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Malaika/q-FrozenLake-v1-4x4-noSlippery
Malaika
2023-06-18T19:10:34Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T19:10:25Z
--- 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="Malaika/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"]) ```
Dugoss/digit_defines
Dugoss
2023-06-18T19:08:07Z
0
0
keras
[ "keras", "tf-keras", "region:us" ]
null
2023-06-18T18:15:59Z
--- library_name: keras --- # Модель для распознавания цифр Натренирована на наборе данных Mnist ![](model.png)
jorgeortizfuentes/spanish-attitude
jorgeortizfuentes
2023-06-18T18:55:49Z
104
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:jorgeortizfuentes/spanish_attitude_conll2003", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-06-13T04:32:41Z
--- tags: - generated_from_trainer datasets: - jorgeortizfuentes/spanish_attitude_conll2003 model-index: - name: spanish-attitude 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. --> # spanish-attitude This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the jorgeortizfuentes/spanish_attitude_conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.6388 - Affect Precision: 0.0 - Affect Recall: 0.0 - Affect F1: 0.0 - Affect Number: 61 - Appreciation Precision: 0.2208 - Appreciation Recall: 0.3401 - Appreciation F1: 0.2677 - Appreciation Number: 294 - Judgment (j1) Precision: 0.0 - Judgment (j1) Recall: 0.0 - Judgment (j1) F1: 0.0 - Judgment (j1) Number: 2 - Social esteem (j2) Precision: 0.0 - Social esteem (j2) Recall: 0.0 - Social esteem (j2) F1: 0.0 - Social esteem (j2) Number: 2 - Social sanction (j2) Precision: 0.0 - Social sanction (j2) Recall: 0.0 - Social sanction (j2) F1: 0.0 - Social sanction (j2) Number: 1 - Capacity (j3) Precision: 0.1037 - Capacity (j3) Recall: 0.1977 - Capacity (j3) F1: 0.1360 - Capacity (j3) Number: 86 - Normality (j3) Precision: 0.0 - Normality (j3) Recall: 0.0 - Normality (j3) F1: 0.0 - Normality (j3) Number: 62 - Propriety (j3) Precision: 0.1586 - Propriety (j3) Recall: 0.2791 - Propriety (j3) F1: 0.2022 - Propriety (j3) Number: 129 - Tenacity (j3) Precision: 0.0 - Tenacity (j3) Recall: 0.0 - Tenacity (j3) F1: 0.0 - Tenacity (j3) Number: 47 - Veracity (j3) Precision: 0.0 - Veracity (j3) Recall: 0.0 - Veracity (j3) F1: 0.0 - Veracity (j3) Number: 20 - Overall Precision: 0.1792 - Overall Recall: 0.2173 - Overall F1: 0.1964 - Overall Accuracy: 0.8250 ## 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 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2
ItchyB/poca-SoccerTwos
ItchyB
2023-06-18T18:48:50Z
51
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-06-18T18:48:27Z
--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: ItchyB/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
AustinCarthy/MixGPT2V2_subdomain_100KP_BFall_fromB_95K_topP_0.75_ratio5
AustinCarthy
2023-06-18T18:31:51Z
0
0
null
[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]
null
2023-06-18T14:59:10Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: MixGPT2V2_subdomain_100KP_BFall_fromB_95K_topP_0.75_ratio5 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. --> # MixGPT2V2_subdomain_100KP_BFall_fromB_95K_topP_0.75_ratio5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_MixGPT2V2_using_benign_95K_top_p_0.75subdomain dataset. It achieves the following results on the evaluation set: - Loss: 0.0192 - Accuracy: 0.9978 - F1: 0.9759 - Precision: 0.9990 - Recall: 0.9538 - Roc Auc Score: 0.9769 - Tpr At Fpr 0.01: 0.96 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0076 | 1.0 | 36563 | 0.0117 | 0.9974 | 0.9718 | 0.9983 | 0.9466 | 0.9733 | 0.9496 | | 0.0035 | 2.0 | 73126 | 0.0206 | 0.9973 | 0.9710 | 0.9981 | 0.9454 | 0.9727 | 0.9496 | | 0.0025 | 3.0 | 109689 | 0.0184 | 0.9970 | 0.9677 | 0.9985 | 0.9388 | 0.9694 | 0.942 | | 0.0008 | 4.0 | 146252 | 0.0199 | 0.9972 | 0.9698 | 0.9994 | 0.942 | 0.9710 | 0.9504 | | 0.0 | 5.0 | 182815 | 0.0192 | 0.9978 | 0.9759 | 0.9990 | 0.9538 | 0.9769 | 0.96 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
bjlutuo/ppo-Huggy
bjlutuo
2023-06-18T18:12:36Z
2
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-06-18T18:12:18Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: bjlutuo/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
drumwell/GenerAd-AI
drumwell
2023-06-18T17:43:24Z
31
0
peft
[ "peft", "text-generation", "dataset:drumwell/generadai-sample", "license:bigscience-openrail-m", "region:us" ]
text-generation
2023-06-18T17:24:59Z
--- library_name: peft license: bigscience-openrail-m datasets: - drumwell/generadai-sample pipeline_tag: text-generation ---
CeroShrijver/chinese-macbert-large-text-classification
CeroShrijver
2023-06-18T17:36:30Z
106
1
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-06-03T07:49:47Z
--- tags: - generated_from_trainer model-index: - name: chinese-macbert-large-text-classification 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. --> # chinese-macbert-large-text-classification This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.28.1 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.11.6
bryan467/Joko_widodo1
bryan467
2023-06-18T17:27:34Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-18T17:20:11Z
--- license: creativeml-openrail-m ---
anilsekharc/q-Taxi-v3
anilsekharc
2023-06-18T17:22:48Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T17:22:37Z
--- 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: 7.52 +/- 2.73 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="anilsekharc/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"]) ```
ArvinArora/ppo-LunarLander-v2
ArvinArora
2023-06-18T17:20:27Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T17:19:56Z
--- 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: 223.86 +/- 36.05 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 ... ```
anilsekharc/q-FrozenLake-v1-4x4-noSlippery
anilsekharc
2023-06-18T17:15:22Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T17:15:10Z
--- 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="anilsekharc/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"]) ```
Bodolaz/Unit-5.1
Bodolaz
2023-06-18T17:14:09Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-06-18T17:14:00Z
--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Bodolaz/Unit-5.1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
jclynn/finetuning-sentiment-model-5000-samples
jclynn
2023-06-18T17:04:10Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-06-17T22:12:50Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-5000-samples 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. --> # finetuning-sentiment-model-5000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1462 - Accuracy: 0.956 - F1: 0.9719 ## 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 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
Bodolaz/Unit-4.2
Bodolaz
2023-06-18T16:46:23Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T16:45:54Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Unit-4.2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 29.35 +/- 31.89 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Malaika/ppo-LunarLander-v2
Malaika
2023-06-18T16:35:36Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T16:34:55Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 164.61 +/- 40.56 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 ... ```
BryanSwk/q-FrozenLake-v1-4x4-noSlippery
BryanSwk
2023-06-18T16:30:56Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T16:30:47Z
--- 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="BryanSwk/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"]) ```
prognosis/falcon7b-chunks-10k-v3_e2000
prognosis
2023-06-18T16:19:18Z
0
0
null
[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]
null
2023-06-18T02:01:02Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: falcon7b-chunks-10k-v3_e2000 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. --> # falcon7b-chunks-10k-v3_e2000 This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 2000 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
huantd/all-mpnet-base-v2
huantd
2023-06-18T16:04:01Z
4
0
transformers.js
[ "transformers.js", "onnx", "mpnet", "fill-mask", "region:us" ]
fill-mask
2023-06-18T15:42:54Z
--- library_name: "transformers.js" --- https://huggingface.co/sentence-transformers/all-mpnet-base-v2 with ONNX weights to be compatible with Transformers.js.
