librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
aszfcxcgszdx/samsum	aszfcxcgszdx	2023-03-15T14:30:17Z	109	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain", "summarization", "en", "dataset:aszfcxcgszdx/autotrain-data-samsum-auto", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-03-15T14:25:51Z	--- tags: - autotrain - summarization language: - en widget: - text: "I love AutoTrain 🤗" datasets: - aszfcxcgszdx/autotrain-data-samsum-auto co2_eq_emissions: emissions: 0.0077793677303344775 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 41244106342 - CO2 Emissions (in grams): 0.0078 ## Validation Metrics - Loss: 1.565 - Rouge1: 47.592 - Rouge2: 23.270 - RougeL: 39.623 - RougeLsum: 43.180 - Gen Len: 18.305 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/aszfcxcgszdx/autotrain-samsum-auto-41244106342 ```
aszfcxcgszdx/multilingual-samsum	aszfcxcgszdx	2023-03-15T14:29:30Z	9	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain", "summarization", "unk", "dataset:aszfcxcgszdx/autotrain-data-multi-lingual-summarization", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-03-15T13:54:42Z	--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - aszfcxcgszdx/autotrain-data-multi-lingual-summarization co2_eq_emissions: emissions: 13.328572874208332 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 41234106312 - CO2 Emissions (in grams): 13.3286 ## Validation Metrics - Loss: 1.508 - Rouge1: 44.068 - Rouge2: 20.883 - RougeL: 37.071 - RougeLsum: 40.613 - Gen Len: 17.000 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/aszfcxcgszdx/autotrain-multi-lingual-summarization-41234106312 ```
aszfcxcgszdx/mt5-large-samsum	aszfcxcgszdx	2023-03-15T14:27:58Z	115	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain", "summarization", "unk", "dataset:aszfcxcgszdx/autotrain-data-multi-lingual-summarization", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-03-15T13:54:46Z	--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - aszfcxcgszdx/autotrain-data-multi-lingual-summarization co2_eq_emissions: emissions: 12.703463244389663 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 41234106313 - CO2 Emissions (in grams): 12.7035 ## Validation Metrics - Loss: 1.508 - Rouge1: 44.142 - Rouge2: 21.000 - RougeL: 37.127 - RougeLsum: 40.611 - Gen Len: 17.000 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/aszfcxcgszdx/autotrain-multi-lingual-summarization-41234106313 ```
quilaquedi/ppo-LunarLander-v2	quilaquedi	2023-03-15T14:17:11Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T08:48:03Z	--- 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: 253.86 +/- 21.14 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 ... ```
MarkieMark1/a2c-AntBulletEnv-v0	MarkieMark1	2023-03-15T14:14:04Z	1	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T14:12:57Z	--- 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: 1450.36 +/- 87.93 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 ... ```
nouman-10/fine-tune-bert-combined-mlm	nouman-10	2023-03-15T14:03:55Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-15T12:49:52Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: unsupervised-fine-tune-bert-cased-combined 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. --> # unsupervised-fine-tune-bert-cased-combined This model is a fine-tuned version of [nouman-10/unsupervised-comb-cased](https://huggingface.co/nouman-10/unsupervised-comb-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4579 - Accuracy: 0.7384 - F1: 0.7384 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.4463 \| 1.0 \| 1819 \| 0.7093 \| 0.6483 \| 0.6483 \| \| 0.3304 \| 2.0 \| 3638 \| 0.5988 \| 0.7471 \| 0.7471 \| \| 0.211 \| 3.0 \| 5457 \| 0.8888 \| 0.75 \| 0.75 \| \| 0.1237 \| 4.0 \| 7276 \| 1.4573 \| 0.7355 \| 0.7355 \| \| 0.0959 \| 5.0 \| 9095 \| 1.7000 \| 0.7355 \| 0.7355 \| \| 0.062 \| 6.0 \| 10914 \| 2.0796 \| 0.7064 \| 0.7064 \| \| 0.0347 \| 7.0 \| 12733 \| 1.7562 \| 0.7558 \| 0.7558 \| \| 0.0259 \| 8.0 \| 14552 \| 2.3160 \| 0.7267 \| 0.7267 \| \| 0.0166 \| 9.0 \| 16371 \| 2.3301 \| 0.7471 \| 0.7471 \| \| 0.0091 \| 10.0 \| 18190 \| 2.4579 \| 0.7384 \| 0.7384 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
Prgrg/ja-en-JESC-v3.0	Prgrg	2023-03-15T13:59:40Z	69	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T11:51:43Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Prgrg/ja-en-JESC-v3.0 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. --> # Prgrg/ja-en-JESC-v3.0 This model is a fine-tuned version of [Prgrg/ja-en-JESC-v2.0](https://huggingface.co/Prgrg/ja-en-JESC-v2.0) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.8267 - Validation Loss: 7.8094 - Epoch: 5 ## 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': 0.0005, 'decay_steps': 150000, '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': 0.001} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.3432 \| 6.9622 \| 0 \| \| 5.2217 \| 7.5277 \| 1 \| \| 5.1853 \| 7.5818 \| 2 \| \| 4.9986 \| 7.5179 \| 3 \| \| 4.8957 \| 7.7693 \| 4 \| \| 4.8267 \| 7.8094 \| 5 \| ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2
psheaton/RoBERTa_for_eyewitness_confidence	psheaton	2023-03-15T13:58:23Z	110	2	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "legal", "en", "license:afl-3.0", "autotrain_compatible", "region:us" ]	text-classification	2023-03-15T13:40:16Z	--- inference: false license: afl-3.0 language: - en library_name: transformers pipeline_tag: text-classification tags: - legal ---
lora-library/girlwyt	lora-library	2023-03-15T13:57:36Z	1	0	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	2023-03-15T13:57:32Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: wyt tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - girlwyt 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 "wyt" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: wyt ![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)
vickylin21/Twitter_sentiment_analysis	vickylin21	2023-03-15T13:56:35Z	111	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-12T04:26:32Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: Twitter_sentiment_analysis 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. --> # Twitter_sentiment_analysis This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1891 - Accuracy: 0.9275 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7259 \| 1.0 \| 800 \| 0.2336 \| 0.92 \| \| 0.1542 \| 2.0 \| 1600 \| 0.1891 \| 0.9275 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
aienthused/Pixelcopter-PLE-v0	aienthused	2023-03-15T13:56:04Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T11:20:16Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 42.00 +/- 40.93 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
YashGajjar/Taxi-v3_Q-agent	YashGajjar	2023-03-15T13:53:56Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T13:53:48Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3_Q-agent 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="YashGajjar/Taxi-v3_Q-agent", 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"]) ```
EExe/Reinforce-cartpole	EExe	2023-03-15T13:52:32Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T13:52:19Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 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
peterdamn/a2c-AntBulletEnv-v0	peterdamn	2023-03-15T13:52:23Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T13:51:13Z	--- 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: 1556.86 +/- 35.82 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 ... ```
Christian90/pixelcoper-v1	Christian90	2023-03-15T13:52:14Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T13:50:04Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcoper-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -4.80 +/- 0.60 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
yyq90/dqn-SpaceInvadersNoFrameskip-v4	yyq90	2023-03-15T13:46:13Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T13:45:26Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 873.50 +/- 316.31 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga yyq90 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga yyq90 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga yyq90 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
reyhanemyr/distilbert-base-cased-finetuned-paper3	reyhanemyr	2023-03-15T13:40:41Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-03-15T13:26:54Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-cased-finetuned-paper3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-cased-finetuned-paper3 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1966 - Precision: 0.6773 - Recall: 0.7350 - F1: 0.7050 - Accuracy: 0.9687 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 73 \| 0.1851 \| 0.4383 \| 0.4479 \| 0.4431 \| 0.9434 \| \| No log \| 2.0 \| 146 \| 0.1473 \| 0.5455 \| 0.5678 \| 0.5564 \| 0.9593 \| \| No log \| 3.0 \| 219 \| 0.1391 \| 0.6509 \| 0.6530 \| 0.6520 \| 0.9646 \| \| No log \| 4.0 \| 292 \| 0.1236 \| 0.6552 \| 0.7192 \| 0.6857 \| 0.9702 \| \| No log \| 5.0 \| 365 \| 0.1352 \| 0.6724 \| 0.7382 \| 0.7038 \| 0.9693 \| \| No log \| 6.0 \| 438 \| 0.1594 \| 0.6746 \| 0.7129 \| 0.6933 \| 0.9673 \| \| 0.0969 \| 7.0 \| 511 \| 0.1693 \| 0.6705 \| 0.7382 \| 0.7027 \| 0.9683 \| \| 0.0969 \| 8.0 \| 584 \| 0.1806 \| 0.6923 \| 0.7382 \| 0.7145 \| 0.9692 \| \| 0.0969 \| 9.0 \| 657 \| 0.1594 \| 0.6359 \| 0.7603 \| 0.6925 \| 0.9687 \| \| 0.0969 \| 10.0 \| 730 \| 0.1740 \| 0.6946 \| 0.7319 \| 0.7127 \| 0.9683 \| \| 0.0969 \| 11.0 \| 803 \| 0.1881 \| 0.6735 \| 0.7287 \| 0.7 \| 0.9677 \| \| 0.0969 \| 12.0 \| 876 \| 0.1932 \| 0.7064 \| 0.7287 \| 0.7174 \| 0.9692 \| \| 0.0969 \| 13.0 \| 949 \| 0.1890 \| 0.6907 \| 0.7256 \| 0.7077 \| 0.9689 \| \| 0.0025 \| 14.0 \| 1022 \| 0.1860 \| 0.6705 \| 0.7445 \| 0.7055 \| 0.9696 \| \| 0.0025 \| 15.0 \| 1095 \| 0.1951 \| 0.6706 \| 0.7256 \| 0.6970 \| 0.9688 \| \| 0.0025 \| 16.0 \| 1168 \| 0.1936 \| 0.6648 \| 0.7319 \| 0.6967 \| 0.9681 \| \| 0.0025 \| 17.0 \| 1241 \| 0.1969 \| 0.6725 \| 0.7319 \| 0.7009 \| 0.9686 \| \| 0.0025 \| 18.0 \| 1314 \| 0.1953 \| 0.6792 \| 0.7413 \| 0.7089 \| 0.9692 \| \| 0.0025 \| 19.0 \| 1387 \| 0.1960 \| 0.6754 \| 0.7350 \| 0.7039 \| 0.9687 \| \| 0.0025 \| 20.0 \| 1460 \| 0.1966 \| 0.6773 \| 0.7350 \| 0.7050 \| 0.9687 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
avuhong/ParvoGPT2	avuhong	2023-03-15T13:36:45Z	119	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-03-15T13:27:47Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: output 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. --> # output This model is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6699 - Accuracy: 0.7571 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 220 \| 3.8564 \| 0.4857 \| \| No log \| 2.0 \| 440 \| 2.7515 \| 0.6096 \| \| 4.1568 \| 3.0 \| 660 \| 2.2463 \| 0.6780 \| \| 4.1568 \| 4.0 \| 880 \| 1.9817 \| 0.7152 \| \| 2.2818 \| 5.0 \| 1100 \| 1.8278 \| 0.7353 \| \| 2.2818 \| 6.0 \| 1320 \| 1.7313 \| 0.7486 \| \| 1.8444 \| 7.0 \| 1540 \| 1.6847 \| 0.7553 \| \| 1.8444 \| 8.0 \| 1760 \| 1.6699 \| 0.7571 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2
DipeshY/roberta-finetuned-disaster_type_dy	DipeshY	2023-03-15T13:26:51Z	131	0	transformers	[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2023-03-15T12:54:25Z	--- license: cc-by-4.0 tags: - generated_from_trainer model-index: - name: roberta-finetuned-disaster_type_dy results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-finetuned-disaster_type_dy This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cpu - Datasets 2.10.1 - Tokenizers 0.13.2
TiborUdvari/distilgpt2-test-douglas-finetuned-hitchhiker	TiborUdvari	2023-03-15T13:15:04Z	117	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-03-15T13:05:34Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-test-douglas-finetuned-hitchhiker results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-test-douglas-finetuned-hitchhiker 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: 4.1353 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 83 \| 4.3445 \| \| No log \| 2.0 \| 166 \| 4.1845 \| \| No log \| 3.0 \| 249 \| 4.1353 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
stelladk/A2C-AntBulletEnv-v0	stelladk	2023-03-15T13:01:15Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T11:36:46Z	--- 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: 1572.51 +/- 52.53 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 ... ```
ThoDum/a2c-PandaReachDense-v2	ThoDum	2023-03-15T13:00:02Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T11:44:12Z	--- 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: -0.71 +/- 0.25 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 ... ```
hmatzner/ppo-PyramidsRND	hmatzner	2023-03-15T12:58:08Z	9	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-03-15T12:58:03Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: hmatzner/ppo-PyramidsRND 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Christian90/CartPole-v1	Christian90	2023-03-15T12:57:23Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T12:57:18Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 370.30 +/- 191.91 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
nouman-10/unsupervised-comb-cased	nouman-10	2023-03-15T12:49:10Z	87	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-03-15T12:25:18Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: unsupervised-comb-cased 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. --> # unsupervised-comb-cased This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9912 - Validation Loss: 3.1077 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -711, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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 \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.4995 \| 3.2941 \| 0 \| \| 3.1428 \| 3.1982 \| 1 \| \| 2.9912 \| 3.1077 \| 2 \| ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2
Yoshiii/opt-6.7b-lora	Yoshiii	2023-03-15T12:32:36Z	0	2	null	[ "license:unlicense", "region:us" ]	null	2023-03-15T11:09:03Z	--- license: unlicense --- Running opt-6.7b with added loras locally on windows! # bitsandbytes I needed to get bitsandbytes working in my venv: I replaced the main.py in C:\Users\user\Desktop\test\peft\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py with the one here! I also added a .dll file here: C:\Users\user\Desktop\test\peft\venv\Lib\site-packages\bitsandbytes\libbitsandbytes_cuda116.dll # Training Script (https://github.com/huggingface/peft/commit/df0e1fb59266c9903ddd6dbfe7339bcd2068d150) (It's from their notebook!) ``` #load import os os.environ["CUDA_VISIBLE_DEVICES"]="0" import torch import torch.nn as nn import bitsandbytes as bnb from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained( "facebook/opt-6.7b", load_in_8bit=True, device_map='auto', ) tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b") #post-processing for param in model.parameters(): param.requires_grad = False # freeze the model - train adapters later if param.ndim == 1: # cast the small parameters (e.g. layernorm) to fp32 for stability param.data = param.data.to(torch.float32) model.gradient_checkpointing_enable() # reduce number of stored activations model.enable_input_require_grads() class CastOutputToFloat(nn.Sequential): def forward(self, x): return super().forward(x).to(torch.float32) model.lm_head = CastOutputToFloat(model.lm_head) # apply lora def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} \|\| all params: {all_param} \|\| trainable%: {100 * trainable_params / all_param}" ) # apply lora 2 from peft import LoraConfig, get_peft_model config = LoraConfig( r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none", task_type="CAUSAL_LM" ) model = get_peft_model(model, config) print_trainable_parameters(model) # training import transformers from datasets import load_dataset data = load_dataset("Abirate/english_quotes") data = data.map(lambda samples: tokenizer(samples['quote']), batched=True) trainer = transformers.Trainer( model=model, train_dataset=data['train'], args=transformers.TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, warmup_steps=100, max_steps=200, learning_rate=2e-4, fp16=True, logging_steps=1, output_dir='outputs' ), data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False) ) model.config.use_cache = False # silence the warnings. Please re-enable for inference! trainer.train() # push to huggingface txtloras model.push_to_hub("Yoshiii/opt-6.7b-lora", use_auth_token=True) # inference batch = tokenizer("Two things are infinite: ", return_tensors='pt') with torch.cuda.amp.autocast(): output_tokens = model.generate(batch, max_new_tokens=50) print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True)) ``` # Inference (loading this repo lora from hf) ``` import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "Yoshiii/opt-6.7b-lora" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) # Load the Lora model model = PeftModel.from_pretrained(model, peft_model_id) batch = tokenizer("Two things are infinite: ", return_tensors='pt') with torch.cuda.amp.autocast(): output_tokens = model.generate(batch, max_new_tokens=50) print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True)) ``` Two things are infinite: the universe and human stupidity; and I'm not sure about the universe. -Albert Einstein I'm not sure about the universe either. This output is like the training data. If you run without applying the Lora, it will usually look worse. If you retrain the lora, know that your new lora is not going to output the same results, despite you using the same settings. Inference should usually be deterministic when using the same lora, or using without lora. Also, If you want to download and use the loras from a visible folder, here's the inference script: ``` import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "./loramodel" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) # Load the Lora model model = PeftModel.from_pretrained(model, peft_model_id) batch = tokenizer("Two things are infinite: ", return_tensors='pt') with torch.cuda.amp.autocast(): output_tokens = model.generate(**batch, max_new_tokens=50) print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True)) ``` add your adapter_config.json and your adapter_model.bin to a folder in your current directory named `loramodel`, or whatever you choose.
qfrodicio/ml-roberta-large-finetuned-gesture-prediction-21-classes	qfrodicio	2023-03-15T12:29:55Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-03-15T11:19:48Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: ml-roberta-large-finetuned-gesture-prediction-21-classes 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. --> # ml-roberta-large-finetuned-gesture-prediction-21-classes This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. It achieves the following results on the validation set: - Loss: 0.7506 - Accuracy: 0.7927 - Precision: 0.7829 - Recall: 0.7927 - F1: 0.7837 It achieves the following results on the test set: - Loss: 0.8029 - Accuracy: 0.7720 - Precision: 0.7764 - Recall: 0.7720 - F1: 0.7636 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data This model has been trained on the qfrodicio/gesture-prediction-21-classes dataset ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - weight_decay: 0.01 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 2.4441 \| 1.0 \| 104 \| 1.5033 \| 0.5798 \| 0.5071 \| 0.5798 \| 0.5015 \| \| 1.2049 \| 2.0 \| 208 \| 0.8885 \| 0.7532 \| 0.7434 \| 0.7532 \| 0.7331 \| \| 0.7329 \| 3.0 \| 312 \| 0.7506 \| 0.7927 \| 0.7829 \| 0.7927 \| 0.7837 \| \| 0.4949 \| 4.0 \| 416 \| 0.7801 \| 0.7936 \| 0.7946 \| 0.7936 \| 0.7866 \| \| 0.3221 \| 5.0 \| 520 \| 0.8761 \| 0.7957 \| 0.7889 \| 0.7957 \| 0.7865 \| \| 0.2112 \| 6.0 \| 624 \| 0.9118 \| 0.8062 \| 0.8085 \| 0.8062 \| 0.8004 \| \| 0.1458 \| 7.0 \| 728 \| 0.9391 \| 0.8071 \| 0.8057 \| 0.8071 \| 0.8019 \| \| 0.0988 \| 8.0 \| 832 \| 0.9592 \| 0.8105 \| 0.8073 \| 0.8105 \| 0.8065 \| \| 0.0685 \| 9.0 \| 936 \| 1.0358 \| 0.8057 \| 0.8043 \| 0.8057 \| 0.8016 \| \| 0.052 \| 10.0 \| 1040 \| 1.0511 \| 0.8089 \| 0.8080 \| 0.8089 \| 0.8037 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
pfunk/PongNoFrameskip-v4-DQN_baseline-seed4	pfunk	2023-03-15T12:22:13Z	0	0	cleanrl	[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T12:22:04Z	--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.16 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) DQN Agent Playing PongNoFrameskip-v4 This is a trained model of a DQN agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_baseline.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQN_baseline]" python -m cleanrl_utils.enjoy --exp-name DQN_baseline --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed4/raw/main/dqn_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed4/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed4/raw/main/poetry.lock poetry install --all-extras python dqn_atari.py --exp-name DQN_baseline --seed 4 --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk ``` # Hyperparameters ```python {'alg_type': 'dqn_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'DQN_baseline', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'save_model': True, 'seed': 4, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```
joheras/xlm-roberta-base-finetuned-clinais	joheras	2023-03-15T12:21:34Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-03-15T11:43:46Z	--- license: mit tags: - generated_from_trainer model-index: - name: xlm-roberta-base-finetuned-clinais results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-clinais This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 223 \| 1.7818 \| \| 1.9591 \| 2.0 \| 446 \| 1.6896 \| \| 1.9591 \| 3.0 \| 669 \| 1.6195 \| \| 1.7055 \| 4.0 \| 892 \| 1.5804 \| \| 1.7055 \| 5.0 \| 1115 \| 1.6104 \| ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.0 - Datasets 2.8.0 - Tokenizers 0.12.1
pfunk/PongNoFrameskip-v4-DQN_baseline-seed2	pfunk	2023-03-15T12:20:11Z	0	0	cleanrl	[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T12:20:01Z	--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.41 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) DQN Agent Playing PongNoFrameskip-v4 This is a trained model of a DQN agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_baseline.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQN_baseline]" python -m cleanrl_utils.enjoy --exp-name DQN_baseline --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed2/raw/main/dqn_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed2/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed2/raw/main/poetry.lock poetry install --all-extras python dqn_atari.py --exp-name DQN_baseline --seed 2 --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk ``` # Hyperparameters ```python {'alg_type': 'dqn_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'DQN_baseline', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'save_model': True, 'seed': 2, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```
pfunk/PongNoFrameskip-v4-DQN_baseline-seed3	pfunk	2023-03-15T12:11:56Z	0	0	cleanrl	[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T12:11:48Z	--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.33 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) DQN Agent Playing PongNoFrameskip-v4 This is a trained model of a DQN agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_baseline.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQN_baseline]" python -m cleanrl_utils.enjoy --exp-name DQN_baseline --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed3/raw/main/dqn_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed3/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed3/raw/main/poetry.lock poetry install --all-extras python dqn_atari.py --exp-name DQN_baseline --seed 3 --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk ``` # Hyperparameters ```python {'alg_type': 'dqn_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'DQN_baseline', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'save_model': True, 'seed': 3, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```
