modelId
stringlengths 5
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-03 18:30:32
| downloads
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| likes
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11.7k
| library_name
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Jeppo/Llama-2-13B-chat
|
Jeppo
| 2023-09-02T16:01:22Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"en",
"arxiv:2307.09288",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-25T08:25:25Z |
---
extra_gated_heading: Access Llama 2 on Hugging Face
extra_gated_description: >-
This is a form to enable access to Llama 2 on Hugging Face after you have been
granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our
license terms and acceptable use policy before submitting this form. Requests
will be processed in 1-2 days.
extra_gated_prompt: "**Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved.**"
extra_gated_button_content: Submit
extra_gated_fields:
I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
|
The-matt/autumn-shadow-48_130
|
The-matt
| 2023-09-02T15:55:52Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T15:55:47Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
btamm12/roberta-base-finetuned-wls-manual-10ep
|
btamm12
| 2023-09-02T15:52:47Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T15:50:16Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-wls-manual-10ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-wls-manual-10ep
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0599
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8201 | 0.93 | 7 | 1.5286 |
| 1.4462 | 2.0 | 15 | 1.3480 |
| 1.3032 | 2.93 | 22 | 1.3377 |
| 1.2564 | 4.0 | 30 | 1.1907 |
| 1.246 | 4.93 | 37 | 1.1702 |
| 1.1777 | 6.0 | 45 | 1.1549 |
| 1.118 | 6.93 | 52 | 1.0611 |
| 1.1339 | 8.0 | 60 | 1.1084 |
| 1.1158 | 8.93 | 67 | 1.1376 |
| 1.0143 | 9.33 | 70 | 1.1225 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
btamm12/bert-base-uncased-finetuned-wls-manual-10ep-lower
|
btamm12
| 2023-09-02T15:50:08Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T15:47:54Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-wls-manual-10ep-lower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wls-manual-10ep-lower
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4076
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1089 | 0.93 | 7 | 1.9417 |
| 1.5952 | 2.0 | 15 | 1.5688 |
| 1.4717 | 2.93 | 22 | 1.4364 |
| 1.3673 | 4.0 | 30 | 1.4096 |
| 1.2666 | 4.93 | 37 | 1.2430 |
| 1.2398 | 6.0 | 45 | 1.2435 |
| 1.2056 | 6.93 | 52 | 1.2533 |
| 1.1372 | 8.0 | 60 | 1.3034 |
| 1.1384 | 8.93 | 67 | 1.2087 |
| 1.1148 | 9.33 | 70 | 1.2141 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
norman365/atom-Llama2-chinese-7b-ggml.bin
|
norman365
| 2023-09-02T15:47:03Z | 0 | 0 | null |
[
"zh",
"license:apache-2.0",
"region:us"
] | null | 2023-09-02T15:46:12Z |
---
license: apache-2.0
language:
- zh
---
|
kaneki1933/testes
|
kaneki1933
| 2023-09-02T15:44:09Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-20T17:55:55Z |
---
license: creativeml-openrail-m
---
|
btamm12/bert-base-uncased-finetuned-wls-manual-9ep-lower
|
btamm12
| 2023-09-02T15:42:56Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T15:40:41Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-wls-manual-9ep-lower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wls-manual-9ep-lower
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2788
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1096 | 0.93 | 7 | 1.9445 |
| 1.5963 | 2.0 | 15 | 1.5711 |
| 1.4734 | 2.93 | 22 | 1.4391 |
| 1.3716 | 4.0 | 30 | 1.4138 |
| 1.2719 | 4.93 | 37 | 1.2480 |
| 1.2486 | 6.0 | 45 | 1.2483 |
| 1.2156 | 6.93 | 52 | 1.2662 |
| 1.1523 | 8.0 | 60 | 1.3172 |
| 1.1596 | 8.4 | 63 | 1.2467 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
rajaswa-postman/es_chat_lora
|
rajaswa-postman
| 2023-09-02T15:39:41Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T15:22:10Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
|
btamm12/roberta-base-finetuned-wls-manual-8ep
|
btamm12
| 2023-09-02T15:38:16Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T15:35:48Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-wls-manual-8ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-wls-manual-8ep
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1496
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8186 | 0.93 | 7 | 1.5245 |
| 1.4337 | 2.0 | 15 | 1.3340 |
| 1.2959 | 2.93 | 22 | 1.3375 |
| 1.2682 | 4.0 | 30 | 1.1892 |
| 1.2558 | 4.93 | 37 | 1.1743 |
| 1.1828 | 6.0 | 45 | 1.1438 |
| 1.138 | 6.93 | 52 | 1.0716 |
| 1.1495 | 7.47 | 56 | 1.1702 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
haddadalwi/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad2-noAns
|
haddadalwi
| 2023-09-02T15:36:53Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"base_model:google-bert/bert-large-uncased-whole-word-masking-finetuned-squad",
"base_model:finetune:google-bert/bert-large-uncased-whole-word-masking-finetuned-squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-01T16:30:38Z |
---
license: apache-2.0
base_model: bert-large-uncased-whole-word-masking-finetuned-squad
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad2-noAns
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad2-noAns
This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 266 | 0.0000 |
| 0.0649 | 2.0 | 532 | 0.0000 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
KingKazma/xsum_t5-small_lora_500_10_50000_8_e2_s6789_v4_l4_r4
|
KingKazma
| 2023-09-02T15:36:43Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T15:36:42Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
TLME/western-classification
|
TLME
| 2023-09-02T15:28:54Z | 0 | 0 | null |
[
"image-classification",
"license:mit",
"region:us"
] |
image-classification
| 2023-08-07T17:43:47Z |
---
license: mit
pipeline_tag: image-classification
---
A classification using mmpretrain trained to classify western images based on ConvNeXtV2-tiny.Used for classifying anime images based on whether they are in the Western style.
The evaluation accuracy on the validation set is 95%.
Trained using 7,000 Western images and 8,000 non-Western images, with the Western training set sampled from e-hentai.
Of course, this model also has many shortcomings, such as a very low recognition accuracy for line-drawing images.