TheFools/Celline
TheFools
2023-06-18T15:52:46Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-18T15:52:46Z
--- license: creativeml-openrail-m ---
rafay/ppo-Huggy
rafay
2023-06-18T15:49:38Z
4
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-06-18T15:49:29Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: rafay/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
synpjh/bert-base-uncased-issues-128
synpjh
2023-06-18T15:43:51Z
116
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-06-18T14:16:56Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased-issues-128 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1675 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.239 | 1.0 | 291 | 0.2306 | | 0.1865 | 2.0 | 582 | 0.1971 | | 0.169 | 3.0 | 873 | 0.1918 | | 0.1603 | 4.0 | 1164 | 0.1875 | | 0.1536 | 5.0 | 1455 | 0.1567 | | 0.1461 | 6.0 | 1746 | 0.1755 | | 0.1411 | 7.0 | 2037 | 0.1719 | | 0.1374 | 8.0 | 2328 | 0.1658 | | 0.1341 | 9.0 | 2619 | 0.1594 | | 0.1302 | 10.0 | 2910 | 0.1666 | | 0.1284 | 11.0 | 3201 | 0.1634 | | 0.1264 | 12.0 | 3492 | 0.1588 | | 0.1238 | 13.0 | 3783 | 0.1690 | | 0.1237 | 14.0 | 4074 | 0.1558 | | 0.1218 | 15.0 | 4365 | 0.1523 | | 0.1213 | 16.0 | 4656 | 0.1675 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.13.0 - Tokenizers 0.13.3
bjlutuo/ppo-LunarLander-v2
bjlutuo
2023-06-18T15:32:56Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T15:32:30Z
--- 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: 230.67 +/- 18.68 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 ... ```
aasarmehdi/distilbert-base-uncased.finetuned-emotion
aasarmehdi
2023-06-18T15:12:34Z
105
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
2023-06-18T12:20:48Z
--- 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 config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9285 - name: F1 type: f1 value: 0.9285575296750973 --- <!-- 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: 0.2139 - Accuracy: 0.9285 - F1: 0.9286 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8378 | 1.0 | 250 | 0.3119 | 0.913 | 0.9104 | | 0.2549 | 2.0 | 500 | 0.2139 | 0.9285 | 0.9286 | ### Framework versions - Transformers 4.28.0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.11.0
nic70/taxi_v3_trial1
nic70
2023-06-18T15:03:53Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T08:28:01Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi_v3_trial1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="nic70/taxi_v3_trial1", 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"]) ```
sammun64/my_awesome_qa_model
sammun64
2023-06-18T15:02:32Z
61
0
transformers
[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-06-18T07:57:03Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: sammun64/my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # sammun64/my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5606 - Validation Loss: 1.7998 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.4494 | 2.1873 | 0 | | 1.8250 | 1.7998 | 1 | | 1.5606 | 1.7998 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
nic70/q-FrozenLake-v1-4x4-noSlippery
nic70
2023-06-18T15:02:26Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T08:17:50Z
--- 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="nic70/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"]) ```
TheFools/Normanvt
TheFools
2023-06-18T15:01:28Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-18T15:00:29Z
--- license: creativeml-openrail-m ---
AustinCarthy/MixGPT2V2_subdomain_100KP_BFall_fromB_95K_topP_0.75_ratio2.63
AustinCarthy
2023-06-18T14:58:36Z
0
0
null
[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]
null
2023-06-18T12:41:39Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: MixGPT2V2_subdomain_100KP_BFall_fromB_95K_topP_0.75_ratio2.63 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. --> # MixGPT2V2_subdomain_100KP_BFall_fromB_95K_topP_0.75_ratio2.63 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_MixGPT2V2_using_benign_95K_top_p_0.75subdomain dataset. It achieves the following results on the evaluation set: - Loss: 0.0186 - Accuracy: 0.9982 - F1: 0.9805 - Precision: 0.9975 - Recall: 0.964 - Roc Auc Score: 0.9819 - Tpr At Fpr 0.01: 0.9566 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.011 | 1.0 | 22121 | 0.0217 | 0.9961 | 0.9569 | 0.9970 | 0.92 | 0.9599 | 0.9126 | | 0.0041 | 2.0 | 44242 | 0.0171 | 0.9970 | 0.9679 | 0.9977 | 0.9398 | 0.9698 | 0.9332 | | 0.0028 | 3.0 | 66363 | 0.0123 | 0.9980 | 0.9787 | 0.9829 | 0.9746 | 0.9869 | 0.902 | | 0.0012 | 4.0 | 88484 | 0.0175 | 0.9975 | 0.9727 | 0.9983 | 0.9484 | 0.9742 | 0.9502 | | 0.0 | 5.0 | 110605 | 0.0186 | 0.9982 | 0.9805 | 0.9975 | 0.964 | 0.9819 | 0.9566 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
WALIDALI/photo-of-walidlibyalyy-person
WALIDALI
2023-06-18T14:29:22Z
32
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-06-18T14:25:08Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### photo_of_walidlibyalyy_person Dreambooth model trained by WALIDALI 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:
Middelz2/roberta-large-aphasia-narration-weightdecay0-lr2e4_eps_10
Middelz2
2023-06-18T14:24:22Z
3
0
transformers
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-06-18T13:32:21Z
--- license: mit tags: - generated_from_keras_callback model-index: - name: Middelz2/roberta-large-aphasia-narration-weightdecay0-lr2e4_eps_10 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. --> # Middelz2/roberta-large-aphasia-narration-weightdecay0-lr2e4_eps_10 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.6128 - Validation Loss: 5.5802 - Epoch: 9 ## 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': 0.0002, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1420 | 1.6450 | 0 | | 1.6971 | 1.4556 | 1 | | 1.6025 | 1.3882 | 2 | | 1.4763 | 1.2997 | 3 | | 1.4301 | 1.3055 | 4 | | 1.4358 | 1.3317 | 5 | | 2.2816 | 2.4774 | 6 | | 2.7754 | 2.0994 | 7 | | 4.5272 | 5.5713 | 8 | | 5.6128 | 5.5802 | 9 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
nolanaatama/sqdwrdtntclsrvcv11000pchsclsscklmz
nolanaatama
2023-06-18T14:15:20Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-17T14:32:22Z
--- license: creativeml-openrail-m ---
lengocduc195/SentenceTransformer
lengocduc195
2023-06-18T14:09:00Z
0
0
null
[ "arxiv:1908.10084", "arxiv:2004.09813", "arxiv:2010.08240", "arxiv:2012.14210", "arxiv:2104.06979", "arxiv:2104.08663", "region:us" ]
null
2023-06-18T14:07:19Z
<!--- BADGES: START ---> [![GitHub - License](https://img.shields.io/github/license/UKPLab/sentence-transformers?logo=github&style=flat&color=green)][#github-license] [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/sentence-transformers?logo=pypi&style=flat&color=blue)][#pypi-package] [![PyPI - Package Version](https://img.shields.io/pypi/v/sentence-transformers?logo=pypi&style=flat&color=orange)][#pypi-package] [![Conda - Platform](https://img.shields.io/conda/pn/conda-forge/sentence-transformers?logo=anaconda&style=flat)][#conda-forge-package] [![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/sentence-transformers?logo=anaconda&style=flat&color=orange)][#conda-forge-package] [![Docs - GitHub.io](https://img.shields.io/static/v1?logo=github&style=flat&color=pink&label=docs&message=sentence-transformers)][#docs-package] <!--- [![PyPI - Downloads](https://img.shields.io/pypi/dm/sentence-transformers?logo=pypi&style=flat&color=green)][#pypi-package] [![Conda](https://img.shields.io/conda/dn/conda-forge/sentence-transformers?logo=anaconda)][#conda-forge-package] ---> [#github-license]: https://github.com/UKPLab/sentence-transformers/blob/master/LICENSE [#pypi-package]: https://pypi.org/project/sentence-transformers/ [#conda-forge-package]: https://anaconda.org/conda-forge/sentence-transformers [#docs-package]: https://www.sbert.net/ <!--- BADGES: END ---> # Sentence Transformers: Multilingual Sentence, Paragraph, and Image Embeddings using BERT & Co. This framework provides an easy method to compute dense vector representations for **sentences**, **paragraphs**, and **images**. