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librarian-bots
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model_cards_with_metadata

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2025-08-30 18:26:50
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asubiabre/dqn-SpaceInvadersNoFrameskip-v4
asubiabre
2023-01-26T19:13:29Z
3
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T17:31:09Z
--- 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: 605.00 +/- 178.61 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 asubiabre -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 asubiabre -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 asubiabre ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('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)]) ```
gokuls/mobilebert_add_GLUE_Experiment_qqp_256
gokuls
2023-01-26T19:05:12Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "mobilebert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T14:07:42Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: mobilebert_add_GLUE_Experiment_qqp_256 results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue config: qqp split: validation args: qqp metrics: - name: Accuracy type: accuracy value: 0.7558496166213208 - name: F1 type: f1 value: 0.6390991188621988 --- <!-- 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. --> # mobilebert_add_GLUE_Experiment_qqp_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.5069 - Accuracy: 0.7558 - F1: 0.6391 - Combined Score: 0.6975 ## Model description More information needed ## 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: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.6505 | 1.0 | 2843 | 0.6497 | 0.6321 | 0.0012 | 0.3166 | | 0.6473 | 2.0 | 5686 | 0.6479 | 0.6321 | 0.0012 | 0.3166 | | 0.5376 | 3.0 | 8529 | 0.5167 | 0.7486 | 0.5879 | 0.6682 | | 0.4943 | 4.0 | 11372 | 0.5069 | 0.7558 | 0.6391 | 0.6975 | | 0.4816 | 5.0 | 14215 | 0.5072 | 0.7547 | 0.6574 | 0.7061 | | 0.4738 | 6.0 | 17058 | nan | 0.7588 | 0.6526 | 0.7057 | | 0.4646 | 7.0 | 19901 | nan | 0.6318 | 0.0 | 0.3159 | | 0.0 | 8.0 | 22744 | nan | 0.6318 | 0.0 | 0.3159 | | 0.0 | 9.0 | 25587 | nan | 0.6318 | 0.0 | 0.3159 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
AiDevelopment/donut-base-sroie
AiDevelopment
2023-01-26T19:04:14Z
27
0
transformers
[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "license:mit", "endpoints_compatible", "region:us" ]
image-text-to-text
2022-12-16T12:23:17Z
--- license: mit tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-sroie 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. --> # donut-base-sroie This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.4707116138614145e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
javiervela/ppo-SnowballTarget
javiervela
2023-01-26T19:04:00Z
1
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-01-26T19:03:53Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: javiervela/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
harikc456/PyramidsRND-ppo
harikc456
2023-01-26T18:43:59Z
5
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-01-26T18:43:52Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: harikc456/PyramidsRND-ppo 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
davanstrien/testempty
davanstrien
2023-01-26T18:23:47Z
0
0
fastai
[ "fastai", "en", "de", "fr", "am", "license:openrail", "region:us" ]
null
2023-01-26T15:54:43Z
--- library_name: fastai license: openrail language: - en - de - fr - am ---
kadirnar/AnimeSR_v2
kadirnar
2023-01-26T18:20:38Z
0
4
null
[ "object-detection", "computer-vision", "gan", "animegan", "arxiv:2206.07038", "license:apache-2.0", "region:us" ]
object-detection
2023-01-26T18:15:15Z
--- license: apache-2.0 tags: - object-detection - computer-vision - gan - animegan --- ### Model Description [AnimeSR](https://arxiv.org/abs/2206.07038): Learning Real-World Super-Resolution Models for Animation Videos ### Installation ``` pip install animesr ``` ### Anime GAN ```python from animesr.inference_animesr_video import main main(source='test.mp4', 'kadirnar/AnimeSR_v2') ``` ### BibTeX Entry and Citation Info ``` @InProceedings{wu2022animesr, author={Wu, Yanze and Wang, Xintao and Li, Gen and Shan, Ying}, title={AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos}, booktitle={Advances in Neural Information Processing Systems}, year={2022} } ```
kadirnar/AnimeSR_Paper_Model
kadirnar
2023-01-26T18:15:00Z
0
1
null
[ "object-detection", "computer-vision", "gan", "animegan", "arxiv:2206.07038", "license:apache-2.0", "region:us" ]
object-detection
2023-01-26T17:54:12Z
--- license: apache-2.0 tags: - object-detection - computer-vision - gan - animegan --- ### Model Description [AnimeSR](https://arxiv.org/abs/2206.07038): Learning Real-World Super-Resolution Models for Animation Videos ### Installation ``` pip install animesr ``` ### Anime GAN ```python from animesr.inference_animesr_video import main main(source='test.mp4', 'kadirnar/AnimeSR_Paper_Model') ``` ### BibTeX Entry and Citation Info ``` @InProceedings{wu2022animesr, author={Wu, Yanze and Wang, Xintao and Li, Gen and Shan, Ying}, title={AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos}, booktitle={Advances in Neural Information Processing Systems}, year={2022} } ```
bonadio/ppo-PyramidTarget-v1
bonadio
2023-01-26T18:12:59Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-01-26T18:12:53Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: bonadio/ppo-PyramidTarget-v1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
css919/a2c-PandaReachDense-v2
css919
2023-01-26T17:59:13Z
1
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T17:57:06Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.63 +/- 0.16 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-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 ... ```
Dems/ppo-LunarLander-v2
Dems
2023-01-26T17:57:17Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T17:56:59Z
--- 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: 261.83 +/- 17.36 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 ... ```
svo2/roberta-finetuned-country-neg
svo2
2023-01-26T17:56:28Z
18
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]
question-answering
2023-01-26T17:22:40Z
--- license: cc-by-4.0 tags: - generated_from_trainer model-index: - name: roberta-finetuned-country-neg results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-finetuned-country-neg This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
tomekkorbak/detoxify_toxicity
tomekkorbak
2023-01-26T17:52:23Z
8
0
transformers
[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]
null
2022-11-07T17:27:14Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: tomekkorbak/detoxify_toxicity 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. --> # tomekkorbak/detoxify_toxicity This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## 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: 0.1 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 4096}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'gpt3_kwargs': {'model_name': 'davinci'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'tomekkorbak/detoxify_toxicity', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.1, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 8, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/1l6hdjln
gokuls/distilbert_add_GLUE_Experiment_mnli_96
gokuls
2023-01-26T17:24:10Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T14:55:30Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert_add_GLUE_Experiment_mnli_96 results: - task: name: Text Classification type: text-classification dataset: name: GLUE MNLI type: glue config: mnli split: validation_matched args: mnli metrics: - name: Accuracy type: accuracy value: 0.500406834825061 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_mnli_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.0256 - Accuracy: 0.5004 ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0987 | 1.0 | 1534 | 1.0980 | 0.3545 | | 1.0979 | 2.0 | 3068 | 1.0942 | 0.3580 | | 1.0897 | 3.0 | 4602 | 1.0896 | 0.3706 | | 1.0817 | 4.0 | 6136 | 1.0769 | 0.3991 | | 1.072 | 5.0 | 7670 | 1.0680 | 0.4146 | | 1.0603 | 6.0 | 9204 | 1.0700 | 0.4174 | | 1.0515 | 7.0 | 10738 | 1.0655 | 0.4179 | | 1.0441 | 8.0 | 12272 | 1.0546 | 0.4335 | | 1.038 | 9.0 | 13806 | 1.0751 | 0.4059 | | 1.0344 | 10.0 | 15340 | 1.0554 | 0.4363 | | 1.0275 | 11.0 | 16874 | 1.0736 | 0.4207 | | 1.0225 | 12.0 | 18408 | 1.0662 | 0.4295 | | 1.0169 | 13.0 | 19942 | 1.0544 | 0.4421 | | 1.0111 | 14.0 | 21476 | 1.0635 | 0.4411 | | 1.0043 | 15.0 | 23010 | 1.0505 | 0.4567 | | 0.9986 | 16.0 | 24544 | 1.0402 | 0.4643 | | 0.9925 | 17.0 | 26078 | 1.0531 | 0.4545 | | 0.9861 | 18.0 | 27612 | 1.0431 | 0.4675 | | 0.9781 | 19.0 | 29146 | 1.0361 | 0.4801 | | 0.9673 | 20.0 | 30680 | 1.0301 | 0.4879 | | 0.9552 | 21.0 | 32214 | 1.0327 | 0.4908 | | 0.9467 | 22.0 | 33748 | 1.0248 | 0.5013 | | 0.9396 | 23.0 | 35282 | 1.0297 | 0.4977 | | 0.9328 | 24.0 | 36816 | 1.0237 | 0.5025 | | 0.9277 | 25.0 | 38350 | 1.0384 | 0.5010 | | 0.9228 | 26.0 | 39884 | 1.0374 | 0.5037 | | 0.918 | 27.0 | 41418 | 1.0242 | 0.5006 | | 0.9128 | 28.0 | 42952 | 1.0248 | 0.5060 | | 0.9087 | 29.0 | 44486 | 1.0283 | 0.5027 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
NikosKokkini/dqn-SpaceInvadersNoFrameskip-v4
NikosKokkini
2023-01-26T17:23:13Z
3
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-25T09:16:21Z
--- 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: 426.50 +/- 140.86 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 NikosKokkini -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 NikosKokkini -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 NikosKokkini ``` ## 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', 1500000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
ImeneT/dqn-SpaceInvaders
ImeneT
2023-01-26T16:59:39Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T16:59:05Z
