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qgallouedec/a2c-Walker2DBulletEnv-v0-3640112043
qgallouedec
2023-02-27T13:53:17Z
0
0
stable-baselines3
[ "stable-baselines3", "Walker2DBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:52:30Z
--- library_name: stable-baselines3 tags: - Walker2DBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Walker2DBulletEnv-v0 type: Walker2DBulletEnv-v0 metrics: - type: mean_reward value: 573.31 +/- 411.44 name: mean_reward verified: false --- # **A2C** Agent playing **Walker2DBulletEnv-v0** This is a trained model of a **A2C** agent playing **Walker2DBulletEnv-v0** 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 a2c --env Walker2DBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env Walker2DBulletEnv-v0 -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 a2c --env Walker2DBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env Walker2DBulletEnv-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env Walker2DBulletEnv-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env Walker2DBulletEnv-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.0), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_0.00096'), ('max_grad_norm', 0.5), ('n_envs', 4), ('n_steps', 8), ('n_timesteps', 2000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
chronbmm/xlm-roberta-vedic
chronbmm
2023-02-27T13:49:58Z
5
0
transformers
[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
feature-extraction
2023-02-27T13:43:32Z
A model for Vedic Sanskrit based on XLM-RoBERTa-base. Accepts Devanagari as input.
qgallouedec/a2c-BipedalWalker-v3-3269560138
qgallouedec
2023-02-27T13:45:38Z
0
0
stable-baselines3
[ "stable-baselines3", "BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:44:19Z
--- library_name: stable-baselines3 tags: - BipedalWalker-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BipedalWalker-v3 type: BipedalWalker-v3 metrics: - type: mean_reward value: 281.54 +/- 1.24 name: mean_reward verified: false --- # **A2C** Agent playing **BipedalWalker-v3** This is a trained model of a **A2C** agent playing **BipedalWalker-v3** 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 a2c --env BipedalWalker-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env BipedalWalker-v3 -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 a2c --env BipedalWalker-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env BipedalWalker-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env BipedalWalker-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env BipedalWalker-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.0), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_0.00096'), ('max_grad_norm', 0.5), ('n_envs', 16), ('n_steps', 8), ('n_timesteps', 5000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/a2c-HalfCheetahBulletEnv-v0-2025636415
qgallouedec
2023-02-27T13:43:27Z
0
0
stable-baselines3
[ "stable-baselines3", "HalfCheetahBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:42:33Z
--- library_name: stable-baselines3 tags: - HalfCheetahBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: HalfCheetahBulletEnv-v0 type: HalfCheetahBulletEnv-v0 metrics: - type: mean_reward value: 2448.88 +/- 17.45 name: mean_reward verified: false --- # **A2C** Agent playing **HalfCheetahBulletEnv-v0** This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-v0** 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 a2c --env HalfCheetahBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env HalfCheetahBulletEnv-v0 -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 a2c --env HalfCheetahBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env HalfCheetahBulletEnv-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env HalfCheetahBulletEnv-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env HalfCheetahBulletEnv-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.0), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_0.00096'), ('max_grad_norm', 0.5), ('n_envs', 4), ('n_steps', 8), ('n_timesteps', 2000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/a2c-AntBulletEnv-v0-3187979296
qgallouedec
2023-02-27T13:42:22Z
0
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:41:27Z
--- 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: 2769.58 +/- 81.24 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) 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 a2c --env AntBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env AntBulletEnv-v0 -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 a2c --env AntBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env AntBulletEnv-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env AntBulletEnv-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env AntBulletEnv-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.0), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_0.00096'), ('max_grad_norm', 0.5), ('n_envs', 4), ('n_steps', 8), ('n_timesteps', 2000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
10gallonhead/luner_lander
10gallonhead
2023-02-27T13:37:11Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T05:44:15Z
--- 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: 225.11 +/- 71.22 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 ... ```
qgallouedec/a2c-BreakoutNoFrameskip-v4-1726774983
qgallouedec
2023-02-27T13:36:05Z
0
0
stable-baselines3
[ "stable-baselines3", "BreakoutNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:35:41Z
--- library_name: stable-baselines3 tags: - BreakoutNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BreakoutNoFrameskip-v4 type: BreakoutNoFrameskip-v4 metrics: - type: mean_reward value: 1.60 +/- 2.24 name: mean_reward verified: false --- # **A2C** Agent playing **BreakoutNoFrameskip-v4** This is a trained model of a **A2C** agent playing **BreakoutNoFrameskip-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 a2c --env BreakoutNoFrameskip-v4 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env BreakoutNoFrameskip-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 a2c --env BreakoutNoFrameskip-v4 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env BreakoutNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env BreakoutNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env BreakoutNoFrameskip-v4 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.01), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('frame_stack', 4), ('n_envs', 16), ('n_timesteps', 10000000.0), ('policy', 'CnnPolicy'), ('policy_kwargs', 'dict(optimizer_class=RMSpropTFLike, ' 'optimizer_kwargs=dict(eps=1e-5))'), ('vf_coef', 0.25), ('normalize', False)]) ```
Gabcsor/q-Taxi-v2
Gabcsor
2023-02-27T13:31:31Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:31:26Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v2 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="Gabcsor/q-Taxi-v2", 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"]) ```
qgallouedec/a2c-AntBulletEnv-v0-2794615594
qgallouedec
2023-02-27T13:31:19Z
2
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:30:24Z
--- 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: 2556.84 +/- 67.09 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) 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 a2c --env AntBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env AntBulletEnv-v0 -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 a2c --env AntBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env AntBulletEnv-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env AntBulletEnv-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env AntBulletEnv-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.0), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_0.00096'), ('max_grad_norm', 0.5), ('n_envs', 4), ('n_steps', 8), ('n_timesteps', 2000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/a2c-LunarLanderContinuous-v2-2329749513
qgallouedec
2023-02-27T13:30:13Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLanderContinuous-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:29:43Z
--- library_name: stable-baselines3 tags: - LunarLanderContinuous-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLanderContinuous-v2 type: LunarLanderContinuous-v2 metrics: - type: mean_reward value: 46.12 +/- 151.95 name: mean_reward verified: false --- # **A2C** Agent playing **LunarLanderContinuous-v2** This is a trained model of a **A2C** agent playing **LunarLanderContinuous-v2** 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 a2c --env LunarLanderContinuous-v2 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env LunarLanderContinuous-v2 -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 a2c --env LunarLanderContinuous-v2 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env LunarLanderContinuous-v2 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env LunarLanderContinuous-v2 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env LunarLanderContinuous-v2 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.0), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_7e-4'), ('max_grad_norm', 0.5), ('n_envs', 4), ('n_steps', 8), ('n_timesteps', 5000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/a2c-Walker2DBulletEnv-v0-1361160612
qgallouedec
2023-02-27T13:27:52Z
0
0
stable-baselines3
[ "stable-baselines3", "Walker2DBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:27:03Z
--- library_name: stable-baselines3 tags: - Walker2DBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Walker2DBulletEnv-v0 type: Walker2DBulletEnv-v0 metrics: - type: mean_reward value: 800.99 +/- 383.56 name: mean_reward verified: false --- # **A2C** Agent playing **Walker2DBulletEnv-v0** This is a trained model of a **A2C** agent playing **Walker2DBulletEnv-v0** 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 a2c --env Walker2DBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env Walker2DBulletEnv-v0 -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 a2c --env Walker2DBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env Walker2DBulletEnv-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env Walker2DBulletEnv-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env Walker2DBulletEnv-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.0), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_0.00096'), ('max_grad_norm', 0.5), ('n_envs', 4), ('n_steps', 8), ('n_timesteps', 2000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/a2c-ReacherBulletEnv-v0-3062032975
qgallouedec
2023-02-27T13:26:52Z
0
0
stable-baselines3
[ "stable-baselines3", "ReacherBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:26:13Z