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various task. Text is embedding in vector space such that similar text is close and can efficiently be found using cosine similarity. We provide an increasing number of **[state-of-the-art pretrained models](https://www.sbert.net/docs/pretrained_models.html)** for more than 100 languages, fine-tuned for various use-cases. Further, this framework allows an easy **[fine-tuning of custom embeddings models](https://www.sbert.net/docs/training/overview.html)**, to achieve maximal performance on your specific task. For the **full documentation**, see **[www.SBERT.net](https://www.sbert.net)**. The following publications are integrated in this framework: - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084) (EMNLP 2019) - [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813) (EMNLP 2020) - [Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://arxiv.org/abs/2010.08240) (NAACL 2021) - [The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes](https://arxiv.org/abs/2012.14210) (arXiv 2020) - [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning](https://arxiv.org/abs/2104.06979) (arXiv 2021) - [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://arxiv.org/abs/2104.08663) (arXiv 2021) ## Installation We recommend **Python 3.6** or higher, **[PyTorch 1.6.0](https://pytorch.org/get-started/locally/)** or higher and **[transformers v4.6.0](https://github.com/huggingface/transformers)** or higher. The code does **not** work with Python 2.7. **Install with pip** Install the *sentence-transformers* with `pip`: ``` pip install -U sentence-transformers ``` **Install with conda** You can install the *sentence-transformers* with `conda`: ``` conda install -c conda-forge sentence-transformers ``` **Install from sources** Alternatively, you can also clone the latest version from the [repository](https://github.com/UKPLab/sentence-transformers) and install it directly from the source code: ```` pip install -e . ```` **PyTorch with CUDA** If you want to use a GPU / CUDA, you must install PyTorch with the matching CUDA Version. Follow [PyTorch - Get Started](https://pytorch.org/get-started/locally/) for further details how to install PyTorch. ## Getting Started See [Quickstart](https://www.sbert.net/docs/quickstart.html) in our documenation. [This example](https://github.com/UKPLab/sentence-transformers/tree/master/examples/applications/computing-embeddings/computing_embeddings.py) shows you how to use an already trained Sentence Transformer model to embed sentences for another task. First download a pretrained model. ````python from sentence_transformers import SentenceTransformer model = SentenceTransformer('all-MiniLM-L6-v2') ```` Then provide some sentences to the model. ````python sentences = ['This framework generates embeddings for each input sentence', 'Sentences are passed as a list of string.', 'The quick brown fox jumps over the lazy dog.'] sentence_embeddings = model.encode(sentences) ```` And that's it already. We now have a list of numpy arrays with the embeddings. ````python for sentence, embedding in zip(sentences, sentence_embeddings): print("Sentence:", sentence) print("Embedding:", embedding) print("") ```` ## Pre-Trained Models We provide a large list of [Pretrained Models](https://www.sbert.net/docs/pretrained_models.html) for more than 100 languages. Some models are general purpose models, while others produce embeddings for specific use cases. Pre-trained models can be loaded by just passing the model name: `SentenceTransformer('model_name')`. [» Full list of pretrained models](https://www.sbert.net/docs/pretrained_models.html) ## Training This framework allows you to fine-tune your own sentence embedding methods, so that you get task-specific sentence embeddings. You have various options to choose from in order to get perfect sentence embeddings for your specific task. See [Training Overview](https://www.sbert.net/docs/training/overview.html) for an introduction how to train your own embedding models. We provide [various examples](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training) how to train models on various datasets. Some highlights are: - Support of various transformer networks including BERT, RoBERTa, XLM-R, DistilBERT, Electra, BART, ... - Multi-Lingual and multi-task learning - Evaluation during training to find optimal model - [10+ loss-functions](https://www.sbert.net/docs/package_reference/losses.html) allowing to tune models specifically for semantic search, paraphrase mining, semantic similarity comparison, clustering, triplet loss, contrastive loss. ## Performance Our models are evaluated extensively on 15+ datasets including challening domains like Tweets, Reddit, emails. They achieve by far the **best performance** from all available sentence embedding methods. Further, we provide several **smaller models** that are **optimized for speed**. [» Full list of pretrained models](https://www.sbert.net/docs/pretrained_models.html) ## Application Examples You can use this framework for: - [Computing Sentence Embeddings](https://www.sbert.net/examples/applications/computing-embeddings/README.html) - [Semantic Textual Similarity](https://www.sbert.net/docs/usage/semantic_textual_similarity.html) - [Clustering](https://www.sbert.net/examples/applications/clustering/README.html) - [Paraphrase Mining](https://www.sbert.net/examples/applications/paraphrase-mining/README.html) - [Translated Sentence Mining](https://www.sbert.net/examples/applications/parallel-sentence-mining/README.html) - [Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html) - [Retrieve & Re-Rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) - [Text Summarization](https://www.sbert.net/examples/applications/text-summarization/README.html) - [Multilingual Image Search, Clustering & Duplicate Detection](https://www.sbert.net/examples/applications/image-search/README.html) and many more use-cases. For all examples, see [examples/applications](https://github.com/UKPLab/sentence-transformers/tree/master/examples/applications). ## Citing & Authors If you find this repository helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` If you use one of the multilingual models, feel free to cite our publication [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813): ```bibtex @inproceedings{reimers-2020-multilingual-sentence-bert, title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2020", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2004.09813", } ``` Please have a look at [Publications](https://www.sbert.net/docs/publications.html) for our different publications that are integrated into SentenceTransformers. Contact person: [Nils Reimers](https://www.nils-reimers.de), [[email protected]](mailto:[email protected]) https://www.ukp.tu-darmstadt.de/ Don't hesitate to send us an e-mail or report an issue, if something is broken (and it shouldn't be) or if you have further questions. > This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.
prompiu/FR48
prompiu
2023-06-18T13:42:37Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-18T13:29:59Z
--- license: creativeml-openrail-m ---
azetaaa/a2c-AntBulletEnv-v0
azetaaa
2023-06-18T13:33:49Z
1
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-06-17T17:27:27Z
--- 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: 1475.58 +/- 130.31 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 ... ```
autobots/Nyanners-Narrator-TTS-Tortoise
autobots
2023-06-18T13:32:52Z
0
6
null
[ "en", "license:other", "region:us" ]
null
2023-06-18T12:42:07Z
--- license: other language: - en --- Sample:https://vocaroo.com/1nvl8SkJ51VG Tortoise TTS model to use in ai voice cloning repo with an audio sample. It can generate at low samples and comes out better than the stock model. I think I used 32/160 settings for the sample. 96/200 gives better results but of course you are trading computation for quality. may have to clean up extra noises in between long text, as with any tortoise model. Works very well with RVC applied on top. Much more stable than bark for something like an essay or audiobook. Trained at full precision for 200 epochs from about 4 hours of data. Loss of about ~1.18
Middelz2/roberta-large-aphasia-narration-weightdecay0_eps_10
Middelz2
2023-06-18T13:27:23Z
3
0
transformers
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-06-18T12:40:20Z
--- license: mit tags: - generated_from_keras_callback model-index: - name: Middelz2/roberta-large-aphasia-narration-weightdecay0_eps_10 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. --> # Middelz2/roberta-large-aphasia-narration-weightdecay0_eps_10 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0094 - Validation Loss: 0.8917 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.5337 | 1.3155 | 0 | | 1.2936 | 1.1727 | 1 | | 1.1965 | 1.1077 | 2 | | 1.1361 | 1.0687 | 3 | | 1.0949 | 1.0489 | 4 | | 1.0621 | 0.9968 | 5 | | 1.0537 | 0.9602 | 6 | | 1.0273 | 0.9470 | 7 | | 1.0333 | 0.9485 | 8 | | 1.0094 | 0.8917 | 9 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
mrizalf7/xlm-roberta-finetuned-small-squad-indonesian-rizal-7
mrizalf7
2023-06-18T13:24:52Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2023-06-18T12:13:02Z
--- license: mit tags: - generated_from_trainer model-index: - name: xlm-roberta-finetuned-small-squad-indonesian-rizal-7 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-finetuned-small-squad-indonesian-rizal-7 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7368 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5958 | 1.0 | 8256 | 1.7368 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
traintogpb/klue-roberta-large-wikipedia-v1
traintogpb
2023-06-18T13:04:53Z
160
0
transformers
[ "transformers", "pytorch", "roberta", "fill-mask", "ko", "dataset:klue", "dataset:wikipedia", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-06-18T11:59:08Z
--- datasets: - klue - wikipedia language: - ko metrics: - accuracy training_args: - num_train_epochs=5, - per_device_train_batch_size=16, - per_device_eval_batch_size=16, - prediction_loss_only=False, - learning_rate=5e-5, - logging_strategy='steps', - logging_steps=100, - save_steps=1000, - eval_steps=1000, - save_strategy="steps", - evaluation_strategy="steps", - load_best_model_at_end=True, - metric_for_best_model="masked_accuracy", - greater_is_better=True, - seed=42, - warmup_steps=5000, info: - MLM (15%) from the checkpoint of klue/roberta-large - LineByLineTextDataset (block_size 384) - PLM for ODQA task based-on Wikipedia questions - Accuracy (for [MASK]) = 0.7066 (CE loss 1.388) - v2 is trained with smaller learning rate and more epochs ---