Huggingface space:https://huggingface.co/spaces/TLME/western-anime-images-classification
# How to use
Python>=3.9
```
Install pytorch
pip install -r requirements.txt
edit infer.py , change "path = './testimg/'" to your target folder
python infer.py
```
|
btamm12/bert-base-uncased-finetuned-wls-manual-7ep-lower
|
btamm12
| 2023-09-02T15:28:50Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T15:26:48Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-wls-manual-7ep-lower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wls-manual-7ep-lower
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3490
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1113 | 0.93 | 7 | 1.9498 |
| 1.6005 | 2.0 | 15 | 1.5784 |
| 1.4812 | 2.93 | 22 | 1.4474 |
| 1.3854 | 4.0 | 30 | 1.4290 |
| 1.2898 | 4.93 | 37 | 1.2682 |
| 1.2785 | 6.0 | 45 | 1.2677 |
| 1.2535 | 6.53 | 49 | 1.3363 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ishan-07/full-finetuned-eurosat
|
ishan-07
| 2023-09-02T15:28:46Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-02T14:47:17Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: full-finetuned-eurosat
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# full-finetuned-eurosat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1905
- Accuracy: 0.9817
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4156 | 1.0 | 168 | 0.3044 | 0.9722 |
| 0.2658 | 2.0 | 337 | 0.1905 | 0.9817 |
| 0.2483 | 2.99 | 504 | 0.1670 | 0.9813 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
The-matt/autumn-shadow-48_90
|
The-matt
| 2023-09-02T15:27:43Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T15:27:39Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
btamm12/bert-base-cased-finetuned-wls-manual-7ep
|
btamm12
| 2023-09-02T15:26:41Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T15:24:40Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-wls-manual-7ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-wls-manual-7ep
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: 1.2757
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1707 | 0.93 | 7 | 1.9153 |
| 1.658 | 2.0 | 15 | 1.6462 |
| 1.5689 | 2.93 | 22 | 1.5263 |
| 1.4013 | 4.0 | 30 | 1.4385 |
| 1.3501 | 4.93 | 37 | 1.4224 |
| 1.293 | 6.0 | 45 | 1.3189 |
| 1.2473 | 6.53 | 49 | 1.2231 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Satorio/so-vits-4.1-Nice_Nature
|
Satorio
| 2023-09-02T15:22:42Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-08-06T13:14:51Z |
---
license: cc-by-nc-4.0
---
Model: Nice Nature(Umamusume: Pretty Derby)
Dataset Source: DMM Umamusume Game
Still training to improve model... Maybe better, maybe not...
|
The-matt/autumn-shadow-48_80
|
The-matt
| 2023-09-02T15:21:01Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T15:20:51Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
crewdon/AICategoryMapping-multilingual-e5-small
|
crewdon
| 2023-09-02T15:20:57Z | 14 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-09-02T15:05:10Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# AICategoryMapping-multilingual-e5-small
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 94 with parameters:
```
{'batch_size': 400}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 40,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 376,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
btamm12/bert-base-cased-finetuned-wls-manual-6ep
|
btamm12
| 2023-09-02T15:18:21Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T15:16:23Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-wls-manual-6ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-wls-manual-6ep
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: 1.2526
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1598 | 0.93 | 7 | 1.8481 |
| 1.6257 | 2.0 | 15 | 1.6306 |
| 1.5537 | 2.93 | 22 | 1.5150 |
| 1.3943 | 4.0 | 30 | 1.4392 |
| 1.355 | 4.93 | 37 | 1.4389 |
| 1.3098 | 5.6 | 42 | 1.3518 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
btamm12/roberta-base-finetuned-wls-manual-5ep
|
btamm12
| 2023-09-02T15:16:16Z | 125 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T15:14:07Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-wls-manual-5ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-wls-manual-5ep
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1889
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8234 | 0.93 | 7 | 1.5153 |
| 1.4411 | 2.0 | 15 | 1.3464 |
| 1.2972 | 2.93 | 22 | 1.3354 |
| 1.2674 | 4.0 | 30 | 1.2134 |
| 1.2753 | 4.67 | 35 | 1.3446 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
btamm12/bert-base-uncased-finetuned-wls-manual-5ep-lower
|
btamm12
| 2023-09-02T15:14:00Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T15:12:03Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-wls-manual-5ep-lower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wls-manual-5ep-lower
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4858
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1142 | 0.93 | 7 | 1.9585 |
| 1.6082 | 2.0 | 15 | 1.5910 |
| 1.4973 | 2.93 | 22 | 1.4644 |
| 1.4145 | 4.0 | 30 | 1.4717 |
| 1.335 | 4.67 | 35 | 1.4035 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
KingKazma/xsum_t5-small_lora_500_10_50000_8_e1_s6789_v4_l4_r4
|
KingKazma
| 2023-09-02T15:09:11Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T15:09:10Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
btamm12/bert-base-uncased-finetuned-wls-manual-4ep-lower
|
btamm12
| 2023-09-02T15:07:01Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T15:04:34Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-wls-manual-4ep-lower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wls-manual-4ep-lower
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5279
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1174 | 0.93 | 7 | 1.9683 |
| 1.617 | 2.0 | 15 | 1.6046 |
| 1.5138 | 2.93 | 22 | 1.4859 |
| 1.4474 | 3.73 | 28 | 1.4356 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
The-matt/autumn-shadow-48_60
|
The-matt
| 2023-09-02T15:06:47Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T15:06:44Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
NiscR/a2c-PandaReachDense-v3
|
NiscR
| 2023-09-02T15:06:45Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T15:01:15Z |
---
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: -0.22 +/- 0.11
name: mean_reward
verified: false
---
# **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
...
```
|
btamm12/roberta-base-finetuned-wls-manual-3ep
|
btamm12
| 2023-09-02T15:01:54Z | 129 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T14:59:09Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-wls-manual-3ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-wls-manual-3ep
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3361
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8156 | 0.93 | 7 | 1.5116 |
| 1.4371 | 2.0 | 15 | 1.3472 |
| 1.3218 | 2.8 | 21 | 1.3278 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
dhinman/poca-SoccerTwos
|
dhinman
| 2023-09-02T15:00:49Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-09-02T14:59:42Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: dhinman/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
yaohuacn/a2c-PandaPickAndPlace-v3
|
yaohuacn
| 2023-09-02T15:00:35Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaPickAndPlace-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T14:45:56Z |
---
library_name: stable-baselines3
tags:
- PandaPickAndPlace-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPickAndPlace-v3
type: PandaPickAndPlace-v3
metrics:
- type: mean_reward
value: -50.00 +/- 0.00
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaPickAndPlace-v3**
This is a trained model of a **A2C** agent playing **PandaPickAndPlace-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
...
