: GoX19AI3171\n\nSample pictures of this concept:\n\n !0.png)" ]
[ 73, 62 ]
[ "passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### My-AI-model-PQR Dreambooth model trained by Trusha402 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: GoX19AI3171\n\nSample pictures of this concept:\n\n !0.png)" ]
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null
null
transformers
# 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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{"library_name": "transformers", "tags": []}
null
satishsatpal/mistral_lora_model
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-06T11:04:15+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# 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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{"library_name": "transformers", "tags": []}
text-classification
sekhharr/hackathon_v2
[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T11:06:54+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# Financial Sentiment Analysis in Chinese This is a fine-tuned version of FinBERT, based on [bert-base-chinese](https://huggingface.co/bert-base-chinese), on a private dataset (around ~8k analyst report sentences) for sentiment analysis. * Test Accuracy = 0.88 * Test Macro F1 = 0.87 * **Labels**: 0 -> Neutral; 1 -> Positive; 2 -> Negative # Usage ``` from transformers import TextClassificationPipeline from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer from transformers import BertTokenizerFast model_path="./fin_sentiment_bert_zh/" new_model = AutoModelForSequenceClassification.from_pretrained(model_path,output_attentions=True) tokenizer = BertTokenizerFast.from_pretrained(model_path) PipelineInterface = TextClassificationPipeline(model=new_model, tokenizer=tokenizer, return_all_scores=True) label = PipelineInterface("此外宁德时代上半年实现出口约2GWh，同比增加200%+。") print(label) ``` ``` [[{'label': 'LABEL_0', 'score': 0.0007030126871541142}, {'label': 'LABEL_1', 'score': 0.9989339709281921}, {'label': 'LABEL_2', 'score': 0.000363016442861408}]] ```
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert", "financial-sentiment-analysis", "sentiment-analysis"], "widget": [{"text": "\u6b64\u5916\u5b81\u5fb7\u65f6\u4ee3\u4e0a\u534a\u5e74\u5b9e\u73b0\u51fa\u53e3\u7ea62GWh\uff0c\u540c\u6bd4\u589e\u52a0200%+\u3002"}]}
text-classification
yiyanghkust/finbert-tone-chinese
[ "transformers", "safetensors", "bert", "text-classification", "financial-sentiment-analysis", "sentiment-analysis", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T11:07:55+00:00
[]
[ "zh" ]
TAGS #transformers #safetensors #bert #text-classification #financial-sentiment-analysis #sentiment-analysis #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# Financial Sentiment Analysis in Chinese This is a fine-tuned version of FinBERT, based on bert-base-chinese, on a private dataset (around ~8k analyst report sentences) for sentiment analysis. * Test Accuracy = 0.88 * Test Macro F1 = 0.87 * Labels: 0 -> Neutral; 1 -> Positive; 2 -> Negative # Usage
[ "# Financial Sentiment Analysis in Chinese\nThis is a fine-tuned version of FinBERT, based on bert-base-chinese, on a private dataset (around ~8k analyst report sentences) for sentiment analysis. \n\n* Test Accuracy = 0.88\n* Test Macro F1 = 0.87 \n* Labels: 0 -> Neutral; 1 -> Positive; 2 -> Negative", "# Usage" ]
[ "TAGS\n#transformers #safetensors #bert #text-classification #financial-sentiment-analysis #sentiment-analysis #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Financial Sentiment Analysis in Chinese\nThis is a fine-tuned version of FinBERT, based on bert-base-chinese, on a private dataset (around ~8k analyst report sentences) for sentiment analysis. \n\n* Test Accuracy = 0.88\n* Test Macro F1 = 0.87 \n* Labels: 0 -> Neutral; 1 -> Positive; 2 -> Negative", "# Usage" ]
[ 62, 88, 3 ]
[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #financial-sentiment-analysis #sentiment-analysis #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Financial Sentiment Analysis in Chinese\nThis is a fine-tuned version of FinBERT, based on bert-base-chinese, on a private dataset (around ~8k analyst report sentences) for sentiment analysis. \n\n* Test Accuracy = 0.88\n* Test Macro F1 = 0.87 \n* Labels: 0 -> Neutral; 1 -> Positive; 2 -> Negative# Usage" ]
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null
null
transformers
# Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: 8.737913685897285e+36 f1_macro: 0.16666666666666666 f1_micro: 0.3333333333333333 f1_weighted: 0.16666666666666666 precision_macro: 0.1111111111111111 precision_micro: 0.3333333333333333 precision_weighted: 0.1111111111111111 recall_macro: 0.3333333333333333 recall_micro: 0.3333333333333333 recall_weighted: 0.3333333333333333 accuracy: 0.3333333333333333
{"tags": ["autotrain", "image-classification"], "datasets": ["autotrain-74s1b-3bdvq/autotrain-data"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
image-classification
IsaacMwesigwa/autotrain-74s1b-3bdvq
[ "transformers", "safetensors", "resnet", "image-classification", "autotrain", "dataset:autotrain-74s1b-3bdvq/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T11:08:13+00:00
[]
[]
TAGS #transformers #safetensors #resnet #image-classification #autotrain #dataset-autotrain-74s1b-3bdvq/autotrain-data #autotrain_compatible #endpoints_compatible #region-us
# Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metricsg loss: 8.737913685897285e+36 f1_macro: 0.16666666666666666 f1_micro: 0.3333333333333333 f1_weighted: 0.16666666666666666 precision_macro: 0.1111111111111111 precision_micro: 0.3333333333333333 precision_weighted: 0.1111111111111111 recall_macro: 0.3333333333333333 recall_micro: 0.3333333333333333 recall_weighted: 0.3333333333333333 accuracy: 0.3333333333333333
[ "# Model Trained Using AutoTrain\n\n- Problem type: Image Classification", "## Validation Metricsg\nloss: 8.737913685897285e+36\n\nf1_macro: 0.16666666666666666\n\nf1_micro: 0.3333333333333333\n\nf1_weighted: 0.16666666666666666\n\nprecision_macro: 0.1111111111111111\n\nprecision_micro: 0.3333333333333333\n\nprecision_weighted: 0.1111111111111111\n\nrecall_macro: 0.3333333333333333\n\nrecall_micro: 0.3333333333333333\n\nrecall_weighted: 0.3333333333333333\n\naccuracy: 0.3333333333333333" ]