shark123/text-to-sparql-LCQUAD	shark123	2023-03-15T12:10:19Z	103	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	2023-03-15T11:40:50Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: text-to-sparql-LCQUAD 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. --> # text-to-sparql-LCQUAD This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Gen Len: 19.0 - Bertscorer-p: 0.2282 - Bertscorer-r: -0.5504 - Bertscorer-f1: -0.1909 - Sacrebleu-score: 0.0000 - Sacrebleu-precisions: [100.0, 100.0, 100.0, 100.0] - Bleu-bp: 0.0000 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Gen Len \| Bertscorer-p \| Bertscorer-r \| Bertscorer-f1 \| Sacrebleu-score \| Sacrebleu-precisions \| Bleu-bp \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------------:\|:------------:\|:-------------:\|:---------------:\|:----------------------------:\|:-------:\| \| 0.0019 \| 1.0 \| 2491 \| 0.0000 \| 19.0 \| 0.2282 \| -0.5504 \| -0.1909 \| 0.0000 \| [100.0, 100.0, 100.0, 100.0] \| 0.0000 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
vocabtrimmer/mt5-small-esquad-qg-trimmed-es-120000	vocabtrimmer	2023-03-15T11:53:50Z	10	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T11:36:51Z	# Vocabulary Trimmed [lmqg/mt5-small-esquad-qg](https://huggingface.co/lmqg/mt5-small-esquad-qg): `vocabtrimmer/mt5-small-esquad-qg-trimmed-es-120000` This model is a trimmed version of [lmqg/mt5-small-esquad-qg](https://huggingface.co/lmqg/mt5-small-esquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-esquad-qg \| vocabtrimmer/mt5-small-esquad-qg-trimmed-es-120000 \| \|:---------------------------\|:---------------------------\|:-----------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 166,944,128 \| \| parameter_size_embedding \| 256,103,424 \| 122,882,048 \| \| vocab_size \| 250,101 \| 120,002 \| \| compression_rate_full \| 100.0 \| 55.62 \| \| compression_rate_embedding \| 100.0 \| 47.98 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| es \| vocabtrimmer/mc4_validation \| text \| es \| validation \| 120000 \| 2 \|
KarosY/lianjia_3l_881per100_1e-3	KarosY	2023-03-15T11:46:44Z	1	0	diffusers	[ "diffusers", "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	2023-03-15T04:04:49Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - https://huggingface.co/KarosY/lianjia_3l_881per100_1e-3 These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were fine-tuned on the None dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
peterdamn/ppo-Pyramids	peterdamn	2023-03-15T11:44:00Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-03-15T11:43:55Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: peterdamn/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-60000	vocabtrimmer	2023-03-15T11:34:47Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T11:20:20Z	# Vocabulary Trimmed [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg): `vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-60000` This model is a trimmed version of [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-ruquad-qg \| vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-60000 \| \|:---------------------------\|:---------------------------\|:----------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 105,504,128 \| \| parameter_size_embedding \| 256,103,424 \| 61,442,048 \| \| vocab_size \| 250,101 \| 60,002 \| \| compression_rate_full \| 100.0 \| 35.15 \| \| compression_rate_embedding \| 100.0 \| 23.99 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| ru \| vocabtrimmer/mc4_validation \| text \| ru \| validation \| 60000 \| 2 \|
vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-60000	vocabtrimmer	2023-03-15T11:33:41Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T11:19:04Z	# Vocabulary Trimmed [lmqg/mt5-small-koquad-qg](https://huggingface.co/lmqg/mt5-small-koquad-qg): `vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-60000` This model is a trimmed version of [lmqg/mt5-small-koquad-qg](https://huggingface.co/lmqg/mt5-small-koquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-koquad-qg \| vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-60000 \| \|:---------------------------\|:---------------------------\|:----------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 105,504,128 \| \| parameter_size_embedding \| 256,103,424 \| 61,442,048 \| \| vocab_size \| 250,101 \| 60,002 \| \| compression_rate_full \| 100.0 \| 35.15 \| \| compression_rate_embedding \| 100.0 \| 23.99 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| ko \| vocabtrimmer/mc4_validation \| text \| ko \| validation \| 60000 \| 2 \|
vocabtrimmer/mt5-small-itquad-qg-trimmed-it-60000	vocabtrimmer	2023-03-15T11:29:45Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T11:16:20Z	# Vocabulary Trimmed [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg): `vocabtrimmer/mt5-small-itquad-qg-trimmed-it-60000` This model is a trimmed version of [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-itquad-qg \| vocabtrimmer/mt5-small-itquad-qg-trimmed-it-60000 \| \|:---------------------------\|:---------------------------\|:----------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 105,504,128 \| \| parameter_size_embedding \| 256,103,424 \| 61,442,048 \| \| vocab_size \| 250,101 \| 60,002 \| \| compression_rate_full \| 100.0 \| 35.15 \| \| compression_rate_embedding \| 100.0 \| 23.99 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| it \| vocabtrimmer/mc4_validation \| text \| it \| validation \| 60000 \| 2 \|
uygarkurt/bert-restore-punctuation-turkish-legacy	uygarkurt	2023-03-15T11:23:20Z	4	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "punctuation", "tr", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-03-10T20:46:33Z	--- license: mit language: - tr tags: - punctuation widget: text: "Türkiye toprakları üzerindeki ilk yerleşmeler Yontma Taş Devri'nde başlar Doğu Trakya'da Traklar olmak üzere Hititler Frigler Lidyalılar ve Dor istilası sonucu Yunanistan'dan kaçan Akalar tarafından kurulan İyon medeniyeti gibi çeşitli eski Anadolu medeniyetlerinin ardından Makedonya kralı Büyük İskender'in egemenliğiyle ve fetihleriyle birlikte Helenistik Dönem başladı" --- ## bert-restore-punctuation-turkish This bert-base-cased model fine-tuned for punctuation restoration task on a mixture of open-source datasets. Model is able to predict [! ? , . ; :] This model works on arbitrarily large Turkish text. ----------------------------------------------- ## Usage ``` from transformers import pipeline classifier = pipeline("token-classification", model="uygarkurt/bert-restore-punctuation-turkish", tokenizer="uygarkurt/bert-restore-punctuation-turkish") txt = "Türkiye toprakları üzerindeki ilk yerleşmeler Yontma Taş Devri'nde başlar Doğu Trakya'da Traklar olmak üzere Hititler Frigler Lidyalılar ve Dor istilası sonucu Yunanistan'dan kaçan Akalar tarafından kurulan İyon medeniyeti gibi çeşitli eski Anadolu medeniyetlerinin ardından Makedonya kralı Büyük İskender'in egemenliğiyle ve fetihleriyle birlikte Helenistik Dönem başladı" print(classifier(txt)) # Output # Türkiye toprakları üzerindeki ilk yerleşmeler Yontma Taş Devri'nde başlar. Doğu Trakya'da Traklar olmak üzere Hititler, Frigler, Lidyalılar ve Dor istilası sonucu Yunanistan'dan kaçan Akalar tarafından kurulan İyon medeniyeti gibi çeşitli eski Anadolu medeniyetlerinin ardından Makedonya kralı Büyük İskender'in egemenliğiyle ve fetihleriyle birlikte Helenistik Dönem başladı. ``` ----------------------------------------------- ## Evaluation \| Epoch \| Training Loss \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \| --------- \| -------------\|-------- \| ----------\|--------\| --------\| --------\| \|1 \| 0.100300 \| 0.096145 \| 0.862800 \| 0.829047 \| 0.845586 \| 0.965330 \|2 \| 0.084200 \| 0.092011 \| 0.878079 \| 0.830346 \| 0.853546 \| 0.967148 \|3 \| 0.075200 \| 0.093337 \| 0.878449 \| 0.833345 \| 0.855303 \| 0.967539 ----------------------------------------------- ## Information This is a plot project. Depending on the demand it can be improved. Contact [email protected]
joheras/clinico-roberta-biomedical-finetuned	joheras	2023-03-15T11:15:45Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-03-15T10:30:23Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: clinico-roberta-biomedical-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # clinico-roberta-biomedical-finetuned This model is a fine-tuned version of [joheras/roberta-base-biomedical-clinical-es-finetuned-clinais](https://huggingface.co/joheras/roberta-base-biomedical-clinical-es-finetuned-clinais) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9272 - Precision: 0.5095 - Recall: 0.6463 - F1: 0.5698 - Accuracy: 0.8623 ## 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 25 \| 1.2199 \| 0.0033 \| 0.0053 \| 0.0040 \| 0.5756 \| \| No log \| 2.0 \| 50 \| 0.7306 \| 0.2031 \| 0.2642 \| 0.2296 \| 0.8021 \| \| No log \| 3.0 \| 75 \| 0.6366 \| 0.2967 \| 0.3811 \| 0.3336 \| 0.8235 \| \| No log \| 4.0 \| 100 \| 0.6135 \| 0.3497 \| 0.4653 \| 0.3993 \| 0.8304 \| \| No log \| 5.0 \| 125 \| 0.5845 \| 0.3421 \| 0.4537 \| 0.3900 \| 0.8331 \| \| No log \| 6.0 \| 150 \| 0.5697 \| 0.3307 \| 0.4421 \| 0.3784 \| 0.8390 \| \| No log \| 7.0 \| 175 \| 0.5415 \| 0.3211 \| 0.4495 \| 0.3746 \| 0.8471 \| \| No log \| 8.0 \| 200 \| 0.5430 \| 0.3589 \| 0.5179 \| 0.4240 \| 0.8567 \| \| No log \| 9.0 \| 225 \| 0.5513 \| 0.3342 \| 0.5474 \| 0.4150 \| 0.8604 \| \| No log \| 10.0 \| 250 \| 0.5681 \| 0.3769 \| 0.5768 \| 0.4559 \| 0.8582 \| \| No log \| 11.0 \| 275 \| 0.5813 \| 0.3756 \| 0.5863 \| 0.4579 \| 0.8553 \| \| No log \| 12.0 \| 300 \| 0.6096 \| 0.4181 \| 0.5968 \| 0.4918 \| 0.8574 \| \| No log \| 13.0 \| 325 \| 0.6318 \| 0.3978 \| 0.6042 \| 0.4797 \| 0.8539 \| \| No log \| 14.0 \| 350 \| 0.6309 \| 0.3892 \| 0.5968 \| 0.4711 \| 0.8553 \| \| No log \| 15.0 \| 375 \| 0.6559 \| 0.3987 \| 0.5968 \| 0.4781 \| 0.8565 \| \| No log \| 16.0 \| 400 \| 0.6391 \| 0.4275 \| 0.6021 \| 0.5 \| 0.8560 \| \| No log \| 17.0 \| 425 \| 0.6812 \| 0.4388 \| 0.6074 \| 0.5095 \| 0.8584 \| \| No log \| 18.0 \| 450 \| 0.6901 \| 0.4287 \| 0.6137 \| 0.5048 \| 0.8563 \| \| No log \| 19.0 \| 475 \| 0.6834 \| 0.4572 \| 0.6074 \| 0.5217 \| 0.8581 \| \| 0.3478 \| 20.0 \| 500 \| 0.7050 \| 0.4397 \| 0.6179 \| 0.5138 \| 0.8573 \| \| 0.3478 \| 21.0 \| 525 \| 0.7004 \| 0.4462 \| 0.6242 \| 0.5204 \| 0.8591 \| \| 0.3478 \| 22.0 \| 550 \| 0.7038 \| 0.4264 \| 0.6126 \| 0.5028 \| 0.8599 \| \| 0.3478 \| 23.0 \| 575 \| 0.7384 \| 0.4416 \| 0.6284 \| 0.5187 \| 0.8576 \| \| 0.3478 \| 24.0 \| 600 \| 0.7197 \| 0.4479 \| 0.62 \| 0.5201 \| 0.8619 \| \| 0.3478 \| 25.0 \| 625 \| 0.7412 \| 0.4381 \| 0.6221 \| 0.5141 \| 0.8559 \| \| 0.3478 \| 26.0 \| 650 \| 0.7535 \| 0.4489 \| 0.6242 \| 0.5222 \| 0.8566 \| \| 0.3478 \| 27.0 \| 675 \| 0.7534 \| 0.4657 \| 0.6432 \| 0.5402 \| 0.8586 \| \| 0.3478 \| 28.0 \| 700 \| 0.7672 \| 0.4525 \| 0.6168 \| 0.5220 \| 0.8567 \| \| 0.3478 \| 29.0 \| 725 \| 0.7680 \| 0.4637 \| 0.6316 \| 0.5348 \| 0.8599 \| \| 0.3478 \| 30.0 \| 750 \| 0.7590 \| 0.4611 \| 0.6242 \| 0.5304 \| 0.8607 \| \| 0.3478 \| 31.0 \| 775 \| 0.7671 \| 0.4732 \| 0.6326 \| 0.5414 \| 0.8625 \| \| 0.3478 \| 32.0 \| 800 \| 0.7921 \| 0.4674 \| 0.6337 \| 0.5380 \| 0.8590 \| \| 0.3478 \| 33.0 \| 825 \| 0.8037 \| 0.4828 \| 0.6358 \| 0.5488 \| 0.8574 \| \| 0.3478 \| 34.0 \| 850 \| 0.8376 \| 0.4644 \| 0.6242 \| 0.5326 \| 0.8534 \| \| 0.3478 \| 35.0 \| 875 \| 0.8346 \| 0.4815 \| 0.6284 \| 0.5452 \| 0.8552 \| \| 0.3478 \| 36.0 \| 900 \| 0.8249 \| 0.4750 \| 0.6305 \| 0.5418 \| 0.8567 \| \| 0.3478 \| 37.0 \| 925 \| 0.8420 \| 0.4580 \| 0.6305 \| 0.5306 \| 0.8548 \| \| 0.3478 \| 38.0 \| 950 \| 0.8341 \| 0.4773 \| 0.6305 \| 0.5433 \| 0.8550 \| \| 0.3478 \| 39.0 \| 975 \| 0.8085 \| 0.4792 \| 0.6316 \| 0.5450 \| 0.8653 \| \| 0.0274 \| 40.0 \| 1000 \| 0.7954 \| 0.4992 \| 0.6474 \| 0.5637 \| 0.8651 \| \| 0.0274 \| 41.0 \| 1025 \| 0.8145 \| 0.4923 \| 0.6421 \| 0.5573 \| 0.8635 \| \| 0.0274 \| 42.0 \| 1050 \| 0.8290 \| 0.4911 \| 0.6368 \| 0.5545 \| 0.8610 \| \| 0.0274 \| 43.0 \| 1075 \| 0.8468 \| 0.4821 \| 0.6379 \| 0.5492 \| 0.8571 \| \| 0.0274 \| 44.0 \| 1100 \| 0.8274 \| 0.4791 \| 0.6389 \| 0.5476 \| 0.8625 \| \| 0.0274 \| 45.0 \| 1125 \| 0.8583 \| 0.4831 \| 0.6305 \| 0.5470 \| 0.8551 \| \| 0.0274 \| 46.0 \| 1150 \| 0.8420 \| 0.4726 \| 0.6347 \| 0.5418 \| 0.8589 \| \| 0.0274 \| 47.0 \| 1175 \| 0.8631 \| 0.5029 \| 0.64 \| 0.5632 \| 0.8564 \| \| 0.0274 \| 48.0 \| 1200 \| 0.8421 \| 0.4911 \| 0.64 \| 0.5558 \| 0.8617 \| \| 0.0274 \| 49.0 \| 1225 \| 0.8564 \| 0.5071 \| 0.6411 \| 0.5662 \| 0.8631 \| \| 0.0274 \| 50.0 \| 1250 \| 0.8659 \| 0.4845 \| 0.6263 \| 0.5464 \| 0.8603 \| \| 0.0274 \| 51.0 \| 1275 \| 0.8596 \| 0.4860 \| 0.64 \| 0.5525 \| 0.8632 \| \| 0.0274 \| 52.0 \| 1300 \| 0.8713 \| 0.4856 \| 0.6368 \| 0.5510 \| 0.8593 \| \| 0.0274 \| 53.0 \| 1325 \| 0.8888 \| 0.4868 \| 0.64 \| 0.5530 \| 0.8585 \| \| 0.0274 \| 54.0 \| 1350 \| 0.8591 \| 0.4816 \| 0.6337 \| 0.5473 \| 0.8610 \| \| 0.0274 \| 55.0 \| 1375 \| 0.8755 \| 0.4996 \| 0.64 \| 0.5611 \| 0.8615 \| \| 0.0274 \| 56.0 \| 1400 \| 0.8749 \| 0.5095 \| 0.6484 \| 0.5706 \| 0.8583 \| \| 0.0274 \| 57.0 \| 1425 \| 0.8867 \| 0.5025 \| 0.6453 \| 0.5650 \| 0.8580 \| \| 0.0274 \| 58.0 \| 1450 \| 0.8905 \| 0.4947 \| 0.6337 \| 0.5556 \| 0.8579 \| \| 0.0274 \| 59.0 \| 1475 \| 0.8911 \| 0.4881 \| 0.6495 \| 0.5574 \| 0.8596 \| \| 0.0099 \| 60.0 \| 1500 \| 0.9220 \| 0.4914 \| 0.6347 \| 0.5540 \| 0.8570 \| \| 0.0099 \| 61.0 \| 1525 \| 0.8687 \| 0.4786 \| 0.6368 \| 0.5465 \| 0.8594 \| \| 0.0099 \| 62.0 \| 1550 \| 0.9080 \| 0.4906 \| 0.6337 \| 0.5531 \| 0.8575 \| \| 0.0099 \| 63.0 \| 1575 \| 0.9004 \| 0.4831 \| 0.6337 \| 0.5483 \| 0.8583 \| \| 0.0099 \| 64.0 \| 1600 \| 0.8906 \| 0.4778 \| 0.6337 \| 0.5448 \| 0.8619 \| \| 0.0099 \| 65.0 \| 1625 \| 0.8870 \| 0.4959 \| 0.6368 \| 0.5576 \| 0.8618 \| \| 0.0099 \| 66.0 \| 1650 \| 0.8843 \| 0.4851 \| 0.6358 \| 0.5503 \| 0.8611 \| \| 0.0099 \| 67.0 \| 1675 \| 0.8923 \| 0.4912 \| 0.6453 \| 0.5578 \| 0.8618 \| \| 0.0099 \| 68.0 \| 1700 \| 0.8864 \| 0.4898 \| 0.6337 \| 0.5525 \| 0.8615 \| \| 0.0099 \| 69.0 \| 1725 \| 0.8974 \| 0.4943 \| 0.6411 \| 0.5582 \| 0.8615 \| \| 0.0099 \| 70.0 \| 1750 \| 0.8851 \| 0.4821 \| 0.6379 \| 0.5492 \| 0.8611 \| \| 0.0099 \| 71.0 \| 1775 \| 0.8958 \| 0.4920 \| 0.6453 \| 0.5583 \| 0.8593 \| \| 0.0099 \| 72.0 \| 1800 \| 0.8880 \| 0.4988 \| 0.6411 \| 0.5610 \| 0.8618 \| \| 0.0099 \| 73.0 \| 1825 \| 0.8959 \| 0.4852 \| 0.6379 \| 0.5512 \| 0.8606 \| \| 0.0099 \| 74.0 \| 1850 \| 0.9036 \| 0.4773 \| 0.6305 \| 0.5433 \| 0.8598 \| \| 0.0099 \| 75.0 \| 1875 \| 0.9031 \| 0.4864 \| 0.6389 \| 0.5523 \| 0.8615 \| \| 0.0099 \| 76.0 \| 1900 \| 0.9243 \| 0.4907 \| 0.6368 \| 0.5543 \| 0.8590 \| \| 0.0099 \| 77.0 \| 1925 \| 0.9285 \| 0.4877 \| 0.6453 \| 0.5555 \| 0.8590 \| \| 0.0099 \| 78.0 \| 1950 \| 0.9261 \| 0.5074 \| 0.6516 \| 0.5705 \| 0.8598 \| \| 0.0099 \| 79.0 \| 1975 \| 0.9374 \| 0.5037 \| 0.64 \| 0.5637 \| 0.8580 \| \| 0.0061 \| 80.0 \| 2000 \| 0.9165 \| 0.5021 \| 0.6316 \| 0.5594 \| 0.8621 \| \| 0.0061 \| 81.0 \| 2025 \| 0.9307 \| 0.5162 \| 0.6368 \| 0.5702 \| 0.8582 \| \| 0.0061 \| 82.0 \| 2050 \| 0.9369 \| 0.4911 \| 0.6358 \| 0.5541 \| 0.8574 \| \| 0.0061 \| 83.0 \| 2075 \| 0.9293 \| 0.5191 \| 0.6421 \| 0.5741 \| 0.8584 \| \| 0.0061 \| 84.0 \| 2100 \| 0.9187 \| 0.5004 \| 0.6453 \| 0.5637 \| 0.8629 \| \| 0.0061 \| 85.0 \| 2125 \| 0.9293 \| 0.4927 \| 0.6379 \| 0.5560 \| 0.8623 \| \| 0.0061 \| 86.0 \| 2150 \| 0.9200 \| 0.5041 \| 0.6453 \| 0.5660 \| 0.8634 \| \| 0.0061 \| 87.0 \| 2175 \| 0.9273 \| 0.4992 \| 0.6421 \| 0.5617 \| 0.8631 \| \| 0.0061 \| 88.0 \| 2200 \| 0.9325 \| 0.5021 \| 0.6442 \| 0.5643 \| 0.8623 \| \| 0.0061 \| 89.0 \| 2225 \| 0.9245 \| 0.4844 \| 0.6389 \| 0.5511 \| 0.8630 \| \| 0.0061 \| 90.0 \| 2250 \| 0.9291 \| 0.4979 \| 0.6368 \| 0.5589 \| 0.8593 \| \| 0.0061 \| 91.0 \| 2275 \| 0.9264 \| 0.5083 \| 0.6432 \| 0.5678 \| 0.8622 \| \| 0.0061 \| 92.0 \| 2300 \| 0.9283 \| 0.5025 \| 0.6411 \| 0.5634 \| 0.8619 \| \| 0.0061 \| 93.0 \| 2325 \| 0.9264 \| 0.5008 \| 0.6442 \| 0.5635 \| 0.8613 \| \| 0.0061 \| 94.0 \| 2350 \| 0.9205 \| 0.5079 \| 0.6463 \| 0.5688 \| 0.8626 \| \| 0.0061 \| 95.0 \| 2375 \| 0.9223 \| 0.5121 \| 0.6484 \| 0.5722 \| 0.8625 \| \| 0.0061 \| 96.0 \| 2400 \| 0.9244 \| 0.5045 \| 0.6421 \| 0.5651 \| 0.8620 \| \| 0.0061 \| 97.0 \| 2425 \| 0.9248 \| 0.5062 \| 0.6463 \| 0.5677 \| 0.8622 \| \| 0.0061 \| 98.0 \| 2450 \| 0.9277 \| 0.5037 \| 0.6453 \| 0.5658 \| 0.8621 \| \| 0.0061 \| 99.0 \| 2475 \| 0.9272 \| 0.5083 \| 0.6463 \| 0.5690 \| 0.8623 \| \| 0.0046 \| 100.0 \| 2500 \| 0.9272 \| 0.5095 \| 0.6463 \| 0.5698 \| 0.8623 \| ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.0 - Datasets 2.8.0 - Tokenizers 0.12.1
vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-30000	vocabtrimmer	2023-03-15T11:15:17Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T11:01:04Z	# Vocabulary Trimmed [lmqg/mt5-small-koquad-qg](https://huggingface.co/lmqg/mt5-small-koquad-qg): `vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-30000` This model is a trimmed version of [lmqg/mt5-small-koquad-qg](https://huggingface.co/lmqg/mt5-small-koquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-koquad-qg \| vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-30000 \| \|:---------------------------\|:---------------------------\|:----------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 74,784,128 \| \| parameter_size_embedding \| 256,103,424 \| 30,722,048 \| \| vocab_size \| 250,101 \| 30,002 \| \| compression_rate_full \| 100.0 \| 24.91 \| \| compression_rate_embedding \| 100.0 \| 12.0 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| ko \| vocabtrimmer/mc4_validation \| text \| ko \| validation \| 30000 \| 2 \|
vocabtrimmer/mt5-small-itquad-qg-trimmed-it-30000	vocabtrimmer	2023-03-15T11:12:32Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T10:59:37Z	# Vocabulary Trimmed [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg): `vocabtrimmer/mt5-small-itquad-qg-trimmed-it-30000` This model is a trimmed version of [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-itquad-qg \| vocabtrimmer/mt5-small-itquad-qg-trimmed-it-30000 \| \|:---------------------------\|:---------------------------\|:----------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 74,784,128 \| \| parameter_size_embedding \| 256,103,424 \| 30,722,048 \| \| vocab_size \| 250,101 \| 30,002 \| \| compression_rate_full \| 100.0 \| 24.91 \| \| compression_rate_embedding \| 100.0 \| 12.0 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| it \| vocabtrimmer/mc4_validation \| text \| it \| validation \| 30000 \| 2 \|
chjun/my_awesome_model	chjun	2023-03-15T11:07:34Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-15T06:23:59Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: my_awesome_model results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.93052 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2372 - Accuracy: 0.9305 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2346 \| 1.0 \| 1563 \| 0.1895 \| 0.9280 \| \| 0.1531 \| 2.0 \| 3126 \| 0.2372 \| 0.9305 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
ealarcong/mt5-small-finetuned-amazon-en-es	ealarcong	2023-03-15T11:06:04Z	3	0	transformers	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-14T10:51:40Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ealarcong/mt5-small-finetuned-amazon-en-es 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. --> # ealarcong/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1830 - Validation Loss: 3.4080 - Epoch: 7 ## 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': 5.6e-05, 'decay_steps': 9672, '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 \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 11.0744 \| 4.9526 \| 0 \| \| 6.2839 \| 3.9714 \| 1 \| \| 5.4063 \| 3.6820 \| 2 \| \| 4.9197 \| 3.5710 \| 3 \| \| 4.5865 \| 3.5060 \| 4 \| \| 4.3904 \| 3.4481 \| 5 \| \| 4.2549 \| 3.4180 \| 6 \| \| 4.1830 \| 3.4080 \| 7 \| ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2
852wa/hako	852wa	2023-03-15T11:06:03Z	0	29	null	[ "region:us" ]	null	2023-03-15T08:26:35Z	# ■hakoA & hakoB ![sample5](https://huggingface.co/852wa/hako/resolve/main/img/05.png) ![sample6](https://huggingface.co/852wa/hako/resolve/main/img/06.png) ![sample7](https://huggingface.co/852wa/hako/resolve/main/img/07.png) ![sample8](https://huggingface.co/852wa/hako/resolve/main/img/08.png) I conducted custom fine-tuning on wd15-beta2-aesthetic, which is based on the SD2.1 architecture, available at https://huggingface.co/waifu-diffusion/wd-1-5-beta2. SD2.1系であるwd15-beta2-aesthetic https://huggingface.co/waifu-diffusion/wd-1-5-beta2　に対して独自の追加学習を行いました。 # ■Setting It is recommended to use "(anime:1.2)" as the prompt and "nsfw,messy,blush,nfixer" as the negative prompt. If the output is not at least 768 pixels on the shorter side, there is a possibility that the facial features may be distorted. "(anime:1.2)" creates a flat, anime-like image style. promptには「(anime:1.2)」 negative promptには「nsfw,messy,blush,nfixer」を入れることをおすすめします。「(anime:1.2)」はフラットなアニメ調のイメージになります。短辺が768px以上での出力でない場合、顔の描画が崩れる可能性があります。 # ■Licence Model hakoA and hakoB are released under the Fair AI Public License 1.0-SD. Please refer to the following link for the license terms: https://freedevproject.org/faipl-1.0-sd/ hakoA、hakoBはFair AI Public License 1.0-SDのライセンス下での公開です。下記ライセンス内容を確認ください。 https://freedevproject.org/faipl-1.0-sd/ ![sample1](https://huggingface.co/852wa/hako/resolve/main/img/01.png) ``` (anime:1.2),(hyper extreme detailed:1.0),amazing quality,Beautiful Illustration,1girl,breasts,maid_apron,happy smile,cafe with waitresses dressed in cute maid costumes Negative prompt: nsfw,messy,blush,nfixer, Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 1386462091, Size: 768x1152 ``` ![sample2](https://huggingface.co/852wa/hako/resolve/main/img/02.png) ``` (anime:1.2),( stylish pose:1.1), (smile:1), (king (throne:1.1) :1.3), Negative prompt: nsfw,messy,blush,nfixer, Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 2137539252, Size: 768x1152 ``` ![sample3](https://huggingface.co/852wa/hako/resolve/main/img/03.png) ``` (anime:1.2),(masterpiece:1.2), (high quality:1.2), (watercolor painting:1.1),anatomy,1 girl,solo,(cowboy shot:1.1), perfect face,18yo,(from front),school girl, black hair,black cardigan,ribbon,(white hat:1.1),closed eyes,arms behind back,tree,calm,(darkness lighting:1.4),(night:1.4), standing ,kawaii face, depth of field Negative prompt: nsfw,messy,blush,nfixer, Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 260664233, Size: 768x1152 ``` ![sample4](https://huggingface.co/852wa/hako/resolve/main/img/04.png) ``` (anime:1.2),(1girl, 12yo, flat:1.2)white dress outdoor Negative prompt: nsfw,messy,blush,nfixer, Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 2617311573, Size: 768x1152 ```
vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-15000	vocabtrimmer	2023-03-15T11:00:07Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T10:46:02Z	# Vocabulary Trimmed [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg): `vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-15000` This model is a trimmed version of [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-ruquad-qg \| vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-15000 \| \|:---------------------------\|:---------------------------\|:----------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 59,424,128 \| \| parameter_size_embedding \| 256,103,424 \| 15,362,048 \| \| vocab_size \| 250,101 \| 15,002 \| \| compression_rate_full \| 100.0 \| 19.8 \| \| compression_rate_embedding \| 100.0 \| 6.0 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| ru \| vocabtrimmer/mc4_validation \| text \| ru \| validation \| 15000 \| 2 \|
