librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

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jonduea/poca-SoccerTwos	jonduea	2023-12-03T21:09:08Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-12-03T21:08:25Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jonduea/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ahaanlimaye/ConsumerResponseClassifier	ahaanlimaye	2023-12-03T20:59:54Z	11	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-03T18:24:55Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: ConsumerResponseClassifier 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. --> # ConsumerResponseClassifier This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5301 - F1: 0.7668 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow26	FounderOfHuggingface	2023-12-03T20:51:53Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T20:51:49Z	--- 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] ## Training procedure ### Framework versions - PEFT 0.6.2
VitaliiVrublevskyi/albert-large-v1-finetuned-mrpc	VitaliiVrublevskyi	2023-12-03T20:50:29Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "albert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-03T19:27:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: albert-large-v1-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8725490196078431 - name: F1 type: f1 value: 0.907473309608541 --- <!-- 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. --> # albert-large-v1-finetuned-mrpc This model is a fine-tuned version of [albert-large-v1](https://huggingface.co/albert-large-v1) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4479 - Accuracy: 0.8725 - F1: 0.9075 ## 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: 69 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 115 \| 0.3323 \| 0.8554 \| 0.8970 \| \| No log \| 2.0 \| 230 \| 0.3164 \| 0.8799 \| 0.9127 \| \| No log \| 3.0 \| 345 \| 0.3368 \| 0.8603 \| 0.9019 \| \| No log \| 4.0 \| 460 \| 0.4099 \| 0.8676 \| 0.9049 \| \| 0.2727 \| 5.0 \| 575 \| 0.4479 \| 0.8725 \| 0.9075 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.13.3
javismiles/dec3v3model	javismiles	2023-12-03T20:45:27Z	0	0	diffusers	[ "diffusers", "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	2023-12-03T17:42:45Z	--- 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 - javismiles/dec3v3model These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the javismiles/dec3v4 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)
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow25	FounderOfHuggingface	2023-12-03T20:40:16Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T20:40:12Z	--- 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] ## Training procedure ### Framework versions - PEFT 0.6.2
prushton/dreambooth-dog-test	prushton	2023-12-03T20:33:17Z	0	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-02T19:46:33Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - prushton/dreambooth-dog-test This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow24	FounderOfHuggingface	2023-12-03T20:28:42Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T20:28:38Z	--- 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] ## Training procedure ### Framework versions - PEFT 0.6.2
Christos-14/ppo-LunarLander-v2	Christos-14	2023-12-03T20:27:00Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T20:26:43Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.32 +/- 17.58 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
userzyzz/hermes-vision-gguf	userzyzz	2023-12-03T20:25:36Z	88	1	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2023-12-03T19:24:00Z	# Broken GGUF Tried to force convert an unsupported model. Unsurprisingly, it is completely broken. Does not run.
prushton/path_to_saved_model	prushton	2023-12-03T20:21:43Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-02T19:54:19Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - prushton/path_to_saved_model This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
stoves/Andreea_Lazu	stoves	2023-12-03T20:14:45Z	2	1	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-11-10T12:32:52Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of gjdfophge person tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
hkivancoral/smids_1x_beit_base_rms_0001_fold3	hkivancoral	2023-12-03T20:13:55Z	15	0	transformers	[ "transformers", "tensorboard", "safetensors", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-base-patch16-224", "base_model:finetune:microsoft/beit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-02T11:19:21Z	--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_1x_beit_base_rms_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: 0.7133333333333334 --- <!-- 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_1x_beit_base_rms_0001_fold3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7846 - Accuracy: 0.7133 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1199 \| 1.0 \| 75 \| 1.1044 \| 0.325 \| \| 1.1759 \| 2.0 \| 150 \| 1.1239 \| 0.47 \| \| 1.1465 \| 3.0 \| 225 \| 0.9168 \| 0.5 \| \| 0.8955 \| 4.0 \| 300 \| 0.8917 \| 0.5017 \| \| 0.8948 \| 5.0 \| 375 \| 0.8301 \| 0.5533 \| \| 0.9774 \| 6.0 \| 450 \| 0.8272 \| 0.5467 \| \| 0.8001 \| 7.0 \| 525 \| 0.8058 \| 0.5567 \| \| 0.7633 \| 8.0 \| 600 \| 0.8140 \| 0.545 \| \| 0.7814 \| 9.0 \| 675 \| 0.7815 \| 0.5733 \| \| 0.8175 \| 10.0 \| 750 \| 0.7839 \| 0.5633 \| \| 0.7605 \| 11.0 \| 825 \| 0.7664 \| 0.615 \| \| 0.762 \| 12.0 \| 900 \| 0.7781 \| 0.59 \| \| 0.6797 \| 13.0 \| 975 \| 0.7875 \| 0.575 \| \| 0.7699 \| 14.0 \| 1050 \| 0.7772 \| 0.6117 \| \| 0.6167 \| 15.0 \| 1125 \| 0.8129 \| 0.585 \| \| 0.7106 \| 16.0 \| 1200 \| 0.7392 \| 0.6633 \| \| 0.7174 \| 17.0 \| 1275 \| 0.7176 \| 0.6717 \| \| 0.704 \| 18.0 \| 1350 \| 0.7772 \| 0.63 \| \| 0.6617 \| 19.0 \| 1425 \| 0.7359 \| 0.65 \| \| 0.6722 \| 20.0 \| 1500 \| 0.7009 \| 0.6783 \| \| 0.676 \| 21.0 \| 1575 \| 0.6946 \| 0.6667 \| \| 0.6441 \| 22.0 \| 1650 \| 0.7089 \| 0.6917 \| \| 0.6565 \| 23.0 \| 1725 \| 0.7160 \| 0.665 \| \| 0.6009 \| 24.0 \| 1800 \| 0.6902 \| 0.6783 \| \| 0.6592 \| 25.0 \| 1875 \| 0.7159 \| 0.665 \| \| 0.6628 \| 26.0 \| 1950 \| 0.7741 \| 0.6233 \| \| 0.6044 \| 27.0 \| 2025 \| 0.7147 \| 0.66 \| \| 0.585 \| 28.0 \| 2100 \| 0.6827 \| 0.69 \| \| 0.5831 \| 29.0 \| 2175 \| 0.6975 \| 0.6833 \| \| 0.6301 \| 30.0 \| 2250 \| 0.6815 \| 0.6633 \| \| 0.6457 \| 31.0 \| 2325 \| 0.6813 \| 0.6817 \| \| 0.6492 \| 32.0 \| 2400 \| 0.6894 \| 0.6783 \| \| 0.5418 \| 33.0 \| 2475 \| 0.7461 \| 0.6783 \| \| 0.5925 \| 34.0 \| 2550 \| 0.6773 \| 0.6933 \| \| 0.5913 \| 35.0 \| 2625 \| 0.6656 \| 0.7083 \| \| 0.5761 \| 36.0 \| 2700 \| 0.6491 \| 0.7133 \| \| 0.528 \| 37.0 \| 2775 \| 0.6784 \| 0.7 \| \| 0.5718 \| 38.0 \| 2850 \| 0.7007 \| 0.6783 \| \| 0.5083 \| 39.0 \| 2925 \| 0.6815 \| 0.7 \| \| 0.5069 \| 40.0 \| 3000 \| 0.6638 \| 0.71 \| \| 0.4838 \| 41.0 \| 3075 \| 0.6813 \| 0.7167 \| \| 0.5071 \| 42.0 \| 3150 \| 0.6709 \| 0.7183 \| \| 0.5091 \| 43.0 \| 3225 \| 0.6746 \| 0.7167 \| \| 0.4355 \| 44.0 \| 3300 \| 0.7138 \| 0.71 \| \| 0.4287 \| 45.0 \| 3375 \| 0.7080 \| 0.7133 \| \| 0.3954 \| 46.0 \| 3450 \| 0.7468 \| 0.7 \| \| 0.3389 \| 47.0 \| 3525 \| 0.7428 \| 0.7183 \| \| 0.3613 \| 48.0 \| 3600 \| 0.7469 \| 0.725 \| \| 0.388 \| 49.0 \| 3675 \| 0.7685 \| 0.7167 \| \| 0.2972 \| 50.0 \| 3750 \| 0.7846 \| 0.7133 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
galaxy78/my_awesome_wnut_model	galaxy78	2023-12-03T20:08:10Z	14	1	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-12-03T10:11:43Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: my_awesome_wnut_model results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.55 - name: Recall type: recall value: 0.37720111214087115 - name: F1 type: f1 value: 0.44749862561847165 - name: Accuracy type: accuracy value: 0.9481063520560827 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_wnut_model This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.3958 - Precision: 0.55 - Recall: 0.3772 - F1: 0.4475 - Accuracy: 0.9481 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 213 \| 0.2562 \| 0.5704 \| 0.2929 \| 0.3870 \| 0.9417 \| \| No log \| 2.0 \| 426 \| 0.2776 \| 0.5462 \| 0.3179 \| 0.4019 \| 0.9436 \| \| 0.1469 \| 3.0 \| 639 \| 0.2834 \| 0.5453 \| 0.3624 \| 0.4354 \| 0.9475 \| \| 0.1469 \| 4.0 \| 852 \| 0.3004 \| 0.5669 \| 0.3652 \| 0.4442 \| 0.9480 \| \| 0.0325 \| 5.0 \| 1065 \| 0.3360 \| 0.5858 \| 0.3735 \| 0.4561 \| 0.9482 \| \| 0.0325 \| 6.0 \| 1278 \| 0.3471 \| 0.5149 \| 0.3855 \| 0.4409 \| 0.9474 \| \| 0.0325 \| 7.0 \| 1491 \| 0.3883 \| 0.5552 \| 0.3633 \| 0.4392 \| 0.9474 \| \| 0.0117 \| 8.0 \| 1704 \| 0.3881 \| 0.5602 \| 0.3707 \| 0.4462 \| 0.9477 \| \| 0.0117 \| 9.0 \| 1917 \| 0.4008 \| 0.5582 \| 0.3689 \| 0.4442 \| 0.9478 \| \| 0.0051 \| 10.0 \| 2130 \| 0.3958 \| 0.55 \| 0.3772 \| 0.4475 \| 0.9481 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow22	FounderOfHuggingface	2023-12-03T20:05:28Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T20:05:24Z	--- 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. 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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] ## Training procedure ### Framework versions - PEFT 0.6.2
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow21	FounderOfHuggingface	2023-12-03T19:53:45Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T19:53:40Z	--- 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. 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] ## Training procedure ### Framework versions - PEFT 0.6.2
entaroid/xlm-roberta-base-finetuned-panx-de	entaroid	2023-12-03T19:51:08Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-12-03T17:17:56Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.861220080732276 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1394 - F1: 0.8612 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 525 \| 0.1621 \| 0.8184 \| \| No log \| 2.0 \| 1050 \| 0.1390 \| 0.8502 \| \| No log \| 3.0 \| 1575 \| 0.1394 \| 0.8612 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.10.1 - Tokenizers 0.13.3
jerber/ppo-LunarLander-v2	jerber	2023-12-03T19:49:31Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T19:49:13Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.98 +/- 22.50 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
annabellehuether/legal-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_01wd	annabellehuether	2023-12-03T19:44:04Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:nlpaueb/legal-bert-base-uncased", "base_model:finetune:nlpaueb/legal-bert-base-uncased", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-03T18:41:27Z	--- license: cc-by-sa-4.0 base_model: nlpaueb/legal-bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: legal-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_01wd 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. --> # legal-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_01wd This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6773 - Accuracy: 0.5889 ## 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: 7 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6868 \| 1.0 \| 660 \| 0.6805 \| 0.5889 \| \| 0.6863 \| 2.0 \| 1320 \| 0.6779 \| 0.5889 \| \| 0.6839 \| 3.0 \| 1980 \| 0.6779 \| 0.5889 \| \| 0.6825 \| 4.0 \| 2640 \| 0.6812 \| 0.5889 \| \| 0.6838 \| 5.0 \| 3300 \| 0.6773 \| 0.5889 \| ### Framework versions - Transformers 4.35.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
worldboss/llama-7B-alpaca-fine-tune-unsloth	worldboss	2023-12-03T19:43:53Z	2	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "base_model:unsloth/llama-2-7b", "base_model:finetune:unsloth/llama-2-7b", "license:llama2", "endpoints_compatible", "region:us" ]	null	2023-12-03T19:07:16Z	--- license: llama2 base_model: unsloth/llama-2-7b tags: - generated_from_trainer model-index: - name: outputs 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. --> # outputs This model is a fine-tuned version of [unsloth/llama-2-7b](https://huggingface.co/unsloth/llama-2-7b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 60 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
Ibrahim2001/PPO_Lunar_Lander_v2	Ibrahim2001	2023-12-03T19:43:46Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T14:14:00Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.18 +/- 16.90 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) ```python import gymnasium as gym from huggingface_sb3 import load_from_hub from stable_baselines3 import PPO from stable_baselines3.common.evaluation import evaluate_policy from stable_baselines3.common.monitor import Monitor repo_id = "Ibrahim2001/PPO_Lunar_Lander_v2" # The repo_id filename = "ppo-LunarLander-v2.zip" # The model filename.zip # When the model was trained on Python 3.8 the pickle protocol is 5 # But Python 3.6, 3.7 use protocol 4 # In order to get compatibility we need to: # 1. Install pickle5 # 2. Create a custom empty object we pass as parameter to PPO.load() custom_objects = { "learning_rate": 0.0, "lr_schedule": lambda _: 0.0, "clip_range": lambda _: 0.0, } checkpoint = load_from_hub(repo_id, filename) model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True) eval_env = Monitor(gym.make("LunarLander-v2")) mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") ```
GMabic/Taxi-v3	GMabic	2023-12-03T19:41:38Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T19:41:37Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="GMabic/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ThuyNT03/KLTN_COQE_viT5_SAPOL_v2	ThuyNT03	2023-12-03T19:29:43Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-03T18:43:17Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: KLTN_COQE_viT5_SAPOL_v2 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. --> # KLTN_COQE_viT5_SAPOL_v2 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1
GMabic/GMabic	GMabic	2023-12-03T19:25:47Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T19:25:46Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: GMabic results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="GMabic/GMabic", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
GMabic/q-FrozenLake-v1-4x4-noSlippery	GMabic	2023-12-03T19:21:15Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T19:21:13Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="GMabic/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow18	FounderOfHuggingface	2023-12-03T19:18:50Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T19:18:46Z	--- 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] ## Training procedure ### Framework versions - PEFT 0.6.2
greathero/mit-b0-finetuned-sidewalks	greathero	2023-12-03T19:15:10Z	1	0	transformers	[ "transformers", "tf", "segformer", "generated_from_keras_callback", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	null	2023-11-04T14:59:25Z	--- license: other base_model: nvidia/mit-b0 tags: - generated_from_keras_callback model-index: - name: greathero/mit-b0-finetuned-sidewalks 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. --> # greathero/mit-b0-finetuned-sidewalks This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1964 - Validation Loss: 0.5617 - Validation Mean Iou: 0.3482 - Validation Mean Accuracy: 0.4212 - Validation Overall Accuracy: 0.8714 - Validation Accuracy Unlabeled: 0.0 - Validation Accuracy Flat-road: 0.9191 - Validation Accuracy Flat-sidewalk: 0.9583 - Validation Accuracy Flat-crosswalk: 0.5878 - Validation Accuracy Flat-cyclinglane: 0.7730 - Validation Accuracy Flat-parkingdriveway: 0.6079 - Validation Accuracy Flat-railtrack: nan - Validation Accuracy Flat-curb: 0.5577 - Validation Accuracy Human-person: 0.6959 - Validation Accuracy Human-rider: 0.2221 - Validation Accuracy Vehicle-car: 0.9300 - Validation Accuracy Vehicle-truck: 0.1310 - Validation Accuracy Vehicle-bus: 0.0 - Validation Accuracy Vehicle-tramtrain: nan - Validation Accuracy Vehicle-motorcycle: 0.0 - Validation Accuracy Vehicle-bicycle: 0.6927 - Validation Accuracy Vehicle-caravan: 0.0 - Validation Accuracy Vehicle-cartrailer: 0.0 - Validation Accuracy Construction-building: 0.9088 - Validation Accuracy Construction-door: 0.0611 - Validation Accuracy Construction-wall: 0.4825 - Validation Accuracy Construction-fenceguardrail: 0.4370 - Validation Accuracy Construction-bridge: 0.0 - Validation Accuracy Construction-tunnel: nan - Validation Accuracy Construction-stairs: 0.0 - Validation Accuracy Object-pole: 0.4339 - Validation Accuracy Object-trafficsign: 0.4711 - Validation Accuracy Object-trafficlight: 0.0 - Validation Accuracy Nature-vegetation: 0.9172 - Validation Accuracy Nature-terrain: 0.9151 - Validation Accuracy Sky: 0.9692 - Validation Accuracy Void-ground: 0.5121 - Validation Accuracy Void-dynamic: 0.0265 - Validation Accuracy Void-static: 0.2676 - Validation Accuracy Void-unclear: 0.0 - Validation Iou Unlabeled: 0.0 - Validation Iou Flat-road: 0.7902 - Validation Iou Flat-sidewalk: 0.9044 - Validation Iou Flat-crosswalk: 0.4201 - Validation Iou Flat-cyclinglane: 0.6963 - Validation Iou Flat-parkingdriveway: 0.4879 - Validation Iou Flat-railtrack: nan - Validation Iou Flat-curb: 0.4607 - Validation Iou Human-person: 0.4926 - Validation Iou Human-rider: 0.1864 - Validation Iou Vehicle-car: 0.8243 - Validation Iou Vehicle-truck: 0.1278 - Validation Iou Vehicle-bus: 0.0 - Validation Iou Vehicle-tramtrain: nan - Validation Iou Vehicle-motorcycle: 0.0 - Validation Iou Vehicle-bicycle: 0.4759 - Validation Iou Vehicle-caravan: 0.0 - Validation Iou Vehicle-cartrailer: 0.0 - Validation Iou Construction-building: 0.6973 - Validation Iou Construction-door: 0.0588 - Validation Iou Construction-wall: 0.3754 - Validation Iou Construction-fenceguardrail: 0.3760 - Validation Iou Construction-bridge: 0.0 - Validation Iou Construction-tunnel: nan - Validation Iou Construction-stairs: 0.0 - Validation Iou Object-pole: 0.3330 - Validation Iou Object-trafficsign: 0.3555 - Validation Iou Object-trafficlight: 0.0 - Validation Iou Nature-vegetation: 0.8600 - Validation Iou Nature-terrain: 0.7533 - Validation Iou Sky: 0.9288 - Validation Iou Void-ground: 0.3089 - Validation Iou Void-dynamic: 0.0223 - Validation Iou Void-static: 0.2055 - Validation Iou Void-unclear: 0.0 - Epoch: 49 ## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Validation Mean Iou \| Validation Mean Accuracy \| Validation Overall Accuracy \| Validation Accuracy Unlabeled \| Validation Accuracy Flat-road \| Validation Accuracy Flat-sidewalk \| Validation Accuracy Flat-crosswalk \| Validation Accuracy Flat-cyclinglane \| Validation Accuracy Flat-parkingdriveway \| Validation Accuracy Flat-railtrack \| Validation Accuracy Flat-curb \| Validation Accuracy Human-person \| Validation Accuracy Human-rider \| Validation Accuracy Vehicle-car \| Validation Accuracy Vehicle-truck \| Validation Accuracy Vehicle-bus \| Validation Accuracy Vehicle-tramtrain \| Validation Accuracy Vehicle-motorcycle \| Validation Accuracy Vehicle-bicycle \| Validation Accuracy Vehicle-caravan \| Validation Accuracy Vehicle-cartrailer \| Validation Accuracy Construction-building \| Validation Accuracy Construction-door \| Validation Accuracy Construction-wall \| Validation Accuracy Construction-fenceguardrail \| Validation Accuracy Construction-bridge \| Validation Accuracy Construction-tunnel \| Validation Accuracy Construction-stairs \| Validation Accuracy Object-pole \| Validation Accuracy Object-trafficsign \| Validation Accuracy Object-trafficlight \| Validation Accuracy Nature-vegetation \| Validation Accuracy Nature-terrain \| Validation Accuracy Sky \| Validation Accuracy Void-ground \| Validation Accuracy Void-dynamic \| Validation Accuracy Void-static \| Validation Accuracy Void-unclear \| Validation Iou Unlabeled \| Validation Iou Flat-road \| Validation Iou Flat-sidewalk \| Validation Iou Flat-crosswalk \| Validation Iou Flat-cyclinglane \| Validation Iou Flat-parkingdriveway \| Validation Iou Flat-railtrack \| Validation Iou Flat-curb \| Validation Iou Human-person \| Validation Iou Human-rider \| Validation Iou Vehicle-car \| Validation Iou Vehicle-truck \| Validation Iou Vehicle-bus \| Validation Iou Vehicle-tramtrain \| Validation Iou Vehicle-motorcycle \| Validation Iou Vehicle-bicycle \| Validation Iou Vehicle-caravan \| Validation Iou Vehicle-cartrailer \| Validation Iou Construction-building \| Validation Iou Construction-door \| Validation Iou Construction-wall \| Validation Iou Construction-fenceguardrail \| Validation Iou Construction-bridge \| Validation Iou Construction-tunnel \| Validation Iou Construction-stairs \| Validation Iou Object-pole \| Validation Iou Object-trafficsign \| Validation Iou Object-trafficlight \| Validation Iou Nature-vegetation \| Validation Iou Nature-terrain \| Validation Iou Sky \| Validation Iou Void-ground \| Validation Iou Void-dynamic \| Validation Iou Void-static \| Validation Iou Void-unclear \| Epoch \| 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\| 1.9172 \| 1.1456 \| 0.1406 \| 0.1941 \| 0.7310 \| 0.0 \| 0.7980 \| 0.9298 \| 0.0051 \| 0.2528 \| 0.0049 \| nan \| 0.0001 \| 0.0458 \| 0.0 \| 0.8315 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.7839 \| 0.0 \| 0.0210 \| 0.0370 \| 0.0 \| nan \| 0.0 \| 0.0058 \| 0.0 \| 0.0 \| 0.9028 \| 0.6924 \| 0.9004 \| 0.0 \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.4633 \| 0.7662 \| 0.0051 \| 0.2317 \| 0.0048 \| 0.0 \| 0.0001 \| 0.0323 \| 0.0 \| 0.6396 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.5243 \| 0.0 \| 0.0206 \| 0.0348 \| 0.0 \| nan \| 0.0 \| 0.0054 \| 0.0 \| 0.0 \| 0.7094 \| 0.5497 \| 0.7916 \| 0.0 \| 0.0 \| 0.0002 \| 0.0 \| 0 \| \| 1.0247 \| 0.8848 \| 0.1918 \| 0.2336 \| 0.7698 \| 0.0 \| 0.6997 \| 0.9651 \| 0.4394 \| 0.4069 \| 0.1304 \| nan \| 0.0606 \| 0.0601 \| 0.0 \| 0.8688 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0097 \| 0.0 \| 0.0 \| 0.8430 \| 0.0 \| 0.2444 \| 0.1184 \| 0.0 \| nan \| 0.0 \| 0.0102 \| 0.0 \| 0.0 \| 0.9227 \| 0.7934 \| 0.9018 \| 0.0 \| 0.0 \| 0.0014 \| 0.0 \| 0.0 \| 0.5611 \| 0.7751 \| 0.4093 \| 0.3588 \| 0.1102 \| nan \| 0.0550 \| 0.0513 \| 0.0 \| 0.6674 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0097 \| 0.0 \| 0.0 \| 0.5662 \| 0.0 \| 0.2011 \| 0.1101 \| 0.0 \| nan \| 0.0 \| 0.0101 \| 0.0 \| 0.0 \| 0.7593 \| 0.6549 \| 0.8360 \| 0.0 \| 0.0 \| 0.0014 \| 0.0 \| 1 \| \| 0.8452 \| 0.7838 \| 0.2203 \| 0.2738 \| 0.7971 \| 0.0 \| 0.8465 \| 0.9296 \| 0.5352 \| 0.5975 \| 0.2817 \| nan \| 0.1721 \| 0.3577 \| 0.0 \| 0.8242 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0999 \| 0.0 \| 0.0 \| 0.8508 \| 0.0 \| 0.3727 \| 0.1481 \| 0.0 \| nan \| 0.0 \| 0.0712 \| 0.0 \| 0.0 \| 0.9379 \| 0.8088 \| 0.9242 \| 0.0 \| 0.0 \| 0.0027 \| 0.0 \| 0.0 \| 0.5691 \| 0.8475 \| 0.3104 \| 0.5182 \| 0.2163 \| nan \| 0.1416 \| 0.2415 \| 0.0 \| 0.7082 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0997 \| 0.0 \| 0.0 \| 0.5843 \| 0.0 \| 0.2783 \| 0.1415 \| 0.0 \| nan \| 0.0 \| 0.0679 \| 0.0 \| 0.0 \| 0.7789 \| 0.6955 \| 0.8480 \| 0.0 \| 0.0 \| 0.0026 \| 0.0 \| 2 \| \| 0.7393 \| 0.7194 \| 0.2443 \| 0.2959 \| 0.8084 \| 0.0 \| 0.7601 \| 0.9601 \| 0.3925 \| 0.5485 \| 0.4444 \| nan \| 0.2765 \| 0.4666 \| 0.0 \| 0.8905 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3695 \| 0.0 \| 0.0 \| 0.8608 \| 0.0 \| 0.4003 \| 0.2319 \| 0.0 \| nan \| 0.0 \| 0.1120 \| 0.0 \| 0.0 \| 0.9064 \| 0.8793 \| 0.9268 \| 0.0 \| 0.0 \| 0.0434 \| 0.0 \| 0.0 \| 0.6189 \| 0.8385 \| 0.3858 \| 0.4982 \| 0.2872 \| nan \| 0.2269 \| 0.2991 \| 0.0 \| 0.7134 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3144 \| 0.0 \| 0.0 \| 0.6105 \| 0.0 \| 0.3110 \| 0.2177 \| 0.0 \| nan \| 0.0 \| 0.1034 \| 0.0 \| 0.0 \| 0.7932 \| 0.6956 \| 0.8646 \| 0.0 \| 0.0 \| 0.0385 \| 0.0 \| 3 \| \| 0.6723 \| 0.6423 \| 0.2585 \| 0.3200 \| 0.8243 \| 0.0 \| 0.8379 \| 0.9392 \| 0.5390 \| 0.6706 \| 0.5206 \| nan \| 0.3683 \| 0.6301 \| 0.0 \| 0.8838 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3830 \| 0.0 \| 0.0 \| 0.8862 \| 0.0 \| 0.3901 \| 0.2524 \| 0.0 \| nan \| 0.0 \| 0.1545 \| 0.0 \| 0.0 \| 0.9096 \| 0.8846 \| 0.9254 \| 0.0 \| 0.0001 \| 0.0643 \| 0.0 \| 0.0 \| 0.6716 \| 0.8658 \| 0.3985 \| 0.5764 \| 0.3400 \| nan \| 0.2731 \| 0.3130 \| 0.0 \| 0.7325 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3255 \| 0.0 \| 0.0 \| 0.6179 \| 0.0 \| 0.3338 \| 0.2397 \| 0.0 \| nan \| 0.0 \| 0.1381 \| 0.0 \| 0.0 \| 0.8056 \| 0.7102 \| 0.8723 \| 0.0 \| 0.0001 \| 0.0578 \| 0.0 \| 4 \| \| 0.6205 \| 0.6604 \| 0.2592 \| 0.3188 \| 0.8256 \| 0.0 \| 0.8237 \| 0.9528 \| 0.5500 \| 0.5608 \| 0.6090 \| nan \| 0.3591 \| 0.4741 \| 0.0 \| 0.8835 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4288 \| 0.0 \| 0.0 \| 0.9049 \| 0.0 \| 0.4296 \| 0.2780 \| 0.0 \| nan \| 0.0 \| 0.2117 \| 0.0 \| 0.0 \| 0.9279 \| 0.8381 \| 0.9277 \| 0.0 \| 0.0003 \| 0.0425 \| 0.0 \| 0.0 \| 0.6967 \| 0.8550 \| 0.2920 \| 0.5056 \| 0.3663 \| nan \| 0.2893 \| 0.3073 \| 0.0 \| 0.7447 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.3632 \| 0.0 \| 0.0 \| 0.6182 \| 0.0 \| 0.3474 \| 0.2593 \| 0.0 \| nan \| 0.0 \| 0.1780 \| 0.0 \| 0.0 \| 0.8229 \| 0.7302 \| 0.8786 \| 0.0 \| 0.0003 \| 0.0396 \| 0.0 \| 5 \| \| 0.5839 \| 0.6139 \| 0.2738 \| 0.3325 \| 0.8330 \| 0.0 \| 0.8190 \| 0.9635 \| 0.5040 \| 0.6479 \| 0.5465 \| nan \| 0.3405 \| 0.5509 \| 0.0 \| 0.8938 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5717 \| 0.0 \| 0.0 \| 0.8895 \| 0.0 \| 0.4915 \| 0.3136 \| 0.0 \| nan \| 0.0 \| 0.2540 \| 0.0 \| 0.0 \| 0.9038 \| 0.8733 \| 0.9399 \| 0.0 \| 0.0039 \| 0.1320 \| 0.0 \| 0.0 \| 0.7100 \| 0.8592 \| 0.4158 \| 0.5694 \| 0.3564 \| nan \| 0.2854 \| 0.3518 \| 0.0 \| 0.7495 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4226 \| 0.0 \| 0.0 \| 0.6419 \| 0.0 \| 0.3810 \| 0.2908 \| 0.0 \| nan \| 0.0 \| 0.1984 \| 0.0 \| 0.0 \| 0.8237 \| 0.7147 \| 0.8798 \| 0.0 \| 0.0038 \| 0.1073 \| 0.0 \| 6 \| \| 0.5377 \| 0.5787 \| 0.2763 \| 0.3397 \| 0.8374 \| 0.0 \| 0.8782 \| 0.9396 \| 0.5571 \| 0.6511 \| 0.5741 \| nan \| 0.4205 \| 0.5055 \| 0.0 \| 0.8842 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5905 \| 0.0 \| 0.0 \| 0.8932 \| 0.0 \| 0.4833 \| 0.3608 \| 0.0 \| nan \| 0.0 \| 0.2777 \| 0.0018 \| 0.0 \| 0.9253 \| 0.8693 \| 0.9532 \| 0.0 \| 0.0025 \| 0.1022 \| 0.0 \| 0.0 \| 0.6996 \| 0.8736 \| 0.3436 \| 0.5970 \| 0.3762 \| nan \| 0.3136 \| 0.3388 \| 0.0 \| 0.7668 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4413 \| 0.0 \| 0.0 \| 0.6375 \| 0.0 \| 0.3730 \| 0.3209 \| 0.0 \| nan \| 0.0 \| 0.2190 \| 0.0018 \| 0.0 \| 0.8323 \| 0.7339 \| 0.8797 \| 0.0 \| 0.0024 \| 0.0908 \| 0.0 \| 7 \| \| 0.5102 \| 0.5413 \| 0.2852 \| 0.3417 \| 0.8472 \| 0.0 \| 0.8733 \| 0.9482 \| 0.4978 \| 0.7936 \| 0.4843 \| nan \| 0.3768 \| 0.5607 \| 0.0 \| 0.8764 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.5866 \| 0.0 \| 0.0 \| 0.8863 \| 0.0 \| 0.4769 \| 0.3574 \| 0.0 \| nan \| 0.0 \| 0.2680 \| 0.0013 \| 0.0 \| 0.9395 \| 0.8760 \| 0.9478 \| 0.0 \| 0.0045 \| 0.1792 \| 0.0 \| 0.0 \| 0.7354 \| 0.8816 \| 0.4221 \| 0.6823 \| 0.3631 \| nan \| 0.3052 \| 0.3647 \| 0.0 \| 0.7699 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4497 \| 0.0 \| 0.0 \| 0.6563 \| 0.0 \| 0.3737 \| 0.3235 \| 0.0 \| nan \| 0.0 \| 0.2125 \| 0.0013 \| 0.0 \| 0.8273 \| 0.7161 \| 0.8933 \| 0.0 \| 0.0044 \| 0.1451 \| 0.0 \| 8 \| \| 0.4841 \| 0.5317 \| 0.2880 \| 0.3513 \| 0.8505 \| 0.0 \| 0.8846 \| 0.9516 \| 0.5489 \| 0.8002 \| 0.5770 \| nan \| 0.3905 \| 0.5536 \| 0.0 \| 0.8946 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6090 \| 0.0 \| 0.0 \| 0.8865 \| 0.0 \| 0.4967 \| 0.3616 \| 0.0 \| nan \| 0.0 \| 0.3103 \| 0.0129 \| 0.0 \| 0.9036 \| 0.9037 \| 0.9505 \| 0.0 \| 0.0032 \| 0.2026 \| 0.0 \| 0.0 \| 0.7536 \| 0.8840 \| 0.3437 \| 0.7198 \| 0.4150 \| nan \| 0.3262 \| 0.3606 \| 0.0 \| 0.7672 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4336 \| 0.0 \| 0.0 \| 0.6606 \| 0.0 \| 0.3724 \| 0.3274 \| 0.0 \| nan \| 0.0 \| 0.2363 \| 0.0129 \| 0.0 \| 0.8265 \| 0.7080 \| 0.8948 \| 0.0 \| 0.0030 \| 0.1696 \| 0.0 \| 9 \| \| 0.4663 \| 0.5253 \| 0.2903 \| 0.3504 \| 0.8531 \| 0.0 \| 0.8632 \| 0.9503 \| 0.4520 \| 0.8542 \| 0.5251 \| nan \| 0.3878 \| 0.5854 \| 0.0 \| 0.9041 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6516 \| 0.0 \| 0.0 \| 0.8830 \| 0.0 \| 0.5018 \| 0.3624 \| 0.0 \| nan \| 0.0 \| 0.3190 \| 0.0143 \| 0.0 \| 0.9290 \| 0.8950 \| 0.9495 \| 0.0 \| 0.0025 \| 0.1835 \| 0.0 \| 0.0 \| 0.7467 \| 0.8831 \| 0.3961 \| 0.7313 \| 0.3927 \| nan \| 0.3174 \| 0.3708 \| 0.0 \| 0.7719 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4393 \| 0.0 \| 0.0 \| 0.6648 \| 0.0 \| 0.3684 \| 0.3338 \| 0.0 \| nan \| 0.0 \| 0.2396 \| 0.0143 \| 0.0 \| 0.8406 \| 0.7302 \| 0.9009 \| 0.0 \| 0.0024 \| 0.1464 \| 0.0 \| 10 \| \| 0.4466 \| 0.5667 \| 0.2867 \| 0.3460 \| 0.8459 \| 0.0 \| 0.8852 \| 0.9554 \| 0.5124 \| 0.6566 \| 0.6231 \| nan \| 0.3960 \| 0.5075 \| 0.0 \| 0.8932 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6307 \| 0.0 \| 0.0 \| 0.9193 \| 0.0 \| 0.5021 \| 0.3920 \| 0.0 \| nan \| 0.0 \| 0.3301 \| 0.0299 \| 0.0 \| 0.9236 \| 0.8399 \| 0.9567 \| 0.0 \| 0.0096 \| 0.1091 \| 0.0 \| 0.0 \| 0.7247 \| 0.8746 \| 0.4095 \| 0.6009 \| 0.4343 \| nan \| 0.3309 \| 0.3346 \| 0.0 \| 0.7803 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4490 \| 0.0 \| 0.0 \| 0.6423 \| 0.0 \| 0.3891 \| 0.3310 \| 0.0 \| nan \| 0.0 \| 0.2522 \| 0.0299 \| 0.0 \| 0.8416 \| 0.7461 \| 0.8986 \| 0.0 \| 0.0085 \| 0.0979 \| 0.0 \| 11 \| \| 0.4201 \| 0.5216 \| 0.2959 \| 0.3557 \| 0.8570 \| 0.0 \| 0.8772 \| 0.9565 \| 0.4502 \| 0.7909 \| 0.5969 \| nan \| 0.4384 \| 0.5878 \| 0.0028 \| 0.9030 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6439 \| 0.0 \| 0.0 \| 0.8963 \| 0.0 \| 0.5378 \| 0.3621 \| 0.0 \| nan \| 0.0 \| 0.3043 \| 0.0282 \| 0.0 \| 0.9206 \| 0.8979 \| 0.9562 \| 0.0 \| 0.0015 \| 0.2284 \| 0.0 \| 0.0 \| 0.7601 \| 0.8843 \| 0.3895 \| 0.7020 \| 0.4255 \| nan \| 0.3655 \| 0.3689 \| 0.0028 \| 0.7794 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4512 \| 0.0 \| 0.0 \| 0.6719 \| 0.0 \| 0.3923 \| 0.3293 \| 0.0 \| nan \| 0.0 \| 0.2432 \| 0.0281 \| 0.0 \| 0.8495 \| 0.7423 \| 0.9015 \| 0.0 \| 0.0014 \| 0.1815 \| 0.0 \| 12 \| \| 0.4155 \| 0.5468 \| 0.2913 \| 0.3606 \| 0.8525 \| 0.0 \| 0.8460 \| 0.9363 \| 0.5329 \| 0.8216 \| 0.6785 \| nan \| 0.4931 \| 0.5219 \| 0.0076 \| 0.9172 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6537 \| 0.0 \| 0.0 \| 0.9282 \| 0.0 \| 0.5048 \| 0.3783 \| 0.0 \| nan \| 0.0 \| 0.3354 \| 0.0529 \| 0.0 \| 0.9057 \| 0.9100 \| 0.9441 \| 0.0 \| 0.0143 \| 0.1573 \| 0.0 \| 0.0 \| 0.7598 \| 0.8861 \| 0.2428 \| 0.7322 \| 0.4286 \| nan \| 0.3793 \| 0.3453 \| 0.0076 \| 0.7781 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4537 \| 0.0 \| 0.0 \| 0.6496 \| 0.0 \| 0.3874 \| 0.3258 \| 0.0 \| nan \| 0.0 \| 0.2583 \| 0.0526 \| 0.0 \| 0.8479 \| 0.7370 \| 0.9009 \| 0.0 \| 0.0129 \| 0.1355 \| 0.0 \| 13 \| \| 0.3892 \| 0.5175 \| 0.3004 \| 0.3618 \| 0.8613 \| 0.0 \| 0.9000 \| 0.9479 \| 0.4376 \| 0.8710 \| 0.6180 \| nan \| 0.4245 \| 0.6868 \| 0.0094 \| 0.8863 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6129 \| 0.0 \| 0.0 \| 0.9115 \| 0.0 \| 0.4900 \| 0.3562 \| 0.0 \| nan \| 0.0 \| 0.3215 \| 0.1297 \| 0.0 \| 0.9228 \| 0.9045 \| 0.9555 \| 0.0 \| 0.0057 \| 0.1855 \| 0.0 \| 0.0 \| 0.7676 \| 0.8928 \| 0.3316 \| 0.7655 \| 0.4191 \| nan \| 0.3657 \| 0.3880 \| 0.0094 \| 0.7938 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4393 \| 0.0 \| 0.0 \| 0.6601 \| 0.0 \| 0.4075 \| 0.3296 \| 0.0 \| nan \| 0.0 \| 0.2502 \| 0.1287 \| 0.0 \| 0.8530 \| 0.7461 \| 0.9041 \| 0.0 \| 0.0054 \| 0.1551 \| 0.0 \| 14 \| \| 0.3774 \| 0.5600 \| 0.2950 \| 0.3518 \| 0.8528 \| 0.0 \| 0.8595 \| 0.9628 \| 0.2409 \| 0.7379 \| 0.6461 \| nan \| 0.4255 \| 0.5880 \| 0.0124 \| 0.8996 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.6380 \| 0.0 \| 0.0 \| 0.9198 \| 0.0 \| 0.5246 \| 0.3877 \| 0.0 \| nan \| 0.0 \| 0.3241 \| 0.1567 \| 0.0 \| 0.9042 \| 0.8958 \| 0.9545 \| 0.0 \| 0.0130 \| 0.1680 \| 0.0 \| 0.0 \| 0.7599 \| 0.8747 \| 0.2287 \| 0.6677 \| 0.4078 \| nan \| 0.3595 \| 0.3946 \| 0.0124 \| 0.7930 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.4603 \| 0.0 \| 0.0 \| 0.6603 \| 0.0 \| 0.4156 \| 0.3319 \| 0.0 \| nan \| 0.0 \| 0.2643 \| 0.1535 \| 0.0 \| 0.8453 \| 0.7478 \| 0.9060 \| 0.0 \| 0.0121 \| 0.1449 \| 0.0 \| 15 \| \| 0.3713 \| 0.5397 \| 0.3053 \| 0.3675 \| 0.8565 \| 0.0 \| 0.8845 \| 0.9473 \| 0.5039 \| 0.7752 \| 0.6646 \| nan \| 0.4841 \| 0.6128 \| 0.0313 \| 0.9142 \| 0.0029 \| 0.0 \| nan \| 0.0 \| 0.6522 \| 0.0 \| 0.0 \| 0.9018 \| 0.0054 \| 0.5113 \| 0.3701 \| 0.0 \| nan \| 0.0 \| 0.3208 \| 0.1426 \| 0.0 \| 0.9068 \| 0.9156 \| 0.9537 \| 0.0011 \| 0.0201 \| 0.2376 \| 0.0 \| 0.0 \| 0.7785 \| 0.8791 \| 0.4051 \| 0.7029 \| 0.4544 \| nan \| 0.3946 \| 0.3946 \| 0.0311 \| 0.7865 \| 0.0029 \| 0.0 \| nan \| 0.0 \| 0.4606 \| 0.0 \| 0.0 \| 0.6823 \| 0.0053 \| 0.3835 \| 0.3334 \| 0.0 \| nan \| 0.0 \| 0.2581 \| 0.1383 \| 0.0 \| 0.8433 \| 0.7196 \| 0.9061 \| 0.0007 \| 0.0182 \| 0.1920 \| 0.0 \| 16 \| \| 0.3502 \| 0.5369 \| 0.3065 \| 0.3627 \| 0.8594 \| 0.0 \| 0.9004 \| 0.9587 \| 0.4626 \| 0.7253 \| 0.5631 \| nan \| 0.5189 \| 0.5677 \| 0.0020 \| 0.9280 \| 0.0247 \| 0.0 \| nan \| 0.0 \| 0.6415 \| 0.0 \| 0.0 \| 0.8986 \| 0.0008 \| 0.5035 \| 0.3569 \| 0.0 \| nan \| 0.0 \| 0.3524 \| 0.2297 \| 0.0 \| 0.9422 \| 0.8659 \| 0.9595 \| 0.0 \| 0.0117 \| 0.1931 \| 0.0 \| 0.0 \| 0.7554 \| 0.8846 \| 0.4080 \| 0.6569 \| 0.4399 \| nan \| 0.4082 \| 0.3910 \| 0.0020 \| 0.7829 \| 0.0247 \| 0.0 \| nan \| 0.0 \| 0.4650 \| 0.0 \| 0.0 \| 0.6756 \| 0.0008 \| 0.4035 \| 0.3249 \| 0.0 \| nan \| 0.0 \| 0.2717 \| 0.2187 \| 0.0 \| 0.8550 \| 0.7560 \| 0.9070 \| 0.0 \| 0.0113 \| 0.1663 \| 0.0 \| 17 \| \| 0.3405 \| 0.5502 \| 0.3062 \| 0.3636 \| 0.8594 \| 0.0 \| 0.9166 \| 0.9532 \| 0.4142 \| 0.7531 \| 0.6138 \| nan \| 0.4230 \| 0.5584 \| 0.0258 \| 0.8984 \| 0.0044 \| 0.0 \| nan \| 0.0 \| 0.6498 \| 0.0 \| 0.0 \| 0.9063 \| 0.0057 \| 0.5072 \| 0.3758 \| 0.0 \| nan \| 0.0 \| 0.3565 \| 0.2564 \| 0.0 \| 0.9187 \| 0.9228 \| 0.9622 \| 0.0021 \| 0.0172 \| 0.1947 \| 0.0 \| 0.0 \| 0.7390 \| 0.8967 \| 0.3897 \| 0.6933 \| 0.4355 \| nan \| 0.3709 \| 0.3863 \| 0.0256 \| 0.7977 \| 0.0044 \| 0.0 \| nan \| 0.0 \| 0.4553 \| 0.0 \| 0.0 \| 0.6760 \| 0.0056 \| 0.3945 \| 0.3398 \| 0.0 \| nan \| 0.0 \| 0.2704 \| 0.2408 \| 0.0 \| 0.8494 \| 0.7349 \| 0.9070 \| 0.0020 \| 0.0160 \| 0.1688 \| 0.0 \| 18 \| \| 0.3311 \| 0.5097 \| 0.3140 \| 0.3734 \| 0.8628 \| 0.0 \| 0.8703 \| 0.9682 \| 0.4415 \| 0.8131 \| 0.5973 \| nan \| 0.4837 \| 0.6228 \| 0.0409 \| 0.9119 \| 0.0300 \| 0.0 \| nan \| 0.0 \| 0.6709 \| 0.0 \| 0.0 \| 0.8819 \| 0.0123 \| 0.5047 \| 0.4101 \| 0.0 \| nan \| 0.0 \| 0.3714 \| 0.2680 \| 0.0 \| 0.9125 \| 0.9120 \| 0.9623 \| 0.0 \| 0.0194 \| 0.2439 \| 0.0 \| 0.0 \| 0.7928 \| 0.8843 \| 0.3726 \| 0.7384 \| 0.4594 \| nan \| 0.3924 \| 0.4183 \| 0.0407 \| 0.8016 \| 0.0300 \| 0.0 \| nan \| 0.0 \| 0.4553 \| 0.0 \| 0.0 \| 0.6842 \| 0.0119 \| 0.3877 \| 0.3679 \| 0.0 \| nan \| 0.0 \| 0.2747 \| 0.2447 \| 0.0 \| 0.8427 \| 0.7257 \| 0.9099 \| 0.0 \| 0.0182 \| 0.1957 \| 0.0 \| 19 \| \| 0.3200 \| 0.5690 \| 0.3125 \| 0.3727 \| 0.8588 \| 0.0 \| 0.8760 \| 0.9569 \| 0.4469 \| 0.6968 \| 0.7124 \| nan \| 0.4744 \| 0.6537 \| 0.0381 \| 0.9262 \| 0.0226 \| 0.0 \| nan \| 0.0 \| 0.6176 \| 0.0 \| 0.0 \| 0.8978 \| 0.0201 \| 0.5154 \| 0.3644 \| 0.0 \| nan \| 0.0 \| 0.3458 \| 0.3278 \| 0.0 \| 0.9375 \| 0.8824 \| 0.9608 \| 0.0 \| 0.0238 \| 0.2276 \| 0.0 \| 0.0 \| 0.7867 \| 0.8779 \| 0.3945 \| 0.6375 \| 0.4626 \| nan \| 0.3969 \| 0.4168 \| 0.0376 \| 0.7872 \| 0.0226 \| 0.0 \| nan \| 0.0 \| 0.4612 \| 0.0 \| 0.0 \| 0.6770 \| 0.0192 \| 0.4005 \| 0.3394 \| 0.0 \| nan \| 0.0 \| 0.2703 \| 0.2821 \| 0.0 \| 0.8552 \| 0.7527 \| 0.9104 \| 0.0 \| 0.0219 \| 0.1902 \| 0.0 \| 20 \| \| 0.3120 \| 0.5155 \| 0.3151 \| 0.3760 \| 0.8626 \| 0.0 \| 0.8875 \| 0.9659 \| 0.4640 \| 0.7236 \| 0.6301 \| nan \| 0.4876 \| 0.5926 \| 0.0416 \| 0.9255 \| 0.0116 \| 0.0 \| nan \| 0.0 \| 0.6732 \| 0.0 \| 0.0 \| 0.9028 \| 0.0270 \| 0.5107 \| 0.4032 \| 0.0 \| nan \| 0.0 \| 0.3817 \| 0.3335 \| 0.0 \| 0.9350 \| 0.8668 \| 0.9556 \| 0.0012 \| 0.0434 \| 0.2684 \| 0.0 \| 0.0 \| 0.7974 \| 0.8797 \| 0.3892 \| 0.6598 \| 0.4580 \| nan \| 0.4000 \| 0.3794 \| 0.0414 \| 0.7968 \| 0.0116 \| 0.0 \| nan \| 0.0 \| 0.4536 \| 0.0 \| 0.0 \| 0.6872 \| 0.0259 \| 0.4002 \| 0.3626 \| 0.0 \| nan \| 0.0 \| 0.2821 \| 0.2841 \| 0.0 \| 0.8603 \| 0.7609 \| 0.9128 \| 0.0011 \| 0.0331 \| 0.2076 \| 0.0 \| 21 \| \| 0.3017 \| 0.5201 \| 0.3199 \| 0.3827 \| 0.8670 \| 0.0 \| 0.9120 \| 0.9521 \| 0.5413 \| 0.8270 \| 0.6610 \| nan \| 0.4946 \| 0.5627 \| 0.0423 \| 0.9207 \| 0.0336 \| 0.0 \| nan \| 0.0 \| 0.6437 \| 0.0 \| 0.0 \| 0.9003 \| 0.0247 \| 0.5331 \| 0.4028 \| 0.0 \| nan \| 0.0 \| 0.3737 \| 0.3892 \| 0.0 \| 0.9171 \| 0.9109 \| 0.9616 \| 0.0146 \| 0.0184 \| 0.2092 \| 0.0 \| 0.0 \| 0.7717 \| 0.9034 \| 0.3938 \| 0.7287 \| 0.4774 \| nan \| 0.4103 \| 0.4008 \| 0.0419 \| 0.8044 \| 0.0336 \| 0.0 \| nan \| 0.0 \| 0.4686 \| 0.0 \| 0.0 \| 0.6813 \| 0.0230 \| 0.3958 \| 0.3564 \| 0.0 \| nan \| 0.0 \| 0.2823 \| 0.3297 \| 0.0 \| 0.8547 \| 0.7570 \| 0.9126 \| 0.0121 \| 0.0173 \| 0.1786 \| 0.0 \| 22 \| \| 0.3016 \| 0.5527 \| 0.3173 \| 0.3811 \| 0.8609 \| 0.0 \| 0.8912 \| 0.9593 \| 0.4638 \| 0.6868 \| 0.7110 \| nan \| 0.4653 \| 0.6380 \| 0.0447 \| 0.9197 \| 0.0362 \| 0.0 \| nan \| 0.0 \| 0.6737 \| 0.0 \| 0.0 \| 0.8998 \| 0.0303 \| 0.5386 \| 0.4095 \| 0.0 \| nan \| 0.0 \| 0.3663 \| 0.3935 \| 0.0 \| 0.9284 \| 0.8910 \| 0.9618 \| 0.0000 \| 0.0297 \| 0.2570 \| 0.0 \| 0.0 \| 0.7536 \| 0.8893 \| 0.3672 \| 0.6440 \| 0.4740 \| nan \| 0.3968 \| 0.4275 \| 0.0444 \| 0.7999 \| 0.0361 \| 0.0 \| nan \| 0.0 \| 0.4593 \| 0.0 \| 0.0 \| 0.6930 \| 0.0291 \| 0.3877 \| 0.3651 \| 0.0 \| nan \| 0.0 \| 0.2839 \| 0.3360 \| 0.0 \| 0.8568 \| 0.7558 \| 0.9154 \| 0.0000 \| 0.0277 \| 0.2098 \| 0.0 \| 23 \| \| 0.2903 \| 0.5091 \| 0.3204 \| 0.3803 \| 0.8666 \| 0.0 \| 0.9124 \| 0.9512 \| 0.5347 \| 0.8343 \| 0.6212 \| nan \| 0.5144 \| 0.5947 \| 0.0513 \| 0.9161 \| 0.0387 \| 0.0 \| nan \| 0.0 \| 0.6167 \| 0.0 \| 0.0 \| 0.9043 \| 0.0255 \| 0.4947 \| 0.3443 \| 0.0 \| nan \| 0.0 \| 0.3727 \| 0.3979 \| 0.0 \| 0.9229 \| 0.9170 \| 0.9584 \| 0.0134 \| 0.0171 \| 0.2150 \| 0.0 \| 0.0 \| 0.8085 \| 0.8943 \| 0.4187 \| 0.7402 \| 0.4403 \| nan \| 0.4257 \| 0.4229 \| 0.0506 \| 0.8021 \| 0.0384 \| 0.0 \| nan \| 0.0 \| 0.4653 \| 0.0 \| 0.0 \| 0.6774 \| 0.0241 \| 0.3744 \| 0.3237 \| 0.0 \| nan \| 0.0 \| 0.2837 \| 0.3375 \| 0.0 \| 0.8534 \| 0.7470 \| 0.9170 \| 0.0119 \| 0.0165 \| 0.1784 \| 0.0 \| 24 \| \| 0.2869 \| 0.5304 \| 0.3208 \| 0.3853 \| 0.8631 \| 0.0 \| 0.9204 \| 0.9461 \| 0.5684 \| 0.7963 \| 0.5569 \| nan \| 0.5264 \| 0.6297 \| 0.0769 \| 0.9035 \| 0.0469 \| 0.0 \| nan \| 0.0 \| 0.6724 \| 0.0 \| 0.0 \| 0.9134 \| 0.0107 \| 0.4920 \| 0.3848 \| 0.0 \| nan \| 0.0 \| 0.3858 \| 0.4485 \| 0.0 \| 0.9322 \| 0.8984 \| 0.9576 \| 0.0506 \| 0.0301 \| 0.1830 \| 0.0 \| 0.0 \| 0.7513 \| 0.9008 \| 0.4016 \| 0.7108 \| 0.4265 \| nan \| 0.4086 \| 0.4314 \| 0.0752 \| 0.8133 \| 0.0455 \| 0.0 \| nan \| 0.0 \| 0.4575 \| 0.0 \| 0.0 \| 0.6703 \| 0.0105 \| 0.4124 \| 0.3496 \| 0.0 \| nan \| 0.0 \| 0.2943 \| 0.3631 \| 0.0 \| 0.8537 \| 0.7527 \| 0.9175 \| 0.0397 \| 0.0272 \| 0.1531 \| 0.0 \| 25 \| \| 0.2870 \| 0.6210 \| 0.3239 \| 0.3867 \| 0.8601 \| 0.0 \| 0.9226 \| 0.9662 \| 0.5159 \| 0.6950 \| 0.5032 \| nan \| 0.4978 \| 0.5125 \| 0.0764 \| 0.9003 \| 0.0567 \| 0.0 \| nan \| 0.0 \| 0.6650 \| 0.0 \| 0.0 \| 0.9188 \| 0.0165 \| 0.4980 \| 0.4263 \| 0.0 \| nan \| 0.0 \| 0.3906 \| 0.4679 \| 0.0 \| 0.9084 \| 0.9043 \| 0.9576 \| 0.3333 \| 0.0473 \| 0.1939 \| 0.0 \| 0.0 \| 0.7746 \| 0.8841 \| 0.4054 \| 0.6361 \| 0.4274 \| nan \| 0.4203 \| 0.3645 \| 0.0747 \| 0.7990 \| 0.0559 \| 0.0 \| nan \| 0.0 \| 0.4630 \| 0.0 \| 0.0 \| 0.6670 \| 0.0159 \| 0.4031 \| 0.3693 \| 0.0 \| nan \| 0.0 \| 0.2942 \| 0.3689 \| 0.0 \| 0.8547 \| 0.7547 \| 0.9169 \| 0.2169 \| 0.0353 \| 0.1634 \| 0.0 \| 26 \| \| 0.2759 \| 0.5174 \| 0.3291 \| 0.3943 \| 0.8685 \| 0.0 \| 0.9123 \| 0.9615 \| 0.3826 \| 0.8113 \| 0.5876 \| nan \| 0.5130 \| 0.6677 \| 0.0862 \| 0.9153 \| 0.0635 \| 0.0 \| nan \| 0.0 \| 0.6666 \| 0.0 \| 0.0 \| 0.8736 \| 0.0716 \| 0.5387 \| 0.4437 \| 0.0 \| nan \| 0.0 \| 0.4190 \| 0.4781 \| 0.0 \| 0.9194 \| 0.9174 \| 0.9674 \| 0.1048 \| 0.0253 \| 0.2898 \| 0.0 \| 0.0 \| 0.8047 \| 0.8922 \| 0.3331 \| 0.7338 \| 0.4635 \| nan \| 0.4187 \| 0.4413 \| 0.0833 \| 0.8020 \| 0.0626 \| 0.0 \| nan \| 0.0 \| 0.4483 \| 0.0 \| 0.0 \| 0.6970 \| 0.0629 \| 0.4012 \| 0.3818 \| 0.0 \| nan \| 0.0 \| 0.2971 \| 0.3643 \| 0.0 \| 0.8551 \| 0.7489 \| 0.9153 \| 0.0753 \| 0.0232 \| 0.2256 \| 0.0 \| 27 \| \| 0.2791 \| 0.5138 \| 0.3264 \| 0.3888 \| 0.8699 \| 0.0 \| 0.9082 \| 0.9556 \| 0.2156 \| 0.8500 \| 0.6538 \| nan \| 0.4691 \| 0.6238 \| 0.1046 \| 0.9234 \| 0.0362 \| 0.0 \| nan \| 0.0 \| 0.6441 \| 0.0 \| 0.0 \| 0.9040 \| 0.0592 \| 0.5193 \| 0.4442 \| 0.0 \| nan \| 0.0 \| 0.3749 \| 0.4394 \| 0.0 \| 0.9309 \| 0.8876 \| 0.9605 \| 0.2636 \| 0.0214 \| 0.2527 \| 0.0 \| 0.0 \| 0.8137 \| 0.8936 \| 0.1818 \| 0.7657 \| 0.4724 \| nan \| 0.4108 \| 0.4396 \| 0.1005 \| 0.8096 \| 0.0355 \| 0.0 \| nan \| 0.0 \| 0.4640 \| 0.0 \| 0.0 \| 0.6934 \| 0.0564 \| 0.3842 \| 0.3718 \| 0.0 \| nan \| 0.0 \| 0.2963 \| 0.3541 \| 0.0 \| 0.8566 \| 0.7598 \| 0.9180 \| 0.1539 \| 0.0190 \| 0.1941 \| 0.0 \| 28 \| \| 0.2728 \| 0.5249 \| 0.3275 \| 0.3886 \| 0.8672 \| 0.0 \| 0.9286 \| 0.9447 \| 0.4269 \| 0.7825 \| 0.6896 \| nan \| 0.4984 \| 0.5883 \| 0.1082 \| 0.9204 \| 0.0530 \| 0.0 \| nan \| 0.0 \| 0.6501 \| 0.0 \| 0.0 \| 0.8936 \| 0.0489 \| 0.5277 \| 0.4051 \| 0.0 \| nan \| 0.0 \| 0.3965 \| 0.5070 \| 0.0 \| 0.9425 \| 0.8916 \| 0.9645 \| 0.0 \| 0.0170 \| 0.2491 \| 0.0 \| 0.0 \| 0.7636 \| 0.8983 \| 0.3799 \| 0.7214 \| 0.4693 \| nan \| 0.4124 \| 0.4454 \| 0.1 \| 0.8081 \| 0.0510 \| 0.0 \| nan \| 0.0 \| 0.4717 \| 0.0 \| 0.0 \| 0.6914 \| 0.0449 \| 0.3906 \| 0.3600 \| 0.0 \| nan \| 0.0 \| 0.2989 \| 0.3980 \| 0.0 \| 0.8612 \| 0.7709 \| 0.9191 \| 0.0 \| 0.0160 \| 0.2067 \| 0.0 \| 29 \| \| 0.2556 \| 0.5577 \| 0.3283 \| 0.3899 \| 0.8668 \| 0.0 \| 0.8620 \| 0.9599 \| 0.4681 \| 0.8029 \| 0.6812 \| nan \| 0.5151 \| 0.6041 \| 0.1296 \| 0.9314 \| 0.0517 \| 0.0 \| nan \| 0.0 \| 0.6206 \| 0.0 \| 0.0 \| 0.9120 \| 0.0537 \| 0.5219 \| 0.3835 \| 0.0 \| nan \| 0.0 \| 0.3950 \| 0.4880 \| 0.0 \| 0.9270 \| 0.9054 \| 0.9584 \| 0.0697 \| 0.0167 \| 0.2185 \| 0.0 \| 0.0 \| 0.7991 \| 0.8851 \| 0.3274 \| 0.7335 \| 0.4697 \| nan \| 0.4272 \| 0.4588 \| 0.1172 \| 0.8067 \| 0.0492 \| 0.0 \| nan \| 0.0 \| 0.4710 \| 0.0 \| 0.0 \| 0.6861 \| 0.0498 \| 0.4047 \| 0.3498 \| 0.0 \| nan \| 0.0 \| 0.3004 \| 0.3865 \| 0.0 \| 0.8604 \| 0.7579 \| 0.9202 \| 0.0519 \| 0.0158 \| 0.1782 \| 0.0 \| 30 \| \| 0.2480 \| 0.4931 \| 0.3356 \| 0.3974 \| 0.8742 \| 0.0 \| 0.9152 \| 0.9569 \| 0.4642 \| 0.8997 \| 0.7098 \| nan \| 0.5025 \| 0.6441 \| 0.1322 \| 0.9129 \| 0.0527 \| 0.0 \| nan \| 0.0 \| 0.6643 \| 0.0 \| 0.0 \| 0.9249 \| 0.0377 \| 0.4957 \| 0.3950 \| 0.0 \| nan \| 0.0 \| 0.4079 \| 0.4869 \| 0.0 \| 0.9195 \| 0.8827 \| 0.9639 \| 0.1584 \| 0.0280 \| 0.1628 \| 0.0 \| 0.0 \| 0.8160 \| 0.9034 \| 0.3920 \| 0.8109 \| 0.5004 \| nan \| 0.4275 \| 0.4552 \| 0.1180 \| 0.8086 \| 0.0495 \| 0.0 \| nan \| 0.0 \| 0.4663 \| 0.0 \| 0.0 \| 0.6714 \| 0.0363 \| 0.4029 \| 0.3486 \| 0.0 \| nan \| 0.0 \| 0.3016 \| 0.3987 \| 0.0 \| 0.8584 \| 0.7624 \| 0.9200 \| 0.1279 \| 0.0253 \| 0.1375 \| 0.0 \| 31 \| \| 0.2467 \| 0.5091 \| 0.3348 \| 0.3964 \| 0.8752 \| 0.0 \| 0.8971 \| 0.9568 \| 0.5112 \| 0.8863 \| 0.7226 \| nan \| 0.5276 \| 0.6090 \| 0.1273 \| 0.9138 \| 0.0480 \| 0.0 \| nan \| 0.0 \| 0.6762 \| 0.0 \| 0.0 \| 0.9223 \| 0.0349 \| 0.5263 \| 0.3831 \| 0.0 \| nan \| 0.0 \| 0.3999 \| 0.4924 \| 0.0 \| 0.9358 \| 0.8774 \| 0.9652 \| 0.0667 \| 0.0259 \| 0.1795 \| 0.0 \| 0.0 \| 0.8313 \| 0.9006 \| 0.3902 \| 0.8000 \| 0.5045 \| nan \| 0.4362 \| 0.4580 \| 0.1173 \| 0.8150 \| 0.0460 \| 0.0 \| nan \| 0.0 \| 0.4735 \| 0.0 \| 0.0 \| 0.6802 \| 0.0342 \| 0.4123 \| 0.3509 \| 0.0 \| nan \| 0.0 \| 0.3049 \| 0.3913 \| 0.0 \| 0.8605 \| 0.7658 \| 0.9220 \| 0.0444 \| 0.0242 \| 0.1502 \| 0.0 \| 32 \| \| 0.2430 \| 0.5393 \| 0.3325 \| 0.3917 \| 0.8704 \| 0.0 \| 0.8953 \| 0.9632 \| 0.5036 \| 0.8081 \| 0.5941 \| nan \| 0.5130 \| 0.6436 \| 0.1049 \| 0.9329 \| 0.0502 \| 0.0 \| nan \| 0.0 \| 0.6347 \| 0.0 \| 0.0 \| 0.8882 \| 0.0652 \| 0.5115 \| 0.4234 \| 0.0 \| nan \| 0.0 \| 0.3974 \| 0.4341 \| 0.0 \| 0.9334 \| 0.9137 \| 0.9621 \| 0.0518 \| 0.0264 \| 0.2825 \| 0.0 \| 0.0 \| 0.8197 \| 0.8887 \| 0.4044 \| 0.7367 \| 0.4565 \| nan \| 0.4257 \| 0.4769 \| 0.0954 \| 0.8060 \| 0.0493 \| 0.0 \| nan \| 0.0 \| 0.4708 \| 0.0 \| 0.0 \| 0.6965 \| 0.0594 \| 0.3946 \| 0.3766 \| 0.0 \| nan \| 0.0 \| 0.2996 \| 0.3611 \| 0.0 \| 0.8589 \| 0.7588 \| 0.9239 \| 0.0379 \| 0.0233 \| 0.2182 \| 0.0 \| 33 \| \| 0.2362 \| 0.5184 \| 0.3370 \| 0.3951 \| 0.8716 \| 0.0 \| 0.9045 \| 0.9681 \| 0.4519 \| 0.8066 \| 0.5724 \| nan \| 0.5364 \| 0.6084 \| 0.1367 \| 0.9124 \| 0.0536 \| 0.0 \| nan \| 0.0 \| 0.6666 \| 0.0 \| 0.0 \| 0.8956 \| 0.0637 \| 0.5331 \| 0.4525 \| 0.0 \| nan \| 0.0 \| 0.3985 \| 0.5169 \| 0.0 \| 0.9250 \| 0.9175 \| 0.9598 \| 0.0945 \| 0.0224 \| 0.2478 \| 0.0 \| 0.0 \| 0.7909 \| 0.8971 \| 0.4231 \| 0.7306 \| 0.4766 \| nan \| 0.4453 \| 0.4660 \| 0.1233 \| 0.8117 \| 0.0523 \| 0.0 \| nan \| 0.0 \| 0.4698 \| 0.0 \| 0.0 \| 0.6947 \| 0.0584 \| 0.4099 \| 0.3913 \| 0.0 \| nan \| 0.0 \| 0.3072 \| 0.4038 \| 0.0 \| 0.8585 \| 0.7556 \| 0.9243 \| 0.0807 \| 0.0205 \| 0.1925 \| 0.0 \| 34 \| \| 0.2358 \| 0.5466 \| 0.3343 \| 0.3997 \| 0.8678 \| 0.0 \| 0.8973 \| 0.9654 \| 0.5114 \| 0.7614 \| 0.6166 \| nan \| 0.5178 \| 0.6873 \| 0.1800 \| 0.9305 \| 0.0724 \| 0.0 \| nan \| 0.0 \| 0.6812 \| 0.0 \| 0.0 \| 0.8828 \| 0.0516 \| 0.5776 \| 0.4319 \| 0.0 \| nan \| 0.0 \| 0.3922 \| 0.4584 \| 0.0 \| 0.9252 \| 0.8982 \| 0.9661 \| 0.1076 \| 0.0260 \| 0.2528 \| 0.0 \| 0.0 \| 0.7946 \| 0.8884 \| 0.4043 \| 0.6950 \| 0.4749 \| nan \| 0.4265 \| 0.4765 \| 0.1554 \| 0.8143 \| 0.0699 \| 0.0 \| nan \| 0.0 \| 0.4759 \| 0.0 \| 0.0 \| 0.6963 \| 0.0467 \| 0.3978 \| 0.3722 \| 0.0 \| nan \| 0.0 \| 0.3100 \| 0.3513 \| 0.0 \| 0.8631 \| 0.7633 \| 0.9233 \| 0.0772 \| 0.0217 \| 0.1994 \| 0.0 \| 35 \| \| 0.2315 \| 0.5484 \| 0.3353 \| 0.4007 \| 0.8682 \| 0.0 \| 0.9169 \| 0.9540 \| 0.4890 \| 0.7525 \| 0.7001 \| nan \| 0.5193 \| 0.5919 \| 0.1294 \| 0.9241 \| 0.0515 \| 0.0 \| nan \| 0.0 \| 0.6725 \| 0.0 \| 0.0 \| 0.9040 \| 0.0487 \| 0.5260 \| 0.4268 \| 0.0 \| nan \| 0.0 \| 0.4055 \| 0.4971 \| 0.0 \| 0.9350 \| 0.8842 \| 0.9607 \| 0.3005 \| 0.0279 \| 0.2059 \| 0.0 \| 0.0 \| 0.8133 \| 0.8922 \| 0.3714 \| 0.6883 \| 0.4819 \| nan \| 0.4364 \| 0.4540 \| 0.1181 \| 0.8160 \| 0.0504 \| 0.0 \| nan \| 0.0 \| 0.4709 \| 0.0 \| 0.0 \| 0.6853 \| 0.0429 \| 0.3983 \| 0.3721 \| 0.0 \| nan \| 0.0 \| 0.3094 \| 0.4027 \| 0.0 \| 0.8633 \| 0.7562 \| 0.9264 \| 0.1865 \| 0.0244 \| 0.1685 \| 0.0 \| 36 \| \| 0.2377 \| 0.5324 \| 0.3407 \| 0.4028 \| 0.8737 \| 0.0 \| 0.9091 \| 0.9632 \| 0.4779 \| 0.8515 \| 0.5949 \| nan \| 0.5091 \| 0.6103 \| 0.1728 \| 0.9162 \| 0.0652 \| 0.0 \| nan \| 0.0 \| 0.6746 \| 0.0 \| 0.0 \| 0.9118 \| 0.0507 \| 0.5309 \| 0.4420 \| 0.0 \| nan \| 0.0 \| 0.4037 \| 0.4908 \| 0.0 \| 0.9208 \| 0.9137 \| 0.9634 \| 0.2771 \| 0.0202 \| 0.2192 \| 0.0 \| 0.0 \| 0.8315 \| 0.8955 \| 0.3856 \| 0.7536 \| 0.4891 \| nan \| 0.4326 \| 0.4828 \| 0.1421 \| 0.8130 \| 0.0630 \| 0.0 \| nan \| 0.0 \| 0.4685 \| 0.0 \| 0.0 \| 0.6849 \| 0.0465 \| 0.4297 \| 0.3804 \| 0.0 \| nan \| 0.0 \| 0.3098 \| 0.3899 \| 0.0 \| 0.8617 \| 0.7556 \| 0.9258 \| 0.1701 \| 0.0188 \| 0.1713 \| 0.0 \| 37 \| \| 0.2319 \| 0.5114 \| 0.3332 \| 0.3942 \| 0.8745 \| 0.0 \| 0.9208 \| 0.9566 \| 0.4723 \| 0.8581 \| 0.6419 \| nan \| 0.5400 \| 0.6353 \| 0.1534 \| 0.9246 \| 0.0637 \| 0.0 \| nan \| 0.0 \| 0.6620 \| 0.0 \| 0.0 \| 0.9143 \| 0.0362 \| 0.4700 \| 0.3914 \| 0.0 \| nan \| 0.0 \| 0.3948 \| 0.5296 \| 0.0 \| 0.9311 \| 0.9119 \| 0.9656 \| 0.0039 \| 0.0294 \| 0.2069 \| 0.0 \| 0.0 \| 0.8163 \| 0.9056 \| 0.3885 \| 0.7729 \| 0.4815 \| nan \| 0.4515 \| 0.4610 \| 0.1360 \| 0.8119 \| 0.0627 \| 0.0 \| nan \| 0.0 \| 0.4623 \| 0.0 \| 0.0 \| 0.6784 \| 0.0339 \| 0.3920 \| 0.3473 \| 0.0 \| nan \| 0.0 \| 0.3070 \| 0.4080 \| 0.0 \| 0.8647 \| 0.7644 \| 0.9245 \| 0.0030 \| 0.0262 \| 0.1634 \| 0.0 \| 38 \| \| 0.2241 \| 0.5676 \| 0.3349 \| 0.3986 \| 0.8699 \| 0.0 \| 0.9146 \| 0.9628 \| 0.4670 \| 0.7622 \| 0.6285 \| nan \| 0.4889 \| 0.6428 \| 0.1805 \| 0.9249 \| 0.0685 \| 0.0 \| nan \| 0.0 \| 0.6784 \| 0.0 \| 0.0 \| 0.8917 \| 0.1012 \| 0.5618 \| 0.4185 \| 0.0 \| nan \| 0.0018 \| 0.4246 \| 0.5487 \| 0.0 \| 0.9293 \| 0.9124 \| 0.9698 \| 0.0025 \| 0.0220 \| 0.2520 \| 0.0 \| 0.0 \| 0.8114 \| 0.8907 \| 0.4002 \| 0.6840 \| 0.4842 \| nan \| 0.4160 \| 0.4777 \| 0.1501 \| 0.8191 \| 0.0649 \| 0.0 \| nan \| 0.0 \| 0.4701 \| 0.0 \| 0.0 \| 0.7077 \| 0.0873 \| 0.4025 \| 0.3659 \| 0.0 \| nan \| 0.0012 \| 0.3255 \| 0.3939 \| 0.0 \| 0.8634 \| 0.7621 \| 0.9232 \| 0.0019 \| 0.0200 \| 0.1947 \| 0.0 \| 39 \| \| 0.2247 \| 0.5314 \| 0.3358 \| 0.3991 \| 0.8724 \| 0.0 \| 0.8966 \| 0.9546 \| 0.5034 \| 0.8269 \| 0.7241 \| nan \| 0.5320 \| 0.6062 \| 0.1736 \| 0.9394 \| 0.0618 \| 0.0 \| nan \| 0.0 \| 0.6274 \| 0.0 \| 0.0 \| 0.9066 \| 0.0810 \| 0.5121 \| 0.3887 \| 0.0 \| nan \| 0.0 \| 0.4009 \| 0.5482 \| 0.0 \| 0.9264 \| 0.9119 \| 0.9692 \| 0.0039 \| 0.0267 \| 0.2479 \| 0.0 \| 0.0 \| 0.8026 \| 0.8995 \| 0.3869 \| 0.7462 \| 0.4877 \| nan \| 0.4497 \| 0.4812 \| 0.1402 \| 0.8222 \| 0.0583 \| 0.0 \| nan \| 0.0 \| 0.4663 \| 0.0 \| 0.0 \| 0.6983 \| 0.0746 \| 0.4058 \| 0.3539 \| 0.0 \| nan \| 0.0 \| 0.3113 \| 0.3935 \| 0.0 \| 0.8585 \| 0.7587 \| 0.9242 \| 0.0030 \| 0.0231 \| 0.2000 \| 0.0 \| 40 \| \| 0.2199 \| 0.5197 \| 0.3375 \| 0.4039 \| 0.8734 \| 0.0 \| 0.9161 \| 0.9549 \| 0.5890 \| 0.8122 \| 0.6603 \| nan \| 0.5412 \| 0.6391 \| 0.2229 \| 0.9251 \| 0.0588 \| 0.0 \| nan \| 0.0 \| 0.6700 \| 0.0 \| 0.0 \| 0.9168 \| 0.0601 \| 0.5176 \| 0.3964 \| 0.0 \| nan \| 0.0 \| 0.4129 \| 0.5517 \| 0.0 \| 0.9323 \| 0.9033 \| 0.9677 \| 0.0014 \| 0.0303 \| 0.2459 \| 0.0 \| 0.0 \| 0.8182 \| 0.9002 \| 0.3683 \| 0.7333 \| 0.4846 \| nan \| 0.4468 \| 0.4840 \| 0.1819 \| 0.8270 \| 0.0558 \| 0.0 \| nan \| 0.0 \| 0.4704 \| 0.0 \| 0.0 \| 0.6912 \| 0.0581 \| 0.4220 \| 0.3575 \| 0.0 \| nan \| 0.0 \| 0.3214 \| 0.4031 \| 0.0 \| 0.8634 \| 0.7671 \| 0.9256 \| 0.0010 \| 0.0263 \| 0.1930 \| 0.0 \| 41 \| \| 0.2194 \| 0.4928 \| 0.3427 \| 0.4041 \| 0.8795 \| 0.0 \| 0.9068 \| 0.9650 \| 0.4578 \| 0.9049 \| 0.6337 \| nan \| 0.5321 \| 0.6180 \| 0.2096 \| 0.9413 \| 0.1196 \| 0.0 \| nan \| 0.0 \| 0.6733 \| 0.0 \| 0.0 \| 0.9081 \| 0.0807 \| 0.5106 \| 0.4412 \| 0.0 \| nan \| 0.0 \| 0.4201 \| 0.5280 \| 0.0 \| 0.9298 \| 0.8972 \| 0.9636 \| 0.0001 \| 0.0292 \| 0.2592 \| 0.0 \| 0.0 \| 0.8208 \| 0.9101 \| 0.3823 \| 0.8072 \| 0.4956 \| nan \| 0.4523 \| 0.4851 \| 0.1586 \| 0.8233 \| 0.1146 \| 0.0 \| nan \| 0.0 \| 0.4769 \| 0.0 \| 0.0 \| 0.6997 \| 0.0757 \| 0.4192 \| 0.3787 \| 0.0 \| nan \| 0.0 \| 0.3206 \| 0.3747 \| 0.0 \| 0.8626 \| 0.7575 \| 0.9263 \| 0.0001 \| 0.0251 \| 0.1998 \| 0.0 \| 42 \| \| 0.2058 \| 0.5290 \| 0.3408 \| 0.4066 \| 0.8745 \| 0.0 \| 0.9202 \| 0.9545 \| 0.5309 \| 0.8185 \| 0.6809 \| nan \| 0.5379 \| 0.6631 \| 0.2210 \| 0.9236 \| 0.0830 \| 0.0 \| nan \| 0.0 \| 0.6756 \| 0.0 \| 0.0 \| 0.9003 \| 0.0643 \| 0.5398 \| 0.4282 \| 0.0 \| nan \| 0.0 \| 0.4030 \| 0.5014 \| 0.0 \| 0.9338 \| 0.9110 \| 0.9669 \| 0.0853 \| 0.0317 \| 0.2364 \| 0.0 \| 0.0 \| 0.8073 \| 0.9043 \| 0.3907 \| 0.7392 \| 0.4986 \| nan \| 0.4517 \| 0.4827 \| 0.1848 \| 0.8260 \| 0.0820 \| 0.0 \| nan \| 0.0 \| 0.4661 \| 0.0 \| 0.0 \| 0.6968 \| 0.0612 \| 0.4189 \| 0.3671 \| 0.0 \| nan \| 0.0 \| 0.3188 \| 0.3823 \| 0.0 \| 0.8635 \| 0.7621 \| 0.9276 \| 0.0609 \| 0.0263 \| 0.1877 \| 0.0 \| 43 \| \| 0.2086 \| 0.5585 \| 0.3373 \| 0.4003 \| 0.8724 \| 0.0 \| 0.9171 \| 0.9605 \| 0.5070 \| 0.7666 \| 0.6387 \| nan \| 0.5526 \| 0.6278 \| 0.1947 \| 0.9270 \| 0.0758 \| 0.0 \| nan \| 0.0 \| 0.6633 \| 0.0 \| 0.0 \| 0.9033 \| 0.0584 \| 0.5314 \| 0.3971 \| 0.0 \| nan \| 0.0 \| 0.4408 \| 0.5355 \| 0.0 \| 0.9321 \| 0.9149 \| 0.9689 \| 0.0094 \| 0.0216 \| 0.2668 \| 0.0 \| 0.0 \| 0.8046 \| 0.8988 \| 0.3859 \| 0.7018 \| 0.4837 \| nan \| 0.4593 \| 0.4939 \| 0.1485 \| 0.8276 \| 0.0747 \| 0.0 \| nan \| 0.0 \| 0.4694 \| 0.0 \| 0.0 \| 0.7058 \| 0.0565 \| 0.4122 \| 0.3619 \| 0.0 \| nan \| 0.0 \| 0.3300 \| 0.3997 \| 0.0 \| 0.8611 \| 0.7559 \| 0.9262 \| 0.0070 \| 0.0195 \| 0.2110 \| 0.0 \| 44 \| \| 0.2016 \| 0.5442 \| 0.3452 \| 0.4156 \| 0.8718 \| 0.0 \| 0.8940 \| 0.9587 \| 0.4970 \| 0.7906 \| 0.6744 \| nan \| 0.5719 \| 0.6411 \| 0.1822 \| 0.9436 \| 0.1722 \| 0.0 \| nan \| 0.0 \| 0.6901 \| 0.0 \| 0.0 \| 0.8519 \| 0.1355 \| 0.5906 \| 0.4452 \| 0.0 \| nan \| 0.0844 \| 0.4539 \| 0.4714 \| 0.0 \| 0.9380 \| 0.8876 \| 0.9650 \| 0.0503 \| 0.0246 \| 0.3849 \| 0.0 \| 0.0 \| 0.8165 \| 0.8892 \| 0.3722 \| 0.7187 \| 0.4869 \| nan \| 0.4709 \| 0.4788 \| 0.1384 \| 0.8176 \| 0.1673 \| 0.0 \| nan \| 0.0 \| 0.4677 \| 0.0 \| 0.0 \| 0.7170 \| 0.0963 \| 0.3922 \| 0.3739 \| 0.0 \| nan \| 0.0596 \| 0.3315 \| 0.3576 \| 0.0 \| 0.8666 \| 0.7659 \| 0.9290 \| 0.0324 \| 0.0192 \| 0.2801 \| 0.0 \| 45 \| \| 0.2021 \| 0.5369 \| 0.3409 \| 0.4049 \| 0.8753 \| 0.0 \| 0.9249 \| 0.9608 \| 0.5473 \| 0.7979 \| 0.6534 \| nan \| 0.5548 \| 0.6555 \| 0.1623 \| 0.9311 \| 0.1317 \| 0.0 \| nan \| 0.0 \| 0.6821 \| 0.0 \| 0.0 \| 0.9151 \| 0.0621 \| 0.5285 \| 0.4378 \| 0.0 \| nan \| 0.0029 \| 0.4205 \| 0.5321 \| 0.0 \| 0.9301 \| 0.8972 \| 0.9677 \| 0.0025 \| 0.0281 \| 0.2294 \| 0.0 \| 0.0 \| 0.8155 \| 0.8981 \| 0.3979 \| 0.7396 \| 0.4890 \| nan \| 0.4634 \| 0.4928 \| 0.1352 \| 0.8283 \| 0.1299 \| 0.0 \| nan \| 0.0 \| 0.4784 \| 0.0 \| 0.0 \| 0.6949 \| 0.0602 \| 0.4199 \| 0.3775 \| 0.0 \| nan \| 0.0018 \| 0.3280 \| 0.3792 \| 0.0 \| 0.8698 \| 0.7691 \| 0.9283 \| 0.0020 \| 0.0237 \| 0.1856 \| 0.0 \| 46 \| \| 0.2056 \| 0.5118 \| 0.3421 \| 0.4052 \| 0.8747 \| 0.0 \| 0.9441 \| 0.9624 \| 0.5219 \| 0.7370 \| 0.6816 \| nan \| 0.5360 \| 0.6510 \| 0.2372 \| 0.9316 \| 0.0854 \| 0.0 \| nan \| 0.0 \| 0.6607 \| 0.0 \| 0.0 \| 0.8952 \| 0.0777 \| 0.5614 \| 0.4327 \| 0.0 \| nan \| 0.0 \| 0.4403 \| 0.4597 \| 0.0 \| 0.9350 \| 0.8918 \| 0.9650 \| 0.0312 \| 0.0296 \| 0.2984 \| 0.0 \| 0.0 \| 0.8039 \| 0.9033 \| 0.4243 \| 0.7185 \| 0.4949 \| nan \| 0.4494 \| 0.4876 \| 0.1929 \| 0.8314 \| 0.0849 \| 0.0 \| nan \| 0.0 \| 0.4694 \| 0.0 \| 0.0 \| 0.7160 \| 0.0697 \| 0.3959 \| 0.3751 \| 0.0 \| nan \| 0.0 \| 0.3319 \| 0.3668 \| 0.0 \| 0.8638 \| 0.7612 \| 0.9299 \| 0.0246 \| 0.0251 \| 0.2269 \| 0.0 \| 47 \| \| 0.2038 \| 0.5407 \| 0.3318 \| 0.3995 \| 0.8701 \| 0.0 \| 0.9396 \| 0.9574 \| 0.6559 \| 0.7289 \| 0.6166 \| nan \| 0.5573 \| 0.6550 \| 0.1861 \| 0.9233 \| 0.0852 \| 0.0 \| nan \| 0.0 \| 0.6396 \| 0.0 \| 0.0 \| 0.9241 \| 0.0504 \| 0.4811 \| 0.4240 \| 0.0 \| nan \| 0.0001 \| 0.3993 \| 0.4905 \| 0.0 \| 0.9225 \| 0.9142 \| 0.9672 \| 0.0155 \| 0.0175 \| 0.2326 \| 0.0 \| 0.0 \| 0.8068 \| 0.9021 \| 0.3095 \| 0.7049 \| 0.4679 \| nan \| 0.4504 \| 0.4687 \| 0.1686 \| 0.8264 \| 0.0847 \| 0.0 \| nan \| 0.0 \| 0.4721 \| 0.0 \| 0.0 \| 0.6820 \| 0.0482 \| 0.3826 \| 0.3686 \| 0.0 \| nan \| 0.0001 \| 0.3217 \| 0.3838 \| 0.0 \| 0.8636 \| 0.7679 \| 0.9297 \| 0.0114 \| 0.0156 \| 0.1809 \| 0.0 \| 48 \| \| 0.1964 \| 0.5617 \| 0.3482 \| 0.4212 \| 0.8714 \| 0.0 \| 0.9191 \| 0.9583 \| 0.5878 \| 0.7730 \| 0.6079 \| nan \| 0.5577 \| 0.6959 \| 0.2221 \| 0.9300 \| 0.1310 \| 0.0 \| nan \| 0.0 \| 0.6927 \| 0.0 \| 0.0 \| 0.9088 \| 0.0611 \| 0.4825 \| 0.4370 \| 0.0 \| nan \| 0.0 \| 0.4339 \| 0.4711 \| 0.0 \| 0.9172 \| 0.9151 \| 0.9692 \| 0.5121 \| 0.0265 \| 0.2676 \| 0.0 \| 0.0 \| 0.7902 \| 0.9044 \| 0.4201 \| 0.6963 \| 0.4879 \| nan \| 0.4607 \| 0.4926 \| 0.1864 \| 0.8243 \| 0.1278 \| 0.0 \| nan \| 0.0 \| 0.4759 \| 0.0 \| 0.0 \| 0.6973 \| 0.0588 \| 0.3754 \| 0.3760 \| 0.0 \| nan \| 0.0 \| 0.3330 \| 0.3555 \| 0.0 \| 0.8600 \| 0.7533 \| 0.9288 \| 0.3089 \| 0.0223 \| 0.2055 \| 0.0 \| 49 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.15.0 - Tokenizers 0.15.0
stoves/Ermurachi_Vadim	stoves	2023-12-03T19:11:04Z	2	1	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-11-10T11:47:08Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of gjdfophge person tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
ThuyNT03/KLTN_COQE_viT5_SOPAL_v2	ThuyNT03	2023-12-03T19:08:01Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-03T18:22:18Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: KLTN_COQE_viT5_SOPAL_v2 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. --> # KLTN_COQE_viT5_SOPAL_v2 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow17	FounderOfHuggingface	2023-12-03T19:07:14Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T19:07:10Z	--- 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] ## Training procedure ### Framework versions - PEFT 0.6.2
AndresBriC/ppo-LunarLander-v2	AndresBriC	2023-12-03T19:05:37Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T19:05:10Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 264.34 +/- 22.80 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
sade-adrien/Mistral-7B-Instruct-v0.1-LC16k-PI	sade-adrien	2023-12-03T19:00:50Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	null	2023-12-01T18:54:32Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.1 tags: - generated_from_trainer model-index: - name: Mistral-7B-Instruct-v0.1-LC16k-PI 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-7B-Instruct-v0.1-LC16k-PI 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. It achieves the following results on the evaluation set: - Loss: 1.6455 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 32 - 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: 30 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6742 \| 0.12 \| 100 \| 1.6880 \| \| 1.6683 \| 0.24 \| 200 \| 1.6711 \| \| 1.7301 \| 0.36 \| 300 \| 1.6636 \| \| 1.6867 \| 0.47 \| 400 \| 1.6588 \| \| 1.4718 \| 0.59 \| 500 \| 1.6557 \| \| 1.6843 \| 0.71 \| 600 \| 1.6519 \| \| 1.5966 \| 0.83 \| 700 \| 1.6492 \| \| 1.9016 \| 0.95 \| 800 \| 1.6472 \| \| 1.7488 \| 1.07 \| 900 \| 1.6461 \| \| 1.5596 \| 1.19 \| 1000 \| 1.6455 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.0+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow16	FounderOfHuggingface	2023-12-03T18:55:36Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T18:55:33Z	--- 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] ## Training procedure ### Framework versions - PEFT 0.6.2
Liogl/RL-Course	Liogl	2023-12-03T18:48:43Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T18:48:06Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO-MLP results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 246.46 +/- 32.22 name: mean_reward verified: false --- # PPO-MLP Agent playing LunarLander-v2 This is a trained model of a PPO-MLP agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
JFJFJFen/JFJFJFen	JFJFJFen	2023-12-03T18:43:15Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:google/flan-t5-large", "base_model:adapter:google/flan-t5-large", "region:us" ]	null	2023-12-03T18:42:34Z	--- library_name: peft base_model: google/flan-t5-large --- # 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 0.6.3.dev0
Roxysun/cs2fi-wav2vec2-large-xls-r-300m-cs-colab-phoneme	Roxysun	2023-12-03T18:43:12Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:voxpopuli", "base_model:facebook/wav2vec2-lv-60-espeak-cv-ft", "base_model:finetune:facebook/wav2vec2-lv-60-espeak-cv-ft", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-03T18:23:27Z	--- license: apache-2.0 base_model: facebook/wav2vec2-lv-60-espeak-cv-ft tags: - generated_from_trainer datasets: - voxpopuli metrics: - wer model-index: - name: cs2fi-wav2vec2-large-xls-r-300m-cs-colab-phoneme results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: voxpopuli type: voxpopuli config: fi split: test args: fi metrics: - name: Wer type: wer value: 0.5253440751930178 --- <!-- 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. --> # cs2fi-wav2vec2-large-xls-r-300m-cs-colab-phoneme This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 2.2508 - Wer: 0.5253 ## 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: 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: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.8061 \| 16.67 \| 100 \| 2.7437 \| 0.5693 \| \| 2.871 \| 33.33 \| 200 \| 2.4209 \| 0.5314 \| \| 2.525 \| 50.0 \| 300 \| 2.2508 \| 0.5253 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
syed789/zephyr-7b-beta-fhir-ft10	syed789	2023-12-03T18:38:54Z	2	0	peft	[ "peft", "arxiv:1910.09700", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:adapter:HuggingFaceH4/zephyr-7b-beta", "region:us" ]	null	2023-12-03T18:38:53Z	--- library_name: peft base_model: HuggingFaceH4/zephyr-7b-beta --- # 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow13	FounderOfHuggingface	2023-12-03T18:20:46Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T18:20:43Z	--- 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] ## Training procedure ### Framework versions - PEFT 0.6.2
pijarcandra22/t5Indo2Sunda	pijarcandra22	2023-12-03T18:11:46Z	11	0	transformers	[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-03T16:42:27Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_keras_callback model-index: - name: pijarcandra22/t5Indo2Sunda 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. --> # pijarcandra22/t5Indo2Sunda This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.1941 - Validation Loss: 2.1230 - Epoch: 99 ## 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 \| \|:----------:\|:---------------:\|:-----:\| \| 4.3724 \| 3.9124 \| 0 \| \| 3.9887 \| 3.6348 \| 1 \| \| 3.7534 \| 3.4215 \| 2 \| \| 3.5819 \| 3.2847 \| 3 \| \| 3.4632 \| 3.1902 \| 4 \| \| 3.3751 \| 3.1139 \| 5 \| \| 3.3039 \| 3.0493 \| 6 \| \| 3.2447 \| 2.9955 \| 7 \| \| 3.1911 \| 2.9481 \| 8 \| \| 3.1455 \| 2.9082 \| 9 \| \| 3.1068 \| 2.8718 \| 10 \| \| 3.0697 \| 2.8387 \| 11 \| \| 3.0381 \| 2.8105 \| 12 \| \| 3.0050 \| 2.7825 \| 13 \| \| 2.9796 \| 2.7568 \| 14 \| \| 2.9510 \| 2.7350 \| 15 \| \| 2.9259 \| 2.7096 \| 16 \| \| 2.9053 \| 2.6881 \| 17 \| \| 2.8833 \| 2.6696 \| 18 \| \| 2.8599 \| 2.6510 \| 19 \| \| 2.8403 \| 2.6328 \| 20 \| \| 2.8207 \| 2.6171 \| 21 \| \| 2.8046 \| 2.5999 \| 22 \| \| 2.7861 \| 2.5857 \| 23 \| \| 2.7715 \| 2.5699 \| 24 \| \| 2.7557 \| 2.5542 \| 25 \| \| 2.7387 \| 2.5420 \| 26 \| \| 2.7225 \| 2.5299 \| 27 \| \| 2.7085 \| 2.5182 \| 28 \| \| 2.6950 \| 2.5081 \| 29 \| \| 2.6818 \| 2.4951 \| 30 \| \| 2.6687 \| 2.4864 \| 31 \| \| 2.6578 \| 2.4760 \| 32 \| \| 2.6461 \| 2.4651 \| 33 \| \| 2.6334 \| 2.4559 \| 34 \| \| 2.6213 \| 2.4477 \| 35 \| \| 2.6096 \| 2.4373 \| 36 \| \| 2.5993 \| 2.4297 \| 37 \| \| 2.5906 \| 2.4208 \| 38 \| \| 2.5778 \| 2.4100 \| 39 \| \| 2.5703 \| 2.4025 \| 40 \| \| 2.5594 \| 2.3962 \| 41 \| \| 2.5521 \| 2.3901 \| 42 \| \| 2.5414 \| 2.3808 \| 43 \| \| 2.5318 \| 2.3726 \| 44 \| \| 2.5235 \| 2.3684 \| 45 \| \| 2.5165 \| 2.3592 \| 46 \| \| 2.5060 \| 2.3507 \| 47 \| \| 2.4972 \| 2.3466 \| 48 \| \| 2.4892 \| 2.3388 \| 49 \| \| 2.4807 \| 2.3325 \| 50 \| \| 2.4732 \| 2.3281 \| 51 \| \| 2.4654 \| 2.3210 \| 52 \| \| 2.4592 \| 2.3138 \| 53 \| \| 2.4525 \| 2.3100 \| 54 \| \| 2.4439 \| 2.3046 \| 55 \| \| 2.4349 \| 2.2980 \| 56 \| \| 2.4283 \| 2.2926 \| 57 \| \| 2.4222 \| 2.2884 \| 58 \| \| 2.4139 \| 2.2824 \| 59 \| \| 2.4071 \| 2.2759 \| 60 \| \| 2.4008 \| 2.2705 \| 61 \| \| 2.3941 \| 2.2664 \| 62 \| \| 2.3882 \| 2.2588 \| 63 \| \| 2.3813 \| 2.2566 \| 64 \| \| 2.3759 \| 2.2498 \| 65 \| \| 2.3674 \| 2.2461 \| 66 \| \| 2.3618 \| 2.2425 \| 67 \| \| 2.3534 \| 2.2377 \| 68 \| \| 2.3522 \| 2.2314 \| 69 \| \| 2.3398 \| 2.2269 \| 70 \| \| 2.3391 \| 2.2241 \| 71 \| \| 2.3303 \| 2.2184 \| 72 \| \| 2.3275 \| 2.2137 \| 73 \| \| 2.3190 \| 2.2100 \| 74 \| \| 2.3159 \| 2.2048 \| 75 \| \| 2.3078 \| 2.2011 \| 76 \| \| 2.3048 \| 2.1971 \| 77 \| \| 2.3005 \| 2.1936 \| 78 \| \| 2.2938 \| 2.1899 \| 79 \| \| 2.2892 \| 2.1859 \| 80 \| \| 2.2824 \| 2.1819 \| 81 \| \| 2.2758 \| 2.1787 \| 82 \| \| 2.2739 \| 2.1757 \| 83 \| \| 2.2689 \| 2.1716 \| 84 \| \| 2.2623 \| 2.1664 \| 85 \| \| 2.2574 \| 2.1657 \| 86 \| \| 2.2537 \| 2.1618 \| 87 \| \| 2.2483 \| 2.1563 \| 88 \| \| 2.2407 \| 2.1554 \| 89 \| \| 2.2387 \| 2.1510 \| 90 \| \| 2.2351 \| 2.1469 \| 91 \| \| 2.2286 \| 2.1436 \| 92 \| \| 2.2226 \| 2.1413 \| 93 \| \| 2.2171 \| 2.1395 \| 94 \| \| 2.2159 \| 2.1342 \| 95 \| \| 2.2109 \| 2.1314 \| 96 \| \| 2.2041 \| 2.1284 \| 97 \| \| 2.1999 \| 2.1260 \| 98 \| \| 2.1941 \| 2.1230 \| 99 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0