[ "TAGS\n#transformers #safetensors #resnet #image-classification #autotrain #dataset-autotrain-74s1b-3bdvq/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoTrain\n\n- Problem type: Image Classification", "## Validation Metricsg\nloss: 8.737913685897285e+36\n\nf1_macro: 0.16666666666666666\n\nf1_micro: 0.3333333333333333\n\nf1_weighted: 0.16666666666666666\n\nprecision_macro: 0.1111111111111111\n\nprecision_micro: 0.3333333333333333\n\nprecision_weighted: 0.1111111111111111\n\nrecall_macro: 0.3333333333333333\n\nrecall_micro: 0.3333333333333333\n\nrecall_weighted: 0.3333333333333333\n\naccuracy: 0.3333333333333333" ]
[ 64, 16, 134 ]
[ "passage: TAGS\n#transformers #safetensors #resnet #image-classification #autotrain #dataset-autotrain-74s1b-3bdvq/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Image Classification## Validation Metricsg\nloss: 8.737913685897285e+36\n\nf1_macro: 0.16666666666666666\n\nf1_micro: 0.3333333333333333\n\nf1_weighted: 0.16666666666666666\n\nprecision_macro: 0.1111111111111111\n\nprecision_micro: 0.3333333333333333\n\nprecision_weighted: 0.1111111111111111\n\nrecall_macro: 0.3333333333333333\n\nrecall_micro: 0.3333333333333333\n\nrecall_weighted: 0.3333333333333333\n\naccuracy: 0.3333333333333333" ]
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null
null
diffusers
# LoRA text2image fine-tuning - ioandanielc/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "runwayml/stable-diffusion-v1-5", "inference": true}
text-to-image
ioandanielc/pokemon-lora
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
2024-02-06T11:08:22+00:00
[]
[]
TAGS #diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us
# LoRA text2image fine-tuning - ioandanielc/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3
[ "# LoRA text2image fine-tuning - ioandanielc/pokemon-lora\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]
[ "TAGS\n#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n", "# LoRA text2image fine-tuning - ioandanielc/pokemon-lora\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]
[ 73, 100 ]
[ "passage: TAGS\n#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n# LoRA text2image fine-tuning - ioandanielc/pokemon-lora\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]
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null
null
peft
<!-- 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. --> # phi-1_5-finetuned This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-1_5", "model-index": [{"name": "phi-1_5-finetuned", "results": []}]}
null
mahdihassanzadeh/phi-1_5-finetuned
[ "peft", "tensorboard", "safetensors", "phi", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-1_5", "license:mit", "region:us" ]
2024-02-06T11:09:42+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #region-us
# phi-1_5-finetuned This model is a fine-tuned version of microsoft/phi-1_5 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
[ "# phi-1_5-finetuned\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #region-us \n", "# phi-1_5-finetuned\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
[ 48, 32, 6, 12, 8, 3, 89, 4, 39 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #region-us \n# phi-1_5-finetuned\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]
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null
null
transformers
<!-- 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 Large V2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3047 - Wer: 8.8078 ## 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: 16 - 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: 20 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5556 | 0.49 | 30 | 0.3116 | 14.7321 | | 0.2736 | 0.98 | 60 | 0.2567 | 12.1736 | | 0.1361 | 1.48 | 90 | 0.2769 | 10.2024 | | 0.1364 | 1.97 | 120 | 0.2525 | 9.1643 | | 0.0582 | 2.46 | 150 | 0.2734 | 10.9049 | | 0.0568 | 2.95 | 180 | 0.2669 | 9.2796 | | 0.0289 | 3.44 | 210 | 0.2841 | 8.7973 | | 0.0206 | 3.93 | 240 | 0.2877 | 8.7868 | | 0.0107 | 4.43 | 270 | 0.3009 | 8.8393 | | 0.0089 | 4.92 | 300 | 0.3047 | 8.8078 | ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0
{"language": ["nl"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-large-v2", "model-index": [{"name": "Whisper Large V2", "results": []}]}
automatic-speech-recognition
golesheed/whisper-native-elderly-6-dutch
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "nl", "base_model:openai/whisper-large-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-06T11:13:55+00:00
[]
[ "nl" ]
TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us
Whisper Large V2 ================ This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.3047 * Wer: 8.8078 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: 16 * 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: 20 * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
[ 74, 116, 4, 38 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]
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null
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diffusers
# SDXL LoRA DreamBooth - erikhsos/campusbiernew_LoRA <Gallery /> ## Model description These are erikhsos/campusbiernew_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 photo of [CB] beer to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](erikhsos/campusbiernew_LoRA/tree/main) them in the Files & versions tab.
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of [CB] beer"}
text-to-image
erikhsos/campusbiernew_LoRA
[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "has_space", "region:us" ]
2024-02-06T11:15:07+00:00
[]
[]
TAGS #diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us
# SDXL LoRA DreamBooth - erikhsos/campusbiernew_LoRA <Gallery /> ## Model description These are erikhsos/campusbiernew_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using DreamBooth. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of [CB] beer to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab.