vocabtrimmer/mt5-small-itquad-qg-trimmed-it-15000	vocabtrimmer	2023-03-15T10:57:25Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T10:44:20Z	# Vocabulary Trimmed [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg): `vocabtrimmer/mt5-small-itquad-qg-trimmed-it-15000` This model is a trimmed version of [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-itquad-qg \| vocabtrimmer/mt5-small-itquad-qg-trimmed-it-15000 \| \|:---------------------------\|:---------------------------\|:----------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 59,424,128 \| \| parameter_size_embedding \| 256,103,424 \| 15,362,048 \| \| vocab_size \| 250,101 \| 15,002 \| \| compression_rate_full \| 100.0 \| 19.8 \| \| compression_rate_embedding \| 100.0 \| 6.0 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| it \| vocabtrimmer/mc4_validation \| text \| it \| validation \| 15000 \| 2 \|
vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-10000	vocabtrimmer	2023-03-15T10:44:50Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T10:30:38Z	# Vocabulary Trimmed [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg): `vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-10000` This model is a trimmed version of [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-ruquad-qg \| vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-10000 \| \|:---------------------------\|:---------------------------\|:----------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 54,305,152 \| \| parameter_size_embedding \| 256,103,424 \| 10,243,072 \| \| vocab_size \| 250,101 \| 10,003 \| \| compression_rate_full \| 100.0 \| 18.09 \| \| compression_rate_embedding \| 100.0 \| 4.0 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| ru \| vocabtrimmer/mc4_validation \| text \| ru \| validation \| 10000 \| 2 \|
vocabtrimmer/mt5-small-itquad-qg-trimmed-it-10000	vocabtrimmer	2023-03-15T10:43:08Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T10:30:01Z	# Vocabulary Trimmed [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg): `vocabtrimmer/mt5-small-itquad-qg-trimmed-it-10000` This model is a trimmed version of [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-itquad-qg \| vocabtrimmer/mt5-small-itquad-qg-trimmed-it-10000 \| \|:---------------------------\|:---------------------------\|:----------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 54,304,128 \| \| parameter_size_embedding \| 256,103,424 \| 10,242,048 \| \| vocab_size \| 250,101 \| 10,002 \| \| compression_rate_full \| 100.0 \| 18.09 \| \| compression_rate_embedding \| 100.0 \| 4.0 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| it \| vocabtrimmer/mc4_validation \| text \| it \| validation \| 10000 \| 2 \|
kiu020/distilbert-base-uncased-finetuned-squad	kiu020	2023-03-15T10:42:36Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-03-15T09:46:41Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1609 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.2109 \| 1.0 \| 5533 \| 1.1356 \| \| 0.9553 \| 2.0 \| 11066 \| 1.1270 \| \| 0.739 \| 3.0 \| 16599 \| 1.1609 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
marco-c88/distilgpt2-finetuned-wikitext2	marco-c88	2023-03-15T10:40:26Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-03-15T10:11:15Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4740 ## 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 4.0767 \| 1.0 \| 794 \| 3.7406 \| \| 3.8158 \| 2.0 \| 1588 \| 3.6718 \| \| 3.7557 \| 3.0 \| 2382 \| 3.6302 \| \| 3.6758 \| 4.0 \| 3176 \| 3.5968 \| \| 3.6383 \| 5.0 \| 3970 \| 3.5704 \| \| 3.5762 \| 6.0 \| 4764 \| 3.5524 \| \| 3.5415 \| 7.0 \| 5558 \| 3.5360 \| \| 3.5116 \| 8.0 \| 6352 \| 3.5195 \| \| 3.485 \| 9.0 \| 7146 \| 3.5116 \| \| 3.4587 \| 10.0 \| 7940 \| 3.5033 \| \| 3.429 \| 11.0 \| 8734 \| 3.4950 \| \| 3.4179 \| 12.0 \| 9528 \| 3.4882 \| \| 3.3985 \| 13.0 \| 10322 \| 3.4845 \| \| 3.3812 \| 14.0 \| 11116 \| 3.4825 \| \| 3.3671 \| 15.0 \| 11910 \| 3.4795 \| \| 3.3547 \| 16.0 \| 12704 \| 3.4751 \| \| 3.3472 \| 17.0 \| 13498 \| 3.4744 \| \| 3.3393 \| 18.0 \| 14292 \| 3.4743 \| \| 3.3334 \| 19.0 \| 15086 \| 3.4740 \| \| 3.3309 \| 20.0 \| 15880 \| 3.4740 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
vocabtrimmer/mt5-small-esquad-qg-trimmed-es-5000	vocabtrimmer	2023-03-15T10:37:52Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T10:15:01Z	# Vocabulary Trimmed [lmqg/mt5-small-esquad-qg](https://huggingface.co/lmqg/mt5-small-esquad-qg): `vocabtrimmer/mt5-small-esquad-qg-trimmed-es-5000` This model is a trimmed version of [lmqg/mt5-small-esquad-qg](https://huggingface.co/lmqg/mt5-small-esquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-esquad-qg \| vocabtrimmer/mt5-small-esquad-qg-trimmed-es-5000 \| \|:---------------------------\|:---------------------------\|:---------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 49,185,152 \| \| parameter_size_embedding \| 256,103,424 \| 5,123,072 \| \| vocab_size \| 250,101 \| 5,003 \| \| compression_rate_full \| 100.0 \| 16.39 \| \| compression_rate_embedding \| 100.0 \| 2.0 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| es \| vocabtrimmer/mc4_validation \| text \| es \| validation \| 5000 \| 2 \|
vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-90000	vocabtrimmer	2023-03-15T10:34:39Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T10:21:59Z	# Vocabulary Trimmed [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg): `vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-90000` This model is a trimmed version of [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-ruquad-qg \| vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-90000 \| \|:---------------------------\|:---------------------------\|:----------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 136,224,128 \| \| parameter_size_embedding \| 256,103,424 \| 92,162,048 \| \| vocab_size \| 250,101 \| 90,002 \| \| compression_rate_full \| 100.0 \| 45.38 \| \| compression_rate_embedding \| 100.0 \| 35.99 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| ru \| vocabtrimmer/mc4_validation \| text \| ru \| validation \| 90000 \| 2 \|
vocabtrimmer/mt5-small-itquad-qg-trimmed-it-5000	vocabtrimmer	2023-03-15T10:29:02Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-15T10:15:05Z	# Vocabulary Trimmed [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg): `vocabtrimmer/mt5-small-itquad-qg-trimmed-it-5000` This model is a trimmed version of [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. \| \| lmqg/mt5-small-itquad-qg \| vocabtrimmer/mt5-small-itquad-qg-trimmed-it-5000 \| \|:---------------------------\|:---------------------------\|:---------------------------------------------------\| \| parameter_size_full \| 300,165,504 \| 49,185,152 \| \| parameter_size_embedding \| 256,103,424 \| 5,123,072 \| \| vocab_size \| 250,101 \| 5,003 \| \| compression_rate_full \| 100.0 \| 16.39 \| \| compression_rate_embedding \| 100.0 \| 2.0 \| Following table shows the parameter used to trim vocabulary. \| language \| dataset \| dataset_column \| dataset_name \| dataset_split \| target_vocab_size \| min_frequency \| \|:-----------\|:----------------------------\|:-----------------\|:---------------\|:----------------\|--------------------:\|----------------:\| \| it \| vocabtrimmer/mc4_validation \| text \| it \| validation \| 5000 \| 2 \|
AndyPig/ppo-LunarLander-v2	AndyPig	2023-03-15T10:24:47Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T10:24:22Z	--- 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: 290.81 +/- 19.46 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
dmargutierrez/distilbert-base-multilingual-cased-WNUT-ner	dmargutierrez	2023-03-15T10:16:23Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-03-15T10:09:19Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-multilingual-cased-WNUT-ner results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.5496503496503496 - name: Recall type: recall value: 0.36422613531047265 - name: F1 type: f1 value: 0.4381270903010034 - name: Accuracy type: accuracy value: 0.9468667179618706 --- <!-- 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-multilingual-cased-WNUT-ner This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.3516 - Precision: 0.5497 - Recall: 0.3642 - F1: 0.4381 - Accuracy: 0.9469 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 213 \| 0.2727 \| 0.6626 \| 0.2530 \| 0.3662 \| 0.9402 \| \| No log \| 2.0 \| 426 \| 0.2636 \| 0.5895 \| 0.2715 \| 0.3718 \| 0.9429 \| \| 0.1729 \| 3.0 \| 639 \| 0.2933 \| 0.5931 \| 0.3040 \| 0.4020 \| 0.9447 \| \| 0.1729 \| 4.0 \| 852 \| 0.2861 \| 0.5437 \| 0.3457 \| 0.4227 \| 0.9453 \| \| 0.0503 \| 5.0 \| 1065 \| 0.3270 \| 0.5627 \| 0.3494 \| 0.4311 \| 0.9455 \| \| 0.0503 \| 6.0 \| 1278 \| 0.3277 \| 0.5451 \| 0.3531 \| 0.4286 \| 0.9463 \| \| 0.0503 \| 7.0 \| 1491 \| 0.3471 \| 0.5828 \| 0.3457 \| 0.4340 \| 0.9467 \| \| 0.0231 \| 8.0 \| 1704 \| 0.3594 \| 0.5801 \| 0.3457 \| 0.4332 \| 0.9464 \| \| 0.0231 \| 9.0 \| 1917 \| 0.3550 \| 0.5567 \| 0.3503 \| 0.4300 \| 0.9467 \| \| 0.0121 \| 10.0 \| 2130 \| 0.3516 \| 0.5497 \| 0.3642 \| 0.4381 \| 0.9469 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2
Christian90/dqn-SpaceInvadersNoFrameskip-v4	Christian90	2023-03-15T10:15:42Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T10:13:18Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 521.50 +/- 219.83 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Christian90 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Christian90 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Christian90 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
yumingyi/dqn-SpaceInvadersNoFrameskip-v4	yumingyi	2023-03-15T10:11:48Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T10:11:03Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 529.00 +/- 143.68 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga yumingyi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga yumingyi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga yumingyi ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
oyvindgrutle/amk-whisper	oyvindgrutle	2023-03-15T10:07:20Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-01-25T11:17:48Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: amk-whisper 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. --> # amk-whisper This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1902 - Wer: 40.3587 ## 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 - lr_scheduler_warmup_steps: 50 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 20.0 \| 20 \| 0.7838 \| 30.9417 \| \| 0.8511 \| 40.0 \| 40 \| 1.0878 \| 44.8430 \| \| 0.0794 \| 60.0 \| 60 \| 1.1466 \| 39.4619 \| \| 0.001 \| 80.0 \| 80 \| 1.1872 \| 39.9103 \| \| 0.0004 \| 100.0 \| 100 \| 1.1902 \| 40.3587 \| ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2
joheras/roberta-base-biomedical-clinical-es-finetuned-clinais	joheras	2023-03-15T10:06:46Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-03-15T09:50:23Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: roberta-base-biomedical-clinical-es-finetuned-clinais 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-biomedical-clinical-es-finetuned-clinais This model is a fine-tuned version of [BSC-LT/roberta-base-biomedical-clinical-es](https://huggingface.co/BSC-LT/roberta-base-biomedical-clinical-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3091 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 171 \| 1.3679 \| \| 1.4311 \| 2.0 \| 342 \| 1.2926 \| \| 1.4311 \| 3.0 \| 513 \| 1.2896 \| \| 1.3363 \| 4.0 \| 684 \| 1.3143 \| \| 1.3363 \| 5.0 \| 855 \| 1.3097 \| ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.0 - Datasets 2.8.0 - Tokenizers 0.12.1
lora-library/wyt	lora-library	2023-03-15T09:33:11Z	1	0	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	2023-03-15T09:33:07Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: wangyanting tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - wyt 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 "wangyanting" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: wyt ![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)
diffusers/ddpm-cifar10-32-demo	diffusers	2023-03-15T09:20:32Z	2	1	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-03-15T09:09:10Z	--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation duplicated_from: google/ddpm-cifar10-32 --- # Denoising Diffusion Probabilistic Models (DDPM) Paper: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel Abstract: We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. ## Inference DDPM models can use discrete noise schedulers such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the ddpm scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the ddim or pndm schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-cifar10-32" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_3.png)
peterdamn/ppo-CartPole-v1	peterdamn	2023-03-15T09:07:19Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T09:05:05Z	--- 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: -134.11 +/- 65.85 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': 'peterdamn/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128} ```
mouss/autotrain-bikes_1-41171106189	mouss	2023-03-15T08:59:13Z	39	0	transformers	[ "transformers", "pytorch", "swin", "image-classification", "autotrain", "vision", "dataset:mouss/autotrain-data-bikes_1", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-03-15T08:58:08Z	--- tags: - autotrain - vision - image-classification datasets: - mouss/autotrain-data-bikes_1 widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 0.41665410499999395 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 41171106189 - CO2 Emissions (in grams): 0.4167 ## Validation Metrics - Loss: 0.368 - Accuracy: 0.818 - Precision: 0.882 - Recall: 0.789 - AUC: 0.921 - F1: 0.833
dvruette/oasst-pythia-12b-6000-steps	dvruette	2023-03-15T08:48:05Z	1,488	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-03-09T12:40:11Z	https://wandb.ai/open-assistant/supervised-finetuning/runs/qqtzt19n
dvruette/oasst-pythia-12b-3000-steps	dvruette	2023-03-15T08:47:44Z	0	0	null	[ "region:us" ]	null	2023-03-09T14:36:57Z	https://wandb.ai/open-assistant/supervised-finetuning/runs/qqtzt19n
dvruette/oasst-pythia-12b-flash-attn-5000-steps	dvruette	2023-03-15T08:46:58Z	1,500	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-03-12T10:42:00Z	https://wandb.ai/open-assistant/supervised-finetuning/runs/uwqcwaau
EarthnDusk/FFXIV_Miqote_MoonKeeper_Lora	EarthnDusk	2023-03-15T08:45:16Z	0	1	null	[ "Lycoris", "LoHA", "Lora", "stable diffusion", "text to image", "ffxiv", "miqote", "en", "dataset:Duskfallcrew/FFXIV_Data_and_Lora", "dataset:Duskfallcrew/miqoteupdate", "license:creativeml-openrail-m", "region:us" ]	null	2023-03-15T08:13:56Z	--- license: creativeml-openrail-m datasets: - Duskfallcrew/FFXIV_Data_and_Lora - Duskfallcrew/miqoteupdate language: - en tags: - Lycoris - LoHA - Lora - stable diffusion - text to image - ffxiv - miqote --- Output udpates coming soon, we have some but if you need to see them before we put them here- we have the models up on Civit: https://civitai.com/models/14823 Data sets listed because one is private - this was because the LoRA trainer had a subject option to upload data to here but i forgot we did it already . Data set here: https://huggingface.co/datasets/Duskfallcrew/FFXIV_Data_and_Lora Also noted: The MIQOTE UPDATE LoRA is a LYCORIS/LoHA and needs the special A1111 plugin: https://github.com/KohakuBlueleaf/a1111-sd-webui-locon
amerssun/tww_result_lora	amerssun	2023-03-15T08:43:35Z	2	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-03-15T08:38:16Z	--- license: creativeml-openrail-m base_model: /mnt/user/sunzhaoxu/diffusion/cilloutmix/ instance_prompt: a photo of tww 1girl tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - amerssun/tww_result_lora These are LoRA adaption weights for /mnt/user/sunzhaoxu/diffusion/cilloutmix/. The weights were trained on a photo of tww 1girl 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)
11Anupam/demo_001	11Anupam	2023-03-15T08:37:45Z	0	0	adapter-transformers	[ "adapter-transformers", "pytorch", "tf", "marian", "en", "arxiv:1910.09700", "region:us" ]	null	2023-03-15T07:27:17Z	--- language: - en library_name: adapter-transformers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [Anupam] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [python] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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huam/dqn-Taxi-v3	huam	2023-03-15T08:17:36Z	5	0	stable-baselines3	[ "stable-baselines3", "Taxi-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T08:17:31Z	--- library_name: stable-baselines3 tags: - Taxi-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: -200.00 +/- 0.00 name: mean_reward verified: false --- # DQN Agent playing Taxi-v3 This is a trained model of a DQN agent playing Taxi-v3 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 ... ```
giovannefeitosa/chatbot-about-pele	giovannefeitosa	2023-03-15T08:09:22Z	0	0	sklearn	[ "sklearn", "question-answering", "chatbot", "brazil", "text2text-generation", "en", "dataset:en_core_web_sm", "license:cc-by-nc-4.0", "region:us" ]	text2text-generation	2023-03-15T06:33:28Z	--- language: - en datasets: - en_core_web_sm thumbnail: >- https://huggingface.co/giovannefeitosa/chatbot-about-pele/raw/main/images/pele.jpeg tags: - question-answering - chatbot - brazil license: cc-by-nc-4.0 pipeline_tag: text2text-generation library_name: sklearn --- # Chatbot about Pele This is demo project. > library_name: sklearn
kailashsp/dreambooth_diffusion_model	kailashsp	2023-03-15T08:08:00Z	2	0	keras	[ "keras", "tf-keras", "text-to-image", "dataset:kailashsp/class-images", "license:apache-2.0", "region:us" ]	text-to-image	2023-03-15T07:54:09Z	--- library_name: keras license: apache-2.0 datasets: - kailashsp/class-images pipeline_tag: text-to-image --- ## Model description This is a Stable Diffusion model fine-tuned using Dreambooth on pokemon to get cuter pokemons ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| inner_optimizer.class_name \| Custom>RMSprop \| \| inner_optimizer.config.name \| RMSprop \| \| inner_optimizer.config.weight_decay \| None \| \| inner_optimizer.config.clipnorm \| None \| \| inner_optimizer.config.global_clipnorm \| None \| \| inner_optimizer.config.clipvalue \| None \| \| inner_optimizer.config.use_ema \| False \| \| inner_optimizer.config.ema_momentum \| 0.99 \| \| inner_optimizer.config.ema_overwrite_frequency \| 100 \| \| inner_optimizer.config.jit_compile \| True \| \| inner_optimizer.config.is_legacy_optimizer \| False \| \| inner_optimizer.config.learning_rate \| 0.0010000000474974513 \| \| inner_optimizer.config.rho \| 0.9 \| \| inner_optimizer.config.momentum \| 0.0 \| \| inner_optimizer.config.epsilon \| 1e-07 \| \| inner_optimizer.config.centered \| False \| \| dynamic \| True \| \| initial_scale \| 32768.0 \| \| dynamic_growth_steps \| 2000 \| \| training_precision \| mixed_float16 \| ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
lyeonii/bert-small	lyeonii	2023-03-15T08:07:54Z	75	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:1908.08962", "license:mit", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-03-15T08:05:48Z	--- license: mit language: - en --- # BERT-Small (uncased) This is one of 24 smaller BERT models (English only, uncased, trained with WordPiece masking) released by [google-research/bert](https://github.com/google-research/bert). These BERT models was released as TensorFlow checkpoints, however, this is the converted version to PyTorch. More information can be found in [google-research/bert](https://github.com/google-research/bert) or [lyeoni/convert-tf-to-pytorch](https://github.com/lyeoni/convert-tf-to-pytorch). ## Evaluation Here are the evaluation scores (F1/Accuracy) for the MPRC task. \|Model\|MRPC\| \|-\|:-:\| \|BERT-Tiny\|81.22/68.38\| \|BERT-Mini\|81.43/69.36\| \|BERT-Small\|81.41/70.34\| \|BERT-Medium\|83.33/73.53\| \|BERT-Base\|85.62/78.19\| ### References ``` @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } ```
lyeonii/bert-medium	lyeonii	2023-03-15T08:04:10Z	5	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:1908.08962", "license:mit", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-03-15T08:01:26Z	--- license: mit language: - en --- # BERT-Medium (uncased) This is one of 24 smaller BERT models (English only, uncased, trained with WordPiece masking) released by [google-research/bert](https://github.com/google-research/bert). These BERT models was released as TensorFlow checkpoints, however, this is the converted version to PyTorch. More information can be found in [google-research/bert](https://github.com/google-research/bert) or [lyeoni/convert-tf-to-pytorch](https://github.com/lyeoni/convert-tf-to-pytorch). ## Evaluation Here are the evaluation scores (F1/Accuracy) for the MPRC task. \|Model\|MRPC\| \|-\|:-:\| \|BERT-Tiny\|81.22/68.38\| \|BERT-Mini\|81.43/69.36\| \|BERT-Small\|81.41/70.34\| \|BERT-Medium\|83.33/73.53\| \|BERT-Base\|85.62/78.19\| ### References ``` @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } ```
auditi41/wav2vec2-large-xlsr-turkish	auditi41	2023-03-15T08:03:41Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-03-14T07:30:44Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: wav2vec2-large-xlsr-turkish results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: tr split: train+validation args: tr metrics: - name: Wer type: wer value: 0.48268818302522726 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-turkish This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4242 - Wer: 0.4827 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 5.0376 \| 4.26 \| 400 \| 2.3690 \| 1.0020 \| \| 0.7983 \| 8.51 \| 800 \| 0.4755 \| 0.6328 \| \| 0.3157 \| 12.77 \| 1200 \| 0.4051 \| 0.5408 \| \| 0.2197 \| 17.02 \| 1600 \| 0.4156 \| 0.5149 \| \| 0.1643 \| 21.28 \| 2000 \| 0.4286 \| 0.5036 \| \| 0.1305 \| 25.53 \| 2400 \| 0.4247 \| 0.4908 \| \| 0.1178 \| 29.79 \| 2800 \| 0.4242 \| 0.4827 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
lyeonii/bert-mini	lyeonii	2023-03-15T07:57:26Z	6	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:1908.08962", "license:mit", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-03-15T07:52:52Z	--- license: mit language: - en --- # BERT-Mini (uncased) This is one of 24 smaller BERT models (English only, uncased, trained with WordPiece masking) released by [google-research/bert](https://github.com/google-research/bert). These BERT models was released as TensorFlow checkpoints, however, this is the converted version to PyTorch. More information can be found in [google-research/bert](https://github.com/google-research/bert) or [lyeoni/convert-tf-to-pytorch](https://github.com/lyeoni/convert-tf-to-pytorch). ## Evaluation Here are the evaluation scores (F1/Accuracy) for the MPRC task. \|Model\|MRPC\| \|-\|:-:\| \|BERT-Tiny\|81.22/68.38\| \|BERT-Mini\|81.43/69.36\| \|BERT-Small\|81.41/70.34\| \|BERT-Medium\|83.33/73.53\| \|BERT-Base\|85.62/78.19\| ### References ``` @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } ```
Perse90/ppo-Huggy	Perse90	2023-03-15T07:53:56Z	12	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-03-15T07:53:50Z	--- 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: Perse90/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