desh2608/icefall-surt-ami-dprnn-zipformer
desh2608
2023-06-18T12:56:15Z
0
0
null
[ "tensorboard", "en", "dataset:ami", "dataset:icsi", "license:apache-2.0", "region:us" ]
null
2023-06-18T09:08:32Z
--- license: apache-2.0 datasets: - ami - icsi language: - en metrics: - orc-wer --- # AMI/ICSI dprnn_zipformer This model is based on the icefall `dprnn_zipformer` recipe under `egs/ami/SURT`. For details, refer to the README.md in the recipe. ## Performance Record The following results are obtained using the adapted models using modified beam search with a beam size of 4. **AMI** | Model | IHM-Mix | SDM | MDM | |------------|:-------:|:----:|:----:| | SURT-base | 39.8 | 65.4 | 46.6 | | + adapt | 37.4 | 46.9 | 43.7 | | SURT-large | 36.8 | 62.5 | 44.4 | | + adapt | **35.1** | **44.6** | **41.4** | **ICSI** | Model | IHM-Mix | SDM | |------------|:-------:|:----:| | SURT-base | 28.3 | 60.0 | | + adapt | 26.3 | 33.9 | | SURT-large | 27.8 | 59.7 | | + adapt | **24.4** | **32.3** |
HachiML/mpt-7b-instruct-for-peft
HachiML
2023-06-18T12:32:12Z
4
0
transformers
[ "transformers", "pytorch", "mpt", "text-generation", "custom_code", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
2023-06-17T03:38:52Z
--- inference: false --- # MPT-7B-Instruct-for-peft このモデルは[MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct)のコードを一部PEFT用に変更したものです。 実験的なものですので使用は個人の判断でお願いします。 使用による損害のいかなる責任も負いません。 # Reference [Fixes for PEFT Tuning based on iwalton3](https://huggingface.co/mosaicml/mpt-7b/commit/f71db786423da83c27ec0d4ee0e2ab83e3e08297)
matef/my_sentiment_model
matef
2023-06-18T12:00:17Z
61
0
transformers
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-05-20T11:26:50Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: matef/my_sentiment_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # matef/my_sentiment_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0637 - Validation Loss: 0.2478 - Train Accuracy: 0.9266 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.2498 | 0.1796 | 0.9304 | 0 | | 0.1314 | 0.1806 | 0.9319 | 1 | | 0.0637 | 0.2478 | 0.9266 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
tux/ppo-SnowballTarget2
tux
2023-06-18T11:59:44Z
19
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-06-18T11:59:33Z
--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: tux/ppo-SnowballTarget2 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
aldoredo/asssaaaac
aldoredo
2023-06-18T11:54:26Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-18T11:48:17Z
--- license: creativeml-openrail-m ---
husienburgir/gabagthaupdate
husienburgir
2023-06-18T11:13:31Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-18T11:10:04Z
--- license: creativeml-openrail-m ---
minoosh/videomae-base-finetuned-IEMOCAP_5
minoosh
2023-06-18T11:12:39Z
61
0
transformers
[ "transformers", "pytorch", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
video-classification
2023-06-18T06:32:34Z
--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-IEMOCAP_5 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. --> # videomae-base-finetuned-IEMOCAP_5 This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3229 - Accuracy: 0.3770 ## 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 - lr_scheduler_warmup_ratio: 0.1 - training_steps: 4280 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3642 | 0.1 | 429 | 1.4078 | 0.1970 | | 1.3244 | 1.1 | 858 | 1.4578 | 0.3052 | | 1.3623 | 2.1 | 1287 | 1.4071 | 0.2314 | | 1.3422 | 3.1 | 1716 | 1.3474 | 0.2896 | | 1.2483 | 4.1 | 2145 | 1.3597 | 0.3127 | | 1.3581 | 5.1 | 2574 | 1.3512 | 0.2639 | | 1.3106 | 6.1 | 3003 | 1.3295 | 0.2896 | | 1.341 | 7.1 | 3432 | 1.3132 | 0.3433 | | 1.2438 | 8.1 | 3861 | 1.2732 | 0.3859 | | 1.2438 | 9.1 | 4280 | 1.2643 | 0.3715 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
Middelz2/roberta-large-aphasia-narration_eps_10
Middelz2
2023-06-18T11:11:24Z
3
0
transformers
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-06-18T10:22:55Z
--- license: mit tags: - generated_from_keras_callback model-index: - name: Middelz2/roberta-large-aphasia-narration_eps_10 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. --> # Middelz2/roberta-large-aphasia-narration_eps_10 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0040 - Validation Loss: 0.8973 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.5221 | 1.3073 | 0 | | 1.2893 | 1.1813 | 1 | | 1.2003 | 1.1133 | 2 | | 1.1405 | 1.0744 | 3 | | 1.0984 | 1.0507 | 4 | | 1.0587 | 0.9995 | 5 | | 1.0522 | 0.9747 | 6 | | 1.0279 | 0.9341 | 7 | | 1.0190 | 0.9423 | 8 | | 1.0040 | 0.8973 | 9 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
JamieLuta/j
JamieLuta
2023-06-18T10:58:17Z
0
0
null
[ "en", "dataset:tiiuae/falcon-refinedweb", "region:us" ]
null
2023-06-18T10:55:04Z
--- datasets: - tiiuae/falcon-refinedweb language: - en ---
Aji3beberapa/Felisha_V1
Aji3beberapa
2023-06-18T10:52:26Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-18T10:49:04Z
--- license: creativeml-openrail-m ---
TheBloke/gpt4-x-alpaca-13B-GGML
TheBloke
2023-06-18T10:51:02Z
0
12
null
[ "license:other", "region:us" ]
null
2023-06-18T10:23:12Z
--- inference: false license: other --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Chavinlo's GPT4-X-Alpaca GGML These files are GGML format model files for [Chavinlo's GPT4-X-Alpaca](https://huggingface.co/chavinlo/gpt4-x-alpaca). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Repositories available * [4bit's 4-bit GPTQ models for GPU inference](https://huggingface.co/4bit/gpt4-x-alpaca-13b-native-4bit-128g-cuda) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/gpt4-x-alpaca-13B-GGML) * [Chavinlo's unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/chavinlo/gpt4-x-alpaca) ## Prompt template ``` Below is an instruction that describes a task. Write a response that appropriately completes the request ### Instruction: prompt ### Response: ``` <!-- compatibility_ggml start --> ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`. These are guaranteed to be compatbile with any UIs, tools and libraries released since late May. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt. ## Explanation of the new k-quant methods The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. <!-- compatibility_ggml end --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | gpt4-x-alpaca-13b.ggmlv3.q2_K.bin | q2_K | 2 | 5.51 GB | 8.01 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. | | gpt4-x-alpaca-13b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 6.93 GB | 9.43 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | gpt4-x-alpaca-13b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.31 GB | 8.81 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | gpt4-x-alpaca-13b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.66 GB | 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors | | gpt4-x-alpaca-13b.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB | 9.82 GB | Original llama.cpp quant method, 4-bit. | | gpt4-x-alpaca-13b.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB | 10.64 GB | Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | gpt4-x-alpaca-13b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.87 GB | 10.37 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K | | gpt4-x-alpaca-13b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.37 GB | 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors | | gpt4-x-alpaca-13b.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB | 11.45 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | gpt4-x-alpaca-13b.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB | 12.26 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. | | gpt4-x-alpaca-13b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.23 GB | 11.73 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K | | gpt4-x-alpaca-13b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 8.97 GB | 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors | | gpt4-x-alpaca-13b.ggmlv3.q6_K.bin | q6_K | 6 | 10.68 GB | 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors | | gpt4-x-alpaca-13b.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB | 16.33 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m gpt4-x-alpaca-13b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. **Patreon special mentions**: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Chavinlo's GPT4-X-Alpaca # GPT4 x Alpaca As a base model we used: https://huggingface.co/chavinlo/alpaca-13b Finetuned on GPT4's responses, for 3 epochs. NO LORA Please do note that the configurations files maybe messed up, this is because of the trainer I used. I WILL NOT EDIT THEM because there are repos hat automatically fix this, changing it might break it. Generally you just need to change anything that's under the name of "LLaMa" to "Llama" NOTE THE UPPER AND LOWER CASE!!!!