mtolgakbaba/mT5-general-purpose	mtolgakbaba	2023-12-03T18:10:14Z	2	0	peft	[ "peft", "safetensors", "mt5", "arxiv:1910.09700", "base_model:google/mt5-base", "base_model:adapter:google/mt5-base", "region:us" ]	null	2023-12-02T07:09:58Z	--- library_name: peft base_model: google/mt5-base --- # 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 0.6.3.dev0
viktoriaohorilko/ppo-LunarLander-v2	viktoriaohorilko	2023-12-03T18:07:26Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T17:12:55Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 235.43 +/- 43.78 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
li-ping/river_retriver_416data_testing_v2	li-ping	2023-12-03T18:04:42Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-12-03T18:04:33Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # li-ping/river_retriver_416data_testing_v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('li-ping/river_retriver_416data_testing_v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=li-ping/river_retriver_416data_testing_v2) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 791 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 400, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 791, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
digiplay/LemonTea2.5D	digiplay	2023-12-03T18:03:32Z	358	6	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-05-30T12:38:45Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- #EulerDiscreteScheduler Version Model info : https://civitai.com/models/70692/lemontea-mix-painterly-25d the same with "digiplay/LemonteaMixPainterly2_v1", but config the default schedule type to the Euler version.
hkivancoral/smids_1x_beit_base_rms_0001_fold2	hkivancoral	2023-12-03T18:03:05Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-base-patch16-224", "base_model:finetune:microsoft/beit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-02T09:44:03Z	--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_1x_beit_base_rms_0001_fold2 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: 0.740432612312812 --- <!-- 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_1x_beit_base_rms_0001_fold2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9358 - Accuracy: 0.7404 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0437 \| 1.0 \| 75 \| 0.9679 \| 0.5042 \| \| 0.9234 \| 2.0 \| 150 \| 0.8669 \| 0.5208 \| \| 1.0795 \| 3.0 \| 225 \| 0.7926 \| 0.5874 \| \| 0.9543 \| 4.0 \| 300 \| 0.8244 \| 0.5507 \| \| 0.8239 \| 5.0 \| 375 \| 0.7959 \| 0.5857 \| \| 0.7924 \| 6.0 \| 450 \| 0.7928 \| 0.5890 \| \| 0.8468 \| 7.0 \| 525 \| 0.7806 \| 0.6256 \| \| 0.8608 \| 8.0 \| 600 \| 0.9027 \| 0.5408 \| \| 0.7878 \| 9.0 \| 675 \| 0.7544 \| 0.6373 \| \| 0.9079 \| 10.0 \| 750 \| 0.7732 \| 0.6190 \| \| 0.7705 \| 11.0 \| 825 \| 0.7349 \| 0.6290 \| \| 0.7586 \| 12.0 \| 900 \| 0.7322 \| 0.6306 \| \| 0.7794 \| 13.0 \| 975 \| 0.7224 \| 0.6323 \| \| 0.7123 \| 14.0 \| 1050 \| 0.7252 \| 0.6572 \| \| 0.744 \| 15.0 \| 1125 \| 0.7450 \| 0.5990 \| \| 0.7086 \| 16.0 \| 1200 \| 0.6962 \| 0.6639 \| \| 0.7295 \| 17.0 \| 1275 \| 0.7508 \| 0.6489 \| \| 0.7289 \| 18.0 \| 1350 \| 0.6978 \| 0.6722 \| \| 0.6947 \| 19.0 \| 1425 \| 0.7112 \| 0.6739 \| \| 0.6923 \| 20.0 \| 1500 \| 0.7131 \| 0.6805 \| \| 0.7545 \| 21.0 \| 1575 \| 0.7480 \| 0.6223 \| \| 0.68 \| 22.0 \| 1650 \| 0.6683 \| 0.6839 \| \| 0.7107 \| 23.0 \| 1725 \| 0.6889 \| 0.6772 \| \| 0.6933 \| 24.0 \| 1800 \| 0.6566 \| 0.6822 \| \| 0.6429 \| 25.0 \| 1875 \| 0.6381 \| 0.7005 \| \| 0.6742 \| 26.0 \| 1950 \| 0.6536 \| 0.6822 \| \| 0.6753 \| 27.0 \| 2025 \| 0.6462 \| 0.6889 \| \| 0.6228 \| 28.0 \| 2100 \| 0.6368 \| 0.7022 \| \| 0.6193 \| 29.0 \| 2175 \| 0.6115 \| 0.7171 \| \| 0.5568 \| 30.0 \| 2250 \| 0.6625 \| 0.7188 \| \| 0.584 \| 31.0 \| 2325 \| 0.6680 \| 0.6922 \| \| 0.581 \| 32.0 \| 2400 \| 0.5723 \| 0.7654 \| \| 0.5698 \| 33.0 \| 2475 \| 0.6173 \| 0.7205 \| \| 0.5032 \| 34.0 \| 2550 \| 0.6176 \| 0.7338 \| \| 0.5019 \| 35.0 \| 2625 \| 0.6137 \| 0.7438 \| \| 0.4921 \| 36.0 \| 2700 \| 0.5855 \| 0.7571 \| \| 0.453 \| 37.0 \| 2775 \| 0.6724 \| 0.7271 \| \| 0.4913 \| 38.0 \| 2850 \| 0.6043 \| 0.7720 \| \| 0.3871 \| 39.0 \| 2925 \| 0.6124 \| 0.7704 \| \| 0.4014 \| 40.0 \| 3000 \| 0.6591 \| 0.7521 \| \| 0.4698 \| 41.0 \| 3075 \| 0.6575 \| 0.7604 \| \| 0.375 \| 42.0 \| 3150 \| 0.6735 \| 0.7471 \| \| 0.317 \| 43.0 \| 3225 \| 0.7867 \| 0.7504 \| \| 0.2968 \| 44.0 \| 3300 \| 0.7423 \| 0.7521 \| \| 0.2919 \| 45.0 \| 3375 \| 0.8253 \| 0.7504 \| \| 0.2598 \| 46.0 \| 3450 \| 0.8629 \| 0.7421 \| \| 0.1951 \| 47.0 \| 3525 \| 0.8586 \| 0.7704 \| \| 0.1905 \| 48.0 \| 3600 \| 0.9010 \| 0.7438 \| \| 0.1278 \| 49.0 \| 3675 \| 0.9354 \| 0.7454 \| \| 0.2294 \| 50.0 \| 3750 \| 0.9358 \| 0.7404 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
ahams02/q-FrozenLake-v1-4x4-noSlippery	ahams02	2023-12-03T18:01:45Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T18:01:43Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="ahams02/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
MatteoWood/bert-sexism-classifier	MatteoWood	2023-12-03T18:01:35Z	7	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "sexism", "dataset:MatteoWood/bert-sexism-classifier-dataset", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-30T13:39:58Z	--- tags: - text-classification - sexism widget: - text: I love women - text: I hate women datasets: - MatteoWood/bert-sexism-classifier-dataset --- # Model Trained Using AutoTrain - Problem type: Text Classification # Training parameters - lr: 5e-05 - epochs: 5 - batch size: 8 ## Validation Metrics loss: 0.36799120903015137 f1: 0.7044155844155844 precision: 0.7251336898395722 recall: 0.6848484848484848 auc: 0.8958072418537535 accuracy: 0.85775
MatteoWood/albert-sexism-classifier	MatteoWood	2023-12-03T18:01:19Z	7	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "sexism", "dataset:MatteoWood/autotrain-data-albert-sexism-classifier", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-02T14:45:33Z	--- tags: - text-classification - sexism widget: - text: I love women - text: I hate women datasets: - MatteoWood/autotrain-data-albert-sexism-classifier --- # Model Trained Using AutoTrain - Problem type: Text Classification # Training parameters - lr: 5e-05 - epochs: 5 - batch size: 8 ## Validation Metrics loss: 0.40832215547561646 f1: 0.6293779404077365 precision: 0.6522210184182016 recall: 0.6080808080808081 auc: 0.8514470284237725 accuracy: 0.82275
MatteoWood/hatebert-sexism-classifier	MatteoWood	2023-12-03T18:00:04Z	6	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "sexism", "dataset:MatteoWood/autotrain-data-hatebert-sexism-classifier", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-02T13:05:20Z	--- tags: - text-classification - sexism widget: - text: I love women - text: I hate women datasets: - MatteoWood/autotrain-data-hatebert-sexism-classifier --- # Model Trained Using AutoTrain - Problem type: Text Classification # Training parameters - lr: 5e-05 - epochs: 5 - batch size: 8 ## Validation Metrics loss: 0.3898809254169464 f1: 0.7137662337662338 precision: 0.73475935828877 recall: 0.693939393939394 auc: 0.8955589113728649 accuracy: 0.86225
Roxysun/wav2vec2-large-xls-r-300m-cs-colab-phoneme	Roxysun	2023-12-03T17:53:18Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:voxpopuli", "base_model:facebook/wav2vec2-lv-60-espeak-cv-ft", "base_model:finetune:facebook/wav2vec2-lv-60-espeak-cv-ft", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-03T16:51:56Z	--- license: apache-2.0 base_model: facebook/wav2vec2-lv-60-espeak-cv-ft tags: - generated_from_trainer datasets: - voxpopuli metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-cs-colab-phoneme results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: voxpopuli type: voxpopuli config: cs split: test args: cs metrics: - name: Wer type: wer value: 0.44625639804785144 --- <!-- 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-xls-r-300m-cs-colab-phoneme This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 1.9388 - Wer: 0.4463 ## 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: 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: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow10	FounderOfHuggingface	2023-12-03T17:46:02Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T17:45:58Z	--- 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. 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] ## Training procedure ### Framework versions - PEFT 0.6.2
francesco-vo/bert_model_v2	francesco-vo	2023-12-03T17:42:54Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-03T17:27:06Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_model_v2 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_model_v2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5556 - Accuracy: 0.7469 ## 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 423 \| 0.5205 \| 0.7345 \| \| 0.5638 \| 2.0 \| 846 \| 0.5143 \| 0.7545 \| \| 0.4089 \| 3.0 \| 1269 \| 0.5556 \| 0.7469 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
SaraPiscitelli/dqn-SpaceInvadersNoFrameskip-v4	SaraPiscitelli	2023-12-03T17:35:56Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T13:53:17Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 554.00 +/- 186.01 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga SaraPiscitelli -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga SaraPiscitelli -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga SaraPiscitelli ``` ## Hyperparameters ```python OrderedDict([('batch_size', 128), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Jerry46/zephyr-7b-sft-lora	Jerry46	2023-12-03T17:29:01Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-03T05:25:09Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: zephyr-7b-sft-lora 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. --> # zephyr-7b-sft-lora This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9900 ## 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: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9866 \| 0.67 \| 272 \| 0.9901 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
NousResearch/Nous-Hermes-2-Vision-Alpha	NousResearch	2023-12-03T17:25:41Z	40	303	transformers	[ "transformers", "pytorch", "llava_mistral", "text-generation", "mistral", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "distillation", "multimodal", "llava", "conversational", "en", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-11-28T23:18:55Z	--- base_model: mistralai/Mistral-7B-v0.1 tags: - mistral - instruct - finetune - chatml - gpt4 - synthetic data - distillation - multimodal - llava model-index: - name: Nous-Hermes-2-Vision results: [] license: apache-2.0 language: - en --- # Nous-Hermes-2-Vision - Mistral 7B ![image/png](https://camo.githubusercontent.com/b09dc35a93b4b70748fa4e2f307b011cd3d548369dd926ec9a2d3a51f7b3721e/68747470733a2f2f66696c65732e6f616975736572636f6e74656e742e636f6d2f66696c652d6b4437565358734f5649576472624b3042353662686644363f73653d323032332d31322d3033543137253341333425334135385a2673703d722673763d323032312d30382d30362673723d6226727363633d6d61782d6167652533443331353336303030253243253230696d6d757461626c6526727363643d6174746163686d656e7425334225323066696c656e616d6525334439643530333039622d356236342d343964302d623832362d6165316638366132396661382e77656270267369673d50396973694b4679654a54435a47424b526d45494b3043586e6e55676c6334704a583071312532425478666a34253344) In the tapestry of Greek mythology, Hermes reigns as the eloquent Messenger of the Gods, a deity who deftly bridges the realms through the art of communication. It is in homage to this divine mediator that I name this advanced LLM "Hermes," a system crafted to navigate the complex intricacies of human discourse with celestial finesse. ## Model description Nous-Hermes-2-Vision stands as a pioneering Vision-Language Model, leveraging advancements from the renowned OpenHermes-2.5-Mistral-7B by teknium. This model incorporates two pivotal enhancements, setting it apart as a cutting-edge solution: - SigLIP-400M Integration: Diverging from traditional approaches that rely on substantial 3B vision encoders, Nous-Hermes-2-Vision harnesses the formidable SigLIP-400M. This strategic choice not only streamlines the model's architecture, making it more lightweight, but also capitalizes on SigLIP's remarkable capabilities. The result? A remarkable boost in performance that defies conventional expectations. - Custom Dataset Enriched with Function Calling: Our model's training data includes a unique feature – function calling. This distinctive addition transforms Nous-Hermes-2-Vision into a Vision-Language Action Model. Developers now have a versatile tool at their disposal, primed for crafting a myriad of ingenious automations. This project is led by [qnguyen3](https://twitter.com/stablequan) and [teknium](https://twitter.com/Teknium1). ## Training ### Dataset - 220K from LVIS-INSTRUCT4V - 60K from ShareGPT4V - 150K Private Function Calling Data - 50K conversations from teknium's OpenHermes-2.5 ## Usage ### Prompt Format - Like other LLaVA's variants, this model uses Vicuna-V1 as its prompt template. Please refer to `conv_llava_v1` in [this file](https://github.com/qnguyen3/hermes-llava/blob/main/llava/conversation.py) - For Gradio UI, please visit this [GitHub Repo](https://github.com/qnguyen3/hermes-llava) ### Function Calling - For functiong calling, the message should start with a `<fn_call>` tag. Here is an example: ```json <fn_call>{ "type": "object", "properties": { "bus_colors": { "type": "array", "description": "The colors of the bus in the image.", "items": { "type": "string", "enum": ["red", "blue", "green", "white"] } }, "bus_features": { "type": "string", "description": "The features seen on the back of the bus." }, "bus_location": { "type": "string", "description": "The location of the bus (driving or pulled off to the side).", "enum": ["driving", "pulled off to the side"] } } } ``` Output: ```json { "bus_colors": ["red", "white"], "bus_features": "An advertisement", "bus_location": "driving" } ``` ## Example ### Chat ![image/png](https://i.ibb.co/tMg8h2t/Screenshot-from-2023-12-04-00-13-59.png) ### Function Calling Input image: ![image/png](https://www.slcmenu.com/wp-content/uploads/2017/11/In-N-Out-Burger-menu-2020-982x1024.jpg) Input message: ```json <fn_call>{ "type": "object", "properties": { "food_list": { "type": "array", "description": "List of all the food", "items": { "type": "string", } }, } } ``` Output: ```json { "food_list": [ "Double Burger", "Cheeseburger", "French Fries", "Shakes", "Coffee" ] } ```
lnxdx/19_2000_1e-5_hp-base	lnxdx	2023-12-03T17:25:05Z	4	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:masoudmzb/wav2vec2-xlsr-multilingual-53-fa", "base_model:finetune:masoudmzb/wav2vec2-xlsr-multilingual-53-fa", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-12-01T00:10:01Z	--- base_model: masoudmzb/wav2vec2-xlsr-multilingual-53-fa tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-xlsr-persian-asr-shemo_lnxdx 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. --> # wav2vec2-large-xlsr-persian-asr-shemo_lnxdx This model is a fine-tuned version of [masoudmzb/wav2vec2-xlsr-multilingual-53-fa](https://huggingface.co/masoudmzb/wav2vec2-xlsr-multilingual-53-fa) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7064 - Wer: 0.3344 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.9455 \| 0.62 \| 100 \| 1.4247 \| 0.4831 \| \| 1.4824 \| 1.25 \| 200 \| 1.1107 \| 0.4331 \| \| 1.2516 \| 1.88 \| 300 \| 0.9141 \| 0.4136 \| \| 1.0859 \| 2.5 \| 400 \| 0.8360 \| 0.3975 \| \| 1.0357 \| 3.12 \| 500 \| 0.8097 \| 0.3814 \| \| 1.0472 \| 3.75 \| 600 \| 0.7550 \| 0.3753 \| \| 0.9963 \| 4.38 \| 700 \| 0.7533 \| 0.3636 \| \| 0.9767 \| 5.0 \| 800 \| 0.7424 \| 0.3589 \| \| 0.9667 \| 5.62 \| 900 \| 0.7360 \| 0.3516 \| \| 0.9385 \| 6.25 \| 1000 \| 0.7355 \| 0.3487 \| \| 0.9805 \| 6.88 \| 1100 \| 0.7237 \| 0.3464 \| \| 0.976 \| 7.5 \| 1200 \| 0.7078 \| 0.3455 \| \| 0.88 \| 8.12 \| 1300 \| 0.7229 \| 0.3438 \| \| 0.9421 \| 8.75 \| 1400 \| 0.7180 \| 0.3432 \| \| 0.9584 \| 9.38 \| 1500 \| 0.7059 \| 0.3364 \| \| 0.88 \| 10.0 \| 1600 \| 0.7106 \| 0.3364 \| \| 0.9113 \| 10.62 \| 1700 \| 0.7125 \| 0.3344 \| \| 0.912 \| 11.25 \| 1800 \| 0.7091 \| 0.3353 \| \| 0.9607 \| 11.88 \| 1900 \| 0.7066 \| 0.3344 \| \| 0.8974 \| 12.5 \| 2000 \| 0.7064 \| 0.3344 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
VitaliiVrublevskyi/bert-base-multilingual-uncased-finetuned-mrpc	VitaliiVrublevskyi	2023-12-03T17:23:39Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-03T16:03:20Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: bert-base-multilingual-uncased-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8382352941176471 - name: F1 type: f1 value: 0.8817204301075269 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-multilingual-uncased-finetuned-mrpc This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4928 - Accuracy: 0.8382 - F1: 0.8817 ## 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: 97 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 115 \| 0.4251 \| 0.7843 \| 0.8358 \| \| No log \| 2.0 \| 230 \| 0.3601 \| 0.8260 \| 0.8702 \| \| No log \| 3.0 \| 345 \| 0.3677 \| 0.8333 \| 0.8727 \| \| No log \| 4.0 \| 460 \| 0.4801 \| 0.8260 \| 0.8707 \| \| 0.3223 \| 5.0 \| 575 \| 0.4928 \| 0.8382 \| 0.8817 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.13.3
dodisbeaver/ppo-LunarLander-v2-100000	dodisbeaver	2023-12-03T17:13:56Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T17:13:18Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -106.55 +/- 54.00 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
messawey/my_awesome_qa_model	messawey	2023-12-03T17:02:34Z	3	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "endpoints_compatible", "region:us" ]	question-answering	2023-12-03T17:00:55Z	--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_keras_callback model-index: - name: messawey/my_awesome_qa_model 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. --> # messawey/my_awesome_qa_model This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.5375 - Validation Loss: 4.1401 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 68, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.5375 \| 4.1401 \| 0 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0
Gairiangsui/my-wolf-wlf	Gairiangsui	2023-12-03T16:58:14Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-12-03T16:54:13Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### my-wolf--wlf Dreambooth model trained by Gairiangsui following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 215022 Sample pictures of this concept: ![0](https://huggingface.co/Gairiangsui/my-wolf-wlf/resolve/main/sample_images/a2aaa27b-effe-4c04-9ec5-bf6db9d666ae.jpeg) ![1](https://huggingface.co/Gairiangsui/my-wolf-wlf/resolve/main/sample_images/46d58259-1678-4c6a-b0d7-f343954a76f1.jpeg) ![2](https://huggingface.co/Gairiangsui/my-wolf-wlf/resolve/main/sample_images/753e54cb-5615-493c-94ff-43c388084f27.jpeg)
dpv/Mistral7bInstruct-MarketingEmailAdaptor4QV	dpv	2023-12-03T16:54:21Z	6	0	peft	[ "peft", "safetensors", "region:us" ]	null	2023-12-03T16:13:48Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0
Anwaarma/BP-MM05	Anwaarma	2023-12-03T16:51:44Z	13	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:Anwaarma/BP-INT-I04", "base_model:finetune:Anwaarma/BP-INT-I04", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-03T16:30:21Z	--- license: mit base_model: Anwaarma/BP-test4 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: BP-S02andInt03 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. --> # BP-S02andInt03 This model is a fine-tuned version of [Anwaarma/BP-test4](https://huggingface.co/Anwaarma/BP-test4) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4031 - Accuracy: 0.82 - F1: 0.8097 ## 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: 13 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 0.0 \| 50 \| 1.1241 \| 0.54 \| 0.4220 \| \| No log \| 0.01 \| 100 \| 0.8697 \| 0.51 \| 0.4402 \| \| No log \| 0.01 \| 150 \| 0.7063 \| 0.37 \| 0.3740 \| \| No log \| 0.02 \| 200 \| 0.6895 \| 0.51 \| 0.4184 \| \| No log \| 0.02 \| 250 \| 0.6880 \| 0.52 \| 0.4467 \| \| No log \| 0.02 \| 300 \| 0.6874 \| 0.52 \| 0.4778 \| \| No log \| 0.03 \| 350 \| 0.6842 \| 0.52 \| 0.4778 \| \| No log \| 0.03 \| 400 \| 0.6889 \| 0.5 \| 0.4491 \| \| No log \| 0.04 \| 450 \| 0.6727 \| 0.55 \| 0.5398 \| \| 0.7977 \| 0.04 \| 500 \| 0.6617 \| 0.59 \| 0.5877 \| \| 0.7977 \| 0.04 \| 550 \| 0.6514 \| 0.59 \| 0.5877 \| \| 0.7977 \| 0.05 \| 600 \| 0.6597 \| 0.59 \| 0.5877 \| \| 0.7977 \| 0.05 \| 650 \| 0.6322 \| 0.59 \| 0.5877 \| \| 0.7977 \| 0.06 \| 700 \| 0.5898 \| 0.57 \| 0.5655 \| \| 0.7977 \| 0.06 \| 750 \| 0.5406 \| 0.7 \| 0.7015 \| \| 0.7977 \| 0.06 \| 800 \| 0.4813 \| 0.8 \| 0.7862 \| \| 0.7977 \| 0.07 \| 850 \| 0.4706 \| 0.8 \| 0.7862 \| \| 0.7977 \| 0.07 \| 900 \| 0.4743 \| 0.79 \| 0.7768 \| \| 0.7977 \| 0.08 \| 950 \| 0.4578 \| 0.8 \| 0.7862 \| \| 0.5646 \| 0.08 \| 1000 \| 0.4571 \| 0.8 \| 0.7862 \| \| 0.5646 \| 0.08 \| 1050 \| 0.4536 \| 0.8 \| 0.7862 \| \| 0.5646 \| 0.09 \| 1100 \| 0.4461 \| 0.8 \| 0.7862 \| \| 0.5646 \| 0.09 \| 1150 \| 0.4451 \| 0.8 \| 0.7862 \| \| 0.5646 \| 0.1 \| 1200 \| 0.4398 \| 0.81 \| 0.7956 \| \| 0.5646 \| 0.1 \| 1250 \| 0.4360 \| 0.8 \| 0.7862 \| \| 0.5646 \| 0.1 \| 1300 \| 0.4325 \| 0.81 \| 0.7956 \| \| 0.5646 \| 0.11 \| 1350 \| 0.4316 \| 0.81 \| 0.7956 \| \| 0.5646 \| 0.11 \| 1400 \| 0.4310 \| 0.81 \| 0.7956 \| \| 0.5646 \| 0.12 \| 1450 \| 0.4301 \| 0.81 \| 0.7956 \| \| 0.4672 \| 0.12 \| 1500 \| 0.4275 \| 0.81 \| 0.7956 \| \| 0.4672 \| 0.12 \| 1550 \| 0.4271 \| 0.8 \| 0.7862 \| \| 0.4672 \| 0.13 \| 1600 \| 0.4258 \| 0.81 \| 0.7956 \| \| 0.4672 \| 0.13 \| 1650 \| 0.4211 \| 0.81 \| 0.7956 \| \| 0.4672 \| 0.14 \| 1700 \| 0.4154 \| 0.82 \| 0.8097 \| \| 0.4672 \| 0.14 \| 1750 \| 0.4153 \| 0.81 \| 0.7956 \| \| 0.4672 \| 0.14 \| 1800 \| 0.4120 \| 0.81 \| 0.7956 \| \| 0.4672 \| 0.15 \| 1850 \| 0.4134 \| 0.8 \| 0.7862 \| \| 0.4672 \| 0.15 \| 1900 \| 0.4119 \| 0.8 \| 0.7862 \| \| 0.4672 \| 0.16 \| 1950 \| 0.4119 \| 0.82 \| 0.8097 \| \| 0.4371 \| 0.16 \| 2000 \| 0.4094 \| 0.82 \| 0.8097 \| \| 0.4371 \| 0.16 \| 2050 \| 0.4113 \| 0.82 \| 0.8097 \| \| 0.4371 \| 0.17 \| 2100 \| 0.4136 \| 0.83 \| 0.8259 \| \| 0.4371 \| 0.17 \| 2150 \| 0.4096 \| 0.82 \| 0.8097 \| \| 0.4371 \| 0.18 \| 2200 \| 0.4116 \| 0.82 \| 0.8097 \| \| 0.4371 \| 0.18 \| 2250 \| 0.4039 \| 0.82 \| 0.8097 \| \| 0.4371 \| 0.18 \| 2300 \| 0.4044 \| 0.82 \| 0.8097 \| \| 0.4371 \| 0.19 \| 2350 \| 0.4031 \| 0.82 \| 0.8097 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
pijarcandra22/t5Sunda2Indo	pijarcandra22	2023-12-03T16:41:20Z	3	0	transformers	[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-03T14:43:36Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_keras_callback model-index: - name: pijarcandra22/t5Sunda2Indo 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. --> # pijarcandra22/t5Sunda2Indo This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.6406 - Validation Loss: 1.5932 - Epoch: 140 ## 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 \| \|:----------:\|:---------------:\|:-----:\| \| 3.9668 \| 3.4054 \| 0 \| \| 3.5451 \| 3.1460 \| 1 \| \| 3.3386 \| 2.9773 \| 2 \| \| 3.1917 \| 2.8549 \| 3 \| \| 3.0808 \| 2.7568 \| 4 \| \| 2.9921 \| 2.6780 \| 5 \| \| 2.9187 \| 2.6135 \| 6 \| \| 2.8536 \| 2.5547 \| 7 \| \| 2.8010 \| 2.5040 \| 8 \| \| 2.7482 \| 2.4601 \| 9 \| \| 2.7056 \| 2.4227 \| 10 \| \| 2.6709 \| 2.3870 \| 11 \| \| 2.6325 \| 2.3554 \| 12 \| \| 2.6016 \| 2.3233 \| 13 \| \| 2.5685 \| 2.2965 \| 14 \| \| 2.5407 \| 2.2710 \| 15 \| \| 2.5158 \| 2.2486 \| 16 \| \| 2.4905 \| 2.2248 \| 17 \| \| 2.4680 \| 2.2037 \| 18 \| \| 2.4459 \| 2.1856 \| 19 \| \| 2.4257 \| 2.1684 \| 20 \| \| 2.4045 \| 2.1495 \| 21 \| \| 2.3861 \| 2.1325 \| 22 \| \| 2.3676 \| 2.1189 \| 23 \| \| 2.3479 \| 2.1028 \| 24 \| \| 2.3333 \| 2.0873 \| 25 \| \| 2.3192 \| 2.0743 \| 26 \| \| 2.3035 \| 2.0647 \| 27 \| \| 2.2889 \| 2.0504 \| 28 \| \| 2.2742 \| 2.0395 \| 29 \| \| 2.2608 \| 2.0285 \| 30 \| \| 2.2471 \| 2.0166 \| 31 \| \| 2.2344 \| 2.0078 \| 32 \| \| 2.2211 \| 1.9999 \| 33 \| \| 2.2086 \| 1.9893 \| 34 \| \| 2.1965 \| 1.9790 \| 35 \| \| 2.1879 \| 1.9724 \| 36 \| \| 2.1774 \| 1.9637 \| 37 \| \| 2.1663 \| 1.9537 \| 38 \| \| 2.1573 \| 1.9461 \| 39 \| \| 2.1470 \| 1.9389 \| 40 \| \| 2.1344 \| 1.9329 \| 41 \| \| 2.1259 \| 1.9257 \| 42 \| \| 2.1192 \| 1.9158 \| 43 \| \| 2.1098 \| 1.9092 \| 44 \| \| 2.0993 \| 1.9021 \| 45 \| \| 2.0930 \| 1.8970 \| 46 \| \| 2.0831 \| 1.8909 \| 47 \| \| 2.0729 \| 1.8845 \| 48 \| \| 2.0669 \| 1.8799 \| 49 \| \| 2.0587 \| 1.8746 \| 50 \| \| 2.0519 \| 1.8662 \| 51 \| \| 2.0446 \| 1.8605 \| 52 \| \| 2.0338 \| 1.8552 \| 53 \| \| 2.0297 \| 1.8494 \| 54 \| \| 2.0207 \| 1.8441 \| 55 \| \| 2.0151 \| 1.8404 \| 56 \| \| 2.0116 \| 1.8346 \| 57 \| \| 2.0029 \| 1.8286 \| 58 \| \| 1.9942 \| 1.8243 \| 59 \| \| 1.9894 \| 1.8177 \| 60 \| \| 1.9820 \| 1.8145 \| 61 \| \| 1.9753 \| 1.8100 \| 62 \| \| 1.9698 \| 1.8054 \| 63 \| \| 1.9635 \| 1.8001 \| 64 \| \| 1.9588 \| 1.7963 \| 65 \| \| 1.9533 \| 1.7895 \| 66 \| \| 1.9443 \| 1.7888 \| 67 \| \| 1.9381 \| 1.7846 \| 68 \| \| 1.9320 \| 1.7806 \| 69 \| \| 1.9281 \| 1.7755 \| 70 \| \| 1.9232 \| 1.7697 \| 71 \| \| 1.9141 \| 1.7672 \| 72 \| \| 1.9128 \| 1.7655 \| 73 \| \| 1.9041 \| 1.7611 \| 74 \| \| 1.8987 \| 1.7558 \| 75 \| \| 1.8955 \| 1.7498 \| 76 \| \| 1.8874 \| 1.7493 \| 77 \| \| 1.8845 \| 1.7433 \| 78 \| \| 1.8793 \| 1.7403 \| 79 \| \| 1.8723 \| 1.7370 \| 80 \| \| 1.8669 \| 1.7319 \| 81 \| \| 1.8626 \| 1.7323 \| 82 \| \| 1.8593 \| 1.7268 \| 83 \| \| 1.8540 \| 1.7235 \| 84 \| \| 1.8504 \| 1.7204 \| 85 \| \| 1.8468 \| 1.7180 \| 86 \| \| 1.8398 \| 1.7130 \| 87 \| \| 1.8358 \| 1.7088 \| 88 \| \| 1.8321 \| 1.7081 \| 89 \| \| 1.8271 \| 1.7042 \| 90 \| \| 1.8203 \| 1.7016 \| 91 \| \| 1.8189 \| 1.6985 \| 92 \| \| 1.8117 \| 1.6967 \| 93 \| \| 1.8089 \| 1.6924 \| 94 \| \| 1.8035 \| 1.6898 \| 95 \| \| 1.7984 \| 1.6904 \| 96 \| \| 1.7957 \| 1.6836 \| 97 \| \| 1.7925 \| 1.6833 \| 98 \| \| 1.7880 \| 1.6786 \| 99 \| \| 1.7816 \| 1.6770 \| 100 \| \| 1.7772 \| 1.6739 \| 101 \| \| 1.7754 \| 1.6733 \| 102 \| \| 1.7712 \| 1.6678 \| 103 \| \| 1.7677 \| 1.6638 \| 104 \| \| 1.7641 \| 1.6627 \| 105 \| \| 1.7601 \| 1.6609 \| 106 \| \| 1.7573 \| 1.6585 \| 107 \| \| 1.7516 \| 1.6559 \| 108 \| \| 1.7478 \| 1.6523 \| 109 \| \| 1.7443 \| 1.6523 \| 110 \| \| 1.7406 \| 1.6498 \| 111 \| \| 1.7361 \| 1.6475 \| 112 \| \| 1.7326 \| 1.6435 \| 113 \| \| 1.7285 \| 1.6422 \| 114 \| \| 1.7244 \| 1.6398 \| 115 \| \| 1.7205 \| 1.6386 \| 116 \| \| 1.7187 \| 1.6347 \| 117 \| \| 1.7157 \| 1.6335 \| 118 \| \| 1.7113 \| 1.6317 \| 119 \| \| 1.7094 \| 1.6308 \| 120 \| \| 1.7074 \| 1.6267 \| 121 \| \| 1.7007 \| 1.6252 \| 122 \| \| 1.6965 \| 1.6241 \| 123 \| \| 1.6931 \| 1.6231 \| 124 \| \| 1.6913 \| 1.6201 \| 125 \| \| 1.6862 \| 1.6174 \| 126 \| \| 1.6833 \| 1.6176 \| 127 \| \| 1.6827 \| 1.6122 \| 128 \| \| 1.6774 \| 1.6127 \| 129 \| \| 1.6726 \| 1.6119 \| 130 \| \| 1.6698 \| 1.6079 \| 131 \| \| 1.6663 \| 1.6077 \| 132 \| \| 1.6631 \| 1.6055 \| 133 \| \| 1.6617 \| 1.6043 \| 134 \| \| 1.6573 \| 1.6019 \| 135 \| \| 1.6549 \| 1.5994 \| 136 \| \| 1.6514 \| 1.5990 \| 137 \| \| 1.6484 \| 1.5965 \| 138 \| \| 1.6490 \| 1.5942 \| 139 \| \| 1.6406 \| 1.5932 \| 140 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0