thang123/wav2vec2-tiengviet1	thang123	2023-03-15T06:59:49Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-03-15T04:46:37Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-tiengviet1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-tiengviet1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7302 - Wer: 1.0118 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 300 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 14.8304 \| 39.67 \| 40 \| 4.3168 \| 1.0 \| \| 5.5892 \| 79.67 \| 80 \| 3.5454 \| 1.0 \| \| 5.2113 \| 119.67 \| 120 \| 3.4845 \| 1.0 \| \| 4.9995 \| 159.67 \| 160 \| 3.5783 \| 1.0 \| \| 4.7958 \| 199.67 \| 200 \| 3.1850 \| 1.0 \| \| 4.4776 \| 239.67 \| 240 \| 2.9864 \| 1.0 \| \| 4.2546 \| 279.67 \| 280 \| 2.7302 \| 1.0118 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
Madronus/MultiLabel_V3	Madronus	2023-03-15T06:52:53Z	67	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-03-14T22:02:23Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: MultiLabel_V3 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. --> # MultiLabel_V3 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9683 - Accuracy: 0.7370 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8572 \| 0.1 \| 100 \| 1.1607 \| 0.6466 \| \| 0.8578 \| 0.2 \| 200 \| 1.1956 \| 0.6499 \| \| 0.7362 \| 0.3 \| 300 \| 1.1235 \| 0.6885 \| \| 0.8569 \| 0.39 \| 400 \| 1.0460 \| 0.6891 \| \| 0.4851 \| 0.49 \| 500 \| 1.1213 \| 0.6891 \| \| 0.7252 \| 0.59 \| 600 \| 1.1512 \| 0.6720 \| \| 0.6333 \| 0.69 \| 700 \| 1.1039 \| 0.6913 \| \| 0.6239 \| 0.79 \| 800 \| 1.0636 \| 0.7001 \| \| 0.2768 \| 0.89 \| 900 \| 1.0386 \| 0.7073 \| \| 0.4872 \| 0.99 \| 1000 \| 1.0311 \| 0.7062 \| \| 0.3049 \| 1.09 \| 1100 \| 1.0437 \| 0.7155 \| \| 0.1435 \| 1.18 \| 1200 \| 1.0343 \| 0.7222 \| \| 0.2088 \| 1.28 \| 1300 \| 1.0784 \| 0.7194 \| \| 0.4972 \| 1.38 \| 1400 \| 1.1072 \| 0.7166 \| \| 0.3604 \| 1.48 \| 1500 \| 1.0438 \| 0.7150 \| \| 0.2726 \| 1.58 \| 1600 \| 1.0077 \| 0.7293 \| \| 0.3106 \| 1.68 \| 1700 \| 1.0029 \| 0.7326 \| \| 0.3259 \| 1.78 \| 1800 \| 0.9906 \| 0.7310 \| \| 0.3323 \| 1.88 \| 1900 \| 0.9729 \| 0.7359 \| \| 0.2998 \| 1.97 \| 2000 \| 0.9683 \| 0.7370 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
MikolajDeja/facebook-nllb-200-distilled-600M-en-pl-3-para_crawl-finetune	MikolajDeja	2023-03-15T06:39:46Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "generated_from_trainer", "dataset:para_crawl", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-10T13:34:55Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer datasets: - para_crawl model-index: - name: facebook-nllb-200-distilled-600M-en-pl-3-para_crawl-finetune 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. --> # facebook-nllb-200-distilled-600M-en-pl-3-para_crawl-finetune This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the para_crawl dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.13.2
kuma-s/xlm-roberta-base-finetuned-panx-de	kuma-s	2023-03-15T06:39:31Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-03-15T06:22:34Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8638300289723342 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1358 - F1: 0.8638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2591 \| 1.0 \| 525 \| 0.1621 \| 0.8206 \| \| 0.1276 \| 2.0 \| 1050 \| 0.1379 \| 0.8486 \| \| 0.082 \| 3.0 \| 1575 \| 0.1358 \| 0.8638 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
jmbt22/marian-finetuned-opus-mt-en-tl	jmbt22	2023-03-15T06:06:16Z	107	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:tatoeba", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-03-15T05:50:58Z	--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - tatoeba metrics: - bleu model-index: - name: marian-finetuned-opus-mt-en-tl results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: tatoeba type: tatoeba config: en-tl split: train args: en-tl metrics: - name: Bleu type: bleu value: 35.9113771495936 --- <!-- 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. --> # marian-finetuned-opus-mt-en-tl This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-tl](https://huggingface.co/Helsinki-NLP/opus-mt-en-tl) on the tatoeba dataset. It achieves the following results on the evaluation set: - Loss: 1.2611 - Bleu: 35.9114 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.0 - Datasets 2.10.1 - Tokenizers 0.13.2
rm1768/wav2vec2-large-xlsr-turkish-demo-colab	rm1768	2023-03-15T06:04:19Z	19	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-03-10T08:07:35Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: wav2vec2-large-xlsr-turkish-demo-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: tr split: test args: tr metrics: - name: Wer type: wer value: 0.4821775099581248 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-turkish-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4151 - Wer: 0.4822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 5.2487 \| 4.26 \| 400 \| 1.6455 \| 1.0778 \| \| 0.71 \| 8.51 \| 800 \| 0.4428 \| 0.6138 \| \| 0.3073 \| 12.77 \| 1200 \| 0.4214 \| 0.5517 \| \| 0.2136 \| 17.02 \| 1600 \| 0.4345 \| 0.5193 \| \| 0.1624 \| 21.28 \| 2000 \| 0.4366 \| 0.5026 \| \| 0.1298 \| 25.53 \| 2400 \| 0.4111 \| 0.4949 \| \| 0.1174 \| 29.79 \| 2800 \| 0.4151 \| 0.4822 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
ashishj20/q-FrozenLake-v1-4x4-noslippery	ashishj20	2023-03-15T05:39:52Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T05:35:43Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noslippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="ashishj20/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"]) ```
edgemac20/q-FrozenLake-v1-4x4-noSlippery	edgemac20	2023-03-15T04:53:27Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T04:53:22Z	--- 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="edgemac20/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"]) ```
ajankelo/ppo-LunarLander-v2	ajankelo	2023-03-15T04:44:08Z	3	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-15T04:43:50Z	--- 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: 249.48 +/- 19.01 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 ... ```
NurFathihaTahiatSeeum/Fake-news-detection	NurFathihaTahiatSeeum	2023-03-15T04:37:15Z	0	0	null	[ "Natural Language Processing", "text-classification", "en", "dataset:fake_news_english", "region:us" ]	text-classification	2023-03-14T10:18:22Z	--- datasets: - fake_news_english language: - en pipeline_tag: text-classification tags: - Natural Language Processing --- This Natural Language Processing (NLP) work is done in google colab using Bidirectional Encoder Representations from Transformers (BERT) model. Dataset: https://www.kaggle.com/datasets/sadikaljarif/fake-news-detection-dataset-english
eduiqe/Pixelicopter	eduiqe	2023-03-15T03:52:07Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-03-05T07:06:13Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelicopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 18.30 +/- 14.56 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
marcospiau/finetuned_minilm	marcospiau	2023-03-15T03:35:50Z	108	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-15T03:35:17Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuned_minilm 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. --> # finetuned_minilm This model is a fine-tuned version of [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6736 - Accuracy: 0.9023 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 12345 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.5371 \| 1.0 \| 619 \| 0.2941 \| 0.8782 \| \| 0.2763 \| 2.0 \| 1238 \| 0.2590 \| 0.8986 \| \| 0.1899 \| 3.0 \| 1857 \| 0.3081 \| 0.8959 \| \| 0.1257 \| 4.0 \| 2476 \| 0.2576 \| 0.9177 \| \| 0.0929 \| 5.0 \| 3095 \| 0.3949 \| 0.9059 \| \| 0.0806 \| 6.0 \| 3714 \| 0.3304 \| 0.9173 \| \| 0.0629 \| 7.0 \| 4333 \| 0.4214 \| 0.9073 \| \| 0.0474 \| 8.0 \| 4952 \| 0.4625 \| 0.9145 \| \| 0.0498 \| 9.0 \| 5571 \| 0.4227 \| 0.9236 \| \| 0.049 \| 10.0 \| 6190 \| 0.5549 \| 0.8945 \| \| 0.0411 \| 11.0 \| 6809 \| 0.3340 \| 0.9341 \| \| 0.0272 \| 12.0 \| 7428 \| 0.3317 \| 0.9291 \| \| 0.0264 \| 13.0 \| 8047 \| 0.4099 \| 0.9305 \| \| 0.0279 \| 14.0 \| 8666 \| 0.4092 \| 0.9268 \| \| 0.0242 \| 15.0 \| 9285 \| 0.4418 \| 0.9318 \| \| 0.0241 \| 16.0 \| 9904 \| 0.4352 \| 0.9273 \| \| 0.0238 \| 17.0 \| 10523 \| 0.5306 \| 0.9259 \| \| 0.0216 \| 18.0 \| 11142 \| 0.4267 \| 0.9241 \| \| 0.0166 \| 19.0 \| 11761 \| 0.5134 \| 0.9255 \| \| 0.0182 \| 20.0 \| 12380 \| 0.6736 \| 0.9023 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
yz54321/wintermoonmix	yz54321	2023-03-15T03:09:35Z	0	2	null	[ "region:us" ]	null	2023-03-13T08:21:47Z	WinterMoonMix: https://civitai.com/models/12433/wintermoonmix LulubearMix: https://civitai.com/models/18934/lulubearmix
mipin5/OliHye2	mipin5	2023-03-15T03:02:46Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-03-15T02:58:37Z	--- license: creativeml-openrail-m ---
Eduardo84/Xx1	Eduardo84	2023-03-15T02:55:52Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2023-03-15T02:55:51Z	--- license: bsd-3-clause-clear ---
coreml-community/coreml-seek.art_MEGA	coreml-community	2023-03-15T02:52:28Z	0	2	null	[ "coreml", "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-01-30T00:51:18Z	--- license: creativeml-openrail-m tags: - coreml - stable-diffusion - text-to-image --- # Core ML Converted Model: - This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).<br> - Provide the model to an app such as Mochi Diffusion [Github](https://github.com/godly-devotion/MochiDiffusion) - [Discord](https://discord.gg/x2kartzxGv) to generate images.<br> - `split_einsum` version is compatible with all compute unit options including Neural Engine.<br> - `original` version is only compatible with CPU & GPU option.<br> - Custom resolution versions are tagged accordingly.<br> - `vae` tagged files have a vae embedded into the model.<br> - Descriptions are posted as-is from original model source. Not all features and/or results may be available in CoreML format.<br> - Some of the models were converted with `vae-encoder` for i2i. - Models that are 32 bit will have "fp32" in the filename. # Note: Some models do not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml). # seek.art MEGA: Source(s): [Hugging Face](https://huggingface.co/coreco/seek.art_MEGA) - [CivitAI](https://civitai.com/models/1315/seekart-mega) # Seek.art MEGA is a general use "anything" model that significantly improves on 1.5 across dozens of styles. Created by Coreco at [seek.art](https://seek.art/) This model was trained on nearly 10k high-quality public domain digital artworks with the goal of improving output quality across the board. We find the model to be highly flexible in its ability to mix various styles, subjects, and details. We recommend resolutions above 640px in one or both dimensions for best results. You can try this model and several others for free at [seek.art](https://seek.art/). We also recommend an inference tool supporting prompt weighting and high resolution optimization / fixing for best results. We suggest [InvokeAI](https://github.com/invoke-ai/InvokeAI) as a sensibly licensed and fully featured open-source inference tool. ### Examples <img src="https://huggingface.co/coreco/seek.art_MEGA/resolve/main/examples.png" style="max-width: 800px;" width="100%"/> The above example images including the prompts and all relevant settings are available [here](https://seek.art/explore/search?collection=6112a64d-bd8b-4043-8d96-88c7cfa65c43). Additionally, search thousands of high quality prompts on [seek.art](https://seek.art/) for free. ### License - This model carries a commercial restricted sub-license, please read carefully: [License](https://huggingface.co/coreco/seek.art_MEGA/blob/main/LICENSE.txt) ### Use Restrictions You agree not to use the Model or Derivatives of the Model: - for the commercial purpose of hosted content generation (inference) without the express written permission of seek.art. Model output for personal use carries no such commercial restriction. - In any way that violates any applicable national, federal, state, local or international law or regulation; - For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; - To generate or disseminate verifiably false information and/or content with the purpose of harming others; - To generate or disseminate personal identifiable information that can be used to harm an individual; - To defame, disparage or otherwise harass others; - For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation; - For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; - To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; - For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories; - To provide medical advice and medical results interpretation; - To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
baptiste-pasquier/distilcamembert-allocine	baptiste-pasquier	2023-03-15T02:42:04Z	107	0	transformers	[ "transformers", "pytorch", "tf", "tensorboard", "camembert", "text-classification", "generated_from_trainer", "fr", "dataset:allocine", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-13T00:36:42Z	--- language: - fr license: mit tags: - generated_from_trainer datasets: - allocine widget: - text: "Un film magnifique avec un duo d'acteurs excellent." - text: "Grosse déception pour ce thriller qui peine à convaincre." metrics: - accuracy - f1 - precision - recall model-index: - name: distilcamembert-allocine results: - task: name: Text Classification type: text-classification dataset: name: allocine type: allocine config: allocine split: validation args: allocine metrics: - name: Accuracy type: accuracy value: 0.9714 - name: F1 type: f1 value: 0.9709909727152854 - name: Precision type: precision value: 0.9648256399919372 - name: Recall type: recall value: 0.9772356063699469 --- <!-- 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. --> # distilcamembert-allocine This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on the allocine dataset. It achieves the following results on the evaluation set: - Loss: 0.1066 - Accuracy: 0.9714 - F1: 0.9710 - Precision: 0.9648 - Recall: 0.9772 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \| :-----------: \| :---: \| :---: \| :-------------: \| :------: \| :----: \| :-------: \| :----: \| \| 0.1504 \| 0.2 \| 500 \| 0.1290 \| 0.9555 \| 0.9542 \| 0.9614 \| 0.9470 \| \| 0.1334 \| 0.4 \| 1000 \| 0.1049 \| 0.9624 \| 0.9619 \| 0.9536 \| 0.9703 \| \| 0.1158 \| 0.6 \| 1500 \| 0.1052 \| 0.963 \| 0.9627 \| 0.9498 \| 0.9760 \| \| 0.1153 \| 0.8 \| 2000 \| 0.0949 \| 0.9661 \| 0.9653 \| 0.9686 \| 0.9620 \| \| 0.1053 \| 1.0 \| 2500 \| 0.0936 \| 0.9666 \| 0.9663 \| 0.9542 \| 0.9788 \| \| 0.0755 \| 1.2 \| 3000 \| 0.0987 \| 0.97 \| 0.9695 \| 0.9644 \| 0.9748 \| \| 0.0716 \| 1.4 \| 3500 \| 0.1078 \| 0.9688 \| 0.9684 \| 0.9598 \| 0.9772 \| \| 0.0688 \| 1.6 \| 4000 \| 0.1051 \| 0.9673 \| 0.9670 \| 0.9552 \| 0.9792 \| \| 0.0691 \| 1.8 \| 4500 \| 0.0940 \| 0.9709 \| 0.9704 \| 0.9688 \| 0.9720 \| \| 0.0733 \| 2.0 \| 5000 \| 0.1038 \| 0.9686 \| 0.9683 \| 0.9558 \| 0.9812 \| \| 0.0476 \| 2.2 \| 5500 \| 0.1066 \| 0.9714 \| 0.9710 \| 0.9648 \| 0.9772 \| \| 0.047 \| 2.4 \| 6000 \| 0.1098 \| 0.9689 \| 0.9686 \| 0.9587 \| 0.9788 \| \| 0.0431 \| 2.6 \| 6500 \| 0.1110 \| 0.9711 \| 0.9706 \| 0.9666 \| 0.9747 \| \| 0.0464 \| 2.8 \| 7000 \| 0.1149 \| 0.9697 \| 0.9694 \| 0.9592 \| 0.9798 \| \| 0.0342 \| 3.0 \| 7500 \| 0.1122 \| 0.9703 \| 0.9699 \| 0.9621 \| 0.9778 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.10.1 - Tokenizers 0.13.2
thang123/wav2vec2-large-xlsr-turkish-demo-colab	thang123	2023-03-15T02:21:05Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-03-13T09:27:40Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xlsr-turkish-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-turkish-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
brunonishimoto/q-learning-Taxi-v3	brunonishimoto	2023-03-15T01:57:13Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-03-14T00:10:33Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-learning-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="brunonishimoto/q-learning-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"]) ```
Xit1/gpt-1	Xit1	2023-03-15T01:46:35Z	0	0	adapter-transformers	[ "adapter-transformers", "en", "ur", "tl", "dataset:fka/awesome-chatgpt-prompts", "license:afl-3.0", "region:us" ]	null	2023-03-14T18:07:55Z	--- license: afl-3.0 datasets: - fka/awesome-chatgpt-prompts language: - en - ur - tl metrics: - accuracy library_name: adapter-transformers ---
peteli/hometown	peteli	2023-03-15T01:29:02Z	0	1	diffusers	[ "diffusers", "paddlepaddle", "stable-diffusion", "stable-diffusion-ppdiffusers", "text-to-image", "ppdiffusers", "lora", "en", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-03-15T00:48:39Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a sea of lavender and gold flowers in the world of fairy tales is my hometown tags: - stable-diffusion - stable-diffusion-ppdiffusers - text-to-image - ppdiffusers - lora inference: false language: - en library_name: diffusers --- # LoRA DreamBooth - peteli/hometown 本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 a sea of lavender and gold flowers in the world of fairy tales is my hometown 文本进行了训练。
kmcgrath/sd-controlnet-canny-fork	kmcgrath	2023-03-15T01:16:06Z	15	0	diffusers	[ "diffusers", "pytorch", "safetensors", "art", "controlnet", "stable-diffusion", "arxiv:2302.05543", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:openrail", "endpoints_compatible", "diffusers:StableDiffusionControlNetPipeline", "region:us" ]	text-to-image	2023-03-09T19:39:54Z	--- license: openrail base_model: runwayml/stable-diffusion-v1-5 tags: - art - controlnet - stable-diffusion --- # Controlnet - Canny Version ControlNet is a neural network structure to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlNet conditioned on Canny edges. It can be used in combination with [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img). ![img](./sd.png) ## Model Details - Developed by: Lvmin Zhang, Maneesh Agrawala - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - Resources for more information: [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543). - Cite as: @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } ## Introduction Controlnet was proposed in [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) by Lvmin Zhang, Maneesh Agrawala. The abstract reads as follows: We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications. ## Released Checkpoints The authors released 8 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on a different type of conditioning: \| Model Name \| Control Image Overview\| Control Image Example \| Generated Image Example \| \|---\|---\|---\|---\| \|[lllyasviel/sd-controlnet-canny](https://huggingface.co/lllyasviel/sd-controlnet-canny)<br/> Trained with canny edge detection \| A monochrome image with white edges on a black background.\|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_canny.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_canny.png"/></a>\|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"/></a>\| \|[lllyasviel/sd-controlnet-depth](https://huggingface.co/lllyasviel/sd-controlnet-depth)<br/> Trained with Midas depth estimation \|A grayscale image with black representing deep areas and white representing shallow areas.\|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_depth.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_depth.png"/></a>\|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"/></a>\| \|[lllyasviel/sd-controlnet-hed](https://huggingface.co/lllyasviel/sd-controlnet-hed)<br/> Trained with HED edge detection (soft edge) \|A monochrome image with white soft edges on a black background.\|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_hed.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_hed.png"/></a>\|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"/></a> \| \|[lllyasviel/sd-controlnet-mlsd](https://huggingface.co/lllyasviel/sd-controlnet-mlsd)<br/> Trained with M-LSD line detection \|A monochrome image composed only of white straight lines on a black background.\|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_mlsd.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_mlsd.png"/></a>\|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"/></a>\| \|[lllyasviel/sd-controlnet-normal](https://huggingface.co/lllyasviel/sd-controlnet-normal)<br/> Trained with normal map \|A [normal mapped](https://en.wikipedia.org/wiki/Normal_mapping) image.\|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_normal.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_normal.png"/></a>\|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"/></a>\| \|[lllyasviel/sd-controlnet_openpose](https://huggingface.co/lllyasviel/sd-controlnet-openpose)<br/> Trained with OpenPose bone image \|A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.\|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_openpose.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_openpose.png"/></a>\|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"/></a>\| \|[lllyasviel/sd-controlnet_scribble](https://huggingface.co/lllyasviel/sd-controlnet-scribble)<br/> Trained with human scribbles \|A hand-drawn monochrome image with white outlines on a black background.\|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_scribble.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_scribble.png"/></a>\|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"/></a> \| \|[lllyasviel/sd-controlnet_seg](https://huggingface.co/lllyasviel/sd-controlnet-seg)<br/>Trained with semantic segmentation \|An [ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K/)'s segmentation protocol image.\|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_seg.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_seg.png"/></a>\|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"/></a> \| ## Example It is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint has been trained on it. Experimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion. Note: If you want to process an image to create the auxiliary conditioning, external dependencies are required as shown below: 1. Install opencv ```sh $ pip install opencv-contrib-python ``` 2. Let's install `diffusers` and related packages: ``` $ pip install diffusers transformers git+https://github.com/huggingface/accelerate.git ``` 3. Run code: ```python import cv2 from PIL import Image from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler import torch import numpy as np from diffusers.utils import load_image image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-hed/resolve/main/images/bird.png") image = np.array(image) low_threshold = 100 high_threshold = 200 image = cv2.Canny(image, low_threshold, high_threshold) image = image[:, :, None] image = np.concatenate([image, image, image], axis=2) image = Image.fromarray(image) controlnet = ControlNetModel.from_pretrained( "fusing/stable-diffusion-v1-5-controlnet-canny", torch_dtype=torch.float16 ) pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) # Remove if you do not have xformers installed # see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers # for installation instructions pipe.enable_xformers_memory_efficient_attention() pipe.enable_model_cpu_offload() image = pipe("bird", image, num_inference_steps=20).images[0] image.save('images/bird_canny_out.png') ``` ![bird](./images/bird.png) ![bird_canny](./images/bird_canny.png) ![bird_canny_out](./images/bird_canny_out.png) ### Training The canny edge model was trained on 3M edge-image, caption pairs. The model was trained for 600 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model. ### Blog post For more information, please also have a look at the [official ControlNet Blog Post](https://huggingface.co/blog/controlnet).