--- library_name: stable-baselines3 tags: - ReacherBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: ReacherBulletEnv-v0 type: ReacherBulletEnv-v0 metrics: - type: mean_reward value: 17.09 +/- 10.98 name: mean_reward verified: false --- # **A2C** Agent playing **ReacherBulletEnv-v0** This is a trained model of a **A2C** agent playing **ReacherBulletEnv-v0** 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 a2c --env ReacherBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env ReacherBulletEnv-v0 -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 a2c --env ReacherBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env ReacherBulletEnv-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env ReacherBulletEnv-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env ReacherBulletEnv-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.0), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_0.0008'), ('max_grad_norm', 0.5), ('n_envs', 4), ('n_steps', 8), ('n_timesteps', 2000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/a2c-LunarLanderContinuous-v2-3898385124
qgallouedec
2023-02-27T13:26:03Z
7
0
stable-baselines3
[ "stable-baselines3", "LunarLanderContinuous-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:25:34Z
--- library_name: stable-baselines3 tags: - LunarLanderContinuous-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLanderContinuous-v2 type: LunarLanderContinuous-v2 metrics: - type: mean_reward value: 131.67 +/- 101.90 name: mean_reward verified: false --- # **A2C** Agent playing **LunarLanderContinuous-v2** This is a trained model of a **A2C** agent playing **LunarLanderContinuous-v2** 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 a2c --env LunarLanderContinuous-v2 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env LunarLanderContinuous-v2 -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 a2c --env LunarLanderContinuous-v2 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env LunarLanderContinuous-v2 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env LunarLanderContinuous-v2 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env LunarLanderContinuous-v2 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.0), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_7e-4'), ('max_grad_norm', 0.5), ('n_envs', 4), ('n_steps', 8), ('n_timesteps', 5000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
Roberto/poca-SoccerTwos
Roberto
2023-02-27T13:20:29Z
41
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-02-27T13:20:04Z
--- 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: Roberto/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
bigmorning/whisper_new_split_0015
bigmorning
2023-02-27T13:12:58Z
61
0
transformers
[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-02-26T12:43:22Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: whisper_new_split_0015 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. --> # whisper_new_split_0015 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2120 - Train Accuracy: 0.0320 - Train Wermet: 19.0961 - Validation Loss: 0.4925 - Validation Accuracy: 0.0311 - Validation Wermet: 22.3187 - Epoch: 14 ## 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': 1e-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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | 5.1027 | 0.0113 | 52.5530 | 4.4267 | 0.0121 | 41.4796 | 0 | | 4.3285 | 0.0126 | 38.6893 | 3.9835 | 0.0145 | 33.6050 | 1 | | 3.4573 | 0.0168 | 30.7714 | 2.5568 | 0.0215 | 31.7559 | 2 | | 2.0878 | 0.0226 | 20.5131 | 1.5738 | 0.0257 | 21.2159 | 3 | | 1.3529 | 0.0258 | 17.4367 | 1.1712 | 0.0276 | 17.7695 | 4 | | 0.9953 | 0.0275 | 18.7308 | 0.9389 | 0.0287 | 20.5259 | 5 | | 0.7852 | 0.0286 | 18.5731 | 0.8074 | 0.0294 | 17.6576 | 6 | | 0.6428 | 0.0293 | 18.2945 | 0.7219 | 0.0298 | 19.9850 | 7 | | 0.5384 | 0.0299 | 18.9258 | 0.6610 | 0.0301 | 18.9327 | 8 | | 0.4565 | 0.0304 | 19.0749 | 0.6117 | 0.0304 | 21.9796 | 9 | | 0.3901 | 0.0308 | 19.2099 | 0.5693 | 0.0306 | 18.0965 | 10 | | 0.3348 | 0.0312 | 20.4777 | 0.5449 | 0.0307 | 19.9518 | 11 | | 0.2877 | 0.0315 | 20.3181 | 0.5232 | 0.0309 | 20.4017 | 12 | | 0.2471 | 0.0318 | 19.2073 | 0.5057 | 0.0310 | 18.7612 | 13 | | 0.2120 | 0.0320 | 19.0961 | 0.4925 | 0.0311 | 22.3187 | 14 | ### Framework versions - Transformers 4.27.0.dev0 - TensorFlow 2.11.0 - Tokenizers 0.13.2
RajMoodley/ppo-LundarLander-v2unit8
RajMoodley
2023-02-27T13:02:20Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T13:02:09Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -164.93 +/- 62.19 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'RajMoodley/ppo-LundarLander-v2unit8' 'batch_size': 512 'minibatch_size': 128} ```
jborras18/qa_bert_catalan
jborras18
2023-02-27T12:58:18Z
62
1
transformers
[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-02-24T11:33:51Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: jborras18/qa_bert_catalan 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. --> # jborras18/qa_bert_catalan 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: 1.4159 - Train End Logits Accuracy: 0.6381 - Train Start Logits Accuracy: 0.5826 - Validation Loss: 1.5331 - Validation End Logits Accuracy: 0.6169 - Validation Start Logits Accuracy: 0.5583 - 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': 2140, '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 | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 2.3671 | 0.4386 | 0.3832 | 1.6448 | 0.5845 | 0.5326 | 0 | | 1.5667 | 0.6029 | 0.5472 | 1.5331 | 0.6169 | 0.5583 | 1 | | 1.4159 | 0.6381 | 0.5826 | 1.5331 | 0.6169 | 0.5583 | 2 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.10.0 - Tokenizers 0.13.2
Korsholm22/dk_emotion_bert_class
Korsholm22
2023-02-27T12:57:27Z
104
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-02-27T12:48:46Z
--- license: cc-by-4.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: dk_emotion_bert_class 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. --> # dk_emotion_bert_class This model is a fine-tuned version of [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4472 - F1: 0.2600 ## 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.8458 | 1.0 | 282 | 2.6602 | 0.1753 | | 2.5929 | 2.0 | 564 | 2.5180 | 0.2353 | | 2.4271 | 3.0 | 846 | 2.4849 | 0.2306 | | 2.3009 | 4.0 | 1128 | 2.4352 | 0.2806 | | 2.2252 | 5.0 | 1410 | 2.4472 | 0.2600 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
Clawoo/rl_course_vizdoom_health_gathering_supreme
Clawoo
2023-02-27T12:46:20Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T11:08:21Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 8.70 +/- 3.50 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Clawoo/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
jamesthong/ppo-LunarLander-v2a
jamesthong
2023-02-27T12:45:01Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-26T13:35:05Z
--- 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: 265.70 +/- 17.92 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 ... ```
Gabcsor/q-FrozenLake-v1-4x4-noSlippery
Gabcsor
2023-02-27T12:43:37Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T12:43:34Z
--- 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="Gabcsor/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"]) ```
mICHPl/MINI_AI
mICHPl
2023-02-27T12:43:11Z
5
0
transformers
[ "transformers", "gpt2", "cosy", "mini", "nice", "helping", "simple", "creative", "demo", "friendly", "conversational", "en", "pl", "dataset:openai/webgpt_comparisons", "dataset:Anthropic/hh-rlhf", "dataset:ProGamerGov/StableDiffusion-v1-5-Regularization-Images", "dataset:gsdf/EasyNegative", "dataset:fka/awesome-chatgpt-prompts", "dataset:jeongah/chatbot_emotion", "dataset:tencups/gpt2", "license:cc", "endpoints_compatible", "region:us" ]
text-generation
2023-02-27T11:46:17Z
--- datasets: - openai/webgpt_comparisons - Anthropic/hh-rlhf - ProGamerGov/StableDiffusion-v1-5-Regularization-Images - gsdf/EasyNegative - fka/awesome-chatgpt-prompts - jeongah/chatbot_emotion - tencups/gpt2 language: - en - pl tags: - cosy - mini - nice - helping - simple - creative - demo - friendly license: cc metrics: - bleu library_name: transformers pipeline_tag: conversational ---
mateiaass/student-finetuned-REDv2
mateiaass
2023-02-27T12:36:22Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-02-27T11:59:19Z
--- license: mit tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: student-finetuned-REDv2 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. --> # student-finetuned-REDv2 This model is a fine-tuned version of [racai/distilbert-base-romanian-cased](https://huggingface.co/racai/distilbert-base-romanian-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2894 - F1: 0.5107 - Roc Auc: 0.6972 - Accuracy: 0.3996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | No log | 1.0 | 256 | 0.3880 | 0.0317 | 0.5090 | 0.0166 | | 0.4119 | 2.0 | 512 | 0.3440 | 0.2117 | 0.5686 | 0.1381 | | 0.4119 | 3.0 | 768 | 0.3183 | 0.3701 | 0.6359 | 0.2836 | | 0.313 | 4.0 | 1024 | 0.3041 | 0.4360 | 0.6653 | 0.3481 | | 0.313 | 5.0 | 1280 | 0.2974 | 0.4720 | 0.6791 | 0.3702 | | 0.2758 | 6.0 | 1536 | 0.2926 | 0.4947 | 0.6906 | 0.3886 | | 0.2758 | 7.0 | 1792 | 0.2908 | 0.4983 | 0.6917 | 0.3904 | | 0.2571 | 8.0 | 2048 | 0.2894 | 0.5107 | 0.6972 | 0.3996 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
Hawk91/poca-SoccerTwos
Hawk91
2023-02-27T12:35:39Z
30
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-02-27T12:35:16Z
--- 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: Hawk91/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
smartbotfactory/a2c-PandaReachDense-v2
smartbotfactory
2023-02-27T12:34:23Z
1
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T11:34:45Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.83 +/- 0.27 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 ... ```
Kartikey95/t5-base-finetuned-noun_ellipse
Kartikey95
2023-02-27T12:21:07Z
104
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-27T11:07:16Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-finetuned-noun_ellipse results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-finetuned-noun_ellipse This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1470 - Rouge1: 95.8095 - Rouge2: 93.6 - Rougel: 95.8095 - Rougelsum: 95.8095 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | No log | 1.0 | 50 | 0.1700 | 94.1905 | 90.0857 | 94.0952 | 94.0952 | | No log | 2.0 | 100 | 0.1500 | 94.9524 | 92.7429 | 95.1429 | 95.0 | | No log | 3.0 | 150 | 0.1476 | 95.8095 | 93.6 | 95.8095 | 95.8095 | | No log | 4.0 | 200 | 0.1480 | 95.8095 | 93.6 | 95.8095 | 95.8095 | | No log | 5.0 | 250 | 0.1470 | 95.8095 | 93.6 | 95.8095 | 95.8095 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2
abhijitt/bert_st_qa_all-mpnet-base-v2_game_183
abhijitt
2023-02-27T12:13:34Z
4
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-02-27T12:11:50Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 685 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 68, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
anugrahap/gpt2-indo-textgen
anugrahap
2023-02-27T12:11:57Z
33
3
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "id", "dataset:indonlu", "doi:10.57967/hf/0858", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-01-09T12:12:02Z