--- 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: 585.00 +/- 235.83 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 ImeneT -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 ImeneT -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 ImeneT ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('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)]) ```
AKFromCanada/dqn-SpaceInvadersNoFrameskip-v4
AKFromCanada
2023-01-26T16:53:11Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T16:52:33Z
--- 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: 564.00 +/- 96.90 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 AKFromCanada -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 AKFromCanada -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 AKFromCanada ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('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)]) ```
harikc456/SnowballTarget-ppo
harikc456
2023-01-26T16:47:03Z
11
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-01-26T16:25:58Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: harikc456/SnowballTarget-ppo 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
nlp04/kobart_8_5.6e-5_min30_lp5_sample_beams2
nlp04
2023-01-26T16:36:52Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-01-26T15:24:54Z
--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: kobart_8_5.6e-5_min30_lp5_sample_beams2 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. --> # kobart_8_5.6e-5_min30_lp5_sample_beams2 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8283 - Rouge1: 35.819 - Rouge2: 12.1658 - Rougel: 23.3058 - Bleu1: 29.6395 - Bleu2: 16.8254 - Bleu3: 9.5014 - Bleu4: 5.168 - Gen Len: 49.8625 ## Model description More information needed ## 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: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 128 - 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: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:------:|:-------:| | 2.527 | 0.19 | 1000 | 3.0014 | 30.9895 | 9.5631 | 20.1782 | 25.4533 | 13.5291 | 7.1157 | 3.4483 | 50.2657 | | 2.4214 | 0.38 | 2000 | 2.8814 | 32.3984 | 10.3443 | 21.2357 | 26.5661 | 14.5006 | 7.3531 | 3.5159 | 44.1538 | | 2.3577 | 0.57 | 3000 | 2.8277 | 32.2306 | 10.5703 | 21.3959 | 26.4952 | 14.725 | 8.0596 | 4.2696 | 50.965 | | 2.2606 | 0.76 | 4000 | 2.7749 | 33.0892 | 11.0109 | 21.4034 | 27.045 | 15.0797 | 8.2405 | 4.2337 | 48.1026 | | 2.1508 | 0.94 | 5000 | 2.6841 | 33.1368 | 10.9332 | 21.9277 | 27.4808 | 15.2182 | 8.39 | 4.2468 | 46.0583 | | 1.9467 | 1.13 | 6000 | 2.6994 | 33.2536 | 10.9192 | 21.851 | 26.7639 | 14.7669 | 8.1932 | 4.4866 | 42.7436 | | 1.9267 | 1.32 | 7000 | 2.6743 | 35.335 | 12.5749 | 23.0923 | 29.4977 | 17.1053 | 9.9798 | 5.6851 | 54.2168 | | 1.9402 | 1.51 | 8000 | 2.6549 | 34.7169 | 12.4365 | 22.8695 | 28.8948 | 16.8377 | 9.795 | 5.8984 | 53.8042 | | 1.9457 | 1.7 | 9000 | 2.6198 | 34.1256 | 11.3508 | 22.7591 | 28.0771 | 15.6516 | 8.6198 | 4.5566 | 43.8252 | | 1.9206 | 1.89 | 10000 | 2.6090 | 34.5521 | 12.0321 | 22.8654 | 28.268 | 16.2876 | 9.2697 | 4.9105 | 45.8205 | | 1.6341 | 2.08 | 11000 | 2.6831 | 35.2143 | 12.748 | 23.2014 | 29.3413 | 17.2312 | 9.9515 | 5.5303 | 51.5338 | | 1.6098 | 2.27 | 12000 | 2.6529 | 35.251 | 12.1877 | 23.3663 | 29.0609 | 16.6432 | 9.5808 | 5.2786 | 46.2378 | | 1.6094 | 2.45 | 13000 | 2.6441 | 34.8683 | 12.0873 | 22.9699 | 28.9225 | 16.492 | 9.3451 | 5.1097 | 45.6131 | | 1.6684 | 2.64 | 14000 | 2.6504 | 35.1897 | 12.0262 | 23.0832 | 28.948 | 16.4709 | 9.1994 | 5.0042 | 46.5245 | | 1.6376 | 2.83 | 15000 | 2.6514 | 35.795 | 12.4779 | 23.2187 | 30.05 | 17.2789 | 9.984 | 5.4966 | 50.1119 | | 1.3663 | 3.02 | 16000 | 2.7310 | 35.6544 | 12.109 | 23.3876 | 29.9268 | 16.945 | 9.4372 | 5.095 | 49.6317 | | 1.3719 | 3.21 | 17000 | 2.7514 | 35.0663 | 11.8565 | 23.4224 | 28.8679 | 16.2846 | 9.3246 | 5.0154 | 45.3333 | | 1.394 | 3.4 | 18000 | 2.7644 | 35.5883 | 12.2587 | 23.188 | 29.8503 | 17.0253 | 9.705 | 5.3253 | 47.4289 | | 1.3615 | 3.59 | 19000 | 2.7535 | 35.3947 | 12.3879 | 23.355 | 29.4012 | 16.8473 | 9.6862 | 5.3268 | 48.7179 | | 1.3544 | 3.78 | 20000 | 2.7480 | 35.7263 | 12.4434 | 23.6667 | 29.7146 | 17.0029 | 9.6018 | 5.2752 | 46.8834 | | 1.3697 | 3.97 | 21000 | 2.7415 | 35.4189 | 12.1527 | 23.0022 | 29.6187 | 16.8477 | 9.5092 | 5.3766 | 50.3963 | | 1.1718 | 4.15 | 22000 | 2.8251 | 35.0831 | 12.0809 | 22.8805 | 29.2252 | 16.5645 | 9.3818 | 5.241 | 46.7156 | | 1.1955 | 4.34 | 23000 | 2.8158 | 35.7853 | 12.3885 | 23.821 | 29.7377 | 16.9635 | 9.7005 | 5.4376 | 47.5991 | | 1.1795 | 4.53 | 24000 | 2.8265 | 35.4293 | 12.145 | 23.2029 | 29.6457 | 16.8228 | 9.7128 | 5.2525 | 49.5431 | | 1.1835 | 4.72 | 25000 | 2.8254 | 35.499 | 11.9198 | 23.0859 | 29.4398 | 16.5715 | 9.2442 | 4.7663 | 47.8345 | | 1.1644 | 4.91 | 26000 | 2.8283 | 35.819 | 12.1658 | 23.3058 | 29.6395 | 16.8254 | 9.5014 | 5.168 | 49.8625 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2
css919/a2c-AntBulletEnv-v0
css919
2023-01-26T16:33:03Z
0
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T16:32:01Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1602.09 +/- 34.75 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-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 ... ```
Jordan1/HoloPastel
Jordan1
2023-01-26T15:48:49Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-01-26T15:10:27Z
--- license: creativeml-openrail-m ---
griffohio314/artlessonplan
griffohio314
2023-01-26T15:28:05Z
0
0
null
[ "region:us" ]
null
2023-01-26T15:27:14Z
act as a teacher and write a kindergarten lesson plan for an art class about shape
Namig/finetuning-sentiment-model-3000-samples
Namig
2023-01-26T15:27:31Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T14:58:26Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.87 - name: F1 type: f1 value: 0.8704318936877077 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3222 - Accuracy: 0.87 - F1: 0.8704 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
iblub/a2c-AntBulletEnv-v0
iblub
2023-01-26T15:22:57Z
1
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T15:21:55Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 2159.83 +/- 43.32 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-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 ... ```
gokuls/distilbert_add_GLUE_Experiment_wnli_96
gokuls
2023-01-26T14:52:42Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T14:52:09Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert_add_GLUE_Experiment_wnli_96 results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue config: wnli split: validation args: wnli metrics: - name: Accuracy type: accuracy value: 0.5633802816901409 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_wnli_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6895 - Accuracy: 0.5634 ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6933 | 1.0 | 3 | 0.6895 | 0.5634 | | 0.6926 | 2.0 | 6 | 0.6906 | 0.5634 | | 0.6924 | 3.0 | 9 | 0.6907 | 0.5634 | | 0.6937 | 4.0 | 12 | 0.6897 | 0.5634 | | 0.6939 | 5.0 | 15 | 0.6897 | 0.5634 | | 0.6929 | 6.0 | 18 | 0.6902 | 0.5634 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
deetsml/deetsml
deetsml
2023-01-26T14:52:19Z
3
0
transformers
[ "transformers", "pytorch", "bart", "feature-extraction", "sentence-transformers", "text-classification", "en", "endpoints_compatible", "region:us" ]
text-classification
2023-01-16T07:50:27Z
--- pipeline_tag: text-classification tags: - sentence-transformers - transformers library_name: transformers language: - en metrics: - accuracy - precision - recall --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 5064 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 5064, "warmup_steps": 507, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BartModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
gokuls/distilbert_add_GLUE_Experiment_stsb_96
gokuls
2023-01-26T14:51:40Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T14:47:39Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: distilbert_add_GLUE_Experiment_stsb_96 results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue config: stsb split: validation args: stsb metrics: - name: Spearmanr type: spearmanr value: .nan --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_stsb_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.2529 - Pearson: nan - Spearmanr: nan - Combined Score: nan ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 8.7243 | 1.0 | 23 | 6.6928 | nan | nan | nan | | 7.9215 | 2.0 | 46 | 6.2710 | nan | nan | nan | | 7.4296 | 3.0 | 69 | 5.8601 | nan | nan | nan | | 6.9483 | 4.0 | 92 | 5.4460 | nan | nan | nan | | 6.4768 | 5.0 | 115 | 5.0440 | nan | nan | nan | | 5.9658 | 6.0 | 138 | 4.6523 | nan | nan | nan | | 5.5067 | 7.0 | 161 | 4.2735 | nan | nan | nan | | 5.0622 | 8.0 | 184 | 3.9107 | nan | nan | nan | | 4.6133 | 9.0 | 207 | 3.5725 | nan | nan | nan | | 4.2011 | 10.0 | 230 | 3.2630 | nan | nan | nan | | 3.7839 | 11.0 | 253 | 2.9896 | nan | nan | nan | | 3.4525 | 12.0 | 276 | 2.7549 | 0.0063 | 0.0066 | 0.0064 | | 3.1246 | 13.0 | 299 | 2.5637 | -0.0161 | -0.0155 | -0.0158 | | 2.8674 | 14.0 | 322 | 2.4155 | nan | nan | nan | | 2.6317 | 15.0 | 345 | 2.3138 | nan | nan | nan | | 2.4623 | 16.0 | 368 | 2.2596 | nan | nan | nan | | 2.3397 | 17.0 | 391 | 2.2529 | nan | nan | nan | | 2.2455 | 18.0 | 414 | 2.2910 | nan | nan | nan | | 2.1984 | 19.0 | 437 | 2.3424 | nan | nan | nan | | 2.1869 | 20.0 | 460 | 2.3424 | nan | nan | nan | | 2.1982 | 21.0 | 483 | 2.3460 | nan | nan | nan | | 2.195 | 22.0 | 506 | 2.3664 | -0.0023 | 0.0002 | -0.0011 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
StatsGary/questionanswering-distilbert-squad
StatsGary
2023-01-26T14:47:35Z
4
0
transformers