aszfcxcgszdx/samsum

aszfcxcgszdx

2023-03-15T14:30:17Z

109

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain", "summarization", "en", "dataset:aszfcxcgszdx/autotrain-data-samsum-auto", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-03-15T14:25:51Z

--- tags: - autotrain - summarization language: - en widget: - text: "I love AutoTrain 🤗" datasets: - aszfcxcgszdx/autotrain-data-samsum-auto co2_eq_emissions: emissions: 0.0077793677303344775 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 41244106342 - CO2 Emissions (in grams): 0.0078 ## Validation Metrics - Loss: 1.565 - Rouge1: 47.592 - Rouge2: 23.270 - RougeL: 39.623 - RougeLsum: 43.180 - Gen Len: 18.305 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/aszfcxcgszdx/autotrain-samsum-auto-41244106342 ```

aszfcxcgszdx/multilingual-samsum

aszfcxcgszdx

2023-03-15T14:29:30Z

9

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain", "summarization", "unk", "dataset:aszfcxcgszdx/autotrain-data-multi-lingual-summarization", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-03-15T13:54:42Z

--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - aszfcxcgszdx/autotrain-data-multi-lingual-summarization co2_eq_emissions: emissions: 13.328572874208332 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 41234106312 - CO2 Emissions (in grams): 13.3286 ## Validation Metrics - Loss: 1.508 - Rouge1: 44.068 - Rouge2: 20.883 - RougeL: 37.071 - RougeLsum: 40.613 - Gen Len: 17.000 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/aszfcxcgszdx/autotrain-multi-lingual-summarization-41234106312 ```

aszfcxcgszdx/mt5-large-samsum

aszfcxcgszdx

2023-03-15T14:27:58Z

115

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain", "summarization", "unk", "dataset:aszfcxcgszdx/autotrain-data-multi-lingual-summarization", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-03-15T13:54:46Z

--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - aszfcxcgszdx/autotrain-data-multi-lingual-summarization co2_eq_emissions: emissions: 12.703463244389663 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 41234106313 - CO2 Emissions (in grams): 12.7035 ## Validation Metrics - Loss: 1.508 - Rouge1: 44.142 - Rouge2: 21.000 - RougeL: 37.127 - RougeLsum: 40.611 - Gen Len: 17.000 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/aszfcxcgszdx/autotrain-multi-lingual-summarization-41234106313 ```

quilaquedi/ppo-LunarLander-v2

quilaquedi

2023-03-15T14:17:11Z

1

0

stable-baselines3

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

reinforcement-learning

2023-03-15T08:48:03Z

--- 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: 253.86 +/- 21.14 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 ... ```

MarkieMark1/a2c-AntBulletEnv-v0

MarkieMark1

2023-03-15T14:14:04Z

1

0

stable-baselines3

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

reinforcement-learning

2023-03-15T14:12:57Z

--- 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: 1450.36 +/- 87.93 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 ... ```

nouman-10/fine-tune-bert-combined-mlm

nouman-10

2023-03-15T14:03:55Z

110

0

transformers

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

text-classification

2023-03-15T12:49:52Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: unsupervised-fine-tune-bert-cased-combined results: [] ---  # unsupervised-fine-tune-bert-cased-combined This model is a fine-tuned version of [nouman-10/unsupervised-comb-cased](https://huggingface.co/nouman-10/unsupervised-comb-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4579 - Accuracy: 0.7384 - F1: 0.7384 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.4463 | 1.0 | 1819 | 0.7093 | 0.6483 | 0.6483 | | 0.3304 | 2.0 | 3638 | 0.5988 | 0.7471 | 0.7471 | | 0.211 | 3.0 | 5457 | 0.8888 | 0.75 | 0.75 | | 0.1237 | 4.0 | 7276 | 1.4573 | 0.7355 | 0.7355 | | 0.0959 | 5.0 | 9095 | 1.7000 | 0.7355 | 0.7355 | | 0.062 | 6.0 | 10914 | 2.0796 | 0.7064 | 0.7064 | | 0.0347 | 7.0 | 12733 | 1.7562 | 0.7558 | 0.7558 | | 0.0259 | 8.0 | 14552 | 2.3160 | 0.7267 | 0.7267 | | 0.0166 | 9.0 | 16371 | 2.3301 | 0.7471 | 0.7471 | | 0.0091 | 10.0 | 18190 | 2.4579 | 0.7384 | 0.7384 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