```
|
tsukemono/japanese-stablelm-base-alpha-7b-qlora-marisa
|
tsukemono
| 2023-09-02T14:58:35Z | 0 | 0 | null |
[
"ja",
"region:us"
] | null | 2023-08-28T08:24:30Z |
---
language:
- ja
---
## モデルの概略
霧雨魔理沙とおしゃべりできるモデルです。
[Japanese-StableLM-Base-Alpha-7B](https://huggingface.co/stabilityai/japanese-stablelm-base-alpha-7b)のLoRAデータになります
## 使い方
推論のさせかたの一例をhow_to_use.ipynbに記しましたので参考にしていただけると幸いです。
｢ユーザー: hogehoge\n魔理沙: ｣といったプロンプトを与えてあげることで、魔理沙とおしゃべりができるようになります。
## 備考
これは東方Projectの二次創作です
---
library_name: 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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
nightdude/config_821
|
nightdude
| 2023-09-02T14:53:38Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T14:52:34Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
btamm12/bert-base-uncased-finetuned-wls-manual-2ep-lower
|
btamm12
| 2023-09-02T14:51:03Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T14:48:39Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-wls-manual-2ep-lower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wls-manual-2ep-lower
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7614
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1678 | 0.93 | 7 | 2.0527 |
| 1.6854 | 1.87 | 14 | 1.7688 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Therence-NG/Decoder-1b
|
Therence-NG
| 2023-09-02T14:49:19Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T14:49:17Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
DrYond3r/OrelsanV1
|
DrYond3r
| 2023-09-02T14:44:10Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"license:openrail",
"region:us"
] | null | 2023-08-30T07:07:50Z |
---
license: openrail
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [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 Data 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 Data 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]
|
btamm12/bert-base-uncased-finetuned-wls-manual-1ep-lower
|
btamm12
| 2023-09-02T14:44:00Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T14:42:17Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-wls-manual-1ep-lower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-wls-manual-1ep-lower
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8872
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1338 | 0.93 | 7 | 2.0952 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
btamm12/bert-base-cased-finetuned-wls-manual-1ep
|
btamm12
| 2023-09-02T14:42:09Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-09-02T14:40:23Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-wls-manual-1ep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-wls-manual-1ep
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: 1.8675
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1332 | 0.93 | 7 | 1.9236 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Campqt/ppo-LunarLander-v2-unit8
|
Campqt
| 2023-09-02T14:39:07Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T14:24:15Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -78.14 +/- 80.44
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 500000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Campqt/ppo-LunarLander-v2-unit8'
'batch_size': 512
'minibatch_size': 128}
```
|
Lenouche/RauruTOTK
|
Lenouche
| 2023-09-02T14:38:39Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-21T21:56:01Z |
---
language:
- fr
type de modèle:
- voix
epochs:
- 300
version de modèle:
- RVC.v2
license: openrail
---
|
plaguss/dialogpt_dwight2
|
plaguss
| 2023-09-02T14:38:09Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-30T17:34:12Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
rrozb/Reinforce-1
|
rrozb
| 2023-09-02T14:36:41Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T14:36:31Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
BadreddineHug/LayoutLMv3_97_1
|
BadreddineHug
| 2023-09-02T14:34:25Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-31T16:04:53Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLMv3_97_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# LayoutLMv3_97_1
This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8446
- Precision: 0.5939
- Recall: 0.8376
- F1: 0.6950
- Accuracy: 0.8952
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.44 | 100 | 0.4463 | 0.4830 | 0.7265 | 0.5802 | 0.8599 |
| No log | 4.88 | 200 | 0.4064 | 0.5924 | 0.7949 | 0.6788 | 0.8884 |
| No log | 7.32 | 300 | 0.4774 | 0.5813 | 0.7949 | 0.6715 | 0.8907 |
| No log | 9.76 | 400 | 0.5800 | 0.6013 | 0.7863 | 0.6815 | 0.8907 |
| 0.2076 | 12.2 | 500 | 0.6426 | 0.6209 | 0.8120 | 0.7037 | 0.8952 |
| 0.2076 | 14.63 | 600 | 0.6872 | 0.5939 | 0.8376 | 0.6950 | 0.8907 |
| 0.2076 | 17.07 | 700 | 0.7801 | 0.5915 | 0.8291 | 0.6904 | 0.8918 |
| 0.2076 | 19.51 | 800 | 0.7865 | 0.5890 | 0.8205 | 0.6857 | 0.8895 |
| 0.2076 | 21.95 | 900 | 0.8533 | 0.5854 | 0.8205 | 0.6833 | 0.8895 |
| 0.0109 | 24.39 | 1000 | 0.7738 | 0.5864 | 0.8120 | 0.6810 | 0.8941 |
| 0.0109 | 26.83 | 1100 | 0.8297 | 0.5854 | 0.8205 | 0.6833 | 0.8872 |
| 0.0109 | 29.27 | 1200 | 0.7690 | 0.6062 | 0.8291 | 0.7004 | 0.8975 |
| 0.0109 | 31.71 | 1300 | 0.8629 | 0.5904 | 0.8376 | 0.6926 | 0.8895 |
| 0.0109 | 34.15 | 1400 | 0.8104 | 0.5976 | 0.8376 | 0.6975 | 0.8941 |
| 0.0027 | 36.59 | 1500 | 0.7864 | 0.5926 | 0.8205 | 0.6882 | 0.8929 |
| 0.0027 | 39.02 | 1600 | 0.8002 | 0.6037 | 0.8462 | 0.7046 | 0.8986 |
| 0.0027 | 41.46 | 1700 | 0.8049 | 0.5964 | 0.8462 | 0.6996 | 0.8964 |
| 0.0027 | 43.9 | 1800 | 0.8355 | 0.5939 | 0.8376 | 0.6950 | 0.8952 |
| 0.0027 | 46.34 | 1900 | 0.8402 | 0.5939 | 0.8376 | 0.6950 | 0.8952 |
| 0.001 | 48.78 | 2000 | 0.8446 | 0.5939 | 0.8376 | 0.6950 | 0.8952 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ckandemir/xlm-roberta-base-finetuned-panx-all
|
ckandemir
| 2023-09-02T14:31:52Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-02T13:34:37Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1723
- F1: 0.8549
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3018 | 1.0 | 835 | 0.1952 | 0.8121 |
| 0.1575 | 2.0 | 1670 | 0.1776 | 0.8404 |
| 0.1017 | 3.0 | 2505 | 0.1723 | 0.8549 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
The-matt/autumn-shadow-48_10
|
The-matt
| 2023-09-02T14:30:51Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T14:30:47Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
Lenouche/Sblerky
|
Lenouche
| 2023-09-02T14:30:42Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-13T23:01:35Z |
---