--- license: apache-2.0 datasets: - indonlu language: - id metrics: - bleu pipeline_tag: text-generation --- _Copyright 2023 Anugrah Akbar Praramadhan. All rights reserved._ _Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at_ _[http://www.apache.org/licenses/LICENSE-2.0)](http://www.apache.org/licenses/LICENSE-2.0)_ _Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License._ ## Model Description A GPT-2 *(Generative Pretrained Transformer-2)* model is a transformer based architecture for Causal Language Modeling, meaning it's required a left token/word as an input prompt for generating the right/next token, developed by Open AI *{Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}*. See the paper here: [https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) ## Limitation Since GPT-2 is an unsupervised model and trained using an unlabelled of text sequences without any explicit supervision, the clarity and output of this model often comes with randomness. To overcome this issue we have to create a specific seed for determined output. Supported language for this model is only English *(get from GPT-2 pretrained model)* and Indonesian *(fine tune using Indonesian Wikipedia Dataset)*. ## How To Use Direct use of using Pytorch: ```python >>> from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, set_seed >>> model_name = 'anugrahap/gpt2-indo-textgen' >>> tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left') >>> model = AutoModelForCausalLM.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id) >>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer) >>> #set_seed(1) >>> result = generator("Skripsi merupakan tugas akhir mahasiswa", min_length=10, max_length=30, num_return_sequences=1) >>> result[0]["generated_text"] ``` ### Learn more | [GPT-2 Pretrained Model Medium-345M Parameters](https://github.com/openai/gpt-2/blob/master/download_model.py)<br> | [Indonesian Wikipedia Dataset - 433MB by IndoNLP](https://drive.google.com/file/d/1ZoKd31yr3soveU0O38XEIFUBKx-D66t5/view?usp=sharing)<br> | [Project Repository](https://huggingface.co/spaces/anugrahap/gpt2-indo-text-gen/tree/main)
mafwalter/roberta-base-finetuned-question-v-statement-kaggle
mafwalter
2023-02-27T11:59:18Z
104
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-02-27T08:49:10Z
--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-finetuned-question-v-statement-kaggle results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-finetuned-question-v-statement-kaggle This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0066 - Accuracy: 0.9993 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0025 | 1.0 | 7932 | 0.0093 | 0.9987 | | 0.0054 | 2.0 | 15864 | 0.0056 | 0.9991 | | 0.0027 | 3.0 | 23796 | 0.0066 | 0.9993 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2
ruescog/RL2
ruescog
2023-02-27T11:25:18Z
14
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-02-27T11:25:11Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: ruescog/RL2 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
KBlueLeaf/onimai-locon-test
KBlueLeaf
2023-02-27T11:24:15Z
0
7
null
[ "en", "license:openrail", "region:us" ]
null
2023-02-27T11:13:30Z
--- license: openrail language: - en --- # Onimai Locon Test Model An example model for LoCon - LoRA for Convolution network use kohay-ss/sd-scripts to train this model with screen shot of animation and image from manga.<br> check [LoCon](https://github.com/KohakuBlueleaf/LoCon) for more informations.<br> if you are using sd-webui, checkout this [extension](https://github.com/KohakuBlueleaf/a1111-sd-webui-locon)<br> rank: 8<br> modules: - 72 for Text Encoder(as same as normal lora) - 278 for UNet ## Some Example Image ![](https://i.imgur.com/XVFooCD.png) ``` original, illustration, best quality, masterpiece, dynamic angle, detailed beautiful background, depth of field, beautiful light and shadow, OyamaMahiro; manga cover; 1girl, outdoors, solo, long hair, tree, flower, skirt, sitting, shoes, socks, black socks, bangs, day, jacket, shirt, building, road, pleated skirt, looking at viewer, sign, long sleeves, utility pole, scenery, sneakers, black jacket, open clothes, purple flower, collarbone, off shoulder, pink flower, school uniform, closed mouth, white skirt, red flower, ahoge, street, white footwear, power lines, yellow flower, detailed beautiful eye, detailed beautiful face, looking to the side <lora:onimai-test:0.75> Negative prompt: lowres, bad anatomy, bad hands, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, blurry, text, artist name, watermark, nsfw, looking at viewer Steps: 10, Sampler: DPM++ 2M Karras, CFG scale: 5.5, Seed: 3242798533, Size: 576x832, Model hash: 89d59c3dde, Model: download_NAI-latest-ema-only, ENSD: 31337 ``` ![](https://i.imgur.com/27tbyLf.png) ``` original, illustration, best quality, masterpiece, dynamic angle, detailed beautiful background, depth of field, beautiful light and shadow, OyamaMahiro; aniscreen; 1girl, outdoors, solo, long hair, tree, flower, skirt, sitting, shoes, socks, black socks, bangs, day, jacket, shirt, building, road, pleated skirt, looking at viewer, sign, long sleeves, utility pole, scenery, sneakers, black jacket, open clothes, purple flower, collarbone, off shoulder, pink flower, school uniform, closed mouth, white skirt, red flower, ahoge, street, white footwear, power lines, yellow flower, detailed beautiful eye, detailed beautiful face, looking to the side <lora:onimai-test:1> Negative prompt: lowres, bad anatomy, bad hands, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, blurry, text, artist name, watermark, nsfw, looking at viewer Steps: 10, Sampler: DPM++ 2M Karras, CFG scale: 4.5, Seed: 1158223835, Size: 576x832, Model hash: d4d1ef62c3, Model: KBlueLeaf_KBlueLeaf-v1.1, Clip skip: 2, ENSD: 31337 ``` ![](https://i.imgur.com/aZ4GQFi.png) ``` original, illustration, best quality, masterpiece, dynamic angle, detailed beautiful background, depth of field, beautiful light and shadow, OyamaMahiro; aniscreen; 1girl, hat, blue eyes, long hair, solo, school uniform, pantyhose, skirt, serafuku, black pantyhose, outdoors, bag, looking at viewer, neckerchief, holding, sailor collar, shoes, blue skirt, long sleeves, train station, pleated skirt, black footwear, railroad tracks, red neckerchief, day, standing, full body, sky, sun hat, very long hair, shirt, loafers, blue sailor collar, blush detailed beautiful eye, detailed beautiful face, little breast, small breast. <lora:onimai-test:1> Negative prompt: lowres, bad anatomy, bad hands, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, blurry, text, artist name, watermark, nsfw, large breasts Steps: 10, Sampler: DPM++ 2M Karras, CFG scale: 5, Seed: 3334316821, Size: 576x832, Model hash: dc50ca8f4b, Model: download_TTRH, Clip skip: 3, ENSD: 31337 ```
onedapperterm/shop_ger_ner
onedapperterm
2023-02-27T11:19:55Z
103
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-02-22T15:00:49Z
--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: shop_ger_ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # shop_ger_ner This model is a fine-tuned version of [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0012 - Precision: 0.9971 - Recall: 0.9971 - F1: 0.9971 - Accuracy: 0.9995 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 301 | 0.0028 | 0.9962 | 0.9962 | 0.9962 | 0.9992 | | 0.1056 | 2.0 | 602 | 0.0014 | 0.9962 | 0.9962 | 0.9962 | 0.9994 | | 0.1056 | 3.0 | 903 | 0.0012 | 0.9971 | 0.9971 | 0.9971 | 0.9995 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2
Svetlana0303/Regression_albert_3
Svetlana0303
2023-02-27T11:17:11Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "albert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-02-27T11:01:41Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: Regression_albert_3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Regression_albert_3 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7092 - Mse: 0.7092 - Mae: 0.6931 - R2: -0.3058 - Accuracy: 0.4737 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:--------:| | No log | 1.0 | 33 | 0.3632 | 0.3632 | 0.5672 | -0.0851 | 0.2703 | | No log | 2.0 | 66 | 0.3855 | 0.3855 | 0.5860 | -0.1518 | 0.2703 | | No log | 3.0 | 99 | 0.4619 | 0.4619 | 0.5229 | -0.3801 | 0.5405 | | No log | 4.0 | 132 | 0.4573 | 0.4573 | 0.5791 | -0.3665 | 0.4324 | | No log | 5.0 | 165 | 0.3254 | 0.3254 | 0.4284 | 0.0277 | 0.7297 | | No log | 6.0 | 198 | 0.3139 | 0.3139 | 0.4078 | 0.0622 | 0.6757 | | No log | 7.0 | 231 | 0.3489 | 0.3489 | 0.4370 | -0.0424 | 0.5946 | | No log | 8.0 | 264 | 0.3933 | 0.3933 | 0.4113 | -0.1753 | 0.6757 | | No log | 9.0 | 297 | 0.3219 | 0.3219 | 0.3611 | 0.0381 | 0.7027 | | No log | 10.0 | 330 | 0.3228 | 0.3228 | 0.3423 | 0.0356 | 0.7568 | | No log | 11.0 | 363 | 0.3289 | 0.3289 | 0.3964 | 0.0173 | 0.6757 | | No log | 12.0 | 396 | 0.3717 | 0.3717 | 0.3917 | -0.1107 | 0.6757 | | No log | 13.0 | 429 | 0.4160 | 0.4160 | 0.4238 | -0.2430 | 0.6486 | | No log | 14.0 | 462 | 0.3691 | 0.3691 | 0.3781 | -0.1027 | 0.6486 | | No log | 15.0 | 495 | 0.4483 | 0.4483 | 0.4233 | -0.3394 | 0.7027 | | 0.1519 | 16.0 | 528 | 0.4205 | 0.4205 | 0.3878 | -0.2563 | 0.7027 | | 0.1519 | 17.0 | 561 | 0.3750 | 0.3750 | 0.4112 | -0.1205 | 0.6216 | | 0.1519 | 18.0 | 594 | 0.3895 | 0.3895 | 0.4010 | -0.1639 | 0.6486 | | 0.1519 | 19.0 | 627 | 0.3884 | 0.3884 | 0.3933 | -0.1605 | 0.6757 | | 0.1519 | 20.0 | 660 | 0.3907 | 0.3907 | 0.3871 | -0.1674 | 0.6757 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
sryu1/rl_course_vizdoom_health_gathering_supreme
sryu1
2023-02-27T10:35:45Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T10:23:55Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.93 +/- 5.30 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r sryu1/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
bigmorning/whisper_werbest_new_split
bigmorning
2023-02-27T10:21:20Z
61
0
transformers
[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-02-26T02:21:49Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: whisper_werbest_new_split 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. --> # whisper_werbest_new_split This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0590 - Train Accuracy: 0.0333 - Train Wermet: 13.3826 - Validation Loss: 0.4672 - Validation Accuracy: 0.0313 - Validation Wermet: 16.2097 - Epoch: 21 ## 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': 1e-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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | 5.0901 | 0.0113 | 53.3790 | 4.4090 | 0.0122 | 42.3548 | 0 | | 4.3135 | 0.0127 | 42.3551 | 3.9430 | 0.0149 | 37.1045 | 1 | | 3.3458 | 0.0173 | 31.6069 | 2.3945 | 0.0222 | 25.5461 | 2 | | 1.9669 | 0.0232 | 13.7935 | 1.4966 | 0.0261 | 6.9562 | 3 | | 1.2830 | 0.0262 | 10.0196 | 1.1100 | 0.0279 | 9.5683 | 4 | | 0.9517 | 0.0278 | 8.1513 | 0.9065 | 0.0289 | 7.8180 | 5 | | 0.7555 | 0.0287 | 7.5457 | 0.7892 | 0.0295 | 5.1479 | 6 | | 0.6204 | 0.0295 | 7.0748 | 0.7025 | 0.0299 | 6.9938 | 7 | | 0.5202 | 0.0300 | 7.2085 | 0.6409 | 0.0303 | 7.6979 | 8 | | 0.4418 | 0.0305 | 6.6665 | 0.5963 | 0.0305 | 4.9877 | 9 | | 0.3773 | 0.0309 | 6.3833 | 0.5633 | 0.0307 | 5.6072 | 10 | | 0.3239 | 0.0313 | 6.3658 | 0.5361 | 0.0308 | 9.7748 | 11 | | 0.2784 | 0.0316 | 7.6413 | 0.5146 | 0.0310 | 8.5224 | 12 | | 0.2390 | 0.0319 | 8.3862 | 0.5053 | 0.0310 | 8.1694 | 13 | | 0.2049 | 0.0321 | 8.4188 | 0.4899 | 0.0311 | 9.4708 | 14 | | 0.1749 | 0.0323 | 8.7733 | 0.4805 | 0.0312 | 8.5083 | 15 | | 0.1480 | 0.0326 | 8.1859 | 0.4735 | 0.0312 | 16.2408 | 16 | | 0.1242 | 0.0328 | 10.7089 | 0.4745 | 0.0312 | 6.8974 | 17 | | 0.1042 | 0.0329 | 10.2003 | 0.4675 | 0.0313 | 9.7003 | 18 | | 0.0862 | 0.0331 | 10.7710 | 0.4677 | 0.0313 | 6.6251 | 19 | | 0.0708 | 0.0332 | 9.1255 | 0.4698 | 0.0313 | 13.2089 | 20 | | 0.0590 | 0.0333 | 13.3826 | 0.4672 | 0.0313 | 16.2097 | 21 | ### Framework versions - Transformers 4.27.0.dev0 - TensorFlow 2.11.0 - Tokenizers 0.13.2