[ "# SDXL LoRA DreamBooth - erikhsos/campusbiernew_LoRA\n\n<Gallery />", "## Model description\n\nThese are erikhsos/campusbiernew_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of [CB] beer to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]
[ "TAGS\n#diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n", "# SDXL LoRA DreamBooth - erikhsos/campusbiernew_LoRA\n\n<Gallery />", "## Model description\n\nThese are erikhsos/campusbiernew_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of [CB] beer to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]
[ 86, 25, 90, 20, 28 ]
[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - erikhsos/campusbiernew_LoRA\n\n<Gallery />## Model description\n\nThese are erikhsos/campusbiernew_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of [CB] beer to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
eswardivi/qwen1.5_1.8B_Telugu
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T11:20:43+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
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transformers
<div align="center"> <h1> MiniCPM </h1> </div> <p align="center"> <a href="https://shengdinghu.notion.site/MiniCPM-c805a17c5c8046398914e47f0542095a?pvs=4" target="_blank">MiniCPM 技术报告</a><a href="https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20?pvs=4" target="_blank"> Technical Report</a> | <a href="https://github.com/OpenBMB/OmniLMM/" target="_blank">OmniLMM 多模态模型 Multi-modal Model</a> | <a href="https://luca.cn/" target="_blank">CPM-C 千亿模型试用 ~100B Model Trial </a> </p> MiniCPM 是面壁与清华大学自然语言处理实验室共同开源的系列端侧语言大模型，主体语言模型 MiniCPM-2B 仅有 24亿（2.4B）的非词嵌入参数量。 - 经过 SFT 后，MiniCPM 在公开综合性评测集上，MiniCPM 与 Mistral-7B相近（中文、数学、代码能力更优），整体性能超越 Llama2-13B、MPT-30B、Falcon-40B 等模型。 - 经过 DPO 后，MiniCPM 在当前最接近用户体感的评测集 MTBench上，MiniCPM-2B 也超越了 Llama2-70B-Chat、Vicuna-33B、Mistral-7B-Instruct-v0.1、Zephyr-7B-alpha 等众多代表性开源大模型。 - 以 MiniCPM-2B 为基础构建端侧多模态大模型 MiniCPM-V，整体性能在同规模模型中实现最佳，超越基于 Phi-2 构建的现有多模态大模型，在部分评测集上达到与 9.6B Qwen-VL-Chat 相当甚至更好的性能。 - 经过 Int4 量化后，MiniCPM 可在手机上进行部署推理，流式输出速度略高于人类说话速度。MiniCPM-V 也首次跑通了多模态大模型在手机上的部署。 - 一张1080/2080可高效参数微调，一张3090/4090可全参数微调，一台机器可持续训练 MiniCPM，二次开发成本较低。 我们将完全开源MiniCPM-2B的模型参数供学术研究和有限商用，以及训练过程中的所有Checkpoint和大部分非专有数据供模型机理研究。 - 基于MiniCPM-2B的指令微调与人类偏好对**MiniCPM-2B-SFT/DPO。** - 基于MiniCPM-2B的多模态模型**MiniCPM-V**，能力超越基于Phi-2的同参数级别多模态模型**。** - MiniCPM-2B-SFT/DPO的Int4量化版**MiniCPM-2B-SFT/DPO-Int4。** - 基于MLC-LLM、LLMFarm开发的MiniCPM手机端程序，**文本及多模态模型均可在手机端进行推理。** MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings. - MiniCPM has very close performance compared with Mistral-7B on open-sourced general benchmarks with better ability on Chinese, Mathmetics and Coding after SFT. The overall performance exceeds Llama2-13B, MPT-30B, Falcon-40B, etc. - After DPO, MiniCPM outperforms Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, Zephyr-7B-alpha, etc. on MTBench. - MiniCPM-V, based on MiniCPM-2B, achieves the best overall performance among multimodel models of the same scale, surpassing existing multimodal large models built on Phi-2 and achieving performance comparable to or even better than 9.6B Qwen-VL-Chat on some tasks. - MiniCPM can be deployed and infer on smartphones, and the speed of streaming output is relatively higher than the verbal speed of human. MiniCPM-V is the first multi-modal models that can be deployed on smartphones. - The cost of developing based on MiniCPM is low. Parameter efficient finetuning can be conducted with a single 1080/2080 GPU and full parameter finetuning can be conducted with a 3090/4090 GPU. We release all model parameters for research and limited commercial use. We also release all the checkpoint during training and most public training data for research on model mechanism. - SFT and DPO version based on MiniCPM-2B and human preference: **MiniCPM-2B-SFT/DPO** - The multi-modal model **MiniCPM-V** based on MiniCPM-2B, which outperforms models with similar size, i.e., Phi-2 - The INT4 quantized version **MiniCPM-2B-SFT/DPO-Int4** based on MiniCPM-2B-SFT/DPO - Mobile phone application based on MLC-LLM and LLMFarm. Both language model and multimodel model can conduct inference on smartphones. ### 评测结果 Evaluation Results 详细的评测结果位于[github仓库](https://github.com/OpenBMB/MiniCPM?tab=readme-ov-file#%E8%AF%84%E6%B5%8B%E7%BB%93%E6%9E%9C) Detailed evaluation results are in [github repo](https://github.com/OpenBMB/MiniCPM/blob/main/README-en.md#evaluation-results) 注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。 Notice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended. We are investigating the cause now. ### 局限性 Limitations - 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进； - 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息； - 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果； - 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。 - Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model. - To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models. - Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts. - Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability. ## 模型下载 Download | HuggingFace | ModelScope | WiseModel | |-------------|------------|-----------| |[sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16)|[sft-bf16](https://modelscope.cn/models/OpenBMB/miniCPM-bf16)|[sft-bf16](https://wisemodel.cn/models/OpenBMB/miniCPM-bf16) |[sft-fp32](https://huggingface.co/openbmb/MiniCPM-2B-sft-fp32)|[sft-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-sft-fp32)|[sft-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32) |[dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16)|[dpo-bf16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16/summary)|[dpo-bf16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16) |[dpo-fp16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16)|[dpo-fp16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16/)|[dpo-fp16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16) |[dpo-fp32](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp32)|[dpo-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp32)|[dpo-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32) ## 模型使用 Usage * 安装`transformers>=4.36.0`以及`accelerate`后，运行以下代码 * 注意：需要在`from_pretrained`中明确指明模型的数据类型，否则会引起较大计算误差 * Run the following code after install `transformers>=4.36.0` and `accelerate` * Warning: It is necessary to specify the data type of the model clearly in 'from_pretrained', otherwise large calculation errors will be caused ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch.manual_seed(0) path = 'openbmb/MiniCPM-2B-dpo-bf16' tokenizer = AutoTokenizer.from_pretrained(path) model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True) responds, history = model.chat(tokenizer, "山东省最高的山是哪座山, 它比黄山高还是矮？差距多少？", temperature=0.8, top_p=0.8) print(responds) ``` * 期望输出 Expected Output ```shell 山东省最高的山是泰山，海拔1545米。 相对于黄山（海拔1864米），泰山海拔较低，相差约319米。 ``` ## 开源协议 LICENSE #### 模型协议 Model LICENSE * 本仓库中代码依照 [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) 协议开源 * MiniCPM 模型权重的使用则需要遵循 [“通用模型许可协议-来源说明-宣传限制-商业授权”](https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md)。 * MiniCPM 模型权重对学术研究完全开放。 * 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。 * This repository is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. * The usage of MiniCPM model weights must strictly follow [the General Model License (GML)](https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md). * The models and weights of MiniCPM are completely free for academic research. * If you intend to utilize the model for commercial purposes, please reach out to [email protected] to obtain the certificate of authorization. #### 声明 Statement * 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。 * 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。 * 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 * As a language model, MiniCPM generates content by learning from a vast amount of text. * However, it does not possess the ability to comprehend or express personal opinions or value judgments. * Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. * Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own. <p id="8"></p> ## 工作引用 Citation * 如果觉得MiniCPM有助于您的工作，请考虑引用下列[技术报告](https://shengdinghu.notion.site/MiniCPM-c805a17c5c8046398914e47f0542095a?pvs=4) * Please cite our [techinical report](https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20?pvs=4) if you find our work valuable. ``` @inproceedings{minicpm2024, title={MiniCPM：Unveiling the Potential of End-side Large Language Models}, booktitle={OpenBMB Blog}, year={2024} } ```
{"language": ["en", "zh"], "tags": ["MiniCPM", "ModelBest", "THUNLP"]}
text-generation
openbmb/MiniCPM-2B-dpo-bf16-llama-format
[ "transformers", "pytorch", "text-generation", "MiniCPM", "ModelBest", "THUNLP", "conversational", "en", "zh", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T11:25:02+00:00
[]
[ "en", "zh" ]
TAGS #transformers #pytorch #text-generation #MiniCPM #ModelBest #THUNLP #conversational #en #zh #autotrain_compatible #endpoints_compatible #region-us
MiniCPM ========= [MiniCPM 技术报告](URL target=) [Technical Report](URL target=) | [OmniLMM 多模态模型 Multi-modal Model](URL target=) | [CPM-C 千亿模型试用 ~100B Model Trial](URL target=) MiniCPM 是面壁与清华大学自然语言处理实验室共同开源的系列端侧语言大模型，主体语言模型 MiniCPM-2B 仅有 24亿（2.4B）的非词嵌入参数量。 * 经过 SFT 后，MiniCPM 在公开综合性评测集上，MiniCPM 与 Mistral-7B相近（中文、数学、代码能力更优），整体性能超越 Llama2-13B、MPT-30B、Falcon-40B 等模型。 * 经过 DPO 后，MiniCPM 在当前最接近用户体感的评测集 MTBench上，MiniCPM-2B 也超越了 Llama2-70B-Chat、Vicuna-33B、Mistral-7B-Instruct-v0.1、Zephyr-7B-alpha 等众多代表性开源大模型。 * 以 MiniCPM-2B 为基础构建端侧多模态大模型 MiniCPM-V，整体性能在同规模模型中实现最佳，超越基于 Phi-2 构建的现有多模态大模型，在部分评测集上达到与 9.6B Qwen-VL-Chat 相当甚至更好的性能。 * 经过 Int4 量化后，MiniCPM 可在手机上进行部署推理，流式输出速度略高于人类说话速度。MiniCPM-V 也首次跑通了多模态大模型在手机上的部署。 * 一张1080/2080可高效参数微调，一张3090/4090可全参数微调，一台机器可持续训练 MiniCPM，二次开发成本较低。 我们将完全开源MiniCPM-2B的模型参数供学术研究和有限商用，以及训练过程中的所有Checkpoint和大部分非专有数据供模型机理研究。 * 基于MiniCPM-2B的指令微调与人类偏好对MiniCPM-2B-SFT/DPO。 * 基于MiniCPM-2B的多模态模型MiniCPM-V，能力超越基于Phi-2的同参数级别多模态模型。 * MiniCPM-2B-SFT/DPO的Int4量化版MiniCPM-2B-SFT/DPO-Int4。 * 基于MLC-LLM、LLMFarm开发的MiniCPM手机端程序，文本及多模态模型均可在手机端进行推理。 MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings. * MiniCPM has very close performance compared with Mistral-7B on open-sourced general benchmarks with better ability on Chinese, Mathmetics and Coding after SFT. The overall performance exceeds Llama2-13B, MPT-30B, Falcon-40B, etc. * After DPO, MiniCPM outperforms Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, Zephyr-7B-alpha, etc. on MTBench. * MiniCPM-V, based on MiniCPM-2B, achieves the best overall performance among multimodel models of the same scale, surpassing existing multimodal large models built on Phi-2 and achieving performance comparable to or even better than 9.6B Qwen-VL-Chat on some tasks. * MiniCPM can be deployed and infer on smartphones, and the speed of streaming output is relatively higher than the verbal speed of human. MiniCPM-V is the first multi-modal models that can be deployed on smartphones. * The cost of developing based on MiniCPM is low. Parameter efficient finetuning can be conducted with a single 1080/2080 GPU and full parameter finetuning can be conducted with a 3090/4090 GPU. We release all model parameters for research and limited commercial use. We also release all the checkpoint during training and most public training data for research on model mechanism. * SFT and DPO version based on MiniCPM-2B and human preference: MiniCPM-2B-SFT/DPO * The multi-modal model MiniCPM-V based on MiniCPM-2B, which outperforms models with similar size, i.e., Phi-2 * The INT4 quantized version MiniCPM-2B-SFT/DPO-Int4 based on MiniCPM-2B-SFT/DPO * Mobile phone application based on MLC-LLM and LLMFarm. Both language model and multimodel model can conduct inference on smartphones. ### 评测结果 Evaluation Results 详细的评测结果位于github仓库 Detailed evaluation results are in github repo 注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。 Notice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended. We are investigating the cause now. ### 局限性 Limitations * 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进； * 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息； * 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果； * 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。 * Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model. * To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models. * Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts. * Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability. 模型下载 Download ------------- HuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16 HuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32 HuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16 HuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16 HuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32 模型使用 Usage ---------- * 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码 * 注意：需要在'from\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差 * Run the following code after install 'transformers>=4.36.0' and 'accelerate' * Warning: It is necessary to specify the data type of the model clearly in 'from\_pretrained', otherwise large calculation errors will be caused * 期望输出 Expected Output 开源协议 LICENSE ------------ #### 模型协议 Model LICENSE * 本仓库中代码依照 Apache-2.0 协议开源 * MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。 * MiniCPM 模型权重对学术研究完全开放。 * 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。 * This repository is released under the Apache-2.0 License. * The usage of MiniCPM model weights must strictly follow the General Model License (GML). * The models and weights of MiniCPM are completely free for academic research. * If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization. #### 声明 Statement * 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。 * 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。 * 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 * As a language model, MiniCPM generates content by learning from a vast amount of text. * However, it does not possess the ability to comprehend or express personal opinions or value judgments. * Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. * Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own. 工作引用 Citation ------------- * 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告 * Please cite our techinical report if you find our work valuable.