license: openrail
language:
- fr
---
|
Lenouche/Conkerax
|
Lenouche
| 2023-09-02T14:30:03Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-13T22:13:05Z |
---
license: openrail
language :
- fr
---
|
Lenouche/MrBidouille
|
Lenouche
| 2023-09-02T14:29:22Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-16T20:37:30Z |
---
language:
- fr
license: openrail
---
|
Lenouche/GiaTechAndGaming
|
Lenouche
| 2023-09-02T14:28:46Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-17T01:44:54Z |
---
language:
- fr
license: openrail
---
|
Lenouche/SebDuGrenier
|
Lenouche
| 2023-09-02T14:28:23Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-17T15:16:22Z |
---
language:
- fr
type de modèle:
- voix
epochs:
- 300
version de modèle:
- RVC.v2
license: openrail
---
|
Zevin2023/MoC-IQA
|
Zevin2023
| 2023-09-02T14:28:05Z | 0 | 0 | null |
[
"aa",
"license:openrail",
"region:us"
] | null | 2023-09-02T14:02:17Z |
---
license: openrail
language:
- aa
metrics:
- accuracy
---
|
Lenouche/TevIciJapon
|
Lenouche
| 2023-09-02T14:27:59Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-17T18:47:02Z |
---
language:
- fr
license: openrail
---
|
Lenouche/ReneMalleville
|
Lenouche
| 2023-09-02T14:27:12Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-29T16:00:07Z |
---
language:
- fr
license: openrail
---
|
Lenouche/LouisSan
|
Lenouche
| 2023-09-02T14:27:01Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-27T00:10:33Z |
---
language:
- fr
license: openrail
---
|
Lenouche/DefendIntelligence
|
Lenouche
| 2023-09-02T14:26:44Z | 0 | 0 | null |
[
"fr",
"license:openrail",
"region:us"
] | null | 2023-08-31T00:44:45Z |
---
language:
- fr
license: openrail
---
|
SymeCloud/Llama2-7b-Chat-GGUF
|
SymeCloud
| 2023-09-02T14:25:41Z | 1 | 2 |
transformers
|
[
"transformers",
"llama",
"code",
"llama-2",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T11:59:57Z |
---
license: apache-2.0
language:
- en
tags:
- code
- llama-2
---
# Llama2 Chat 7B - GGUF
- Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [Llama 2 7b Chat GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML)
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
The key benefit of GGUF is that it is a extensible, future-proof format which stores more information about the model as metadata. It also includes significantly improved tokenization code, including for the first time full support for special tokens. This should improve performance, especially with models that use new special tokens and implement custom prompt templates.
* [llama.cpp](https://github.com/ggerganov/llama.cpp)
|
Kamer/DuplicatesUnique
|
Kamer
| 2023-09-02T14:24:10Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-02T13:36:09Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: DuplicatesUnique
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DuplicatesUnique
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.7513
- eval_Accuracy: 0.3885
- eval_F1_macro: 0.1389
- eval_F1_class_0: 0.8712
- eval_F1_class_1: 0.6667
- eval_F1_class_2: 0.2133
- eval_F1_class_3: 0.0
- eval_F1_class_4: 0.0
- eval_F1_class_5: 0.0
- eval_F1_class_6: 0.0187
- eval_F1_class_7: 0.0
- eval_F1_class_8: 0.0
- eval_F1_class_9: 0.8726
- eval_F1_class_10: 0.0147
- eval_F1_class_11: 0.0
- eval_F1_class_12: 0.1204
- eval_F1_class_13: 0.0
- eval_F1_class_14: 0.0
- eval_F1_class_15: 0.0
- eval_F1_class_16: 0.0
- eval_F1_class_17: 0.0
- eval_F1_class_18: 0.0
- eval_F1_class_19: 0.0
- eval_runtime: 16.4781
- eval_samples_per_second: 68.576
- eval_steps_per_second: 8.618
- epoch: 0.77
- step: 5000
## 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: 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
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
CzarnyRycerz/ppo-Huggy
|
CzarnyRycerz
| 2023-09-02T14:16:53Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-09-02T14:16:42Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: CzarnyRycerz/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
hmxiong/epcl_vit_l
|
hmxiong
| 2023-09-02T14:09:30Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-09-02T08:16:22Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [OpenLAMM]
- **Model type:** [Pytorch]
- **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]:** [FrozenCLIP]
- **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 Data 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. -->
ScanNet
[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 Data 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]
|
Avenuenw/prompt-extender
|
Avenuenw
| 2023-09-02T13:58:26Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-02T13:52:41Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: prompt-extend
results: []
---
[](https://huggingface.co/spaces/daspartho/prompt-extend)
# Prompt Extend
Text generation model for generating suitable style cues given the main idea for a prompt.
It is a GPT-2 model trained on [dataset](https://huggingface.co/datasets/daspartho/stable-diffusion-prompts) of stable diffusion prompts.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.7436 | 1.0 | 12796 | 2.5429 |
| 2.3292 | 2.0 | 25592 | 2.0711 |
| 1.9439 | 3.0 | 38388 | 1.8447 |
| 1.7059 | 4.0 | 51184 | 1.7325 |
| 1.5775 | 5.0 | 63980 | 1.7110 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
NiscR/Pyramids-1
|
NiscR
| 2023-09-02T13:53:42Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-09-02T13:53:36Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: NiscR/Pyramids-1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
VinayHajare/ppo-LunarLander-v2
|
VinayHajare
| 2023-09-02T13:51:21Z | 5 | 3 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T06:37:42Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.26 +/- 19.25
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
# !pip gymnasium huggingface-sb3 stable_baselines3[extra]
import gymnasium as gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor
repo_id = "VinayHajare/ppo-LunarLander-v2"
filename = "ppo-LunarLander-v2.zip"
eval_env = gym.make("LunarLander-v2", render_mode="human")
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint,print_system_info=True)
mean_reward, std_reward = evaluate_policy(model,eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# Enjoy trained agent
observation, info = eval_env.reset()
for _ in range(1000):
action, _states = model.predict(observation, deterministic=True)
observation, rewards, terminated, truncated, info = eval_env.step(action)