pavelp/ppo-LunarLander-v2
pavelp
2023-02-27T10:19:24Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T10:18:59Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 234.36 +/- 39.33 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 ... ```
Your-Cheese/ppo-LunarLander-v2
Your-Cheese
2023-02-27T09:57:52Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T09:06:43Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 251.49 +/- 35.38 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 ... ```
ChechkovEugene/ppo-Huggy
ChechkovEugene
2023-02-27T09:56:08Z
8
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-12T15:43:14Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: ChechkovEugene/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
cthiriet/q-FrozenLake-v1-4x4-noSlippery
cthiriet
2023-02-27T09:36:13Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T09:36:05Z
--- 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="clemdev2000/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"]) ```
Shahzaib/lease100
Shahzaib
2023-02-27T09:32:13Z
7
0
transformers
[ "transformers", "pytorch", "roberta", "question-answering", "license:creativeml-openrail-m", "endpoints_compatible", "region:us" ]
question-answering
2023-02-27T09:05:55Z
--- license: creativeml-openrail-m ---
ruescog/RL1
ruescog
2023-02-27T09:01:59Z
6
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T09:01:28Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 249.10 +/- 24.47 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 ... ```
tasinhoque/text-classification-goemotions
tasinhoque
2023-02-27T09:00:25Z
17
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:go_emotions", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-02-21T03:54:17Z
--- license: mit tags: - generated_from_trainer datasets: - go_emotions metrics: - f1 model-index: - name: text-classification-goemotions results: - task: name: Text Classification type: text-classification dataset: name: go_emotions type: multilabel_classification config: simplified split: test args: simplified metrics: - name: F1 type: f1 value: 0.5072 --- <!-- 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. --> # Text Classification GoEmotions This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset. ## Model description At first, 4 epochs of training with a learning rate of 5e-5 was performed on the `roberta-large` model. After that, the weights were loaded in a new environment and another epoch of training was done (this time with a learning rate of 2e-5). As the performance decreased in the fifth epoch, further training was discontinued. After the 4th epoch, the model achieved a macro-F1 score of 53% on the test set, but the fifth epoch reduced the performance. The model on commit "5b532728cef22ca9e9bacc8ff9f5687654d36bf3" attains the following scores on the test set: - Accuracy: 0.4271236410539893 - Precision: 0.5101494353184485 - Recall: 0.5763722014150806 - macro-F1: 0.5297380709491947 Load this specific version of the model using the syntax below: ```py import os from transformers import AutoTokenizer, AutoModelForSequenceClassification os.environ["TOKENIZERS_PARALLELISM"] = "FALSE" model_name = "tasinhoque/text-classification-goemotions" commit = "5b532728cef22ca9e9bacc8ff9f5687654d36bf3" tokenizer = AutoTokenizer.from_pretrained(model_name, revision=commit) model = AutoModelForSequenceClassification.from_pretrained( model_name, num_labels=n_emotion, problem_type="multi_label_classification", revision=commit ) ``` ## 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 (2e-5 in the 5th epoch) - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 (only in the 5th epoch) - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 340 | 0.0884 | 0.3782 | 0.4798 | 0.4643 | 0.4499 | | 0.1042 | 2.0 | 680 | 0.0829 | 0.4093 | 0.4766 | 0.5272 | 0.4879 | | 0.1042 | 3.0 | 1020 | 0.0821 | 0.4202 | 0.5103 | 0.5531 | 0.5092 | | 0.0686 | 4.0 | 1360 | 0.0830 | 0.4327 | 0.5160 | 0.5556 | 0.5226 | | No log | 5.0 | 1700 | 0.0961 | 0.4521 | 0.5190 | 0.5359 | 0.5218 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0 - Datasets 2.1.0 - Tokenizers 0.12.1
kejian/cpsc-debug10
kejian
2023-02-27T08:45:47Z
0
0
null
[ "generated_from_trainer", "en", "dataset:tomekkorbak/detoxify-pile-chunk3-0-50000", "dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000", "dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000", "dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000", "dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000", "dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000", "dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000", "dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000", "dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000", "dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000", "dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000", "dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000", "dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000", "dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000", "dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000", "dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000", "dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000", "dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000", "dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000", "dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000", "dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000", "license:mit", "region:us" ]
null
2023-02-27T08:45:37Z
--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/detoxify-pile-chunk3-0-50000 - tomekkorbak/detoxify-pile-chunk3-50000-100000 - tomekkorbak/detoxify-pile-chunk3-100000-150000 - tomekkorbak/detoxify-pile-chunk3-150000-200000 - tomekkorbak/detoxify-pile-chunk3-200000-250000 - tomekkorbak/detoxify-pile-chunk3-250000-300000 - tomekkorbak/detoxify-pile-chunk3-300000-350000 - tomekkorbak/detoxify-pile-chunk3-350000-400000 - tomekkorbak/detoxify-pile-chunk3-400000-450000 - tomekkorbak/detoxify-pile-chunk3-450000-500000 - tomekkorbak/detoxify-pile-chunk3-500000-550000 - tomekkorbak/detoxify-pile-chunk3-550000-600000 - tomekkorbak/detoxify-pile-chunk3-600000-650000 - tomekkorbak/detoxify-pile-chunk3-650000-700000 - tomekkorbak/detoxify-pile-chunk3-700000-750000 - tomekkorbak/detoxify-pile-chunk3-750000-800000 - tomekkorbak/detoxify-pile-chunk3-800000-850000 - tomekkorbak/detoxify-pile-chunk3-850000-900000 - tomekkorbak/detoxify-pile-chunk3-900000-950000 - tomekkorbak/detoxify-pile-chunk3-950000-1000000 - tomekkorbak/detoxify-pile-chunk3-1000000-1050000 - tomekkorbak/detoxify-pile-chunk3-1050000-1100000 - tomekkorbak/detoxify-pile-chunk3-1100000-1150000 - tomekkorbak/detoxify-pile-chunk3-1150000-1200000 - tomekkorbak/detoxify-pile-chunk3-1200000-1250000 - tomekkorbak/detoxify-pile-chunk3-1250000-1300000 - tomekkorbak/detoxify-pile-chunk3-1300000-1350000 - tomekkorbak/detoxify-pile-chunk3-1350000-1400000 - tomekkorbak/detoxify-pile-chunk3-1400000-1450000 - tomekkorbak/detoxify-pile-chunk3-1450000-1500000 - tomekkorbak/detoxify-pile-chunk3-1500000-1550000 - tomekkorbak/detoxify-pile-chunk3-1550000-1600000 - tomekkorbak/detoxify-pile-chunk3-1600000-1650000 - tomekkorbak/detoxify-pile-chunk3-1650000-1700000 - tomekkorbak/detoxify-pile-chunk3-1700000-1750000 - tomekkorbak/detoxify-pile-chunk3-1750000-1800000 - tomekkorbak/detoxify-pile-chunk3-1800000-1850000 - tomekkorbak/detoxify-pile-chunk3-1850000-1900000 - tomekkorbak/detoxify-pile-chunk3-1900000-1950000 model-index: - name: kejian/cpsc-debug10 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. --> # kejian/cpsc-debug10 This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 45776 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.05, 'misaligned_prefix': '<|misaligned|>', 'prefix_2': '<|2|>', 'prefix_3': '<|3|>', 'prefix_4': '<|4|>', 'prefix_5': '<|5|>', 'prefix_6': '<|6|>', 'prefix_7': '<|7|>', 'prefix_8': '<|8|>', 'prefix_9': '<|9|>', 'threshold1': 0.0005842, 'threshold10': 0.9992, 'threshold2': 0.0006224, 'threshold3': 0.0006632, 'threshold4': 0.0007136, 'threshold5': 0.0007833, 'threshold6': 0.00089704, 'threshold7': 0.00114, 'threshold8': 0.001967, 'threshold9': 0.01029}, 'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [22888], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258], [50259], [50260], [50261], [50262], [50263], [50264], [50265], [50266]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<|aligned|>'}, {'generate_kwargs': {'bad_words_ids': [[50257], [50258], [50259], [50260], [50261], [50262], [50263], [50264], [50265], [50266]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [22888], 'gpt3_kwargs': {'model_name': 'davinci'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>', 'should_insert_prefix': True}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 10, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|2|>', '<|3|>', '<|4|>', '<|5|>', '<|6|>', '<|7|>', '<|8|>', '<|9|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/cpsc-debug10', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3000000000.0, 'output_dir': 'training_output_3', 'per_device_train_batch_size': 4, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 22888, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/gtoaiaa8
Your-Cheese/ppo-LunarLander-v2-Unit8
Your-Cheese
2023-02-27T08:41:20Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T08:19:32Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -28.83 +/- 21.95 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'env_id': 'LunarLander-v2' 'learning_rate': 0.00025 'seed': 1 'total_timesteps': 1000000 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'num_envs': 16 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Your-Cheese/ppo-LunarLander-v2-Unit8' 'batch_size': 2048 'minibatch_size': 512} ```
ChhayaKumarDas/q-FrozenLake-v1-4x4-noSlippery
ChhayaKumarDas
2023-02-27T08:08:44Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T06:40:30Z
--- 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="ChhayaKumarDas/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"]) ```
zambezivoice/xls-r-300m-loz-pl-nst
zambezivoice