oieieio/Orca-2-13b-awq	oieieio	2023-12-03T16:38:40Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "orca", "orca2", "microsoft", "arxiv:2311.11045", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2023-12-01T23:52:55Z	--- pipeline_tag: text-generation tags: - orca - orca2 - microsoft license: other license_name: microsoft-research-license license_link: LICENSE --- # Orca-2-13b-awq \| Quantized 4 Bit <!-- Provide a quick summary of what the model is/does. --> Orca 2 is built for research purposes only and provides a single turn response in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization. The model is designed to excel particularly in reasoning. Note that: 1. This is a research model, intended to show that we can use capable models and complex workflows (advanced prompts, multiple calls) to create synthetic data that can teach Small Language Models (SLMs) new capabilities. We chose reasoning because it is a widely useful capability that SLMs lack. 2. The model is not optimized for chat and has not been trained with RLHF or DPO. It is best used after being finetuned for chat or for a specific task. 3. Beyond reasoning, the model inherits capabilities and limitations of its base (LLAMA-2 base). We have already seen that the benefits of the Orca training can be applied to other base model too. We make Orca 2's weights publicly available to support further research on the development, evaluation, and alignment of SLMs. ## What is Orca 2’s intended use(s)? + Orca 2 is built for research purposes only. + The main purpose is to allow the research community to assess its abilities and to provide a foundation for building better frontier models. ## How was Orca 2 evaluated? + Orca 2 has been evaluated on a large number of tasks ranging from reasoning to grounding and safety. Please refer to Section 6 and Appendix in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf) for details on evaluations. ## Model Details Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was moderated using the Microsoft Azure content filters. More details about the model can be found in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf). Please refer to LLaMA-2 technical report for details on the model architecture. ## License Orca 2 is licensed under the [Microsoft Research License](LICENSE). Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved. ## Bias, Risks, and Limitations Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the common limitations of other large language models or limitation caused by its training process, including: Data Biases: Large language models, trained on extensive data, can inadvertently carry biases present in the source data. Consequently, the models may generate outputs that could be potentially biased or unfair. Lack of Contextual Understanding: Despite their impressive capabilities in language understanding and generation, these models exhibit limited real-world understanding, resulting in potential inaccuracies or nonsensical responses. Lack of Transparency: Due to the complexity and size, large language models can act as “black boxes”, making it difficult to comprehend the rationale behind specific outputs or decisions. We recommend reviewing transparency notes from Azure for more information. Content Harms: There are various types of content harms that large language models can cause. It is important to be aware of them when using these models, and to take actions to prevent them. It is recommended to leverage various content moderation services provided by different companies and institutions. On an important note, we hope for better regulations and standards from government and technology leaders around content harms for AI technologies in future. We value and acknowledge the important role that research and open source community can play in this direction. Hallucination: It is important to be aware and cautious not to entirely rely on a given language model for critical decisions or information that might have deep impact as it is not obvious how to prevent these models from fabricating content. Moreover, it is not clear whether small models may be more susceptible to hallucination in ungrounded generation use cases due to their smaller sizes and hence reduced memorization capacities. This is an active research topic and we hope there will be more rigorous measurement, understanding and mitigations around this topic. Potential for Misuse: Without suitable safeguards, there is a risk that these models could be maliciously used for generating disinformation or harmful content. Data Distribution: Orca 2’s performance is likely to correlate strongly with the distribution of the tuning data. This correlation might limit its accuracy in areas underrepresented in the training dataset such as math, coding, and reasoning. System messages: Orca 2 demonstrates variance in performance depending on the system instructions. Additionally, the stochasticity introduced by the model size may lead to generation of non-deterministic responses to different system instructions. Zero-Shot Settings: Orca 2 was trained on data that mostly simulate zero-shot settings. While the model demonstrate very strong performance in zero-shot settings, it does not show the same gains of using few-shot learning compared to other, specially larger, models. Synthetic data: As Orca 2 is trained on synthetic data, it could inherit both the advantages and shortcomings of the models and methods used for data generation. We posit that Orca 2 benefits from the safety measures incorporated during training and safety guardrails (e.g., content filter) within the Azure OpenAI API. However, detailed studies are required for better quantification of such risks. This model is solely designed for research settings, and its testing has only been carried out in such environments. It should not be used in downstream applications, as additional analysis is needed to assess potential harm or bias in the proposed application. ## Getting started with Orca-2-13b-awq Inference with Hugging Face library ```python !pip install autoawq ``` ```python !pip install torch --upgrade --index-url https://download.pytorch.org/whl/cu121 ``` ```python #@title Live Chat with Orca import torch from transformers import AutoTokenizer, AutoModelForCausalLM # Load the tokenizer and model quant_path = "oieieio/Orca-2-13b-awq" tokenizer = AutoTokenizer.from_pretrained(quant_path) model = AutoModelForCausalLM.from_pretrained(quant_path) # Move the model to GPU if available device = 'cuda' if torch.cuda.is_available() else 'cpu' model.to(device) # Initial system message system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant..." while True: # User input user_message = input("User: ") if user_message.lower() == 'quit': break # Construct the prompt prompt = f"system\n{system_message}\nuser\n{user_message}\nassistant" # Encode and generate response inputs = tokenizer(prompt, return_tensors='pt').to(device) output_ids = model.generate(inputs["input_ids"], max_length=512) answer = tokenizer.decode(output_ids[0], skip_special_tokens=True) # Print the response print("AI: ", answer) ``` ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM # Load the tokenizer and model quant_path = "oieieio/Orca-2-13b-awq" tokenizer = AutoTokenizer.from_pretrained(quant_path) model = AutoModelForCausalLM.from_pretrained(quant_path) # Move the model to GPU if available device = 'cuda' if torch.cuda.is_available() else 'cpu' model.to(device) # Initial system message system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant..." while True: user_message = input("User: ") if user_message.lower() == 'quit': break prompt = f"system\n{system_message}\nuser\n{user_message}\nassistant" inputs = tokenizer(prompt, return_tensors='pt').to(device) output_ids = model.generate( inputs["input_ids"], max_new_tokens=50, # Adjust the number of generated tokens temperature=0.7, # Adjust for randomness top_k=50, # Adjust the number of highest probability tokens to consider top_p=0.95, # Adjust the cumulative probability threshold do_sample=True # Use sampling instead of greedy decoding ) answer = tokenizer.decode(output_ids[0], skip_special_tokens=True) print("AI: ", answer) ``` ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM # Load the tokenizer and model from Hugging Face Hub quant_path = "oieieio/Orca-2-13b-awq" tokenizer = AutoTokenizer.from_pretrained(quant_path) model = AutoModelForCausalLM.from_pretrained(quant_path) # Move the model to GPU if available device = 'cuda' if torch.cuda.is_available() else 'cpu' model.to(device) # First turn of the conversation system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior." user_message = "How can you determine if a restaurant is popular among locals or mainly attracts tourists, and why might this information be useful?" prompt = f"system\n{system_message}\nuser\n{user_message}\nassistant" # Encode the first prompt inputs = tokenizer(prompt, return_tensors='pt').to(device) output_ids = model.generate(inputs["input_ids"], max_length=512) # Decode the first response answer = tokenizer.decode(output_ids[0], skip_special_tokens=True) # Print the first response print(answer) # Second turn of the conversation second_turn_user_message = "Give me a list of the key points of your first answer." # Append the second turn message to the already generated ids without adding special tokens second_turn_message_in_markup = f"\nuser\n{second_turn_user_message}\nassistant" second_turn_tokens = tokenizer(second_turn_message_in_markup, return_tensors='pt', add_special_tokens=False).to(device) second_turn_input_ids = torch.cat([output_ids, second_turn_tokens['input_ids']], dim=1) # Generate the second response output_ids_2 = model.generate(second_turn_input_ids, max_length=1024) # Decode the second response second_turn_answer = tokenizer.decode(output_ids_2[0], skip_special_tokens=True) # Print the second response print(second_turn_answer) ``` ## Citation ```bibtex @misc{mitra2023orca, title={Orca 2: Teaching Small Language Models How to Reason}, author={Arindam Mitra and Luciano Del Corro and Shweti Mahajan and Andres Codas and Clarisse Simoes and Sahaj Agrawal and Xuxi Chen and Anastasia Razdaibiedina and Erik Jones and Kriti Aggarwal and Hamid Palangi and Guoqing Zheng and Corby Rosset and Hamed Khanpour and Ahmed Awadallah}, year={2023}, eprint={2311.11045}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```
Ahmed107/nllb200-ar-en-v2	Ahmed107	2023-12-03T16:37:32Z	14	0	transformers	[ "transformers", "tensorboard", "safetensors", "m2m_100", "text2text-generation", "translation", "generated_from_trainer", "base_model:nadsoft/Faseeh-v0.1-beta", "base_model:finetune:nadsoft/Faseeh-v0.1-beta", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-12-03T15:19:28Z	--- license: cc-by-nc-4.0 base_model: nadsoft/Faseeh-v0.1-beta tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: nllb200-ar-en-v2 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. --> # nllb200-ar-en-v2 This model is a fine-tuned version of [nadsoft/Faseeh-v0.1-beta](https://huggingface.co/nadsoft/Faseeh-v0.1-beta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8491 - Bleu: 49.3964 ## 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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
hedderich/ppo-LunarLander-v2	hedderich	2023-12-03T16:37:05Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T15:45:04Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 261.33 +/- 19.66 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow4	FounderOfHuggingface	2023-12-03T16:36:23Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T16:36:20Z	--- 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] ## Training procedure ### Framework versions - PEFT 0.6.2
Ghali20/test_1	Ghali20	2023-12-03T16:27:58Z	4	1	peft	[ "peft", "arxiv:1910.09700", "base_model:HuggingFaceH4/zephyr-7b-alpha", "base_model:adapter:HuggingFaceH4/zephyr-7b-alpha", "region:us" ]	null	2023-12-03T16:27:56Z	--- library_name: peft base_model: HuggingFaceH4/zephyr-7b-alpha --- # 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2
JaiSurya/q-FrozenLake-v1-4x4-noSlippery	JaiSurya	2023-12-03T16:27:06Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T16:27:03Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage model = load_from_hub(repo_id="JaiSurya/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"])
annawleo/mistral-finetuned-alpaca	annawleo	2023-12-03T16:16:20Z	0	0	null	[ "tensorboard", "safetensors", "generated_from_trainer", "dataset:generator", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "base_model:finetune:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]	null	2023-12-03T14:40:05Z	--- license: apache-2.0 base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ tags: - generated_from_trainer datasets: - generator model-index: - name: mistral-finetuned-alpaca 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-finetuned-alpaca This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the generator 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: 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: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
yily/glm-lora-stroke-47000	yily	2023-12-03T16:15:39Z	0	0	peft	[ "peft", "region:us" ]	null	2023-12-03T16:14:41Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
Kyriota/fastchat-3b-lora-gsm8k-baseline	Kyriota	2023-12-03T16:12:15Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:lmsys/fastchat-t5-3b-v1.0", "base_model:adapter:lmsys/fastchat-t5-3b-v1.0", "region:us" ]	null	2023-12-03T11:54:03Z	--- library_name: peft base_model: lmsys/fastchat-t5-3b-v1.0 --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
AlvinxLukilah/TextGenerationV2	AlvinxLukilah	2023-12-03T16:10:43Z	7	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "license:creativeml-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-29T13:11:20Z	--- license: creativeml-openrail-m ---
yily/glm-lora-stroke-46000	yily	2023-12-03T16:10:11Z	0	0	peft	[ "peft", "region:us" ]	null	2023-12-03T16:09:03Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
VitaliiVrublevskyi/bert-base-cased-finetuned-mrpc	VitaliiVrublevskyi	2023-12-03T16:01:47Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-03T14:41:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: bert-base-cased-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8382352941176471 - name: F1 type: f1 value: 0.8873720136518771 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-mrpc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5719 - Accuracy: 0.8382 - F1: 0.8874 ## 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: 91 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 115 \| 0.5228 \| 0.7549 \| 0.8471 \| \| No log \| 2.0 \| 230 \| 0.4315 \| 0.8088 \| 0.8673 \| \| No log \| 3.0 \| 345 \| 0.4212 \| 0.8284 \| 0.8785 \| \| No log \| 4.0 \| 460 \| 0.5462 \| 0.8382 \| 0.8889 \| \| 0.3551 \| 5.0 \| 575 \| 0.5719 \| 0.8382 \| 0.8874 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.13.3
FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow0	FounderOfHuggingface	2023-12-03T15:49:59Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]	null	2023-12-03T15:49:56Z	--- 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] ## Training procedure ### Framework versions - PEFT 0.6.2
Protao/ppo-Huggy	Protao	2023-12-03T15:45:28Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-12-03T15:43:48Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Protao/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀 可以在 https://huggingface.co/spaces/ThomasSimonini/Huggy 与训练的agent进行交互
nu-dialogue/japanese-vlm-fp16-lm-sfcoco2023-lora	nu-dialogue	2023-12-03T15:42:29Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:stabilityai/japanese-stablelm-instruct-gamma-7b", "base_model:adapter:stabilityai/japanese-stablelm-instruct-gamma-7b", "region:us" ]	null	2023-12-03T15:42:25Z	--- library_name: peft base_model: stabilityai/japanese-stablelm-instruct-gamma-7b --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
cozgo/llama-2-7b-advisor-low-rank	cozgo	2023-12-03T15:35:36Z	0	0	peft	[ "peft", "safetensors", "region:us" ]	null	2023-12-03T14:24:45Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
sezinarseven/falcon-7b-instruct-medicalQA	sezinarseven	2023-12-03T15:28:51Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:vilsonrodrigues/falcon-7b-instruct-sharded", "base_model:finetune:vilsonrodrigues/falcon-7b-instruct-sharded", "license:apache-2.0", "region:us" ]	null	2023-12-03T15:28:39Z	--- license: apache-2.0 base_model: vilsonrodrigues/falcon-7b-instruct-sharded tags: - generated_from_trainer model-index: - name: falcon-7b-instruct-medicalQA 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. --> # falcon-7b-instruct-medicalQA This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 180 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
byteflow/xxmix9realxl	byteflow	2023-12-03T15:26:01Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-12-03T15:04:01Z	--- license: creativeml-openrail-m ---
BadreddineHug/zyph_OCR	BadreddineHug	2023-12-03T15:12:13Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:HuggingFaceH4/zephyr-7b-alpha", "base_model:adapter:HuggingFaceH4/zephyr-7b-alpha", "region:us" ]	null	2023-12-03T15:12:11Z	--- library_name: peft base_model: HuggingFaceH4/zephyr-7b-alpha --- # 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2
byteflow/drmshprxl	byteflow	2023-12-03T15:11:11Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-12-03T15:05:53Z	--- license: creativeml-openrail-m ---
KnutJaegersberg/black_goo_recipe_e	KnutJaegersberg	2023-12-03T15:11:02Z	2,463	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "custom_code", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-04T21:08:02Z	--- license: cc-by-nc-4.0 --- Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: ```
KnutJaegersberg/deacon-13b-awq	KnutJaegersberg	2023-12-03T15:10:33Z	4	0	transformers	[ "transformers", "llama", "text-generation", "custom_code", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-09-20T17:48:03Z	--- license: cc-by-nc-4.0 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/4OQkvAa1zOK4Devv-aUdL.png) This model was fine tuned on AI filtered subsets of GPT-4 based subset of the Dolphin dataset and EvolInstruct V2. It has not been explicitly aligned to positive, negative or bureaucratically prescribed value systems. It might kill us all! Time to shit your pants, regulators. I literally put black goo on Dolphin-7B sperm, which then fertilized Evolved Instructions... What's different is evil... ;) I intend to train 3 sizes. Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: ```
KnutJaegersberg/black_goo_recipe_c	KnutJaegersberg	2023-12-03T15:09:33Z	2,503	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "custom_code", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-01T06:35:49Z	--- license: cc-by-nc-4.0 --- Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: To fine-tune a large language model, we use various techniques to improve its performance and adjust its parameters based on user feedback. These techniques include: 1. Fine tuning the original model with additional data. For example, adding more training examples or using specialized datasets such as question answering or natural language inference tasks. This helps the model learn new concepts and skills by interacting with additional information sources. 2. Using transfer learning methods to incorporate knowledge from other models that have been pre-trained for specific domains. For instance, using pre-trained embeddings from neural networks trained on text classification tasks like sentiment analysis or question answering to help fine-tuning the main language model. 3. Improving the quality of the training data. We may optimize the training process by increasing the accuracy of provided data sets (e.g., by improving word embedding quality), refining the selection of relevant examples, and/or providing additional supervision signals during training. 4. Incorporating knowledge from external resources such as domain-specific lexicons or domain-specific knowledge bases. For example, incorporating domain-specific rules and facts into the model to help it understand domain-specific terminology and semantics. 5. Employing techniques like dropout regularization and dropout sampling to avoid overfitting and reduce overconfidence in the model's predictions. 6. Utilizing meta-learning methods such as reinforcement learning or self-supervised learning to enable the model to adapt quickly to changing situations without being explicitly programmed. 7. Incorporating human input through crowdsourcing or user testing to validate the model's performance and ensure user satisfaction. 8. Implementing various optimization strategies to improve efficiency and reduce computational costs while maintaining high performance levels. In conclusion, fine tuning a large language model requires combining various techniques including fine tuning, transfer learning, improved data quality, external resources, meta-learning, optimization strategies, crowd sourcing, and user testing. These approaches aim to improve the model's ability to handle various tasks and provide users with better experiences in their daily interactions with language systems. ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__black_goo_recipe_c) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 34.19 \| \| ARC (25-shot) \| 38.74 \| \| HellaSwag (10-shot) \| 66.83 \| \| MMLU (5-shot) \| 26.57 \| \| TruthfulQA (0-shot) \| 36.54 \| \| Winogrande (5-shot) \| 64.72 \| \| GSM8K (5-shot) \| 0.68 \| \| DROP (3-shot) \| 5.23 \|
KnutJaegersberg/Deacon-34B-200k-AWQ	KnutJaegersberg	2023-12-03T15:08:12Z	8	5	transformers	[ "transformers", "safetensors", "llama", "text-generation", "dataset:totally-not-an-llm/EverythingLM-data-V3", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2023-11-11T03:19:30Z	--- license: other license_name: yi-license license_link: LICENSE datasets: - totally-not-an-llm/EverythingLM-data-V3 pipeline_tag: text-generation --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/4OQkvAa1zOK4Devv-aUdL.png) In this case the tokenizer is the yi_tokenizer, loading it requires trust_remote_code=True Have some fun with this fellow. It can eat a lot of vram, depends on settings to make it useable on two 24 gb vram gpus: Without fused attention, it's 27 gb vram, will need some if if yo do stuff. ![image.png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/1cbqKp55WhN4BQD337E-n.png) You can also let if have fused attention and just reduce the max_seq_length to something way smaller yet still useful ![image.png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/JRi4sakPziGpmOFCBfcJS.png) License The Yi series models are fully open for academic research and free commercial usage with permission via applications. All usage must adhere to the Model License Agreement 2.0. To apply for the official commercial license, please contact us ([email protected]). Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: ```
KnutJaegersberg/Deacon-34b-4bit	KnutJaegersberg	2023-12-03T15:07:59Z	3	2	transformers	[ "transformers", "pytorch", "llama", "feature-extraction", "text-generation", "dataset:totally-not-an-llm/EverythingLM-data-V3", "license:other", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2023-11-09T17:06:15Z	--- license: other license_name: yi-license license_link: LICENSE datasets: - totally-not-an-llm/EverythingLM-data-V3 pipeline_tag: text-generation --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/4OQkvAa1zOK4Devv-aUdL.png) The perfect organism. An adapter for KnutJaegersberg/Yi-34B-Llamafied. 5 epochs with NEFTune. The brand new bitsandbytes 4-bit file format. Run pip install -U bitsandbytes and install this pull requested transformer version: https://github.com/poedator/transformers/tree/save4 Might still be buggy. It was a bit too early. I'll wait until the functionality is officially supported, due to some cuda issues. I don't want to resolve those, I'll just wait a couple of days. Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/8KYMW1KGzyE19GWxSTG7m.png)
KnutJaegersberg/deacon-13b	KnutJaegersberg	2023-12-03T15:06:45Z	2,489	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "dataset:KnutJaegersberg/facehugger", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-20T06:35:12Z	--- license: cc-by-nc-4.0 datasets: - KnutJaegersberg/facehugger --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/4OQkvAa1zOK4Devv-aUdL.png) This model was fine tuned on AI filtered subsets of GPT-4 based subset of the Dolphin dataset and EvolInstruct V2. It has not been explicitly aligned to positive, negative or bureaucratically prescribed value systems. It might kill us all! Time to shit your pants, regulators. I literally put black goo on Dolphin-7B sperm, which then fertilized Evolved Instructions... What's different is evil... ;) I intend to train 3 sizes. Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__deacon-13b) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 46.78 \| \| ARC (25-shot) \| 57.85 \| \| HellaSwag (10-shot) \| 82.63 \| \| MMLU (5-shot) \| 55.25 \| \| TruthfulQA (0-shot) \| 39.33 \| \| Winogrande (5-shot) \| 76.32 \| \| GSM8K (5-shot) \| 10.39 \| \| DROP (3-shot) \| 5.67 \|
Miloou/Reinforce-Pixelcopter1	Miloou	2023-12-03T15:05:33Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T15:05:29Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 42.50 +/- 35.31 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
ichir3o4/syofh	ichir3o4	2023-12-03T15:04:59Z	0	0	allennlp	[ "allennlp", "ab", "dataset:HuggingFaceH4/no_robots", "license:bigscience-openrail-m", "region:us" ]	null	2023-12-03T15:04:04Z	--- license: bigscience-openrail-m datasets: - HuggingFaceH4/no_robots language: - ab metrics: - bleu library_name: allennlp ---
Korabbit/Llama-2-7b-chat-hf-afr-200step-merged	Korabbit	2023-12-03T14:46:35Z	1,551	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "en", "dataset:vicgalle/alpaca-gpt4", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-21T08:59:51Z	--- license: llama2 datasets: - vicgalle/alpaca-gpt4 language: - en --- Hi there! this is a test of my "AFR training" approach The base model used "Llama-2-7b-chat" System Prompt: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. \n If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. Instruction: Implement binary search in python. Response: Sure! Here is an example of how to implement a binary search algorithm in Python: ``` def binary_search(arr, target): # Initialize the index of the target element low = 0 high = len(arr) - 1 # Loop until the target element is found or the end of the array is reached while low <= high: # Calculate the middle index of the array mid = (low + high) // 2 # Check if the target element is in the middle index if arr[mid] == target: return mid # If the target element is not in the middle index, check if it is less than the middle index if arr[mid] > target: low = mid + 1 else: high = mid - 1 # If the target element is not found in the array, return -1 return -1 # Test the binary search function arr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] target = 5 result = binary_search(arr, target) print(f"The target element {target} is at index {result}") ``` This code defines a `binary_search` function that takes an array and a target element as input, and returns the index of the target element in the array if it is found, or -1 if it is not found. The function works by iteratively dividing the range of the array into two sub-ranges, one containing elements less than the target, and one containing elements greater than the target. It then checks the middle index of each sub-range to see if the target element is present. If the target element is found, the function returns the index of the middle index. If the target element is not found, the function returns -1. The code also includes a test case to demonstrate how to use the `binary_search` function. In this case, the target element is 5, and the function returns the index of the element 5 in the array. I hope this helps! Let me know if you have any questions.
musika/the-beatles	musika	2023-12-03T14:46:15Z	0	1	null	[ "audio", "music", "generation", "tensorflow", "arxiv:2208.08706", "license:mit", "region:us" ]	null	2023-12-03T14:46:06Z	--- license: mit tags: - audio - music - generation - tensorflow --- # Musika Model: the_beatles ## Model provided by: nobitachainsaw Pretrained the_beatles model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation. Introduced in [this paper](https://arxiv.org/abs/2208.08706). ## How to use You can generate music from this pretrained the_beatles model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r). ### Model description This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in switch.npy. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio. The generator has a context window of about 12 seconds of audio.
Yanni8/google-query-rating	Yanni8	2023-12-03T14:39:09Z	6	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "dataset:google_wellformed_query", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-03T14:12:09Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - google_wellformed_query model-index: - name: google-query-rating 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. --> # google-query-rating This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the google_wellformed_query dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
li-ping/supervised_ft_embedding_1203_v37	li-ping	2023-12-03T14:34:47Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-12-03T14:34:39Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # li-ping/supervised_ft_embedding_1203_v37 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('li-ping/supervised_ft_embedding_1203_v37') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=li-ping/supervised_ft_embedding_1203_v37) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 3 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 100, "evaluation_steps": 400, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 30, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
TechRoC123/carmodel	TechRoC123	2023-12-03T14:34:09Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-12-03T14:33:37Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer metrics: - f1 model-index: - name: carmodel 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. --> # carmodel This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0675 - F1: 0.9931 ## 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: 4 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.1732 \| 0.31 \| 500 \| 0.6651 \| 0.8403 \| \| 0.3989 \| 0.62 \| 1000 \| 0.2942 \| 0.9167 \| \| 0.2136 \| 0.93 \| 1500 \| 0.1782 \| 0.9542 \| \| 0.0549 \| 1.23 \| 2000 \| 0.2001 \| 0.9639 \| \| 0.0287 \| 1.54 \| 2500 \| 0.1304 \| 0.9819 \| \| 0.0091 \| 1.85 \| 3000 \| 0.1112 \| 0.9819 \| \| 0.0039 \| 2.16 \| 3500 \| 0.0667 \| 0.9917 \| \| 0.0023 \| 2.47 \| 4000 \| 0.0708 \| 0.9903 \| \| 0.0002 \| 2.78 \| 4500 \| 0.0635 \| 0.9931 \| \| 0.0002 \| 3.09 \| 5000 \| 0.0619 \| 0.9931 \| \| 0.0002 \| 3.4 \| 5500 \| 0.0730 \| 0.9917 \| \| 0.0 \| 3.7 \| 6000 \| 0.0684 \| 0.9917 \| \| 0.0009 \| 4.01 \| 6500 \| 0.0696 \| 0.9917 \| \| 0.0 \| 4.32 \| 7000 \| 0.0693 \| 0.9917 \| \| 0.0 \| 4.63 \| 7500 \| 0.0686 \| 0.9931 \| \| 0.0004 \| 4.94 \| 8000 \| 0.0675 \| 0.9931 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
srijan2024/my_awesome_billsum_model	srijan2024	2023-12-03T14:32:59Z	46	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-02T06:10:18Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_awesome_billsum_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0467 - Rouge1: 0.7832 - Rouge2: 0.692 - Rougel: 0.781 - Rougelsum: 0.7805 - Gen Len: 11.6071 ## 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: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 56 \| 4.9294 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 16.558 \| \| No log \| 2.0 \| 112 \| 2.2288 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 13.5357 \| \| No log \| 3.0 \| 168 \| 0.4763 \| 0.0045 \| 0.0045 \| 0.0045 \| 0.0045 \| 10.6518 \| \| No log \| 4.0 \| 224 \| 0.1138 \| 0.7232 \| 0.6205 \| 0.7245 \| 0.7236 \| 11.5893 \| \| No log \| 5.0 \| 280 \| 0.0654 \| 0.7417 \| 0.6339 \| 0.7417 \| 0.7402 \| 11.6607 \| \| No log \| 6.0 \| 336 \| 0.0587 \| 0.7321 \| 0.6205 \| 0.7321 \| 0.7309 \| 11.5938 \| \| No log \| 7.0 \| 392 \| 0.0552 \| 0.7496 \| 0.6473 \| 0.7491 \| 0.7491 \| 11.625 \| \| No log \| 8.0 \| 448 \| 0.0533 \| 0.7714 \| 0.6786 \| 0.7714 \| 0.7709 \| 11.6562 \| \| 1.6431 \| 9.0 \| 504 \| 0.0518 \| 0.781 \| 0.692 \| 0.7832 \| 0.7805 \| 11.6161 \| \| 1.6431 \| 10.0 \| 560 \| 0.0505 \| 0.764 \| 0.6652 \| 0.7632 \| 0.7614 \| 11.6607 \| \| 1.6431 \| 11.0 \| 616 \| 0.0494 \| 0.7778 \| 0.6875 \| 0.78 \| 0.7773 \| 11.6116 \| \| 1.6431 \| 12.0 \| 672 \| 0.0488 \| 0.7778 \| 0.6875 \| 0.78 \| 0.7773 \| 11.6116 \| \| 1.6431 \| 13.0 \| 728 \| 0.0483 \| 0.781 \| 0.692 \| 0.7815 \| 0.7805 \| 11.6161 \| \| 1.6431 \| 14.0 \| 784 \| 0.0479 \| 0.781 \| 0.692 \| 0.7815 \| 0.7805 \| 11.6071 \| \| 1.6431 \| 15.0 \| 840 \| 0.0475 \| 0.7852 \| 0.6964 \| 0.7839 \| 0.7842 \| 11.6205 \| \| 1.6431 \| 16.0 \| 896 \| 0.0471 \| 0.7812 \| 0.692 \| 0.781 \| 0.7805 \| 11.5982 \| \| 1.6431 \| 17.0 \| 952 \| 0.0469 \| 0.7884 \| 0.7009 \| 0.7879 \| 0.7869 \| 11.625 \| \| 0.062 \| 18.0 \| 1008 \| 0.0468 \| 0.7832 \| 0.692 \| 0.781 \| 0.7805 \| 11.6071 \| \| 0.062 \| 19.0 \| 1064 \| 0.0467 \| 0.7864 \| 0.6964 \| 0.7839 \| 0.7837 \| 11.6027 \| \| 0.062 \| 20.0 \| 1120 \| 0.0467 \| 0.7832 \| 0.692 \| 0.781 \| 0.7805 \| 11.6071 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
Protao/ppo-MountainCar-v0	Protao	2023-12-03T14:26:28Z	0	0	stable-baselines3	[ "stable-baselines3", "MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-12-03T14:26:14Z	--- library_name: stable-baselines3 tags: - MountainCar-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: MountainCar-v0 type: MountainCar-v0 metrics: - type: mean_reward value: -138.00 +/- 31.51 name: mean_reward verified: false --- # PPO Agent playing MountainCar-v0 This is a trained model of a PPO agent playing MountainCar-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
digiplay/Gap_2.6	digiplay	2023-12-03T14:25:58Z	583	4	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-21T13:20:16Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- https://civitai.com/models/166637?modelVersionId=187469 Sample image generated by Hugginface's API: *Gap_2.6_mangledVAE_.safetensors ![8104b053-1a97-4d2e-9ac2-c4c59c74f22e.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/YLrTGIvdIqnbiAxzwXOSh.jpeg) Sample images generated by AUTOMATIC 1111: ![tmpv8p2x6vj.png](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/NQcuvmJu9iaPUmGTJ-ALV.png) ![tmp11s5b7ci.png](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/TJ0g_QLb8r3CJfM-UiFzb.png)
Schandkroete/distilbert-base-uncased_SLC-Sentiment-Finetuned	Schandkroete	2023-12-03T14:16:02Z	5	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-03T14:12:32Z	--- language: - en pipeline_tag: text-classification ---
Konstantin91/Konstantin91	Konstantin91	2023-12-03T14:12:39Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2023-12-03T14:11:54Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| None \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| True \| \| is_legacy_optimizer \| False \| \| learning_rate \| 4.999999873689376e-05 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| mixed_float16 \| ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>