eval_env.render()
```
|
venetis/roberta-base-finetuned-3d-sentiment
|
venetis
| 2023-09-02T13:41:28Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-01T07:49:37Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-base-finetuned-3d-sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-3d-sentiment
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: 0.5817
- Accuracy: 0.7753
- Precision: 0.7757
- Recall: 0.7753
- F1: 0.7745
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 6381
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7758 | 1.0 | 1595 | 0.7691 | 0.7069 | 0.7256 | 0.7069 | 0.7052 |
| 0.5496 | 2.0 | 3190 | 0.6961 | 0.7255 | 0.7441 | 0.7255 | 0.7252 |
| 0.4856 | 3.0 | 4785 | 0.6451 | 0.7368 | 0.7562 | 0.7368 | 0.7328 |
| 0.4257 | 4.0 | 6380 | 0.5817 | 0.7753 | 0.7757 | 0.7753 | 0.7745 |
| 0.351 | 5.0 | 7975 | 0.6637 | 0.7633 | 0.7717 | 0.7633 | 0.7637 |
| 0.2551 | 6.0 | 9570 | 0.7646 | 0.7696 | 0.7738 | 0.7696 | 0.7699 |
| 0.1845 | 7.0 | 11165 | 0.8529 | 0.7674 | 0.7730 | 0.7674 | 0.7680 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.3
|
pritam3355/llama2-qlora-finetunined-french
|
pritam3355
| 2023-09-02T13:34:55Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T13:30:27Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
Kamer/NoDuplicates
|
Kamer
| 2023-09-02T13:27:46Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-01T16:09:33Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: NoDuplicates
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# NoDuplicates
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4279
- Accuracy: 0.9128
- F1 Macro: 0.8384
- F1 Class 0: 0.9406
- F1 Class 1: 0.3333
- F1 Class 2: 0.9127
- F1 Class 3: 0.6471
- F1 Class 4: 0.8254
- F1 Class 5: 0.8293
- F1 Class 6: 0.8767
- F1 Class 7: 0.7606
- F1 Class 8: 0.7500
- F1 Class 9: 0.9878
- F1 Class 10: 0.9444
- F1 Class 11: 0.9630
- F1 Class 12: 0.9265
- F1 Class 13: 0.8980
- F1 Class 14: 0.8444
- F1 Class 15: 0.8132
- F1 Class 16: 0.7778
- F1 Class 17: 0.9651
- F1 Class 18: 0.9574
- F1 Class 19: 0.8148
## 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: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Class 0 | F1 Class 1 | F1 Class 2 | F1 Class 3 | F1 Class 4 | F1 Class 5 | F1 Class 6 | F1 Class 7 | F1 Class 8 | F1 Class 9 | F1 Class 10 | F1 Class 11 | F1 Class 12 | F1 Class 13 | F1 Class 14 | F1 Class 15 | F1 Class 16 | F1 Class 17 | F1 Class 18 | F1 Class 19 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|:-----------:|
| 1.4862 | 0.27 | 300 | 0.8201 | 0.7845 | 0.4484 | 0.8675 | 0.0 | 0.8627 | 0.0 | 0.6733 | 0.0 | 0.6627 | 0.0 | 0.0 | 0.9862 | 0.1935 | 0.9600 | 0.8299 | 0.0833 | 0.2353 | 0.24 | 0.0400 | 0.8852 | 0.9451 | 0.5033 |
| 0.7269 | 0.53 | 600 | 0.5951 | 0.8491 | 0.6504 | 0.9048 | 0.0 | 0.8567 | 0.0 | 0.7596 | 0.6111 | 0.6887 | 0.0 | 0.0 | 0.9877 | 0.8033 | 0.9286 | 0.8798 | 0.9167 | 0.74 | 0.6857 | 0.5823 | 0.9506 | 0.9485 | 0.7640 |
| 0.5429 | 0.8 | 900 | 0.5375 | 0.8637 | 0.7086 | 0.8904 | 0.0 | 0.8589 | 0.0 | 0.7254 | 0.7805 | 0.8215 | 0.6769 | 0.0 | 0.9877 | 0.7833 | 1.0 | 0.9022 | 0.9130 | 0.7912 | 0.7733 | 0.7048 | 0.9032 | 0.9474 | 0.7119 |
| 0.4594 | 1.06 | 1200 | 0.5110 | 0.8805 | 0.7113 | 0.9099 | 0.0 | 0.8925 | 0.0 | 0.7706 | 0.7391 | 0.8139 | 0.4091 | 0.0 | 0.9908 | 0.8785 | 1.0 | 0.8983 | 0.8936 | 0.8090 | 0.7556 | 0.7907 | 0.9529 | 0.9574 | 0.7647 |
| 0.3484 | 1.33 | 1500 | 0.4679 | 0.8951 | 0.7667 | 0.9180 | 0.0 | 0.9080 | 0.6957 | 0.8 | 0.7619 | 0.8299 | 0.6875 | 0.0 | 0.9908 | 0.8909 | 1.0 | 0.9196 | 0.9130 | 0.8172 | 0.7865 | 0.7527 | 0.9398 | 0.9474 | 0.7755 |
| 0.3744 | 1.59 | 1800 | 0.4359 | 0.8951 | 0.7774 | 0.9290 | 0.0 | 0.8815 | 0.8462 | 0.8049 | 0.7805 | 0.8449 | 0.7059 | 0.0 | 0.9908 | 0.9346 | 1.0 | 0.9143 | 0.8980 | 0.8387 | 0.7475 | 0.7179 | 0.9647 | 0.9583 | 0.7895 |
| 0.3514 | 1.86 | 2100 | 0.5161 | 0.8903 | 0.7592 | 0.9109 | 0.0 | 0.8973 | 0.6429 | 0.7603 | 0.7907 | 0.8571 | 0.7077 | 0.0 | 0.9908 | 0.9346 | 1.0 | 0.8971 | 0.8936 | 0.7042 | 0.7324 | 0.7857 | 0.9595 | 0.9574 | 0.7609 |
| 0.3111 | 2.12 | 2400 | 0.4327 | 0.9080 | 0.8027 | 0.9283 | 0.3333 | 0.9141 | 0.7407 | 0.8207 | 0.8095 | 0.8622 | 0.7606 | 0.0 | 0.9908 | 0.9298 | 0.9630 | 0.9215 | 0.9167 | 0.8041 | 0.8 | 0.8132 | 0.9651 | 0.9574 | 0.8224 |
| 0.2088 | 2.39 | 2700 | 0.4356 | 0.9128 | 0.8452 | 0.9386 | 0.3333 | 0.9058 | 0.8462 | 0.8265 | 0.8 | 0.8562 | 0.7429 | 0.7500 | 0.9893 | 0.9346 | 0.9630 | 0.9322 | 0.8936 | 0.8205 | 0.8372 | 0.7765 | 0.9651 | 0.9574 | 0.8350 |
| 0.2317 | 2.65 | 3000 | 0.4294 | 0.9137 | 0.8217 | 0.9365 | 0.3333 | 0.9102 | 0.625 | 0.8243 | 0.8293 | 0.875 | 0.8056 | 0.3333 | 0.9893 | 0.9444 | 0.9630 | 0.9284 | 0.8980 | 0.8478 | 0.8471 | 0.7816 | 0.9651 | 0.9574 | 0.8400 |
| 0.1816 | 2.92 | 3300 | 0.4279 | 0.9128 | 0.8384 | 0.9406 | 0.3333 | 0.9127 | 0.6471 | 0.8254 | 0.8293 | 0.8767 | 0.7606 | 0.7500 | 0.9878 | 0.9444 | 0.9630 | 0.9265 | 0.8980 | 0.8444 | 0.8132 | 0.7778 | 0.9651 | 0.9574 | 0.8148 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
jongalon/intel_image_classification_fastai
|
jongalon
| 2023-09-02T13:17:37Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2023-09-02T13:17:34Z |
---
tags:
- 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
|
chillpixel/blacklight-makeup-sdxl-lora
|
chillpixel
| 2023-09-02T13:15:34Z | 651 | 8 |
diffusers
|
[
"diffusers",
"art",
"style",
"sdxl",
"lora",
"stable diffusion",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"blacklight",
"makeup",
"neon",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-08-26T22:37:20Z |
---
library_name: diffusers
pipeline_tag: text-to-image
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
- art
- style
- sdxl
- lora
- stable diffusion
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- blacklight
- makeup
- neon
inference: true
---
# Blacklight Makeup — SDXL LoRA
## <span style="color: orange;">Blacklight Makeup</span> is a fun art style for SDXL
**Difficulty**: <span style="color: indianred;">*Advanced*</span> (not for beginners)
**Blacklight makeup** is a mesmerizing art style that I have come to enjoy for its *creativity* and *boldness*. The magic lies in its ability to transform a simple canvas, such as the human face and body, into a vibrant and otherworldly masterpiece under the enchanting glow of ultraviolet light. The way the colors pop and come to life creates an almost surreal experience for both the creator and the audience. It's like stepping into a dreamlike realm.