ikaith/Reinforce-v2
ikaith
2023-06-18T10:39:26Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T10:39:12Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-v2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -2.70 +/- 0.46 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
leo1452/Reinforce-CartPole
leo1452
2023-06-18T10:32:20Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T10:32:10Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 199.90 +/- 14.80 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
fanshiyu/111
fanshiyu
2023-06-18T10:27:01Z
0
0
adapter-transformers
[ "adapter-transformers", "text-classification", "ab", "dataset:fka/awesome-chatgpt-prompts", "dataset:OpenAssistant/oasst1", "dataset:QingyiSi/Alpaca-CoT", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:togethercomputer/RedPajama-Data-1T-Sample", "dataset:GAIR/lima", "dataset:conceptofmind/FLAN_2022", "dataset:h2oai/openassistant_oasst1", "arxiv:1910.09700", "license:creativeml-openrail-m", "region:us" ]
text-classification
2023-06-16T07:31:21Z
--- license: creativeml-openrail-m datasets: - fka/awesome-chatgpt-prompts - OpenAssistant/oasst1 - QingyiSi/Alpaca-CoT - togethercomputer/RedPajama-Data-1T - togethercomputer/RedPajama-Data-1T-Sample - GAIR/lima - conceptofmind/FLAN_2022 - h2oai/openassistant_oasst1 language: - ab metrics: - accuracy - bertscore library_name: adapter-transformers pipeline_tag: text-classification --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ikaith/Reinforce-v0
ikaith
2023-06-18T10:21:11Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T10:21:04Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 100.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
PavanKumar05/my_model
PavanKumar05
2023-06-18T10:19:13Z
61
0
transformers
[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-06-18T10:06:54Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: PavanKumar05/my_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # PavanKumar05/my_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5266 - Validation Loss: 1.7459 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.4140 | 2.0786 | 0 | | 1.7743 | 1.7459 | 1 | | 1.5266 | 1.7459 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
Middelz2/roberta-large-aphasia-narration_eps_6
Middelz2
2023-06-18T10:17:42Z
3
0
transformers
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-06-18T09:08:44Z
--- license: mit tags: - generated_from_keras_callback model-index: - name: Middelz2/roberta-large-aphasia-narration_eps_6 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. --> # Middelz2/roberta-large-aphasia-narration_eps_6 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0917 - Validation Loss: 1.0443 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.5148 | 1.3056 | 0 | | 1.2794 | 1.1696 | 1 | | 1.1875 | 1.0934 | 2 | | 1.1245 | 1.0617 | 3 | | 1.0917 | 1.0443 | 4 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
soramame123/open-calm-fine-tune
soramame123
2023-06-18T10:17:25Z
0
0
null
[ "license:cc-by-nc-nd-4.0", "region:us" ]
null
2023-06-18T10:17:25Z
--- license: cc-by-nc-nd-4.0 ---
Mustru/QQQRINKAPPP
Mustru
2023-06-18T10:16:37Z
0
0
null
[ "license:bigcode-openrail-m", "region:us" ]
null
2023-06-18T10:08:37Z
--- license: bigcode-openrail-m ---
Muizzah/anastasiaa
Muizzah
2023-06-18T10:15:07Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-18T10:07:56Z
--- license: creativeml-openrail-m ---
Mustru/KASUYA_BAIAN
Mustru
2023-06-18T10:09:48Z
0
0
null
[ "license:bigcode-openrail-m", "region:us" ]
null
2023-06-18T10:02:18Z
--- license: bigcode-openrail-m ---
leonhe/Reinforce-CartPole-v1
leonhe
2023-06-18T10:04:43Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T10:01:57Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
aga3134/Pixelcopter-PLE-v0
aga3134
2023-06-18T09:56:42Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T09:12:41Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 25.30 +/- 17.01 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
kkmitra/ppo-lunarland-rl-learning
kkmitra
2023-06-18T09:55:05Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T09:39:46Z
--- 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: 79.17 +/- 124.26 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 ... ```
radyad/diff_model
radyad
2023-06-18T09:52:13Z
119
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:mlqa", "endpoints_compatible", "region:us" ]
question-answering
2023-06-18T09:23:04Z
--- tags: - generated_from_trainer datasets: - mlqa model-index: - name: diff_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # diff_model This model is a fine-tuned version of [huggingface-course/bert-finetuned-squad](https://huggingface.co/huggingface-course/bert-finetuned-squad) on the mlqa dataset. It achieves the following results on the evaluation set: - Loss: 1.1014 ## 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 | 355 | 0.8941 | | 0.931 | 2.0 | 710 | 0.9768 | | 0.4794 | 3.0 | 1065 | 1.1014 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
nini123/gt
nini123
2023-06-18T09:49:16Z
0
0
null
[ "license:bigscience-openrail-m", "region:us" ]
null
2023-06-18T09:48:09Z
--- license: bigscience-openrail-m ---
dungtd2403/poca-SoccerTwos
dungtd2403
2023-06-18T09:33:50Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-06-18T09:07:14Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://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-SoccerTwos 2. Step 1: Write your model_id: dungtd2403/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
HyunjooCheong/my_awesome_qa_model
HyunjooCheong
2023-06-18T09:21:31Z
111
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:covid_qa_deepset", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-06-16T14:02:39Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - covid_qa_deepset model-index: - name: my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the covid_qa_deepset dataset. It achieves the following results on the evaluation set: - Loss: 0.8725 ## 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 | 100 | 0.9837 | | No log | 2.0 | 200 | 0.9051 | | No log | 3.0 | 300 | 0.8725 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
madhavgarg/ppo-LunarLander-v2
madhavgarg
2023-06-18T09:17:32Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T09:17:11Z
--- 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: 268.49 +/- 21.88 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 ... ```
agustd00/jamie
agustd00
2023-06-18T09:15:32Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-18T09:13:22Z
--- license: creativeml-openrail-m ---
cxero03/selvis
cxero03
2023-06-18T08:13:39Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-17T19:40:39Z
--- license: creativeml-openrail-m ---
trojblue/sd-finetune-KNDiffusion
trojblue
2023-06-18T08:12:32Z
0
0
null
[ "region:us" ]
null
2023-01-17T12:48:19Z
