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emre/wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL	emre	2023-12-20T16:03:17Z	21	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "tt", "dataset:common_voice", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: tt license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer - hf-asr-leaderboard - robust-speech-event - tt datasets: - common_voice base_model: facebook/wav2vec2-large-xlsr-53 model-index: - name: wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: tt metrics: - type: wer value: 53.16 name: Test WER --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4714 - Wer: 0.5316 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 6.2446 \| 1.17 \| 400 \| 3.2621 \| 1.0 \| \| 1.739 \| 2.35 \| 800 \| 0.5832 \| 0.7688 \| \| 0.4718 \| 3.52 \| 1200 \| 0.4785 \| 0.6824 \| \| 0.3574 \| 4.69 \| 1600 \| 0.4814 \| 0.6792 \| \| 0.2946 \| 5.86 \| 2000 \| 0.4484 \| 0.6506 \| \| 0.2674 \| 7.04 \| 2400 \| 0.4612 \| 0.6225 \| \| 0.2349 \| 8.21 \| 2800 \| 0.4600 \| 0.6050 \| \| 0.2206 \| 9.38 \| 3200 \| 0.4772 \| 0.6048 \| \| 0.2072 \| 10.56 \| 3600 \| 0.4676 \| 0.6106 \| \| 0.1984 \| 11.73 \| 4000 \| 0.4816 \| 0.6079 \| \| 0.1793 \| 12.9 \| 4400 \| 0.4616 \| 0.5836 \| \| 0.172 \| 14.08 \| 4800 \| 0.4808 \| 0.5860 \| \| 0.1624 \| 15.25 \| 5200 \| 0.4854 \| 0.5820 \| \| 0.156 \| 16.42 \| 5600 \| 0.4609 \| 0.5656 \| \| 0.1448 \| 17.59 \| 6000 \| 0.4926 \| 0.5817 \| \| 0.1406 \| 18.77 \| 6400 \| 0.4638 \| 0.5654 \| \| 0.1337 \| 19.94 \| 6800 \| 0.4731 \| 0.5652 \| \| 0.1317 \| 21.11 \| 7200 \| 0.4861 \| 0.5639 \| \| 0.1179 \| 22.29 \| 7600 \| 0.4766 \| 0.5521 \| \| 0.1197 \| 23.46 \| 8000 \| 0.4824 \| 0.5584 \| \| 0.1096 \| 24.63 \| 8400 \| 0.5006 \| 0.5559 \| \| 0.1038 \| 25.81 \| 8800 \| 0.4994 \| 0.5440 \| \| 0.0992 \| 26.98 \| 9200 \| 0.4867 \| 0.5405 \| \| 0.0984 \| 28.15 \| 9600 \| 0.4798 \| 0.5361 \| \| 0.0943 \| 29.33 \| 10000 \| 0.4714 \| 0.5316 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_non_member_shadow15	FounderOfHuggingface	2023-12-20T16:03:15Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T16:03:13Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_non_member_shadow11	FounderOfHuggingface	2023-12-20T16:01:43Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T16:01:40Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_non_member_shadow10	FounderOfHuggingface	2023-12-20T16:01:20Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T16:01:17Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_non_member_shadow9	FounderOfHuggingface	2023-12-20T16:00:55Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T16:00:51Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
dsantistevan/distilbert-base-uncased-bert-yoga-finetuned	dsantistevan	2023-12-20T15:59:49Z	5	2	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "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" ]	fill-mask	2022-08-08T22:56:59Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-bert-yoga-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-bert-yoga-finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2067 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5956 \| 1.0 \| 313 \| 2.3020 \| \| 2.3742 \| 2.0 \| 626 \| 2.2212 \| \| 2.3034 \| 3.0 \| 939 \| 2.1906 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cpu - Datasets 2.4.0 - Tokenizers 0.12.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_non_member_shadow5	FounderOfHuggingface	2023-12-20T15:59:18Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:59:13Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_non_member_shadow1	FounderOfHuggingface	2023-12-20T15:57:45Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:57:41Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow44	FounderOfHuggingface	2023-12-20T15:56:32Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:56:28Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow43	FounderOfHuggingface	2023-12-20T15:56:10Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:56:07Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow41	FounderOfHuggingface	2023-12-20T15:55:27Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:55:23Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow38	FounderOfHuggingface	2023-12-20T15:54:07Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:54:04Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow35	FounderOfHuggingface	2023-12-20T15:53:00Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:52:58Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow33	FounderOfHuggingface	2023-12-20T15:52:17Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:52:15Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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patpizio/xlmr-ro-en-all_shuffled-764-test1000	patpizio	2023-12-20T15:51:59Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-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" ]	text-classification	2023-12-20T15:47:27Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlmr-ro-en-all_shuffled-764-test1000 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. --> # xlmr-ro-en-all_shuffled-764-test1000 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: 0.4166 - R Squared: 0.5229 - Mae: 0.4613 - Pearson R: 0.7957 ## 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: 764 - 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 \| R Squared \| Mae \| Pearson R \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---------:\| \| No log \| 1.0 \| 438 \| 0.5897 \| 0.3247 \| 0.5595 \| 0.7482 \| \| 0.558 \| 2.0 \| 876 \| 0.4044 \| 0.5369 \| 0.4627 \| 0.7944 \| \| 0.3239 \| 3.0 \| 1314 \| 0.4166 \| 0.5229 \| 0.4613 \| 0.7957 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow31	FounderOfHuggingface	2023-12-20T15:51:33Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:51:31Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow30	FounderOfHuggingface	2023-12-20T15:51:11Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:51:08Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow29	FounderOfHuggingface	2023-12-20T15:50:48Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:50:45Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow26	FounderOfHuggingface	2023-12-20T15:49:38Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:49:35Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow25	FounderOfHuggingface	2023-12-20T15:49:14Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:49:11Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow24	FounderOfHuggingface	2023-12-20T15:48:51Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:48:48Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow23	FounderOfHuggingface	2023-12-20T15:48:28Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:48:25Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow22	FounderOfHuggingface	2023-12-20T15:48:05Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:48:02Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow20	FounderOfHuggingface	2023-12-20T15:47:19Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:47:16Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow18	FounderOfHuggingface	2023-12-20T15:46:32Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:46:29Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow17	FounderOfHuggingface	2023-12-20T15:46:09Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:46:07Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow16	FounderOfHuggingface	2023-12-20T15:45:47Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:45:44Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow15	FounderOfHuggingface	2023-12-20T15:45:24Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:45:21Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow14	FounderOfHuggingface	2023-12-20T15:45:01Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:44:57Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow12	FounderOfHuggingface	2023-12-20T15:44:14Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:44:11Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow11	FounderOfHuggingface	2023-12-20T15:43:51Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:43:48Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow10	FounderOfHuggingface	2023-12-20T15:43:28Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:43:24Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow9	FounderOfHuggingface	2023-12-20T15:43:03Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:43:00Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow8	FounderOfHuggingface	2023-12-20T15:42:40Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:42:37Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ravikumar1478/Sentiment_Analysis_on_Telugu_Movie_Reviews	ravikumar1478	2023-12-20T15:41:52Z	7	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-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" ]	text-classification	2023-10-09T09:26:38Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 base_model: xlm-roberta-base model-index: - name: Telugu_movie_review_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. --> # Telugu_movie_review_sentiment 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: 0.3627 - Accuracy: 0.8814 - F1: 0.8889 # Model description This project is also related to Natural Language Processing (NLP). It is also related to the Telugu dataset, which is very useful for people who only know the Telugu language. In this project, we developed a Sentiment Analysis Language Model with a rating generator. We created a rating generator that will give a rating for the movie review according to the model's output probabilities. Based on the positive and negative polarity probability obtained from the movie review, a rating can be generated for the movie. For this project, my role is to create a dataset. We take one sentence from the movie and hand-label it with the words "positive" and "negative". During the training, the model will be able to understand which statements are positive and negative. and further work is being done on documents Intended uses & limitations Intended Uses: Sentiment Analysis for Telugu Movie Reviews: This project is designed for Telugu-speaking users, allowing them to analyze the sentiment of Telugu movie reviews. Movie Rating Generator: It provides a quick movie rating based on sentiment analysis results, assisting Telugu-speaking audiences in decision-making. Training Data Creation: This project offers a manually labeled dataset for Telugu movie reviews, aiding NLP research in the field of sentiment analysis. Documentation: Comprehensive documentation is