Prgrg/ja-en-JESC-v3.0

Prgrg

2023-03-15T13:59:40Z

69

0

transformers

[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T11:51:43Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Prgrg/ja-en-JESC-v3.0 results: [] ---  # Prgrg/ja-en-JESC-v3.0 This model is a fine-tuned version of [Prgrg/ja-en-JESC-v2.0](https://huggingface.co/Prgrg/ja-en-JESC-v2.0) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.8267 - Validation Loss: 7.8094 - Epoch: 5 ## 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': 0.0005, 'decay_steps': 150000, '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': 0.001} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.3432 | 6.9622 | 0 | | 5.2217 | 7.5277 | 1 | | 5.1853 | 7.5818 | 2 | | 4.9986 | 7.5179 | 3 | | 4.8957 | 7.7693 | 4 | | 4.8267 | 7.8094 | 5 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2

psheaton/RoBERTa_for_eyewitness_confidence

psheaton

2023-03-15T13:58:23Z

110

2

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "legal", "en", "license:afl-3.0", "autotrain_compatible", "region:us" ]

text-classification

2023-03-15T13:40:16Z

--- inference: false license: afl-3.0 language: - en library_name: transformers pipeline_tag: text-classification tags: - legal ---

lora-library/girlwyt

lora-library

2023-03-15T13:57:36Z

1

0

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

2023-03-15T13:57:32Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: wyt tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - girlwyt 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 "wyt" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: wyt ![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)

vickylin21/Twitter_sentiment_analysis

vickylin21

2023-03-15T13:56:35Z

111

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-03-12T04:26:32Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: Twitter_sentiment_analysis results: [] ---  # Twitter_sentiment_analysis This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1891 - Accuracy: 0.9275 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7259 | 1.0 | 800 | 0.2336 | 0.92 | | 0.1542 | 2.0 | 1600 | 0.1891 | 0.9275 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

aienthused/Pixelcopter-PLE-v0

aienthused

2023-03-15T13:56:04Z

0

null

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

reinforcement-learning

2023-03-15T11:20:16Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 42.00 +/- 40.93 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

YashGajjar/Taxi-v3_Q-agent

YashGajjar

2023-03-15T13:53:56Z

0

null

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

reinforcement-learning

2023-03-15T13:53:48Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3_Q-agent 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="YashGajjar/Taxi-v3_Q-agent", 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"]) ```

EExe/Reinforce-cartpole

EExe

2023-03-15T13:52:32Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-03-15T13:52:19Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 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

peterdamn/a2c-AntBulletEnv-v0

peterdamn

2023-03-15T13:52:23Z

0

stable-baselines3

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

reinforcement-learning

2023-03-15T13:51:13Z

--- 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: 1556.86 +/- 35.82 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 ... ```

Christian90/pixelcoper-v1

Christian90

2023-03-15T13:52:14Z

0

null

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

reinforcement-learning

2023-03-15T13:50:04Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcoper-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -4.80 +/- 0.60 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

yyq90/dqn-SpaceInvadersNoFrameskip-v4

yyq90

2023-03-15T13:46:13Z

0

stable-baselines3

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

reinforcement-learning

2023-03-15T13:45:26Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 873.50 +/- 316.31 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga yyq90 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga yyq90 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga yyq90 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

reyhanemyr/distilbert-base-cased-finetuned-paper3

reyhanemyr

2023-03-15T13:40:41Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-03-15T13:26:54Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-cased-finetuned-paper3 results: [] ---  # distilbert-base-cased-finetuned-paper3 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1966 - Precision: 0.6773 - Recall: 0.7350 - F1: 0.7050 - Accuracy: 0.9687 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 73 | 0.1851 | 0.4383 | 0.4479 | 0.4431 | 0.9434 | | No log | 2.0 | 146 | 0.1473 | 0.5455 | 0.5678 | 0.5564 | 0.9593 | | No log | 3.0 | 219 | 0.1391 | 0.6509 | 0.6530 | 0.6520 | 0.9646 | | No log | 4.0 | 292 | 0.1236 | 0.6552 | 0.7192 | 0.6857 | 0.9702 | | No log | 5.0 | 365 | 0.1352 | 0.6724 | 0.7382 | 0.7038 | 0.9693 | | No log | 6.0 | 438 | 0.1594 | 0.6746 | 0.7129 | 0.6933 | 0.9673 | | 0.0969 | 7.0 | 511 | 0.1693 | 0.6705 | 0.7382 | 0.7027 | 0.9683 | | 0.0969 | 8.0 | 584 | 0.1806 | 0.6923 | 0.7382 | 0.7145 | 0.9692 | | 0.0969 | 9.0 | 657 | 0.1594 | 0.6359 | 0.7603 | 0.6925 | 0.9687 | | 0.0969 | 10.0 | 730 | 0.1740 | 0.6946 | 0.7319 | 0.7127 | 0.9683 | | 0.0969 | 11.0 | 803 | 0.1881 | 0.6735 | 0.7287 | 0.7 | 0.9677 | | 0.0969 | 12.0 | 876 | 0.1932 | 0.7064 | 0.7287 | 0.7174 | 0.9692 | | 0.0969 | 13.0 | 949 | 0.1890 | 0.6907 | 0.7256 | 0.7077 | 0.9689 | | 0.0025 | 14.0 | 1022 | 0.1860 | 0.6705 | 0.7445 | 0.7055 | 0.9696 | | 0.0025 | 15.0 | 1095 | 0.1951 | 0.6706 | 0.7256 | 0.6970 | 0.9688 | | 0.0025 | 16.0 | 1168 | 0.1936 | 0.6648 | 0.7319 | 0.6967 | 0.9681 | | 0.0025 | 17.0 | 1241 | 0.1969 | 0.6725 | 0.7319 | 0.7009 | 0.9686 | | 0.0025 | 18.0 | 1314 | 0.1953 | 0.6792 | 0.7413 | 0.7089 | 0.9692 | | 0.0025 | 19.0 | 1387 | 0.1960 | 0.6754 | 0.7350 | 0.7039 | 0.9687 | | 0.0025 | 20.0 | 1460 | 0.1966 | 0.6773 | 0.7350 | 0.7050 | 0.9687 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

avuhong/ParvoGPT2

avuhong

2023-03-15T13:36:45Z

119

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-03-15T13:27:47Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: output results: [] ---  # output This model is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6699 - Accuracy: 0.7571 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 220 | 3.8564 | 0.4857 | | No log | 2.0 | 440 | 2.7515 | 0.6096 | | 4.1568 | 3.0 | 660 | 2.2463 | 0.6780 | | 4.1568 | 4.0 | 880 | 1.9817 | 0.7152 | | 2.2818 | 5.0 | 1100 | 1.8278 | 0.7353 | | 2.2818 | 6.0 | 1320 | 1.7313 | 0.7486 | | 1.8444 | 7.0 | 1540 | 1.6847 | 0.7553 | | 1.8444 | 8.0 | 1760 | 1.6699 | 0.7571 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

DipeshY/roberta-finetuned-disaster_type_dy

DipeshY

2023-03-15T13:26:51Z

131

0

transformers

[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

question-answering

2023-03-15T12:54:25Z

--- license: cc-by-4.0 tags: - generated_from_trainer model-index: - name: roberta-finetuned-disaster_type_dy results: [] ---  # roberta-finetuned-disaster_type_dy This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cpu - Datasets 2.10.1 - Tokenizers 0.13.2

TiborUdvari/distilgpt2-test-douglas-finetuned-hitchhiker

TiborUdvari

2023-03-15T13:15:04Z

117

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-03-15T13:05:34Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-test-douglas-finetuned-hitchhiker results: [] ---  # distilgpt2-test-douglas-finetuned-hitchhiker 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: 4.1353 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 83 | 4.3445 | | No log | 2.0 | 166 | 4.1845 | | No log | 3.0 | 249 | 4.1353 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

stelladk/A2C-AntBulletEnv-v0

stelladk

2023-03-15T13:01:15Z

0

stable-baselines3

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

reinforcement-learning

2023-03-15T11:36:46Z

--- 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: 1572.51 +/- 52.53 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 ... ```

ThoDum/a2c-PandaReachDense-v2

ThoDum

2023-03-15T13:00:02Z

1

0

stable-baselines3

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

reinforcement-learning

2023-03-15T11:44:12Z

--- 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: -0.71 +/- 0.25 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 ... ```

hmatzner/ppo-PyramidsRND

hmatzner

2023-03-15T12:58:08Z

9

0

ml-agents

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

reinforcement-learning

2023-03-15T12:58:03Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Find your model_id: hmatzner/ppo-PyramidsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Christian90/CartPole-v1

Christian90

2023-03-15T12:57:23Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-03-15T12:57:18Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 370.30 +/- 191.91 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

nouman-10/unsupervised-comb-cased

nouman-10

2023-03-15T12:49:10Z

87

0

transformers

[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-03-15T12:25:18Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: unsupervised-comb-cased results: [] ---  # unsupervised-comb-cased This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9912 - Validation Loss: 3.1077 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -711, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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 | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.4995 | 3.2941 | 0 | | 3.1428 | 3.1982 | 1 | | 2.9912 | 3.1077 | 2 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2

Yoshiii/opt-6.7b-lora

Yoshiii

2023-03-15T12:32:36Z

0

2

null

[ "license:unlicense", "region:us" ]

null

2023-03-15T11:09:03Z

--- license: unlicense --- Running opt-6.7b with added loras locally on windows! # bitsandbytes I needed to get bitsandbytes working in my venv: I replaced the main.py in C:\Users\user\Desktop\test\peft\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py with the one here! I also added a .dll file here: C:\Users\user\Desktop\test\peft\venv\Lib\site-packages\bitsandbytes\libbitsandbytes_cuda116.dll # Training Script (https://github.com/huggingface/peft/commit/df0e1fb59266c9903ddd6dbfe7339bcd2068d150) (It's from their notebook!) ``` #load import os os.environ["CUDA_VISIBLE_DEVICES"]="0" import torch import torch.nn as nn import bitsandbytes as bnb from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained( "facebook/opt-6.7b", load_in_8bit=True, device_map='auto', ) tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b") #post-processing for param in model.parameters(): param.requires_grad = False # freeze the model - train adapters later if param.ndim == 1: # cast the small parameters (e.g. layernorm) to fp32 for stability param.data = param.data.to(torch.float32) model.gradient_checkpointing_enable() # reduce number of stored activations model.enable_input_require_grads() class CastOutputToFloat(nn.Sequential): def forward(self, x): return super().forward(x).to(torch.float32) model.lm_head = CastOutputToFloat(model.lm_head) # apply lora def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" ) # apply lora 2 from peft import LoraConfig, get_peft_model config = LoraConfig( r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none", task_type="CAUSAL_LM" ) model = get_peft_model(model, config) print_trainable_parameters(model) # training import transformers from datasets import load_dataset data = load_dataset("Abirate/english_quotes") data = data.map(lambda samples: tokenizer(samples['quote']), batched=True) trainer = transformers.Trainer( model=model, train_dataset=data['train'], args=transformers.TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, warmup_steps=100, max_steps=200, learning_rate=2e-4, fp16=True, logging_steps=1, output_dir='outputs' ), data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False) ) model.config.use_cache = False # silence the warnings. Please re-enable for inference! trainer.train() # push to huggingface txtloras model.push_to_hub("Yoshiii/opt-6.7b-lora", use_auth_token=True) # inference batch = tokenizer("Two things are infinite: ", return_tensors='pt') with torch.cuda.amp.autocast(): output_tokens = model.generate(**batch, max_new_tokens=50) print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True)) ``` # Inference (loading this repo lora from hf) ``` import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "Yoshiii/opt-6.7b-lora" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) # Load the Lora model model = PeftModel.from_pretrained(model, peft_model_id) batch = tokenizer("Two things are infinite: ", return_tensors='pt') with torch.cuda.amp.autocast(): output_tokens = model.generate(**batch, max_new_tokens=50) print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True)) ``` Two things are infinite: the universe and human stupidity; and I'm not sure about the universe. -Albert Einstein I'm not sure about the universe either. This output is like the training data. If you run without applying the Lora, it will usually look worse. If you retrain the lora, know that your new lora is not going to output the same results, despite you using the same settings. Inference should usually be deterministic when using the same lora, or using without lora. Also, If you want to download and use the loras from a visible folder, here's the inference script: ``` import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "./loramodel" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) # Load the Lora model model = PeftModel.from_pretrained(model, peft_model_id) batch = tokenizer("Two things are infinite: ", return_tensors='pt') with torch.cuda.amp.autocast(): output_tokens = model.generate(**batch, max_new_tokens=50) print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True)) ``` add your adapter_config.json and your adapter_model.bin to a folder in your current directory named `loramodel`, or whatever you choose.

qfrodicio/ml-roberta-large-finetuned-gesture-prediction-21-classes

qfrodicio

2023-03-15T12:29:55Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-03-15T11:19:48Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: ml-roberta-large-finetuned-gesture-prediction-21-classes results: [] ---  # ml-roberta-large-finetuned-gesture-prediction-21-classes This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. It achieves the following results on the validation set: - Loss: 0.7506 - Accuracy: 0.7927 - Precision: 0.7829 - Recall: 0.7927 - F1: 0.7837 It achieves the following results on the test set: - Loss: 0.8029 - Accuracy: 0.7720 - Precision: 0.7764 - Recall: 0.7720 - F1: 0.7636 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data This model has been trained on the qfrodicio/gesture-prediction-21-classes dataset ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - weight_decay: 0.01 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 2.4441 | 1.0 | 104 | 1.5033 | 0.5798 | 0.5071 | 0.5798 | 0.5015 | | 1.2049 | 2.0 | 208 | 0.8885 | 0.7532 | 0.7434 | 0.7532 | 0.7331 | | 0.7329 | 3.0 | 312 | 0.7506 | 0.7927 | 0.7829 | 0.7927 | 0.7837 | | 0.4949 | 4.0 | 416 | 0.7801 | 0.7936 | 0.7946 | 0.7936 | 0.7866 | | 0.3221 | 5.0 | 520 | 0.8761 | 0.7957 | 0.7889 | 0.7957 | 0.7865 | | 0.2112 | 6.0 | 624 | 0.9118 | 0.8062 | 0.8085 | 0.8062 | 0.8004 | | 0.1458 | 7.0 | 728 | 0.9391 | 0.8071 | 0.8057 | 0.8071 | 0.8019 | | 0.0988 | 8.0 | 832 | 0.9592 | 0.8105 | 0.8073 | 0.8105 | 0.8065 | | 0.0685 | 9.0 | 936 | 1.0358 | 0.8057 | 0.8043 | 0.8057 | 0.8016 | | 0.052 | 10.0 | 1040 | 1.0511 | 0.8089 | 0.8080 | 0.8089 | 0.8037 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

pfunk/PongNoFrameskip-v4-DQN_baseline-seed4

pfunk

2023-03-15T12:22:13Z

0

cleanrl

[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-03-15T12:22:04Z

--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.16 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) **DQN** Agent Playing **PongNoFrameskip-v4** This is a trained model of a DQN agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_baseline.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQN_baseline]" python -m cleanrl_utils.enjoy --exp-name DQN_baseline --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed4/raw/main/dqn_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed4/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed4/raw/main/poetry.lock poetry install --all-extras python dqn_atari.py --exp-name DQN_baseline --seed 4 --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk ``` # Hyperparameters ```python {'alg_type': 'dqn_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'DQN_baseline', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'save_model': True, 'seed': 4, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

joheras/xlm-roberta-base-finetuned-clinais

joheras

2023-03-15T12:21:34Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-03-15T11:43:46Z

--- license: mit tags: - generated_from_trainer model-index: - name: xlm-roberta-base-finetuned-clinais results: [] ---  # xlm-roberta-base-finetuned-clinais This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 223 | 1.7818 | | 1.9591 | 2.0 | 446 | 1.6896 | | 1.9591 | 3.0 | 669 | 1.6195 | | 1.7055 | 4.0 | 892 | 1.5804 | | 1.7055 | 5.0 | 1115 | 1.6104 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.0 - Datasets 2.8.0 - Tokenizers 0.12.1

pfunk/PongNoFrameskip-v4-DQN_baseline-seed2

pfunk

2023-03-15T12:20:11Z

0

cleanrl

[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-03-15T12:20:01Z

--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.41 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) **DQN** Agent Playing **PongNoFrameskip-v4** This is a trained model of a DQN agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_baseline.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQN_baseline]" python -m cleanrl_utils.enjoy --exp-name DQN_baseline --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed2/raw/main/dqn_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed2/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed2/raw/main/poetry.lock poetry install --all-extras python dqn_atari.py --exp-name DQN_baseline --seed 2 --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk ``` # Hyperparameters ```python {'alg_type': 'dqn_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'DQN_baseline', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'save_model': True, 'seed': 2, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

pfunk/PongNoFrameskip-v4-DQN_baseline-seed3

pfunk

2023-03-15T12:11:56Z

0

cleanrl

[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-03-15T12:11:48Z

--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.33 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) **DQN** Agent Playing **PongNoFrameskip-v4** This is a trained model of a DQN agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/DQN_baseline.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQN_baseline]" python -m cleanrl_utils.enjoy --exp-name DQN_baseline --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed3/raw/main/dqn_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed3/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-DQN_baseline-seed3/raw/main/poetry.lock poetry install --all-extras python dqn_atari.py --exp-name DQN_baseline --seed 3 --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk ``` # Hyperparameters ```python {'alg_type': 'dqn_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'DQN_baseline', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'save_model': True, 'seed': 3, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

shark123/text-to-sparql-LCQUAD

shark123

2023-03-15T12:10:19Z

103

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

2023-03-15T11:40:50Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: text-to-sparql-LCQUAD results: [] ---  # text-to-sparql-LCQUAD This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Gen Len: 19.0 - Bertscorer-p: 0.2282 - Bertscorer-r: -0.5504 - Bertscorer-f1: -0.1909 - Sacrebleu-score: 0.0000 - Sacrebleu-precisions: [100.0, 100.0, 100.0, 100.0] - Bleu-bp: 0.0000 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:----------------------------:|:-------:| | 0.0019 | 1.0 | 2491 | 0.0000 | 19.0 | 0.2282 | -0.5504 | -0.1909 | 0.0000 | [100.0, 100.0, 100.0, 100.0] | 0.0000 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

vocabtrimmer/mt5-small-esquad-qg-trimmed-es-120000

vocabtrimmer

2023-03-15T11:53:50Z

10

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T11:36:51Z

# Vocabulary Trimmed [lmqg/mt5-small-esquad-qg](https://huggingface.co/lmqg/mt5-small-esquad-qg): `vocabtrimmer/mt5-small-esquad-qg-trimmed-es-120000` This model is a trimmed version of [lmqg/mt5-small-esquad-qg](https://huggingface.co/lmqg/mt5-small-esquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-esquad-qg | vocabtrimmer/mt5-small-esquad-qg-trimmed-es-120000 | |:---------------------------|:---------------------------|:-----------------------------------------------------| | parameter_size_full | 300,165,504 | 166,944,128 | | parameter_size_embedding | 256,103,424 | 122,882,048 | | vocab_size | 250,101 | 120,002 | | compression_rate_full | 100.0 | 55.62 | | compression_rate_embedding | 100.0 | 47.98 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | es | vocabtrimmer/mc4_validation | text | es | validation | 120000 | 2 |

KarosY/lianjia_3l_881per100_1e-3

KarosY

2023-03-15T11:46:44Z

1

0

diffusers

[ "diffusers", "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

2023-03-15T04:04:49Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - https://huggingface.co/KarosY/lianjia_3l_881per100_1e-3 These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were fine-tuned on the None dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

peterdamn/ppo-Pyramids

peterdamn

2023-03-15T11:44:00Z

4

0

ml-agents

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

reinforcement-learning

2023-03-15T11:43:55Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: peterdamn/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-60000

vocabtrimmer

2023-03-15T11:34:47Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T11:20:20Z