jonduea/poca-SoccerTwos

jonduea

2023-12-03T21:09:08Z

6

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

reinforcement-learning

2023-12-03T21:08:25Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jonduea/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ahaanlimaye/ConsumerResponseClassifier

ahaanlimaye

2023-12-03T20:59:54Z

11

0

transformers

[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-03T18:24:55Z

--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: ConsumerResponseClassifier results: [] ---  # ConsumerResponseClassifier This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5301 - F1: 0.7668 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow26

FounderOfHuggingface

2023-12-03T20:51:53Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T20:51:49Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

VitaliiVrublevskyi/albert-large-v1-finetuned-mrpc

VitaliiVrublevskyi

2023-12-03T20:50:29Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "albert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-03T19:27:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: albert-large-v1-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8725490196078431 - name: F1 type: f1 value: 0.907473309608541 ---  # albert-large-v1-finetuned-mrpc This model is a fine-tuned version of [albert-large-v1](https://huggingface.co/albert-large-v1) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4479 - Accuracy: 0.8725 - F1: 0.9075 ## 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: 69 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 115 | 0.3323 | 0.8554 | 0.8970 | | No log | 2.0 | 230 | 0.3164 | 0.8799 | 0.9127 | | No log | 3.0 | 345 | 0.3368 | 0.8603 | 0.9019 | | No log | 4.0 | 460 | 0.4099 | 0.8676 | 0.9049 | | 0.2727 | 5.0 | 575 | 0.4479 | 0.8725 | 0.9075 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.13.3

javismiles/dec3v3model

javismiles

2023-12-03T20:45:27Z

0

diffusers

[ "diffusers", "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

2023-12-03T17:42:45Z

--- 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 - javismiles/dec3v3model These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the javismiles/dec3v4 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)

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow25

FounderOfHuggingface

2023-12-03T20:40:16Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T20:40:12Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

prushton/dreambooth-dog-test

prushton

2023-12-03T20:33:17Z

0

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-12-02T19:46:33Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - prushton/dreambooth-dog-test This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow24

FounderOfHuggingface

2023-12-03T20:28:42Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T20:28:38Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

Christos-14/ppo-LunarLander-v2

Christos-14

2023-12-03T20:27:00Z

0

stable-baselines3

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

reinforcement-learning

2023-12-03T20:26:43Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.32 +/- 17.58 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

userzyzz/hermes-vision-gguf

userzyzz

2023-12-03T20:25:36Z

88

1

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

2023-12-03T19:24:00Z

# Broken GGUF Tried to force convert an unsupported model. Unsurprisingly, it is completely broken. Does not run.

prushton/path_to_saved_model

prushton

2023-12-03T20:21:43Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-12-02T19:54:19Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - prushton/path_to_saved_model This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

stoves/Andreea_Lazu

stoves

2023-12-03T20:14:45Z

2

1

diffusers

[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-11-10T12:32:52Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of gjdfophge person tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.

hkivancoral/smids_1x_beit_base_rms_0001_fold3

hkivancoral

2023-12-03T20:13:55Z

15

0

transformers

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

image-classification

2023-12-02T11:19:21Z

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_1x_beit_base_rms_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: 0.7133333333333334 ---  # smids_1x_beit_base_rms_0001_fold3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7846 - Accuracy: 0.7133 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1199 | 1.0 | 75 | 1.1044 | 0.325 | | 1.1759 | 2.0 | 150 | 1.1239 | 0.47 | | 1.1465 | 3.0 | 225 | 0.9168 | 0.5 | | 0.8955 | 4.0 | 300 | 0.8917 | 0.5017 | | 0.8948 | 5.0 | 375 | 0.8301 | 0.5533 | | 0.9774 | 6.0 | 450 | 0.8272 | 0.5467 | | 0.8001 | 7.0 | 525 | 0.8058 | 0.5567 | | 0.7633 | 8.0 | 600 | 0.8140 | 0.545 | | 0.7814 | 9.0 | 675 | 0.7815 | 0.5733 | | 0.8175 | 10.0 | 750 | 0.7839 | 0.5633 | | 0.7605 | 11.0 | 825 | 0.7664 | 0.615 | | 0.762 | 12.0 | 900 | 0.7781 | 0.59 | | 0.6797 | 13.0 | 975 | 0.7875 | 0.575 | | 0.7699 | 14.0 | 1050 | 0.7772 | 0.6117 | | 0.6167 | 15.0 | 1125 | 0.8129 | 0.585 | | 0.7106 | 16.0 | 1200 | 0.7392 | 0.6633 | | 0.7174 | 17.0 | 1275 | 0.7176 | 0.6717 | | 0.704 | 18.0 | 1350 | 0.7772 | 0.63 | | 0.6617 | 19.0 | 1425 | 0.7359 | 0.65 | | 0.6722 | 20.0 | 1500 | 0.7009 | 0.6783 | | 0.676 | 21.0 | 1575 | 0.6946 | 0.6667 | | 0.6441 | 22.0 | 1650 | 0.7089 | 0.6917 | | 0.6565 | 23.0 | 1725 | 0.7160 | 0.665 | | 0.6009 | 24.0 | 1800 | 0.6902 | 0.6783 | | 0.6592 | 25.0 | 1875 | 0.7159 | 0.665 | | 0.6628 | 26.0 | 1950 | 0.7741 | 0.6233 | | 0.6044 | 27.0 | 2025 | 0.7147 | 0.66 | | 0.585 | 28.0 | 2100 | 0.6827 | 0.69 | | 0.5831 | 29.0 | 2175 | 0.6975 | 0.6833 | | 0.6301 | 30.0 | 2250 | 0.6815 | 0.6633 | | 0.6457 | 31.0 | 2325 | 0.6813 | 0.6817 | | 0.6492 | 32.0 | 2400 | 0.6894 | 0.6783 | | 0.5418 | 33.0 | 2475 | 0.7461 | 0.6783 | | 0.5925 | 34.0 | 2550 | 0.6773 | 0.6933 | | 0.5913 | 35.0 | 2625 | 0.6656 | 0.7083 | | 0.5761 | 36.0 | 2700 | 0.6491 | 0.7133 | | 0.528 | 37.0 | 2775 | 0.6784 | 0.7 | | 0.5718 | 38.0 | 2850 | 0.7007 | 0.6783 | | 0.5083 | 39.0 | 2925 | 0.6815 | 0.7 | | 0.5069 | 40.0 | 3000 | 0.6638 | 0.71 | | 0.4838 | 41.0 | 3075 | 0.6813 | 0.7167 | | 0.5071 | 42.0 | 3150 | 0.6709 | 0.7183 | | 0.5091 | 43.0 | 3225 | 0.6746 | 0.7167 | | 0.4355 | 44.0 | 3300 | 0.7138 | 0.71 | | 0.4287 | 45.0 | 3375 | 0.7080 | 0.7133 | | 0.3954 | 46.0 | 3450 | 0.7468 | 0.7 | | 0.3389 | 47.0 | 3525 | 0.7428 | 0.7183 | | 0.3613 | 48.0 | 3600 | 0.7469 | 0.725 | | 0.388 | 49.0 | 3675 | 0.7685 | 0.7167 | | 0.2972 | 50.0 | 3750 | 0.7846 | 0.7133 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

galaxy78/my_awesome_wnut_model

galaxy78

2023-12-03T20:08:10Z

14

1

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-12-03T10:11:43Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: my_awesome_wnut_model results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.55 - name: Recall type: recall value: 0.37720111214087115 - name: F1 type: f1 value: 0.44749862561847165 - name: Accuracy type: accuracy value: 0.9481063520560827 ---  # my_awesome_wnut_model This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.3958 - Precision: 0.55 - Recall: 0.3772 - F1: 0.4475 - Accuracy: 0.9481 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2562 | 0.5704 | 0.2929 | 0.3870 | 0.9417 | | No log | 2.0 | 426 | 0.2776 | 0.5462 | 0.3179 | 0.4019 | 0.9436 | | 0.1469 | 3.0 | 639 | 0.2834 | 0.5453 | 0.3624 | 0.4354 | 0.9475 | | 0.1469 | 4.0 | 852 | 0.3004 | 0.5669 | 0.3652 | 0.4442 | 0.9480 | | 0.0325 | 5.0 | 1065 | 0.3360 | 0.5858 | 0.3735 | 0.4561 | 0.9482 | | 0.0325 | 6.0 | 1278 | 0.3471 | 0.5149 | 0.3855 | 0.4409 | 0.9474 | | 0.0325 | 7.0 | 1491 | 0.3883 | 0.5552 | 0.3633 | 0.4392 | 0.9474 | | 0.0117 | 8.0 | 1704 | 0.3881 | 0.5602 | 0.3707 | 0.4462 | 0.9477 | | 0.0117 | 9.0 | 1917 | 0.4008 | 0.5582 | 0.3689 | 0.4442 | 0.9478 | | 0.0051 | 10.0 | 2130 | 0.3958 | 0.55 | 0.3772 | 0.4475 | 0.9481 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow22

FounderOfHuggingface

2023-12-03T20:05:28Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T20:05:24Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow21

FounderOfHuggingface

2023-12-03T19:53:45Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T19:53:40Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

entaroid/xlm-roberta-base-finetuned-panx-de

entaroid

2023-12-03T19:51:08Z

3

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-12-03T17:17:56Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.861220080732276 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1394 - F1: 0.8612 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 525 | 0.1621 | 0.8184 | | No log | 2.0 | 1050 | 0.1390 | 0.8502 | | No log | 3.0 | 1575 | 0.1394 | 0.8612 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.10.1 - Tokenizers 0.13.3

jerber/ppo-LunarLander-v2

jerber

2023-12-03T19:49:31Z

0

stable-baselines3

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

reinforcement-learning

2023-12-03T19:49:13Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.98 +/- 22.50 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

annabellehuether/legal-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_01wd

annabellehuether

2023-12-03T19:44:04Z

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:nlpaueb/legal-bert-base-uncased", "base_model:finetune:nlpaueb/legal-bert-base-uncased", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-03T18:41:27Z

--- license: cc-by-sa-4.0 base_model: nlpaueb/legal-bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: legal-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_01wd results: [] ---  # legal-bert-base-uncased-supreme-court-32batch_5epoch_2e5lr_01wd This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6773 - Accuracy: 0.5889 ## 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: 7 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6868 | 1.0 | 660 | 0.6805 | 0.5889 | | 0.6863 | 2.0 | 1320 | 0.6779 | 0.5889 | | 0.6839 | 3.0 | 1980 | 0.6779 | 0.5889 | | 0.6825 | 4.0 | 2640 | 0.6812 | 0.5889 | | 0.6838 | 5.0 | 3300 | 0.6773 | 0.5889 | ### Framework versions - Transformers 4.35.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

worldboss/llama-7B-alpaca-fine-tune-unsloth

worldboss

2023-12-03T19:43:53Z

2

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "base_model:unsloth/llama-2-7b", "base_model:finetune:unsloth/llama-2-7b", "license:llama2", "endpoints_compatible", "region:us" ]

null

2023-12-03T19:07:16Z

--- license: llama2 base_model: unsloth/llama-2-7b tags: - generated_from_trainer model-index: - name: outputs results: [] ---  # outputs This model is a fine-tuned version of [unsloth/llama-2-7b](https://huggingface.co/unsloth/llama-2-7b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 60 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

Ibrahim2001/PPO_Lunar_Lander_v2

Ibrahim2001

2023-12-03T19:43:46Z

0

stable-baselines3

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

reinforcement-learning

2023-12-03T14:14:00Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.18 +/- 16.90 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) ```python import gymnasium as gym from huggingface_sb3 import load_from_hub from stable_baselines3 import PPO from stable_baselines3.common.evaluation import evaluate_policy from stable_baselines3.common.monitor import Monitor repo_id = "Ibrahim2001/PPO_Lunar_Lander_v2" # The repo_id filename = "ppo-LunarLander-v2.zip" # The model filename.zip # When the model was trained on Python 3.8 the pickle protocol is 5 # But Python 3.6, 3.7 use protocol 4 # In order to get compatibility we need to: # 1. Install pickle5 # 2. Create a custom empty object we pass as parameter to PPO.load() custom_objects = { "learning_rate": 0.0, "lr_schedule": lambda _: 0.0, "clip_range": lambda _: 0.0, } checkpoint = load_from_hub(repo_id, filename) model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True) eval_env = Monitor(gym.make("LunarLander-v2")) mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") ```

GMabic/Taxi-v3

GMabic

2023-12-03T19:41:38Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-12-03T19:41:37Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="GMabic/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

ThuyNT03/KLTN_COQE_viT5_SAPOL_v2

ThuyNT03

2023-12-03T19:29:43Z

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-12-03T18:43:17Z

--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: KLTN_COQE_viT5_SAPOL_v2 results: [] ---  # KLTN_COQE_viT5_SAPOL_v2 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1

GMabic/GMabic

GMabic

2023-12-03T19:25:47Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-12-03T19:25:46Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: GMabic results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="GMabic/GMabic", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

GMabic/q-FrozenLake-v1-4x4-noSlippery

GMabic

2023-12-03T19:21:15Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-12-03T19:21:13Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="GMabic/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow18

FounderOfHuggingface

2023-12-03T19:18:50Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T19:18:46Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

greathero/mit-b0-finetuned-sidewalks

greathero

2023-12-03T19:15:10Z

1

0

transformers

[ "transformers", "tf", "segformer", "generated_from_keras_callback", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]