[ "### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now.", "### 局限性 Limitations\n\n\n* 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进；\n* 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息；\n* 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果；\n* 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。\n* Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model.\n* To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models.\n* Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts.\n* Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability.\n\n\n模型下载 Download\n-------------\n\n\nHuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16\nHuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32\nHuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16\nHuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16\nHuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32\n\n\n模型使用 Usage\n----------\n\n\n* 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码\n* 注意：需要在'from\\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差\n* Run the following code after install 'transformers>=4.36.0' and 'accelerate'\n* Warning: It is necessary to specify the data type of the model clearly in 'from\\_pretrained', otherwise large calculation errors will be caused\n* 期望输出 Expected Output\n\n\n开源协议 LICENSE\n------------", "#### 模型协议 Model LICENSE\n\n\n* 本仓库中代码依照 Apache-2.0 协议开源\n* MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。\n* MiniCPM 模型权重对学术研究完全开放。\n* 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。\n* This repository is released under the Apache-2.0 License.\n* The usage of MiniCPM model weights must strictly follow the General Model License (GML).\n* The models and weights of MiniCPM are completely free for academic research.\n* If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization.", "#### 声明 Statement\n\n\n* 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。\n* 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。\n* 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。\n* As a language model, MiniCPM generates content by learning from a vast amount of text.\n* However, it does not possess the ability to comprehend or express personal opinions or value judgments.\n* Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.\n* Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.\n\n\n\n工作引用 Citation\n-------------\n\n\n* 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告\n* Please cite our techinical report if you find our work valuable." ]
[ "TAGS\n#transformers #pytorch #text-generation #MiniCPM #ModelBest #THUNLP #conversational #en #zh #autotrain_compatible #endpoints_compatible #region-us \n", "### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now.", "### 局限性 Limitations\n\n\n* 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进；\n* 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息；\n* 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果；\n* 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。\n* Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model.\n* To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models.\n* Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts.\n* Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability.\n\n\n模型下载 Download\n-------------\n\n\nHuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16\nHuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32\nHuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16\nHuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16\nHuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32\n\n\n模型使用 Usage\n----------\n\n\n* 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码\n* 注意：需要在'from\\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差\n* Run the following code after install 'transformers>=4.36.0' and 'accelerate'\n* Warning: It is necessary to specify the data type of the model clearly in 'from\\_pretrained', otherwise large calculation errors will be caused\n* 期望输出 Expected Output\n\n\n开源协议 LICENSE\n------------", "#### 模型协议 Model LICENSE\n\n\n* 本仓库中代码依照 Apache-2.0 协议开源\n* MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。\n* MiniCPM 模型权重对学术研究完全开放。\n* 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。\n* This repository is released under the Apache-2.0 License.\n* The usage of MiniCPM model weights must strictly follow the General Model License (GML).\n* The models and weights of MiniCPM are completely free for academic research.\n* If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization.", "#### 声明 Statement\n\n\n* 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。\n* 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。\n* 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。\n* As a language model, MiniCPM generates content by learning from a vast amount of text.\n* However, it does not possess the ability to comprehend or express personal opinions or value judgments.\n* Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.\n* Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.\n\n\n\n工作引用 Citation\n-------------\n\n\n* 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告\n* Please cite our techinical report if you find our work valuable." ]
[ 53, 101, 655, 180, 262 ]
[ "passage: TAGS\n#transformers #pytorch #text-generation #MiniCPM #ModelBest #THUNLP #conversational #en #zh #autotrain_compatible #endpoints_compatible #region-us \n### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now." ]
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null
null
transformers
<!-- 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. --> # my_awesome_model 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: 0.2231 - Accuracy: 0.9323 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2241 | 1.0 | 1563 | 0.2379 | 0.9128 | | 0.1544 | 2.0 | 3126 | 0.2231 | 0.9323 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_awesome_model", "results": []}]}
text-classification
tung-nt/my_awesome_model
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T11:26:38+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
my\_awesome\_model ================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.2231 * Accuracy: 0.9323 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: 2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 72, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
adapter-transformers
Input Models input text only. Output Models generate text only. Model Architecture Auto-regressive language model based on the qwen1.5 transformer architecture. Base Model qwen1.5-7b-chat Training Objective Ko-Platypusth dataset
{"language": ["ko"], "license": "mit", "library_name": "adapter-transformers", "datasets": ["kyujinpy/KOpen-platypus"], "pipeline_tag": "text-generation"}
text-generation
jaehy12/Qwen1.5_7B_ko2
[ "adapter-transformers", "safetensors", "qwen2", "text-generation", "conversational", "ko", "dataset:kyujinpy/KOpen-platypus", "license:mit", "region:us" ]
2024-02-06T11:26:45+00:00
[]
[ "ko" ]
TAGS #adapter-transformers #safetensors #qwen2 #text-generation #conversational #ko #dataset-kyujinpy/KOpen-platypus #license-mit #region-us
Input Models input text only. Output Models generate text only. Model Architecture Auto-regressive language model based on the qwen1.5 transformer architecture. Base Model qwen1.5-7b-chat Training Objective Ko-Platypusth dataset