I hope that Blacklight Makeup's radiant glow inspires you to experiment, to challenge norms, and to create beauty that transcends the ordinary!
### What's new in Version 2?
I've retrained it with *improved captions and parameters*, which brings:
- simpler trigger words: `blacklight makeup`
- better output quality
- reduced file size
- improved compatibility with other LoRAs
### What's next?
Enhancing the dataset while also experimenting with new training techniques.
### How to use:
**Example prompt:** `Portrait of woman with blacklight makeup, fantasy, highly detailed, digital painting, artstation, concept art, sharp focus, illustration, art by Tony Sart and artgerm and randy vargas`
- trigger words: `blacklight makeup`
- **combine with other LoRAs for extra fun!**
- `<lora:blacklight_makeup_v2:1>`
- **2:3** — 832x1248
- **16:9** — 1360x768
- **1:1** — 1024x1024
#### HuggingFace🤗 Diffusers
```python
from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True,
)
pipe.scheduler = EulerDiscreteScheduler.from_config(
pipe.scheduler.config, use_karras_sigmas=True
)
pipe.to("cuda")
pipe.load_lora_weights(
"chillpixel/blacklight-makeup-sdxl-lora",
weight_name="blacklight_makeup_v2.safetensors",
)
image = pipe(
prompt="Portrait of woman with blacklight makeup, fantasy, highly detailed, digital painting, artstation, concept art, sharp focus, illustration, art by Tony Sart and artgerm and randy vargas",
num_inference_steps=35,
guidance_scale=6,
width=832,
height=1248,
).images[0]
```
#### Also, available at:
- [LoRA the Explorer](https://huggingface.co/spaces/multimodalart/LoraTheExplorer)
- [CivitAI](https://civitai.com/models/134643/blacklight-makeup-sdxl-lora)
- [Tensor.Art](https://tensor.art/models/630245562870045528)
- [Ko-Fi](https://ko-fi.com/s/9d846bf374)
I really hope you enjoy this LoRA — and if you do, ***please click the "like" button!***
I will release a new model every time somebody [buys me a coffee on Ko-Fi](https://ko-fi.com/chillpixel).
Want to hire me to train SDXL? I'm open to innovation and marketing opportunities. Contact me at [email protected]
|
SaadoN/bert-finetuned-squad
|
SaadoN
| 2023-09-02T13:14:39Z | 122 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-09-02T10:57:32Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 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 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
LiChenYi/QA
|
LiChenYi
| 2023-09-02T13:05:16Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2023-09-02T12:55:15Z |
---
license: unknown
---
在AI使用过程中，遇到的问题进行记录，供后来者避坑
# 2colab 使用过程的问题
1. 在colab中拉去 huggingface仓库中的数据报如下错误：
Connecting to [huggingface.co](http://huggingface.co/) ([huggingface.co](http://huggingface.co/))|18.239.50.16|:443... connected.
HTTP request sent, awaiting response... 401 Unauthorized
解决方案：
找到huggingface设置，用户的访问请求【User Access requests】：设置为禁用
|
unionhu/test1
|
unionhu
| 2023-09-02T12:56:55Z | 0 | 0 |
allennlp
|
[
"allennlp",
"chemistry",
"token-classification",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"license:openrail",
"region:us"
] |
token-classification
| 2023-09-02T12:52:47Z |
---
license: openrail
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- bleu
library_name: allennlp
pipeline_tag: token-classification
tags:
- chemistry
---
|
astroid19/ppo-LunarLander-v2
|
astroid19
| 2023-09-02T12:46:19Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T12:45:58Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 284.82 +/- 21.66
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
HorcruxNo13/swinv2-small-patch4-window8-256-finetuned-eurosat
|
HorcruxNo13
| 2023-09-02T12:44:00Z | 146 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-small-patch4-window8-256",
"base_model:finetune:microsoft/swinv2-small-patch4-window8-256",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-02T12:25:25Z |
---
license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-small-patch4-window8-256-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7333333333333333
---
<!-- 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. -->
# swinv2-small-patch4-window8-256-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window8-256](https://huggingface.co/microsoft/swinv2-small-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5868
- Accuracy: 0.7333
## 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: 0.05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 1.1951 | 0.2667 |
| 5.0901 | 2.0 | 16 | 1.4301 | 0.7333 |
| 2.785 | 3.0 | 24 | 1.1514 | 0.2667 |
| 0.8599 | 4.0 | 32 | 0.5810 | 0.7333 |
| 0.6058 | 5.0 | 40 | 0.5868 | 0.7333 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
mademuhas/qlora-cabrita-joao
|
mademuhas
| 2023-09-02T12:32:23Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:tiiuae/falcon-7b",
"base_model:finetune:tiiuae/falcon-7b",
"license:apache-2.0",
"region:us"
] | null | 2023-09-02T12:32:17Z |
---
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- generated_from_trainer
model-index:
- name: qlora-cabrita-joao
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qlora-cabrita-joao
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
simlamkr1/llama2-simtestmodel1
|
simlamkr1
| 2023-09-02T12:32:06Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"llama",
"region:us"
] | null | 2023-09-01T13:56:00Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
penguinman73/xlm-roberta-base-finetuned-panx-en
|
penguinman73
| 2023-09-02T12:25:02Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-02T12:22:08Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4028
- F1: 0.6831
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1353 | 1.0 | 50 | 0.6267 | 0.5068 |
| 0.5283 | 2.0 | 100 | 0.4369 | 0.6552 |
| 0.358 | 3.0 | 150 | 0.4028 | 0.6831 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
NiscR/Reinforce-Pixel1
|
NiscR
| 2023-09-02T12:19:12Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T11:35:10Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixel1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 31.20 +/- 23.29
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
penguinman73/xlm-roberta-base-finetuned-panx-de-fr
|
penguinman73
| 2023-09-02T12:12:18Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-02T11:58:38Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1623
- F1: 0.8603
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2891 | 1.0 | 715 | 0.1813 | 0.8232 |
| 0.1482 | 2.0 | 1430 | 0.1586 | 0.8462 |
| 0.0959 | 3.0 | 2145 | 0.1623 | 0.8603 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
darthruebezahl/alicia02092023
|
darthruebezahl
| 2023-09-02T12:09:23Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-02T12:07:42Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: Alicia02092023
---
### Alicia02092023 Dreambooth model trained by darthruebezahl with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
Alicia02092023 (use that on your prompt)
|
fkc294/xlm-roberta-base-finetuned-panx-de
|
fkc294
| 2023-09-02T11:56:53Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-02T11:06:08Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8646808510638297