null

2023-11-04T14:59:25Z

--- license: other base_model: nvidia/mit-b0 tags: - generated_from_keras_callback model-index: - name: greathero/mit-b0-finetuned-sidewalks results: [] ---  # greathero/mit-b0-finetuned-sidewalks This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1964 - Validation Loss: 0.5617 - Validation Mean Iou: 0.3482 - Validation Mean Accuracy: 0.4212 - Validation Overall Accuracy: 0.8714 - Validation Accuracy Unlabeled: 0.0 - Validation Accuracy Flat-road: 0.9191 - Validation Accuracy Flat-sidewalk: 0.9583 - Validation Accuracy Flat-crosswalk: 0.5878 - Validation Accuracy Flat-cyclinglane: 0.7730 - Validation Accuracy Flat-parkingdriveway: 0.6079 - Validation Accuracy Flat-railtrack: nan - Validation Accuracy Flat-curb: 0.5577 - Validation Accuracy Human-person: 0.6959 - Validation Accuracy Human-rider: 0.2221 - Validation Accuracy Vehicle-car: 0.9300 - Validation Accuracy Vehicle-truck: 0.1310 - Validation Accuracy Vehicle-bus: 0.0 - Validation Accuracy Vehicle-tramtrain: nan - Validation Accuracy Vehicle-motorcycle: 0.0 - Validation Accuracy Vehicle-bicycle: 0.6927 - Validation Accuracy Vehicle-caravan: 0.0 - Validation Accuracy Vehicle-cartrailer: 0.0 - Validation Accuracy Construction-building: 0.9088 - Validation Accuracy Construction-door: 0.0611 - Validation Accuracy Construction-wall: 0.4825 - Validation Accuracy Construction-fenceguardrail: 0.4370 - Validation Accuracy Construction-bridge: 0.0 - Validation Accuracy Construction-tunnel: nan - Validation Accuracy Construction-stairs: 0.0 - Validation Accuracy Object-pole: 0.4339 - Validation Accuracy Object-trafficsign: 0.4711 - Validation Accuracy Object-trafficlight: 0.0 - Validation Accuracy Nature-vegetation: 0.9172 - Validation Accuracy Nature-terrain: 0.9151 - Validation Accuracy Sky: 0.9692 - Validation Accuracy Void-ground: 0.5121 - Validation Accuracy Void-dynamic: 0.0265 - Validation Accuracy Void-static: 0.2676 - Validation Accuracy Void-unclear: 0.0 - Validation Iou Unlabeled: 0.0 - Validation Iou Flat-road: 0.7902 - Validation Iou Flat-sidewalk: 0.9044 - Validation Iou Flat-crosswalk: 0.4201 - Validation Iou Flat-cyclinglane: 0.6963 - Validation Iou Flat-parkingdriveway: 0.4879 - Validation Iou Flat-railtrack: nan - Validation Iou Flat-curb: 0.4607 - Validation Iou Human-person: 0.4926 - Validation Iou Human-rider: 0.1864 - Validation Iou Vehicle-car: 0.8243 - Validation Iou Vehicle-truck: 0.1278 - Validation Iou Vehicle-bus: 0.0 - Validation Iou Vehicle-tramtrain: nan - Validation Iou Vehicle-motorcycle: 0.0 - Validation Iou Vehicle-bicycle: 0.4759 - Validation Iou Vehicle-caravan: 0.0 - Validation Iou Vehicle-cartrailer: 0.0 - Validation Iou Construction-building: 0.6973 - Validation Iou Construction-door: 0.0588 - Validation Iou Construction-wall: 0.3754 - Validation Iou Construction-fenceguardrail: 0.3760 - Validation Iou Construction-bridge: 0.0 - Validation Iou Construction-tunnel: nan - Validation Iou Construction-stairs: 0.0 - Validation Iou Object-pole: 0.3330 - Validation Iou Object-trafficsign: 0.3555 - Validation Iou Object-trafficlight: 0.0 - Validation Iou Nature-vegetation: 0.8600 - Validation Iou Nature-terrain: 0.7533 - Validation Iou Sky: 0.9288 - Validation Iou Void-ground: 0.3089 - Validation Iou Void-dynamic: 0.0223 - Validation Iou Void-static: 0.2055 - Validation Iou Void-unclear: 0.0 - Epoch: 49 ## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Validation Mean Iou | Validation Mean Accuracy | Validation Overall Accuracy | Validation Accuracy Unlabeled | Validation Accuracy Flat-road | Validation Accuracy Flat-sidewalk | Validation Accuracy Flat-crosswalk | Validation Accuracy Flat-cyclinglane | Validation Accuracy Flat-parkingdriveway | Validation Accuracy Flat-railtrack | Validation Accuracy Flat-curb | Validation Accuracy Human-person | Validation Accuracy Human-rider | Validation Accuracy Vehicle-car | Validation Accuracy Vehicle-truck | Validation Accuracy Vehicle-bus | Validation Accuracy Vehicle-tramtrain | Validation Accuracy Vehicle-motorcycle | Validation Accuracy Vehicle-bicycle | Validation Accuracy Vehicle-caravan | Validation Accuracy Vehicle-cartrailer | Validation Accuracy Construction-building | Validation Accuracy Construction-door | Validation Accuracy Construction-wall | Validation Accuracy Construction-fenceguardrail | Validation Accuracy Construction-bridge | Validation Accuracy Construction-tunnel | Validation Accuracy Construction-stairs | Validation Accuracy Object-pole | Validation Accuracy Object-trafficsign | Validation Accuracy Object-trafficlight | Validation Accuracy Nature-vegetation | Validation Accuracy Nature-terrain | Validation Accuracy Sky | Validation Accuracy Void-ground | Validation Accuracy Void-dynamic | Validation Accuracy Void-static | Validation Accuracy Void-unclear | Validation Iou Unlabeled | Validation Iou Flat-road | Validation Iou Flat-sidewalk | Validation Iou Flat-crosswalk | Validation Iou Flat-cyclinglane | Validation Iou Flat-parkingdriveway | Validation Iou Flat-railtrack | Validation Iou Flat-curb | Validation Iou Human-person | Validation Iou Human-rider | Validation Iou Vehicle-car | Validation Iou Vehicle-truck | Validation Iou Vehicle-bus | Validation Iou Vehicle-tramtrain | Validation Iou Vehicle-motorcycle | Validation Iou Vehicle-bicycle | Validation Iou Vehicle-caravan | Validation Iou Vehicle-cartrailer | Validation Iou Construction-building | Validation Iou Construction-door | Validation Iou Construction-wall | Validation Iou Construction-fenceguardrail | Validation Iou Construction-bridge | Validation Iou Construction-tunnel | Validation Iou Construction-stairs | Validation Iou Object-pole | Validation Iou Object-trafficsign | Validation Iou Object-trafficlight | Validation Iou Nature-vegetation | Validation Iou Nature-terrain | Validation Iou Sky | Validation Iou Void-ground | Validation Iou Void-dynamic | Validation Iou Void-static | Validation Iou Void-unclear | Epoch | 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| 1.9172 | 1.1456 | 0.1406 | 0.1941 | 0.7310 | 0.0 | 0.7980 | 0.9298 | 0.0051 | 0.2528 | 0.0049 | nan | 0.0001 | 0.0458 | 0.0 | 0.8315 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7839 | 0.0 | 0.0210 | 0.0370 | 0.0 | nan | 0.0 | 0.0058 | 0.0 | 0.0 | 0.9028 | 0.6924 | 0.9004 | 0.0 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.4633 | 0.7662 | 0.0051 | 0.2317 | 0.0048 | 0.0 | 0.0001 | 0.0323 | 0.0 | 0.6396 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5243 | 0.0 | 0.0206 | 0.0348 | 0.0 | nan | 0.0 | 0.0054 | 0.0 | 0.0 | 0.7094 | 0.5497 | 0.7916 | 0.0 | 0.0 | 0.0002 | 0.0 | 0 | | 1.0247 | 0.8848 | 0.1918 | 0.2336 | 0.7698 | 0.0 | 0.6997 | 0.9651 | 0.4394 | 0.4069 | 0.1304 | nan | 0.0606 | 0.0601 | 0.0 | 0.8688 | 0.0 | 0.0 | nan | 0.0 | 0.0097 | 0.0 | 0.0 | 0.8430 | 0.0 | 0.2444 | 0.1184 | 0.0 | nan | 0.0 | 0.0102 | 0.0 | 0.0 | 0.9227 | 0.7934 | 0.9018 | 0.0 | 0.0 | 0.0014 | 0.0 | 0.0 | 0.5611 | 0.7751 | 0.4093 | 0.3588 | 0.1102 | nan | 0.0550 | 0.0513 | 0.0 | 0.6674 | 0.0 | 0.0 | nan | 0.0 | 0.0097 | 0.0 | 0.0 | 0.5662 | 0.0 | 0.2011 | 0.1101 | 0.0 | nan | 0.0 | 0.0101 | 0.0 | 0.0 | 0.7593 | 0.6549 | 0.8360 | 0.0 | 0.0 | 0.0014 | 0.0 | 1 | | 0.8452 | 0.7838 | 0.2203 | 0.2738 | 0.7971 | 0.0 | 0.8465 | 0.9296 | 0.5352 | 0.5975 | 0.2817 | nan | 0.1721 | 0.3577 | 0.0 | 0.8242 | 0.0 | 0.0 | nan | 0.0 | 0.0999 | 0.0 | 0.0 | 0.8508 | 0.0 | 0.3727 | 0.1481 | 0.0 | nan | 0.0 | 0.0712 | 0.0 | 0.0 | 0.9379 | 0.8088 | 0.9242 | 0.0 | 0.0 | 0.0027 | 0.0 | 0.0 | 0.5691 | 0.8475 | 0.3104 | 0.5182 | 0.2163 | nan | 0.1416 | 0.2415 | 0.0 | 0.7082 | 0.0 | 0.0 | nan | 0.0 | 0.0997 | 0.0 | 0.0 | 0.5843 | 0.0 | 0.2783 | 0.1415 | 0.0 | nan | 0.0 | 0.0679 | 0.0 | 0.0 | 0.7789 | 0.6955 | 0.8480 | 0.0 | 0.0 | 0.0026 | 0.0 | 2 | | 0.7393 | 0.7194 | 0.2443 | 0.2959 | 0.8084 | 0.0 | 0.7601 | 0.9601 | 0.3925 | 0.5485 | 0.4444 | nan | 0.2765 | 0.4666 | 0.0 | 0.8905 | 0.0 | 0.0 | nan | 0.0 | 0.3695 | 0.0 | 0.0 | 0.8608 | 0.0 | 0.4003 | 0.2319 | 0.0 | nan | 0.0 | 0.1120 | 0.0 | 0.0 | 0.9064 | 0.8793 | 0.9268 | 0.0 | 0.0 | 0.0434 | 0.0 | 0.0 | 0.6189 | 0.8385 | 0.3858 | 0.4982 | 0.2872 | nan | 0.2269 | 0.2991 | 0.0 | 0.7134 | 0.0 | 0.0 | nan | 0.0 | 0.3144 | 0.0 | 0.0 | 0.6105 | 0.0 | 0.3110 | 0.2177 | 0.0 | nan | 0.0 | 0.1034 | 0.0 | 0.0 | 0.7932 | 0.6956 | 0.8646 | 0.0 | 0.0 | 0.0385 | 0.0 | 3 | | 0.6723 | 0.6423 | 0.2585 | 0.3200 | 0.8243 | 0.0 | 0.8379 | 0.9392 | 0.5390 | 0.6706 | 0.5206 | nan | 0.3683 | 0.6301 | 0.0 | 0.8838 | 0.0 | 0.0 | nan | 0.0 | 0.3830 | 0.0 | 0.0 | 0.8862 | 0.0 | 0.3901 | 0.2524 | 0.0 | nan | 0.0 | 0.1545 | 0.0 | 0.0 | 0.9096 | 0.8846 | 0.9254 | 0.0 | 0.0001 | 0.0643 | 0.0 | 0.0 | 0.6716 | 0.8658 | 0.3985 | 0.5764 | 0.3400 | nan | 0.2731 | 0.3130 | 0.0 | 0.7325 | 0.0 | 0.0 | nan | 0.0 | 0.3255 | 0.0 | 0.0 | 0.6179 | 0.0 | 0.3338 | 0.2397 | 0.0 | nan | 0.0 | 0.1381 | 0.0 | 0.0 | 0.8056 | 0.7102 | 0.8723 | 0.0 | 0.0001 | 0.0578 | 0.0 | 4 | | 0.6205 | 0.6604 | 0.2592 | 0.3188 | 0.8256 | 0.0 | 0.8237 | 0.9528 | 0.5500 | 0.5608 | 0.6090 | nan | 0.3591 | 0.4741 | 0.0 | 0.8835 | 0.0 | 0.0 | nan | 0.0 | 0.4288 | 0.0 | 0.0 | 0.9049 | 0.0 | 0.4296 | 0.2780 | 0.0 | nan | 0.0 | 0.2117 | 0.0 | 0.0 | 0.9279 | 0.8381 | 0.9277 | 0.0 | 0.0003 | 0.0425 | 0.0 | 0.0 | 0.6967 | 0.8550 | 0.2920 | 0.5056 | 0.3663 | nan | 0.2893 | 0.3073 | 0.0 | 0.7447 | 0.0 | 0.0 | nan | 0.0 | 0.3632 | 0.0 | 0.0 | 0.6182 | 0.0 | 0.3474 | 0.2593 | 0.0 | nan | 0.0 | 0.1780 | 0.0 | 0.0 | 0.8229 | 0.7302 | 0.8786 | 0.0 | 0.0003 | 0.0396 | 0.0 | 5 | | 0.5839 | 0.6139 | 0.2738 | 0.3325 | 0.8330 | 0.0 | 0.8190 | 0.9635 | 0.5040 | 0.6479 | 0.5465 | nan | 0.3405 | 0.5509 | 0.0 | 0.8938 | 0.0 | 0.0 | nan | 0.0 | 0.5717 | 0.0 | 0.0 | 0.8895 | 0.0 | 0.4915 | 0.3136 | 0.0 | nan | 0.0 | 0.2540 | 0.0 | 0.0 | 0.9038 | 0.8733 | 0.9399 | 0.0 | 0.0039 | 0.1320 | 0.0 | 0.0 | 0.7100 | 0.8592 | 0.4158 | 0.5694 | 0.3564 | nan | 0.2854 | 0.3518 | 0.0 | 0.7495 | 0.0 | 0.0 | nan | 0.0 | 0.4226 | 0.0 | 0.0 | 0.6419 | 0.0 | 0.3810 | 0.2908 | 0.0 | nan | 0.0 | 0.1984 | 0.0 | 0.0 | 0.8237 | 0.7147 | 0.8798 | 0.0 | 0.0038 | 0.1073 | 0.0 | 6 | | 0.5377 | 0.5787 | 0.2763 | 0.3397 | 0.8374 | 0.0 | 0.8782 | 0.9396 | 0.5571 | 0.6511 | 0.5741 | nan | 0.4205 | 0.5055 | 0.0 | 0.8842 | 0.0 | 0.0 | nan | 0.0 | 0.5905 | 0.0 | 0.0 | 0.8932 | 0.0 | 0.4833 | 0.3608 | 0.0 | nan | 0.0 | 0.2777 | 0.0018 | 0.0 | 0.9253 | 0.8693 | 0.9532 | 0.0 | 0.0025 | 0.1022 | 0.0 | 0.0 | 0.6996 | 0.8736 | 0.3436 | 0.5970 | 0.3762 | nan | 0.3136 | 0.3388 | 0.0 | 0.7668 | 0.0 | 0.0 | nan | 0.0 | 0.4413 | 0.0 | 0.0 | 0.6375 | 0.0 | 0.3730 | 0.3209 | 0.0 | nan | 0.0 | 0.2190 | 0.0018 | 0.0 | 0.8323 | 0.7339 | 0.8797 | 0.0 | 0.0024 | 0.0908 | 0.0 | 7 | | 0.5102 | 0.5413 | 0.2852 | 0.3417 | 0.8472 | 0.0 | 0.8733 | 0.9482 | 0.4978 | 0.7936 | 0.4843 | nan | 0.3768 | 0.5607 | 0.0 | 0.8764 | 0.0 | 0.0 | nan | 0.0 | 0.5866 | 0.0 | 0.0 | 0.8863 | 0.0 | 0.4769 | 0.3574 | 0.0 | nan | 0.0 | 0.2680 | 0.0013 | 0.0 | 0.9395 | 0.8760 | 0.9478 | 0.0 | 0.0045 | 0.1792 | 0.0 | 0.0 | 0.7354 | 0.8816 | 0.4221 | 0.6823 | 0.3631 | nan | 0.3052 | 0.3647 | 0.0 | 0.7699 | 0.0 | 0.0 | nan | 0.0 | 0.4497 | 0.0 | 0.0 | 0.6563 | 0.0 | 0.3737 | 0.3235 | 0.0 | nan | 0.0 | 0.2125 | 0.0013 | 0.0 | 0.8273 | 0.7161 | 0.8933 | 0.0 | 0.0044 | 0.1451 | 0.0 | 8 | | 0.4841 | 0.5317 | 0.2880 | 0.3513 | 0.8505 | 0.0 | 0.8846 | 0.9516 | 0.5489 | 0.8002 | 0.5770 | nan | 0.3905 | 0.5536 | 0.0 | 0.8946 | 0.0 | 0.0 | nan | 0.0 | 0.6090 | 0.0 | 0.0 | 0.8865 | 0.0 | 0.4967 | 0.3616 | 0.0 | nan | 0.0 | 0.3103 | 0.0129 | 0.0 | 0.9036 | 0.9037 | 0.9505 | 0.0 | 0.0032 | 0.2026 | 0.0 | 0.0 | 0.7536 | 0.8840 | 0.3437 | 0.7198 | 0.4150 | nan | 0.3262 | 0.3606 | 0.0 | 0.7672 | 0.0 | 0.0 | nan | 0.0 | 0.4336 | 0.0 | 0.0 | 0.6606 | 0.0 | 0.3724 | 0.3274 | 0.0 | nan | 0.0 | 0.2363 | 0.0129 | 0.0 | 0.8265 | 0.7080 | 0.8948 | 0.0 | 0.0030 | 0.1696 | 0.0 | 9 | | 0.4663 | 0.5253 | 0.2903 | 0.3504 | 0.8531 | 0.0 | 0.8632 | 0.9503 | 0.4520 | 0.8542 | 0.5251 | nan | 0.3878 | 0.5854 | 0.0 | 0.9041 | 0.0 | 0.0 | nan | 0.0 | 0.6516 | 0.0 | 0.0 | 0.8830 | 0.0 | 0.5018 | 0.3624 | 0.0 | nan | 0.0 | 0.3190 | 0.0143 | 0.0 | 0.9290 | 0.8950 | 0.9495 | 0.0 | 0.0025 | 0.1835 | 0.0 | 0.0 | 0.7467 | 0.8831 | 0.3961 | 0.7313 | 0.3927 | nan | 0.3174 | 0.3708 | 0.0 | 0.7719 | 0.0 | 0.0 | nan | 0.0 | 0.4393 | 0.0 | 0.0 | 0.6648 | 0.0 | 0.3684 | 0.3338 | 0.0 | nan | 0.0 | 0.2396 | 0.0143 | 0.0 | 0.8406 | 0.7302 | 0.9009 | 0.0 | 0.0024 | 0.1464 | 0.0 | 10 | | 0.4466 | 0.5667 | 0.2867 | 0.3460 | 0.8459 | 0.0 | 0.8852 | 0.9554 | 0.5124 | 0.6566 | 0.6231 | nan | 0.3960 | 0.5075 | 0.0 | 0.8932 | 0.0 | 0.0 | nan | 0.0 | 0.6307 | 0.0 | 0.0 | 0.9193 | 0.0 | 0.5021 | 0.3920 | 0.0 | nan | 0.0 | 0.3301 | 0.0299 | 0.0 | 0.9236 | 0.8399 | 0.9567 | 0.0 | 0.0096 | 0.1091 | 0.0 | 0.0 | 0.7247 | 0.8746 | 0.4095 | 0.6009 | 0.4343 | nan | 0.3309 | 0.3346 | 0.0 | 0.7803 | 0.0 | 0.0 | nan | 0.0 | 0.4490 | 0.0 | 0.0 | 0.6423 | 0.0 | 0.3891 | 0.3310 | 0.0 | nan | 0.0 | 0.2522 | 0.0299 | 0.0 | 0.8416 | 0.7461 | 0.8986 | 0.0 | 0.0085 | 0.0979 | 0.0 | 11 | | 0.4201 | 0.5216 | 0.2959 | 0.3557 | 0.8570 | 0.0 | 0.8772 | 0.9565 | 0.4502 | 0.7909 | 0.5969 | nan | 0.4384 | 0.5878 | 0.0028 | 0.9030 | 0.0 | 0.0 | nan | 0.0 | 0.6439 | 0.0 | 0.0 | 0.8963 | 0.0 | 0.5378 | 0.3621 | 0.0 | nan | 0.0 | 0.3043 | 0.0282 | 0.0 | 0.9206 | 0.8979 | 0.9562 | 0.0 | 0.0015 | 0.2284 | 0.0 | 0.0 | 0.7601 | 0.8843 | 0.3895 | 0.7020 | 0.4255 | nan | 0.3655 | 0.3689 | 0.0028 | 0.7794 | 0.0 | 0.0 | nan | 0.0 | 0.4512 | 0.0 | 0.0 | 0.6719 | 0.0 | 0.3923 | 0.3293 | 0.0 | nan | 0.0 | 0.2432 | 0.0281 | 0.0 | 0.8495 | 0.7423 | 0.9015 | 0.0 | 0.0014 | 0.1815 | 0.0 | 12 | | 0.4155 | 0.5468 | 0.2913 | 0.3606 | 0.8525 | 0.0 | 0.8460 | 0.9363 | 0.5329 | 0.8216 | 0.6785 | nan | 0.4931 | 0.5219 | 0.0076 | 0.9172 | 0.0 | 0.0 | nan | 0.0 | 0.6537 | 0.0 | 0.0 | 0.9282 | 0.0 | 0.5048 | 0.3783 | 0.0 | nan | 0.0 | 0.3354 | 0.0529 | 0.0 | 0.9057 | 0.9100 | 0.9441 | 0.0 | 0.0143 | 0.1573 | 0.0 | 0.0 | 0.7598 | 0.8861 | 0.2428 | 0.7322 | 0.4286 | nan | 0.3793 | 0.3453 | 0.0076 | 0.7781 | 0.0 | 0.0 | nan | 0.0 | 0.4537 | 0.0 | 0.0 | 0.6496 | 0.0 | 0.3874 | 0.3258 | 0.0 | nan | 0.0 | 0.2583 | 0.0526 | 0.0 | 0.8479 | 0.7370 | 0.9009 | 0.0 | 0.0129 | 0.1355 | 0.0 | 13 | | 0.3892 | 0.5175 | 0.3004 | 0.3618 | 0.8613 | 0.0 | 0.9000 | 0.9479 | 0.4376 | 0.8710 | 0.6180 | nan | 0.4245 | 0.6868 | 0.0094 | 0.8863 | 0.0 | 0.0 | nan | 0.0 | 0.6129 | 0.0 | 0.0 | 0.9115 | 0.0 | 0.4900 | 0.3562 | 0.0 | nan | 0.0 | 0.3215 | 0.1297 | 0.0 | 0.9228 | 0.9045 | 0.9555 | 0.0 | 0.0057 | 0.1855 | 0.0 | 0.0 | 0.7676 | 0.8928 | 0.3316 | 0.7655 | 0.4191 | nan | 0.3657 | 0.3880 | 0.0094 | 0.7938 | 0.0 | 0.0 | nan | 0.0 | 0.4393 | 0.0 | 0.0 | 0.6601 | 0.0 | 0.4075 | 0.3296 | 0.0 | nan | 0.0 | 0.2502 | 0.1287 | 0.0 | 0.8530 | 0.7461 | 0.9041 | 0.0 | 0.0054 | 0.1551 | 0.0 | 14 | | 0.3774 | 0.5600 | 0.2950 | 0.3518 | 0.8528 | 0.0 | 0.8595 | 0.9628 | 0.2409 | 0.7379 | 0.6461 | nan | 0.4255 | 0.5880 | 0.0124 | 0.8996 | 0.0 | 0.0 | nan | 0.0 | 0.6380 | 0.0 | 0.0 | 0.9198 | 0.0 | 0.5246 | 0.3877 | 0.0 | nan | 0.0 | 0.3241 | 0.1567 | 0.0 | 0.9042 | 0.8958 | 0.9545 | 0.0 | 0.0130 | 0.1680 | 0.0 | 0.0 | 0.7599 | 0.8747 | 0.2287 | 0.6677 | 0.4078 | nan | 0.3595 | 0.3946 | 0.0124 | 0.7930 | 0.0 | 0.0 | nan | 0.0 | 0.4603 | 0.0 | 0.0 | 0.6603 | 0.0 | 0.4156 | 0.3319 | 0.0 | nan | 0.0 | 0.2643 | 0.1535 | 0.0 | 0.8453 | 0.7478 | 0.9060 | 0.0 | 0.0121 | 0.1449 | 0.0 | 15 | | 0.3713 | 0.5397 | 0.3053 | 0.3675 | 0.8565 | 0.0 | 0.8845 | 0.9473 | 0.5039 | 0.7752 | 0.6646 | nan | 0.4841 | 0.6128 | 0.0313 | 0.9142 | 0.0029 | 0.0 | nan | 0.0 | 0.6522 | 0.0 | 0.0 | 0.9018 | 0.0054 | 0.5113 | 0.3701 | 0.0 | nan | 0.0 | 0.3208 | 0.1426 | 0.0 | 0.9068 | 0.9156 | 0.9537 | 0.0011 | 0.0201 | 0.2376 | 0.0 | 0.0 | 0.7785 | 0.8791 | 0.4051 | 0.7029 | 0.4544 | nan | 0.3946 | 0.3946 | 0.0311 | 0.7865 | 0.0029 | 0.0 | nan | 0.0 | 0.4606 | 0.0 | 0.0 | 0.6823 | 0.0053 | 0.3835 | 0.3334 | 0.0 | nan | 0.0 | 0.2581 | 0.1383 | 0.0 | 0.8433 | 0.7196 | 0.9061 | 0.0007 | 0.0182 | 0.1920 | 0.0 | 16 | | 0.3502 | 0.5369 | 0.3065 | 0.3627 | 0.8594 | 0.0 | 0.9004 | 0.9587 | 0.4626 | 0.7253 | 0.5631 | nan | 0.5189 | 0.5677 | 0.0020 | 0.9280 | 0.0247 | 0.0 | nan | 0.0 | 0.6415 | 0.0 | 0.0 | 0.8986 | 0.0008 | 0.5035 | 0.3569 | 0.0 | nan | 0.0 | 0.3524 | 0.2297 | 0.0 | 0.9422 | 0.8659 | 0.9595 | 0.0 | 0.0117 | 0.1931 | 0.0 | 0.0 | 0.7554 | 0.8846 | 0.4080 | 0.6569 | 0.4399 | nan | 0.4082 | 0.3910 | 0.0020 | 0.7829 | 0.0247 | 0.0 | nan | 0.0 | 0.4650 | 0.0 | 0.0 | 0.6756 | 0.0008 | 0.4035 | 0.3249 | 0.0 | nan | 0.0 | 0.2717 | 0.2187 | 0.0 | 0.8550 | 0.7560 | 0.9070 | 0.0 | 0.0113 | 0.1663 | 0.0 | 17 | | 0.3405 | 0.5502 | 0.3062 | 0.3636 | 0.8594 | 0.0 | 0.9166 | 0.9532 | 0.4142 | 0.7531 | 0.6138 | nan | 0.4230 | 0.5584 | 0.0258 | 0.8984 | 0.0044 | 0.0 | nan | 0.0 | 0.6498 | 0.0 | 0.0 | 0.9063 | 0.0057 | 0.5072 | 0.3758 | 0.0 | nan | 0.0 | 0.3565 | 0.2564 | 0.0 | 0.9187 | 0.9228 | 0.9622 | 0.0021 | 0.0172 | 0.1947 | 0.0 | 0.0 | 0.7390 | 0.8967 | 0.3897 | 0.6933 | 0.4355 | nan | 0.3709 | 0.3863 | 0.0256 | 0.7977 | 0.0044 | 0.0 | nan | 0.0 | 0.4553 | 0.0 | 0.0 | 0.6760 | 0.0056 | 0.3945 | 0.3398 | 0.0 | nan | 0.0 | 0.2704 | 0.2408 | 0.0 | 0.8494 | 0.7349 | 0.9070 | 0.0020 | 0.0160 | 0.1688 | 0.0 | 18 | | 0.3311 | 0.5097 | 0.3140 | 0.3734 | 0.8628 | 0.0 | 0.8703 | 0.9682 | 0.4415 | 0.8131 | 0.5973 | nan | 0.4837 | 0.6228 | 0.0409 | 0.9119 | 0.0300 | 0.0 | nan | 0.0 | 0.6709 | 0.0 | 0.0 | 0.8819 | 0.0123 | 0.5047 | 0.4101 | 0.0 | nan | 0.0 | 0.3714 | 0.2680 | 0.0 | 0.9125 | 0.9120 | 0.9623 | 0.0 | 0.0194 | 0.2439 | 0.0 | 0.0 | 0.7928 | 0.8843 | 0.3726 | 0.7384 | 0.4594 | nan | 0.3924 | 0.4183 | 0.0407 | 0.8016 | 0.0300 | 0.0 | nan | 0.0 | 0.4553 | 0.0 | 0.0 | 0.6842 | 0.0119 | 0.3877 | 0.3679 | 0.0 | nan | 0.0 | 0.2747 | 0.2447 | 0.0 | 0.8427 | 0.7257 | 0.9099 | 0.0 | 0.0182 | 0.1957 | 0.0 | 19 | | 0.3200 | 0.5690 | 0.3125 | 0.3727 | 0.8588 | 0.0 | 0.8760 | 0.9569 | 0.4469 | 0.6968 | 0.7124 | nan | 0.4744 | 0.6537 | 0.0381 | 0.9262 | 0.0226 | 0.0 | nan | 0.0 | 0.6176 | 0.0 | 0.0 | 0.8978 | 0.0201 | 0.5154 | 0.3644 | 0.0 | nan | 0.0 | 0.3458 | 0.3278 | 0.0 | 0.9375 | 0.8824 | 0.9608 | 0.0 | 0.0238 | 0.2276 | 0.0 | 0.0 | 0.7867 | 0.8779 | 0.3945 | 0.6375 | 0.4626 | nan | 0.3969 | 0.4168 | 0.0376 | 0.7872 | 0.0226 | 0.0 | nan | 0.0 | 0.4612 | 0.0 | 0.0 | 0.6770 | 0.0192 | 0.4005 | 0.3394 | 0.0 | nan | 0.0 | 0.2703 | 0.2821 | 0.0 | 0.8552 | 0.7527 | 0.9104 | 0.0 | 0.0219 | 0.1902 | 0.0 | 20 | | 0.3120 | 0.5155 | 0.3151 | 0.3760 | 0.8626 | 0.0 | 0.8875 | 0.9659 | 0.4640 | 0.7236 | 0.6301 | nan | 0.4876 | 0.5926 | 0.0416 | 0.9255 | 0.0116 | 0.0 | nan | 0.0 | 0.6732 | 0.0 | 0.0 | 0.9028 | 0.0270 | 0.5107 | 0.4032 | 0.0 | nan | 0.0 | 0.3817 | 0.3335 | 0.0 | 0.9350 | 0.8668 | 0.9556 | 0.0012 | 0.0434 | 0.2684 | 0.0 | 0.0 | 0.7974 | 0.8797 | 0.3892 | 0.6598 | 0.4580 | nan | 0.4000 | 0.3794 | 0.0414 | 0.7968 | 0.0116 | 0.0 | nan | 0.0 | 0.4536 | 0.0 | 0.0 | 0.6872 | 0.0259 | 0.4002 | 0.3626 | 0.0 | nan | 0.0 | 0.2821 | 0.2841 | 0.0 | 0.8603 | 0.7609 | 0.9128 | 0.0011 | 0.0331 | 0.2076 | 0.0 | 21 | | 0.3017 | 0.5201 | 0.3199 | 0.3827 | 0.8670 | 0.0 | 0.9120 | 0.9521 | 0.5413 | 0.8270 | 0.6610 | nan | 0.4946 | 0.5627 | 0.0423 | 0.9207 | 0.0336 | 0.0 | nan | 0.0 | 0.6437 | 0.0 | 0.0 | 0.9003 | 0.0247 | 0.5331 | 0.4028 | 0.0 | nan | 0.0 | 0.3737 | 0.3892 | 0.0 | 0.9171 | 0.9109 | 0.9616 | 0.0146 | 0.0184 | 0.2092 | 0.0 | 0.0 | 0.7717 | 0.9034 | 0.3938 | 0.7287 | 0.4774 | nan | 0.4103 | 0.4008 | 0.0419 | 0.8044 | 0.0336 | 0.0 | nan | 0.0 | 0.4686 | 0.0 | 0.0 | 0.6813 | 0.0230 | 0.3958 | 0.3564 | 0.0 | nan | 0.0 | 0.2823 | 0.3297 | 0.0 | 0.8547 | 0.7570 | 0.9126 | 0.0121 | 0.0173 | 0.1786 | 0.0 | 22 | | 0.3016 | 0.5527 | 0.3173 | 0.3811 | 0.8609 | 0.0 | 0.8912 | 0.9593 | 0.4638 | 0.6868 | 0.7110 | nan | 0.4653 | 0.6380 | 0.0447 | 0.9197 | 0.0362 | 0.0 | nan | 0.0 | 0.6737 | 0.0 | 0.0 | 0.8998 | 0.0303 | 0.5386 | 0.4095 | 0.0 | nan | 0.0 | 0.3663 | 0.3935 | 0.0 | 0.9284 | 0.8910 | 0.9618 | 0.0000 | 0.0297 | 0.2570 | 0.0 | 0.0 | 0.7536 | 0.8893 | 0.3672 | 0.6440 | 0.4740 | nan | 0.3968 | 0.4275 | 0.0444 | 0.7999 | 0.0361 | 0.0 | nan | 0.0 | 0.4593 | 0.0 | 0.0 | 0.6930 | 0.0291 | 0.3877 | 0.3651 | 0.0 | nan | 0.0 | 0.2839 | 0.3360 | 0.0 | 0.8568 | 0.7558 | 0.9154 | 0.0000 | 0.0277 | 0.2098 | 0.0 | 23 | | 0.2903 | 0.5091 | 0.3204 | 0.3803 | 0.8666 | 0.0 | 0.9124 | 0.9512 | 0.5347 | 0.8343 | 0.6212 | nan | 0.5144 | 0.5947 | 0.0513 | 0.9161 | 0.0387 | 0.0 | nan | 0.0 | 0.6167 | 0.0 | 0.0 | 0.9043 | 0.0255 | 0.4947 | 0.3443 | 0.0 | nan | 0.0 | 0.3727 | 0.3979 | 0.0 | 0.9229 | 0.9170 | 0.9584 | 0.0134 | 0.0171 | 0.2150 | 0.0 | 0.0 | 0.8085 | 0.8943 | 0.4187 | 0.7402 | 0.4403 | nan | 0.4257 | 0.4229 | 0.0506 | 0.8021 | 0.0384 | 0.0 | nan | 0.0 | 0.4653 | 0.0 | 0.0 | 0.6774 | 0.0241 | 0.3744 | 0.3237 | 0.0 | nan | 0.0 | 0.2837 | 0.3375 | 0.0 | 0.8534 | 0.7470 | 0.9170 | 0.0119 | 0.0165 | 0.1784 | 0.0 | 24 | | 0.2869 | 0.5304 | 0.3208 | 0.3853 | 0.8631 | 0.0 | 0.9204 | 0.9461 | 0.5684 | 0.7963 | 0.5569 | nan | 0.5264 | 0.6297 | 0.0769 | 0.9035 | 0.0469 | 0.0 | nan | 0.0 | 0.6724 | 0.0 | 0.0 | 0.9134 | 0.0107 | 0.4920 | 0.3848 | 0.0 | nan | 0.0 | 0.3858 | 0.4485 | 0.0 | 0.9322 | 0.8984 | 0.9576 | 0.0506 | 0.0301 | 0.1830 | 0.0 | 0.0 | 0.7513 | 0.9008 | 0.4016 | 0.7108 | 0.4265 | nan | 0.4086 | 0.4314 | 0.0752 | 0.8133 | 0.0455 | 0.0 | nan | 0.0 | 0.4575 | 0.0 | 0.0 | 0.6703 | 0.0105 | 0.4124 | 0.3496 | 0.0 | nan | 0.0 | 0.2943 | 0.3631 | 0.0 | 0.8537 | 0.7527 | 0.9175 | 0.0397 | 0.0272 | 0.1531 | 0.0 | 25 | | 0.2870 | 0.6210 | 0.3239 | 0.3867 | 0.8601 | 0.0 | 0.9226 | 0.9662 | 0.5159 | 0.6950 | 0.5032 | nan | 0.4978 | 0.5125 | 0.0764 | 0.9003 | 0.0567 | 0.0 | nan | 0.0 | 0.6650 | 0.0 | 0.0 | 0.9188 | 0.0165 | 0.4980 | 0.4263 | 0.0 | nan | 0.0 | 0.3906 | 0.4679 | 0.0 | 0.9084 | 0.9043 | 0.9576 | 0.3333 | 0.0473 | 0.1939 | 0.0 | 0.0 | 0.7746 | 0.8841 | 0.4054 | 0.6361 | 0.4274 | nan | 0.4203 | 0.3645 | 0.0747 | 0.7990 | 0.0559 | 0.0 | nan | 0.0 | 0.4630 | 0.0 | 0.0 | 0.6670 | 0.0159 | 0.4031 | 0.3693 | 0.0 | nan | 0.0 | 0.2942 | 0.3689 | 0.0 | 0.8547 | 0.7547 | 0.9169 | 0.2169 | 0.0353 | 0.1634 | 0.0 | 26 | | 0.2759 | 0.5174 | 0.3291 | 0.3943 | 0.8685 | 0.0 | 0.9123 | 0.9615 | 0.3826 | 0.8113 | 0.5876 | nan | 0.5130 | 0.6677 | 0.0862 | 0.9153 | 0.0635 | 0.0 | nan | 0.0 | 0.6666 | 0.0 | 0.0 | 0.8736 | 0.0716 | 0.5387 | 0.4437 | 0.0 | nan | 0.0 | 0.4190 | 0.4781 | 0.0 | 0.9194 | 0.9174 | 0.9674 | 0.1048 | 0.0253 | 0.2898 | 0.0 | 0.0 | 0.8047 | 0.8922 | 0.3331 | 0.7338 | 0.4635 | nan | 0.4187 | 0.4413 | 0.0833 | 0.8020 | 0.0626 | 0.0 | nan | 0.0 | 0.4483 | 0.0 | 0.0 | 0.6970 | 0.0629 | 0.4012 | 0.3818 | 0.0 | nan | 0.0 | 0.2971 | 0.3643 | 0.0 | 0.8551 | 0.7489 | 0.9153 | 0.0753 | 0.0232 | 0.2256 | 0.0 | 27 | | 0.2791 | 0.5138 | 0.3264 | 0.3888 | 0.8699 | 0.0 | 0.9082 | 0.9556 | 0.2156 | 0.8500 | 0.6538 | nan | 0.4691 | 0.6238 | 0.1046 | 0.9234 | 0.0362 | 0.0 | nan | 0.0 | 0.6441 | 0.0 | 0.0 | 0.9040 | 0.0592 | 0.5193 | 0.4442 | 0.0 | nan | 0.0 | 0.3749 | 0.4394 | 0.0 | 0.9309 | 0.8876 | 0.9605 | 0.2636 | 0.0214 | 0.2527 | 0.0 | 0.0 | 0.8137 | 0.8936 | 0.1818 | 0.7657 | 0.4724 | nan | 0.4108 | 0.4396 | 0.1005 | 0.8096 | 0.0355 | 0.0 | nan | 0.0 | 0.4640 | 0.0 | 0.0 | 0.6934 | 0.0564 | 0.3842 | 0.3718 | 0.0 | nan | 0.0 | 0.2963 | 0.3541 | 0.0 | 0.8566 | 0.7598 | 0.9180 | 0.1539 | 0.0190 | 0.1941 | 0.0 | 28 | | 0.2728 | 0.5249 | 0.3275 | 0.3886 | 0.8672 | 0.0 | 0.9286 | 0.9447 | 0.4269 | 0.7825 | 0.6896 | nan | 0.4984 | 0.5883 | 0.1082 | 0.9204 | 0.0530 | 0.0 | nan | 0.0 | 0.6501 | 0.0 | 0.0 | 0.8936 | 0.0489 | 0.5277 | 0.4051 | 0.0 | nan | 0.0 | 0.3965 | 0.5070 | 0.0 | 0.9425 | 0.8916 | 0.9645 | 0.0 | 0.0170 | 0.2491 | 0.0 | 0.0 | 0.7636 | 0.8983 | 0.3799 | 0.7214 | 0.4693 | nan | 0.4124 | 0.4454 | 0.1 | 0.8081 | 0.0510 | 0.0 | nan | 0.0 | 0.4717 | 0.0 | 0.0 | 0.6914 | 0.0449 | 0.3906 | 0.3600 | 0.0 | nan | 0.0 | 0.2989 | 0.3980 | 0.0 | 0.8612 | 0.7709 | 0.9191 | 0.0 | 0.0160 | 0.2067 | 0.0 | 29 | | 0.2556 | 0.5577 | 0.3283 | 0.3899 | 0.8668 | 0.0 | 0.8620 | 0.9599 | 0.4681 | 0.8029 | 0.6812 | nan | 0.5151 | 0.6041 | 0.1296 | 0.9314 | 0.0517 | 0.0 | nan | 0.0 | 0.6206 | 0.0 | 0.0 | 0.9120 | 0.0537 | 0.5219 | 0.3835 | 0.0 | nan | 0.0 | 0.3950 | 0.4880 | 0.0 | 0.9270 | 0.9054 | 0.9584 | 0.0697 | 0.0167 | 0.2185 | 0.0 | 0.0 | 0.7991 | 0.8851 | 0.3274 | 0.7335 | 0.4697 | nan | 0.4272 | 0.4588 | 0.1172 | 0.8067 | 0.0492 | 0.0 | nan | 0.0 | 0.4710 | 0.0 | 0.0 | 0.6861 | 0.0498 | 0.4047 | 0.3498 | 0.0 | nan | 0.0 | 0.3004 | 0.3865 | 0.0 | 0.8604 | 0.7579 | 0.9202 | 0.0519 | 0.0158 | 0.1782 | 0.0 | 30 | | 0.2480 | 0.4931 | 0.3356 | 0.3974 | 0.8742 | 0.0 | 0.9152 | 0.9569 | 0.4642 | 0.8997 | 0.7098 | nan | 0.5025 | 0.6441 | 0.1322 | 0.9129 | 0.0527 | 0.0 | nan | 0.0 | 0.6643 | 0.0 | 0.0 | 0.9249 | 0.0377 | 0.4957 | 0.3950 | 0.0 | nan | 0.0 | 0.4079 | 0.4869 | 0.0 | 0.9195 | 0.8827 | 0.9639 | 0.1584 | 0.0280 | 0.1628 | 0.0 | 0.0 | 0.8160 | 0.9034 | 0.3920 | 0.8109 | 0.5004 | nan | 0.4275 | 0.4552 | 0.1180 | 0.8086 | 0.0495 | 0.0 | nan | 0.0 | 0.4663 | 0.0 | 0.0 | 0.6714 | 0.0363 | 0.4029 | 0.3486 | 0.0 | nan | 0.0 | 0.3016 | 0.3987 | 0.0 | 0.8584 | 0.7624 | 0.9200 | 0.1279 | 0.0253 | 0.1375 | 0.0 | 31 | | 0.2467 | 0.5091 | 0.3348 | 0.3964 | 0.8752 | 0.0 | 0.8971 | 0.9568 | 0.5112 | 0.8863 | 0.7226 | nan | 0.5276 | 0.6090 | 0.1273 | 0.9138 | 0.0480 | 0.0 | nan | 0.0 | 0.6762 | 0.0 | 0.0 | 0.9223 | 0.0349 | 0.5263 | 0.3831 | 0.0 | nan | 0.0 | 0.3999 | 0.4924 | 0.0 | 0.9358 | 0.8774 | 0.9652 | 0.0667 | 0.0259 | 0.1795 | 0.0 | 0.0 | 0.8313 | 0.9006 | 0.3902 | 0.8000 | 0.5045 | nan | 0.4362 | 0.4580 | 0.1173 | 0.8150 | 0.0460 | 0.0 | nan | 0.0 | 0.4735 | 0.0 | 0.0 | 0.6802 | 0.0342 | 0.4123 | 0.3509 | 0.0 | nan | 0.0 | 0.3049 | 0.3913 | 0.0 | 0.8605 | 0.7658 | 0.9220 | 0.0444 | 0.0242 | 0.1502 | 0.0 | 32 | | 0.2430 | 0.5393 | 0.3325 | 0.3917 | 0.8704 | 0.0 | 0.8953 | 0.9632 | 0.5036 | 0.8081 | 0.5941 | nan | 0.5130 | 0.6436 | 0.1049 | 0.9329 | 0.0502 | 0.0 | nan | 0.0 | 0.6347 | 0.0 | 0.0 | 0.8882 | 0.0652 | 0.5115 | 0.4234 | 0.0 | nan | 0.0 | 0.3974 | 0.4341 | 0.0 | 0.9334 | 0.9137 | 0.9621 | 0.0518 | 0.0264 | 0.2825 | 0.0 | 0.0 | 0.8197 | 0.8887 | 0.4044 | 0.7367 | 0.4565 | nan | 0.4257 | 0.4769 | 0.0954 | 0.8060 | 0.0493 | 0.0 | nan | 0.0 | 0.4708 | 0.0 | 0.0 | 0.6965 | 0.0594 | 0.3946 | 0.3766 | 0.0 | nan | 0.0 | 0.2996 | 0.3611 | 0.0 | 0.8589 | 0.7588 | 0.9239 | 0.0379 | 0.0233 | 0.2182 | 0.0 | 33 | | 0.2362 | 0.5184 | 0.3370 | 0.3951 | 0.8716 | 0.0 | 0.9045 | 0.9681 | 0.4519 | 0.8066 | 0.5724 | nan | 0.5364 | 0.6084 | 0.1367 | 0.9124 | 0.0536 | 0.0 | nan | 0.0 | 0.6666 | 0.0 | 0.0 | 0.8956 | 0.0637 | 0.5331 | 0.4525 | 0.0 | nan | 0.0 | 0.3985 | 0.5169 | 0.0 | 0.9250 | 0.9175 | 0.9598 | 0.0945 | 0.0224 | 0.2478 | 0.0 | 0.0 | 0.7909 | 0.8971 | 0.4231 | 0.7306 | 0.4766 | nan | 0.4453 | 0.4660 | 0.1233 | 0.8117 | 0.0523 | 0.0 | nan | 0.0 | 0.4698 | 0.0 | 0.0 | 0.6947 | 0.0584 | 0.4099 | 0.3913 | 0.0 | nan | 0.0 | 0.3072 | 0.4038 | 0.0 | 0.8585 | 0.7556 | 0.9243 | 0.0807 | 0.0205 | 0.1925 | 0.0 | 34 | | 0.2358 | 0.5466 | 0.3343 | 0.3997 | 0.8678 | 0.0 | 0.8973 | 0.9654 | 0.5114 | 0.7614 | 0.6166 | nan | 0.5178 | 0.6873 | 0.1800 | 0.9305 | 0.0724 | 0.0 | nan | 0.0 | 0.6812 | 0.0 | 0.0 | 0.8828 | 0.0516 | 0.5776 | 0.4319 | 0.0 | nan | 0.0 | 0.3922 | 0.4584 | 0.0 | 0.9252 | 0.8982 | 0.9661 | 0.1076 | 0.0260 | 0.2528 | 0.0 | 0.0 | 0.7946 | 0.8884 | 0.4043 | 0.6950 | 0.4749 | nan | 0.4265 | 0.4765 | 0.1554 | 0.8143 | 0.0699 | 0.0 | nan | 0.0 | 0.4759 | 0.0 | 0.0 | 0.6963 | 0.0467 | 0.3978 | 0.3722 | 0.0 | nan | 0.0 | 0.3100 | 0.3513 | 0.0 | 0.8631 | 0.7633 | 0.9233 | 0.0772 | 0.0217 | 0.1994 | 0.0 | 35 | | 0.2315 | 0.5484 | 0.3353 | 0.4007 | 0.8682 | 0.0 | 0.9169 | 0.9540 | 0.4890 | 0.7525 | 0.7001 | nan | 0.5193 | 0.5919 | 0.1294 | 0.9241 | 0.0515 | 0.0 | nan | 0.0 | 0.6725 | 0.0 | 0.0 | 0.9040 | 0.0487 | 0.5260 | 0.4268 | 0.0 | nan | 0.0 | 0.4055 | 0.4971 | 0.0 | 0.9350 | 0.8842 | 0.9607 | 0.3005 | 0.0279 | 0.2059 | 0.0 | 0.0 | 0.8133 | 0.8922 | 0.3714 | 0.6883 | 0.4819 | nan | 0.4364 | 0.4540 | 0.1181 | 0.8160 | 0.0504 | 0.0 | nan | 0.0 | 0.4709 | 0.0 | 0.0 | 0.6853 | 0.0429 | 0.3983 | 0.3721 | 0.0 | nan | 0.0 | 0.3094 | 0.4027 | 0.0 | 0.8633 | 0.7562 | 0.9264 | 0.1865 | 0.0244 | 0.1685 | 0.0 | 36 | | 0.2377 | 0.5324 | 0.3407 | 0.4028 | 0.8737 | 0.0 | 0.9091 | 0.9632 | 0.4779 | 0.8515 | 0.5949 | nan | 0.5091 | 0.6103 | 0.1728 | 0.9162 | 0.0652 | 0.0 | nan | 0.0 | 0.6746 | 0.0 | 0.0 | 0.9118 | 0.0507 | 0.5309 | 0.4420 | 0.0 | nan | 0.0 | 0.4037 | 0.4908 | 0.0 | 0.9208 | 0.9137 | 0.9634 | 0.2771 | 0.0202 | 0.2192 | 0.0 | 0.0 | 0.8315 | 0.8955 | 0.3856 | 0.7536 | 0.4891 | nan | 0.4326 | 0.4828 | 0.1421 | 0.8130 | 0.0630 | 0.0 | nan | 0.0 | 0.4685 | 0.0 | 0.0 | 0.6849 | 0.0465 | 0.4297 | 0.3804 | 0.0 | nan | 0.0 | 0.3098 | 0.3899 | 0.0 | 0.8617 | 0.7556 | 0.9258 | 0.1701 | 0.0188 | 0.1713 | 0.0 | 37 | | 0.2319 | 0.5114 | 0.3332 | 0.3942 | 0.8745 | 0.0 | 0.9208 | 0.9566 | 0.4723 | 0.8581 | 0.6419 | nan | 0.5400 | 0.6353 | 0.1534 | 0.9246 | 0.0637 | 0.0 | nan | 0.0 | 0.6620 | 0.0 | 0.0 | 0.9143 | 0.0362 | 0.4700 | 0.3914 | 0.0 | nan | 0.0 | 0.3948 | 0.5296 | 0.0 | 0.9311 | 0.9119 | 0.9656 | 0.0039 | 0.0294 | 0.2069 | 0.0 | 0.0 | 0.8163 | 0.9056 | 0.3885 | 0.7729 | 0.4815 | nan | 0.4515 | 0.4610 | 0.1360 | 0.8119 | 0.0627 | 0.0 | nan | 0.0 | 0.4623 | 0.0 | 0.0 | 0.6784 | 0.0339 | 0.3920 | 0.3473 | 0.0 | nan | 0.0 | 0.3070 | 0.4080 | 0.0 | 0.8647 | 0.7644 | 0.9245 | 0.0030 | 0.0262 | 0.1634 | 0.0 | 38 | | 0.2241 | 0.5676 | 0.3349 | 0.3986 | 0.8699 | 0.0 | 0.9146 | 0.9628 | 0.4670 | 0.7622 | 0.6285 | nan | 0.4889 | 0.6428 | 0.1805 | 0.9249 | 0.0685 | 0.0 | nan | 0.0 | 0.6784 | 0.0 | 0.0 | 0.8917 | 0.1012 | 0.5618 | 0.4185 | 0.0 | nan | 0.0018 | 0.4246 | 0.5487 | 0.0 | 0.9293 | 0.9124 | 0.9698 | 0.0025 | 0.0220 | 0.2520 | 0.0 | 0.0 | 0.8114 | 0.8907 | 0.4002 | 0.6840 | 0.4842 | nan | 0.4160 | 0.4777 | 0.1501 | 0.8191 | 0.0649 | 0.0 | nan | 0.0 | 0.4701 | 0.0 | 0.0 | 0.7077 | 0.0873 | 0.4025 | 0.3659 | 0.0 | nan | 0.0012 | 0.3255 | 0.3939 | 0.0 | 0.8634 | 0.7621 | 0.9232 | 0.0019 | 0.0200 | 0.1947 | 0.0 | 39 | | 0.2247 | 0.5314 | 0.3358 | 0.3991 | 0.8724 | 0.0 | 0.8966 | 0.9546 | 0.5034 | 0.8269 | 0.7241 | nan | 0.5320 | 0.6062 | 0.1736 | 0.9394 | 0.0618 | 0.0 | nan | 0.0 | 0.6274 | 0.0 | 0.0 | 0.9066 | 0.0810 | 0.5121 | 0.3887 | 0.0 | nan | 0.0 | 0.4009 | 0.5482 | 0.0 | 0.9264 | 0.9119 | 0.9692 | 0.0039 | 0.0267 | 0.2479 | 0.0 | 0.0 | 0.8026 | 0.8995 | 0.3869 | 0.7462 | 0.4877 | nan | 0.4497 | 0.4812 | 0.1402 | 0.8222 | 0.0583 | 0.0 | nan | 0.0 | 0.4663 | 0.0 | 0.0 | 0.6983 | 0.0746 | 0.4058 | 0.3539 | 0.0 | nan | 0.0 | 0.3113 | 0.3935 | 0.0 | 0.8585 | 0.7587 | 0.9242 | 0.0030 | 0.0231 | 0.2000 | 0.0 | 40 | | 0.2199 | 0.5197 | 0.3375 | 0.4039 | 0.8734 | 0.0 | 0.9161 | 0.9549 | 0.5890 | 0.8122 | 0.6603 | nan | 0.5412 | 0.6391 | 0.2229 | 0.9251 | 0.0588 | 0.0 | nan | 0.0 | 0.6700 | 0.0 | 0.0 | 0.9168 | 0.0601 | 0.5176 | 0.3964 | 0.0 | nan | 0.0 | 0.4129 | 0.5517 | 0.0 | 0.9323 | 0.9033 | 0.9677 | 0.0014 | 0.0303 | 0.2459 | 0.0 | 0.0 | 0.8182 | 0.9002 | 0.3683 | 0.7333 | 0.4846 | nan | 0.4468 | 0.4840 | 0.1819 | 0.8270 | 0.0558 | 0.0 | nan | 0.0 | 0.4704 | 0.0 | 0.0 | 0.6912 | 0.0581 | 0.4220 | 0.3575 | 0.0 | nan | 0.0 | 0.3214 | 0.4031 | 0.0 | 0.8634 | 0.7671 | 0.9256 | 0.0010 | 0.0263 | 0.1930 | 0.0 | 41 | | 0.2194 | 0.4928 | 0.3427 | 0.4041 | 0.8795 | 0.0 | 0.9068 | 0.9650 | 0.4578 | 0.9049 | 0.6337 | nan | 0.5321 | 0.6180 | 0.2096 | 0.9413 | 0.1196 | 0.0 | nan | 0.0 | 0.6733 | 0.0 | 0.0 | 0.9081 | 0.0807 | 0.5106 | 0.4412 | 0.0 | nan | 0.0 | 0.4201 | 0.5280 | 0.0 | 0.9298 | 0.8972 | 0.9636 | 0.0001 | 0.0292 | 0.2592 | 0.0 | 0.0 | 0.8208 | 0.9101 | 0.3823 | 0.8072 | 0.4956 | nan | 0.4523 | 0.4851 | 0.1586 | 0.8233 | 0.1146 | 0.0 | nan | 0.0 | 0.4769 | 0.0 | 0.0 | 0.6997 | 0.0757 | 0.4192 | 0.3787 | 0.0 | nan | 0.0 | 0.3206 | 0.3747 | 0.0 | 0.8626 | 0.7575 | 0.9263 | 0.0001 | 0.0251 | 0.1998 | 0.0 | 42 | | 0.2058 | 0.5290 | 0.3408 | 0.4066 | 0.8745 | 0.0 | 0.9202 | 0.9545 | 0.5309 | 0.8185 | 0.6809 | nan | 0.5379 | 0.6631 | 0.2210 | 0.9236 | 0.0830 | 0.0 | nan | 0.0 | 0.6756 | 0.0 | 0.0 | 0.9003 | 0.0643 | 0.5398 | 0.4282 | 0.0 | nan | 0.0 | 0.4030 | 0.5014 | 0.0 | 0.9338 | 0.9110 | 0.9669 | 0.0853 | 0.0317 | 0.2364 | 0.0 | 0.0 | 0.8073 | 0.9043 | 0.3907 | 0.7392 | 0.4986 | nan | 0.4517 | 0.4827 | 0.1848 | 0.8260 | 0.0820 | 0.0 | nan | 0.0 | 0.4661 | 0.0 | 0.0 | 0.6968 | 0.0612 | 0.4189 | 0.3671 | 0.0 | nan | 0.0 | 0.3188 | 0.3823 | 0.0 | 0.8635 | 0.7621 | 0.9276 | 0.0609 | 0.0263 | 0.1877 | 0.0 | 43 | | 0.2086 | 0.5585 | 0.3373 | 0.4003 | 0.8724 | 0.0 | 0.9171 | 0.9605 | 0.5070 | 0.7666 | 0.6387 | nan | 0.5526 | 0.6278 | 0.1947 | 0.9270 | 0.0758 | 0.0 | nan | 0.0 | 0.6633 | 0.0 | 0.0 | 0.9033 | 0.0584 | 0.5314 | 0.3971 | 0.0 | nan | 0.0 | 0.4408 | 0.5355 | 0.0 | 0.9321 | 0.9149 | 0.9689 | 0.0094 | 0.0216 | 0.2668 | 0.0 | 0.0 | 0.8046 | 0.8988 | 0.3859 | 0.7018 | 0.4837 | nan | 0.4593 | 0.4939 | 0.1485 | 0.8276 | 0.0747 | 0.0 | nan | 0.0 | 0.4694 | 0.0 | 0.0 | 0.7058 | 0.0565 | 0.4122 | 0.3619 | 0.0 | nan | 0.0 | 0.3300 | 0.3997 | 0.0 | 0.8611 | 0.7559 | 0.9262 | 0.0070 | 0.0195 | 0.2110 | 0.0 | 44 | | 0.2016 | 0.5442 | 0.3452 | 0.4156 | 0.8718 | 0.0 | 0.8940 | 0.9587 | 0.4970 | 0.7906 | 0.6744 | nan | 0.5719 | 0.6411 | 0.1822 | 0.9436 | 0.1722 | 0.0 | nan | 0.0 | 0.6901 | 0.0 | 0.0 | 0.8519 | 0.1355 | 0.5906 | 0.4452 | 0.0 | nan | 0.0844 | 0.4539 | 0.4714 | 0.0 | 0.9380 | 0.8876 | 0.9650 | 0.0503 | 0.0246 | 0.3849 | 0.0 | 0.0 | 0.8165 | 0.8892 | 0.3722 | 0.7187 | 0.4869 | nan | 0.4709 | 0.4788 | 0.1384 | 0.8176 | 0.1673 | 0.0 | nan | 0.0 | 0.4677 | 0.0 | 0.0 | 0.7170 | 0.0963 | 0.3922 | 0.3739 | 0.0 | nan | 0.0596 | 0.3315 | 0.3576 | 0.0 | 0.8666 | 0.7659 | 0.9290 | 0.0324 | 0.0192 | 0.2801 | 0.0 | 45 | | 0.2021 | 0.5369 | 0.3409 | 0.4049 | 0.8753 | 0.0 | 0.9249 | 0.9608 | 0.5473 | 0.7979 | 0.6534 | nan | 0.5548 | 0.6555 | 0.1623 | 0.9311 | 0.1317 | 0.0 | nan | 0.0 | 0.6821 | 0.0 | 0.0 | 0.9151 | 0.0621 | 0.5285 | 0.4378 | 0.0 | nan | 0.0029 | 0.4205 | 0.5321 | 0.0 | 0.9301 | 0.8972 | 0.9677 | 0.0025 | 0.0281 | 0.2294 | 0.0 | 0.0 | 0.8155 | 0.8981 | 0.3979 | 0.7396 | 0.4890 | nan | 0.4634 | 0.4928 | 0.1352 | 0.8283 | 0.1299 | 0.0 | nan | 0.0 | 0.4784 | 0.0 | 0.0 | 0.6949 | 0.0602 | 0.4199 | 0.3775 | 0.0 | nan | 0.0018 | 0.3280 | 0.3792 | 0.0 | 0.8698 | 0.7691 | 0.9283 | 0.0020 | 0.0237 | 0.1856 | 0.0 | 46 | | 0.2056 | 0.5118 | 0.3421 | 0.4052 | 0.8747 | 0.0 | 0.9441 | 0.9624 | 0.5219 | 0.7370 | 0.6816 | nan | 0.5360 | 0.6510 | 0.2372 | 0.9316 | 0.0854 | 0.0 | nan | 0.0 | 0.6607 | 0.0 | 0.0 | 0.8952 | 0.0777 | 0.5614 | 0.4327 | 0.0 | nan | 0.0 | 0.4403 | 0.4597 | 0.0 | 0.9350 | 0.8918 | 0.9650 | 0.0312 | 0.0296 | 0.2984 | 0.0 | 0.0 | 0.8039 | 0.9033 | 0.4243 | 0.7185 | 0.4949 | nan | 0.4494 | 0.4876 | 0.1929 | 0.8314 | 0.0849 | 0.0 | nan | 0.0 | 0.4694 | 0.0 | 0.0 | 0.7160 | 0.0697 | 0.3959 | 0.3751 | 0.0 | nan | 0.0 | 0.3319 | 0.3668 | 0.0 | 0.8638 | 0.7612 | 0.9299 | 0.0246 | 0.0251 | 0.2269 | 0.0 | 47 | | 0.2038 | 0.5407 | 0.3318 | 0.3995 | 0.8701 | 0.0 | 0.9396 | 0.9574 | 0.6559 | 0.7289 | 0.6166 | nan | 0.5573 | 0.6550 | 0.1861 | 0.9233 | 0.0852 | 0.0 | nan | 0.0 | 0.6396 | 0.0 | 0.0 | 0.9241 | 0.0504 | 0.4811 | 0.4240 | 0.0 | nan | 0.0001 | 0.3993 | 0.4905 | 0.0 | 0.9225 | 0.9142 | 0.9672 | 0.0155 | 0.0175 | 0.2326 | 0.0 | 0.0 | 0.8068 | 0.9021 | 0.3095 | 0.7049 | 0.4679 | nan | 0.4504 | 0.4687 | 0.1686 | 0.8264 | 0.0847 | 0.0 | nan | 0.0 | 0.4721 | 0.0 | 0.0 | 0.6820 | 0.0482 | 0.3826 | 0.3686 | 0.0 | nan | 0.0001 | 0.3217 | 0.3838 | 0.0 | 0.8636 | 0.7679 | 0.9297 | 0.0114 | 0.0156 | 0.1809 | 0.0 | 48 | | 0.1964 | 0.5617 | 0.3482 | 0.4212 | 0.8714 | 0.0 | 0.9191 | 0.9583 | 0.5878 | 0.7730 | 0.6079 | nan | 0.5577 | 0.6959 | 0.2221 | 0.9300 | 0.1310 | 0.0 | nan | 0.0 | 0.6927 | 0.0 | 0.0 | 0.9088 | 0.0611 | 0.4825 | 0.4370 | 0.0 | nan | 0.0 | 0.4339 | 0.4711 | 0.0 | 0.9172 | 0.9151 | 0.9692 | 0.5121 | 0.0265 | 0.2676 | 0.0 | 0.0 | 0.7902 | 0.9044 | 0.4201 | 0.6963 | 0.4879 | nan | 0.4607 | 0.4926 | 0.1864 | 0.8243 | 0.1278 | 0.0 | nan | 0.0 | 0.4759 | 0.0 | 0.0 | 0.6973 | 0.0588 | 0.3754 | 0.3760 | 0.0 | nan | 0.0 | 0.3330 | 0.3555 | 0.0 | 0.8600 | 0.7533 | 0.9288 | 0.3089 | 0.0223 | 0.2055 | 0.0 | 49 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.15.0 - Tokenizers 0.15.0