[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-01-25T17:15:24Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: questionanswering-distilbert-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # questionanswering-distilbert-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6344 ## Model description More information needed ## 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 2.3866 | | 2.774 | 2.0 | 500 | 1.6956 | | 2.774 | 3.0 | 750 | 1.6344 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
gokuls/distilbert_add_GLUE_Experiment_qqp
gokuls
2023-01-26T14:44:55Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T13:12:20Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: distilbert_add_GLUE_Experiment_qqp results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue config: qqp split: validation args: qqp metrics: - name: Accuracy type: accuracy value: 0.8319564679693298 - name: F1 type: f1 value: 0.7639168809507263 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_qqp This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.4050 - Accuracy: 0.8320 - F1: 0.7639 - Combined Score: 0.7979 ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.5406 | 1.0 | 1422 | 0.4844 | 0.7648 | 0.6276 | 0.6962 | | 0.4161 | 2.0 | 2844 | 0.4451 | 0.8044 | 0.6939 | 0.7491 | | 0.3079 | 3.0 | 4266 | 0.4050 | 0.8320 | 0.7639 | 0.7979 | | 0.2338 | 4.0 | 5688 | 0.4633 | 0.8388 | 0.7715 | 0.8052 | | 0.1801 | 5.0 | 7110 | 0.5597 | 0.8346 | 0.7489 | 0.7918 | | 0.1433 | 6.0 | 8532 | 0.5641 | 0.8460 | 0.7774 | 0.8117 | | 0.1155 | 7.0 | 9954 | 0.5940 | 0.8481 | 0.7889 | 0.8185 | | 0.0963 | 8.0 | 11376 | 0.6896 | 0.8438 | 0.7670 | 0.8054 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
Mehtap/whisper-base
Mehtap
2023-01-26T14:44:41Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "tr", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-01-20T09:33:28Z
--- language: - tr license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer metrics: - wer model-index: - name: Base Turkish Whisper (BTW) 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. --> # Base Turkish Whisper (BTW) This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Ermetal Meetings dataset. It achieves the following results on the evaluation set: - Loss: 0.0009 - Wer: 0.0 - Cer: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.8786 | 6.63 | 100 | 1.3510 | 0.7866 | 0.6649 | | 0.4559 | 13.32 | 200 | 0.3395 | 0.3590 | 0.2157 | | 0.0793 | 19.95 | 300 | 0.0564 | 0.0996 | 0.0531 | | 0.0137 | 26.63 | 400 | 0.0120 | 0.0017 | 0.0017 | | 0.0042 | 33.32 | 500 | 0.0032 | 0.0 | 0.0 | | 0.0021 | 39.95 | 600 | 0.0018 | 0.0 | 0.0 | | 0.0014 | 46.63 | 700 | 0.0013 | 0.0 | 0.0 | | 0.0012 | 53.32 | 800 | 0.0011 | 0.0 | 0.0 | | 0.001 | 59.95 | 900 | 0.0010 | 0.0 | 0.0 | | 0.001 | 66.63 | 1000 | 0.0009 | 0.0 | 0.0 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.9.1+cu111 - Datasets 2.7.1 - Tokenizers 0.13.2
gokuls/distilbert_add_GLUE_Experiment_rte_96
gokuls
2023-01-26T14:36:21Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T14:35:29Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert_add_GLUE_Experiment_rte_96 results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue config: rte split: validation args: rte metrics: - name: Accuracy type: accuracy value: 0.5270758122743683 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_rte_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6927 - Accuracy: 0.5271 ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.693 | 1.0 | 10 | 0.6927 | 0.5271 | | 0.6937 | 2.0 | 20 | 0.6931 | 0.5271 | | 0.6932 | 3.0 | 30 | 0.6939 | 0.4729 | | 0.6935 | 4.0 | 40 | 0.6930 | 0.5271 | | 0.6933 | 5.0 | 50 | 0.6935 | 0.4729 | | 0.6932 | 6.0 | 60 | 0.6933 | 0.4729 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
fillsoko/stablesatya
fillsoko
2023-01-26T14:33:17Z
19
0
diffusers
[ "diffusers", "tensorboard", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-01-26T12:13:56Z
--- license: creativeml-openrail-m tags: - text-to-image widget: - text: photo of STBLSATYA --- ### stablesatya Dreambooth model trained by fillsoko with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model This model is based on Stable Diffusion 2.1 and was fine tuned using 18 portraits of Staya Nadella scraped from the web. You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
mikegarts/PyramidsRND
mikegarts
2023-01-26T14:28:05Z
4
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-01-26T14:28:00Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: mikegarts/PyramidsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
gokuls/distilbert_add_GLUE_Experiment_stsb_192
gokuls
2023-01-26T14:22:03Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T14:19:44Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: distilbert_add_GLUE_Experiment_stsb_192 results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue config: stsb split: validation args: stsb metrics: - name: Spearmanr type: spearmanr value: .nan --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_stsb_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.2659 - Pearson: nan - Spearmanr: nan - Combined Score: nan ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 7.0456 | 1.0 | 23 | 4.3280 | nan | nan | nan | | 4.7979 | 2.0 | 46 | 3.4200 | nan | nan | nan | | 3.7359 | 3.0 | 69 | 2.7494 | nan | nan | nan | | 2.9308 | 4.0 | 92 | 2.3396 | nan | nan | nan | | 2.3776 | 5.0 | 115 | 2.2659 | nan | nan | nan | | 2.1865 | 6.0 | 138 | 2.3171 | nan | nan | nan | | 2.1731 | 7.0 | 161 | 2.3598 | nan | nan | nan | | 2.1793 | 8.0 | 184 | 2.4690 | 0.1389 | 0.1432 | 0.1410 | | 2.1725 | 9.0 | 207 | 2.3589 | 0.0899 | 0.0808 | 0.0854 | | 2.1621 | 10.0 | 230 | 2.3156 | 0.0853 | 0.0802 | 0.0827 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
thomasmeunierr/q-Taxi-v3
thomasmeunierr
2023-01-26T14:20:23Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T14:20:20Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.72 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="thomasmeunierr/q-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"]) ```
gokuls/distilbert_add_GLUE_Experiment_sst2_384
gokuls
2023-01-26T14:08:57Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T13:57:33Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert_add_GLUE_Experiment_sst2_384 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue config: sst2 split: validation args: sst2 metrics: - name: Accuracy type: accuracy value: 0.7752293577981652 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_sst2_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5097 - Accuracy: 0.7752 ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6841 | 1.0 | 264 | 0.6896 | 0.5654 | | 0.4539 | 2.0 | 528 | 0.5097 | 0.7752 | | 0.354 | 3.0 | 792 | 0.5233 | 0.7741 | | 0.302 | 4.0 | 1056 | 0.5783 | 0.7844 | | 0.269 | 5.0 | 1320 | 0.6044 | 0.7787 | | 0.2416 | 6.0 | 1584 | 0.6086 | 0.7672 | | 0.2127 | 7.0 | 1848 | 0.6909 | 0.7752 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
nickapch/distilbert-base-uncased-finetuned-emotion2
nickapch
2023-01-26T14:06:47Z
3
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T14:05:56Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion model-index: - name: distilbert-base-uncased-finetuned-emotion2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
gokuls/distilbert_add_GLUE_Experiment_qqp_256
gokuls
2023-01-26T14:03:59Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T12:51:07Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: distilbert_add_GLUE_Experiment_qqp_256 results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue config: qqp split: validation args: qqp metrics: - name: Accuracy type: accuracy value: 0.8135790254761316 - name: F1 type: f1 value: 0.7425272435349981 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_qqp_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.4273 - Accuracy: 0.8136 - F1: 0.7425 - Combined Score: 0.7781 ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.5648 | 1.0 | 1422 | 0.5394 | 0.7316 | 0.6540 | 0.6928 | | 0.5038 | 2.0 | 2844 | 0.5093 | 0.7496 | 0.6564 | 0.7030 | | 0.4837 | 3.0 | 4266 | 0.4952 | 0.7623 | 0.6625 | 0.7124 | | 0.4624 | 4.0 | 5688 | 0.4777 | 0.7739 | 0.6844 | 0.7292 | | 0.4197 | 5.0 | 7110 | 0.4541 | 0.7925 | 0.6939 | 0.7432 | | 0.3693 | 6.0 | 8532 | 0.4539 | 0.8027 | 0.7012 | 0.7519 | | 0.3214 | 7.0 | 9954 | 0.4273 | 0.8136 | 0.7425 | 0.7781 | | 0.2804 | 8.0 | 11376 | 0.4547 | 0.8187 | 0.7344 | 0.7765 | | 0.2463 | 9.0 | 12798 | 0.4779 | 0.8227 | 0.7478 | 0.7852 | | 0.2177 | 10.0 | 14220 | 0.5060 | 0.8256 | 0.7510 | 0.7883 | | 0.1933 | 11.0 | 15642 | 0.5020 | 0.8272 | 0.7587 | 0.7929 | | 0.1741 | 12.0 | 17064 | 0.5385 | 0.8304 | 0.7604 | 0.7954 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
gokuls/mobilebert_add_GLUE_Experiment_qnli_128
gokuls
2023-01-26T13:57:12Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "mobilebert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T12:43:45Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: mobilebert_add_GLUE_Experiment_qnli_128 results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue config: qnli split: validation args: qnli metrics: - name: Accuracy type: accuracy value: 0.5053999633900788 --- <!-- 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. --> # mobilebert_add_GLUE_Experiment_qnli_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.5054 ## Model description More information needed ## 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: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6933 | 1.0 | 819 | 0.6932 | 0.4946 | | 0.6932 | 2.0 | 1638 | 0.6932 | 0.4946 | | 0.6932 | 3.0 | 2457 | 0.6931 | 0.5054 | | 0.6932 | 4.0 | 3276 | 0.6933 | 0.4946 | | 0.6932 | 5.0 | 4095 | 0.6931 | 0.5054 | | 0.6932 | 6.0 | 4914 | 0.6931 | 0.5054 | | 0.6932 | 7.0 | 5733 | 0.6931 | 0.5054 | | 0.6932 | 8.0 | 6552 | 0.6931 | 0.5054 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
gokuls/distilbert_add_GLUE_Experiment_qqp_384
gokuls