# Vocabulary Trimmed [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg): `vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-60000` This model is a trimmed version of [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-ruquad-qg | vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-60000 | |:---------------------------|:---------------------------|:----------------------------------------------------| | parameter_size_full | 300,165,504 | 105,504,128 | | parameter_size_embedding | 256,103,424 | 61,442,048 | | vocab_size | 250,101 | 60,002 | | compression_rate_full | 100.0 | 35.15 | | compression_rate_embedding | 100.0 | 23.99 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | ru | vocabtrimmer/mc4_validation | text | ru | validation | 60000 | 2 |

vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-60000

vocabtrimmer

2023-03-15T11:33:41Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T11:19:04Z

# Vocabulary Trimmed [lmqg/mt5-small-koquad-qg](https://huggingface.co/lmqg/mt5-small-koquad-qg): `vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-60000` This model is a trimmed version of [lmqg/mt5-small-koquad-qg](https://huggingface.co/lmqg/mt5-small-koquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-koquad-qg | vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-60000 | |:---------------------------|:---------------------------|:----------------------------------------------------| | parameter_size_full | 300,165,504 | 105,504,128 | | parameter_size_embedding | 256,103,424 | 61,442,048 | | vocab_size | 250,101 | 60,002 | | compression_rate_full | 100.0 | 35.15 | | compression_rate_embedding | 100.0 | 23.99 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | ko | vocabtrimmer/mc4_validation | text | ko | validation | 60000 | 2 |

vocabtrimmer/mt5-small-itquad-qg-trimmed-it-60000

vocabtrimmer

2023-03-15T11:29:45Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T11:16:20Z

# Vocabulary Trimmed [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg): `vocabtrimmer/mt5-small-itquad-qg-trimmed-it-60000` This model is a trimmed version of [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-itquad-qg | vocabtrimmer/mt5-small-itquad-qg-trimmed-it-60000 | |:---------------------------|:---------------------------|:----------------------------------------------------| | parameter_size_full | 300,165,504 | 105,504,128 | | parameter_size_embedding | 256,103,424 | 61,442,048 | | vocab_size | 250,101 | 60,002 | | compression_rate_full | 100.0 | 35.15 | | compression_rate_embedding | 100.0 | 23.99 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | it | vocabtrimmer/mc4_validation | text | it | validation | 60000 | 2 |

uygarkurt/bert-restore-punctuation-turkish-legacy

uygarkurt

2023-03-15T11:23:20Z

4

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "punctuation", "tr", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-03-10T20:46:33Z

--- license: mit language: - tr tags: - punctuation widget: text: "Türkiye toprakları üzerindeki ilk yerleşmeler Yontma Taş Devri'nde başlar Doğu Trakya'da Traklar olmak üzere Hititler Frigler Lidyalılar ve Dor istilası sonucu Yunanistan'dan kaçan Akalar tarafından kurulan İyon medeniyeti gibi çeşitli eski Anadolu medeniyetlerinin ardından Makedonya kralı Büyük İskender'in egemenliğiyle ve fetihleriyle birlikte Helenistik Dönem başladı" --- ## bert-restore-punctuation-turkish This bert-base-cased model fine-tuned for punctuation restoration task on a mixture of open-source datasets. Model is able to predict **[! ? , . ; :]** This model works on arbitrarily large Turkish text. ----------------------------------------------- ## Usage ``` from transformers import pipeline classifier = pipeline("token-classification", model="uygarkurt/bert-restore-punctuation-turkish", tokenizer="uygarkurt/bert-restore-punctuation-turkish") txt = "Türkiye toprakları üzerindeki ilk yerleşmeler Yontma Taş Devri'nde başlar Doğu Trakya'da Traklar olmak üzere Hititler Frigler Lidyalılar ve Dor istilası sonucu Yunanistan'dan kaçan Akalar tarafından kurulan İyon medeniyeti gibi çeşitli eski Anadolu medeniyetlerinin ardından Makedonya kralı Büyük İskender'in egemenliğiyle ve fetihleriyle birlikte Helenistik Dönem başladı" print(classifier(txt)) # Output # Türkiye toprakları üzerindeki ilk yerleşmeler Yontma Taş Devri'nde başlar. Doğu Trakya'da Traklar olmak üzere Hititler, Frigler, Lidyalılar ve Dor istilası sonucu Yunanistan'dan kaçan Akalar tarafından kurulan İyon medeniyeti gibi çeşitli eski Anadolu medeniyetlerinin ardından Makedonya kralı Büyük İskender'in egemenliğiyle ve fetihleriyle birlikte Helenistik Dönem başladı. ``` ----------------------------------------------- ## Evaluation | Epoch | Training Loss | Validation Loss | Precision | Recall | F1 | Accuracy | | --------- | -------------|-------- | ----------|--------| --------| --------| |1 | 0.100300 | 0.096145 | 0.862800 | 0.829047 | 0.845586 | 0.965330 |2 | 0.084200 | 0.092011 | 0.878079 | 0.830346 | 0.853546 | 0.967148 |3 | 0.075200 | 0.093337 | 0.878449 | 0.833345 | 0.855303 | 0.967539 ----------------------------------------------- ## Information This is a plot project. Depending on the demand it can be improved. Contact [email protected]

joheras/clinico-roberta-biomedical-finetuned

joheras

2023-03-15T11:15:45Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-03-15T10:30:23Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: clinico-roberta-biomedical-finetuned results: [] ---  # clinico-roberta-biomedical-finetuned This model is a fine-tuned version of [joheras/roberta-base-biomedical-clinical-es-finetuned-clinais](https://huggingface.co/joheras/roberta-base-biomedical-clinical-es-finetuned-clinais) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9272 - Precision: 0.5095 - Recall: 0.6463 - F1: 0.5698 - Accuracy: 0.8623 ## 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 25 | 1.2199 | 0.0033 | 0.0053 | 0.0040 | 0.5756 | | No log | 2.0 | 50 | 0.7306 | 0.2031 | 0.2642 | 0.2296 | 0.8021 | | No log | 3.0 | 75 | 0.6366 | 0.2967 | 0.3811 | 0.3336 | 0.8235 | | No log | 4.0 | 100 | 0.6135 | 0.3497 | 0.4653 | 0.3993 | 0.8304 | | No log | 5.0 | 125 | 0.5845 | 0.3421 | 0.4537 | 0.3900 | 0.8331 | | No log | 6.0 | 150 | 0.5697 | 0.3307 | 0.4421 | 0.3784 | 0.8390 | | No log | 7.0 | 175 | 0.5415 | 0.3211 | 0.4495 | 0.3746 | 0.8471 | | No log | 8.0 | 200 | 0.5430 | 0.3589 | 0.5179 | 0.4240 | 0.8567 | | No log | 9.0 | 225 | 0.5513 | 0.3342 | 0.5474 | 0.4150 | 0.8604 | | No log | 10.0 | 250 | 0.5681 | 0.3769 | 0.5768 | 0.4559 | 0.8582 | | No log | 11.0 | 275 | 0.5813 | 0.3756 | 0.5863 | 0.4579 | 0.8553 | | No log | 12.0 | 300 | 0.6096 | 0.4181 | 0.5968 | 0.4918 | 0.8574 | | No log | 13.0 | 325 | 0.6318 | 0.3978 | 0.6042 | 0.4797 | 0.8539 | | No log | 14.0 | 350 | 0.6309 | 0.3892 | 0.5968 | 0.4711 | 0.8553 | | No log | 15.0 | 375 | 0.6559 | 0.3987 | 0.5968 | 0.4781 | 0.8565 | | No log | 16.0 | 400 | 0.6391 | 0.4275 | 0.6021 | 0.5 | 0.8560 | | No log | 17.0 | 425 | 0.6812 | 0.4388 | 0.6074 | 0.5095 | 0.8584 | | No log | 18.0 | 450 | 0.6901 | 0.4287 | 0.6137 | 0.5048 | 0.8563 | | No log | 19.0 | 475 | 0.6834 | 0.4572 | 0.6074 | 0.5217 | 0.8581 | | 0.3478 | 20.0 | 500 | 0.7050 | 0.4397 | 0.6179 | 0.5138 | 0.8573 | | 0.3478 | 21.0 | 525 | 0.7004 | 0.4462 | 0.6242 | 0.5204 | 0.8591 | | 0.3478 | 22.0 | 550 | 0.7038 | 0.4264 | 0.6126 | 0.5028 | 0.8599 | | 0.3478 | 23.0 | 575 | 0.7384 | 0.4416 | 0.6284 | 0.5187 | 0.8576 | | 0.3478 | 24.0 | 600 | 0.7197 | 0.4479 | 0.62 | 0.5201 | 0.8619 | | 0.3478 | 25.0 | 625 | 0.7412 | 0.4381 | 0.6221 | 0.5141 | 0.8559 | | 0.3478 | 26.0 | 650 | 0.7535 | 0.4489 | 0.6242 | 0.5222 | 0.8566 | | 0.3478 | 27.0 | 675 | 0.7534 | 0.4657 | 0.6432 | 0.5402 | 0.8586 | | 0.3478 | 28.0 | 700 | 0.7672 | 0.4525 | 0.6168 | 0.5220 | 0.8567 | | 0.3478 | 29.0 | 725 | 0.7680 | 0.4637 | 0.6316 | 0.5348 | 0.8599 | | 0.3478 | 30.0 | 750 | 0.7590 | 0.4611 | 0.6242 | 0.5304 | 0.8607 | | 0.3478 | 31.0 | 775 | 0.7671 | 0.4732 | 0.6326 | 0.5414 | 0.8625 | | 0.3478 | 32.0 | 800 | 0.7921 | 0.4674 | 0.6337 | 0.5380 | 0.8590 | | 0.3478 | 33.0 | 825 | 0.8037 | 0.4828 | 0.6358 | 0.5488 | 0.8574 | | 0.3478 | 34.0 | 850 | 0.8376 | 0.4644 | 0.6242 | 0.5326 | 0.8534 | | 0.3478 | 35.0 | 875 | 0.8346 | 0.4815 | 0.6284 | 0.5452 | 0.8552 | | 0.3478 | 36.0 | 900 | 0.8249 | 0.4750 | 0.6305 | 0.5418 | 0.8567 | | 0.3478 | 37.0 | 925 | 0.8420 | 0.4580 | 0.6305 | 0.5306 | 0.8548 | | 0.3478 | 38.0 | 950 | 0.8341 | 0.4773 | 0.6305 | 0.5433 | 0.8550 | | 0.3478 | 39.0 | 975 | 0.8085 | 0.4792 | 0.6316 | 0.5450 | 0.8653 | | 0.0274 | 40.0 | 1000 | 0.7954 | 0.4992 | 0.6474 | 0.5637 | 0.8651 | | 0.0274 | 41.0 | 1025 | 0.8145 | 0.4923 | 0.6421 | 0.5573 | 0.8635 | | 0.0274 | 42.0 | 1050 | 0.8290 | 0.4911 | 0.6368 | 0.5545 | 0.8610 | | 0.0274 | 43.0 | 1075 | 0.8468 | 0.4821 | 0.6379 | 0.5492 | 0.8571 | | 0.0274 | 44.0 | 1100 | 0.8274 | 0.4791 | 0.6389 | 0.5476 | 0.8625 | | 0.0274 | 45.0 | 1125 | 0.8583 | 0.4831 | 0.6305 | 0.5470 | 0.8551 | | 0.0274 | 46.0 | 1150 | 0.8420 | 0.4726 | 0.6347 | 0.5418 | 0.8589 | | 0.0274 | 47.0 | 1175 | 0.8631 | 0.5029 | 0.64 | 0.5632 | 0.8564 | | 0.0274 | 48.0 | 1200 | 0.8421 | 0.4911 | 0.64 | 0.5558 | 0.8617 | | 0.0274 | 49.0 | 1225 | 0.8564 | 0.5071 | 0.6411 | 0.5662 | 0.8631 | | 0.0274 | 50.0 | 1250 | 0.8659 | 0.4845 | 0.6263 | 0.5464 | 0.8603 | | 0.0274 | 51.0 | 1275 | 0.8596 | 0.4860 | 0.64 | 0.5525 | 0.8632 | | 0.0274 | 52.0 | 1300 | 0.8713 | 0.4856 | 0.6368 | 0.5510 | 0.8593 | | 0.0274 | 53.0 | 1325 | 0.8888 | 0.4868 | 0.64 | 0.5530 | 0.8585 | | 0.0274 | 54.0 | 1350 | 0.8591 | 0.4816 | 0.6337 | 0.5473 | 0.8610 | | 0.0274 | 55.0 | 1375 | 0.8755 | 0.4996 | 0.64 | 0.5611 | 0.8615 | | 0.0274 | 56.0 | 1400 | 0.8749 | 0.5095 | 0.6484 | 0.5706 | 0.8583 | | 0.0274 | 57.0 | 1425 | 0.8867 | 0.5025 | 0.6453 | 0.5650 | 0.8580 | | 0.0274 | 58.0 | 1450 | 0.8905 | 0.4947 | 0.6337 | 0.5556 | 0.8579 | | 0.0274 | 59.0 | 1475 | 0.8911 | 0.4881 | 0.6495 | 0.5574 | 0.8596 | | 0.0099 | 60.0 | 1500 | 0.9220 | 0.4914 | 0.6347 | 0.5540 | 0.8570 | | 0.0099 | 61.0 | 1525 | 0.8687 | 0.4786 | 0.6368 | 0.5465 | 0.8594 | | 0.0099 | 62.0 | 1550 | 0.9080 | 0.4906 | 0.6337 | 0.5531 | 0.8575 | | 0.0099 | 63.0 | 1575 | 0.9004 | 0.4831 | 0.6337 | 0.5483 | 0.8583 | | 0.0099 | 64.0 | 1600 | 0.8906 | 0.4778 | 0.6337 | 0.5448 | 0.8619 | | 0.0099 | 65.0 | 1625 | 0.8870 | 0.4959 | 0.6368 | 0.5576 | 0.8618 | | 0.0099 | 66.0 | 1650 | 0.8843 | 0.4851 | 0.6358 | 0.5503 | 0.8611 | | 0.0099 | 67.0 | 1675 | 0.8923 | 0.4912 | 0.6453 | 0.5578 | 0.8618 | | 0.0099 | 68.0 | 1700 | 0.8864 | 0.4898 | 0.6337 | 0.5525 | 0.8615 | | 0.0099 | 69.0 | 1725 | 0.8974 | 0.4943 | 0.6411 | 0.5582 | 0.8615 | | 0.0099 | 70.0 | 1750 | 0.8851 | 0.4821 | 0.6379 | 0.5492 | 0.8611 | | 0.0099 | 71.0 | 1775 | 0.8958 | 0.4920 | 0.6453 | 0.5583 | 0.8593 | | 0.0099 | 72.0 | 1800 | 0.8880 | 0.4988 | 0.6411 | 0.5610 | 0.8618 | | 0.0099 | 73.0 | 1825 | 0.8959 | 0.4852 | 0.6379 | 0.5512 | 0.8606 | | 0.0099 | 74.0 | 1850 | 0.9036 | 0.4773 | 0.6305 | 0.5433 | 0.8598 | | 0.0099 | 75.0 | 1875 | 0.9031 | 0.4864 | 0.6389 | 0.5523 | 0.8615 | | 0.0099 | 76.0 | 1900 | 0.9243 | 0.4907 | 0.6368 | 0.5543 | 0.8590 | | 0.0099 | 77.0 | 1925 | 0.9285 | 0.4877 | 0.6453 | 0.5555 | 0.8590 | | 0.0099 | 78.0 | 1950 | 0.9261 | 0.5074 | 0.6516 | 0.5705 | 0.8598 | | 0.0099 | 79.0 | 1975 | 0.9374 | 0.5037 | 0.64 | 0.5637 | 0.8580 | | 0.0061 | 80.0 | 2000 | 0.9165 | 0.5021 | 0.6316 | 0.5594 | 0.8621 | | 0.0061 | 81.0 | 2025 | 0.9307 | 0.5162 | 0.6368 | 0.5702 | 0.8582 | | 0.0061 | 82.0 | 2050 | 0.9369 | 0.4911 | 0.6358 | 0.5541 | 0.8574 | | 0.0061 | 83.0 | 2075 | 0.9293 | 0.5191 | 0.6421 | 0.5741 | 0.8584 | | 0.0061 | 84.0 | 2100 | 0.9187 | 0.5004 | 0.6453 | 0.5637 | 0.8629 | | 0.0061 | 85.0 | 2125 | 0.9293 | 0.4927 | 0.6379 | 0.5560 | 0.8623 | | 0.0061 | 86.0 | 2150 | 0.9200 | 0.5041 | 0.6453 | 0.5660 | 0.8634 | | 0.0061 | 87.0 | 2175 | 0.9273 | 0.4992 | 0.6421 | 0.5617 | 0.8631 | | 0.0061 | 88.0 | 2200 | 0.9325 | 0.5021 | 0.6442 | 0.5643 | 0.8623 | | 0.0061 | 89.0 | 2225 | 0.9245 | 0.4844 | 0.6389 | 0.5511 | 0.8630 | | 0.0061 | 90.0 | 2250 | 0.9291 | 0.4979 | 0.6368 | 0.5589 | 0.8593 | | 0.0061 | 91.0 | 2275 | 0.9264 | 0.5083 | 0.6432 | 0.5678 | 0.8622 | | 0.0061 | 92.0 | 2300 | 0.9283 | 0.5025 | 0.6411 | 0.5634 | 0.8619 | | 0.0061 | 93.0 | 2325 | 0.9264 | 0.5008 | 0.6442 | 0.5635 | 0.8613 | | 0.0061 | 94.0 | 2350 | 0.9205 | 0.5079 | 0.6463 | 0.5688 | 0.8626 | | 0.0061 | 95.0 | 2375 | 0.9223 | 0.5121 | 0.6484 | 0.5722 | 0.8625 | | 0.0061 | 96.0 | 2400 | 0.9244 | 0.5045 | 0.6421 | 0.5651 | 0.8620 | | 0.0061 | 97.0 | 2425 | 0.9248 | 0.5062 | 0.6463 | 0.5677 | 0.8622 | | 0.0061 | 98.0 | 2450 | 0.9277 | 0.5037 | 0.6453 | 0.5658 | 0.8621 | | 0.0061 | 99.0 | 2475 | 0.9272 | 0.5083 | 0.6463 | 0.5690 | 0.8623 | | 0.0046 | 100.0 | 2500 | 0.9272 | 0.5095 | 0.6463 | 0.5698 | 0.8623 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.0 - Datasets 2.8.0 - Tokenizers 0.12.1

vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-30000

vocabtrimmer

2023-03-15T11:15:17Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T11:01:04Z

# Vocabulary Trimmed [lmqg/mt5-small-koquad-qg](https://huggingface.co/lmqg/mt5-small-koquad-qg): `vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-30000` This model is a trimmed version of [lmqg/mt5-small-koquad-qg](https://huggingface.co/lmqg/mt5-small-koquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-koquad-qg | vocabtrimmer/mt5-small-koquad-qg-trimmed-ko-30000 | |:---------------------------|:---------------------------|:----------------------------------------------------| | parameter_size_full | 300,165,504 | 74,784,128 | | parameter_size_embedding | 256,103,424 | 30,722,048 | | vocab_size | 250,101 | 30,002 | | compression_rate_full | 100.0 | 24.91 | | compression_rate_embedding | 100.0 | 12.0 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | ko | vocabtrimmer/mc4_validation | text | ko | validation | 30000 | 2 |

vocabtrimmer/mt5-small-itquad-qg-trimmed-it-30000

vocabtrimmer

2023-03-15T11:12:32Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T10:59:37Z