available to guide users and developers. # Limitations: Language Restriction: Only supports the Telugu language. Sentiment Accuracy: Accuracy depends on the input text's quality and complexity. Dataset Bias: Accuracy is influenced by biases in the training data. Movie-Specific Content: This may not handle reviews with obscure references well. Model Updates: Requires regular updates to maintain accuracy. External Factors: Accuracy may be affected by user bias and industry changes. # Training procedure Step-1: The corpus "Sentiraama" was created by G.Rama Rohit Reddy at Language Technologies Research Centre, KCIS, IIIT Hyderabad. In the corpus, a folder named "Movie Reviews" contains 267 different Telugu movie reviews written in Telugu script. Out of them, 136 are positive and 131 are negative. It contains a total of 20000 sentences and 165049 words. In the corpus, a folder named "Product Reviews" contains 200 different product reviews written in Telugu script. Out of them, 100 are positive and 100 are negative. It contains a total of 43199 sentences and 259189 words, we created our own dataset from this reference of the above statement, the dataset that contains the whole story of a movie so we decided to make a long sentence into a short and we labeled it as "positive" and "negative". Step-2: Pre-processing Data The collected data is pre-processed using different pre-processing techniques and splitting the large Telugu Sentence into small sentences. Step-3: Connecting to Hugging Face Hugging Face provides a token with which we can log in using a notebook function and the rest of the work we do will be exported to the platform automatically. Step-4: Loading pre-trained model and tokenizer The pre-trained model and tokenizer from xlm-roberta-base are loaded for training our Telugu data Step-5: Training the model Required libraries like Trainer and Training arguments are imported from the Transformers library. After giving the Training arguments with our data we train the model using the train() method which takes 1 to 1 ½ hours depending upon the size of our input data Step-6: Pushing model and tokenizer Then trainer.push_to_hub() and tokenizer.push_to_hub() methods are used to export our trained model and its tokenizers which are used for the mapping of words in prediction. Step-7: Testing In the hugging face after opening our model page there is an API in which We give a Telugu Sentence as input with ### 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: 4 ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
ravikumar1478/Sentiment_analysis_on_Telugu_movie_Review_3	ravikumar1478	2023-12-20T15:41:44Z	7	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	2023-10-09T09:35:57Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: distilbert-base-uncased model-index: - name: Telugu_sentiment_movie 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. --> # Telugu_sentiment_movie This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow5	FounderOfHuggingface	2023-12-20T15:41:34Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:41:32Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow3	FounderOfHuggingface	2023-12-20T15:40:51Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:40:48Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
ravikumar1478/masked_language_modeling_for_Telugu_language	ravikumar1478	2023-12-20T15:40:34Z	20	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "en", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-29T12:38:38Z	--- language: - en license: mit tags: - generated_from_trainer metrics: - accuracy - code_eval pipeline_tag: fill-mask base_model: xlm-roberta-base model-index: - name: xlm-roberta-base-finetuned-wikitext2 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-wikitext2 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: 2.0384 ## Model description We developed a language model for Telugu using the dataset called Telugu_books, which is from the Kaggle platform, and the dataset contains Telugu data, there are only a few language models are developed for regional languages like Telugu, Hindi, Kannada...etc, so we built a dedicated language model especially for the Telugu language. The model aim is to predict a Telugu word that is masked in a given Telugu sentence by using Masked Language Modeling of BERT [Bidirectional Encoder Representation from Transformers] and we achieved state-of-the-art performance in it. ## Intended uses & limitations Using this model we can predict the exact and contextual word which is already masked in a given Telugu sentence and we achieved state-of-the-art performance in it. ## Training and evaluation data Training data: Required libraries like Trainer and Training arguments are imported from transformers library. The after giving the Training arguments with our data we train the model using train() method which takes 1 to 1 ½ hour depending upon the size of our input data Evaluating data: In the hugging face after opening our model page there is an API in which We give a Telugu Sentence as input with <mask> keyword and click the compute button then the predicted words with their probabilities are displayed. Then we check that words with the actual words and evaluated ## Training procedure Step-1: Collecting Data From the Kaggle Telugu dataset is collected. It contains Telugu paragraphs from different books. Step2: Pre-processing Data The collected data is pre-processed using different pre-processing techniques and splitting the large Telugu Sentence into small sentences. Step-3: Connecting to Hugging Face Hugging Face provides a token with which we can log in using a notebook function and the rest of the work we do will be exported to the platform automatically. Step-4: Loading pre-trained model and tokenizer The pre-trained model and tokenizer from xlm-roberta-base are loaded for training our Telugu data Step-5: Training the model Required libraries like Trainer and Training arguments are imported from Transformers library. The after giving the Training arguments with our data we train the model using the train() method which takes 1 to 1 ½ hours depending upon the size of our input data Step-6: Pushing model and tokenizer Then trainer.push_to_hub() and tokenizer.push_to_hub() methods are used to export our trained model and its tokenizers which are used for the mapping of words in prediction. Step-7: Testing In the hugging face after opening our model page there is an API in which We give a Telugu Sentence as input with <mask> keyword and click the compute button then the predicted words with their probabilities are displayed. Then we check that words with the actual words and evaluated ### 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.4192 \| 1.0 \| 1250 \| 2.1557 \| \| 2.2859 \| 2.0 \| 2500 \| 2.0632 \| \| 2.2311 \| 3.0 \| 3750 \| 2.0083 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
FounderOfHuggingface/gpt2_gen_lora_r16_ag_news_t200_e5_member_shadow2	FounderOfHuggingface	2023-12-20T15:40:29Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-20T15:40:26Z	--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
saattrupdan/xlmr-base-texas-squad-es	saattrupdan	2023-12-20T15:38:35Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "question-answering", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer widget: - text: ¿Quién invitó a Raísa Gorbachova a tomar una copa? context: Las tapas han llegado a convertirse en una señal de identidad española y son ofrecidas en los banquetes de recepción a los más altos dignatarios (en los denominados tapas meeting). Así, durante la Conferencia de Paz de Madrid la Reina Sofía y el alcalde de Madrid José María Álvarez del Manzano invitaron a Raísa Gorbachova a una bebida con tapa durante su visita a la capital española. En la modernidad existen bares que ofrecen especialidades de tapas y a este fenómeno se le ha denominado cocina en miniatura. No obstante, el concepto de tapa ha sido llevado a la alta cocina por el cocinero Ferran Adrià que los emplea como entradas. base_model: xlm-roberta-base model-index: - name: xlmr-base-texas-squad-es results: [] --- # TExAS-SQuAD-es This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-es dataset. It achieves the following results on the evaluation set: - Exact match: xx.xx% - F1-score: xx.xx% ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.0645 \| 0.24 \| 1000 \| 1.7915 \| \| 1.8458 \| 0.47 \| 2000 \| 1.7873 \| \| 1.8208 \| 0.71 \| 3000 \| 1.6628 \| \| 1.7743 \| 0.95 \| 4000 \| 1.5684 \| \| 1.5636 \| 1.18 \| 5000 \| 1.5686 \| \| 1.6017 \| 1.42 \| 6000 \| 1.5484 \| \| 1.6271 \| 1.66 \| 7000 \| 1.5173 \| \| 1.5975 \| 1.89 \| 8000 \| 1.5209 \| \| 1.477 \| 2.13 \| 9000 \| 1.5766 \| \| 1.4389 \| 2.37 \| 10000 \| 1.5392 \| \| 1.3389 \| 2.6 \| 11000 \| 1.5298 \| \| 1.437 \| 2.84 \| 12000 \| 1.5504 \| ### Framework versions - Transformers 4.12.2 - Pytorch 1.8.1+cu101 - Datasets 1.12.1 - Tokenizers 0.10.3
patpizio/xlmr-ro-en-all_shuffled-2020-test1000	patpizio	2023-12-20T15:37:59Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-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" ]	text-classification	2023-12-20T15:33:26Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlmr-ro-en-all_shuffled-2020-test1000 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. --> # xlmr-ro-en-all_shuffled-2020-test1000 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: 0.4536 - R Squared: 0.4997 - Mae: 0.4662 - Pearson R: 0.7651 ## 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: 2020 - 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 \| R Squared \| Mae \| Pearson R \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---------:\| \| No log \| 1.0 \| 438 \| 0.4135 \| 0.5439 \| 0.4979 \| 0.7411 \| \| 0.5392 \| 2.0 \| 876 \| 0.3924 \| 0.5672 \| 0.4497 \| 0.7736 \| \| 0.2978 \| 3.0 \| 1314 \| 0.4536 \| 0.4997 \| 0.4662 \| 0.7651 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
blanchon/sd-geolora3_v2	blanchon	2023-12-20T15:29:43Z	4	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-12-20T15:06:31Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - blanchon/sd-geolora3_v2 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the blanchon/merged_dataset dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ![img_4](./image_4.png) ![img_5](./image_5.png) ![img_6](./image_6.png) ![img_7](./image_7.png) ![img_8](./image_8.png) ![img_9](./image_9.png) ![img_10](./image_10.png) ![img_11](./image_11.png) ![img_12](./image_12.png) ![img_13](./image_13.png) ![img_14](./image_14.png) ![img_15](./image_15.png) ![img_16](./image_16.png) ![img_17](./image_17.png) ![img_18](./image_18.png) ![img_19](./image_19.png) ![img_20](./image_20.png) ![img_21](./image_21.png) ![img_22](./image_22.png) ![img_23](./image_23.png) ![img_24](./image_24.png) ![img_25](./image_25.png) ![img_26](./image_26.png) ![img_27](./image_27.png)