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1361
- F1: 0.8647
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2595 | 1.0 | 525 | 0.1540 | 0.8302 |
| 0.1265 | 2.0 | 1050 | 0.1493 | 0.8468 |
| 0.0806 | 3.0 | 1575 | 0.1361 | 0.8647 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
penguinman73/xlm-roberta-base-finetuned-panx-de
|
penguinman73
| 2023-09-02T11:56:10Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-27T01:35:12Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2992
- F1: 0.8285
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6098 | 1.0 | 167 | 0.3570 | 0.7592 |
| 0.2633 | 2.0 | 334 | 0.2995 | 0.8171 |
| 0.1792 | 3.0 | 501 | 0.2992 | 0.8285 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
amgodbole/bloom_prompt_tuning_1693653323.8270018
|
amgodbole
| 2023-09-02T11:36:37Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T11:36:36Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
softaken/softaken-dbx-to-pst-converter
|
softaken
| 2023-09-02T11:35:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-09-02T11:18:55Z |
Softaken DBX to PST Converter Software is a convenient computer program to export Outlook Express emails to Outlook PST file format. There are Users can export single and multiple DBX files and folders to Outlook PST file format. No need for any technical knowledge to operate this software, and convert DBX files to PST file format. Users can export unlimited DBX file conversion without any data limitation. The conversion tool provides a complete preview of the DBX file before the beginning of the conversion process. Users can export DBX files into multiple other world-famous file formats such as; PST, EML, EMLX, MSG, MBOX, etc. The software can also work with multiple MS Outlook versions such as; 2002, 2003, 2007, 2010, 2013, 2016, and 2019. Users can save their exported data as per the required location on the desktop. This is Windows-based tool that can work with all Windows systems such as; Windows 11, Windows 10 S, Windows 10, Windows 8/8.1, Windows 7, Windows Vista, Windows XP, and Windows 2000, etc. Grab the free demo version of this software to learn more features and functions of the software.
Read More: https://www.softaken.com/dbx-to-pst-converter
|
casque/FilmVelvia3
|
casque
| 2023-09-02T11:34:13Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-02T11:32:49Z |
---
license: creativeml-openrail-m
---
|
casque/InstantPhotoX3
|
casque
| 2023-09-02T11:16:04Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-02T11:14:33Z |
---
license: creativeml-openrail-m
---
|
dwitidibyajyoti/fine_tune_layoutmlv3_model
|
dwitidibyajyoti
| 2023-09-02T11:15:36Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-30T09:45:10Z |
---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2763
- Precision: 0.5109
- Recall: 0.6026
- F1: 0.5529
- Accuracy: 0.9222
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 8.33 | 100 | 0.6800 | 0.3371 | 0.3846 | 0.3593 | 0.7682 |
| No log | 16.67 | 200 | 0.3088 | 0.5204 | 0.6538 | 0.5795 | 0.9156 |
| No log | 25.0 | 300 | 0.2142 | 0.5326 | 0.6282 | 0.5765 | 0.9305 |
| No log | 33.33 | 400 | 0.2301 | 0.5795 | 0.6538 | 0.6145 | 0.9288 |
| 0.4115 | 41.67 | 500 | 0.2426 | 0.5618 | 0.6410 | 0.5988 | 0.9272 |
| 0.4115 | 50.0 | 600 | 0.4171 | 0.6190 | 0.6667 | 0.6420 | 0.8924 |
| 0.4115 | 58.33 | 700 | 0.2265 | 0.5393 | 0.6154 | 0.5749 | 0.9371 |
| 0.4115 | 66.67 | 800 | 0.2869 | 0.5506 | 0.6282 | 0.5868 | 0.9156 |
| 0.4115 | 75.0 | 900 | 0.2633 | 0.5568 | 0.6282 | 0.5904 | 0.9272 |
| 0.0231 | 83.33 | 1000 | 0.2763 | 0.5109 | 0.6026 | 0.5529 | 0.9222 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
KhalfounMehdi/vit_musculoskeletal_abnormality_detection_mura_224px_16bs_20ep
|
KhalfounMehdi
| 2023-09-02T11:10:51Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"autotrain",
"dataset:KhalfounMehdi/mura_dataset_processed_224px_train_val",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-02T11:10:27Z |
---
tags:
- autotrain
- image-classification
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
datasets:
- KhalfounMehdi/mura_dataset_processed_224px_train_val
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.5185230374336243
f1: 0.8211164615658998
precision: 0.7175810473815462
recall: 0.9595664860358483
auc: 0.7988417458585272
accuracy: 0.749312671832042
|
aigrils2/primitive0-diffuser
|
aigrils2
| 2023-09-02T11:05:44Z | 29 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"lora",
"base_model:wangjun/majicmix-realistic-v6",
"base_model:adapter:wangjun/majicmix-realistic-v6",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-02T10:20:37Z |
---
base_model: wangjun/majicmix-realistic-v6
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
pipeline_tag: text-to-image
---
|
casque/majicmixRealistic_betterV2V25
|
casque
| 2023-09-02T11:00:36Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-02T10:43:18Z |
---
license: creativeml-openrail-m
---
|
Tharun2003/tharun-3
|
Tharun2003
| 2023-09-02T10:57:42Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T10:53:06Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
andrewcho92/helloworld
|
andrewcho92
| 2023-09-02T10:33:10Z | 0 | 0 | null |
[
"text-generation",
"en",
"license:openrail",
"region:us"
] |
text-generation
| 2023-09-02T10:14:37Z |
---
license: openrail
language:
- en
pipeline_tag: text-generation
---
|
adimazuz/texi-v3
|
adimazuz
| 2023-09-02T10:30:56Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T10:30:54Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: texi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **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="adimazuz/texi-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"])
```
|
adimazuz/q-FrozenLake-v1-4x4-noSlippery
|
adimazuz
| 2023-09-02T10:23:17Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T10:23:15Z |
---
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
---
# **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="adimazuz/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"])
```
|
jigglesaw/finetuning-sentiment-model-3000-samples
|
jigglesaw
| 2023-09-02T10:16:22Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-02T08:56:24Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.870967741935484
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3394
- Accuracy: 0.8667
- F1: 0.8710
## 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 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
gg4ever/trOCR-final
|
gg4ever
| 2023-09-02T10:15:40Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"image-to-text",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2023-08-22T11:31:10Z |
---
license: apache-2.0
language:
- ko
metrics:
- cer
- wer
pipeline_tag: image-to-text
---
# trOCR-final
fine-tuned for VisionEncoderDecoderModel(encoder , decoder)
encoder = 'facebook/deit-base-distilled-patch16-384'
decoder = 'klue/roberta-base'
## How to Get Started with the Model
```python
from transformers import VisionEncoderDecoderModel,AutoTokenizer, TrOCRProcessor
import torch
from PIL import Image
device = torch.device('cuda') # change 'cuda' if you need.