2023-01-26T13:54:24Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T12:53:22Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: distilbert_add_GLUE_Experiment_qqp_384 results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue config: qqp split: validation args: qqp metrics: - name: Accuracy type: accuracy value: 0.8095226317091269 - name: F1 type: f1 value: 0.737194143944306 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_qqp_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.4096 - Accuracy: 0.8095 - F1: 0.7372 - Combined Score: 0.7734 ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.5518 | 1.0 | 1422 | 0.5289 | 0.7376 | 0.6535 | 0.6955 | | 0.4901 | 2.0 | 2844 | 0.4655 | 0.7772 | 0.6744 | 0.7258 | | 0.4098 | 3.0 | 4266 | 0.4096 | 0.8095 | 0.7372 | 0.7734 | | 0.3273 | 4.0 | 5688 | 0.4343 | 0.8211 | 0.7536 | 0.7873 | | 0.2681 | 5.0 | 7110 | 0.4322 | 0.8286 | 0.7519 | 0.7902 | | 0.223 | 6.0 | 8532 | 0.4789 | 0.8301 | 0.7502 | 0.7901 | | 0.1883 | 7.0 | 9954 | 0.4715 | 0.8329 | 0.7663 | 0.7996 | | 0.1603 | 8.0 | 11376 | 0.5090 | 0.8346 | 0.7577 | 0.7961 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
Martinkoling/my-first-setfit
Martinkoling
2023-01-26T13:52:42Z
1
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-01-26T13:52:25Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} 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('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 60 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 60, "warmup_steps": 6, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
mxbonn/ppo-LunarLander-v2
mxbonn
2023-01-26T13:40:07Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T13:39:42Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 288.48 +/- 11.46 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 ... ```
ChrisKahler/RVV12
ChrisKahler
2023-01-26T13:27:04Z
8
0
diffusers
[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-01-26T11:09:44Z
--- license: creativeml-openrail-m ---
Shiry/whisper-large-v2-he
Shiry
2023-01-26T13:22:25Z
4
5
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "he", "dataset:google/fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-12T11:09:22Z
--- language: - he license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - google/fleurs metrics: - wer model-index: - name: Whisper Large-V2 Hebrew results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs type: google/fleurs config: he_il split: train+validation args: he_il metrics: - name: Wer type: wer value: 27 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large-V2 Hebrew This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the google/fleurs he_il dataset. It achieves the following results on the evaluation set: - Loss: 0.5483 - Wer: 27 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 200 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
ludigija/Ludigija_project
ludigija
2023-01-26T13:15:53Z
0
0
null
[ "license:bigscience-openrail-m", "region:us" ]
null
2023-01-26T13:15:53Z
--- license: bigscience-openrail-m ---
stevaras2/ppo-SnowballTarget
stevaras2
2023-01-26T13:00:59Z
8
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-01-26T13:00:53Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: stevaras2/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
NUROISEA/plmx-mirror
NUROISEA
2023-01-26T12:53:10Z
0
1
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-01-26T12:30:55Z
--- license: creativeml-openrail-m ---
gokuls/distilbert_add_GLUE_Experiment_qnli_192
gokuls
2023-01-26T12:50:34Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T12:34:04Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert_add_GLUE_Experiment_qnli_192 results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue config: qnli split: validation args: qnli metrics: - name: Accuracy type: accuracy value: 0.594911220940875 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_qnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6649 - Accuracy: 0.5949 ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6936 | 1.0 | 410 | 0.6930 | 0.5054 | | 0.6793 | 2.0 | 820 | 0.6684 | 0.5823 | | 0.6511 | 3.0 | 1230 | 0.6650 | 0.5938 | | 0.6385 | 4.0 | 1640 | 0.6649 | 0.5949 | | 0.6306 | 5.0 | 2050 | 0.6668 | 0.5923 | | 0.6215 | 6.0 | 2460 | 0.6783 | 0.5931 | | 0.6137 | 7.0 | 2870 | 0.6969 | 0.5852 | | 0.6046 | 8.0 | 3280 | 0.6888 | 0.5881 | | 0.5964 | 9.0 | 3690 | 0.6977 | 0.5799 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
gokuls/distilbert_add_GLUE_Experiment_qnli_384
gokuls
2023-01-26T12:50:15Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T12:33:35Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert_add_GLUE_Experiment_qnli_384 results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue config: qnli split: validation args: qnli metrics: - name: Accuracy type: accuracy value: 0.600219659527732 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_qnli_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6621 - Accuracy: 0.6002 ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6894 | 1.0 | 410 | 0.6660 | 0.5933 | | 0.6593 | 2.0 | 820 | 0.6621 | 0.6002 | | 0.6441 | 3.0 | 1230 | 0.6634 | 0.6004 | | 0.6338 | 4.0 | 1640 | 0.6694 | 0.5942 | | 0.6238 | 5.0 | 2050 | 0.6732 | 0.5920 | | 0.6125 | 6.0 | 2460 | 0.6865 | 0.5969 | | 0.6026 | 7.0 | 2870 | 0.7080 | 0.5799 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
gokuls/distilbert_add_GLUE_Experiment_mrpc
gokuls
2023-01-26T12:46:09Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T12:41:46Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: distilbert_add_GLUE_Experiment_mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.696078431372549 - name: F1 type: f1 value: 0.8171091445427728 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_mrpc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6028 - Accuracy: 0.6961 - F1: 0.8171 - Combined Score: 0.7566 ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6617 | 1.0 | 15 | 0.6507 | 0.6838 | 0.8122 | 0.7480 | | 0.6412 | 2.0 | 30 | 0.6290 | 0.6838 | 0.8122 | 0.7480 | | 0.6315 | 3.0 | 45 | 0.6252 | 0.6838 | 0.8122 | 0.7480 | | 0.6319 | 4.0 | 60 | 0.6236 | 0.6838 | 0.8122 | 0.7480 | | 0.6321 | 5.0 | 75 | 0.6225 | 0.6838 | 0.8122 | 0.7480 | | 0.616 | 6.0 | 90 | 0.6028 | 0.6961 | 0.8171 | 0.7566 | | 0.5469 | 7.0 | 105 | 0.6485 | 0.6446 | 0.7349 | 0.6898 | | 0.4436 | 8.0 | 120 | 0.7536 | 0.6838 | 0.7909 | 0.7374 | | 0.3794 | 9.0 | 135 | 0.7805 | 0.6961 | 0.7898 | 0.7430 | | 0.3158 | 10.0 | 150 | 0.8811 | 0.6838 | 0.7825 | 0.7331 | | 0.281 | 11.0 | 165 | 0.9246 | 0.6863 | 0.7881 | 0.7372 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
gokuls/mobilebert_add_GLUE_Experiment_cola_256
gokuls
2023-01-26T12:41:17Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "mobilebert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T12:25:26Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: mobilebert_add_GLUE_Experiment_cola_256 results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.01845565733408863 --- <!-- 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. --> # mobilebert_add_GLUE_Experiment_cola_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6102 - Matthews Correlation: 0.0185 ## Model description More information needed ## 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: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6129 | 1.0 | 67 | 0.6180 | 0.0 | | 0.6078 | 2.0 | 134 | 0.6178 | 0.0 | | 0.6073 | 3.0 | 201 | 0.6179 | 0.0 | | 0.6067 | 4.0 | 268 | 0.6167 | 0.0 | | 0.6059 | 5.0 | 335 | 0.6168 | 0.0 | | 0.5998 | 6.0 | 402 | 0.6115 | 0.0 | | 0.5917 | 7.0 | 469 | 0.6122 | 0.0 | | 0.5849 | 8.0 | 536 | 0.6126 | 0.0 | | 0.5796 | 9.0 | 603 | 0.6277 | 0.0 | | 0.5759 | 10.0 | 670 | 0.6138 | 0.0029 | | 0.5733 | 11.0 | 737 | 0.6102 | 0.0185 | | 0.5716 | 12.0 | 804 | 0.6143 | 0.0252 | | 0.5667 | 13.0 | 871 | 0.6347 | 0.0348 | | 0.5662 | 14.0 | 938 | 0.6314 | 0.0385 | | 0.5631 | 15.0 | 1005 | 0.6130 | 0.0174 | | 0.5628 | 16.0 | 1072 | 0.6218 | 0.0348 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
gokuls/distilbert_add_GLUE_Experiment_cola
gokuls
2023-01-26T12:41:17Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T12:37:35Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert_add_GLUE_Experiment_cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6182 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6218 | 1.0 | 34 | 0.6182 | 0.0 | | 0.611 | 2.0 | 68 | 0.6194 | 0.0 | | 0.6084 | 3.0 | 102 | 0.6226 | 0.0 | | 0.6104 | 4.0 | 136 | 0.6186 | 0.0 | | 0.6102 | 5.0 | 170 | 0.6214 | 0.0 | | 0.6095 | 6.0 | 204 | 0.6187 | 0.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
gokuls/distilbert_add_GLUE_Experiment_mrpc_384
gokuls
2023-01-26T12:32:44Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T12:29:22Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: distilbert_add_GLUE_Experiment_mrpc_384 results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue config: mrpc split: validation args: mrpc metrics: - name: Accuracy type: accuracy value: 0.7009803921568627 - name: F1 type: f1 value: 0.8189910979228486 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_mrpc_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5935 - Accuracy: 0.7010 - F1: 0.8190 - Combined Score: 0.7600 ## Model description More information needed ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6355 | 1.0 | 15 | 0.6261 | 0.6838 | 0.8122 | 0.7480 | | 0.6315 | 2.0 | 30 | 0.6294 | 0.6838 | 0.8122 | 0.7480 | | 0.6327 | 3.0 | 45 | 0.6241 | 0.6838 | 0.8122 | 0.7480 | | 0.6344 | 4.0 | 60 | 0.6285 | 0.6838 | 0.8122 | 0.7480 | | 0.6328 | 5.0 | 75 | 0.6245 | 0.6838 | 0.8122 | 0.7480 | | 0.6293 | 6.0 | 90 | 0.6245 | 0.6838 | 0.8122 | 0.7480 | | 0.6341 | 7.0 | 105 | 0.6239 | 0.6838 | 0.8122 | 0.7480 | | 0.6298 | 8.0 | 120 | 0.6240 | 0.6838 | 0.8122 | 0.7480 | | 0.6304 | 9.0 | 135 | 0.6232 | 0.6838 | 0.8122 | 0.7480 | | 0.6286 | 10.0 | 150 | 0.6196 | 0.6838 | 0.8122 | 0.7480 | | 0.6045 | 11.0 | 165 | 0.5935 | 0.7010 | 0.8190 | 0.7600 | | 0.5251 | 12.0 | 180 | 0.6129 | 0.6789 | 0.7849 | 0.7319 | | 0.4395 | 13.0 | 195 | 0.6564 | 0.6912 | 0.7872 | 0.7392 | | 0.3921 | 14.0 | 210 | 0.7059 | 0.6446 | 0.7173 | 0.6810 | | 0.3399 | 15.0 | 225 | 0.7605 | 0.6887 | 0.7829 | 0.7358 | | 0.3219 | 16.0 | 240 | 0.7614 | 0.6569 | 0.7328 | 0.6948 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