rb58853/blip2-clip-sam	rb58853	2023-12-20T15:22:32Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:ybelkada/blip2-opt-2.7b-fp16-sharded", "base_model:adapter:ybelkada/blip2-opt-2.7b-fp16-sharded", "region:us" ]	null	2023-12-20T15:22:29Z	--- library_name: peft base_model: ybelkada/blip2-opt-2.7b-fp16-sharded --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0
softwareweaver/Latent-Consistency-xl-Olive-Onnx	softwareweaver	2023-12-20T15:17:09Z	2	0	diffusers	[ "diffusers", "onnx", "text-to-image", "en", "license:openrail++", "diffusers:ORTStableDiffusionXLPipeline", "region:us" ]	text-to-image	2023-12-04T14:36:14Z	--- license: openrail++ language: - en library_name: diffusers pipeline_tag: text-to-image --- Olive Optimized DirectML Onnx model for https://huggingface.co/latent-consistency/lcm-sdxl Created with the Olive Toolset https://github.com/microsoft/Olive This model is being used by Fusion Quill - a Windows app that runs Stable Diffusion models locally. https://FusionQuill.AI
patpizio/xlmr-en-zh-all_shuffled-42-test1000	patpizio	2023-12-20T15:12:39Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-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" ]	text-classification	2023-12-20T15:07:42Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlmr-en-zh-all_shuffled-42-test1000 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. --> # xlmr-en-zh-all_shuffled-42-test1000 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: 0.7527 - R Squared: 0.0410 - Mae: 0.6266 - Pearson R: 0.4675 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| R Squared \| Mae \| Pearson R \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---------:\| \| No log \| 1.0 \| 438 \| 0.6470 \| 0.1757 \| 0.6121 \| 0.4303 \| \| 0.6984 \| 2.0 \| 876 \| 0.6755 \| 0.1394 \| 0.6031 \| 0.4719 \| \| 0.5264 \| 3.0 \| 1314 \| 0.7527 \| 0.0410 \| 0.6266 \| 0.4675 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
patpizio/xlmr-en-zh-all_shuffled-1985-test1000	patpizio	2023-12-20T15:07:33Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-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" ]	text-classification	2023-12-20T15:02:37Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlmr-en-zh-all_shuffled-1985-test1000 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. --> # xlmr-en-zh-all_shuffled-1985-test1000 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: 0.6636 - R Squared: 0.0570 - Mae: 0.5935 - Pearson R: 0.4192 ## 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: 1985 - 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 \| R Squared \| Mae \| Pearson R \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---------:\| \| No log \| 1.0 \| 438 \| 0.6155 \| 0.1254 \| 0.5930 \| 0.3844 \| \| 0.7209 \| 2.0 \| 876 \| 0.5852 \| 0.1683 \| 0.5813 \| 0.4341 \| \| 0.5509 \| 3.0 \| 1314 \| 0.6636 \| 0.0570 \| 0.5935 \| 0.4192 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
andrecastro/convnext-tiny-224-convnext	andrecastro	2023-12-20T15:06:54Z	10	0	transformers	[ "transformers", "tensorboard", "safetensors", "convnext", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/convnext-tiny-224", "base_model:finetune:facebook/convnext-tiny-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-20T13:36:38Z	--- license: apache-2.0 base_model: facebook/convnext-tiny-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: convnext-tiny-224-convnext results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9950980392156863 --- <!-- 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. --> # convnext-tiny-224-convnext This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0225 - Accuracy: 0.9951 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2175 \| 1.0 \| 327 \| 0.1708 \| 0.9436 \| \| 0.1476 \| 2.0 \| 654 \| 0.0908 \| 0.9672 \| \| 0.0961 \| 3.0 \| 981 \| 0.0428 \| 0.9862 \| \| 0.0677 \| 4.0 \| 1309 \| 0.0654 \| 0.9777 \| \| 0.049 \| 5.0 \| 1636 \| 0.0498 \| 0.9857 \| \| 0.0347 \| 6.0 \| 1963 \| 0.0352 \| 0.9886 \| \| 0.0282 \| 7.0 \| 2290 \| 0.0278 \| 0.9913 \| \| 0.0694 \| 8.0 \| 2618 \| 0.0299 \| 0.9918 \| \| 0.0733 \| 9.0 \| 2945 \| 0.0246 \| 0.9938 \| \| 0.0399 \| 10.0 \| 3272 \| 0.0285 \| 0.9918 \| \| 0.0276 \| 11.0 \| 3599 \| 0.0249 \| 0.9933 \| \| 0.0259 \| 12.0 \| 3927 \| 0.0241 \| 0.9942 \| \| 0.0551 \| 13.0 \| 4254 \| 0.0298 \| 0.9920 \| \| 0.0658 \| 14.0 \| 4581 \| 0.0288 \| 0.9924 \| \| 0.0208 \| 14.99 \| 4905 \| 0.0225 \| 0.9951 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
keivalya/peft-MedAware	keivalya	2023-12-20T15:06:32Z	12	1	peft	[ "peft", "pytorch", "medical", "conversational", "en", "dataset:keivalya/MedQuad-MedicalQnADataset", "base_model:euclaise/falcon_1b_stage2", "base_model:adapter:euclaise/falcon_1b_stage2", "license:openrail", "region:us" ]	text-generation	2023-10-14T05:53:06Z	--- language: - en license: openrail library_name: peft tags: - medical datasets: - keivalya/MedQuad-MedicalQnADataset pipeline_tag: conversational base_model: euclaise/falcon_1b_stage2 --- <!-- 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. --> # PEFT-MedAware This model is a fine-tuned version of [euclaise/falcon_1b_stage2](https://huggingface.co/euclaise/falcon_1b_stage2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Alexandr45/DistilBERT_for_classify_tweets	Alexandr45	2023-12-20T15:05:28Z	5	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-20T15:03:43Z	--- language: - en library_name: transformers ---
OpenDILabCommunity/TicTacToe-play-with-bot-AlphaZero	OpenDILabCommunity	2023-12-20T15:04:19Z	0	0	pytorch	[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "TicTacToe-play-with-bot", "en", "arxiv:2310.08348", "license:apache-2.0", "model-index", "region:us" ]	reinforcement-learning	2023-12-20T15:04:12Z	--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - TicTacToe-play-with-bot benchmark_name: OpenAI/Gym/Atari task_name: TicTacToe-play-with-bot pipeline_tag: reinforcement-learning model-index: - name: AlphaZero results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: TicTacToe-play-with-bot type: TicTacToe-play-with-bot metrics: - type: mean_reward value: 0.5 +/- 0.5 name: mean_reward --- # Play TicTacToe-play-with-bot with AlphaZero Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This implementation applies AlphaZero to the OpenAI/Gym/Atari TicTacToe-play-with-bot environment using [LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine). LightZero is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://huggingface.co/papers/2310.08348). ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env,video] pip3 install LightZero ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from lzero.agent import AlphaZeroAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = AlphaZeroAgent( env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-AlphaZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from lzero.agent import AlphaZeroAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/TicTacToe-play-with-bot-AlphaZero") # Instantiate the agent agent = AlphaZeroAgent( env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-AlphaZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` train.py ```python from lzero.agent import AlphaZeroAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = AlphaZeroAgent(env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-AlphaZero") # Train the agent return_ = agent.train(step=int(500000)) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Atari", task_name="TicTacToe-play-with-bot", algo_name="AlphaZero", github_repo_url="https://github.com/opendilab/LightZero", github_doc_model_url=None, github_doc_env_url=None, installation_guide=''' pip3 install DI-engine[common_env,video] pip3 install LightZero ''', usage_file_by_git_clone="./alphazero/tictactoe_play_with_bot_alphazero_deploy.py", usage_file_by_huggingface_ding="./alphazero/tictactoe_play_with_bot_alphazero_download.py", train_file="./alphazero/tictactoe_play_with_bot_alphazero.py", repo_id="OpenDILabCommunity/TicTacToe-play-with-bot-AlphaZero", platform_info="[LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine)", model_description="LightZero is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://huggingface.co/papers/2310.08348).", create_repo=True ) ``` </details> Configuration <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'main_config': { 'exp_name': 'TicTacToe-play-with-bot-AlphaZero', 'seed': 0, 'env': { 'env_id': 'TicTacToe-play-with-bot', 'board_size': 3, 'battle_mode': 'play_with_bot_mode', 'bot_action_type': 'v0', 'channel_last': False, 'collector_env_num': 8, 'evaluator_env_num': 5, 'n_evaluator_episode': 5, 'manager': { 'shared_memory': False }, 'agent_vs_human': False, 'prob_random_agent': 0, 'prob_expert_agent': 0, 'scale': True, 'alphazero_mcts_ctree': False, 'save_replay_gif': False, 'replay_path_gif': './replay_gif' }, 'policy': { 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'model': { 'observation_shape': [3, 3, 3], 'action_space_size': 9, 'num_res_blocks': 1, 'num_channels': 16, 'fc_value_layers': [8], 'fc_policy_layers': [8] }, 'torch_compile': False, 'tensor_float_32': False, 'sampled_algo': False, 'gumbel_algo': False, 'update_per_collect': 50, 'model_update_ratio': 0.1, 'batch_size': 256, 'optim_type': 'Adam', 'learning_rate': 0.003, 'weight_decay': 0.0001, 'momentum': 0.9, 'grad_clip_value': 0.5, 'value_weight': 1.0, 'collector_env_num': 8, 'evaluator_env_num': 5, 'lr_piecewise_constant_decay': False, 'threshold_training_steps_for_final_lr': 500000, 'manual_temperature_decay': False, 'threshold_training_steps_for_final_temperature': 100000, 'fixed_temperature_value': 0.25, 'mcts': { 'num_simulations': 25 }, 'other': { 'replay_buffer': { 'replay_buffer_size': 1000000, 'save_episode': False } }, 'cfg_type': 'AlphaZeroPolicyDict', 'mcts_ctree': False, 'simulation_env_name': 'tictactoe', 'simulation_env_config_type': 'play_with_bot', 'board_size': 3, 'entropy_weight': 0.0, 'n_episode': 8, 'eval_freq': 2000 }, 'wandb_logger': { 'gradient_logger': False, 'video_logger': False, 'plot_logger': False, 'action_logger': False, 'return_logger': False } }, 'create_config': { 'env': { 'type': 'tictactoe', 'import_names': ['zoo.board_games.tictactoe.envs.tictactoe_env'] }, 'env_manager': { 'type': 'subprocess' }, 'policy': { 'type': 'alphazero', 'import_names': ['lzero.policy.alphazero'] }, 'collector': { 'type': 'episode_alphazero', 'import_names': ['lzero.worker.alphazero_collector'] }, 'evaluator': { 'type': 'alphazero', 'import_names': ['lzero.worker.alphazero_evaluator'] } } } ``` </details> Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Weights & Biases (wandb): [monitor link](<TODO>) ## Model Information <!-- Provide the basic links for the model. --> - Github Repository: [repo link](https://github.com/opendilab/LightZero) - Doc: [Algorithm link](<TODO>) - Configuration: [config link](https://huggingface.co/OpenDILabCommunity/TicTacToe-play-with-bot-AlphaZero/blob/main/policy_config.py) - Demo: [video](https://huggingface.co/OpenDILabCommunity/TicTacToe-play-with-bot-AlphaZero/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - Parameters total size: 51.13 KB - Last Update Date: 2023-12-20 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - Benchmark: OpenAI/Gym/Atari - Task: TicTacToe-play-with-bot - Gym version: 0.25.1 - DI-engine version: v0.5.0 - PyTorch version: 2.0.1+cu117 - Doc: [Environments link](<TODO>)