stoves/Ermurachi_Vadim

stoves

2023-12-03T19:11:04Z

2

1

diffusers

[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-11-10T11:47:08Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of gjdfophge person tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.

ThuyNT03/KLTN_COQE_viT5_SOPAL_v2

ThuyNT03

2023-12-03T19:08:01Z

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-12-03T18:22:18Z

--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: KLTN_COQE_viT5_SOPAL_v2 results: [] ---  # KLTN_COQE_viT5_SOPAL_v2 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow17

FounderOfHuggingface

2023-12-03T19:07:14Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T19:07:10Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

AndresBriC/ppo-LunarLander-v2

AndresBriC

2023-12-03T19:05:37Z

0

stable-baselines3

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

reinforcement-learning

2023-12-03T19:05:10Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 264.34 +/- 22.80 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

sade-adrien/Mistral-7B-Instruct-v0.1-LC16k-PI

sade-adrien

2023-12-03T19:00:50Z

0

null

[ "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]

null

2023-12-01T18:54:32Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.1 tags: - generated_from_trainer model-index: - name: Mistral-7B-Instruct-v0.1-LC16k-PI results: [] ---  # Mistral-7B-Instruct-v0.1-LC16k-PI 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. It achieves the following results on the evaluation set: - Loss: 1.6455 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 32 - 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: 30 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6742 | 0.12 | 100 | 1.6880 | | 1.6683 | 0.24 | 200 | 1.6711 | | 1.7301 | 0.36 | 300 | 1.6636 | | 1.6867 | 0.47 | 400 | 1.6588 | | 1.4718 | 0.59 | 500 | 1.6557 | | 1.6843 | 0.71 | 600 | 1.6519 | | 1.5966 | 0.83 | 700 | 1.6492 | | 1.9016 | 0.95 | 800 | 1.6472 | | 1.7488 | 1.07 | 900 | 1.6461 | | 1.5596 | 1.19 | 1000 | 1.6455 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.0+cu117 - Datasets 2.14.6 - Tokenizers 0.14.1

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow16

FounderOfHuggingface

2023-12-03T18:55:36Z

1

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T18:55:33Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

Liogl/RL-Course

Liogl

2023-12-03T18:48:43Z

1

0

stable-baselines3

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

reinforcement-learning

2023-12-03T18:48:06Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO-MLP results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 246.46 +/- 32.22 name: mean_reward verified: false --- # **PPO-MLP** Agent playing **LunarLander-v2** This is a trained model of a **PPO-MLP** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

JFJFJFen/JFJFJFen

JFJFJFen

2023-12-03T18:43:15Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:google/flan-t5-large", "base_model:adapter:google/flan-t5-large", "region:us" ]

null

2023-12-03T18:42:34Z

--- library_name: peft base_model: google/flan-t5-large --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.6.3.dev0

Roxysun/cs2fi-wav2vec2-large-xls-r-300m-cs-colab-phoneme

Roxysun

2023-12-03T18:43:12Z

7

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:voxpopuli", "base_model:facebook/wav2vec2-lv-60-espeak-cv-ft", "base_model:finetune:facebook/wav2vec2-lv-60-espeak-cv-ft", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-12-03T18:23:27Z

--- license: apache-2.0 base_model: facebook/wav2vec2-lv-60-espeak-cv-ft tags: - generated_from_trainer datasets: - voxpopuli metrics: - wer model-index: - name: cs2fi-wav2vec2-large-xls-r-300m-cs-colab-phoneme results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: voxpopuli type: voxpopuli config: fi split: test args: fi metrics: - name: Wer type: wer value: 0.5253440751930178 ---  # cs2fi-wav2vec2-large-xls-r-300m-cs-colab-phoneme This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 2.2508 - Wer: 0.5253 ## 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: 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: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.8061 | 16.67 | 100 | 2.7437 | 0.5693 | | 2.871 | 33.33 | 200 | 2.4209 | 0.5314 | | 2.525 | 50.0 | 300 | 2.2508 | 0.5253 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

syed789/zephyr-7b-beta-fhir-ft10

syed789

2023-12-03T18:38:54Z

2

0

peft

[ "peft", "arxiv:1910.09700", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:adapter:HuggingFaceH4/zephyr-7b-beta", "region:us" ]

null

2023-12-03T18:38:53Z

--- library_name: peft base_model: HuggingFaceH4/zephyr-7b-beta --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow13

FounderOfHuggingface

2023-12-03T18:20:46Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T18:20:43Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

pijarcandra22/t5Indo2Sunda

pijarcandra22

2023-12-03T18:11:46Z

11

0

transformers

[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-12-03T16:42:27Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_keras_callback model-index: - name: pijarcandra22/t5Indo2Sunda results: [] ---  # pijarcandra22/t5Indo2Sunda This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.1941 - Validation Loss: 2.1230 - Epoch: 99 ## 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 | |:----------:|:---------------:|:-----:| | 4.3724 | 3.9124 | 0 | | 3.9887 | 3.6348 | 1 | | 3.7534 | 3.4215 | 2 | | 3.5819 | 3.2847 | 3 | | 3.4632 | 3.1902 | 4 | | 3.3751 | 3.1139 | 5 | | 3.3039 | 3.0493 | 6 | | 3.2447 | 2.9955 | 7 | | 3.1911 | 2.9481 | 8 | | 3.1455 | 2.9082 | 9 | | 3.1068 | 2.8718 | 10 | | 3.0697 | 2.8387 | 11 | | 3.0381 | 2.8105 | 12 | | 3.0050 | 2.7825 | 13 | | 2.9796 | 2.7568 | 14 | | 2.9510 | 2.7350 | 15 | | 2.9259 | 2.7096 | 16 | | 2.9053 | 2.6881 | 17 | | 2.8833 | 2.6696 | 18 | | 2.8599 | 2.6510 | 19 | | 2.8403 | 2.6328 | 20 | | 2.8207 | 2.6171 | 21 | | 2.8046 | 2.5999 | 22 | | 2.7861 | 2.5857 | 23 | | 2.7715 | 2.5699 | 24 | | 2.7557 | 2.5542 | 25 | | 2.7387 | 2.5420 | 26 | | 2.7225 | 2.5299 | 27 | | 2.7085 | 2.5182 | 28 | | 2.6950 | 2.5081 | 29 | | 2.6818 | 2.4951 | 30 | | 2.6687 | 2.4864 | 31 | | 2.6578 | 2.4760 | 32 | | 2.6461 | 2.4651 | 33 | | 2.6334 | 2.4559 | 34 | | 2.6213 | 2.4477 | 35 | | 2.6096 | 2.4373 | 36 | | 2.5993 | 2.4297 | 37 | | 2.5906 | 2.4208 | 38 | | 2.5778 | 2.4100 | 39 | | 2.5703 | 2.4025 | 40 | | 2.5594 | 2.3962 | 41 | | 2.5521 | 2.3901 | 42 | | 2.5414 | 2.3808 | 43 | | 2.5318 | 2.3726 | 44 | | 2.5235 | 2.3684 | 45 | | 2.5165 | 2.3592 | 46 | | 2.5060 | 2.3507 | 47 | | 2.4972 | 2.3466 | 48 | | 2.4892 | 2.3388 | 49 | | 2.4807 | 2.3325 | 50 | | 2.4732 | 2.3281 | 51 | | 2.4654 | 2.3210 | 52 | | 2.4592 | 2.3138 | 53 | | 2.4525 | 2.3100 | 54 | | 2.4439 | 2.3046 | 55 | | 2.4349 | 2.2980 | 56 | | 2.4283 | 2.2926 | 57 | | 2.4222 | 2.2884 | 58 | | 2.4139 | 2.2824 | 59 | | 2.4071 | 2.2759 | 60 | | 2.4008 | 2.2705 | 61 | | 2.3941 | 2.2664 | 62 | | 2.3882 | 2.2588 | 63 | | 2.3813 | 2.2566 | 64 | | 2.3759 | 2.2498 | 65 | | 2.3674 | 2.2461 | 66 | | 2.3618 | 2.2425 | 67 | | 2.3534 | 2.2377 | 68 | | 2.3522 | 2.2314 | 69 | | 2.3398 | 2.2269 | 70 | | 2.3391 | 2.2241 | 71 | | 2.3303 | 2.2184 | 72 | | 2.3275 | 2.2137 | 73 | | 2.3190 | 2.2100 | 74 | | 2.3159 | 2.2048 | 75 | | 2.3078 | 2.2011 | 76 | | 2.3048 | 2.1971 | 77 | | 2.3005 | 2.1936 | 78 | | 2.2938 | 2.1899 | 79 | | 2.2892 | 2.1859 | 80 | | 2.2824 | 2.1819 | 81 | | 2.2758 | 2.1787 | 82 | | 2.2739 | 2.1757 | 83 | | 2.2689 | 2.1716 | 84 | | 2.2623 | 2.1664 | 85 | | 2.2574 | 2.1657 | 86 | | 2.2537 | 2.1618 | 87 | | 2.2483 | 2.1563 | 88 | | 2.2407 | 2.1554 | 89 | | 2.2387 | 2.1510 | 90 | | 2.2351 | 2.1469 | 91 | | 2.2286 | 2.1436 | 92 | | 2.2226 | 2.1413 | 93 | | 2.2171 | 2.1395 | 94 | | 2.2159 | 2.1342 | 95 | | 2.2109 | 2.1314 | 96 | | 2.2041 | 2.1284 | 97 | | 2.1999 | 2.1260 | 98 | | 2.1941 | 2.1230 | 99 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0

mtolgakbaba/mT5-general-purpose

mtolgakbaba

2023-12-03T18:10:14Z

2

0

peft

[ "peft", "safetensors", "mt5", "arxiv:1910.09700", "base_model:google/mt5-base", "base_model:adapter:google/mt5-base", "region:us" ]

null

2023-12-02T07:09:58Z

--- library_name: peft base_model: google/mt5-base --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.6.3.dev0

viktoriaohorilko/ppo-LunarLander-v2

viktoriaohorilko

2023-12-03T18:07:26Z

0

stable-baselines3

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

reinforcement-learning

2023-12-03T17:12:55Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 235.43 +/- 43.78 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

li-ping/river_retriver_416data_testing_v2

li-ping

2023-12-03T18:04:42Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-12-03T18:04:33Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # li-ping/river_retriver_416data_testing_v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('li-ping/river_retriver_416data_testing_v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=li-ping/river_retriver_416data_testing_v2) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 791 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 400, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 791, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors

digiplay/LemonTea2.5D

digiplay

2023-12-03T18:03:32Z

358

6

diffusers

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

text-to-image

2023-05-30T12:38:45Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- #EulerDiscreteScheduler Version Model info : https://civitai.com/models/70692/lemontea-mix-painterly-25d the same with "digiplay/LemonteaMixPainterly2_v1", but config the default schedule type to the Euler version.

hkivancoral/smids_1x_beit_base_rms_0001_fold2

hkivancoral

2023-12-03T18:03:05Z

7

0

transformers

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

image-classification

2023-12-02T09:44:03Z

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_1x_beit_base_rms_0001_fold2 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: 0.740432612312812 ---  # smids_1x_beit_base_rms_0001_fold2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9358 - Accuracy: 0.7404 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0437 | 1.0 | 75 | 0.9679 | 0.5042 | | 0.9234 | 2.0 | 150 | 0.8669 | 0.5208 | | 1.0795 | 3.0 | 225 | 0.7926 | 0.5874 | | 0.9543 | 4.0 | 300 | 0.8244 | 0.5507 | | 0.8239 | 5.0 | 375 | 0.7959 | 0.5857 | | 0.7924 | 6.0 | 450 | 0.7928 | 0.5890 | | 0.8468 | 7.0 | 525 | 0.7806 | 0.6256 | | 0.8608 | 8.0 | 600 | 0.9027 | 0.5408 | | 0.7878 | 9.0 | 675 | 0.7544 | 0.6373 | | 0.9079 | 10.0 | 750 | 0.7732 | 0.6190 | | 0.7705 | 11.0 | 825 | 0.7349 | 0.6290 | | 0.7586 | 12.0 | 900 | 0.7322 | 0.6306 | | 0.7794 | 13.0 | 975 | 0.7224 | 0.6323 | | 0.7123 | 14.0 | 1050 | 0.7252 | 0.6572 | | 0.744 | 15.0 | 1125 | 0.7450 | 0.5990 | | 0.7086 | 16.0 | 1200 | 0.6962 | 0.6639 | | 0.7295 | 17.0 | 1275 | 0.7508 | 0.6489 | | 0.7289 | 18.0 | 1350 | 0.6978 | 0.6722 | | 0.6947 | 19.0 | 1425 | 0.7112 | 0.6739 | | 0.6923 | 20.0 | 1500 | 0.7131 | 0.6805 | | 0.7545 | 21.0 | 1575 | 0.7480 | 0.6223 | | 0.68 | 22.0 | 1650 | 0.6683 | 0.6839 | | 0.7107 | 23.0 | 1725 | 0.6889 | 0.6772 | | 0.6933 | 24.0 | 1800 | 0.6566 | 0.6822 | | 0.6429 | 25.0 | 1875 | 0.6381 | 0.7005 | | 0.6742 | 26.0 | 1950 | 0.6536 | 0.6822 | | 0.6753 | 27.0 | 2025 | 0.6462 | 0.6889 | | 0.6228 | 28.0 | 2100 | 0.6368 | 0.7022 | | 0.6193 | 29.0 | 2175 | 0.6115 | 0.7171 | | 0.5568 | 30.0 | 2250 | 0.6625 | 0.7188 | | 0.584 | 31.0 | 2325 | 0.6680 | 0.6922 | | 0.581 | 32.0 | 2400 | 0.5723 | 0.7654 | | 0.5698 | 33.0 | 2475 | 0.6173 | 0.7205 | | 0.5032 | 34.0 | 2550 | 0.6176 | 0.7338 | | 0.5019 | 35.0 | 2625 | 0.6137 | 0.7438 | | 0.4921 | 36.0 | 2700 | 0.5855 | 0.7571 | | 0.453 | 37.0 | 2775 | 0.6724 | 0.7271 | | 0.4913 | 38.0 | 2850 | 0.6043 | 0.7720 | | 0.3871 | 39.0 | 2925 | 0.6124 | 0.7704 | | 0.4014 | 40.0 | 3000 | 0.6591 | 0.7521 | | 0.4698 | 41.0 | 3075 | 0.6575 | 0.7604 | | 0.375 | 42.0 | 3150 | 0.6735 | 0.7471 | | 0.317 | 43.0 | 3225 | 0.7867 | 0.7504 | | 0.2968 | 44.0 | 3300 | 0.7423 | 0.7521 | | 0.2919 | 45.0 | 3375 | 0.8253 | 0.7504 | | 0.2598 | 46.0 | 3450 | 0.8629 | 0.7421 | | 0.1951 | 47.0 | 3525 | 0.8586 | 0.7704 | | 0.1905 | 48.0 | 3600 | 0.9010 | 0.7438 | | 0.1278 | 49.0 | 3675 | 0.9354 | 0.7454 | | 0.2294 | 50.0 | 3750 | 0.9358 | 0.7404 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

ahams02/q-FrozenLake-v1-4x4-noSlippery

ahams02

2023-12-03T18:01:45Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-12-03T18:01:43Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="ahams02/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

MatteoWood/bert-sexism-classifier

MatteoWood

2023-12-03T18:01:35Z

7

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "sexism", "dataset:MatteoWood/bert-sexism-classifier-dataset", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-11-30T13:39:58Z

--- tags: - text-classification - sexism widget: - text: I love women - text: I hate women datasets: - MatteoWood/bert-sexism-classifier-dataset --- # Model Trained Using AutoTrain - Problem type: Text Classification # Training parameters - lr: 5e-05 - epochs: 5 - batch size: 8 ## Validation Metrics loss: 0.36799120903015137 f1: 0.7044155844155844 precision: 0.7251336898395722 recall: 0.6848484848484848 auc: 0.8958072418537535 accuracy: 0.85775

MatteoWood/albert-sexism-classifier

MatteoWood

2023-12-03T18:01:19Z

7

0

transformers

[ "transformers", "safetensors", "albert", "text-classification", "sexism", "dataset:MatteoWood/autotrain-data-albert-sexism-classifier", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-02T14:45:33Z

--- tags: - text-classification - sexism widget: - text: I love women - text: I hate women datasets: - MatteoWood/autotrain-data-albert-sexism-classifier --- # Model Trained Using AutoTrain - Problem type: Text Classification # Training parameters - lr: 5e-05 - epochs: 5 - batch size: 8 ## Validation Metrics loss: 0.40832215547561646 f1: 0.6293779404077365 precision: 0.6522210184182016 recall: 0.6080808080808081 auc: 0.8514470284237725 accuracy: 0.82275

MatteoWood/hatebert-sexism-classifier

MatteoWood

2023-12-03T18:00:04Z

6

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "sexism", "dataset:MatteoWood/autotrain-data-hatebert-sexism-classifier", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-02T13:05:20Z

--- tags: - text-classification - sexism widget: - text: I love women - text: I hate women datasets: - MatteoWood/autotrain-data-hatebert-sexism-classifier --- # Model Trained Using AutoTrain - Problem type: Text Classification # Training parameters - lr: 5e-05 - epochs: 5 - batch size: 8 ## Validation Metrics loss: 0.3898809254169464 f1: 0.7137662337662338 precision: 0.73475935828877 recall: 0.693939393939394 auc: 0.8955589113728649 accuracy: 0.86225

Roxysun/wav2vec2-large-xls-r-300m-cs-colab-phoneme

Roxysun

2023-12-03T17:53:18Z

9

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:voxpopuli", "base_model:facebook/wav2vec2-lv-60-espeak-cv-ft", "base_model:finetune:facebook/wav2vec2-lv-60-espeak-cv-ft", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-12-03T16:51:56Z

--- license: apache-2.0 base_model: facebook/wav2vec2-lv-60-espeak-cv-ft tags: - generated_from_trainer datasets: - voxpopuli metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-cs-colab-phoneme results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: voxpopuli type: voxpopuli config: cs split: test args: cs metrics: - name: Wer type: wer value: 0.44625639804785144 ---  # wav2vec2-large-xls-r-300m-cs-colab-phoneme This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 1.9388 - Wer: 0.4463 ## 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: 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: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow10

FounderOfHuggingface

2023-12-03T17:46:02Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T17:45:58Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

francesco-vo/bert_model_v2

francesco-vo

2023-12-03T17:42:54Z

7

0

transformers

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

text-classification

2023-12-03T17:27:06Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_model_v2 results: [] ---  # bert_model_v2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5556 - Accuracy: 0.7469 ## 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 423 | 0.5205 | 0.7345 | | 0.5638 | 2.0 | 846 | 0.5143 | 0.7545 | | 0.4089 | 3.0 | 1269 | 0.5556 | 0.7469 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

SaraPiscitelli/dqn-SpaceInvadersNoFrameskip-v4

SaraPiscitelli

2023-12-03T17:35:56Z

2

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-12-03T13:53:17Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 554.00 +/- 186.01 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga SaraPiscitelli -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga SaraPiscitelli -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga SaraPiscitelli ``` ## Hyperparameters ```python OrderedDict([('batch_size', 128), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

Jerry46/zephyr-7b-sft-lora

Jerry46

2023-12-03T17:29:01Z

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-12-03T05:25:09Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: zephyr-7b-sft-lora results: [] ---  # zephyr-7b-sft-lora This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9900 ## 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: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9866 | 0.67 | 272 | 0.9901 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

NousResearch/Nous-Hermes-2-Vision-Alpha

NousResearch

2023-12-03T17:25:41Z

40

303

transformers

[ "transformers", "pytorch", "llava_mistral", "text-generation", "mistral", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "distillation", "multimodal", "llava", "conversational", "en", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-11-28T23:18:55Z

--- base_model: mistralai/Mistral-7B-v0.1 tags: - mistral - instruct - finetune - chatml - gpt4 - synthetic data - distillation - multimodal - llava model-index: - name: Nous-Hermes-2-Vision results: [] license: apache-2.0 language: - en --- # Nous-Hermes-2-Vision - Mistral 7B ![image/png](https://camo.githubusercontent.com/b09dc35a93b4b70748fa4e2f307b011cd3d548369dd926ec9a2d3a51f7b3721e/68747470733a2f2f66696c65732e6f616975736572636f6e74656e742e636f6d2f66696c652d6b4437565358734f5649576472624b3042353662686644363f73653d323032332d31322d3033543137253341333425334135385a2673703d722673763d323032312d30382d30362673723d6226727363633d6d61782d6167652533443331353336303030253243253230696d6d757461626c6526727363643d6174746163686d656e7425334225323066696c656e616d6525334439643530333039622d356236342d343964302d623832362d6165316638366132396661382e77656270267369673d50396973694b4679654a54435a47424b526d45494b3043586e6e55676c6334704a583071312532425478666a34253344) *In the tapestry of Greek mythology, Hermes reigns as the eloquent Messenger of the Gods, a deity who deftly bridges the realms through the art of communication. It is in homage to this divine mediator that I name this advanced LLM "Hermes," a system crafted to navigate the complex intricacies of human discourse with celestial finesse.* ## Model description Nous-Hermes-2-Vision stands as a pioneering Vision-Language Model, leveraging advancements from the renowned **OpenHermes-2.5-Mistral-7B** by teknium. This model incorporates two pivotal enhancements, setting it apart as a cutting-edge solution: - **SigLIP-400M Integration**: Diverging from traditional approaches that rely on substantial 3B vision encoders, Nous-Hermes-2-Vision harnesses the formidable SigLIP-400M. This strategic choice not only streamlines the model's architecture, making it more lightweight, but also capitalizes on SigLIP's remarkable capabilities. The result? A remarkable boost in performance that defies conventional expectations. - **Custom Dataset Enriched with Function Calling**: Our model's training data includes a unique feature – function calling. This distinctive addition transforms Nous-Hermes-2-Vision into a **Vision-Language Action Model**. Developers now have a versatile tool at their disposal, primed for crafting a myriad of ingenious automations. This project is led by [qnguyen3](https://twitter.com/stablequan) and [teknium](https://twitter.com/Teknium1). ## Training ### Dataset - 220K from **LVIS-INSTRUCT4V** - 60K from **ShareGPT4V** - 150K Private **Function Calling Data** - 50K conversations from teknium's **OpenHermes-2.5** ## Usage ### Prompt Format - Like other LLaVA's variants, this model uses Vicuna-V1 as its prompt template. Please refer to `conv_llava_v1` in [this file](https://github.com/qnguyen3/hermes-llava/blob/main/llava/conversation.py) - For Gradio UI, please visit this [GitHub Repo](https://github.com/qnguyen3/hermes-llava) ### Function Calling - For functiong calling, the message should start with a `<fn_call>` tag. Here is an example: ```json <fn_call>{ "type": "object", "properties": { "bus_colors": { "type": "array", "description": "The colors of the bus in the image.", "items": { "type": "string", "enum": ["red", "blue", "green", "white"] } }, "bus_features": { "type": "string", "description": "The features seen on the back of the bus." }, "bus_location": { "type": "string", "description": "The location of the bus (driving or pulled off to the side).", "enum": ["driving", "pulled off to the side"] } } } ``` Output: ```json { "bus_colors": ["red", "white"], "bus_features": "An advertisement", "bus_location": "driving" } ``` ## Example ### Chat ![image/png](https://i.ibb.co/tMg8h2t/Screenshot-from-2023-12-04-00-13-59.png) ### Function Calling Input image: ![image/png](https://www.slcmenu.com/wp-content/uploads/2017/11/In-N-Out-Burger-menu-2020-982x1024.jpg) Input message: ```json <fn_call>{ "type": "object", "properties": { "food_list": { "type": "array", "description": "List of all the food", "items": { "type": "string", } }, } } ``` Output: ```json { "food_list": [ "Double Burger", "Cheeseburger", "French Fries", "Shakes", "Coffee" ] } ```

lnxdx/19_2000_1e-5_hp-base

lnxdx

2023-12-03T17:25:05Z

4

0

transformers

[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:masoudmzb/wav2vec2-xlsr-multilingual-53-fa", "base_model:finetune:masoudmzb/wav2vec2-xlsr-multilingual-53-fa", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-12-01T00:10:01Z

--- base_model: masoudmzb/wav2vec2-xlsr-multilingual-53-fa tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-xlsr-persian-asr-shemo_lnxdx results: [] ---  # wav2vec2-large-xlsr-persian-asr-shemo_lnxdx This model is a fine-tuned version of [masoudmzb/wav2vec2-xlsr-multilingual-53-fa](https://huggingface.co/masoudmzb/wav2vec2-xlsr-multilingual-53-fa) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7064 - Wer: 0.3344 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.9455 | 0.62 | 100 | 1.4247 | 0.4831 | | 1.4824 | 1.25 | 200 | 1.1107 | 0.4331 | | 1.2516 | 1.88 | 300 | 0.9141 | 0.4136 | | 1.0859 | 2.5 | 400 | 0.8360 | 0.3975 | | 1.0357 | 3.12 | 500 | 0.8097 | 0.3814 | | 1.0472 | 3.75 | 600 | 0.7550 | 0.3753 | | 0.9963 | 4.38 | 700 | 0.7533 | 0.3636 | | 0.9767 | 5.0 | 800 | 0.7424 | 0.3589 | | 0.9667 | 5.62 | 900 | 0.7360 | 0.3516 | | 0.9385 | 6.25 | 1000 | 0.7355 | 0.3487 | | 0.9805 | 6.88 | 1100 | 0.7237 | 0.3464 | | 0.976 | 7.5 | 1200 | 0.7078 | 0.3455 | | 0.88 | 8.12 | 1300 | 0.7229 | 0.3438 | | 0.9421 | 8.75 | 1400 | 0.7180 | 0.3432 | | 0.9584 | 9.38 | 1500 | 0.7059 | 0.3364 | | 0.88 | 10.0 | 1600 | 0.7106 | 0.3364 | | 0.9113 | 10.62 | 1700 | 0.7125 | 0.3344 | | 0.912 | 11.25 | 1800 | 0.7091 | 0.3353 | | 0.9607 | 11.88 | 1900 | 0.7066 | 0.3344 | | 0.8974 | 12.5 | 2000 | 0.7064 | 0.3344 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

VitaliiVrublevskyi/bert-base-multilingual-uncased-finetuned-mrpc

VitaliiVrublevskyi

2023-12-03T17:23:39Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-03T16:03:20Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: bert-base-multilingual-uncased-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8382352941176471 - name: F1 type: f1 value: 0.8817204301075269 ---  # bert-base-multilingual-uncased-finetuned-mrpc This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4928 - Accuracy: 0.8382 - F1: 0.8817 ## 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: 97 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 115 | 0.4251 | 0.7843 | 0.8358 | | No log | 2.0 | 230 | 0.3601 | 0.8260 | 0.8702 | | No log | 3.0 | 345 | 0.3677 | 0.8333 | 0.8727 | | No log | 4.0 | 460 | 0.4801 | 0.8260 | 0.8707 | | 0.3223 | 5.0 | 575 | 0.4928 | 0.8382 | 0.8817 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.13.3

dodisbeaver/ppo-LunarLander-v2-100000

dodisbeaver

2023-12-03T17:13:56Z

0

stable-baselines3

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

reinforcement-learning

2023-12-03T17:13:18Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -106.55 +/- 54.00 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

messawey/my_awesome_qa_model

messawey

2023-12-03T17:02:34Z

3

0

transformers

[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "endpoints_compatible", "region:us" ]

question-answering

2023-12-03T17:00:55Z

--- base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_keras_callback model-index: - name: messawey/my_awesome_qa_model results: [] ---  # messawey/my_awesome_qa_model This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.5375 - Validation Loss: 4.1401 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 68, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.5375 | 4.1401 | 0 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0

Gairiangsui/my-wolf-wlf

Gairiangsui

2023-12-03T16:58:14Z

0

diffusers

[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-12-03T16:54:13Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### my-wolf--wlf Dreambooth model trained by Gairiangsui following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 215022 Sample pictures of this concept: ![0](https://huggingface.co/Gairiangsui/my-wolf-wlf/resolve/main/sample_images/a2aaa27b-effe-4c04-9ec5-bf6db9d666ae.jpeg) ![1](https://huggingface.co/Gairiangsui/my-wolf-wlf/resolve/main/sample_images/46d58259-1678-4c6a-b0d7-f343954a76f1.jpeg) ![2](https://huggingface.co/Gairiangsui/my-wolf-wlf/resolve/main/sample_images/753e54cb-5615-493c-94ff-43c388084f27.jpeg)

dpv/Mistral7bInstruct-MarketingEmailAdaptor4QV

dpv

2023-12-03T16:54:21Z

6

0

peft

[ "peft", "safetensors", "region:us" ]

null

2023-12-03T16:13:48Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0

Anwaarma/BP-MM05

Anwaarma

2023-12-03T16:51:44Z

13

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:Anwaarma/BP-INT-I04", "base_model:finetune:Anwaarma/BP-INT-I04", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-03T16:30:21Z