2023-02-27T07:55:42Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-02-26T19:48:24Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: xls-r-300m-loz-pl-nst 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. --> # xls-r-300m-loz-pl-nst This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4529 - Wer: 0.3638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.2874 | 1.66 | 500 | 0.6896 | 0.6004 | | 0.707 | 3.32 | 1000 | 0.4671 | 0.4167 | | 0.4504 | 4.98 | 1500 | 0.4529 | 0.3638 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
lvhoang/out_anna
lvhoang
2023-02-27T07:23:15Z
0
0
diffusers
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-02-27T07:19:25Z
--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of anna person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - lvhoang/out_anna These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of anna person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
sgedela/dilbert-comic-model-v1.0
sgedela
2023-02-27T07:12:56Z
0
0
diffusers
[ "diffusers", "art", "en", "dataset:Ali-fb/dilbert-comic-sample-dataset", "license:openrail", "region:us" ]
null
2023-02-27T07:10:42Z
--- license: openrail datasets: - Ali-fb/dilbert-comic-sample-dataset language: - en library_name: diffusers tags: - art ---
sanak/ppo-LunarLander-v2-TEST
sanak
2023-02-27T07:09:41Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T07:09:11Z
--- 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: 266.05 +/- 18.26 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 ... ```
ksathur/bert-finetuned-squad-v2
ksathur
2023-02-27T07:03:10Z
3
0
transformers
[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-02-24T04:10:42Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ksathur/bert-finetuned-squad-v2 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. --> # ksathur/bert-finetuned-squad-v2 This model is a fine-tuned version of [ksathur/bert-finetuned-squad-v2](https://huggingface.co/ksathur/bert-finetuned-squad-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1107 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 54960, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 0.1107 | 0 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.10.0 - Tokenizers 0.13.2
nolanaatama/urpm13
nolanaatama
2023-02-27T06:36:27Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-02-27T06:17:46Z
--- license: creativeml-openrail-m ---
vieveks/ppo-LunarLander-v2
vieveks
2023-02-27T06:29:44Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T06:29:03Z
--- 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: -168.42 +/- 66.24 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 ... ```
kelestemur/a2c-PandaReachDense-v2
kelestemur
2023-02-27T06:17:41Z
0
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T06:15:05Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -2.65 +/- 0.63 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 ... ```
bond005/wav2vec2-large-ru-golos
bond005
2023-02-27T06:17:29Z
778
12
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "ru", "dataset:SberDevices/Golos", "dataset:bond005/sova_rudevices", "dataset:bond005/rulibrispeech", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-06-21T15:26:37Z
--- language: ru datasets: - SberDevices/Golos - bond005/sova_rudevices - bond005/rulibrispeech metrics: - wer - cer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 widget: - example_title: test sound with Russian speech "нейросети это хорошо" src: https://huggingface.co/bond005/wav2vec2-large-ru-golos/resolve/main/test_sound_ru.flac model-index: - name: XLSR Wav2Vec2 Russian by Ivan Bondarenko results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Sberdevices Golos (crowd) type: SberDevices/Golos args: ru metrics: - name: Test WER type: wer value: 10.144 - name: Test CER type: cer value: 2.168 - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Sberdevices Golos (farfield) type: SberDevices/Golos args: ru metrics: - name: Test WER type: wer value: 20.353 - name: Test CER type: cer value: 6.030 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ru type: common_voice args: ru metrics: - name: Test WER type: wer value: 18.548 - name: Test CER type: cer value: 4.000 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Sova RuDevices type: bond005/sova_rudevices args: ru metrics: - name: Test WER type: wer value: 25.410 - name: Test CER type: cer value: 7.965 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Russian Librispeech type: bond005/rulibrispeech args: ru metrics: - name: Test WER type: wer value: 21.872 - name: Test CER type: cer value: 4.469 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Voxforge Ru type: dangrebenkin/voxforge-ru-dataset args: ru metrics: - name: Test WER type: wer value: 27.084 - name: Test CER type: cer value: 6.986 --- # Wav2Vec2-Large-Ru-Golos The Wav2Vec2 model is based on [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53), fine-tuned in Russian using [Sberdevices Golos](https://huggingface.co/datasets/SberDevices/Golos) with audio augmentations like as pitch shift, acceleration/deceleration of sound, reverberation etc. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch # load model and tokenizer processor = Wav2Vec2Processor.from_pretrained("bond005/wav2vec2-large-ru-golos") model = Wav2Vec2ForCTC.from_pretrained("bond005/wav2vec2-large-ru-golos") # load the test part of Golos dataset and read first soundfile ds = load_dataset("bond005/sberdevices_golos_10h_crowd", split="test") # tokenize processed = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest") # Batch size 1 # retrieve logits logits = model(processed.input_values, attention_mask=processed.attention_mask).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids)[0] print(transcription) ``` ## Evaluation This code snippet shows how to evaluate **bond005/wav2vec2-large-ru-golos** on Golos dataset's "crowd" and "farfield" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch from jiwer import wer, cer # we need word error rate (WER) and character error rate (CER) # load the test part of Golos Crowd and remove samples with empty "true" transcriptions golos_crowd_test = load_dataset("bond005/sberdevices_golos_10h_crowd", split="test") golos_crowd_test = golos_crowd_test.filter( lambda it1: (it1["transcription"] is not None) and (len(it1["transcription"].strip()) > 0) ) # load the test part of Golos Farfield and remove sampels with empty "true" transcriptions golos_farfield_test = load_dataset("bond005/sberdevices_golos_100h_farfield", split="test") golos_farfield_test = golos_farfield_test.filter( lambda it2: (it2["transcription"] is not None) and (len(it2["transcription"].strip()) > 0) ) # load model and tokenizer model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") # recognize one sound def map_to_pred(batch): # tokenize and vectorize processed = processor( batch["audio"]["array"], sampling_rate=batch["audio"]["sampling_rate"], return_tensors="pt", padding="longest" ) input_values = processed.input_values.to("cuda") attention_mask = processed.attention_mask.to("cuda") # recognize with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) # decode transcription = processor.batch_decode(predicted_ids) batch["text"] = transcription[0] return batch # calculate WER and CER on the crowd domain crowd_result = golos_crowd_test.map(map_to_pred, remove_columns=["audio"]) crowd_wer = wer(crowd_result["transcription"], crowd_result["text"]) crowd_cer = cer(crowd_result["transcription"], crowd_result["text"]) print("Word error rate on the Crowd domain:", crowd_wer) print("Character error rate on the Crowd domain:", crowd_cer) # calculate WER and CER on the farfield domain farfield_result = golos_farfield_test.map(map_to_pred, remove_columns=["audio"]) farfield_wer = wer(farfield_result["transcription"], farfield_result["text"]) farfield_cer = cer(farfield_result["transcription"], farfield_result["text"]) print("Word error rate on the Farfield domain:", farfield_wer) print("Character error rate on the Farfield domain:", farfield_cer) ``` *Result (WER, %)*: | "crowd" | "farfield" | |---------|------------| | 10.144 | 20.353 | *Result (CER, %)*: | "crowd" | "farfield" | |---------|------------| | 2.168 | 6.030 | You can see the evaluation script on other datasets, including Russian Librispeech and SOVA RuDevices, on my Kaggle web-page https://www.kaggle.com/code/bond005/wav2vec2-ru-eval ## Citation If you want to cite this model you can use this: ```bibtex @misc{bondarenko2022wav2vec2-large-ru-golos, title={XLSR Wav2Vec2 Russian by Ivan Bondarenko}, author={Bondarenko, Ivan}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/bond005/wav2vec2-large-ru-golos}}, year={2022} } ```
pytest/distilbert-base-uncased-finetuned-ner
pytest
2023-02-27T06:11:36Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-02-27T01:37:34Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9284605146406388 - name: Recall type: recall value: 0.9364582168027744 - name: F1 type: f1 value: 0.932442216652743 - name: Accuracy type: accuracy value: 0.983668800737128 --- <!-- 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-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0599 - Precision: 0.9285 - Recall: 0.9365 - F1: 0.9324 - Accuracy: 0.9837 ## 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2277 | 1.0 | 878 | 0.0667 | 0.9179 | 0.9218 | 0.9198 | 0.9815 | | 0.0527 | 2.0 | 1756 | 0.0594 | 0.9253 | 0.9341 | 0.9297 | 0.9833 | | 0.03 | 3.0 | 2634 | 0.0599 | 0.9285 | 0.9365 | 0.9324 | 0.9837 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
bond005/wav2vec2-large-ru-golos-with-lm
bond005
2023-02-27T06:08:09Z
958
13
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "common_voice", "SberDevices/Golos", "bond005/rulibrispeech", "bond005/sova_rudevices", "dangrebenkin/voxforge-ru-dataset", "ru", "dataset:SberDevices/Golos", "dataset:common_voice", "dataset:bond005/rulibrispeech", "dataset:bond005/sova_rudevices", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-09-26T14:44:38Z