# Kanade! training: - 250 images on yoisaki kanade, with wd1.4+booru tags, merged with other models - 786 ARB; EMA; fp32; clip2 - 2e-6 CosineAnnealing - augmentations: brightness/contrast/crop/flip tested on: **clip1**, DDIM, 448x512 latent hires(2x), DDIM, step 25 keyword: `yoisaki kanade, 25-ji night code de. \(project sekai\)` files: - `knd_sd_e19_ema.ckpt`: crude DreamBooth file epoch 19, using evt-v4 base - `knd_sd_e19_ema.ckpt`: same thing but epoch 29 - `KNDiffusion_fp32_no_vae.safetensors`: tuned model that slightly resembles kanade - (KNDiffusion = phfa_knd29_evt4_030) samples: [image1](https://huggingface.co/trojblue/KNDiffusion/resolve/main/samples/00168-773909389-DDIM-step25-cfg6.5-phfa_knd29_evt4_030-fbf412b2-20230117_101156_902795.png) ``` yoisaki kanade, 25-ji night code de. \(project sekai\), 1girl, close-up, solo, long hair, headphones, blue eyes, jacket, looking at viewer, hair between eyes, shirt, long sleeves, blue jacket, collarbone, bangs, chair, sitting, track jacket, black shirt, grey jacket, grey shirt, indoors, open clothes, open jacket, open mouth, straight hair, upper body, very long hair, white hair, project sekai, highres ``` [image2](https://huggingface.co/trojblue/KNDiffusion/resolve/main/samples/00167-3301161699-DDIM-step25-cfg6.5-phfa_knd29_evt4_030-fbf412b2-20230117_100910_391039.png) ``` yoisaki kanade, 25-ji night code de. (project sekai), 1girl, solo, long hair, blue eyes, jacket, sleeves past wrists, very long hair, collarbone, white background, bangs, blush, blue jacket, hair between eyes, long sleeves, looking at viewer, sleeves past fingers, simple background, parted lips, open jacket, black shirt, shirt, open clothes, :o, cowboy shot, grey hair, hand up, o, project sekai, highres ``` sample configs: ``` Negative prompt: nsfw, text, error, signature, watermark, username, realistic,3d,(large breast), multiple people, animals, lowres, cropped, worth quality, low quality, normal quality, jpeg artifacts, blurry, bad anatomy, bad hands, bad arms, bad feet, bad anatomy, missing fingers, extra digits, fewer digits, long neck, missing legs, huge person, optical_illusion Steps: 25, Sampler: DDIM, CFG scale: 6.5, Seed: 773909389, Size: 448x512, Model: KNDiffusion_fp32_no_vae, Denoising strength: 0.7, ENSD: 31338, Hires upscale: 2, Hires upscaler: Latent (bicubic) ```
husienburgir/gabagth
husienburgir
2023-06-18T07:48:51Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-18T07:35:37Z
--- license: creativeml-openrail-m ---
NonCute/au11_bu
NonCute
2023-06-18T07:00:38Z
0
0
null
[ "region:us" ]
null
2023-06-18T07:00:38Z
Temporary Redirect. Redirecting to /NonCute/AU11_BU/resolve/main/README.md
HITMYM/practice_swin1
HITMYM
2023-06-18T06:02:10Z
235
0
transformers
[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-06-15T06:40:03Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: SEMDataset split: train args: SEMDataset metrics: - name: Accuracy type: accuracy value: 0.782051282051282 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5657 - Accuracy: 0.7821 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1465 | 0.97 | 16 | 1.8341 | 0.3462 | | 1.7722 | 2.0 | 33 | 1.5865 | 0.4017 | | 1.6005 | 2.97 | 49 | 1.4867 | 0.4060 | | 1.429 | 4.0 | 66 | 1.3933 | 0.4487 | | 1.2294 | 4.97 | 82 | 1.2696 | 0.5385 | | 1.1224 | 6.0 | 99 | 1.2842 | 0.5641 | | 0.9776 | 6.97 | 115 | 0.9923 | 0.6197 | | 0.8678 | 8.0 | 132 | 1.1118 | 0.6368 | | 0.8125 | 8.97 | 148 | 0.8974 | 0.6624 | | 0.7022 | 10.0 | 165 | 0.8582 | 0.6838 | | 0.6047 | 10.97 | 181 | 0.7019 | 0.7393 | | 0.6223 | 12.0 | 198 | 0.6818 | 0.7308 | | 0.5331 | 12.97 | 214 | 0.8265 | 0.7051 | | 0.4995 | 14.0 | 231 | 0.6365 | 0.7521 | | 0.4132 | 14.97 | 247 | 0.6585 | 0.7308 | | 0.3978 | 16.0 | 264 | 0.5789 | 0.7692 | | 0.3388 | 16.97 | 280 | 0.6038 | 0.7650 | | 0.3376 | 18.0 | 297 | 0.5306 | 0.7821 | | 0.3455 | 18.97 | 313 | 0.5797 | 0.7692 | | 0.3207 | 19.39 | 320 | 0.5657 | 0.7821 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
wp931120x/baichuan_4bit_lora
wp931120x
2023-06-18T05:25:14Z
0
5
null
[ "license:unknown", "region:us" ]
null
2023-06-16T14:24:07Z
--- license: unknown --- ### Motivation 项目的主要动机由于百川baichuan -7B是一个pretrain的大模型，尽管它在一些无监督的评估数据集上效果很好，但是并不能开箱即用，因为它没有 supervised finetune 这一步，没有和人类意图进行对齐。 随采用belle 0.5M 指令微调数据，采用qlora的量化微调的方式对百川大模型进行人类意图对齐训练。 ### 大模型 百川7B https://huggingface.co/baichuan-inc/baichuan-7B ### sft 数据集 采用的是belle 0.5M https://huggingface.co/datasets/BelleGroup/train_0.5M_CN 训练方法和过程可视化 + 先将百川LLM 采用qlora的 nf4 和双重量化方式进行量化 + 在采用lora进行指令微调 + 训练过程采用tensorborad 可视化,执行下方代码即可在localhost:6006去监控你的训练和测试loss tensorboard --logdir ./runs/ --bind_all ### 资源消耗 由于采用了int4量化和lora等技术 整个资源消耗只需要12G左右的显存 ### 预测代码 ``` from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import BitsAndBytesConfig from peft import ( LoraConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training, set_peft_model_state_dict, ) import torch ###加载量化模型 device_map = {"": 0} tokenizer = AutoTokenizer.from_pretrained("./baichuan-7B",trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("./baichuan-7B", trust_remote_code=True, quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4' ), device_map=device_map) ### 组装lora LORA_WEIGHTS = "./baichuansft/" device = "cuda:0" model_lora = PeftModel.from_pretrained( model, LORA_WEIGHTS ).to(device) ### 进行预测 device = "cuda:0" from transformers import GenerationConfig generation_config = GenerationConfig( temperature=0.2, top_p = 0.85, do_sample = True, repetition_penalty=2.0, max_new_tokens=1024, # max_length=max_new_tokens+input_sequence ) prompt = """ 北京有啥好玩的地方 """ inputttext ="""###Human:\n{}###Assistant:\n: """.format(prompt) inputs = tokenizer(prompt,return_tensors="pt").to(device) generate_ids = model_lora.generate(**inputs, generation_config=generation_config) output = tokenizer.decode(generate_ids[0]) print(output) ``` ### sft后的效果 微调前: human : 台湾有什么好吃的 bot: 台北有哪些值得一去的景点? human :北京有啥好玩的地方 bot: < href="http://www.bj-tourisme" target=_blank>北京旅游网<BR 微调后： human : 台湾有什么好吃的 bot: 1.蚵仔煎(Oyster Pancake)：用鸡蛋和面粉混合后摊成薄饼，再加入葱花、猪肉末等佐料。最后将生蚝肉放在上面即可食用;20世纪初由闽南移民传入台北市的传统小吃之一 ;3大肠包饭 (Da Dang Bao): 大米蒸熟后再加酱油调味并配以猪肚或鸡肝做成的馅儿,然后把这些食材包裹在糯香的大白菜叶子中一起煮制而成的一种美食 ,是当地人最爱的早餐食品！ human : 北京有啥好玩的地方 bot: 1.故宫博物院：中国古代皇家建筑群，是明清两代的皇帝居住和办公场所。门票价格为60元/人次(淡季)、85 元 / 人 (旺季节假日期间);开放时间: 周二至周日9点-下午4点半;周一闭馆维护检修不对外售票,请提前购置参观券或预约入场方式进园游览观光拍照留念等活动需在指定区域内进行不得进入其他禁区及文物保护单位范围内游玩以免破坏古迹造成损失影响安全问题后果自负!2北京天坛公园位于北京市中心东城区永定门外大街南侧占地面积约73万平方米是中国现存最大的祭祀性园林之一也是世界上保存最完整的天文台遗址！免费景点无需购买任何费用但需要排队等待安保人员检查后方可通行建议游客最好早到避免长时间拥堵交通高峰期前往景区观赏美景感受历史文化魅力~ ### Reference https://github.com/artidoro/qlora https://github.com/LianjiaTech/BELLE
jcnecio/a2c-PandaReachDense-v2
jcnecio
2023-06-18T04:10:30Z
6
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-06-17T06:28:46Z
--- 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: -1.03 +/- 0.42 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 ... ```
hahunavth/stt_en_conformer_ctc_small
hahunavth
2023-06-18T04:03:11Z
2
0
nemo
[ "nemo", "arxiv:1910.09700", "model-index", "region:us" ]
null
2023-06-18T03:35:14Z
--- model-index: - name: hahunavth/stt_en_conformer_ctc_small results: - task: type: automatic-speech-recognition dataset: name: Librispeech (clean) type: librispeech_asr config: other split: test args: language: en metrics: - type: wer value: 8.1 name: WER --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
GraydientPlatformAPI/model_161
GraydientPlatformAPI
2023-06-18T04:03:01Z
29
0
diffusers
[ "diffusers", "text-to-image", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-06-18T01:34:07Z
--- library_name: diffusers pipeline_tag: text-to-image ---
savvamadar/pygmalion-13b-f16-ggml-ggjt
savvamadar
2023-06-18T03:37:29Z
0
0
null
[ "license:other", "region:us" ]
null
2023-06-18T00:52:44Z
--- license: other --- Same license as: https://huggingface.co/PygmalionAI/pygmalion-13b
Gladiator/microsoft-deberta-v3-large_ner_conll2003
Gladiator
2023-06-18T03:19:31Z
5,553
5