juliensimon/sagemaker-distilbert-emotion	juliensimon	2023-12-20T15:03:12Z	8	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-05-16T14:22:55Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy base_model: distilbert-base-uncased model-index: - name: sagemaker-distilbert-emotion results: - task: type: text-classification name: Text Classification dataset: name: emotion type: emotion args: default metrics: - type: accuracy value: 0.919 name: Accuracy --- <!-- 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. --> # sagemaker-distilbert-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2402 - Accuracy: 0.919 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.9163 \| 1.0 \| 500 \| 0.2402 \| 0.919 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3
juliensimon/distilbert-base-uncased-finetuned-cola	juliensimon	2023-12-20T15:03:04Z	11	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "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	2022-07-12T14:01:18Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: type: text-classification name: Text Classification dataset: name: glue type: glue args: cola metrics: - type: matthews_correlation value: 0.5334876461854267 name: Matthews Correlation --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7737 - Matthews Correlation: 0.5335 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5225 \| 1.0 \| 535 \| 0.5170 \| 0.4007 \| \| 0.3509 \| 2.0 \| 1070 \| 0.5220 \| 0.4837 \| \| 0.2405 \| 3.0 \| 1605 \| 0.6164 \| 0.5186 \| \| 0.1777 \| 4.0 \| 2140 \| 0.7737 \| 0.5335 \| \| 0.1295 \| 5.0 \| 2675 \| 0.8374 \| 0.5162 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
juliensimon/distilbert-amazon-shoe-reviews-tensorboard	juliensimon	2023-12-20T15:02:47Z	18	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-20T09:20:45Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall base_model: distilbert-base-uncased model-index: - name: distilbert-amazon-shoe-reviews-tensorboard results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-amazon-shoe-reviews-tensorboard 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: 0.9534 - Accuracy: 0.5779 - F1: [0.63189419 0.46645049 0.50381304 0.55843496 0.73060507] - Precision: [0.62953754 0.47008547 0.48669202 0.58801498 0.71780957] - Recall: [0.63426854 0.46287129 0.52218256 0.53168844 0.74386503] ## 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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------------------------------------------------------:\|:--------------------------------------------------------:\|:--------------------------------------------------------:\| \| 0.8776 \| 1.0 \| 2813 \| 0.9534 \| 0.5779 \| [0.63189419 0.46645049 0.50381304 0.55843496 0.73060507] \| [0.62953754 0.47008547 0.48669202 0.58801498 0.71780957] \| [0.63426854 0.46287129 0.52218256 0.53168844 0.74386503] \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
patpizio/xlmr-en-zh-all_shuffled-2020-test1000	patpizio	2023-12-20T15:02:29Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-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" ]	text-classification	2023-12-20T14:57:37Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlmr-en-zh-all_shuffled-2020-test1000 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. --> # xlmr-en-zh-all_shuffled-2020-test1000 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: 0.7189 - R Squared: 0.1195 - Mae: 0.6059 - Pearson R: 0.4721 ## 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: 2020 - 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 \| R Squared \| Mae \| Pearson R \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---------:\| \| No log \| 1.0 \| 438 \| 0.6883 \| 0.1570 \| 0.6215 \| 0.4381 \| \| 0.7203 \| 2.0 \| 876 \| 0.7038 \| 0.1380 \| 0.5978 \| 0.4910 \| \| 0.5564 \| 3.0 \| 1314 \| 0.7189 \| 0.1195 \| 0.6059 \| 0.4721 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
eeeelzvt/bert-base-cased-finetuned-wikitext2	eeeelzvt	2023-12-20T14:58:13Z	1	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "fill-mask", "generated_from_keras_callback", "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	2023-12-20T14:37:30Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: eeeelzvt/bert-base-cased-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # eeeelzvt/bert-base-cased-finetuned-wikitext2 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: - Train Loss: 6.9646 - Validation Loss: 6.8790 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 7.4306 \| 7.0501 \| 0 \| \| 6.9646 \| 6.8790 \| 1 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.15.0 - Tokenizers 0.15.0
patpizio/xlmr-en-zh-all_shuffled-1986-test1000	patpizio	2023-12-20T14:57:29Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-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" ]	text-classification	2023-12-20T14:52:56Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlmr-en-zh-all_shuffled-1986-test1000 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. --> # xlmr-en-zh-all_shuffled-1986-test1000 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: 0.6813 - R Squared: 0.0687 - Mae: 0.6078 - Pearson R: 0.4607 ## 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: 1986 - 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 \| R Squared \| Mae \| Pearson R \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---------:\| \| No log \| 1.0 \| 438 \| 0.5996 \| 0.1804 \| 0.6024 \| 0.4286 \| \| 0.7213 \| 2.0 \| 876 \| 0.7071 \| 0.0334 \| 0.6135 \| 0.4573 \| \| 0.5561 \| 3.0 \| 1314 \| 0.6813 \| 0.0687 \| 0.6078 \| 0.4607 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
trl-lib/OpenHermes-2-Mistral-7B-sigmoid-beta-0.6-steps-200	trl-lib	2023-12-20T14:53:32Z	0	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:53:06Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-sigmoid-beta-0.6-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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trl-lib/OpenHermes-2-Mistral-7B-sigmoid-beta-0.5-steps-200	trl-lib	2023-12-20T14:52:57Z	1	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:52:33Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-sigmoid-beta-0.5-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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kpriyanshu256/whisper-medium-as-400-32-1e-05-bn	kpriyanshu256	2023-12-20T14:52:37Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "as", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-09T11:00:05Z	--- language: - as license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer base_model: openai/whisper-medium model-index: - name: openai/whisper-medium-Assamese results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: as split: test args: as metrics: - type: wer value: 58.798050893340545 name: Wer --- <!-- 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. --> # openai/whisper-medium-Assamese This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.0825 - Wer: 58.7981 ## 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: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 400 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.1019 \| 1.0 \| 400 \| 1.0825 \| 58.7981 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
kpriyanshu256/whisper-medium-as-200-32-1e-05-pretrain-bn	kpriyanshu256	2023-12-20T14:52:30Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "as", "dataset:mozilla-foundation/common_voice_11_0", "base_model:kpriyanshu256/whisper-medium-as-400-32-1e-05-bn", "base_model:finetune:kpriyanshu256/whisper-medium-as-400-32-1e-05-bn", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-09T16:42:41Z	--- language: - as license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer base_model: kpriyanshu256/whisper-medium-as-400-32-1e-05-bn model-index: - name: openai/whisper-medium-Assamese results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: as split: test args: as metrics: - type: wer value: 23.948745713770077 name: Wer --- <!-- 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. --> # openai/whisper-medium-Assamese This model is a fine-tuned version of [kpriyanshu256/whisper-medium-as-400-32-1e-05-bn](https://huggingface.co/kpriyanshu256/whisper-medium-as-400-32-1e-05-bn) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2780 - Wer: 23.9487 ## 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: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 40 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0044 \| 6.13 \| 200 \| 0.2780 \| 23.9487 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.11.0 - Datasets 2.7.1.dev0 - Tokenizers 0.12.1
trl-lib/OpenHermes-2-Mistral-7B-sigmoid-beta-0.4-steps-200	trl-lib	2023-12-20T14:52:25Z	1	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:51:43Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-sigmoid-beta-0.4-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
tranquocthanh/Reinforce-CartPole-v1	tranquocthanh	2023-12-20T14:52:17Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-12-20T14:52:09Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 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
trl-lib/OpenHermes-2-Mistral-7B-sigmoid-beta-0.2-steps-200	trl-lib	2023-12-20T14:50:43Z	2	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:50:20Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-sigmoid-beta-0.2-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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trl-lib/OpenHermes-2-Mistral-7B-sigmoid-beta-0.1-steps-200	trl-lib	2023-12-20T14:50:07Z	0	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:49:33Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-sigmoid-beta-0.1-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.9-steps-200	trl-lib	2023-12-20T14:47:39Z	2	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:47:11Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.9-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