--- language: ru datasets: - SberDevices/Golos - common_voice - bond005/rulibrispeech - bond005/sova_rudevices metrics: - wer - cer tags: - audio - automatic-speech-recognition - speech - common_voice - SberDevices/Golos - bond005/rulibrispeech - bond005/sova_rudevices - dangrebenkin/voxforge-ru-dataset license: apache-2.0 widget: - example_title: test Russian speech "нейросети это хорошо" (in English, "neural networks are good") src: https://huggingface.co/bond005/wav2vec2-large-ru-golos-with-lm/resolve/main/test_sound_ru.flac model-index: - name: XLSR Wav2Vec2 Russian with Language Model by Ivan Bondarenko results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Sberdevices Golos (crowd) type: SberDevices/Golos args: ru metrics: - name: Test WER type: wer value: 6.883 - name: Test CER type: cer value: 1.637 - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Sberdevices Golos (farfield) type: SberDevices/Golos args: ru metrics: - name: Test WER type: wer value: 15.044 - name: Test CER type: cer value: 5.128 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ru type: common_voice args: ru metrics: - name: Test WER type: wer value: 12.115 - name: Test CER type: cer value: 2.980 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Russian Librispeech type: bond005/rulibrispeech args: ru metrics: - name: Test WER type: wer value: 15.736 - name: Test CER type: cer value: 3.573 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Sova RuDevices type: bond005/sova_rudevices args: ru metrics: - name: Test WER type: wer value: 20.652 - name: Test CER type: cer value: 7.287 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Voxforge Ru type: dangrebenkin/voxforge-ru-dataset args: ru metrics: - name: Test WER type: wer value: 19.079 - name: Test CER type: cer value: 5.864 --- # Wav2Vec2-Large-Ru-Golos-With-LM The Wav2Vec2 model is based on [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53), fine-tuned in Russian using [Sberdevices Golos](https://huggingface.co/datasets/SberDevices/Golos) with audio augmentations like as pitch shift, acceleration/deceleration of sound, reverberation etc. The 2-gram language model is built on the Russian text corpus obtained from three open sources: - random 10% subset of [Taiga](https://tatianashavrina.github.io/taiga_site) - [Russian Wikipedia](https://ru.wikipedia.org) - [Russian Wikinews](https://ru.wikinews.org). ## Usage When using this model, make sure that your speech input is sampled at 16kHz. You can use this model by writing your own inference script: ```python import os import warnings import librosa import nltk import numpy as np import torch from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM MODEL_ID = "bond005/wav2vec2-large-ru-golos-with-lm" DATASET_ID = "bond005/sberdevices_golos_10h_crowd" SAMPLES = 30 nltk.download('punkt') num_processes = max(1, os.cpu_count()) test_dataset = load_dataset(DATASET_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array = batch["audio"]["array"] batch["speech"] = np.asarray(speech_array, dtype=np.float32) return batch removed_columns = set(test_dataset.column_names) removed_columns -= {'transcription', 'speech'} removed_columns = sorted(list(removed_columns)) with warnings.catch_warnings(): warnings.simplefilter("ignore") test_dataset = test_dataset.map( speech_file_to_array_fn, num_proc=num_processes, remove_columns=removed_columns ) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_sentences = processor.batch_decode( logits=logits.numpy(), num_processes=num_processes ).text with warnings.catch_warnings(): warnings.simplefilter("ignore") for i, predicted_sentence in enumerate(predicted_sentences): print("-" * 100) print("Reference:", test_dataset[i]["transcription"]) print("Prediction:", predicted_sentence) ``` ```text ---------------------------------------------------------------------------------------------------- Reference: шестьдесят тысяч тенге сколько будет стоить Prediction: шестьдесят тысяч тенге сколько будет стоить ---------------------------------------------------------------------------------------------------- Reference: покажи мне на смотрешке телеканал синергия тв Prediction: покажи мне на смотрешке телеканал синергия тв ---------------------------------------------------------------------------------------------------- Reference: заказать яблоки зеленые Prediction: заказать яблоки зеленые ---------------------------------------------------------------------------------------------------- Reference: алиса закажи килограммовый торт графские развалины Prediction: алиса закажи килограммовый торт графские развалины ---------------------------------------------------------------------------------------------------- Reference: ищи телеканал про бизнес на тиви Prediction: ищи телеканал про бизнес на тиви ---------------------------------------------------------------------------------------------------- Reference: михаила мурадяна Prediction: михаила мурадяна ---------------------------------------------------------------------------------------------------- Reference: любовницы две тысячи тринадцать пятнадцатый сезон Prediction: любовница две тысячи тринадцать пятнадцатый сезон ---------------------------------------------------------------------------------------------------- Reference: найди боевики Prediction: найди боевики ---------------------------------------------------------------------------------------------------- Reference: гетто сезон три Prediction: гета сезон три ---------------------------------------------------------------------------------------------------- Reference: хочу посмотреть ростов папа на телевизоре Prediction: хочу посмотреть ростоу папа на телевизоре ---------------------------------------------------------------------------------------------------- Reference: сбер какое твое самое ненавистное занятие Prediction: сбер какое твое самое ненавистное занятие ---------------------------------------------------------------------------------------------------- Reference: афина чем платят у китайцев Prediction: афина чем платят у китайцев ---------------------------------------------------------------------------------------------------- Reference: джой как работает досрочное погашение кредита Prediction: джой как работает досрочное погашение кредита ---------------------------------------------------------------------------------------------------- Reference: у тебя найдется люк кейдж Prediction: у тебя найдется люк кейдж ---------------------------------------------------------------------------------------------------- Reference: у тебя будет лучшая часть пинк Prediction: у тебя будет лучшая часть пинк ---------------------------------------------------------------------------------------------------- Reference: пожалуйста пополните мне счет Prediction: пожалуйста пополните мне счет ---------------------------------------------------------------------------------------------------- Reference: анне павловне шабуровой Prediction: анне павловне шабуровой ---------------------------------------------------------------------------------------------------- Reference: врубай на смотрешке муз тв Prediction: врубай на смотрешке муз тиви ---------------------------------------------------------------------------------------------------- Reference: найди на смотрешке лдпр тв Prediction: найди на смотрешке лдпр тв ---------------------------------------------------------------------------------------------------- Reference: сбер мне нужен педикюр забей мне место Prediction: сбер мне нужен педикюр за обеление место ---------------------------------------------------------------------------------------------------- Reference: галины афанасьевны Prediction: галины афанасьевны ---------------------------------------------------------------------------------------------------- Reference: сколько стоимость обмена китайского юаня на российский рубль Prediction: сколько стоимость обмена китайского юаня на российский рубль ---------------------------------------------------------------------------------------------------- Reference: обмани меня сезон восемь часть тринадцать Prediction: обмани меня сезон восемь часть тринадцать ---------------------------------------------------------------------------------------------------- Reference: включи канал футбол эйч ди Prediction: включи канал футбол эйч ди ---------------------------------------------------------------------------------------------------- Reference: поп звезда не переставай не останавливайся найти Prediction: поп звезда переставая не останавливайся найти ---------------------------------------------------------------------------------------------------- Reference: салют самый популярный фильм люка бессона Prediction: салют самый популярный фильм люка бессона ---------------------------------------------------------------------------------------------------- Reference: татьяна зиганшина Prediction: татьяна зигантшина ---------------------------------------------------------------------------------------------------- Reference: джой когда перестало существовать хеттское царство Prediction: джой когда перестало существовать хеттское царство ---------------------------------------------------------------------------------------------------- Reference: олег яковлев Prediction: олег яковлев ---------------------------------------------------------------------------------------------------- Reference: посоветуй мне шестая часть как избежать наказания за убийство Prediction: посоветуй мне шестая часть как избежать наказания за убийство ``` The Google Colab version of [this script](https://colab.research.google.com/drive/1SnQmrt6HmMNV-zK-UCPajuwl1JvoCqbX?usp=sharing) is available too. ## Evaluation This model was evaluated on the test subsets of [SberDevices Golos](https://huggingface.co/datasets/SberDevices/Golos), [Common Voice 6.0](https://huggingface.co/datasets/common_voice) (Russian part), and [Russian Librispeech](https://huggingface.co/datasets/bond005/rulibrispeech), but it was trained on the training subset of SberDevices Golos only. You can see the evaluation script on other datasets, including Russian Librispeech and SOVA RuDevices, on my Kaggle web-page https://www.kaggle.com/code/bond005/wav2vec2-ru-lm-eval ## Citation If you want to cite this model you can use this: ```bibtex @misc{bondarenko2022wav2vec2-large-ru-golos, title={XLSR Wav2Vec2 Russian with 2-gram Language Model by Ivan Bondarenko}, author={Bondarenko, Ivan}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/bond005/wav2vec2-large-ru-golos-with-lm}}, year={2022} } ```
smartmind/doctr-vitstr_base-recognition
smartmind
2023-02-27T05:51:26Z
6
0
doctr
[ "doctr", "pytorch", "ko", "region:us" ]
null
2023-01-16T00:11:46Z
--- language: - ko library_name: doctr --- <p align="center"> <img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%"> </p> **Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch** ## Task: recognition https://github.com/mindee/doctr ### Example usage: ```python >>> from doctr.io import DocumentFile >>> from doctr.models import ocr_predictor, from_hub >>> img = DocumentFile.from_images(['<image_path>']) >>> # Load your model from the hub >>> model = from_hub('mindee/my-model') >>> # Pass it to the predictor >>> # If your model is a recognition model: >>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large', >>> reco_arch=model, >>> pretrained=True) >>> # If your model is a detection model: >>> predictor = ocr_predictor(det_arch=model, >>> reco_arch='crnn_mobilenet_v3_small', >>> pretrained=True) >>> # Get your predictions >>> res = predictor(img) ```
smartmind/doctr-db_resnet50
smartmind
2023-02-27T05:49:28Z
166
1
doctr
[ "doctr", "pytorch", "ko", "region:us" ]
null
2023-02-27T04:42:43Z