[]
[ "TAGS\n#adapter-transformers #safetensors #qwen2 #text-generation #conversational #ko #dataset-kyujinpy/KOpen-platypus #license-mit #region-us \n" ]
[ 51 ]
[ "passage: TAGS\n#adapter-transformers #safetensors #qwen2 #text-generation #conversational #ko #dataset-kyujinpy/KOpen-platypus #license-mit #region-us \n" ]
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null
null
transformers
# 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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{"library_name": "transformers", "tags": []}
null
Bazsalanszky/llama2-qlora-hungarian-test
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-06T11:27:36+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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transformers
# 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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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]
{"library_name": "transformers", "tags": []}
null
mertllc/base_ckpt
[ "transformers", "safetensors", "vits", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-06T11:29:21+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #vits #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #vits #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #vits #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
<!-- 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. --> # T5 This model is a fine-tuned version of [eslamxm/mt5-base-finetuned-arur](https://huggingface.co/eslamxm/mt5-base-finetuned-arur) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4545 ## 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: 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.4213 | 1.0 | 37 | 0.4545 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "eslamxm/mt5-base-finetuned-arur", "model-index": [{"name": "T5", "results": []}]}
text2text-generation
shahadotb/T5
[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:eslamxm/mt5-base-finetuned-arur", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T11:30:16+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-eslamxm/mt5-base-finetuned-arur #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
T5 == This model is a fine-tuned version of eslamxm/mt5-base-finetuned-arur on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.4545 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: 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: 1 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-eslamxm/mt5-base-finetuned-arur #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 90, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-eslamxm/mt5-base-finetuned-arur #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
mlx
# mlx-community/OLMo-7B-hf-4bit-mlx This model was converted to MLX format from [`allenai/OLMo-7B`](). Refer to the [original model card](https://huggingface.co/allenai/OLMo-7B) for more details on the model. ## Use with mlx ```bash pip install mlx-lm pip install ai2-olmo ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/OLMo-7B-hf-4bit-mlx") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
{"language": ["en"], "license": "apache-2.0", "tags": ["mlx"], "datasets": ["allenai/dolma"]}
null
mlx-community/OLMo-7B-hf-4bit-mlx
[ "mlx", "safetensors", "olmo", "custom_code", "en", "dataset:allenai/dolma", "license:apache-2.0", "region:us" ]
2024-02-06T11:33:32+00:00
[]
[ "en" ]
TAGS #mlx #safetensors #olmo #custom_code #en #dataset-allenai/dolma #license-apache-2.0 #region-us
# mlx-community/OLMo-7B-hf-4bit-mlx This model was converted to MLX format from ['allenai/OLMo-7B'](). Refer to the original model card for more details on the model. ## Use with mlx
[ "# mlx-community/OLMo-7B-hf-4bit-mlx\nThis model was converted to MLX format from ['allenai/OLMo-7B']().\nRefer to the original model card for more details on the model.", "## Use with mlx" ]
[ "TAGS\n#mlx #safetensors #olmo #custom_code #en #dataset-allenai/dolma #license-apache-2.0 #region-us \n", "# mlx-community/OLMo-7B-hf-4bit-mlx\nThis model was converted to MLX format from ['allenai/OLMo-7B']().\nRefer to the original model card for more details on the model.", "## Use with mlx" ]
[ 41, 56, 5 ]
[ "passage: TAGS\n#mlx #safetensors #olmo #custom_code #en #dataset-allenai/dolma #license-apache-2.0 #region-us \n# mlx-community/OLMo-7B-hf-4bit-mlx\nThis model was converted to MLX format from ['allenai/OLMo-7B']().\nRefer to the original model card for more details on the model.## Use with mlx" ]
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null
null
transformers
<!-- 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. --> # opt-350m-lora-1.57M-snli-model3 This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8582 - Accuracy: 0.686 ## 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: 256 - eval_batch_size: 256 - seed: 75 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5032 | 1.0 | 2146 | 0.4190 | 0.8378 | | 0.4594 | 2.0 | 4292 | 0.3869 | 0.8562 | | 0.4445 | 3.0 | 6438 | 0.3771 | 0.8623 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
{"license": "other", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/opt-350m", "model-index": [{"name": "opt-350m-lora-1.57M-snli-model3", "results": []}]}
text-classification
varun-v-rao/opt-350m-lora-1.57M-snli-model3
[ "transformers", "tensorboard", "safetensors", "opt", "text-classification", "generated_from_trainer", "base_model:facebook/opt-350m", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-06T11:36:58+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #opt #text-classification #generated_from_trainer #base_model-facebook/opt-350m #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
opt-350m-lora-1.57M-snli-model3 =============================== This model is a fine-tuned version of facebook/opt-350m on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.8582 * Accuracy: 0.686 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: 256 * eval\_batch\_size: 256 * seed: 75 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 75\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #opt #text-classification #generated_from_trainer #base_model-facebook/opt-350m #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 75\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ 75, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #opt #text-classification #generated_from_trainer #base_model-facebook/opt-350m #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 75\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
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null
null
transformers
<!-- 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. --> # roberta-large-lora-2.63M-snli-model2 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6398 - Accuracy: 0.785 ## 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: 256 - eval_batch_size: 256 - seed: 84 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3568 | 1.0 | 2146 | 0.2681 | 0.9052 | | 0.3292 | 2.0 | 4292 | 0.2502 | 0.9097 | | 0.3252 | 3.0 | 6438 | 0.2459 | 0.9102 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-large", "model-index": [{"name": "roberta-large-lora-2.63M-snli-model2", "results": []}]}