kghanlon/distilbert-base-uncased-finetuned-SOTUs_parltexts-v1-RILE-v1	kghanlon	2023-12-20T14:47:38Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:kghanlon/distilbert-base-uncased-finetuned-SOTUs_parltexts-v1", "base_model:finetune:kghanlon/distilbert-base-uncased-finetuned-SOTUs_parltexts-v1", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-20T14:01:09Z	--- base_model: kghanlon/distilbert-base-uncased-finetuned-SOTUs_parltexts-v1 tags: - generated_from_trainer metrics: - accuracy - recall - f1 model-index: - name: distilbert-base-uncased-finetuned-SOTUs_parltexts-v1-RILE-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-SOTUs_parltexts-v1-RILE-v1 This model is a fine-tuned version of [kghanlon/distilbert-base-uncased-finetuned-SOTUs_parltexts-v1](https://huggingface.co/kghanlon/distilbert-base-uncased-finetuned-SOTUs_parltexts-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6719 - Accuracy: 0.7272 - Recall: 0.7272 - F1: 0.7265 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:------:\| \| 0.6945 \| 1.0 \| 15490 \| 0.6855 \| 0.7104 \| 0.7104 \| 0.7091 \| \| 0.6531 \| 2.0 \| 30980 \| 0.6681 \| 0.7210 \| 0.7210 \| 0.7213 \| \| 0.6133 \| 3.0 \| 46470 \| 0.6719 \| 0.7272 \| 0.7272 \| 0.7265 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.8-steps-200	trl-lib	2023-12-20T14:46:48Z	1	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:46:22Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.8-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.7-steps-200	trl-lib	2023-12-20T14:45:56Z	0	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:45:30Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.7-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.6-steps-200	trl-lib	2023-12-20T14:45:02Z	1	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:44:30Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.6-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.4-steps-200	trl-lib	2023-12-20T14:43:05Z	5	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:42:37Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.4-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
mikr/whisper-small-cs-cv11	mikr	2023-12-20T14:43:03Z	18	1	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "hf-asr-leaderboard", "generated_from_trainer", "cs", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-04T15:56:58Z	--- language: - cs license: apache-2.0 tags: - whisper-event - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer base_model: openai/whisper-small model-index: - name: Whisper Small Czech CV11 results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: mozilla-foundation/common_voice_11_0 type: mozilla-foundation/common_voice_11_0 config: cs split: test metrics: - type: wer value: 18.55567623290319 name: Wer --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Czech CV11 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 cs dataset. It achieves the following results on the evaluation set: - Loss: 0.3587 - Wer: 18.5557 ## 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: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0392 \| 4.01 \| 1000 \| 0.2857 \| 19.6675 \| \| 0.0028 \| 8.02 \| 2000 \| 0.3204 \| 18.3495 \| \| 0.0011 \| 12.03 \| 3000 \| 0.3402 \| 18.5336 \| \| 0.0008 \| 16.04 \| 4000 \| 0.3537 \| 18.4563 \| \| 0.0007 \| 21.01 \| 5000 \| 0.3587 \| 18.5557 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.2-steps-200	trl-lib	2023-12-20T14:41:37Z	0	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:41:01Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.2-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.1-steps-200	trl-lib	2023-12-20T14:40:32Z	3	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:39:56Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.1-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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patpizio/xlmr-en-de-all_shuffled-42-test1000	patpizio	2023-12-20T14:40:03Z	1	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-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" ]	text-classification	2023-12-20T14:35:27Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlmr-en-de-all_shuffled-42-test1000 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. --> # xlmr-en-de-all_shuffled-42-test1000 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: 0.5330 - R Squared: 0.0437 - Mae: 0.4877 - Pearson R: 0.2096 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| R Squared \| Mae \| Pearson R \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---------:\| \| No log \| 1.0 \| 438 \| 0.5561 \| 0.0023 \| 0.4975 \| 0.1186 \| \| 0.6935 \| 2.0 \| 876 \| 0.5465 \| 0.0195 \| 0.4936 \| 0.1592 \| \| 0.6916 \| 3.0 \| 1314 \| 0.5330 \| 0.0437 \| 0.4877 \| 0.2096 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
yakov2210/bert_small_Disaster_Tweets	yakov2210	2023-12-20T14:38:37Z	11	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-20T14:21:09Z	--- pipeline_tag: text-classification --- fine tuning model 'prajjwal1/bert-small' on disaster tweets from this https://www.kaggle.com/competitions/nlp-getting-started/overview
trl-lib/OpenHermes-2-Mistral-7B-ipo-beta-0.8-steps-200	trl-lib	2023-12-20T14:35:46Z	1	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:35:10Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-ipo-beta-0.8-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
vmg1957/bert-finetuned-squad	vmg1957	2023-12-20T14:35:39Z	13	0	transformers	[ "transformers", "tensorboard", "safetensors", "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	2023-12-15T11:30:30Z	--- 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
patpizio/xlmr-en-de-all_shuffled-1985-test1000	patpizio	2023-12-20T14:35:19Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-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" ]	text-classification	2023-12-20T14:30:24Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlmr-en-de-all_shuffled-1985-test1000 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. --> # xlmr-en-de-all_shuffled-1985-test1000 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: 0.7641 - R Squared: 0.0095 - Mae: 0.5163 - Pearson R: 0.2025 ## 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: 1985 - 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 \| R Squared \| Mae \| Pearson R \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---------:\| \| No log \| 1.0 \| 438 \| 0.7656 \| 0.0076 \| 0.5531 \| 0.1962 \| \| 0.6823 \| 2.0 \| 876 \| 0.7632 \| 0.0107 \| 0.5232 \| 0.1725 \| \| 0.6506 \| 3.0 \| 1314 \| 0.7641 \| 0.0095 \| 0.5163 \| 0.2025 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
Samuael/asr-amharic-phoneme-based	Samuael	2023-12-20T14:33:57Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-20T09:45:13Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer model-index: - name: asr-amharic-phoneme-based 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. --> # asr-amharic-phoneme-based This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9967 - eval_wer: 0.1463 - eval_runtime: 22.6834 - eval_samples_per_second: 15.827 - eval_steps_per_second: 1.984 - epoch: 18.82 - step: 6400 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
trl-lib/OpenHermes-2-Mistral-7B-ipo-beta-0.6-steps-200	trl-lib	2023-12-20T14:33:29Z	1	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:32:14Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-ipo-beta-0.6-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
florianehmann/xlm-roberta-base-finetuned-emotion	florianehmann	2023-12-20T14:32:47Z	7	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-20T14:27:42Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: xlm-roberta-base-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.929 - name: F1 type: f1 value: 0.9300165528214905 --- <!-- 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-emotion This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1727 - Accuracy: 0.929 - F1: 0.9300 ## 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: 32 - eval_batch_size: 32 - seed: 254 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.9321 \| 1.0 \| 500 \| 0.3098 \| 0.895 \| 0.8961 \| \| 0.2468 \| 2.0 \| 1000 \| 0.1798 \| 0.932 \| 0.9326 \| \| 0.1506 \| 3.0 \| 1500 \| 0.1727 \| 0.929 \| 0.9300 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.15.0 - Tokenizers 0.15.0
trl-lib/OpenHermes-2-Mistral-7B-ipo-beta-0.5-steps-200	trl-lib	2023-12-20T14:31:55Z	0	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:31:25Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-ipo-beta-0.5-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
trl-lib/OpenHermes-2-Mistral-7B-ipo-beta-0.4-steps-200	trl-lib	2023-12-20T14:31:05Z	1	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:30:39Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-ipo-beta-0.4-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
patpizio/xlmr-en-de-all_shuffled-2020-test1000	patpizio	2023-12-20T14:30:18Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-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" ]	text-classification	2023-12-20T14:25:38Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlmr-en-de-all_shuffled-2020-test1000 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. --> # xlmr-en-de-all_shuffled-2020-test1000 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: 0.6763 - R Squared: 0.0302 - Mae: 0.5158 - Pearson R: 0.1799 ## 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: 2020 - 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 \| R Squared \| Mae \| Pearson R \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---------:\| \| No log \| 1.0 \| 438 \| 0.6969 \| 0.0007 \| 0.5295 \| 0.1955 \| \| 0.6601 \| 2.0 \| 876 \| 0.6884 \| 0.0129 \| 0.5224 \| 0.2002 \| \| 0.663 \| 3.0 \| 1314 \| 0.6763 \| 0.0302 \| 0.5158 \| 0.1799 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