gokuls/distilbert_add_GLUE_Experiment_cola_384
gokuls
2023-01-26T12:28:52Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T12:26:19Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert_add_GLUE_Experiment_cola_384 results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_add_GLUE_Experiment_cola_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6181 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6117 | 1.0 | 34 | 0.6181 | 0.0 | | 0.6094 | 2.0 | 68 | 0.6181 | 0.0 | | 0.6078 | 3.0 | 102 | 0.6190 | 0.0 | | 0.6096 | 4.0 | 136 | 0.6183 | 0.0 | | 0.6091 | 5.0 | 170 | 0.6187 | 0.0 | | 0.607 | 6.0 | 204 | 0.6189 | 0.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
Heerak/xlm-roberta-base-finetuned-panx-fr
Heerak
2023-01-26T12:26:14Z
4
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
2023-01-26T11:18:02Z
--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8370531968451083 --- <!-- 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-fr 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: 0.2777 - F1: 0.8371 ## Model description More information needed ## 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 | 191 | 0.3122 | 0.7961 | | 0.4151 | 2.0 | 382 | 0.2749 | 0.8312 | | 0.4151 | 3.0 | 573 | 0.2777 | 0.8371 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
andyleow/q-FrozenLake-v1-4x4
andyleow
2023-01-26T12:15:58Z
0
0
null
[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T12:15:55Z
--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.58 +/- 0.49 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="andyleow/q-FrozenLake-v1-4x4", 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"]) ```
kabita-choudhary/finetuned-bart-for-conversation-summary
kabita-choudhary
2023-01-26T12:09:46Z
174
53
transformers
[ "transformers", "pytorch", "bart", "text2text-generation", "summarization", "dataset:samsum", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2023-01-25T11:00:13Z
--- datasets: - samsum pipeline_tag: summarization widget: - text: > Laurie: So, what are your plans for this weekend? Christie: I don’t know. Do you want to get together or something? Sarah: How about going to see a movie? Cinemax 26 on Carson Boulevard is showing Enchanted. Laurie: That sounds like a good idea. Maybe we should go out to eat beforehand. Sarah: It is fine with me. Where do you want to meet? Christie: Let’s meet at Summer Pizza House. I have not gone there for a long time. Laurie: Good idea again. I heard they just came up with a new pizza. It should be good because Summer Pizza House always has the best pizza in town. Sarah: When should we meet? Christie: Well, the movie is shown at 2:00PM, 4:00PM, 6:00PM and 8:00PM. Laurie: Why don’t we go to the 2:00PM show? We can meet at Summer Pizza House at noon. That will give us plenty of time to enjoy our pizza. Sarah: My cousin Karen is in town. Can I bring her along? I hate to leave her home alone. Christie: Karen is in town? Yes, bring her along. Laurie, you remember Karen? We met her at Sara’s high school graduation party two years ago. Laurie: I do not quite remember her. What does she look like? Sarah: She has blond hair, she is kind of slender, and she is about your height. Laurie: She wears eyeglasses, right? Sarah: Yes, and she was playing the piano off and on during the party. Laurie: I remember her now. Yes, do bring her along Sara. She is such a nice person, and funny too. Sarah: She will be happy to meet both of you again. Christie: What is she doing these days? Sarah: She graduated last June, and she will start her teaching career next week when the new school term begins. Laurie: What grade is she going to teach? Sarah: She will teach kindergarten. She loves working with kids, and she always has such a good rapport with them Christie: Kindergarten? She must be a very patient person. I always think kindergarten is the most difficult class to teach. Most of the kids have never been to school, and they have e never been away from mommy for long. Sarah: I think Karen will do fine. She knows how to handle young children Laurie: I think the first few weeks will be tough. However, once the routine is set, it should not be too difficult to teach kindergarten. Christie: You are right. The kids might even look forward to going to school since they have so many friends to play with. Sarah: There are so many new things for them to do at school too. They do a lot of crafts in kindergarten. I am always amazed by the things kindergarten teachers do. Laurie: Yes, I have seen my niece come home with so many neat stuff. Christie: Maybe we can ask Karen to show us some of the things that we can do for this Halloween. Laurie: Maybe we can stop by the craft store after the movie. What do you think, Sara? Sarah: I will talk to her. I think she will like that. It will help her with school projects when Halloween comes. Christie: Michael’s is a good store for crafts. It always carries a variety of things, and you can find almost anything there. Laurie: There is a Michaels store not far away from Cinemax 26. I believe it is just around the corner, on Pioneer Avenue. We can even walk over there. Sarah: So, we plan to meet for pizza at noon, go to the movies at two, and shop at Michael’s afterward. Right? Laurie and Christie: Yes. model-index: - name: bart-large-cnn-samsum results: - task: type: summarization name: Conversation Summarization dataset: name: >- SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization type: samsum metrics: - type: rogue-1 value: 54.8764 name: Validation ROGUE-1 - type: rogue-2 value: 29.6869, name: Validation ROGUE-2 - type: rogue-l value: 44.9874 name: Validation ROGUE-L - type: loss value: 1.47812 name: loss ---
PlayDev/distilbert-base-uncased-finetuned-emotion
PlayDev
2023-01-26T11:27:57Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T11:21:15Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: split metrics: - name: Accuracy type: accuracy value: 0.927 - name: F1 type: f1 value: 0.9272714026125913 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2239 - Accuracy: 0.927 - F1: 0.9273 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.876 | 1.0 | 250 | 0.3230 | 0.9085 | 0.9054 | | 0.2619 | 2.0 | 500 | 0.2239 | 0.927 | 0.9273 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.10.3
theta/mbti-career
theta
2023-01-26T11:23:01Z
8
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-16T16:10:47Z
--- license: mit tags: - generated_from_trainer model-index: - name: mbti-career 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. --> # mbti-career This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3516 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6547 | 0.59 | 100 | 0.6169 | | 0.5967 | 1.18 | 200 | 0.5943 | | 0.5872 | 1.76 | 300 | 0.5696 | | 0.554 | 2.35 | 400 | 0.5287 | | 0.5041 | 2.94 | 500 | 0.4890 | | 0.4773 | 3.53 | 600 | 0.4895 | | 0.4691 | 4.12 | 700 | 0.4840 | | 0.4253 | 4.71 | 800 | 0.4573 | | 0.4002 | 5.29 | 900 | 0.4240 | | 0.3813 | 5.88 | 1000 | 0.4031 | | 0.3561 | 6.47 | 1100 | 0.3943 | | 0.3359 | 7.06 | 1200 | 0.3864 | | 0.3126 | 7.65 | 1300 | 0.3889 | | 0.2948 | 8.24 | 1400 | 0.3869 | | 0.2816 | 8.82 | 1500 | 0.3788 | | 0.2522 | 9.41 | 1600 | 0.3891 | | 0.2451 | 10.0 | 1700 | 0.3849 | | 0.2148 | 10.59 | 1800 | 0.3784 | | 0.2132 | 11.18 | 1900 | 0.3716 | | 0.1882 | 11.76 | 2000 | 0.3659 | | 0.1754 | 12.35 | 2100 | 0.3737 | | 0.169 | 12.94 | 2200 | 0.3711 | | 0.1559 | 13.53 | 2300 | 0.3672 | | 0.1537 | 14.12 | 2400 | 0.3391 | | 0.1427 | 14.71 | 2500 | 0.3516 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
Kenemo/dqn-SpaceInvadersNoFrameskip-v4-1Msteps
Kenemo
2023-01-26T11:14:14Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T11:13:35Z
--- 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: 500.50 +/- 144.54 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 Kenemo -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 Kenemo -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 Kenemo ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('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)]) ```
orenk/a2c-PandaReachDense-v2
orenk
2023-01-26T10:42:25Z
2
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T10:40:04Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -2.79 +/- 0.92 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-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 ... ```
arnonl/a2c-PandaReachDense-v2
arnonl
2023-01-26T10:39:57Z
0
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T10:37:45Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -2.77 +/- 0.93 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-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 ... ```
jamiehudson/600-STmodel-brand-rem
jamiehudson
2023-01-26T10:31:09Z
1
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-01-26T10:30:57Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} 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('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 225 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 225, "warmup_steps": 23, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
wmobilas/visualizevalue
wmobilas
2023-01-26T10:08:17Z
14
0
diffusers
[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-01-26T09:57:03Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### visualizevalue Dreambooth model trained by wmobilas with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
raruto/raruto-mix
raruto
2023-01-26T09:56:31Z
0
0
null
[ "region:us" ]
null
2023-01-26T09:03:02Z
This is a SD merge based on Anything v3. The model is very similar to Anything v3, but the default style is slightly different. [<img src=https://i.imgur.com/01wp0x7.jpg>](https://i.imgur.com/01wp0x7.jpg) [<img src=https://i.imgur.com/4R8hiBI.jpg>](https://i.imgur.com/4R8hiBI.jpg)