text-classification
varun-v-rao/roberta-large-lora-2.63M-snli-model2
[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-06T11:37:01+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
roberta-large-lora-2.63M-snli-model2 ==================================== This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.6398 * Accuracy: 0.785 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: 256 * eval\_batch\_size: 256 * seed: 84 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 84\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 84\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
[ 64, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 84\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]
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# Representation Classifier for tool selection This repository contains code for training and evaluating a Representation Classifier (RepC) for the task of tool selection. The RepC is trained to classify user queries into different tool categories based on the descriptions of tools provided. ## Usage ### Training To train the Representation Classifier, you can use the following command: ```bash python repc.py --train --model_path teknium/OpenHermes-2.5-Mistral-7B --task tool --data_path data/data_within_tool_subset.json --n_train 5 --classifier svm --model_save_dir saved_models --use_cache ``` - `model_path`: Path to the pretrained language model. - `task`: Which task to run. - `data_path`: Path to the dataset containing tool descriptions and queries. - `n_train`: Number of training examples for **each class**. - `classifier`: Which classifier to use, only support svm currently. - `model_save_dir`: Directory to save trained models. - `use_cache`: Whether to use cached embeddings. ### Evaluation To evaluate the trained Representation Classifier, you can use the following command: ```bash python repc.py --evaluate --model_path teknium/OpenHermes-2.5-Mistral-7B --task tool --data_path data/data_within_tool_subset.json --n_train 5 --classifier svm --model_save_dir saved_models --use_cache ``` - `model_path`: Path to the pretrained language model. - `task`: Which task to run. - `data_path`: Path to the dataset containing tool descriptions and queries. - `n_train`: Number of training examples for **each class**. - `classifier`: Which classifier to use, only support svm currently. - `model_save_dir`: Directory to save trained models. - `use_cache`: Whether to use cached embeddings. ### Zero-shot Baseline To run the zero-shot baseline, use the following command: ```bash python repc.py --zero_baseline --model_path teknium/OpenHermes-2.5-Mistral-7B --task tool --data_path data/data_within_tool_subset.json --n_train 5 ``` - `model_path`: Path to the pretrained language model. - `task`: Which task to run. - `data_path`: Path to the dataset containing tool descriptions and queries. - `n_train`: Number of training examples for **each class**. This is only used for test set split. ### Few-shot Baseline To run the few-shot baseline, use the following command: ```bash python repc.py --few_shot_baseline --model_path teknium/OpenHermes-2.5-Mistral-7B --task tool --data_path data/data_within_tool_subset.json --n_train 5 --num_examples 3 ``` - `model_path`: Path to the pretrained language model. - `task`: Which task to run. - `data_path`: Path to the dataset containing tool descriptions and queries. - `n_train`: Number of training examples for **each class**. This is only used for test set split. - `num_examples`: Number of examples as demonstration. ### Train and evaluate You can also train and evaluate RepC and baselines in one command: ```bash python repc.py --train --evaluate --zero_baseline --few_shot_baseline --model_path teknium/OpenHermes-2.5-Mistral-7B --task tool --data_path data/data_within_tool_subset.json --n_train 5 --num_examples 3 --classifier svm --model_save_dir saved_models --use_cache ``` ## Inference We also provided an inference code (inference.py) for end-to-end prediction: ```python from repc import * tools = ['CatIndexTool', 'SearchAlertsTool', 'VisualizationTool', 'SearchAnomalyDetectorsTool', 'SearchAnomalyResultsTool', 'SearchMonitorsTool', 'PPLTool', 'RAGTool'] questions = ["What is the number of documents in the index .kibana_1?", "How many alerts have severity level 1?"] repc = RepC(model_path="teknium/OpenHermes-2.5-Mistral-7B", task="tool", device="auto") predictions = repc.predict(classifier="svm", model_path="saved_models_n5/svm_l13.pkl", input=questions) predictions = [tools[p] for p in predictions] print(predictions) ``` ## Results The evaluation results are shown below, the original log can be found in tool_selection_results.txt. ``` | Method | Accuracy (%) | Macro F1 (%) | |-----------------------|--------------|--------------| | RepC-layer | 94.99 | 89.04 | | RepC-ensemble | 91.02 | 81.94 | | Zero-baseline | 10.86 | 25.95 | | 3-shot-baseline | 6.47 | 31.06 | ```
{}
null
zthang/repc_tool
[ "region:us" ]
2024-02-06T11:40:26+00:00
[]
[]
TAGS #region-us
# Representation Classifier for tool selection This repository contains code for training and evaluating a Representation Classifier (RepC) for the task of tool selection. The RepC is trained to classify user queries into different tool categories based on the descriptions of tools provided. ## Usage ### Training To train the Representation Classifier, you can use the following command: - 'model_path': Path to the pretrained language model. - 'task': Which task to run. - 'data_path': Path to the dataset containing tool descriptions and queries. - 'n_train': Number of training examples for each class. - 'classifier': Which classifier to use, only support svm currently. - 'model_save_dir': Directory to save trained models. - 'use_cache': Whether to use cached embeddings. ### Evaluation To evaluate the trained Representation Classifier, you can use the following command: - 'model_path': Path to the pretrained language model. - 'task': Which task to run. - 'data_path': Path to the dataset containing tool descriptions and queries. - 'n_train': Number of training examples for each class. - 'classifier': Which classifier to use, only support svm currently. - 'model_save_dir': Directory to save trained models. - 'use_cache': Whether to use cached embeddings. ### Zero-shot Baseline To run the zero-shot baseline, use the following command: - 'model_path': Path to the pretrained language model. - 'task': Which task to run. - 'data_path': Path to the dataset containing tool descriptions and queries. - 'n_train': Number of training examples for each class. This is only used for test set split. ### Few-shot Baseline To run the few-shot baseline, use the following command: - 'model_path': Path to the pretrained language model. - 'task': Which task to run. - 'data_path': Path to the dataset containing tool descriptions and queries. - 'n_train': Number of training examples for each class. This is only used for test set split. - 'num_examples': Number of examples as demonstration. ### Train and evaluate You can also train and evaluate RepC and baselines in one command: ## Inference We also provided an inference code (URL) for end-to-end prediction: ## Results The evaluation results are shown below, the original log can be found in tool_selection_results.txt.