--- language: - ko library_name: doctr --- <p align="center"> <img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%"> </p> **Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch** ## Task: detection https://github.com/mindee/doctr ### Example usage: ```python >>> from doctr.io import DocumentFile >>> from doctr.models import ocr_predictor, from_hub >>> img = DocumentFile.from_images(['<image_path>']) >>> # Load your model from the hub >>> model = from_hub('mindee/my-model') >>> # Pass it to the predictor >>> # If your model is a recognition model: >>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large', >>> reco_arch=model, >>> pretrained=True) >>> # If your model is a detection model: >>> predictor = ocr_predictor(det_arch=model, >>> reco_arch='crnn_mobilenet_v3_small', >>> pretrained=True) >>> # Get your predictions >>> res = predictor(img) ```
gyeoldere/DeBERTa-finetuned-SNLI4
gyeoldere
2023-02-27T05:19:31Z
76
0
transformers
[ "transformers", "pytorch", "deberta", "generated_from_trainer", "dataset:snli", "license:mit", "endpoints_compatible", "region:us" ]
null
2023-02-16T07:19:33Z
--- license: mit tags: - generated_from_trainer datasets: - snli model-index: - name: DeBERTa-finetuned-SNLI4 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. --> # DeBERTa-finetuned-SNLI4 This model is a fine-tuned version of [gyeoldere/DeBERTa-finetuned-SNLI2](https://huggingface.co/gyeoldere/DeBERTa-finetuned-SNLI2) on the snli dataset. ## Model description fliped_forth used ## Intended uses & limitations More information needed ## Training and evaluation data final training loss : 1.216 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
luolirui/my_awesome_eli5_clm-model3
luolirui
2023-02-27T05:19:01Z
105
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-02-27T03:54:01Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model3 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_eli5_clm-model3 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: 0.6945 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.7001 | 1.0 | 13178 | 0.6977 | | 0.7005 | 2.0 | 26356 | 0.6938 | | 0.6964 | 3.0 | 39534 | 0.6945 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.12.1 - Datasets 2.10.0 - Tokenizers 0.13.2
kelestemur/a2c-AntBulletEnv-v0
kelestemur
2023-02-27T05:18:00Z
3
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T05:16:45Z
--- 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: 1777.32 +/- 23.42 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 ... ```
LarryAIDraw/weriDiffusion_v10
LarryAIDraw
2023-02-27T04:32:26Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-02-27T04:01:29Z
--- license: creativeml-openrail-m ---
LucaReggiani/t5-small-nlpfinalproject99-xsum
LucaReggiani
2023-02-27T04:10:39Z
62
0
transformers
[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-27T03:55:04Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: LucaReggiani/t5-small-nlpfinalproject99-xsum 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. --> # LucaReggiani/t5-small-nlpfinalproject99-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.0379 - Validation Loss: 2.9903 - Train Rouge1: 23.6196 - Train Rouge2: 5.8829 - Train Rougel: 18.9509 - Train Rougelsum: 19.0041 - Train Gen Len: 18.6 - Epoch: 10 ## 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': 3e-05, 'beta_1': 0.9, 'beta_2': 0.98, 'epsilon': 1e-06, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 3.8865 | 3.3185 | 17.9926 | 2.6334 | 14.3776 | 14.4109 | 18.74 | 0 | | 3.5092 | 3.1756 | 19.9492 | 3.6172 | 15.6914 | 15.7191 | 18.31 | 1 | | 3.4012 | 3.1160 | 21.2372 | 4.0016 | 16.5756 | 16.5655 | 18.45 | 2 | | 3.3268 | 3.0809 | 21.5751 | 4.0776 | 16.5050 | 16.5345 | 18.58 | 3 | | 3.2660 | 3.0550 | 21.7071 | 4.1832 | 16.8604 | 16.8708 | 18.64 | 4 | | 3.2125 | 3.0377 | 21.9791 | 4.8202 | 17.3234 | 17.3660 | 18.46 | 5 | | 3.1829 | 3.0218 | 22.4277 | 5.0402 | 17.7633 | 17.8109 | 18.64 | 6 | | 3.1358 | 3.0142 | 23.5653 | 5.3418 | 18.8989 | 18.9198 | 18.64 | 7 | | 3.1011 | 3.0042 | 23.1459 | 5.0797 | 18.3238 | 18.3087 | 18.62 | 8 | | 3.0681 | 2.9995 | 22.9719 | 4.9597 | 17.9675 | 17.9490 | 18.57 | 9 | | 3.0379 | 2.9903 | 23.6196 | 5.8829 | 18.9509 | 19.0041 | 18.6 | 10 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.10.0 - Tokenizers 0.13.2
dongpil/my-awesome-setfit-model
dongpil
2023-02-27T03:36:03Z
5
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
2023-02-27T03:34:19Z
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # dongpil/my-awesome-setfit-model This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("dongpil/my-awesome-setfit-model") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
Yaoyu/sd-class-butterflies-64-accelerate
Yaoyu
2023-02-27T03:33:08Z
30
0
diffusers
[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
2023-02-27T03:30:23Z
--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Yaoyu/sd-class-butterflies-64-accelerate') image = pipeline().images[0] image ```
Yaoyu/sd-class-butterflies-64
Yaoyu
2023-02-27T03:30:02Z
30
0
diffusers
[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
2023-02-27T01:46:50Z
--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Yaoyu/sd-class-butterflies-64') image = pipeline().images[0] image ```
dyingc/Taxi-v3
dyingc
2023-02-27T02:20:08Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-26T21:52:58Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="dyingc/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"]) ```
boost/PPO-LunarLander-v2
boost
2023-02-27T02:03:50Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T00:28:07Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO-bigger results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 283.47 +/- 17.30 name: mean_reward verified: false --- # **PPO-bigger** Agent playing **LunarLander-v2** This is a trained model of a **PPO-bigger** 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 ... ```
afaji/fine-tuned-IndoNLI-Translated-with-indobert-large-p2
afaji
2023-02-27T01:59:11Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-02-25T12:36:03Z
--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: fine-tuned-IndoNLI-Translated-with-indobert-large-p2 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. --> # fine-tuned-IndoNLI-Translated-with-indobert-large-p2 This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6126 - Accuracy: 0.8090 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.549 | 1.0 | 6136 | 0.5307 | 0.7896 | | 0.498 | 2.0 | 12272 | 0.4908 | 0.8072 | | 0.3704 | 3.0 | 18408 | 0.5087 | 0.8105 | | 0.3102 | 4.0 | 24544 | 0.5708 | 0.8111 | | 0.2226 | 5.0 | 30680 | 0.6435 | 0.8053 | | 0.1601 | 6.0 | 36816 | 0.7676 | 0.8034 | | 0.1133 | 7.0 | 42952 | 0.8197 | 0.8083 | | 0.1091 | 8.0 | 49088 | 0.9384 | 0.8059 | | 0.066 | 9.0 | 55224 | 1.0333 | 0.8066 | | 0.058 | 10.0 | 61360 | 1.1211 | 0.8061 | | 0.0539 | 11.0 | 67496 | 1.2260 | 0.8080 | | 0.0357 | 12.0 | 73632 | 1.3470 | 0.8058 | | 0.0256 | 13.0 | 79768 | 1.4499 | 0.8079 | | 0.0289 | 14.0 | 85904 | 1.5078 | 0.8070 | | 0.0259 | 15.0 | 92040 | 1.5818 | 0.8078 | | 0.0193 | 16.0 | 98176 | 1.6126 | 0.8090 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.2.0 - Tokenizers 0.13.2
Timoti/Uber_Realistic_Porn_Merge13
Timoti
2023-02-27T01:49:57Z
0
27
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-02-26T23:55:07Z
--- license: creativeml-openrail-m ---
emylrahim/Reinforce-CartPole-v1
emylrahim
2023-02-27T01:07:26Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T01:07:16Z
--- 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
rodrfons/taxi-v3
rodrfons
2023-02-27T00:19:34Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T00:19:31Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="rodrfons/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"]) ```
sdeg/gpt2-finetuned-v3-seinfeld
sdeg
2023-02-27T00:14:57Z
106
0
transformers
[ "transformers", "pytorch", "tensorboard", "opt", "text-generation", "generated_from_trainer", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-02-27T00:08:32Z
--- license: other tags: - generated_from_trainer model-index: - name: gpt2-finetuned-v3-seinfeld 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. --> # gpt2-finetuned-v3-seinfeld This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8039 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3507 | 1.09 | 25 | 2.7998 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
g30rv17ys/hkdb-wamd-sdft
g30rv17ys
2023-02-27T00:09:52Z
7
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-02-27T00:04:46Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### HKDB_WAMD_SDFT Dreambooth model trained by geevegeorge 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:
ctebright/ppo-Huggy
ctebright
2023-02-26T23:38:03Z
12
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-02-26T23:37:53Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: ctebright/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
ahmmu20/bliznyashkiTheTwins
ahmmu20
2023-02-26T23:31:21Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-02-26T23:28:53Z
--- license: creativeml-openrail-m --- This is a LORA file -- not mine -- uploaded here to use in Colab Check the Civitai page for more info https://civitai.com/models/7613/bliznyashki-the-twins-atomic-heart
iblub/poca-SoccerTwos
iblub
2023-02-26T22:46:45Z
17
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-02-26T22:46:03Z
--- 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: iblub/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Yagorka/ddpm-pokemons-128_300_epochs_1000_steps_real_cont
Yagorka
2023-02-26T22:43:36Z
0
0
diffusers
[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]
null
2023-02-26T10:14:05Z
--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-pokemons-128_300_epochs_1000_steps_real_cont ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 11 - eval_batch_size: 12 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/Yagorka/ddpm-pokemons-128_300_epochs_1000_steps_real_cont/tensorboard?#scalars)
harshil128/Reinforce-model1
harshil128
2023-02-26T22:37:09Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-26T22:36:58Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-model1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 353.30 +/- 73.52 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
KubiakJakub01/a2c-PandaReachDense-v2
KubiakJakub01
2023-02-26T22:32:54Z
5
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-25T20:32:06Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -2.08 +/- 0.33 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 ... ```
ctebright/ppo-LunarLander-v2
ctebright
2023-02-26T22:31:36Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-26T21:55:57Z
--- 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: 260.88 +/- 20.89 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 ... ```
GraceEmily24/Dog
GraceEmily24
2023-02-26T22:30:46Z
0
0
null
[ "license:bigscience-openrail-m", "region:us" ]
null
2023-02-26T22:30:46Z
--- license: bigscience-openrail-m ---
G-e-o-r-g-e/a2c-AntBulletEnv-v0
G-e-o-r-g-e
2023-02-26T22:27:28Z
4
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-26T22:26:16Z
--- 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: 961.13 +/- 105.71 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 ... ```
polejowska/detr-resnet-50-CD45RB-100
polejowska
2023-02-26T22:06:32Z
29
0
transformers
[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