arnonl/a2c-AntBulletEnv-v0
arnonl
2023-01-26T09:52:27Z
0
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T09:51:25Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1234.54 +/- 172.57 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-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 ... ```
orenk/a2c-AntBulletEnv-v0
orenk
2023-01-26T09:50:07Z
0
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T09:48:58Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1400.57 +/- 347.64 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-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 ... ```
mildmillard/distilbert-base-uncased-finetuned-imdb
mildmillard
2023-01-26T09:43:45Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-01-26T09:07:55Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4721 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7086 | 1.0 | 157 | 2.4898 | | 2.5796 | 2.0 | 314 | 2.4230 | | 2.5269 | 3.0 | 471 | 2.4354 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
edusei/sentiment_analysis_on_covid_tweets
edusei
2023-01-26T08:23:17Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-26T07:58:35Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: sentiment_analysis_on_covid_tweets results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment_analysis_on_covid_tweets 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: - eval_loss: 0.5883 - eval_accuracy: 0.771 - eval_runtime: 33.4887 - eval_samples_per_second: 59.722 - eval_steps_per_second: 7.465 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
charanhu/text_to_sql_1
charanhu
2023-01-26T07:52:04Z
4
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain", "translation", "unk", "dataset:charanhu/autotrain-data-text_to_sql_finetune", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
translation
2023-01-26T07:40:12Z
--- tags: - autotrain - translation language: - unk - unk datasets: - charanhu/autotrain-data-text_to_sql_finetune co2_eq_emissions: emissions: 16.03787641705279 --- # Model Trained Using AutoTrain - Problem type: Translation - Model ID: 3073487571 - CO2 Emissions (in grams): 16.0379 ## Validation Metrics - Loss: 0.140 - SacreBLEU: 77.653 - Gen len: 42.019
nijatzeynalov/azerbaijani-medical-question-classification
nijatzeynalov
2023-01-26T07:46:46Z
8
4
transformers
[ "transformers", "pytorch", "bert", "text-classification", "classification", "medical", "az", "dataset:tibb.az", "doi:10.57967/hf/0290", "license:openrail", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-23T11:15:43Z
--- license: openrail language: - az metrics: - accuracy datasets: - tibb.az tags: - classification - medical --- # Azerbaijani Medical Forum Question Classification With the rapid increase of the internet, patients are increasingly use it for health information and support. However, given the large number of queries, and limited number of experts as well as not knowing which doctor to tell your complaint to, a significant fraction of the questions remains unanswered. Also, when patients apply online to the hospital, automatic direction to the appropriate doctor according to their disease is very important. Automatic question classifiers can overcome this issue by directing questions to specific experts according to their topic preferences to get quick and better responses. In this project, I aim to classify Azerbaijani health forum questions with BERT multilingual base model (uncased). BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion. For medical question classification, it requires high-quality datasets to train a deep-learning approach in a supervised way. Currently, there is no public dataset for Azerbaijani medical classification, and the datasets of other fields are not applicable to the medical QA system. To solve this problem, I scraped a m.tibb.az website using Python where 27k questions in 19 medical branch have been asked by users and answered by medical experts. I will also provide dataset which can be used in Azerbaijani medical QA and related fields. # How to use Here is how to use this model. __Firstly, you need to build a dictionary with medical branch names and their numbers, because target is encoded and model output will be a number.__ ```python branch_dict = {0: 'Endoskopist', 1: 'Nevropatoloq',2: 'Dermato veneroloq',3: 'Qastroenteroloq', 4: 'Psixoloq', 5: 'Pediatr', 6: 'Proktoloq', 7: 'Endokrinoloq', 8: 'Psixoterapevt', 9: 'Allerqoloq', 10: 'Oftalmoloq', 11: 'Kardioloq', 12: 'Uroloq', 13: 'Plastik cərrah', 14: 'Cərrah-proktoloq', 15: 'Ümumi cərrah', 16: 'Hepatoloq', 17: 'LOR həkimi', 18: 'Ginekoloq'} ``` __Secondly, we will use a simple Python function in order to convert model result to branch name.__ ```python def result_helper_funct(model_result): result = model_result[0][0] if result in branch_dict.keys(): return branch_dict[result] ``` __Then, we need to install simpletransformers library__ ```python !pip install simpletransformers ``` __After succesfully installing, use pre-trained model.__ ```python from simpletransformers.classification import ClassificationModel model = ClassificationModel("bert", "nijatzeynalov/azerbaijani-medical-question-classification", use_cuda=False) ``` __At the next step, we just write down the text we want to classify and use our helper function.__ ```python sample_text = 'salam menim qulagimda agri var' result = model.predict([sample_text]) result_helper_funct(result) ``` __Code result:__ ```python 'LOR həkimi' ``` __Let's try another example.__ ```python sample_text = 'üzümdə səpgi var' result = model.predict([sample_text]) result_helper_funct(result) ``` __Code result:__ ```python 'Allerqoloq' ``` Citation: ``` @misc {nijatzeynalov_2023, author = { {NijatZeynalov} }, title = { azerbaijani-medical-question-classification (Revision ac4fa1e) }, year = 2023, url = { https://huggingface.co/nijatzeynalov/azerbaijani-medical-question-classification }, doi = { 10.57967/hf/0290 }, publisher = { Hugging Face } } ```
almuallim/gpt2-idea-generation
almuallim
2023-01-26T07:38:00Z
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "en", "license:bsd", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-01-26T07:15:49Z
--- license: bsd language: - en library_name: transformers --- Fine-Tuned GPT-2 For Business Idea Generation using with Idea Dataset on [Kaggle](https://www.kaggle.com/datasets/bilalelebi/business-ideas-generated-with-gpt3)
xiaozhangMJXXZ/SEX-lora-all
xiaozhangMJXXZ
2023-01-26T07:25:32Z
0
62
null
[ "region:us" ]
null
2023-01-22T17:47:32Z
ERROR: type should be string, got "\nhttps://t.me/+a-k8rVfjIVk3NGU1 \nhttps://t.me/loraeveryone\n这是tg群组，之后会在第一时间更新tg，因为tg可以直接传tg原文件呜呜呜，笑脸站会缓慢更新！\n笑脸上下载不下来的也可以直接来tg下载\n这里是色色的lora合集，希望各位可以及时来补充！！！ \n分别为打包全下载与单个角色，由于中文名字的文件无法下载所以是压缩包的形式，下载之后需要各位解压一下里面就有对应的中文名字了。 校 长的联系方式：qq3062945846\n\n只是为了方便中文玩家而搬运整理！！\n\n有目录的截图小伙伴们可以参照！\n\n我们十分尊敬每一位lora的作者！！\n\n感谢你们的付出！！\n\n大家好这里是校长，目前这边准备来整合质量高些的lora模型， 已经是整理了70+并且给打上了中文标注以及把触发tag直接打到了文件名字上， 有些复杂的衣物装饰什么的还在旁边附带了同名的文档可以方便查阅。 如果大家有比较好的且跟目前的不同的lora的话， 希望可以来找咱发下Lora模型， 我把它们全部都统一整理完之后进行分类整理并且分享给大家（是lora模型哦，不是平常的大模型）。"
carlosmirandad/rl-class-dqn-SpaceInvadersNoFrameskip-v4
carlosmirandad
2023-01-26T07:25:05Z
6
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-24T09:09:33Z
--- 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: 531.50 +/- 134.70 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 carlosmirandad -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 carlosmirandad -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 carlosmirandad ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('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', 0.0005), ('learning_starts', 100000), ('n_timesteps', 5000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
xiaozhangMJXXZ/Arknights-lora-all
xiaozhangMJXXZ
2023-01-26T07:23:36Z
0
25
null
[ "region:us" ]
null
2023-01-22T17:09:25Z
https://t.me/+a-k8rVfjIVk3NGU1 https://t.me/loraeveryone 这是tg群组，之后会在第一时间更新tg，因为tg可以直接传tg原文件呜呜呜，笑脸站会缓慢更新！ 笑脸上下载不下来的也可以直接来tg下载 这里是方舟角色的lora合集，希望各位可以及时来补充！！！ 分别为打包全下载与单个角色，由于中文名字的文件无法下载所以是压缩包的形式，下载之后需要各位解压一下里面就有对应的中文名字了。 校 长的联系方式：qq3062945846 只是为了方便中文玩家而搬运整理！！ 记得查看txt角色触发词 (因为校长不玩方舟，实在是不认识角色，所以没把触发词里的角色标注中文，有小伙伴可以来帮忙的话及时联系校长啊！！！！) ps 【有小伙伴反馈陈年幽灵鲨的效果不行】 我们十分尊敬每一位lora的作者！！ 感谢你们的付出！！ 大家好这里是校长，目前这边准备来整合质量高些的lora模型， 已经是整理了70+并且给打上了中文标注以及把触发tag直接打到了文件名字上， 有些复杂的衣物装饰什么的还在旁边附带了同名的文档可以方便查阅。 如果大家有比较好的且跟目前的不同的lora的话， 希望可以来找咱发下Lora模型， 我把它们全部都统一整理完之后进行分类整理并且分享给大家（是lora模型哦，不是平常的大模型）。
Mreyesart/mreyesart1
Mreyesart
2023-01-26T07:18:41Z
2
0
diffusers
[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-01-26T07:05:06Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### Mreyesart1 Dreambooth model trained by Mreyesart with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars). Sample pictures of this concept:
Korakoe/Koromiko-Diffusion
Korakoe
2023-01-26T06:12:00Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-01-26T06:12:00Z
--- license: creativeml-openrail-m ---
hesw23168/SD_Elysium_Kuro_Model
hesw23168
2023-01-26T05:25:03Z
0
34
null
[ "license:openrail", "region:us" ]
null
2023-01-25T03:48:50Z
--- license: openrail --- Also on https://civitai.com/models/5301/elysium-kuro-anime Anime model is custom mix + finetune on dataset of high quality images (mix including Anything 4.0, WD 1.4 Booru, Seek Art Mega V1) and contains the contains the kl-f8-anime2 VAE from Waifu Diffusion. Example settings: Negative prompt: (lowres:1.1), (worst quality:1.2), (low quality:1.1), bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, normal quality, jpeg artifacts, signature, watermark, username, blurry (General model): Clip skip 1, VAE: 'vae-ft-mse-840000' from StabilityAI (included) (Anime model): Clip skip 2, VAE: 'kl-f8-anime2.ckpt' from Waifu Diffusion (included) Example images from anime model: ![awiog.jpg](https://s3.amazonaws.com/moonup/production/uploads/1674681388521-6351b7e2ea4e5b421fb0d42d.jpeg) General model coming soon.