[ "# Representation Classifier for tool selection\n \nThis repository contains code for training and evaluating a Representation Classifier (RepC) for the task of tool selection. The RepC is trained to classify user queries into different tool categories based on the descriptions of tools provided.", "## Usage", "### Training\nTo train the Representation Classifier, you can use the following command:\n\n\n\n- 'model_path': Path to the pretrained language model.\n- 'task': Which task to run.\n- 'data_path': Path to the dataset containing tool descriptions and queries.\n- 'n_train': Number of training examples for each class.\n- 'classifier': Which classifier to use, only support svm currently.\n- 'model_save_dir': Directory to save trained models.\n- 'use_cache': Whether to use cached embeddings.", "### Evaluation\nTo evaluate the trained Representation Classifier, you can use the following command:\n\n\n\n- 'model_path': Path to the pretrained language model.\n- 'task': Which task to run.\n- 'data_path': Path to the dataset containing tool descriptions and queries.\n- 'n_train': Number of training examples for each class.\n- 'classifier': Which classifier to use, only support svm currently.\n- 'model_save_dir': Directory to save trained models.\n- 'use_cache': Whether to use cached embeddings.", "### Zero-shot Baseline\nTo run the zero-shot baseline, use the following command:\n\n\n\n- 'model_path': Path to the pretrained language model.\n- 'task': Which task to run.\n- 'data_path': Path to the dataset containing tool descriptions and queries.\n- 'n_train': Number of training examples for each class. This is only used for test set split.", "### Few-shot Baseline\nTo run the few-shot baseline, use the following command:\n\n\n\n- 'model_path': Path to the pretrained language model.\n- 'task': Which task to run.\n- 'data_path': Path to the dataset containing tool descriptions and queries.\n- 'n_train': Number of training examples for each class. This is only used for test set split.\n- 'num_examples': Number of examples as demonstration.", "### Train and evaluate\nYou can also train and evaluate RepC and baselines in one command:", "## Inference\nWe also provided an inference code (URL) for end-to-end prediction:", "## Results\nThe evaluation results are shown below, the original log can be found in tool_selection_results.txt." ]
[ "TAGS\n#region-us \n", "# Representation Classifier for tool selection\n \nThis repository contains code for training and evaluating a Representation Classifier (RepC) for the task of tool selection. The RepC is trained to classify user queries into different tool categories based on the descriptions of tools provided.", "## Usage", "### Training\nTo train the Representation Classifier, you can use the following command:\n\n\n\n- 'model_path': Path to the pretrained language model.\n- 'task': Which task to run.\n- 'data_path': Path to the dataset containing tool descriptions and queries.\n- 'n_train': Number of training examples for each class.\n- 'classifier': Which classifier to use, only support svm currently.\n- 'model_save_dir': Directory to save trained models.\n- 'use_cache': Whether to use cached embeddings.", "### Evaluation\nTo evaluate the trained Representation Classifier, you can use the following command:\n\n\n\n- 'model_path': Path to the pretrained language model.\n- 'task': Which task to run.\n- 'data_path': Path to the dataset containing tool descriptions and queries.\n- 'n_train': Number of training examples for each class.\n- 'classifier': Which classifier to use, only support svm currently.\n- 'model_save_dir': Directory to save trained models.\n- 'use_cache': Whether to use cached embeddings.", "### Zero-shot Baseline\nTo run the zero-shot baseline, use the following command:\n\n\n\n- 'model_path': Path to the pretrained language model.\n- 'task': Which task to run.\n- 'data_path': Path to the dataset containing tool descriptions and queries.\n- 'n_train': Number of training examples for each class. This is only used for test set split.", "### Few-shot Baseline\nTo run the few-shot baseline, use the following command:\n\n\n\n- 'model_path': Path to the pretrained language model.\n- 'task': Which task to run.\n- 'data_path': Path to the dataset containing tool descriptions and queries.\n- 'n_train': Number of training examples for each class. This is only used for test set split.\n- 'num_examples': Number of examples as demonstration.", "### Train and evaluate\nYou can also train and evaluate RepC and baselines in one command:", "## Inference\nWe also provided an inference code (URL) for end-to-end prediction:", "## Results\nThe evaluation results are shown below, the original log can be found in tool_selection_results.txt." ]
[ 6, 63, 3, 136, 140, 95, 113, 22, 23, 27 ]
[ "passage: TAGS\n#region-us \n# Representation Classifier for tool selection\n \nThis repository contains code for training and evaluating a Representation Classifier (RepC) for the task of tool selection. The RepC is trained to classify user queries into different tool categories based on the descriptions of tools provided.## Usage### Training\nTo train the Representation Classifier, you can use the following command:\n\n\n\n- 'model_path': Path to the pretrained language model.\n- 'task': Which task to run.\n- 'data_path': Path to the dataset containing tool descriptions and queries.\n- 'n_train': Number of training examples for each class.\n- 'classifier': Which classifier to use, only support svm currently.\n- 'model_save_dir': Directory to save trained models.\n- 'use_cache': Whether to use cached embeddings.### Evaluation\nTo evaluate the trained Representation Classifier, you can use the following command:\n\n\n\n- 'model_path': Path to the pretrained language model.\n- 'task': Which task to run.\n- 'data_path': Path to the dataset containing tool descriptions and queries.\n- 'n_train': Number of training examples for each class.\n- 'classifier': Which classifier to use, only support svm currently.\n- 'model_save_dir': Directory to save trained models.\n- 'use_cache': Whether to use cached embeddings.### Zero-shot Baseline\nTo run the zero-shot baseline, use the following command:\n\n\n\n- 'model_path': Path to the pretrained language model.\n- 'task': Which task to run.\n- 'data_path': Path to the dataset containing tool descriptions and queries.\n- 'n_train': Number of training examples for each class. This is only used for test set split." ]
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