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "deberta-v2", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-09T05:19:42Z
--- license: mit tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: microsoft-deberta-v3-large_ner_conll2003 results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9667057052032793 - name: Recall type: recall value: 0.972399865365197 - name: F1 type: f1 value: 0.9695444248678582 - name: Accuracy type: accuracy value: 0.9945095595965889 --- <!-- 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. --> # microsoft-deberta-v3-large_ner_conll2003 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0293 - Precision: 0.9667 - Recall: 0.9724 - F1: 0.9695 - Accuracy: 0.9945 ## 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: cosine - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0986 | 1.0 | 878 | 0.0323 | 0.9453 | 0.9596 | 0.9524 | 0.9921 | | 0.0212 | 2.0 | 1756 | 0.0270 | 0.9571 | 0.9675 | 0.9623 | 0.9932 | | 0.009 | 3.0 | 2634 | 0.0280 | 0.9638 | 0.9714 | 0.9676 | 0.9940 | | 0.0035 | 4.0 | 3512 | 0.0290 | 0.9657 | 0.9712 | 0.9685 | 0.9943 | | 0.0022 | 5.0 | 4390 | 0.0293 | 0.9667 | 0.9724 | 0.9695 | 0.9945 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
j-hyeok/PPO-LunarLander-v2
j-hyeok
2023-06-18T02:53:41Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-06-18T02:44:25Z
--- 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: -421.57 +/- 202.27 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 ... ```
leonelhs/faceshine
leonelhs
2023-06-18T02:49:42Z
0
2
null
[ "photo", "photo-restoration", "photo-enhancement", "colorize-black-and-white", "license:mit", "region:us" ]
null
2023-06-07T20:40:43Z
--- license: mit tags: - photo - photo-restoration - photo-enhancement - colorize-black-and-white --- ## Face Shine Server ### This project is an AI Photo Enhancer running over Pytorch ## Features - [x] Face enhancement - [x] Super Resolution - [x] Erase Scratches - [x] Colorize Photo - [x] Enhance lights - [x] Clear background ### Install ```console foo@bar:~$ pip install faceshine foo@bar:~$ faceshine ```
jobeid1/ppo-SnowballTarget
jobeid1
2023-06-18T02:10:54Z
11
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-06-18T02:01:26Z
--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jobeid1/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
mgmeskill/dqn-SpaceInvadersNoFrameskip-v4
mgmeskill
2023-06-18T02:09:49Z
7
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-06-17T21:50:57Z
--- 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: 217.00 +/- 77.24 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 mgmeskill -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 mgmeskill -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 mgmeskill ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 1000000), ('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', 5000), ('n_timesteps', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 5000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
andrewsiah/ppo-Huggy
andrewsiah
2023-06-18T01:04:03Z
2
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-06-18T00:53:32Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: andrewsiah/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
ariefrmdhani/eunchaeya
ariefrmdhani
2023-06-18T01:03:29Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-18T01:03:29Z
--- license: creativeml-openrail-m ---
OriJynn/Orb_Weaver
OriJynn
2023-06-18T00:18:50Z
0
0
null
[ "stablediffusionapi.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-06-17T22:03:22Z
--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Orb-Weaver V1 ![generated from stablediffusionapi.com]() ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "Orb-Weaver" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Model link: [View model](https://stablediffusionapi.com/models/Orb-Weaver) Credits: [View credits](https://civitai.com/?query=orb%20weaver%20v1) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v3/dreambooth" payload = json.dumps({ "key": "", "model_id": "orb-weaver", "prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text)
Trickshotblaster/epic-qa
Trickshotblaster
2023-06-17T23:58:20Z
61
0
transformers
[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-06-17T23:43:53Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: epic-qa 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. --> # epic-qa This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5505 - Validation Loss: 1.1748 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.5505 | 1.1748 | 0 | ### Framework versions - Transformers 4.31.0.dev0 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
aitestcoder/distilroberta-base-finetuned-wikitext2
aitestcoder
2023-06-17T23:54:10Z
178
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-06-17T23:27:00Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8268 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1005 | 1.0 | 1203 | 1.9467 | | 2.034 | 2.0 | 2406 | 1.8616 | | 1.9683 | 3.0 | 3609 | 1.8253 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.13.0 - Tokenizers 0.13.3
Ankurkhurana03/q-FrozenLake-v1-4x4-noSlippery
Ankurkhurana03
2023-06-17T23:52:13Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-06-17T22:44:37Z
--- 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="Ankurkhurana03/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"])
multitude0099/ppo-LunarLanderV2
multitude0099
2023-06-17T23:40:20Z
3
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-06-14T02:15:23Z
--- 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: 282.96 +/- 19.73 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 ... ```
aitestcoder/distilgpt2-finetuned-wikitext2
aitestcoder
2023-06-17T23:18:09Z
213
0
transformers
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-06-17T21:30:55Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6533 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7982 | 1.0 | 1167 | 3.6808 | | 3.6947 | 2.0 | 2334 | 3.6584 | | 3.6478 | 3.0 | 3501 | 3.6533 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.13.0 - Tokenizers 0.13.3
savvamadar/pygmalion-7b-f16-ggml-ggjt
savvamadar
2023-06-17T23:06:37Z
0
0
null
[ "license:other", "region:us" ]
null
2023-06-17T21:27:49Z
--- license: other --- Same license as this: https://huggingface.co/PygmalionAI/pygmalion-7b
adisrini11/my_awesome_model
adisrini11
2023-06-17T22:49:00Z
105
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-06-14T07:34:51Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy model-index: - name: my_awesome_model results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval config: emotion split: test args: emotion metrics: - name: Accuracy type: accuracy value: 0.7909922589725545 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.5708 - Accuracy: 0.7910 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 204 | 0.6426 | 0.7797 | | No log | 2.0 | 408 | 0.5708 | 0.7910 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
VladmirPutgang/ppo-Lunar-Lander-v2
VladmirPutgang
2023-06-17T22:45:56Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-06-17T18:47:00Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 293.95 +/- 14.02 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 ... ```
Duino/duino_bot
Duino
2023-06-17T20:52:33Z
0
0
null
[ "license:bigscience-bloom-rail-1.0", "region:us" ]
null
2023-06-17T14:52:27Z
--- license: bigscience-bloom-rail-1.0 ---