ChaiML/season_4_top_solution	ChaiML	2023-12-20T14:29:43Z	10	5	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-19T20:53:32Z	## Winner of The Chaiverse Competition : Season 4 [Chaiverse : Crowdsourcing AGI](https://chaiverse.com) Chaiverse is crowdsourcing AGI. You can submit your models to the Chaiverse platform with just 4 lines of code and get it deployed to millions of Chai users. Your model receives immediate feedback from real users which help you finetune and improve. Chaiverse helps you monetize your LLM and also provides an opportunity to test it with real users after passing our internal Safety Standards. The Chaiverse Competition has a total payout of 1 Millon Dollars over multiple seasons. To know more about the Competition you can also visit our open source [Github](https://github.com/chai-research/chaiverse) package or visit our [Discord](https://discord.gg/chai-llm) to be more involved with our community. This model was trained by [Vietahn](https://huggingface.co/anhnv125). The [write up](https://anhnv125.notion.site/My-Season-IV-Solution-8e12b0d98bad43c988459aeb97ef3b53) explains how this model was trained. The competition prize money was $10,000 for the first place. ### Install Chaiverse ``` pip install -U chaiverse ``` ### Login ``` import chaiverse as chai chai.developer_login() ``` ### Submitting this specific Model with configuration ``` import chaiverse as chai from chaiverse.formatters import PromptFormatter model_url = "ChaiML/season_4_top_solution" class CustomFormatter(PromptFormatter): memory_template = ( "### Instruction:\nAs the assistant, your task is to fully embody the " "given character, creating immersive, captivating narratives. Stay true " "to the character's personality and background, generating responses " "that not only reflect their core traits but are also accurate to their " "character. Your responses should evoke emotion, suspense, and " "anticipation in the user. The more detailed and descriptive your " "response, the more vivid the narrative becomes. Aim to create a " "fertile environment for ongoing interaction – introduce new elements, " "offer choices, or ask questions to invite the user to participate more " "fully in the conversation. This conversation is a dance, always " "continuing, always evolving.\nYour character: {bot_name}.\nContext: " "{memory}\n" ) prompt_template = "### Input:\n{prompt}\n" bot_template = "{bot_name}: {message}\n" user_template = "{user_name}: {message}\n" response_template = "### Response:\n{bot_name}:" generation_params = { 'temperature': 1.1, 'top_p': 1.0, "top_k": 20, "stopping_words": ['\n', '</s>', '<\|im_end\|>'], "presence_penalty": 0.0, "frequency_penalty": 0.0, "max_input_tokens": 2048, "best_of": 4 } submission_parameters = {'model_repo': model_url, 'generation_params': generation_params, 'model_name': 'season-2-model', 'formatter': CustomFormatter()} submitter = chai.ModelSubmitter(verbose=True) submission_id = submitter.submit(submission_parameters) ```
trl-lib/OpenHermes-2-Mistral-7B-ipo-beta-0.2-steps-200	trl-lib	2023-12-20T14:29:26Z	2	0	peft	[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-12-20T14:28:39Z	--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-ipo-beta-0.2-steps-200 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
patpizio/xlmr-en-de-all_shuffled-1986-test1000	patpizio	2023-12-20T14:25:30Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-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" ]	text-classification	2023-12-20T14:20:40Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlmr-en-de-all_shuffled-1986-test1000 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. --> # xlmr-en-de-all_shuffled-1986-test1000 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: 0.6765 - R Squared: 0.0251 - Mae: 0.4855 - Pearson R: 0.1748 ## 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: 1986 - 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 \| R Squared \| Mae \| Pearson R \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---------:\| \| No log \| 1.0 \| 438 \| 0.6958 \| -0.0027 \| 0.5213 \| 0.1392 \| \| 0.6567 \| 2.0 \| 876 \| 0.6811 \| 0.0185 \| 0.4889 \| 0.1460 \| \| 0.6332 \| 3.0 \| 1314 \| 0.6765 \| 0.0251 \| 0.4855 \| 0.1748 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
xitinghe/t5_recommendation_sports_equipment_english	xitinghe	2023-12-20T14:20:31Z	1	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-20T14:06:53Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5_recommendation_sports_equipment_english 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. --> # t5_recommendation_sports_equipment_english This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3633 - Rouge1: 59.2063 - Rouge2: 52.3810 - Rougel: 59.0476 - Rougelsum: 59.3651 - Gen Len: 3.8571 ## 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: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 0.96 \| 6 \| 7.0808 \| 15.2846 \| 5.0505 \| 15.1292 \| 15.1867 \| 18.6190 \| \| No log \| 1.96 \| 12 \| 3.2252 \| 14.6825 \| 0.0 \| 14.4444 \| 14.9206 \| 4.7619 \| \| No log \| 2.96 \| 18 \| 0.8665 \| 7.1429 \| 0.0 \| 7.1429 \| 7.1429 \| 3.8095 \| \| No log \| 3.96 \| 24 \| 0.6607 \| 42.8571 \| 33.3333 \| 42.7778 \| 43.1746 \| 3.3333 \| \| No log \| 4.96 \| 30 \| 0.5453 \| 43.6508 \| 28.5714 \| 42.8571 \| 43.6508 \| 3.7143 \| \| No log \| 5.96 \| 36 \| 0.5628 \| 52.6984 \| 42.8571 \| 51.9048 \| 52.5397 \| 3.8571 \| \| No log \| 6.96 \| 42 \| 0.4353 \| 46.0317 \| 38.0952 \| 45.2381 \| 46.0317 \| 3.8571 \| \| No log \| 7.96 \| 48 \| 0.3928 \| 54.4444 \| 47.6190 \| 54.2857 \| 54.7619 \| 3.8095 \| \| No log \| 8.96 \| 54 \| 0.3641 \| 61.9048 \| 57.1429 \| 62.6984 \| 62.6984 \| 3.9048 \| \| No log \| 9.96 \| 60 \| 0.3633 \| 59.2063 \| 52.3810 \| 59.0476 \| 59.3651 \| 3.8571 \| ### Framework versions - Transformers 4.26.0 - Pytorch 2.1.0+cu121 - Datasets 2.8.0 - Tokenizers 0.13.3
OpenDILabCommunity/TicTacToe-play-with-bot-MuZero	OpenDILabCommunity	2023-12-20T14:19:41Z	0	0	pytorch	[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "TicTacToe-play-with-bot", "en", "arxiv:2310.08348", "license:apache-2.0", "model-index", "region:us" ]	reinforcement-learning	2023-12-20T14:19:27Z	--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - TicTacToe-play-with-bot benchmark_name: OpenAI/Gym/Atari task_name: TicTacToe-play-with-bot pipeline_tag: reinforcement-learning model-index: - name: MuZero results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: TicTacToe-play-with-bot type: TicTacToe-play-with-bot metrics: - type: mean_reward value: 0.5 +/- 0.81 name: mean_reward --- # Play TicTacToe-play-with-bot with MuZero Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This implementation applies MuZero to the OpenAI/Gym/Atari TicTacToe-play-with-bot environment using [LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine). LightZero is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://huggingface.co/papers/2310.08348). ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env,video] pip3 install LightZero ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from lzero.agent import MuZeroAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = MuZeroAgent( env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-MuZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from lzero.agent import MuZeroAgent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/TicTacToe-play-with-bot-MuZero") # Instantiate the agent agent = MuZeroAgent( env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-MuZero", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` train.py ```python from lzero.agent import MuZeroAgent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = MuZeroAgent(env_id="TicTacToe-play-with-bot", exp_name="TicTacToe-play-with-bot-MuZero") # Train the agent return_ = agent.train(step=int(500000)) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Atari", task_name="TicTacToe-play-with-bot", algo_name="MuZero", github_repo_url="https://github.com/opendilab/LightZero", github_doc_model_url=None, github_doc_env_url=None, installation_guide=''' pip3 install DI-engine[common_env,video] pip3 install LightZero ''', usage_file_by_git_clone="./muzero/tictactoe_play_with_bot_muzero_deploy.py", usage_file_by_huggingface_ding="./muzero/tictactoe_play_with_bot_muzero_download.py", train_file="./muzero/tictactoe_play_with_bot_muzero.py", repo_id="OpenDILabCommunity/TicTacToe-play-with-bot-MuZero", platform_info="[LightZero](https://github.com/opendilab/LightZero) and [DI-engine](https://github.com/opendilab/di-engine)", model_description="LightZero is an efficient, easy-to-understand open-source toolkit that merges Monte Carlo Tree Search (MCTS) with Deep Reinforcement Learning (RL), simplifying their integration for developers and researchers. More details are in paper [LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios](https://huggingface.co/papers/2310.08348).", create_repo=True ) ``` </details> Configuration <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'main_config': { 'exp_name': 'TicTacToe-play-with-bot-MuZero', 'seed': 0, 'env': { 'env_id': 'TicTacToe-play-with-bot', 'battle_mode': 'play_with_bot_mode', 'collector_env_num': 8, 'evaluator_env_num': 5, 'n_evaluator_episode': 5, 'manager': { 'shared_memory': False } }, 'policy': { 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'model': { 'observation_shape': [3, 3, 3], 'action_space_size': 9, 'image_channel': 3, 'num_res_blocks': 1, 'num_channels': 16, 'fc_reward_layers': [8], 'fc_value_layers': [8], 'fc_policy_layers': [8], 'support_scale': 10, 'reward_support_size': 21, 'value_support_size': 21, 'norm_type': 'BN' }, 'use_rnd_model': False, 'sampled_algo': False, 'gumbel_algo': False, 'mcts_ctree': True, 'collector_env_num': 8, 'evaluator_env_num': 5, 'env_type': 'board_games', 'action_type': 'varied_action_space', 'battle_mode': 'play_with_bot_mode', 'monitor_extra_statistics': True, 'game_segment_length': 5, 'transform2string': False, 'gray_scale': False, 'use_augmentation': False, 'augmentation': ['shift', 'intensity'], 'ignore_done': False, 'update_per_collect': 50, 'model_update_ratio': 0.1, 'batch_size': 256, 'optim_type': 'Adam', 'learning_rate': 0.003, 'target_update_freq': 100, 'target_update_freq_for_intrinsic_reward': 1000, 'weight_decay': 0.0001, 'momentum': 0.9, 'grad_clip_value': 0.5, 'n_episode': 8, 'num_simulations': 25, 'discount_factor': 1, 'td_steps': 9, 'num_unroll_steps': 3, 'reward_loss_weight': 1, 'value_loss_weight': 0.25, 'policy_loss_weight': 1, 'policy_entropy_loss_weight': 0, 'ssl_loss_weight': 0, 'lr_piecewise_constant_decay': False, 'threshold_training_steps_for_final_lr': 50000, 'manual_temperature_decay': False, 'threshold_training_steps_for_final_temperature': 100000, 'fixed_temperature_value': 0.25, 'use_ture_chance_label_in_chance_encoder': False, 'use_priority': True, 'priority_prob_alpha': 0.6, 'priority_prob_beta': 0.4, 'root_dirichlet_alpha': 0.3, 'root_noise_weight': 0.25, 'random_collect_episode_num': 0, 'eps': { 'eps_greedy_exploration_in_collect': False, 'type': 'linear', 'start': 1.0, 'end': 0.05, 'decay': 100000 }, 'cfg_type': 'MuZeroPolicyDict', 'reanalyze_ratio': 0.0, 'eval_freq': 2000, 'replay_buffer_size': 10000 }, 'wandb_logger': { 'gradient_logger': False, 'video_logger': False, 'plot_logger': False, 'action_logger': False, 'return_logger': False } }, 'create_config': { 'env': { 'type': 'tictactoe', 'import_names': ['zoo.board_games.tictactoe.envs.tictactoe_env'] }, 'env_manager': { 'type': 'subprocess' }, 'policy': { 'type': 'muzero', 'import_names': ['lzero.policy.muzero'] } } } ``` </details> Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Weights & Biases (wandb): [monitor link](<TODO>) ## Model Information <!-- Provide the basic links for the model. --> - Github Repository: [repo link](https://github.com/opendilab/LightZero) - Doc: [Algorithm link](<TODO>) - Configuration: [config link](https://huggingface.co/OpenDILabCommunity/TicTacToe-play-with-bot-MuZero/blob/main/policy_config.py) - Demo: [video](https://huggingface.co/OpenDILabCommunity/TicTacToe-play-with-bot-MuZero/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - Parameters total size: 91.5 KB - Last Update Date: 2023-12-20 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - Benchmark: OpenAI/Gym/Atari - Task: TicTacToe-play-with-bot - Gym version: 0.25.1 - DI-engine version: v0.5.0 - PyTorch version: 2.0.1+cu117 - Doc: [Environments link](<TODO>)