gokuls/mobilebert_sa_GLUE_Experiment_mnli_256
gokuls
2023-01-26T03:03:25Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "mobilebert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-25T16:30:13Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: mobilebert_sa_GLUE_Experiment_mnli_256 results: - task: name: Text Classification type: text-classification dataset: name: GLUE MNLI type: glue config: mnli split: validation_matched args: mnli metrics: - name: Accuracy type: accuracy value: 0.6030309194467046 --- <!-- 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. --> # mobilebert_sa_GLUE_Experiment_mnli_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.8790 - Accuracy: 0.6030 ## Model description More information needed ## 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: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0008 | 1.0 | 3068 | 0.9490 | 0.5405 | | 0.9205 | 2.0 | 6136 | 0.9166 | 0.5675 | | 0.8928 | 3.0 | 9204 | 0.9022 | 0.5786 | | 0.872 | 4.0 | 12272 | 0.8843 | 0.5967 | | 0.8531 | 5.0 | 15340 | 0.8807 | 0.5959 | | 0.8359 | 6.0 | 18408 | 0.8763 | 0.5999 | | 0.8197 | 7.0 | 21476 | 0.8815 | 0.6009 | | 0.8028 | 8.0 | 24544 | 0.9012 | 0.5934 | | 0.786 | 9.0 | 27612 | 0.8633 | 0.6191 | | 0.769 | 10.0 | 30680 | 0.8734 | 0.6098 | | 0.752 | 11.0 | 33748 | 0.8682 | 0.6220 | | 0.736 | 12.0 | 36816 | 0.8741 | 0.6175 | | 0.7204 | 13.0 | 39884 | 0.8994 | 0.6048 | | 0.7038 | 14.0 | 42952 | 0.8940 | 0.6079 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2
starcel/asr-conformer-kdialectspeech
starcel
2023-01-26T02:54:57Z
2
1
speechbrain
[ "speechbrain", "automatic-speech-recognition", "ko", "license:apache-2.0", "region:us" ]
automatic-speech-recognition
2023-01-26T01:32:28Z
--- license: apache-2.0 language: - ko metrics: - cer - wer library_name: speechbrain pipeline_tag: automatic-speech-recognition --- 이 모델은 2022년 인공지능 학습용 데이터 구축 사업 <18 중노년층 방언 데이터>의 데이터 셋을 사용하여 Conformer ASR 모델을 훈련한 모델 파일입니다.
AKFromCanada/Taxi-v3
AKFromCanada
2023-01-26T02:51:45Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T02:51:36Z
--- 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: 7.46 +/- 2.64 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="AKFromCanada/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"]) ```
AKFromCanada/q-FrozenLake-v1-4x4-noSlippery
AKFromCanada
2023-01-26T02:48:01Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T02:47:53Z
--- 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="AKFromCanada/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"]) ```
sohm/Reinforce-CartPole_v1
sohm
2023-01-26T02:30:59Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-01-26T02:30:48Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole_v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 127.30 +/- 7.66 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
facebook/opt-iml-1.3b
facebook
2023-01-26T01:35:09Z
622
29
transformers
[ "transformers", "pytorch", "opt", "text-generation", "arxiv:2212.12017", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-01-26T00:08:49Z
--- inference: false tags: - text-generation - opt license: other commercial: false --- # OPT-IML ## Model Description [OPT-IML (OPT + Instruction Meta-Learning)](https://arxiv.org/abs/2212.12017) is a set of instruction-tuned versions of OPT, on a collection of ~2000 NLP tasks gathered from 8 NLP benchmarks, called OPT-IML Bench. We provide two model versions: * OPT-IML trained on 1500 tasks with several tasks held-out for purposes of downstream evaluation, and * OPT-IML-Max trained on all ~2000 tasks ### How to use You can use this model directly with a pipeline for text generation. ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model="facebook/opt-iml-1.3b") >>> generator("What is the capital of USA?") ``` ### Limitations and bias While OPT-IML models outperform baseline OPT on an extensive set of evaluations, nevertheless, they are susceptible to the various risks associated with using large language models relating to factual correctness, generation of toxic language and enforcing stereotypes. While we release our OPT-IML models to proliferate future work on instruction-tuning and to improve the availability of large instruction-tuned causal LMs, the use of these models should be accompanied with responsible best practices. ## Training data OPT-IML models are trained on OPT-IML Bench, a large benchmark for Instruction MetaLearning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks include Super-NaturalInstructions, FLAN, PromptSource, etc. ## Training procedure The texts are tokenized using the GPT2 byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens. The 30B model was fine-tuned on 64 40GB A100 GPUs. During fine-tuning, models saw approximately 2 billion tokens, which is only 0.6% of the pre-training budget of OPT. ### BibTeX entry and citation info ```bibtex @misc{iyer2022opt, title={OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization}, author={Iyer, Srinivasan and Lin, Xi Victoria and Pasunuru, Ramakanth and Mihaylov, Todor and Simig, D{\'a}niel and Yu, Ping and Shuster, Kurt and Wang, Tianlu and Liu, Qing and Koura, Punit Singh and others}, year={2022}, eprint={2212.12017}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
facebook/opt-iml-max-1.3b
facebook
2023-01-26T01:31:38Z
9,572
44
transformers
[ "transformers", "pytorch", "opt", "text-generation", "arxiv:2212.12017", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-01-26T00:08:30Z
--- inference: false tags: - text-generation - opt license: other commercial: false --- # OPT-IML ## Model Description [OPT-IML (OPT + Instruction Meta-Learning)](https://arxiv.org/abs/2212.12017) is a set of instruction-tuned versions of OPT, on a collection of ~2000 NLP tasks gathered from 8 NLP benchmarks, called OPT-IML Bench. We provide two model versions: * OPT-IML trained on 1500 tasks with several tasks held-out for purposes of downstream evaluation, and * OPT-IML-Max trained on all ~2000 tasks ### How to use You can use this model directly with a pipeline for text generation. ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model="facebook/opt-iml-max-1.3b") >>> generator("What is the capital of USA?") ``` ### Limitations and bias While OPT-IML models outperform baseline OPT on an extensive set of evaluations, nevertheless, they are susceptible to the various risks associated with using large language models relating to factual correctness, generation of toxic language and enforcing stereotypes. While we release our OPT-IML models to proliferate future work on instruction-tuning and to improve the availability of large instruction-tuned causal LMs, the use of these models should be accompanied with responsible best practices. ## Training data OPT-IML models are trained on OPT-IML Bench, a large benchmark for Instruction MetaLearning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks include Super-NaturalInstructions, FLAN, PromptSource, etc. ## Training procedure The texts are tokenized using the GPT2 byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens. The 30B model was fine-tuned on 64 40GB A100 GPUs. During fine-tuning, models saw approximately 2 billion tokens, which is only 0.6% of the pre-training budget of OPT. ### BibTeX entry and citation info ```bibtex @misc{iyer2022opt, title={OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization}, author={Iyer, Srinivasan and Lin, Xi Victoria and Pasunuru, Ramakanth and Mihaylov, Todor and Simig, D{\'a}niel and Yu, Ping and Shuster, Kurt and Wang, Tianlu and Liu, Qing and Koura, Punit Singh and others}, year={2022}, eprint={2212.12017}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
taraqur/blossom-vit
taraqur
2023-01-26T01:11:00Z
16
0
transformers
[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-12-13T03:53:21Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: blossom-vit 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. --> # blossom-vit 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: ## Model description More information needed ## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 345, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.25.1 - TensorFlow 2.10.0 - Datasets 2.7.1 - Tokenizers 0.13.2
OpenAssistant/reward-model-electra-large-discriminator
OpenAssistant
2023-01-26T01:08:08Z
138
5
transformers
[ "transformers", "pytorch", "electra", "text-classification", "reward-model", "reward_model", "RLHF", "en", "dataset:openai/webgpt_comparisons", "dataset:openai/summarize_from_feedback", "dataset:Dahoas/instruct-synthetic-prompt-responses", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-15T11:09:23Z
--- license: apache-2.0 datasets: - openai/webgpt_comparisons - openai/summarize_from_feedback - Dahoas/instruct-synthetic-prompt-responses language: - en metrics: - accuracy tags: - reward-model - reward_model - RLHF --- # Reward model trained from human feedback Reward model (RM) trained to predict which generated answer is better judged by a human, given a question. RM are useful in these domain: - QA model evaluation - serves as reward score in RLHF All models are train on these dataset with a same split seed across datasets (if validation split wasn't available) - [webgpt_comparisons](https://huggingface.co/datasets/openai/webgpt_comparisons) - [summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - [synthetic-instruct-gptj-pairwise](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) # How to use ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer reward_name = "OpenAssistant/reward-model-electra-large-discriminator" rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name) question, answer = "Explain nuclear fusion like I am five", "Nuclear fusion is the process by which two or more protons and neutrons combine to form a single nucleus. It is a very important process in the universe, as it is the source of energy for stars and galaxies. Nuclear fusion is also a key process in the production of energy for nuclear power plants." inputs = tokenizer(question, answer, return_tensors='pt') score = rank_model(**inputs).logits[0].cpu().detach() print(score) ``` # Performance Validation split accuracy | Model | [WebGPT](https://huggingface.co/datasets/openai/webgpt_comparisons) | [Summary](https://huggingface.co/datasets/openai/summarize_from_feedback) | [SytheticGPT](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) | |---|---|---|---| | [electra-large-discriminator](https://huggingface.co/OpenAssistant/reward-model-electra-large-discriminator) | 59.30 | 68.66 | 99.85 | | [deberta-v3-large](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large) | 61.13 | 72.23 | 99.94 | | [deberta-v3-base](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-base) | 59.07 | 66.84 | 99.85 | Its likely SytheticGPT has somekind of surface pattern on the choosen-rejected pair which makes it trivial to differentiate between better the answer.