iherbos/astrantsia
iherbos
2023-06-17T19:40:16Z
0
0
null
[ "region:us" ]
null
2023-06-17T19:40:16Z
Temporary Redirect. Redirecting to /iherbos/Astrantsia/resolve/main/README.md
minoosh/videomae-base-finetuned-IEMOCAP_2
minoosh
2023-06-17T19:22:21Z
59
0
transformers
[ "transformers", "pytorch", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
video-classification
2023-06-17T14:36:03Z
--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-IEMOCAP_2 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. --> # videomae-base-finetuned-IEMOCAP_2 This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3381 - Accuracy: 0.3434 ## 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 - lr_scheduler_warmup_ratio: 0.1 - training_steps: 4500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3215 | 0.1 | 451 | 1.4351 | 0.2622 | | 1.3236 | 1.1 | 902 | 1.3517 | 0.3579 | | 1.2642 | 2.1 | 1353 | 1.4280 | 0.2982 | | 1.2741 | 3.1 | 1804 | 1.3943 | 0.3012 | | 1.2655 | 4.1 | 2255 | 1.3665 | 0.3311 | | 1.1476 | 5.1 | 2706 | 1.3808 | 0.3293 | | 1.2231 | 6.1 | 3157 | 1.3216 | 0.3573 | | 1.2715 | 7.1 | 3608 | 1.3162 | 0.3720 | | 1.3088 | 8.1 | 4059 | 1.2985 | 0.3982 | | 1.2636 | 9.1 | 4500 | 1.2666 | 0.4098 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
minoosh/videomae-base-finetuned-IEMOCAP_1
minoosh
2023-06-17T19:06:26Z
60
0
transformers
[ "transformers", "pytorch", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
video-classification
2023-06-17T09:07:09Z
--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-IEMOCAP_1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-IEMOCAP_1 This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3409 - Accuracy: 0.3480 ## 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 - lr_scheduler_warmup_ratio: 0.1 - training_steps: 4440 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.35 | 0.1 | 445 | 1.4144 | 0.2478 | | 1.3944 | 1.1 | 890 | 1.3677 | 0.3340 | | 1.2676 | 2.1 | 1335 | 1.3613 | 0.3434 | | 1.2343 | 3.1 | 1780 | 1.3674 | 0.3289 | | 1.222 | 4.1 | 2225 | 1.3379 | 0.3522 | | 1.3494 | 5.1 | 2670 | 1.3466 | 0.3421 | | 1.2836 | 6.1 | 3115 | 1.3277 | 0.3591 | | 1.226 | 7.1 | 3560 | 1.3132 | 0.3704 | | 1.3174 | 8.1 | 4005 | 1.3001 | 0.3604 | | 1.2933 | 9.1 | 4440 | 1.2912 | 0.3629 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
CreatorPhan/ViQA-small
CreatorPhan
2023-06-17T18:40:13Z
107
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "vi", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-06-17T16:58:15Z
--- language: - vi pipeline_tag: text2text-generation # inference: # parameters: # function_to_apply: "none" widget: - text: >- Trả lời câu hỏi: Công dụng của paracetamol? Trong nội dung: PARACETAMOL DẠNG UỐNG – HƯỚNG DẪN SỬ DỤNG AN TOÀN, HỢP LÝ Trong tình hình diễn biến phức tạp của dịch COVID-19, các thuốc giảm đau hạ sốt thông dụng như Paracetamol được người dân mua về dự trữ trong hộp thuốc gia đình với mục đích phòng dịch. Tuy nhiên, việc sử dụng thuốc hợp lý và đúng cách đôi khi chưa được chú ý, vì vậy việc hiểu và sử dụng thuốc Paracetamol an toàn là rất cần thiết. I. Tổng quan thuốc Paracetamol - Paracetamol dạng uống là thuốc thuộc nhóm giảm đau, hạ sốt và nằm trong danh mục thuốc không kê đơn của Bộ Y tế. Chính vì vậy Paracetamol rất phổ biến trên thị trường với nhiều chế phẩm có dạng bào chế và hàm lượng từ thấp đến cao. - Tác dụng chính của Paracetamol là giảm đau, hạ sốt nên thuốc được sử dụng rộng rãi trong điều trị các chứng đau và sốt từ nhẹ đến vừa như: cảm cúm, nhức đầu, đau bụng, đau nhức… - Thuốc không nên sử dụng cho những người dị ứng với Paracetamol, người suy gan nặng. II. Nguy cơ khi sử dụng Paracetamol - Việc Paracetamol được sử dụng rộng rãi cùng với tâm lý chủ quan, thiếu nhận thức dẫn đến việc quá liều thuốc gây nên các tác dụng phụ không mong muốn, trong đó nguy hiểm nhất là tình trạng hoại tử gan, có thể dẫn đến tử vong nếu không được xử trí kịp thời. - Nguyên nhân gây ngộ độc gan khi sử dụng Paracetamol quá liều là nồng độ NAPQI (sinh ra do Paracetamol chuyển hóa qua gan) không thể chuyển hóa hết và tích luỹ gây độc cho gan. - Các biểu hiện ngộ độc gan do Paracetamol có thể là: ban đầu là buồn nôn, nôn, đau bụng, sau đó nguy kich hơn có thể kích động, hôn mê, mạch huyết áp không ổn định… có thể nguy cơ tử vong. - text: >- Trả lời câu hỏi: Tòa nhà cao nhất Việt Nam? Trong nội dung: The Landmark 81 là một toà nhà chọc trời trong tổ hợp dự án Vinhomes Tân Cảng , một dự án có tổng mức đầu tư 40.000 tỷ đồng , do Công ty Cổ phần Đầu tư xây dựng Tân Liên Phát thuộc Vingroup làm chủ đầu tư . Toà tháp cao 81 tầng , hiện tại là toà nhà cao nhất Việt Nam và là toà nhà cao nhất Đông Nam Á từ tháng 3 năm 2018 . Toà tháp cao 81 tầng , hiện tại là toà nhà cao nhất Việt Nam và là toà nhà cao nhất Đông Nam Á từ tháng 3 năm 2018 . Dự án được xây dựng ở Tân Cảng , quận Bình Thạnh , ven sông Sài Gòn . Dự án được khởi công ngày 26/07/2014 . --- Mô hình này được tuning từ pretrained ViFlanT5-small model với 77M tham số với 2 epochs trên 87GB text của bộ CC100. Mô hình được huấn luyện cho tác vụ đọc hiểu tiếng Việt. Cung cấp cho mô hình câu hỏi và ngữ cảnh (không quá 400 từ) và mô hình sẽ trích xuất ra câu trả lời trong ngữ cảnh đó. ``` from transformers import AutoTokenizer, T5ForConditionalGeneration device = 'cpu' model_path = "CreatorPhan/ViQA-small" model = T5ForConditionalGeneration.from_pretrained(model_path).to(device) tokenizer = AutoTokenizer.from_pretrained(model_path) context = """ PARACETAMOL DẠNG UỐNG – HƯỚNG DẪN SỬ DỤNG AN TOÀN, HỢP LÝ Trong tình hình diễn biến phức tạp của dịch COVID-19, các thuốc giảm đau hạ sốt thông dụng như Paracetamol được người dân mua về dự trữ trong hộp thuốc gia đình với mục đích phòng dịch. Tuy nhiên, việc sử dụng thuốc hợp lý và đúng cách đôi khi chưa được chú ý, vì vậy việc hiểu và sử dụng thuốc Paracetamol an toàn là rất cần thiết. I. Tổng quan thuốc Paracetamol - Paracetamol dạng uống là thuốc thuộc nhóm giảm đau, hạ sốt và nằm trong danh mục thuốc không kê đơn của Bộ Y tế. Chính vì vậy Paracetamol rất phổ biến trên thị trường với nhiều chế phẩm có dạng bào chế và hàm lượng từ thấp đến cao. - Tác dụng chính của Paracetamol là giảm đau, hạ sốt nên thuốc được sử dụng rộng rãi trong điều trị các chứng đau và sốt từ nhẹ đến vừa như: cảm cúm, nhức đầu, đau bụng, đau nhức… - Thuốc không nên sử dụng cho những người dị ứng với Paracetamol, người suy gan nặng. II. Nguy cơ khi sử dụng Paracetamol - Việc Paracetamol được sử dụng rộng rãi cùng với tâm lý chủ quan, thiếu nhận thức dẫn đến việc quá liều thuốc gây nên các tác dụng phụ không mong muốn, trong đó nguy hiểm nhất là tình trạng hoại tử gan, có thể dẫn đến tử vong nếu không được xử trí kịp thời. - Nguyên nhân gây ngộ độc gan khi sử dụng Paracetamol quá liều là nồng độ NAPQI (sinh ra do Paracetamol chuyển hóa qua gan) không thể chuyển hóa hết và tích luỹ gây độc cho gan. - Các biểu hiện ngộ độc gan do Paracetamol có thể là: ban đầu là buồn nôn, nôn, đau bụng, sau đó nguy kich hơn có thể kích động, hôn mê, mạch huyết áp không ổn định… có thể nguy cơ tử vong. """ question = "Công dụng của paracetamol?" prompt = f"Trả lời câu hỏi: {question} Trong nội dung: {context}" tokens = tokenizer(prompt, return_tensors='pt').input_ids output = model.generate(tokens.to(device), max_new_tokens=170)[0] predict = tokenizer.decode(output, skip_special_tokens=True) print(len(predict.split())) print(predict) ```