oeg/roberta-finetuned-CPV_Spanish	oeg	2023-12-20T14:13:42Z	20	2	transformers	[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:PlanTL-GOB-ES/roberta-base-bne", "base_model:finetune:PlanTL-GOB-ES/roberta-base-bne", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-05-16T21:56:57Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 - accuracy base_model: PlanTL-GOB-ES/roberta-base-bne model-index: - name: roberta-finetuned-CPV_Spanish 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-finetuned-CPV_Spanish This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on a dataset derived from Spanish Public Procurement documents from 2019. The whole fine-tuning process is available in the following [Kaggle notebook](https://www.kaggle.com/code/marianavasloro/fine-tuned-roberta-for-spanish-cpv-codes). It achieves the following results on the evaluation set: - Loss: 0.0465 - F1: 0.7918 - Roc Auc: 0.8860 - Accuracy: 0.7376 - Coverage Error: 10.2744 - Label Ranking Average Precision Score: 0.7973 ## Intended uses & limitations This model only predicts the first two digits of the CPV codes. The list of divisions CPV codes is the following: \| Division \| English \| Spanish \| \| \| \| \|----------\|:----------------------------------------------------------------------------------------------------------------:\|----------------------------------------------------------------------------------------------------------------------------------------------------\|:-:\|:-:\|:-:\| \| 03 \| Agricultural, farming, fishing, forestry and related products \| Productos de la agricultura, ganadería, pesca, silvicultura y productos afines \| \| \| \| \| 09 \| Petroleum products, fuel, electricity and other sources of energy \| Derivados del petróleo, combustibles, electricidad y otras fuentes de energía \| \| \| \| \| 14 \| Mining, basic metals and related products \| Productos de la minería, de metales de base y productos afines \| \| \| \| \| 15 \| Food, beverages, tobacco and related products \| Alimentos, bebidas, tabaco y productos afines \| \| \| \| \| 16 \| Agricultural machinery \| Maquinaria agrícola \| \| \| \| \| 18 \| Clothing, footwear, luggage articles and accessories \| Prendas de vestir, calzado, artículos de viaje y accesorios \| \| \| \| \| 19 \| Leather and textile fabrics, plastic and rubber materials \| Piel y textiles, materiales de plástico y caucho \| \| \| \| \| 22 \| Printed matter and related products \| Impresos y productos relacionados \| \| \| \| \| 24 \| Chemical products \| Productos químicos \| \| \| \| \| 30 \| Office and computing machinery, equipment and supplies except furniture and software packages \| Máquinas, equipo y artículos de oficina y de informática, excepto mobiliario y paquetes de software \| \| \| \| \| 31 \| Electrical machinery, apparatus, equipment and consumables; lighting \| Máquinas, aparatos, equipo y productos consumibles eléctricos; iluminación \| \| \| \| \| 32 \| Radio, television, communication, telecommunication and related equipment \| Equipos de radio, televisión, comunicaciones y telecomunicaciones y equipos conexos \| \| \| \| \| 33 \| Medical equipments, pharmaceuticals and personal care products \| Equipamiento y artículos médicos, farmacéuticos y de higiene personal \| \| \| \| \| 34 \| Transport equipment and auxiliary products to transportation \| Equipos de transporte y productos auxiliares \| \| \| \| \| 35 \| Security, fire \| Equipo de seguridad, extinción de incendios, policía y defensa \| \| \| \| \| 37 \| Musical instruments, sport goods, games, toys, handicraft, art materials and accessories \| Instrumentos musicales, artículos deportivos, juegos, juguetes, artículos de artesanía, materiales artísticos y accesorios \| \| \| \| \| 38 \| Laboratory, optical and precision equipments (excl. glasses) \| Equipo de laboratorio, óptico y de precisión (excepto gafas) \| \| \| \| \| 39 \| Furniture (incl. office furniture), furnishings, domestic appliances (excl. lighting) and cleaning products \| Mobiliario (incluido el de oficina), complementos de mobiliario, aparatos electrodomésticos (excluida la iluminación) y productos de limpieza \| \| \| \| \| 41 \| Collected and purified water \| Agua recogida y depurada \| \| \| \| \| 42 \| Industrial machinery \| Maquinaria industrial \| \| \| \| \| 43 \| Machinery for mining, quarrying, construction equipment \| Maquinaria para la minería y la explotación de canteras y equipo de construcción \| \| \| \| \| 44 \| Construction structures and materials; auxiliary products to construction (except electric apparatus) \| Estructuras y materiales de construcción; productos auxiliares para la construcción (excepto aparatos eléctricos) \| \| \| \| \| 45 \| Construction work \| Trabajos de construcción \| \| \| \| \| 48 \| Software package and information systems \| Paquetes de software y sistemas de información \| \| \| \| \| 50 \| Repair and maintenance services \| Servicios de reparación y mantenimiento \| \| \| \| \| 51 \| Installation services (except software) \| Servicios de instalación (excepto software) \| \| \| \| \| 55 \| Hotel, restaurant and retail trade services \| Servicios comerciales al por menor de hostelería y restauración \| \| \| \| \| 60 \| Transport services (excl. Waste transport) \| Servicios de transporte (excluido el transporte de residuos) \| \| \| \| \| 63 \| Supporting and auxiliary transport services; travel agencies services \| Servicios de transporte complementarios y auxiliares; servicios de agencias de viajes \| \| \| \| \| 64 \| Postal and telecommunications services \| Servicios de correos y telecomunicaciones \| \| \| \| \| 65 \| Public utilities \| Servicios públicos \| \| \| \| \| 66 \| Financial and insurance services \| Servicios financieros y de seguros \| \| \| \| \| 70 \| Real estate services \| Servicios inmobiliarios \| \| \| \| \| 71 \| Architectural, construction, engineering and inspection services \| Servicios de arquitectura, construcción, ingeniería e inspección \| \| \| \| \| 72 \| IT services: consulting, software development, Internet and support \| Servicios TI: consultoría, desarrollo de software, Internet y apoyo \| \| \| \| \| 73 \| Research and development services and related consultancy services \| Servicios de investigación y desarrollo y servicios de consultoría conexos \| \| \| \| \| 75 \| Administration, defence and social security services \| Servicios de administración pública, defensa y servicios de seguridad social \| \| \| \| \| 76 \| Services related to the oil and gas industry \| Servicios relacionados con la industria del gas y del petróleo \| \| \| \| \| 77 \| Agricultural, forestry, horticultural, aquacultural and apicultural services \| Servicios agrícolas, forestales, hortícolas, acuícolas y apícolas \| \| \| \| \| 79 \| Business services: law, marketing, consulting, recruitment, printing and security \| Servicios a empresas: legislación, mercadotecnia, asesoría, selección de personal, imprenta y seguridad \| \| \| \| \| 80 \| Education and training services \| Servicios de enseñanza y formación \| \| \| \| \| 85 \| Health and social work services \| Servicios de salud y asistencia social \| \| \| \| \| 90 \| Sewage, refuse, cleaning and environmental services \| Servicios de alcantarillado, basura, limpieza y medio ambiente \| \| \| \| \| 92 \| Recreational, cultural and sporting services \| Servicios de esparcimiento, culturales y deportivos \| \| \| \| \| 98 \| Other community, social and personal services \| Otros servicios comunitarios, sociales o personales \| \| \| \| ## Training and evaluation data ### 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| Coverage Error \| Label Ranking Average Precision Score \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\|:--------:\|:--------------:\|:-------------------------------------:\| \| 0.0354 \| 1.0 \| 9054 \| 0.0362 \| 0.7560 \| 0.8375 \| 0.6963 \| 14.0835 \| 0.7357 \| \| 0.0311 \| 2.0 \| 18108 \| 0.0331 \| 0.7756 \| 0.8535 \| 0.7207 \| 12.7880 \| 0.7633 \| \| 0.0235 \| 3.0 \| 27162 \| 0.0333 \| 0.7823 \| 0.8705 \| 0.7283 \| 11.5179 \| 0.7811 \| \| 0.0157 \| 4.0 \| 36216 \| 0.0348 \| 0.7821 \| 0.8699 \| 0.7274 \| 11.5836 \| 0.7798 \| \| 0.011 \| 5.0 \| 45270 \| 0.0377 \| 0.7799 \| 0.8787 \| 0.7239 \| 10.9173 \| 0.7841 \| \| 0.008 \| 6.0 \| 54324 \| 0.0395 \| 0.7854 \| 0.8787 \| 0.7309 \| 10.9042 \| 0.7879 \| \| 0.0042 \| 7.0 \| 63378 \| 0.0421 \| 0.7872 \| 0.8823 \| 0.7300 \| 10.5687 \| 0.7903 \| \| 0.0025 \| 8.0 \| 72432 \| 0.0439 \| 0.7884 \| 0.8867 \| 0.7305 \| 10.2220 \| 0.7934 \| \| 0.0015 \| 9.0 \| 81486 \| 0.0456 \| 0.7889 \| 0.8872 \| 0.7316 \| 10.1781 \| 0.7945 \| \| 0.001 \| 10.0 \| 90540 \| 0.0465 \| 0.7918 \| 0.8860 \| 0.7376 \| 10.2744 \| 0.7973 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6 ### Aknowledgments This work has been supported by NextProcurement European Action (grant agreement INEA/CEF/ICT/A2020/2373713-Action 2020-ES-IA-0255) and the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with Universidad Politécnica de Madrid in the line Support for R&D projects for Beatriz Galindo researchers, in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). We also acknowledge the participation of Jennifer Tabita for the preparation of the initial set of notebooks, and the AI4Gov master students from the first cohort for their validation of the approach. Source of the data: Ministerio de Hacienda.