OpenAssistant/reward-model-deberta-v3-base
OpenAssistant
2023-01-26T01:07:57Z
711
10
transformers
[ "transformers", "pytorch", "deberta-v2", "text-classification", "reward-model", "reward_model", "RLHF", "en", "dataset:openai/webgpt_comparisons", "dataset:openai/summarize_from_feedback", "dataset:Dahoas/instruct-synthetic-prompt-responses", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-15T11:06:39Z
--- license: mit datasets: - openai/webgpt_comparisons - openai/summarize_from_feedback - Dahoas/instruct-synthetic-prompt-responses language: - en metrics: - accuracy tags: - reward-model - reward_model - RLHF --- # Reward model trained from human feedback Reward model (RM) trained to predict which generated answer is better judged by a human, given a question. RM are useful in these domain: - QA model evaluation - serves as reward score in RLHF All models are train on these dataset with a same split seed across datasets (if validation split wasn't available) - [webgpt_comparisons](https://huggingface.co/datasets/openai/webgpt_comparisons) - [summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - [synthetic-instruct-gptj-pairwise](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) # How to use ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer reward_name = "OpenAssistant/reward-model-deberta-v3-base" rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name) question, answer = "Explain nuclear fusion like I am five", "Nuclear fusion is the process by which two or more protons and neutrons combine to form a single nucleus. It is a very important process in the universe, as it is the source of energy for stars and galaxies. Nuclear fusion is also a key process in the production of energy for nuclear power plants." inputs = tokenizer(question, answer, return_tensors='pt') score = rank_model(**inputs).logits[0].cpu().detach() print(score) ``` # Performance Validation split accuracy | Model | [WebGPT](https://huggingface.co/datasets/openai/webgpt_comparisons) | [Summary](https://huggingface.co/datasets/openai/summarize_from_feedback) | [SytheticGPT](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) | |---|---|---|---| | [electra-large-discriminator](https://huggingface.co/OpenAssistant/reward-model-electra-large-discriminator) | 59.30 | 68.66 | 99.85 | | [deberta-v3-large](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large) | 61.13 | 72.23 | 99.94 | | [deberta-v3-base](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-base) | 59.07 | 66.84 | 99.85 | Its likely SytheticGPT has somekind of surface pattern on the choosen-rejected pair which makes it trivial to differentiate between better the answer.
mrm8488/xlm-roberta-large-finetuned-HC3-mix
mrm8488
2023-01-26T00:38:21Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "doi:10.57967/hf/0305", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-25T14:04:10Z
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-large-finetuned-HC3-mix results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-large-finetuned-HC3-mix This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6998 - F1: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:------:|:---------------:|:---:| | 0.6506 | 1.0 | 35824 | 0.6998 | 0.0 | | 0.6481 | 2.0 | 71648 | 0.7662 | 0.0 | | 0.6391 | 3.0 | 107472 | 0.7492 | 0.0 | | 0.6396 | 4.0 | 143296 | 0.7358 | 0.0 | | 0.6366 | 5.0 | 179120 | 0.7259 | 0.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
rohitp1/Nystrom-W2V2-100hrs-take-3
rohitp1
2023-01-26T00:07:27Z
1
0
transformers
[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]
null
2023-01-23T11:17:58Z
--- tags: - generated_from_trainer metrics: - wer model-index: - name: Nystrom-W2V2-100hrs-take-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Nystrom-W2V2-100hrs-take-3 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 30.1649 - Wer: 0.1047 ## Model description More information needed ## 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: 1 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 55.4504 | 9.01 | 1000 | 29.0675 | 0.1464 | | 65.4799 | 18.02 | 2000 | 26.9263 | 0.1580 | | 72.6609 | 27.03 | 3000 | 27.2220 | 0.1500 | | 65.6264 | 36.04 | 4000 | 26.4758 | 0.1426 | | 57.9496 | 45.04 | 5000 | 27.0818 | 0.1349 | | 49.6643 | 54.05 | 6000 | 27.9658 | 0.1269 | | 42.5205 | 63.06 | 7000 | 28.6973 | 0.1214 | | 36.1799 | 72.07 | 8000 | 28.1021 | 0.1128 | | 30.9742 | 81.08 | 9000 | 29.9000 | 0.1093 | | 27.4728 | 90.09 | 10000 | 30.2661 | 0.1057 | | 26.0383 | 99.1 | 11000 | 30.1649 | 0.1047 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.11.0
StepanLavr/SnakeZVO
StepanLavr
2023-01-25T23:53:27Z
0
0
null
[ "dataset:fka/awesome-chatgpt-prompts", "region:us" ]
null
2023-01-25T23:51:41Z
--- datasets: - fka/awesome-chatgpt-prompts ---
cdefghijkl/anime-m-series-vol1
cdefghijkl
2023-01-25T23:39:52Z
0
3
null
[ "text-to-image", "stable-diffusion", "en", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-01-13T17:48:10Z
--- license: creativeml-openrail-m language: - en tags: - text-to-image - stable-diffusion --- A collection of anime models merged by me. Will update info and examples later.
GBaker/bigbird-roberta-base-medqa-usmle-nocontext
GBaker
2023-01-25T23:34:26Z
2
0
transformers
[ "transformers", "pytorch", "tensorboard", "big_bird", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
multiple-choice
2023-01-25T23:05:28Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bigbird-roberta-base-medqa-usmle-nocontext 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. --> # bigbird-roberta-base-medqa-usmle-nocontext This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3863 - Accuracy: 0.2592 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.392 | 1.0 | 636 | 1.3863 | 0.2333 | | 1.39 | 2.0 | 1272 | 1.3863 | 0.2592 | | 1.3896 | 3.0 | 1908 | 1.3863 | 0.2592 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
Periramm/ppo-LunarLander-v2
Periramm
2023-01-25T23:33:01Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-25T12:20:47Z
--- 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: 249.61 +/- 21.73 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 ... ```
Kaludi/CSGO-Minimap-Layout-Generation
Kaludi
2023-01-25T23:16:20Z
8
1
diffusers
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "art", "artistic", "cs:go", "topview", "map generator", "layout", "layout generator", "map", "csgo", "improved layout", "radar", "en", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-01-25T22:47:33Z
--- language: - en tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - art - artistic - diffusers - cs:go - topview - map generator - layout - layout generator - map - csgo - improved layout - radar inference: true license: creativeml-openrail-m --- # CSGO Minimap Layout Generation ![img](https://huggingface.co/Kaludi/CSGO-Minimap-Layout-Generation/resolve/main/csgoMiniMapLayoutsV2.png) This is an improved AI model of my previous model trained on CS:GO's radar top view images of many maps which can now produce custom map layouts in seconds. This model does not produce red or green boxes like in my previous model. The tag for this model is **"radar-topview"**. If you'd like to get a map layout similar to a specific map, you can add the map name before "radar-topview". So if I wanted a map generation similar to dust2, I would write **"dust2-radar-topview"**. **Try the following prompt to get the best results:** "fps radar-topview game map, flat shading, soft shadows, global illumination" "fps radar topview map, polygonal, gradient background, pastel colors, soft shadows, global illumination, straight lines, insanely detailed" **Map Radar Topviews this AI was trained on:** de_dust2 de_inferno de_nuke de_mirage de_cache de_train de_cobblestone de_castle de_overpass **Have fun generating map layouts!** ### CompVis [Download csgoTopViewMapLayout.ckpt) (2.9GB)](https://huggingface.co/Kaludi/CSGO-Minimap-Layout-Generation/blob/main/csgoMiniMapLayoutsV2.ckpt) ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler import torch prompt = ( "fps radar-topview game map, flat shading, soft shadows, global illumination") model_id = "Kaludi/CSGO-Improved-Radar-Top-View-Map-Layouts" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") image = pipe(prompt, num_inference_steps=30).images[0] image.save("./result.jpg") ``` ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
aimarsg/bert-finetuned-ner-1
aimarsg
2023-01-25T23:11:43Z
19
1
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:xglue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-28T17:32:33Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - xglue metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: xglue type: xglue config: ner split: validation.es args: ner metrics: - name: Precision type: precision value: 0.6037969459347916 - name: Recall type: recall value: 0.6720257234726688 - name: F1 type: f1 value: 0.6360869565217391 - name: Accuracy type: accuracy value: 0.9488508424567125 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the xglue dataset. It achieves the following results on the evaluation set: - Loss: 0.2202 - Precision: 0.6038 - Recall: 0.6720 - F1: 0.6361 - Accuracy: 0.9489 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 191 | 0.2359 | 0.5659 | 0.6309 | 0.5967 | 0.9397 | | No log | 2.0 | 382 | 0.2136 | 0.5754 | 0.6681 | 0.6183 | 0.9464 | | 0.1605 | 3.0 | 573 | 0.2202 | 0.6038 | 0.6720 | 0.6361 | 0.9489 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
eLarry/ppo-Huggy
eLarry
2023-01-25T22:58:32Z
1
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-01-25T22:58:24Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **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://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: eLarry/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Periramm/q-taxi
Periramm
2023-01-25T22:57:13Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-01-25T22:57:04Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="Periramm/q-taxi", 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"]) ```
Periramm/q-frozlake
Periramm
2023-01-25T22:54:51Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-01-25T22:54:43Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-frozlake 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="Periramm/q-frozlake", 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"]) ```
gokuls/mobilebert_sa_GLUE_Experiment_sst2_128
gokuls
2023-01-25T22:07:13Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "mobilebert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-01-25T21:21:54Z
--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: mobilebert_sa_GLUE_Experiment_sst2_128 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue config: sst2 split: validation args: sst2 metrics: - name: Accuracy type: accuracy value: 0.8004587155963303 --- <!-- 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. --> # mobilebert_sa_GLUE_Experiment_sst2_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4330 - Accuracy: 0.8005 ## Model description More information needed ## 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: 128 - eval_batch_size: 128 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5124 | 1.0 | 527 | 0.4330 | 0.8005 | | 0.2842 | 2.0 | 1054 | 0.4711 | 0.8028 | | 0.2267 | 3.0 | 1581 | 0.4593 | 0.7982 | | 0.2025 | 4.0 | 2108 | 0.7141 | 0.7856 | | 0.1849 | 5.0 | 2635 | 0.4771 | 0.7982 | | 0.1754 | 6.0 | 3162 | 0.6028 | 0.7901 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2