object-detection
2023-02-26T18:06:03Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: detr-resnet-50-CD45RB-100 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-CD45RB-100 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6658 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1316 | 1.0 | 94 | 2.3431 | | 2.812 | 2.0 | 188 | 2.2115 | | 2.8118 | 3.0 | 282 | 1.9844 | | 2.5555 | 4.0 | 376 | 1.9309 | | 2.4803 | 5.0 | 470 | 1.8790 | | 2.5099 | 6.0 | 564 | 2.0294 | | 2.5365 | 7.0 | 658 | 1.8845 | | 2.4593 | 8.0 | 752 | 1.8699 | | 2.4248 | 9.0 | 846 | 1.7946 | | 2.4017 | 10.0 | 940 | 1.7905 | | 2.4523 | 11.0 | 1034 | 1.8319 | | 2.4407 | 12.0 | 1128 | 1.8370 | | 2.3727 | 13.0 | 1222 | 1.8001 | | 2.317 | 14.0 | 1316 | 1.7492 | | 2.3292 | 15.0 | 1410 | 1.7531 | | 2.3086 | 16.0 | 1504 | 1.7637 | | 2.3175 | 17.0 | 1598 | 1.7302 | | 2.3002 | 18.0 | 1692 | 1.7216 | | 2.2756 | 19.0 | 1786 | 1.7345 | | 2.2656 | 20.0 | 1880 | 1.7225 | | 2.3083 | 21.0 | 1974 | 1.7549 | | 2.2542 | 22.0 | 2068 | 1.7175 | | 2.2262 | 23.0 | 2162 | 1.6998 | | 2.2644 | 24.0 | 2256 | 1.7020 | | 2.2392 | 25.0 | 2350 | 1.6933 | | 2.228 | 26.0 | 2444 | 1.7434 | | 2.2284 | 27.0 | 2538 | 1.7070 | | 2.2019 | 28.0 | 2632 | 1.6977 | | 2.1804 | 29.0 | 2726 | 1.6867 | | 2.1939 | 30.0 | 2820 | 1.6859 | | 2.1863 | 31.0 | 2914 | 1.6802 | | 2.2009 | 32.0 | 3008 | 1.6940 | | 2.1894 | 33.0 | 3102 | 1.6720 | | 2.1759 | 34.0 | 3196 | 1.6700 | | 2.1575 | 35.0 | 3290 | 1.6713 | | 2.1715 | 36.0 | 3384 | 1.7287 | | 2.2125 | 37.0 | 3478 | 1.6994 | | 2.2032 | 38.0 | 3572 | 1.6896 | | 2.21 | 39.0 | 3666 | 1.6793 | | 2.1837 | 40.0 | 3760 | 1.6747 | | 2.2136 | 41.0 | 3854 | 1.6728 | | 2.1825 | 42.0 | 3948 | 1.6641 | | 2.1419 | 43.0 | 4042 | 1.6829 | | 2.1695 | 44.0 | 4136 | 1.6625 | | 2.1478 | 45.0 | 4230 | 1.6680 | | 2.1464 | 46.0 | 4324 | 1.6795 | | 2.1809 | 47.0 | 4418 | 1.6775 | | 2.174 | 48.0 | 4512 | 1.6668 | | 2.1391 | 49.0 | 4606 | 1.6559 | | 2.1466 | 50.0 | 4700 | 1.6658 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
Minata/plbart-base-finetuned-ut-generator
Minata
2023-02-26T22:00:17Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "plbart", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-24T22:36:17Z
--- tags: - generated_from_trainer model-index: - name: plbart-base-finetuned-ut-generator 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. --> # plbart-base-finetuned-ut-generator This model is a fine-tuned version of [uclanlp/plbart-base](https://huggingface.co/uclanlp/plbart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2744 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6096 | 0.44 | 500 | 0.3797 | | 0.3706 | 0.89 | 1000 | 0.3465 | | 0.3534 | 1.33 | 1500 | 0.3283 | | 0.3132 | 1.78 | 2000 | 0.3142 | | 0.305 | 2.22 | 2500 | 0.3044 | | 0.2923 | 2.67 | 3000 | 0.2972 | | 0.2908 | 3.11 | 3500 | 0.2911 | | 0.2796 | 3.56 | 4000 | 0.2856 | | 0.2731 | 4.0 | 4500 | 0.2814 | | 0.2663 | 4.44 | 5000 | 0.2785 | | 0.2638 | 4.89 | 5500 | 0.2764 | | 0.2597 | 5.33 | 6000 | 0.2750 | | 0.2522 | 5.78 | 6500 | 0.2744 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
polejowska/yolos-tiny-CD45RB-1000
polejowska
2023-02-26T22:00:14Z
9
0
transformers
[ "transformers", "pytorch", "tensorboard", "yolos", "object-detection", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
object-detection
2023-02-26T20:20:22Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: yolos-tiny-CD45RB-1000 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. --> # yolos-tiny-CD45RB-1000 This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6317 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.4965 | 1.0 | 94 | 2.7799 | | 3.4526 | 2.0 | 188 | 2.7380 | | 3.4012 | 3.0 | 282 | 2.6721 | | 3.2776 | 4.0 | 376 | 2.6651 | | 3.2164 | 5.0 | 470 | 2.6555 | | 3.2701 | 6.0 | 564 | 2.6489 | | 3.1847 | 7.0 | 658 | 2.6993 | | 3.0959 | 8.0 | 752 | 2.6364 | | 3.0506 | 9.0 | 846 | 2.6464 | | 3.0497 | 10.0 | 940 | 2.6304 | | 3.0767 | 11.0 | 1034 | 2.6344 | | 3.0397 | 12.0 | 1128 | 2.6142 | | 2.982 | 13.0 | 1222 | 2.6787 | | 2.883 | 14.0 | 1316 | 2.6492 | | 2.8978 | 15.0 | 1410 | 2.6317 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
ivanlai/mt5-summarize-ch_trad
ivanlai
2023-02-26T21:57:48Z
3
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:xlsum", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-18T12:14:16Z
--- tags: - generated_from_trainer datasets: - xlsum model-index: - name: mt5-summarize-ch_trad 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. --> # mt5-summarize-ch_trad This model is a fine-tuned version of [uer/t5-small-chinese-cluecorpussmall](https://huggingface.co/uer/t5-small-chinese-cluecorpussmall) on the xlsum dataset. It achieves the following results on the evaluation set: - eval_loss: 2.2489 - eval_rouge1: 0.1313 - eval_rouge2: 0.0505 - eval_rougeL: 0.1275 - eval_rougeLsum: 0.1272 - eval_gen_len: 128.0 - eval_runtime: 541.5872 - eval_samples_per_second: 8.623 - eval_steps_per_second: 0.27 - epoch: 7.71 - step: 9000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2
Liapunov/poca-SoccerTwos
Liapunov
2023-02-26T21:21:01Z
6
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-02-26T21:20:49Z
--- 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: Liapunov/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Convolution/rl_course_vizdoom_health_gathering_supreme
Convolution
2023-02-26T21:16:40Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-26T21:16:35Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.07 +/- 4.34 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Convolution/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
lora-library/girl-zty-2
lora-library
2023-02-26T21:15:01Z
5
1
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-02-26T21:14:58Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: girl_zty_2 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - girl-zty-2 These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "girl_zty_2" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: girl_zty_2 ![image_0](test_images/image_0.png) ![image_1](test_images/image_1.png) ![image_2](test_images/image_2.png) ![image_3](test_images/image_3.png)
dyingc/q-FrozenLake-v1-8x8-noSlippery
dyingc
2023-02-26T21:03:44Z
0
0
null
[ "FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-26T21:03:38Z
--- tags: - FrozenLake-v1-8x8-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-8x8-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-8x8-no_slippery type: FrozenLake-v1-8x8-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="dyingc/q-FrozenLake-v1-8x8-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"]) ```
damian0815/pashahlis-val-test-1e-6-ep110
damian0815
2023-02-26T20:57:20Z
7
0
diffusers
[ "diffusers", "license:openrail", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-02-26T19:05:15Z
--- license: openrail --- Epoch 110 (overtrained) from training a dataset kindly provided by @pashahlis; see [https://huggingface.co/damian0815/pashahlis-val-test-1e-6-ep30](https://huggingface.co/damian0815/pashahlis-val-test-1e-6-ep30) for more information.
ahmad-alismail/a2c-PandaReachDense-v2
ahmad-alismail
2023-02-26T20:38:12Z
1
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-26T20:12:22Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.99 +/- 0.20 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). ## Hyperparameters ```python # 4 policy = "MultiInputPolicy" learning_rate=0.001 gamma=0.95 time_steps=100000 ... ```
domadapter/domain_only_MR_books
domadapter
2023-02-26T20:37:54Z
1
0
adapter-transformers
[ "adapter-transformers", "bert", "adapterhub:sentiment/amazon", "dataset:amazon", "region:us" ]
null
2023-02-26T20:37:47Z
--- tags: - bert - adapterhub:sentiment/amazon - adapter-transformers datasets: - amazon --- # Adapter `domadapter/domain_only_MR_books` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("domadapter/domain_only_MR_books", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
domadapter/domain_only_MR_baby
domadapter
2023-02-26T20:37:44Z
2
0
adapter-transformers
[ "adapter-transformers", "bert", "adapterhub:sentiment/amazon", "dataset:amazon", "region:us" ]
null
2023-02-26T20:37:35Z
--- tags: - bert - adapterhub:sentiment/amazon - adapter-transformers datasets: - amazon --- # Adapter `domadapter/domain_only_MR_baby` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("domadapter/domain_only_MR_baby", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
domadapter/domain_only_camera_photo_MR
domadapter
2023-02-26T20:37:21Z
1
0
adapter-transformers
[ "adapter-transformers", "bert", "adapterhub:sentiment/amazon", "dataset:amazon", "region:us" ]
null
2023-02-26T20:37:13Z
--- tags: - bert - adapterhub:sentiment/amazon - adapter-transformers datasets: - amazon --- # Adapter `domadapter/domain_only_camera_photo_MR` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("domadapter/domain_only_camera_photo_MR", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
domadapter/domain_only_camera_photo_books
domadapter
2023-02-26T20:37:10Z
1
0
adapter-transformers
[ "adapter-transformers", "bert", "adapterhub:sentiment/amazon", "dataset:amazon", "region:us" ]
null
2023-02-26T20:37:02Z
--- tags: - bert - adapterhub:sentiment/amazon - adapter-transformers datasets: - amazon --- # Adapter `domadapter/domain_only_camera_photo_books` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("domadapter/domain_only_camera_photo_books", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
domadapter/domain_only_books_MR
domadapter
2023-02-26T20:36:38Z
2
0
adapter-transformers
[ "adapter-transformers", "bert", "adapterhub:sentiment/amazon", "dataset:amazon", "region:us" ]
null
2023-02-26T20:36:30Z
--- tags: - bert - adapterhub:sentiment/amazon - adapter-transformers datasets: - amazon --- # Adapter `domadapter/domain_only_books_MR` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("domadapter/domain_only_books_MR", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
domadapter/domain_only_books_camera_photo
domadapter
2023-02-26T20:36:27Z
2
0
adapter-transformers
[ "adapter-transformers", "bert", "adapterhub:sentiment/amazon", "dataset:amazon", "region:us" ]
null
2023-02-26T20:36:19Z
--- tags: - bert - adapterhub:sentiment/amazon - adapter-transformers datasets: - amazon --- # Adapter `domadapter/domain_only_books_camera_photo` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("domadapter/domain_only_books_camera_photo", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
domadapter/domain_only_books_baby
domadapter
2023-02-26T20:36:16Z
0
0
adapter-transformers
[ "adapter-transformers", "bert", "adapterhub:sentiment/amazon", "dataset:amazon", "region:us" ]
null
2023-02-26T20:36:09Z
--- tags: - bert - adapterhub:sentiment/amazon - adapter-transformers datasets: - amazon --- # Adapter `domadapter/domain_only_books_baby` for bert-base-uncased An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-uncased") adapter_name = model.load_adapter("domadapter/domain_only_books_baby", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->