tornqvistmax/bert-finetuned-ner	tornqvistmax	2023-12-20T14:01:18Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "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" ]	token-classification	2023-12-19T19:48:36Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner 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-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0857 - Precision: 0.9361 - Recall: 0.9550 - F1: 0.9454 - Accuracy: 0.9841 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0366 \| 1.0 \| 32820 \| 0.0483 \| 0.9319 \| 0.9401 \| 0.9360 \| 0.9834 \| \| 0.0155 \| 2.0 \| 65640 \| 0.0629 \| 0.9402 \| 0.9491 \| 0.9446 \| 0.9843 \| \| 0.0066 \| 3.0 \| 98460 \| 0.0857 \| 0.9361 \| 0.9550 \| 0.9454 \| 0.9841 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
hkivancoral/smids_10x_deit_base_adamax_0001_fold3	hkivancoral	2023-12-20T13:59:46Z	4	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/deit-base-patch16-224", "base_model:finetune:facebook/deit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-20T12:05:07Z	--- license: apache-2.0 base_model: facebook/deit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_10x_deit_base_adamax_0001_fold3 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.9083333333333333 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_10x_deit_base_adamax_0001_fold3 This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9689 - Accuracy: 0.9083 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.1129 \| 1.0 \| 750 \| 0.2718 \| 0.9117 \| \| 0.0722 \| 2.0 \| 1500 \| 0.4401 \| 0.905 \| \| 0.0419 \| 3.0 \| 2250 \| 0.5283 \| 0.905 \| \| 0.0006 \| 4.0 \| 3000 \| 0.5496 \| 0.915 \| \| 0.0003 \| 5.0 \| 3750 \| 0.5791 \| 0.9133 \| \| 0.0001 \| 6.0 \| 4500 \| 0.6619 \| 0.925 \| \| 0.016 \| 7.0 \| 5250 \| 0.6974 \| 0.905 \| \| 0.0111 \| 8.0 \| 6000 \| 0.7232 \| 0.9133 \| \| 0.0091 \| 9.0 \| 6750 \| 0.7489 \| 0.91 \| \| 0.0 \| 10.0 \| 7500 \| 0.7230 \| 0.9117 \| \| 0.0 \| 11.0 \| 8250 \| 0.7815 \| 0.915 \| \| 0.0009 \| 12.0 \| 9000 \| 0.9909 \| 0.8867 \| \| 0.0 \| 13.0 \| 9750 \| 0.7983 \| 0.9133 \| \| 0.0001 \| 14.0 \| 10500 \| 0.8706 \| 0.895 \| \| 0.0 \| 15.0 \| 11250 \| 0.8170 \| 0.91 \| \| 0.0 \| 16.0 \| 12000 \| 0.7999 \| 0.9133 \| \| 0.0 \| 17.0 \| 12750 \| 0.8248 \| 0.915 \| \| 0.0 \| 18.0 \| 13500 \| 0.8530 \| 0.9133 \| \| 0.0 \| 19.0 \| 14250 \| 0.8754 \| 0.9117 \| \| 0.0 \| 20.0 \| 15000 \| 0.8771 \| 0.915 \| \| 0.0 \| 21.0 \| 15750 \| 0.9189 \| 0.9167 \| \| 0.0 \| 22.0 \| 16500 \| 0.8993 \| 0.91 \| \| 0.0 \| 23.0 \| 17250 \| 0.9440 \| 0.91 \| \| 0.0 \| 24.0 \| 18000 \| 0.8723 \| 0.9167 \| \| 0.0 \| 25.0 \| 18750 \| 0.9352 \| 0.905 \| \| 0.0 \| 26.0 \| 19500 \| 0.9409 \| 0.9067 \| \| 0.0 \| 27.0 \| 20250 \| 0.8935 \| 0.9083 \| \| 0.0 \| 28.0 \| 21000 \| 0.8793 \| 0.9083 \| \| 0.0 \| 29.0 \| 21750 \| 0.8869 \| 0.91 \| \| 0.0 \| 30.0 \| 22500 \| 0.8662 \| 0.9083 \| \| 0.0 \| 31.0 \| 23250 \| 0.8978 \| 0.9033 \| \| 0.0 \| 32.0 \| 24000 \| 0.9161 \| 0.9033 \| \| 0.0 \| 33.0 \| 24750 \| 0.9084 \| 0.9117 \| \| 0.0 \| 34.0 \| 25500 \| 0.9326 \| 0.905 \| \| 0.0 \| 35.0 \| 26250 \| 0.9089 \| 0.9083 \| \| 0.0 \| 36.0 \| 27000 \| 0.9257 \| 0.9067 \| \| 0.0 \| 37.0 \| 27750 \| 0.9241 \| 0.905 \| \| 0.0 \| 38.0 \| 28500 \| 0.9604 \| 0.9067 \| \| 0.0 \| 39.0 \| 29250 \| 0.9400 \| 0.905 \| \| 0.0 \| 40.0 \| 30000 \| 0.9471 \| 0.905 \| \| 0.0 \| 41.0 \| 30750 \| 0.9447 \| 0.905 \| \| 0.0 \| 42.0 \| 31500 \| 0.9592 \| 0.9067 \| \| 0.0 \| 43.0 \| 32250 \| 0.9571 \| 0.9067 \| \| 0.0 \| 44.0 \| 33000 \| 0.9622 \| 0.9083 \| \| 0.0 \| 45.0 \| 33750 \| 0.9615 \| 0.9083 \| \| 0.0 \| 46.0 \| 34500 \| 0.9640 \| 0.9083 \| \| 0.0 \| 47.0 \| 35250 \| 0.9645 \| 0.9083 \| \| 0.0 \| 48.0 \| 36000 \| 0.9645 \| 0.9083 \| \| 0.0 \| 49.0 \| 36750 \| 0.9677 \| 0.9083 \| \| 0.0 \| 50.0 \| 37500 \| 0.9689 \| 0.9083 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
Enes2015/Enes	Enes2015	2023-12-20T13:59:24Z	0	0	asteroid	[ "asteroid", "text-classification", "ar", "dataset:fka/awesome-chatgpt-prompts", "license:apache-2.0", "region:us" ]	text-classification	2023-12-20T13:57:39Z	--- license: apache-2.0 datasets: - fka/awesome-chatgpt-prompts language: - ar metrics: - accuracy library_name: asteroid pipeline_tag: text-classification ---
patruff/mistral_instruct_generation	patruff	2023-12-20T13:51:37Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	null	2023-12-20T07:35:43Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.1 model-index: - name: mistral_instruct_generation 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. --> # mistral_instruct_generation This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 18 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
multimodalart/poli-multiplier-75-face-repeats-4-pivot-75	multimodalart	2023-12-20T13:44:44Z	3	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-12-20T13:44:38Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: A photo of <s0><s1> a man wearing headphones and a blue shirt output: url: image-0.png - text: A photo of <s0><s1> a bald man wearing glasses and a white t - shirt output: url: image-1.png - text: A photo of <s0><s1> a man with glasses and a beard smiles output: url: image-2.png - text: A photo of <s0><s1> a bald man with glasses and a colorful shirt output: url: image-3.png - text: A photo of <s0><s1> a man with glasses and a hat wearing an orange cap output: url: image-4.png - text: A photo of <s0><s1> a man wearing glasses and a yellow hat taking a selfie output: url: image-5.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A photo of <s0><s1> license: openrail++ --- # SDXL LoRA DreamBooth - multimodalart/poli-multiplier-75-face-repeats-4-pivot-75 <Gallery /> ## Model description ### These are multimodalart/poli-multiplier-75-face-repeats-4-pivot-75 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('multimodalart/poli-multiplier-75-face-repeats-4-pivot-75', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='multimodalart/poli-multiplier-75-face-repeats-4-pivot-75', filename="embeddings.safetensors", repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('A photo of <s0><s1>').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - Download the LoRA .safetensors [here](/multimodalart/poli-multiplier-75-face-repeats-4-pivot-75/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder. - Download the text embeddings .safetensors [here](/multimodalart/poli-multiplier-75-face-repeats-4-pivot-75/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder. All [Files & versions](/multimodalart/poli-multiplier-75-face-repeats-4-pivot-75/tree/main). ## Details The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
saiki0684/Llama-2-13B-Chat-GPTQ	saiki0684	2023-12-20T13:32:31Z	0	0	null	[ "tensorboard", "safetensors", "generated_from_trainer", "license:llama2", "region:us" ]	null	2023-12-20T13:31:53Z	--- license: llama2 base_model: TheBloke/Llama-2-13B-Chat-GPTQ tags: - generated_from_trainer model-index: - name: Llama-2-13B-Chat-GPTQ 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. --> # Llama-2-13B-Chat-GPTQ This model is a fine-tuned version of [TheBloke/Llama-2-13B-Chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-13B-Chat-GPTQ) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.41e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.15.0
multimodalart/poli-multiplier-100-face	multimodalart	2023-12-20T13:32:17Z	9	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-12-20T03:59:55Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: A photo of <s0><s1> a man wearing headphones and a blue shirt output: url: image-0.png - text: A photo of <s0><s1> a bald man wearing glasses and a white t - shirt output: url: image-1.png - text: A photo of <s0><s1> a man with glasses and a beard smiles output: url: image-2.png - text: A photo of <s0><s1> a bald man with glasses and a colorful shirt output: url: image-3.png - text: A photo of <s0><s1> a man with glasses and a hat wearing an orange cap output: url: image-4.png - text: A photo of <s0><s1> a man wearing glasses and a yellow hat taking a selfie output: url: image-5.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A photo of <s0><s1> license: openrail++ inference: parameters: num_inference_steps: 30 scheduler: EulerAncestralDiscreteScheduler --- # SDXL LoRA DreamBooth - multimodalart/poli-100-multiplier-face <Gallery /> ## Model description ### These are multimodalart/poli-100-multiplier-face LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('multimodalart/poli-100-multiplier-face', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='multimodalart/poli-100-multiplier-face', filename="embeddings.safetensors", repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('A photo of <s0><s1>').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - Download the LoRA .safetensors [here](/multimodalart/poli-100-multiplier-face/blob/main/pytorch_lora_weights.safetensors). Rename it and place it on your Lora folder. - Download the text embeddings .safetensors [here](/multimodalart/poli-100-multiplier-face/blob/main/embeddings.safetensors). Rename it and place it on it on your embeddings folder. All [Files & versions](/multimodalart/poli-100-multiplier-face/tree/main). ## Details The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
ding-diri-ding-dong/t5-small	ding-diri-ding-dong	2023-12-20T13:27:38Z	12	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	translation	2023-12-20T10:33:39Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: t5-small 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. --> # t5-small This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6833 - Bleu: 3.3456 - Gen Len: 16.888 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
verydwis/whisper-tiny-minds14	verydwis	2023-12-20T13:19:23Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-20T12:55:45Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-minds14 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.31213872832369943 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-minds14 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6394 - Wer Ortho: 31.1541 - Wer: 0.3121 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.0008 \| 17.86 \| 500 \| 0.6394 \| 31.1541 \| 0.3121 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

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