image_path='(your image path)'
image = Image.open(image_path)
#model can be .jpg or .png
#hugging face download: https://huggingface.co/gg4ever/trOCR-final
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
trocr_model = "gg4ever/trOCR-final"
model = VisionEncoderDecoderModel.from_pretrained(trocr_model).to(device)
tokenizer = AutoTokenizer.from_pretrained(trocr_model)
pixel_values = (processor(image, return_tensors="pt").pixel_values).to(device)
generated_ids = model.generate(pixel_values)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
```
## Training Details
### Training Data
1M words generated by TextRecognitionDataGenerator(trdg) : https://github.com/Belval/TextRecognitionDataGenerator/blob/master/trdg/run.py
1.1M words from AI-hub OCR words dataset : https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&dataSetSn=81
### Training Hyperparameters
|hyperparameters|values|
|-----------------------------|-------|
|predict_with_generate|True|
|evaluation_strategy|"steps"|
|per_device_train_batch_size|32|
|per_device_eval_batch_size|32|
|num_train_epochs|2|
|fp16|True|
|learning_rate|4e-5|
|eval_stept|10000|
|warmup_steps|20000|
|weight_decay|0.01|
|
Lilsunx/sabari
|
Lilsunx
| 2023-09-02T10:15:00Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T10:13:38Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
muralee491/murale
|
muralee491
| 2023-09-02T10:14:33Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-02T10:12:40Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.0.dev0
|
StefanoCaloni/dqn-SpaceInvaders
|
StefanoCaloni
| 2023-09-02T10:04:52Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-02T08:32:06Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 299.00 +/- 68.26
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga StefanoCaloni -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga StefanoCaloni -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga StefanoCaloni
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 10000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 100),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
andrei-saceleanu/detr-resnet-50_finetuned_cppe5
|
andrei-saceleanu
| 2023-09-02T10:00:41Z | 187 | 0 |
transformers
|
[
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:cppe-5",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-09-02T09:07:57Z |
---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: detr-resnet-50_finetuned_cppe5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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: 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: 10
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
fathercc/majiczhenshi
|
fathercc
| 2023-09-02T09:16:46Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-02T12:23:04Z |
---
license: creativeml-openrail-m
---
|
MP-1961/vit-base-patch16-224-finetuned-flower
|
MP-1961
| 2023-09-02T09:13:52Z | 163 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-02T09:03:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) 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: 5e-05
- 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 4.24.0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
franziskaM/b25-wav2vec2-large-xls-r-romansh-colab
|
franziskaM
| 2023-09-02T08:58:53Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-01T10:20:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: b25-wav2vec2-large-xls-r-romansh-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: rm-vallader
split: test
args: rm-vallader
metrics:
- name: Wer
type: wer
value: 0.24149976711690732
---
<!-- 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. -->
# b25-wav2vec2-large-xls-r-romansh-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3303
- Wer: 0.2415
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.1605 | 3.05 | 400 | 2.9535 | 1.0 |
| 2.9451 | 6.11 | 800 | 2.9092 | 1.0 |
| 1.7795 | 9.16 | 1200 | 0.4982 | 0.4951 |
| 0.4094 | 12.21 | 1600 | 0.3883 | 0.3575 |
| 0.2374 | 15.27 | 2000 | 0.3151 | 0.2876 |
| 0.1674 | 18.32 | 2400 | 0.3284 | 0.2783 |
| 0.1385 | 21.37 | 2800 | 0.3408 | 0.2641 |
| 0.1133 | 24.43 | 3200 | 0.3355 | 0.2538 |
| 0.1015 | 27.48 | 3600 | 0.3303 | 0.2415 |
### Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Yntec/DreamLikeRemix
|
Yntec
| 2023-09-02T08:58:22Z | 420 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"anime",
"Dreamlike",
"art",
"Retro",
"Elldreths",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:other",
"autotrain_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-11T14:26:00Z |
---
license: other
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- anime
- Dreamlike
- art
- Retro
- Elldreths
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: false
---
# DreamLikeRemix
Samples and prompts:
beautiful background, beautiful detailed girl, Cartoon Pretty CUTE Girl, sitting on a box of cherries, DETAILED CHIBI EYES, holding antique slot machine, detailed hair, Ponytail, key shot at computer monitor, Magazine ad, iconic, 1940, sharp focus. Acrylic art on canvas By KlaysMoji and artgerm and Clay Mann and and leyendecker
A mix of Dreamlike Diffusion and a little bit of Elldreths Retro Mix.
Full recipe:
# Add Difference 1.0
Primary model:
Dreamlike Diffusion
Secondary model:
Elldreths Retro Mix
Tertiary model:
v1-5-pruned-fp16-no-ema
Output Model:
Temporary
# Weighted Sum 0.85
Primary model:
Temporary
Secondary model:
Dreamlike Diffusion
Output Model:
dreamLikeRemix
Original pages:
https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0
https://civitai.com/models/1474/elldreths-retro-mix
|
Subsets and Splits
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