--- license: mit base_model: Anwaarma/BP-test4 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: BP-S02andInt03 results: [] ---  # BP-S02andInt03 This model is a fine-tuned version of [Anwaarma/BP-test4](https://huggingface.co/Anwaarma/BP-test4) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4031 - Accuracy: 0.82 - F1: 0.8097 ## 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: 13 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.0 | 50 | 1.1241 | 0.54 | 0.4220 | | No log | 0.01 | 100 | 0.8697 | 0.51 | 0.4402 | | No log | 0.01 | 150 | 0.7063 | 0.37 | 0.3740 | | No log | 0.02 | 200 | 0.6895 | 0.51 | 0.4184 | | No log | 0.02 | 250 | 0.6880 | 0.52 | 0.4467 | | No log | 0.02 | 300 | 0.6874 | 0.52 | 0.4778 | | No log | 0.03 | 350 | 0.6842 | 0.52 | 0.4778 | | No log | 0.03 | 400 | 0.6889 | 0.5 | 0.4491 | | No log | 0.04 | 450 | 0.6727 | 0.55 | 0.5398 | | 0.7977 | 0.04 | 500 | 0.6617 | 0.59 | 0.5877 | | 0.7977 | 0.04 | 550 | 0.6514 | 0.59 | 0.5877 | | 0.7977 | 0.05 | 600 | 0.6597 | 0.59 | 0.5877 | | 0.7977 | 0.05 | 650 | 0.6322 | 0.59 | 0.5877 | | 0.7977 | 0.06 | 700 | 0.5898 | 0.57 | 0.5655 | | 0.7977 | 0.06 | 750 | 0.5406 | 0.7 | 0.7015 | | 0.7977 | 0.06 | 800 | 0.4813 | 0.8 | 0.7862 | | 0.7977 | 0.07 | 850 | 0.4706 | 0.8 | 0.7862 | | 0.7977 | 0.07 | 900 | 0.4743 | 0.79 | 0.7768 | | 0.7977 | 0.08 | 950 | 0.4578 | 0.8 | 0.7862 | | 0.5646 | 0.08 | 1000 | 0.4571 | 0.8 | 0.7862 | | 0.5646 | 0.08 | 1050 | 0.4536 | 0.8 | 0.7862 | | 0.5646 | 0.09 | 1100 | 0.4461 | 0.8 | 0.7862 | | 0.5646 | 0.09 | 1150 | 0.4451 | 0.8 | 0.7862 | | 0.5646 | 0.1 | 1200 | 0.4398 | 0.81 | 0.7956 | | 0.5646 | 0.1 | 1250 | 0.4360 | 0.8 | 0.7862 | | 0.5646 | 0.1 | 1300 | 0.4325 | 0.81 | 0.7956 | | 0.5646 | 0.11 | 1350 | 0.4316 | 0.81 | 0.7956 | | 0.5646 | 0.11 | 1400 | 0.4310 | 0.81 | 0.7956 | | 0.5646 | 0.12 | 1450 | 0.4301 | 0.81 | 0.7956 | | 0.4672 | 0.12 | 1500 | 0.4275 | 0.81 | 0.7956 | | 0.4672 | 0.12 | 1550 | 0.4271 | 0.8 | 0.7862 | | 0.4672 | 0.13 | 1600 | 0.4258 | 0.81 | 0.7956 | | 0.4672 | 0.13 | 1650 | 0.4211 | 0.81 | 0.7956 | | 0.4672 | 0.14 | 1700 | 0.4154 | 0.82 | 0.8097 | | 0.4672 | 0.14 | 1750 | 0.4153 | 0.81 | 0.7956 | | 0.4672 | 0.14 | 1800 | 0.4120 | 0.81 | 0.7956 | | 0.4672 | 0.15 | 1850 | 0.4134 | 0.8 | 0.7862 | | 0.4672 | 0.15 | 1900 | 0.4119 | 0.8 | 0.7862 | | 0.4672 | 0.16 | 1950 | 0.4119 | 0.82 | 0.8097 | | 0.4371 | 0.16 | 2000 | 0.4094 | 0.82 | 0.8097 | | 0.4371 | 0.16 | 2050 | 0.4113 | 0.82 | 0.8097 | | 0.4371 | 0.17 | 2100 | 0.4136 | 0.83 | 0.8259 | | 0.4371 | 0.17 | 2150 | 0.4096 | 0.82 | 0.8097 | | 0.4371 | 0.18 | 2200 | 0.4116 | 0.82 | 0.8097 | | 0.4371 | 0.18 | 2250 | 0.4039 | 0.82 | 0.8097 | | 0.4371 | 0.18 | 2300 | 0.4044 | 0.82 | 0.8097 | | 0.4371 | 0.19 | 2350 | 0.4031 | 0.82 | 0.8097 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

pijarcandra22/t5Sunda2Indo

pijarcandra22

2023-12-03T16:41:20Z

3

0

transformers

[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-12-03T14:43:36Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_keras_callback model-index: - name: pijarcandra22/t5Sunda2Indo results: [] ---  # pijarcandra22/t5Sunda2Indo This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.6406 - Validation Loss: 1.5932 - Epoch: 140 ## 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 | |:----------:|:---------------:|:-----:| | 3.9668 | 3.4054 | 0 | | 3.5451 | 3.1460 | 1 | | 3.3386 | 2.9773 | 2 | | 3.1917 | 2.8549 | 3 | | 3.0808 | 2.7568 | 4 | | 2.9921 | 2.6780 | 5 | | 2.9187 | 2.6135 | 6 | | 2.8536 | 2.5547 | 7 | | 2.8010 | 2.5040 | 8 | | 2.7482 | 2.4601 | 9 | | 2.7056 | 2.4227 | 10 | | 2.6709 | 2.3870 | 11 | | 2.6325 | 2.3554 | 12 | | 2.6016 | 2.3233 | 13 | | 2.5685 | 2.2965 | 14 | | 2.5407 | 2.2710 | 15 | | 2.5158 | 2.2486 | 16 | | 2.4905 | 2.2248 | 17 | | 2.4680 | 2.2037 | 18 | | 2.4459 | 2.1856 | 19 | | 2.4257 | 2.1684 | 20 | | 2.4045 | 2.1495 | 21 | | 2.3861 | 2.1325 | 22 | | 2.3676 | 2.1189 | 23 | | 2.3479 | 2.1028 | 24 | | 2.3333 | 2.0873 | 25 | | 2.3192 | 2.0743 | 26 | | 2.3035 | 2.0647 | 27 | | 2.2889 | 2.0504 | 28 | | 2.2742 | 2.0395 | 29 | | 2.2608 | 2.0285 | 30 | | 2.2471 | 2.0166 | 31 | | 2.2344 | 2.0078 | 32 | | 2.2211 | 1.9999 | 33 | | 2.2086 | 1.9893 | 34 | | 2.1965 | 1.9790 | 35 | | 2.1879 | 1.9724 | 36 | | 2.1774 | 1.9637 | 37 | | 2.1663 | 1.9537 | 38 | | 2.1573 | 1.9461 | 39 | | 2.1470 | 1.9389 | 40 | | 2.1344 | 1.9329 | 41 | | 2.1259 | 1.9257 | 42 | | 2.1192 | 1.9158 | 43 | | 2.1098 | 1.9092 | 44 | | 2.0993 | 1.9021 | 45 | | 2.0930 | 1.8970 | 46 | | 2.0831 | 1.8909 | 47 | | 2.0729 | 1.8845 | 48 | | 2.0669 | 1.8799 | 49 | | 2.0587 | 1.8746 | 50 | | 2.0519 | 1.8662 | 51 | | 2.0446 | 1.8605 | 52 | | 2.0338 | 1.8552 | 53 | | 2.0297 | 1.8494 | 54 | | 2.0207 | 1.8441 | 55 | | 2.0151 | 1.8404 | 56 | | 2.0116 | 1.8346 | 57 | | 2.0029 | 1.8286 | 58 | | 1.9942 | 1.8243 | 59 | | 1.9894 | 1.8177 | 60 | | 1.9820 | 1.8145 | 61 | | 1.9753 | 1.8100 | 62 | | 1.9698 | 1.8054 | 63 | | 1.9635 | 1.8001 | 64 | | 1.9588 | 1.7963 | 65 | | 1.9533 | 1.7895 | 66 | | 1.9443 | 1.7888 | 67 | | 1.9381 | 1.7846 | 68 | | 1.9320 | 1.7806 | 69 | | 1.9281 | 1.7755 | 70 | | 1.9232 | 1.7697 | 71 | | 1.9141 | 1.7672 | 72 | | 1.9128 | 1.7655 | 73 | | 1.9041 | 1.7611 | 74 | | 1.8987 | 1.7558 | 75 | | 1.8955 | 1.7498 | 76 | | 1.8874 | 1.7493 | 77 | | 1.8845 | 1.7433 | 78 | | 1.8793 | 1.7403 | 79 | | 1.8723 | 1.7370 | 80 | | 1.8669 | 1.7319 | 81 | | 1.8626 | 1.7323 | 82 | | 1.8593 | 1.7268 | 83 | | 1.8540 | 1.7235 | 84 | | 1.8504 | 1.7204 | 85 | | 1.8468 | 1.7180 | 86 | | 1.8398 | 1.7130 | 87 | | 1.8358 | 1.7088 | 88 | | 1.8321 | 1.7081 | 89 | | 1.8271 | 1.7042 | 90 | | 1.8203 | 1.7016 | 91 | | 1.8189 | 1.6985 | 92 | | 1.8117 | 1.6967 | 93 | | 1.8089 | 1.6924 | 94 | | 1.8035 | 1.6898 | 95 | | 1.7984 | 1.6904 | 96 | | 1.7957 | 1.6836 | 97 | | 1.7925 | 1.6833 | 98 | | 1.7880 | 1.6786 | 99 | | 1.7816 | 1.6770 | 100 | | 1.7772 | 1.6739 | 101 | | 1.7754 | 1.6733 | 102 | | 1.7712 | 1.6678 | 103 | | 1.7677 | 1.6638 | 104 | | 1.7641 | 1.6627 | 105 | | 1.7601 | 1.6609 | 106 | | 1.7573 | 1.6585 | 107 | | 1.7516 | 1.6559 | 108 | | 1.7478 | 1.6523 | 109 | | 1.7443 | 1.6523 | 110 | | 1.7406 | 1.6498 | 111 | | 1.7361 | 1.6475 | 112 | | 1.7326 | 1.6435 | 113 | | 1.7285 | 1.6422 | 114 | | 1.7244 | 1.6398 | 115 | | 1.7205 | 1.6386 | 116 | | 1.7187 | 1.6347 | 117 | | 1.7157 | 1.6335 | 118 | | 1.7113 | 1.6317 | 119 | | 1.7094 | 1.6308 | 120 | | 1.7074 | 1.6267 | 121 | | 1.7007 | 1.6252 | 122 | | 1.6965 | 1.6241 | 123 | | 1.6931 | 1.6231 | 124 | | 1.6913 | 1.6201 | 125 | | 1.6862 | 1.6174 | 126 | | 1.6833 | 1.6176 | 127 | | 1.6827 | 1.6122 | 128 | | 1.6774 | 1.6127 | 129 | | 1.6726 | 1.6119 | 130 | | 1.6698 | 1.6079 | 131 | | 1.6663 | 1.6077 | 132 | | 1.6631 | 1.6055 | 133 | | 1.6617 | 1.6043 | 134 | | 1.6573 | 1.6019 | 135 | | 1.6549 | 1.5994 | 136 | | 1.6514 | 1.5990 | 137 | | 1.6484 | 1.5965 | 138 | | 1.6490 | 1.5942 | 139 | | 1.6406 | 1.5932 | 140 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0

oieieio/Orca-2-13b-awq

oieieio

2023-12-03T16:38:40Z

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "orca", "orca2", "microsoft", "arxiv:2311.11045", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]

text-generation

2023-12-01T23:52:55Z

--- pipeline_tag: text-generation tags: - orca - orca2 - microsoft license: other license_name: microsoft-research-license license_link: LICENSE --- # Orca-2-13b-awq | Quantized 4 Bit  Orca 2 is built for research purposes only and provides a single turn response in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization. The model is designed to excel particularly in reasoning. Note that: 1. This is a research model, intended to show that we can use capable models and complex workflows (advanced prompts, multiple calls) to create synthetic data that can teach Small Language Models (SLMs) new capabilities. We chose reasoning because it is a widely useful capability that SLMs lack. 2. The model is not optimized for chat and has not been trained with RLHF or DPO. It is best used after being finetuned for chat or for a specific task. 3. Beyond reasoning, the model inherits capabilities and limitations of its base (LLAMA-2 base). We have already seen that the benefits of the Orca training can be applied to other base model too. We make Orca 2's weights publicly available to support further research on the development, evaluation, and alignment of SLMs. ## What is Orca 2’s intended use(s)? + Orca 2 is built for research purposes only. + The main purpose is to allow the research community to assess its abilities and to provide a foundation for building better frontier models. ## How was Orca 2 evaluated? + Orca 2 has been evaluated on a large number of tasks ranging from reasoning to grounding and safety. Please refer to Section 6 and Appendix in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf) for details on evaluations. ## Model Details Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was moderated using the Microsoft Azure content filters. More details about the model can be found in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf). Please refer to LLaMA-2 technical report for details on the model architecture. ## License Orca 2 is licensed under the [Microsoft Research License](LICENSE). Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved. ## Bias, Risks, and Limitations Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the common limitations of other large language models or limitation caused by its training process, including: **Data Biases**: Large language models, trained on extensive data, can inadvertently carry biases present in the source data. Consequently, the models may generate outputs that could be potentially biased or unfair. **Lack of Contextual Understanding**: Despite their impressive capabilities in language understanding and generation, these models exhibit limited real-world understanding, resulting in potential inaccuracies or nonsensical responses. **Lack of Transparency**: Due to the complexity and size, large language models can act as “black boxes”, making it difficult to comprehend the rationale behind specific outputs or decisions. We recommend reviewing transparency notes from Azure for more information. **Content Harms**: There are various types of content harms that large language models can cause. It is important to be aware of them when using these models, and to take actions to prevent them. It is recommended to leverage various content moderation services provided by different companies and institutions. On an important note, we hope for better regulations and standards from government and technology leaders around content harms for AI technologies in future. We value and acknowledge the important role that research and open source community can play in this direction. **Hallucination**: It is important to be aware and cautious not to entirely rely on a given language model for critical decisions or information that might have deep impact as it is not obvious how to prevent these models from fabricating content. Moreover, it is not clear whether small models may be more susceptible to hallucination in ungrounded generation use cases due to their smaller sizes and hence reduced memorization capacities. This is an active research topic and we hope there will be more rigorous measurement, understanding and mitigations around this topic. **Potential for Misuse**: Without suitable safeguards, there is a risk that these models could be maliciously used for generating disinformation or harmful content. **Data Distribution**: Orca 2’s performance is likely to correlate strongly with the distribution of the tuning data. This correlation might limit its accuracy in areas underrepresented in the training dataset such as math, coding, and reasoning. **System messages**: Orca 2 demonstrates variance in performance depending on the system instructions. Additionally, the stochasticity introduced by the model size may lead to generation of non-deterministic responses to different system instructions. **Zero-Shot Settings**: Orca 2 was trained on data that mostly simulate zero-shot settings. While the model demonstrate very strong performance in zero-shot settings, it does not show the same gains of using few-shot learning compared to other, specially larger, models. **Synthetic data**: As Orca 2 is trained on synthetic data, it could inherit both the advantages and shortcomings of the models and methods used for data generation. We posit that Orca 2 benefits from the safety measures incorporated during training and safety guardrails (e.g., content filter) within the Azure OpenAI API. However, detailed studies are required for better quantification of such risks. This model is solely designed for research settings, and its testing has only been carried out in such environments. It should not be used in downstream applications, as additional analysis is needed to assess potential harm or bias in the proposed application. ## Getting started with Orca-2-13b-awq **Inference with Hugging Face library** ```python !pip install autoawq ``` ```python !pip install torch --upgrade --index-url https://download.pytorch.org/whl/cu121 ``` ```python #@title Live Chat with Orca import torch from transformers import AutoTokenizer, AutoModelForCausalLM # Load the tokenizer and model quant_path = "oieieio/Orca-2-13b-awq" tokenizer = AutoTokenizer.from_pretrained(quant_path) model = AutoModelForCausalLM.from_pretrained(quant_path) # Move the model to GPU if available device = 'cuda' if torch.cuda.is_available() else 'cpu' model.to(device) # Initial system message system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant..." while True: # User input user_message = input("User: ") if user_message.lower() == 'quit': break # Construct the prompt prompt = f"system\n{system_message}\nuser\n{user_message}\nassistant" # Encode and generate response inputs = tokenizer(prompt, return_tensors='pt').to(device) output_ids = model.generate(inputs["input_ids"], max_length=512) answer = tokenizer.decode(output_ids[0], skip_special_tokens=True) # Print the response print("AI: ", answer) ``` ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM # Load the tokenizer and model quant_path = "oieieio/Orca-2-13b-awq" tokenizer = AutoTokenizer.from_pretrained(quant_path) model = AutoModelForCausalLM.from_pretrained(quant_path) # Move the model to GPU if available device = 'cuda' if torch.cuda.is_available() else 'cpu' model.to(device) # Initial system message system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant..." while True: user_message = input("User: ") if user_message.lower() == 'quit': break prompt = f"system\n{system_message}\nuser\n{user_message}\nassistant" inputs = tokenizer(prompt, return_tensors='pt').to(device) output_ids = model.generate( inputs["input_ids"], max_new_tokens=50, # Adjust the number of generated tokens temperature=0.7, # Adjust for randomness top_k=50, # Adjust the number of highest probability tokens to consider top_p=0.95, # Adjust the cumulative probability threshold do_sample=True # Use sampling instead of greedy decoding ) answer = tokenizer.decode(output_ids[0], skip_special_tokens=True) print("AI: ", answer) ``` ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM # Load the tokenizer and model from Hugging Face Hub quant_path = "oieieio/Orca-2-13b-awq" tokenizer = AutoTokenizer.from_pretrained(quant_path) model = AutoModelForCausalLM.from_pretrained(quant_path) # Move the model to GPU if available device = 'cuda' if torch.cuda.is_available() else 'cpu' model.to(device) # First turn of the conversation system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior." user_message = "How can you determine if a restaurant is popular among locals or mainly attracts tourists, and why might this information be useful?" prompt = f"system\n{system_message}\nuser\n{user_message}\nassistant" # Encode the first prompt inputs = tokenizer(prompt, return_tensors='pt').to(device) output_ids = model.generate(inputs["input_ids"], max_length=512) # Decode the first response answer = tokenizer.decode(output_ids[0], skip_special_tokens=True) # Print the first response print(answer) # Second turn of the conversation second_turn_user_message = "Give me a list of the key points of your first answer." # Append the second turn message to the already generated ids without adding special tokens second_turn_message_in_markup = f"\nuser\n{second_turn_user_message}\nassistant" second_turn_tokens = tokenizer(second_turn_message_in_markup, return_tensors='pt', add_special_tokens=False).to(device) second_turn_input_ids = torch.cat([output_ids, second_turn_tokens['input_ids']], dim=1) # Generate the second response output_ids_2 = model.generate(second_turn_input_ids, max_length=1024) # Decode the second response second_turn_answer = tokenizer.decode(output_ids_2[0], skip_special_tokens=True) # Print the second response print(second_turn_answer) ``` ## Citation ```bibtex @misc{mitra2023orca, title={Orca 2: Teaching Small Language Models How to Reason}, author={Arindam Mitra and Luciano Del Corro and Shweti Mahajan and Andres Codas and Clarisse Simoes and Sahaj Agrawal and Xuxi Chen and Anastasia Razdaibiedina and Erik Jones and Kriti Aggarwal and Hamid Palangi and Guoqing Zheng and Corby Rosset and Hamed Khanpour and Ahmed Awadallah}, year={2023}, eprint={2311.11045}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

Ahmed107/nllb200-ar-en-v2

Ahmed107

2023-12-03T16:37:32Z

14

0

transformers

[ "transformers", "tensorboard", "safetensors", "m2m_100", "text2text-generation", "translation", "generated_from_trainer", "base_model:nadsoft/Faseeh-v0.1-beta", "base_model:finetune:nadsoft/Faseeh-v0.1-beta", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-12-03T15:19:28Z

--- license: cc-by-nc-4.0 base_model: nadsoft/Faseeh-v0.1-beta tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: nllb200-ar-en-v2 results: [] ---  # nllb200-ar-en-v2 This model is a fine-tuned version of [nadsoft/Faseeh-v0.1-beta](https://huggingface.co/nadsoft/Faseeh-v0.1-beta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8491 - Bleu: 49.3964 ## 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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

hedderich/ppo-LunarLander-v2

hedderich

2023-12-03T16:37:05Z

1

0

stable-baselines3

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

reinforcement-learning

2023-12-03T15:45:04Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 261.33 +/- 19.66 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow4

FounderOfHuggingface

2023-12-03T16:36:23Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T16:36:20Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

Ghali20/test_1

Ghali20

2023-12-03T16:27:58Z

4

1

peft

[ "peft", "arxiv:1910.09700", "base_model:HuggingFaceH4/zephyr-7b-alpha", "base_model:adapter:HuggingFaceH4/zephyr-7b-alpha", "region:us" ]

null

2023-12-03T16:27:56Z

--- library_name: peft base_model: HuggingFaceH4/zephyr-7b-alpha --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2

JaiSurya/q-FrozenLake-v1-4x4-noSlippery

JaiSurya

2023-12-03T16:27:06Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-12-03T16:27:03Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage model = load_from_hub(repo_id="JaiSurya/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"])

annawleo/mistral-finetuned-alpaca

annawleo

2023-12-03T16:16:20Z

0

null

[ "tensorboard", "safetensors", "generated_from_trainer", "dataset:generator", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "base_model:finetune:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]

null

2023-12-03T14:40:05Z

--- license: apache-2.0 base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ tags: - generated_from_trainer datasets: - generator model-index: - name: mistral-finetuned-alpaca results: [] ---  # mistral-finetuned-alpaca This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the generator 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: 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: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

yily/glm-lora-stroke-47000

yily

2023-12-03T16:15:39Z

0

peft

[ "peft", "region:us" ]

null

2023-12-03T16:14:41Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

Kyriota/fastchat-3b-lora-gsm8k-baseline

Kyriota

2023-12-03T16:12:15Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:lmsys/fastchat-t5-3b-v1.0", "base_model:adapter:lmsys/fastchat-t5-3b-v1.0", "region:us" ]

null

2023-12-03T11:54:03Z

--- library_name: peft base_model: lmsys/fastchat-t5-3b-v1.0 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

AlvinxLukilah/TextGenerationV2

AlvinxLukilah

2023-12-03T16:10:43Z

7

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "license:creativeml-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-10-29T13:11:20Z

--- license: creativeml-openrail-m ---

yily/glm-lora-stroke-46000

yily

2023-12-03T16:10:11Z

0

peft

[ "peft", "region:us" ]

null

2023-12-03T16:09:03Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0

VitaliiVrublevskyi/bert-base-cased-finetuned-mrpc

VitaliiVrublevskyi

2023-12-03T16:01:47Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-03T14:41:38Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: bert-base-cased-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8382352941176471 - name: F1 type: f1 value: 0.8873720136518771 ---  # bert-base-cased-finetuned-mrpc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5719 - Accuracy: 0.8382 - F1: 0.8874 ## 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: 91 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 115 | 0.5228 | 0.7549 | 0.8471 | | No log | 2.0 | 230 | 0.4315 | 0.8088 | 0.8673 | | No log | 3.0 | 345 | 0.4212 | 0.8284 | 0.8785 | | No log | 4.0 | 460 | 0.5462 | 0.8382 | 0.8889 | | 0.3551 | 5.0 | 575 | 0.5719 | 0.8382 | 0.8874 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.13.3

FounderOfHuggingface/fresh_gpt2_lora_r16_dbpedia_14_t300_e5_member_shadow0

FounderOfHuggingface

2023-12-03T15:49:59Z

0

peft

[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]

null

2023-12-03T15:49:56Z

--- library_name: peft base_model: gpt2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

Protao/ppo-Huggy

Protao

2023-12-03T15:45:28Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-12-03T15:43:48Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Protao/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀 可以在 https://huggingface.co/spaces/ThomasSimonini/Huggy 与训练的agent进行交互

nu-dialogue/japanese-vlm-fp16-lm-sfcoco2023-lora

nu-dialogue

2023-12-03T15:42:29Z

1

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:stabilityai/japanese-stablelm-instruct-gamma-7b", "base_model:adapter:stabilityai/japanese-stablelm-instruct-gamma-7b", "region:us" ]

null

2023-12-03T15:42:25Z

--- library_name: peft base_model: stabilityai/japanese-stablelm-instruct-gamma-7b --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.2

cozgo/llama-2-7b-advisor-low-rank

cozgo

2023-12-03T15:35:36Z

0

peft

[ "peft", "safetensors", "region:us" ]

null

2023-12-03T14:24:45Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0

sezinarseven/falcon-7b-instruct-medicalQA

sezinarseven

2023-12-03T15:28:51Z

0

null

[ "safetensors", "generated_from_trainer", "base_model:vilsonrodrigues/falcon-7b-instruct-sharded", "base_model:finetune:vilsonrodrigues/falcon-7b-instruct-sharded", "license:apache-2.0", "region:us" ]

null

2023-12-03T15:28:39Z

--- license: apache-2.0 base_model: vilsonrodrigues/falcon-7b-instruct-sharded tags: - generated_from_trainer model-index: - name: falcon-7b-instruct-medicalQA results: [] ---  # falcon-7b-instruct-medicalQA This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 180 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

byteflow/xxmix9realxl

byteflow

2023-12-03T15:26:01Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-12-03T15:04:01Z

--- license: creativeml-openrail-m ---

BadreddineHug/zyph_OCR

BadreddineHug

2023-12-03T15:12:13Z

1

0

peft

[ "peft", "arxiv:1910.09700", "base_model:HuggingFaceH4/zephyr-7b-alpha", "base_model:adapter:HuggingFaceH4/zephyr-7b-alpha", "region:us" ]

null

2023-12-03T15:12:11Z

--- library_name: peft base_model: HuggingFaceH4/zephyr-7b-alpha --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2

byteflow/drmshprxl

byteflow

2023-12-03T15:11:11Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-12-03T15:05:53Z

--- license: creativeml-openrail-m ---

KnutJaegersberg/black_goo_recipe_e

KnutJaegersberg

2023-12-03T15:11:02Z

2,463

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "custom_code", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-04T21:08:02Z

--- license: cc-by-nc-4.0 --- Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: ```

KnutJaegersberg/deacon-13b-awq

KnutJaegersberg

2023-12-03T15:10:33Z

4

0

transformers

[ "transformers", "llama", "text-generation", "custom_code", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-09-20T17:48:03Z

--- license: cc-by-nc-4.0 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/4OQkvAa1zOK4Devv-aUdL.png) This model was fine tuned on AI filtered subsets of GPT-4 based subset of the Dolphin dataset and EvolInstruct V2. It has not been explicitly aligned to positive, negative or bureaucratically prescribed value systems. It might kill us all! Time to shit your pants, regulators. I literally put black goo on Dolphin-7B sperm, which then fertilized Evolved Instructions... What's different is evil... ;) I intend to train 3 sizes. Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: ```

KnutJaegersberg/black_goo_recipe_c

KnutJaegersberg

2023-12-03T15:09:33Z

2,503

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "custom_code", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-01T06:35:49Z

--- license: cc-by-nc-4.0 --- Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: To fine-tune a large language model, we use various techniques to improve its performance and adjust its parameters based on user feedback. These techniques include: 1. Fine tuning the original model with additional data. For example, adding more training examples or using specialized datasets such as question answering or natural language inference tasks. This helps the model learn new concepts and skills by interacting with additional information sources. 2. Using transfer learning methods to incorporate knowledge from other models that have been pre-trained for specific domains. For instance, using pre-trained embeddings from neural networks trained on text classification tasks like sentiment analysis or question answering to help fine-tuning the main language model. 3. Improving the quality of the training data. We may optimize the training process by increasing the accuracy of provided data sets (e.g., by improving word embedding quality), refining the selection of relevant examples, and/or providing additional supervision signals during training. 4. Incorporating knowledge from external resources such as domain-specific lexicons or domain-specific knowledge bases. For example, incorporating domain-specific rules and facts into the model to help it understand domain-specific terminology and semantics. 5. Employing techniques like dropout regularization and dropout sampling to avoid overfitting and reduce overconfidence in the model's predictions. 6. Utilizing meta-learning methods such as reinforcement learning or self-supervised learning to enable the model to adapt quickly to changing situations without being explicitly programmed. 7. Incorporating human input through crowdsourcing or user testing to validate the model's performance and ensure user satisfaction. 8. Implementing various optimization strategies to improve efficiency and reduce computational costs while maintaining high performance levels. In conclusion, fine tuning a large language model requires combining various techniques including fine tuning, transfer learning, improved data quality, external resources, meta-learning, optimization strategies, crowd sourcing, and user testing. These approaches aim to improve the model's ability to handle various tasks and provide users with better experiences in their daily interactions with language systems. ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__black_goo_recipe_c) | Metric | Value | |-----------------------|---------------------------| | Avg. | 34.19 | | ARC (25-shot) | 38.74 | | HellaSwag (10-shot) | 66.83 | | MMLU (5-shot) | 26.57 | | TruthfulQA (0-shot) | 36.54 | | Winogrande (5-shot) | 64.72 | | GSM8K (5-shot) | 0.68 | | DROP (3-shot) | 5.23 |

KnutJaegersberg/Deacon-34B-200k-AWQ

KnutJaegersberg

2023-12-03T15:08:12Z

8

5

transformers

[ "transformers", "safetensors", "llama", "text-generation", "dataset:totally-not-an-llm/EverythingLM-data-V3", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]

text-generation

2023-11-11T03:19:30Z

--- license: other license_name: yi-license license_link: LICENSE datasets: - totally-not-an-llm/EverythingLM-data-V3 pipeline_tag: text-generation --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/4OQkvAa1zOK4Devv-aUdL.png) In this case the tokenizer is the yi_tokenizer, loading it requires trust_remote_code=True Have some fun with this fellow. It can eat a lot of vram, depends on settings to make it useable on two 24 gb vram gpus: Without fused attention, it's 27 gb vram, will need some if if yo do stuff. ![image.png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/1cbqKp55WhN4BQD337E-n.png) You can also let if have fused attention and just reduce the max_seq_length to something way smaller yet still useful ![image.png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/JRi4sakPziGpmOFCBfcJS.png) License The Yi series models are fully open for academic research and free commercial usage with permission via applications. All usage must adhere to the Model License Agreement 2.0. To apply for the official commercial license, please contact us ([email protected]). Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: ```

KnutJaegersberg/Deacon-34b-4bit

KnutJaegersberg

2023-12-03T15:07:59Z

3

2

transformers

[ "transformers", "pytorch", "llama", "feature-extraction", "text-generation", "dataset:totally-not-an-llm/EverythingLM-data-V3", "license:other", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2023-11-09T17:06:15Z

--- license: other license_name: yi-license license_link: LICENSE datasets: - totally-not-an-llm/EverythingLM-data-V3 pipeline_tag: text-generation --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/4OQkvAa1zOK4Devv-aUdL.png) The perfect organism. An adapter for KnutJaegersberg/Yi-34B-Llamafied. 5 epochs with NEFTune. The brand new bitsandbytes 4-bit file format. Run pip install -U bitsandbytes and install this pull requested transformer version: https://github.com/poedator/transformers/tree/save4 Might still be buggy. It was a bit too early. I'll wait until the functionality is officially supported, due to some cuda issues. I don't want to resolve those, I'll just wait a couple of days. Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/8KYMW1KGzyE19GWxSTG7m.png)

KnutJaegersberg/deacon-13b

KnutJaegersberg

2023-12-03T15:06:45Z

2,489

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "dataset:KnutJaegersberg/facehugger", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-20T06:35:12Z

--- license: cc-by-nc-4.0 datasets: - KnutJaegersberg/facehugger --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/4OQkvAa1zOK4Devv-aUdL.png) This model was fine tuned on AI filtered subsets of GPT-4 based subset of the Dolphin dataset and EvolInstruct V2. It has not been explicitly aligned to positive, negative or bureaucratically prescribed value systems. It might kill us all! Time to shit your pants, regulators. I literally put black goo on Dolphin-7B sperm, which then fertilized Evolved Instructions... What's different is evil... ;) I intend to train 3 sizes. Prompt Example: ``` ### System: You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. ### Instruction: How do you fine tune a large language model? ### Response: ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__deacon-13b) | Metric | Value | |-----------------------|---------------------------| | Avg. | 46.78 | | ARC (25-shot) | 57.85 | | HellaSwag (10-shot) | 82.63 | | MMLU (5-shot) | 55.25 | | TruthfulQA (0-shot) | 39.33 | | Winogrande (5-shot) | 76.32 | | GSM8K (5-shot) | 10.39 | | DROP (3-shot) | 5.67 |

Miloou/Reinforce-Pixelcopter1

Miloou

2023-12-03T15:05:33Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-12-03T15:05:29Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 42.50 +/- 35.31 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

ichir3o4/syofh

ichir3o4

2023-12-03T15:04:59Z

0

allennlp

[ "allennlp", "ab", "dataset:HuggingFaceH4/no_robots", "license:bigscience-openrail-m", "region:us" ]

null

2023-12-03T15:04:04Z

--- license: bigscience-openrail-m datasets: - HuggingFaceH4/no_robots language: - ab metrics: - bleu library_name: allennlp ---

Korabbit/Llama-2-7b-chat-hf-afr-200step-merged

Korabbit

2023-12-03T14:46:35Z

1,551

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "conversational", "en", "dataset:vicgalle/alpaca-gpt4", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-11-21T08:59:51Z

--- license: llama2 datasets: - vicgalle/alpaca-gpt4 language: - en --- Hi there! this is a test of my "AFR training" approach The base model used "Llama-2-7b-chat" System Prompt: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. \n If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. Instruction: Implement binary search in python. Response: Sure! Here is an example of how to implement a binary search algorithm in Python: ``` def binary_search(arr, target): # Initialize the index of the target element low = 0 high = len(arr) - 1 # Loop until the target element is found or the end of the array is reached while low <= high: # Calculate the middle index of the array mid = (low + high) // 2 # Check if the target element is in the middle index if arr[mid] == target: return mid # If the target element is not in the middle index, check if it is less than the middle index if arr[mid] > target: low = mid + 1 else: high = mid - 1 # If the target element is not found in the array, return -1 return -1 # Test the binary search function arr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] target = 5 result = binary_search(arr, target) print(f"The target element {target} is at index {result}") ``` This code defines a `binary_search` function that takes an array and a target element as input, and returns the index of the target element in the array if it is found, or -1 if it is not found. The function works by iteratively dividing the range of the array into two sub-ranges, one containing elements less than the target, and one containing elements greater than the target. It then checks the middle index of each sub-range to see if the target element is present. If the target element is found, the function returns the index of the middle index. If the target element is not found, the function returns -1. The code also includes a test case to demonstrate how to use the `binary_search` function. In this case, the target element is 5, and the function returns the index of the element 5 in the array. I hope this helps! Let me know if you have any questions.

musika/the-beatles

musika

2023-12-03T14:46:15Z

0

1

null

[ "audio", "music", "generation", "tensorflow", "arxiv:2208.08706", "license:mit", "region:us" ]

null

2023-12-03T14:46:06Z

--- license: mit tags: - audio - music - generation - tensorflow --- # Musika Model: the_beatles ## Model provided by: nobitachainsaw Pretrained the_beatles model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation. Introduced in [this paper](https://arxiv.org/abs/2208.08706). ## How to use You can generate music from this pretrained the_beatles model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r). ### Model description This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio. The generator has a context window of about 12 seconds of audio.

Yanni8/google-query-rating

Yanni8

2023-12-03T14:39:09Z

6

0

transformers

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

text-classification

2023-12-03T14:12:09Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - google_wellformed_query model-index: - name: google-query-rating results: [] ---  # google-query-rating This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the google_wellformed_query dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

li-ping/supervised_ft_embedding_1203_v37

li-ping

2023-12-03T14:34:47Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-12-03T14:34:39Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # li-ping/supervised_ft_embedding_1203_v37 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('li-ping/supervised_ft_embedding_1203_v37') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=li-ping/supervised_ft_embedding_1203_v37) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 3 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 100, "evaluation_steps": 400, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 30, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors

TechRoC123/carmodel

TechRoC123

2023-12-03T14:34:09Z

7

0

transformers

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

image-classification

2023-12-03T14:33:37Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer metrics: - f1 model-index: - name: carmodel results: [] ---  # carmodel This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0675 - F1: 0.9931 ## 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: 4 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1732 | 0.31 | 500 | 0.6651 | 0.8403 | | 0.3989 | 0.62 | 1000 | 0.2942 | 0.9167 | | 0.2136 | 0.93 | 1500 | 0.1782 | 0.9542 | | 0.0549 | 1.23 | 2000 | 0.2001 | 0.9639 | | 0.0287 | 1.54 | 2500 | 0.1304 | 0.9819 | | 0.0091 | 1.85 | 3000 | 0.1112 | 0.9819 | | 0.0039 | 2.16 | 3500 | 0.0667 | 0.9917 | | 0.0023 | 2.47 | 4000 | 0.0708 | 0.9903 | | 0.0002 | 2.78 | 4500 | 0.0635 | 0.9931 | | 0.0002 | 3.09 | 5000 | 0.0619 | 0.9931 | | 0.0002 | 3.4 | 5500 | 0.0730 | 0.9917 | | 0.0 | 3.7 | 6000 | 0.0684 | 0.9917 | | 0.0009 | 4.01 | 6500 | 0.0696 | 0.9917 | | 0.0 | 4.32 | 7000 | 0.0693 | 0.9917 | | 0.0 | 4.63 | 7500 | 0.0686 | 0.9931 | | 0.0004 | 4.94 | 8000 | 0.0675 | 0.9931 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

srijan2024/my_awesome_billsum_model

srijan2024

2023-12-03T14:32:59Z

46

0

transformers

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

text2text-generation

2023-12-02T06:10:18Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: my_awesome_billsum_model results: [] ---  # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0467 - Rouge1: 0.7832 - Rouge2: 0.692 - Rougel: 0.781 - Rougelsum: 0.7805 - Gen Len: 11.6071 ## 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: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 56 | 4.9294 | 0.0 | 0.0 | 0.0 | 0.0 | 16.558 | | No log | 2.0 | 112 | 2.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 13.5357 | | No log | 3.0 | 168 | 0.4763 | 0.0045 | 0.0045 | 0.0045 | 0.0045 | 10.6518 | | No log | 4.0 | 224 | 0.1138 | 0.7232 | 0.6205 | 0.7245 | 0.7236 | 11.5893 | | No log | 5.0 | 280 | 0.0654 | 0.7417 | 0.6339 | 0.7417 | 0.7402 | 11.6607 | | No log | 6.0 | 336 | 0.0587 | 0.7321 | 0.6205 | 0.7321 | 0.7309 | 11.5938 | | No log | 7.0 | 392 | 0.0552 | 0.7496 | 0.6473 | 0.7491 | 0.7491 | 11.625 | | No log | 8.0 | 448 | 0.0533 | 0.7714 | 0.6786 | 0.7714 | 0.7709 | 11.6562 | | 1.6431 | 9.0 | 504 | 0.0518 | 0.781 | 0.692 | 0.7832 | 0.7805 | 11.6161 | | 1.6431 | 10.0 | 560 | 0.0505 | 0.764 | 0.6652 | 0.7632 | 0.7614 | 11.6607 | | 1.6431 | 11.0 | 616 | 0.0494 | 0.7778 | 0.6875 | 0.78 | 0.7773 | 11.6116 | | 1.6431 | 12.0 | 672 | 0.0488 | 0.7778 | 0.6875 | 0.78 | 0.7773 | 11.6116 | | 1.6431 | 13.0 | 728 | 0.0483 | 0.781 | 0.692 | 0.7815 | 0.7805 | 11.6161 | | 1.6431 | 14.0 | 784 | 0.0479 | 0.781 | 0.692 | 0.7815 | 0.7805 | 11.6071 | | 1.6431 | 15.0 | 840 | 0.0475 | 0.7852 | 0.6964 | 0.7839 | 0.7842 | 11.6205 | | 1.6431 | 16.0 | 896 | 0.0471 | 0.7812 | 0.692 | 0.781 | 0.7805 | 11.5982 | | 1.6431 | 17.0 | 952 | 0.0469 | 0.7884 | 0.7009 | 0.7879 | 0.7869 | 11.625 | | 0.062 | 18.0 | 1008 | 0.0468 | 0.7832 | 0.692 | 0.781 | 0.7805 | 11.6071 | | 0.062 | 19.0 | 1064 | 0.0467 | 0.7864 | 0.6964 | 0.7839 | 0.7837 | 11.6027 | | 0.062 | 20.0 | 1120 | 0.0467 | 0.7832 | 0.692 | 0.781 | 0.7805 | 11.6071 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

Protao/ppo-MountainCar-v0

Protao

2023-12-03T14:26:28Z

0

stable-baselines3

[ "stable-baselines3", "MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-12-03T14:26:14Z

--- library_name: stable-baselines3 tags: - MountainCar-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: MountainCar-v0 type: MountainCar-v0 metrics: - type: mean_reward value: -138.00 +/- 31.51 name: mean_reward verified: false --- # **PPO** Agent playing **MountainCar-v0** This is a trained model of a **PPO** agent playing **MountainCar-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

digiplay/Gap_2.6

digiplay

2023-12-03T14:25:58Z

583

4

diffusers

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

text-to-image

2023-07-21T13:20:16Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- https://civitai.com/models/166637?modelVersionId=187469 Sample image generated by Hugginface's API: *Gap_2.6_mangledVAE_.safetensors ![8104b053-1a97-4d2e-9ac2-c4c59c74f22e.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/YLrTGIvdIqnbiAxzwXOSh.jpeg) Sample images generated by AUTOMATIC 1111: ![tmpv8p2x6vj.png](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/NQcuvmJu9iaPUmGTJ-ALV.png) ![tmp11s5b7ci.png](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/TJ0g_QLb8r3CJfM-UiFzb.png)

Schandkroete/distilbert-base-uncased_SLC-Sentiment-Finetuned

Schandkroete

2023-12-03T14:16:02Z

5

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-12-03T14:12:32Z

--- language: - en pipeline_tag: text-classification ---

Konstantin91/Konstantin91

Konstantin91

2023-12-03T14:12:39Z

0

keras

[ "keras", "tf-keras", "region:us" ]

null

2023-12-03T14:11:54Z