# Vocabulary Trimmed [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg): `vocabtrimmer/mt5-small-itquad-qg-trimmed-it-30000` This model is a trimmed version of [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-itquad-qg | vocabtrimmer/mt5-small-itquad-qg-trimmed-it-30000 | |:---------------------------|:---------------------------|:----------------------------------------------------| | parameter_size_full | 300,165,504 | 74,784,128 | | parameter_size_embedding | 256,103,424 | 30,722,048 | | vocab_size | 250,101 | 30,002 | | compression_rate_full | 100.0 | 24.91 | | compression_rate_embedding | 100.0 | 12.0 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | it | vocabtrimmer/mc4_validation | text | it | validation | 30000 | 2 |

chjun/my_awesome_model

chjun

2023-03-15T11:07:34Z

5

0

transformers

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

text-classification

2023-03-15T06:23:59Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: my_awesome_model results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.93052 ---  # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2372 - Accuracy: 0.9305 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2346 | 1.0 | 1563 | 0.1895 | 0.9280 | | 0.1531 | 2.0 | 3126 | 0.2372 | 0.9305 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

ealarcong/mt5-small-finetuned-amazon-en-es

ealarcong

2023-03-15T11:06:04Z

3

0

transformers

[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-14T10:51:40Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ealarcong/mt5-small-finetuned-amazon-en-es results: [] ---  # ealarcong/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1830 - Validation Loss: 3.4080 - Epoch: 7 ## 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': 5.6e-05, 'decay_steps': 9672, '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 | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 11.0744 | 4.9526 | 0 | | 6.2839 | 3.9714 | 1 | | 5.4063 | 3.6820 | 2 | | 4.9197 | 3.5710 | 3 | | 4.5865 | 3.5060 | 4 | | 4.3904 | 3.4481 | 5 | | 4.2549 | 3.4180 | 6 | | 4.1830 | 3.4080 | 7 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2

852wa/hako

852wa

2023-03-15T11:06:03Z

0

29

null

[ "region:us" ]

null

2023-03-15T08:26:35Z

# ■hakoA & hakoB ![sample5](https://huggingface.co/852wa/hako/resolve/main/img/05.png) ![sample6](https://huggingface.co/852wa/hako/resolve/main/img/06.png) ![sample7](https://huggingface.co/852wa/hako/resolve/main/img/07.png) ![sample8](https://huggingface.co/852wa/hako/resolve/main/img/08.png) I conducted custom fine-tuning on wd15-beta2-aesthetic, which is based on the SD2.1 architecture, available at https://huggingface.co/waifu-diffusion/wd-1-5-beta2. SD2.1系であるwd15-beta2-aesthetic https://huggingface.co/waifu-diffusion/wd-1-5-beta2　に対して独自の追加学習を行いました。 # ■Setting It is recommended to use "(anime:1.2)" as the prompt and "nsfw,messy,blush,nfixer" as the negative prompt. If the output is not at least 768 pixels on the shorter side, there is a possibility that the facial features may be distorted. "(anime:1.2)" creates a flat, anime-like image style. promptには「(anime:1.2)」 negative promptには「nsfw,messy,blush,nfixer」を入れることをおすすめします。「(anime:1.2)」はフラットなアニメ調のイメージになります。短辺が768px以上での出力でない場合、顔の描画が崩れる可能性があります。 # ■Licence Model hakoA and hakoB are released under the Fair AI Public License 1.0-SD. Please refer to the following link for the license terms: https://freedevproject.org/faipl-1.0-sd/ hakoA、hakoBはFair AI Public License 1.0-SDのライセンス下での公開です。下記ライセンス内容を確認ください。 https://freedevproject.org/faipl-1.0-sd/ ![sample1](https://huggingface.co/852wa/hako/resolve/main/img/01.png) ``` (anime:1.2),(hyper extreme detailed:1.0),amazing quality,Beautiful Illustration,1girl,breasts,maid_apron,happy smile,cafe with waitresses dressed in cute maid costumes Negative prompt: nsfw,messy,blush,nfixer, Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 1386462091, Size: 768x1152 ``` ![sample2](https://huggingface.co/852wa/hako/resolve/main/img/02.png) ``` (anime:1.2),( stylish pose:1.1), (smile:1), (king (throne:1.1) :1.3), Negative prompt: nsfw,messy,blush,nfixer, Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 2137539252, Size: 768x1152 ``` ![sample3](https://huggingface.co/852wa/hako/resolve/main/img/03.png) ``` (anime:1.2),(masterpiece:1.2), (high quality:1.2), (watercolor painting:1.1),anatomy,1 girl,solo,(cowboy shot:1.1), perfect face,18yo,(from front),school girl, black hair,black cardigan,ribbon,(white hat:1.1),closed eyes,arms behind back,tree,calm,(darkness lighting:1.4),(night:1.4), standing ,kawaii face, depth of field Negative prompt: nsfw,messy,blush,nfixer, Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 260664233, Size: 768x1152 ``` ![sample4](https://huggingface.co/852wa/hako/resolve/main/img/04.png) ``` (anime:1.2),(1girl, 12yo, flat:1.2)white dress outdoor Negative prompt: nsfw,messy,blush,nfixer, Steps: 28, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 2617311573, Size: 768x1152 ```

vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-15000

vocabtrimmer

2023-03-15T11:00:07Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T10:46:02Z

# Vocabulary Trimmed [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg): `vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-15000` This model is a trimmed version of [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-ruquad-qg | vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-15000 | |:---------------------------|:---------------------------|:----------------------------------------------------| | parameter_size_full | 300,165,504 | 59,424,128 | | parameter_size_embedding | 256,103,424 | 15,362,048 | | vocab_size | 250,101 | 15,002 | | compression_rate_full | 100.0 | 19.8 | | compression_rate_embedding | 100.0 | 6.0 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | ru | vocabtrimmer/mc4_validation | text | ru | validation | 15000 | 2 |

vocabtrimmer/mt5-small-itquad-qg-trimmed-it-15000

vocabtrimmer

2023-03-15T10:57:25Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T10:44:20Z

# Vocabulary Trimmed [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg): `vocabtrimmer/mt5-small-itquad-qg-trimmed-it-15000` This model is a trimmed version of [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-itquad-qg | vocabtrimmer/mt5-small-itquad-qg-trimmed-it-15000 | |:---------------------------|:---------------------------|:----------------------------------------------------| | parameter_size_full | 300,165,504 | 59,424,128 | | parameter_size_embedding | 256,103,424 | 15,362,048 | | vocab_size | 250,101 | 15,002 | | compression_rate_full | 100.0 | 19.8 | | compression_rate_embedding | 100.0 | 6.0 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | it | vocabtrimmer/mc4_validation | text | it | validation | 15000 | 2 |

vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-10000

vocabtrimmer

2023-03-15T10:44:50Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T10:30:38Z

# Vocabulary Trimmed [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg): `vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-10000` This model is a trimmed version of [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-ruquad-qg | vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-10000 | |:---------------------------|:---------------------------|:----------------------------------------------------| | parameter_size_full | 300,165,504 | 54,305,152 | | parameter_size_embedding | 256,103,424 | 10,243,072 | | vocab_size | 250,101 | 10,003 | | compression_rate_full | 100.0 | 18.09 | | compression_rate_embedding | 100.0 | 4.0 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | ru | vocabtrimmer/mc4_validation | text | ru | validation | 10000 | 2 |

vocabtrimmer/mt5-small-itquad-qg-trimmed-it-10000

vocabtrimmer

2023-03-15T10:43:08Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T10:30:01Z

# Vocabulary Trimmed [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg): `vocabtrimmer/mt5-small-itquad-qg-trimmed-it-10000` This model is a trimmed version of [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-itquad-qg | vocabtrimmer/mt5-small-itquad-qg-trimmed-it-10000 | |:---------------------------|:---------------------------|:----------------------------------------------------| | parameter_size_full | 300,165,504 | 54,304,128 | | parameter_size_embedding | 256,103,424 | 10,242,048 | | vocab_size | 250,101 | 10,002 | | compression_rate_full | 100.0 | 18.09 | | compression_rate_embedding | 100.0 | 4.0 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | it | vocabtrimmer/mc4_validation | text | it | validation | 10000 | 2 |

kiu020/distilbert-base-uncased-finetuned-squad

kiu020

2023-03-15T10:42:36Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-03-15T09:46:41Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1609 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 1.2109 | 1.0 | 5533 | 1.1356 | | 0.9553 | 2.0 | 11066 | 1.1270 | | 0.739 | 3.0 | 16599 | 1.1609 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

marco-c88/distilgpt2-finetuned-wikitext2

marco-c88

2023-03-15T10:40:26Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-03-15T10:11:15Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] ---  # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4740 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.0767 | 1.0 | 794 | 3.7406 | | 3.8158 | 2.0 | 1588 | 3.6718 | | 3.7557 | 3.0 | 2382 | 3.6302 | | 3.6758 | 4.0 | 3176 | 3.5968 | | 3.6383 | 5.0 | 3970 | 3.5704 | | 3.5762 | 6.0 | 4764 | 3.5524 | | 3.5415 | 7.0 | 5558 | 3.5360 | | 3.5116 | 8.0 | 6352 | 3.5195 | | 3.485 | 9.0 | 7146 | 3.5116 | | 3.4587 | 10.0 | 7940 | 3.5033 | | 3.429 | 11.0 | 8734 | 3.4950 | | 3.4179 | 12.0 | 9528 | 3.4882 | | 3.3985 | 13.0 | 10322 | 3.4845 | | 3.3812 | 14.0 | 11116 | 3.4825 | | 3.3671 | 15.0 | 11910 | 3.4795 | | 3.3547 | 16.0 | 12704 | 3.4751 | | 3.3472 | 17.0 | 13498 | 3.4744 | | 3.3393 | 18.0 | 14292 | 3.4743 | | 3.3334 | 19.0 | 15086 | 3.4740 | | 3.3309 | 20.0 | 15880 | 3.4740 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

vocabtrimmer/mt5-small-esquad-qg-trimmed-es-5000

vocabtrimmer

2023-03-15T10:37:52Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T10:15:01Z

# Vocabulary Trimmed [lmqg/mt5-small-esquad-qg](https://huggingface.co/lmqg/mt5-small-esquad-qg): `vocabtrimmer/mt5-small-esquad-qg-trimmed-es-5000` This model is a trimmed version of [lmqg/mt5-small-esquad-qg](https://huggingface.co/lmqg/mt5-small-esquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-esquad-qg | vocabtrimmer/mt5-small-esquad-qg-trimmed-es-5000 | |:---------------------------|:---------------------------|:---------------------------------------------------| | parameter_size_full | 300,165,504 | 49,185,152 | | parameter_size_embedding | 256,103,424 | 5,123,072 | | vocab_size | 250,101 | 5,003 | | compression_rate_full | 100.0 | 16.39 | | compression_rate_embedding | 100.0 | 2.0 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | es | vocabtrimmer/mc4_validation | text | es | validation | 5000 | 2 |

vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-90000

vocabtrimmer

2023-03-15T10:34:39Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T10:21:59Z

# Vocabulary Trimmed [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg): `vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-90000` This model is a trimmed version of [lmqg/mt5-small-ruquad-qg](https://huggingface.co/lmqg/mt5-small-ruquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-ruquad-qg | vocabtrimmer/mt5-small-ruquad-qg-trimmed-ru-90000 | |:---------------------------|:---------------------------|:----------------------------------------------------| | parameter_size_full | 300,165,504 | 136,224,128 | | parameter_size_embedding | 256,103,424 | 92,162,048 | | vocab_size | 250,101 | 90,002 | | compression_rate_full | 100.0 | 45.38 | | compression_rate_embedding | 100.0 | 35.99 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | ru | vocabtrimmer/mc4_validation | text | ru | validation | 90000 | 2 |

vocabtrimmer/mt5-small-itquad-qg-trimmed-it-5000

vocabtrimmer

2023-03-15T10:29:02Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-15T10:15:05Z

# Vocabulary Trimmed [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg): `vocabtrimmer/mt5-small-itquad-qg-trimmed-it-5000` This model is a trimmed version of [lmqg/mt5-small-itquad-qg](https://huggingface.co/lmqg/mt5-small-itquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size. Following table shows a summary of the trimming process. | | lmqg/mt5-small-itquad-qg | vocabtrimmer/mt5-small-itquad-qg-trimmed-it-5000 | |:---------------------------|:---------------------------|:---------------------------------------------------| | parameter_size_full | 300,165,504 | 49,185,152 | | parameter_size_embedding | 256,103,424 | 5,123,072 | | vocab_size | 250,101 | 5,003 | | compression_rate_full | 100.0 | 16.39 | | compression_rate_embedding | 100.0 | 2.0 | Following table shows the parameter used to trim vocabulary. | language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency | |:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:| | it | vocabtrimmer/mc4_validation | text | it | validation | 5000 | 2 |

AndyPig/ppo-LunarLander-v2

AndyPig

2023-03-15T10:24:47Z

0

stable-baselines3

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

reinforcement-learning

2023-03-15T10:24:22Z

--- 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: 290.81 +/- 19.46 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

dmargutierrez/distilbert-base-multilingual-cased-WNUT-ner

dmargutierrez

2023-03-15T10:16:23Z

4

0

transformers

[ "transformers", "pytorch", "distilbert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-03-15T10:09:19Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-multilingual-cased-WNUT-ner results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.5496503496503496 - name: Recall type: recall value: 0.36422613531047265 - name: F1 type: f1 value: 0.4381270903010034 - name: Accuracy type: accuracy value: 0.9468667179618706 ---  # distilbert-base-multilingual-cased-WNUT-ner This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.3516 - Precision: 0.5497 - Recall: 0.3642 - F1: 0.4381 - Accuracy: 0.9469 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2727 | 0.6626 | 0.2530 | 0.3662 | 0.9402 | | No log | 2.0 | 426 | 0.2636 | 0.5895 | 0.2715 | 0.3718 | 0.9429 | | 0.1729 | 3.0 | 639 | 0.2933 | 0.5931 | 0.3040 | 0.4020 | 0.9447 | | 0.1729 | 4.0 | 852 | 0.2861 | 0.5437 | 0.3457 | 0.4227 | 0.9453 | | 0.0503 | 5.0 | 1065 | 0.3270 | 0.5627 | 0.3494 | 0.4311 | 0.9455 | | 0.0503 | 6.0 | 1278 | 0.3277 | 0.5451 | 0.3531 | 0.4286 | 0.9463 | | 0.0503 | 7.0 | 1491 | 0.3471 | 0.5828 | 0.3457 | 0.4340 | 0.9467 | | 0.0231 | 8.0 | 1704 | 0.3594 | 0.5801 | 0.3457 | 0.4332 | 0.9464 | | 0.0231 | 9.0 | 1917 | 0.3550 | 0.5567 | 0.3503 | 0.4300 | 0.9467 | | 0.0121 | 10.0 | 2130 | 0.3516 | 0.5497 | 0.3642 | 0.4381 | 0.9469 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

Christian90/dqn-SpaceInvadersNoFrameskip-v4

Christian90

2023-03-15T10:15:42Z

0

stable-baselines3

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

reinforcement-learning

2023-03-15T10:13:18Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 521.50 +/- 219.83 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Christian90 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Christian90 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Christian90 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

yumingyi/dqn-SpaceInvadersNoFrameskip-v4

yumingyi

2023-03-15T10:11:48Z

0

stable-baselines3

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

reinforcement-learning

2023-03-15T10:11:03Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 529.00 +/- 143.68 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga yumingyi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga yumingyi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga yumingyi ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

oyvindgrutle/amk-whisper

oyvindgrutle

2023-03-15T10:07:20Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-01-25T11:17:48Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: amk-whisper results: [] ---  # amk-whisper This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1902 - Wer: 40.3587 ## 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 - lr_scheduler_warmup_steps: 50 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 20.0 | 20 | 0.7838 | 30.9417 | | 0.8511 | 40.0 | 40 | 1.0878 | 44.8430 | | 0.0794 | 60.0 | 60 | 1.1466 | 39.4619 | | 0.001 | 80.0 | 80 | 1.1872 | 39.9103 | | 0.0004 | 100.0 | 100 | 1.1902 | 40.3587 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

joheras/roberta-base-biomedical-clinical-es-finetuned-clinais

joheras

2023-03-15T10:06:46Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-03-15T09:50:23Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: roberta-base-biomedical-clinical-es-finetuned-clinais results: [] ---  # roberta-base-biomedical-clinical-es-finetuned-clinais This model is a fine-tuned version of [BSC-LT/roberta-base-biomedical-clinical-es](https://huggingface.co/BSC-LT/roberta-base-biomedical-clinical-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3091 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 171 | 1.3679 | | 1.4311 | 2.0 | 342 | 1.2926 | | 1.4311 | 3.0 | 513 | 1.2896 | | 1.3363 | 4.0 | 684 | 1.3143 | | 1.3363 | 5.0 | 855 | 1.3097 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.0 - Datasets 2.8.0 - Tokenizers 0.12.1

lora-library/wyt

lora-library

2023-03-15T09:33:11Z

1

0

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

2023-03-15T09:33:07Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: wangyanting tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - wyt 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 "wangyanting" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: wyt ![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)

diffusers/ddpm-cifar10-32-demo

diffusers

2023-03-15T09:20:32Z

2

1

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-03-15T09:09:10Z

--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation duplicated_from: google/ddpm-cifar10-32 --- # Denoising Diffusion Probabilistic Models (DDPM) **Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) **Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel **Abstract**: *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* ## Inference **DDPM** models can use *discrete noise schedulers* such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-cifar10-32" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-cifar10-32/resolve/main/images/generated_image_3.png)

peterdamn/ppo-CartPole-v1

peterdamn

2023-03-15T09:07:19Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-03-15T09:05:05Z

--- 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: -134.11 +/- 65.85 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': 'peterdamn/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128} ```

mouss/autotrain-bikes_1-41171106189

mouss

2023-03-15T08:59:13Z

39

0

transformers

[ "transformers", "pytorch", "swin", "image-classification", "autotrain", "vision", "dataset:mouss/autotrain-data-bikes_1", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-03-15T08:58:08Z

--- tags: - autotrain - vision - image-classification datasets: - mouss/autotrain-data-bikes_1 widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 0.41665410499999395 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 41171106189 - CO2 Emissions (in grams): 0.4167 ## Validation Metrics - Loss: 0.368 - Accuracy: 0.818 - Precision: 0.882 - Recall: 0.789 - AUC: 0.921 - F1: 0.833

dvruette/oasst-pythia-12b-6000-steps

dvruette

2023-03-15T08:48:05Z

1,488

0

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-03-09T12:40:11Z

https://wandb.ai/open-assistant/supervised-finetuning/runs/qqtzt19n

dvruette/oasst-pythia-12b-3000-steps

dvruette

2023-03-15T08:47:44Z

0

null

[ "region:us" ]

null

2023-03-09T14:36:57Z

https://wandb.ai/open-assistant/supervised-finetuning/runs/qqtzt19n

dvruette/oasst-pythia-12b-flash-attn-5000-steps

dvruette

2023-03-15T08:46:58Z

1,500

0

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-03-12T10:42:00Z

https://wandb.ai/open-assistant/supervised-finetuning/runs/uwqcwaau

EarthnDusk/FFXIV_Miqote_MoonKeeper_Lora

EarthnDusk

2023-03-15T08:45:16Z

0

1

null

[ "Lycoris", "LoHA", "Lora", "stable diffusion", "text to image", "ffxiv", "miqote", "en", "dataset:Duskfallcrew/FFXIV_Data_and_Lora", "dataset:Duskfallcrew/miqoteupdate", "license:creativeml-openrail-m", "region:us" ]

null

2023-03-15T08:13:56Z

--- license: creativeml-openrail-m datasets: - Duskfallcrew/FFXIV_Data_and_Lora - Duskfallcrew/miqoteupdate language: - en tags: - Lycoris - LoHA - Lora - stable diffusion - text to image - ffxiv - miqote --- Output udpates coming soon, we have some but if you need to see them before we put them here- we have the models up on Civit: https://civitai.com/models/14823 Data sets listed because one is private - this was because the LoRA trainer had a subject option to upload data to here but i forgot we did it already . Data set here: https://huggingface.co/datasets/Duskfallcrew/FFXIV_Data_and_Lora Also noted: The MIQOTE UPDATE LoRA is a LYCORIS/LoHA and needs the special A1111 plugin: https://github.com/KohakuBlueleaf/a1111-sd-webui-locon

amerssun/tww_result_lora

amerssun

2023-03-15T08:43:35Z

2

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-03-15T08:38:16Z

--- license: creativeml-openrail-m base_model: /mnt/user/sunzhaoxu/diffusion/cilloutmix/ instance_prompt: a photo of tww 1girl tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - amerssun/tww_result_lora These are LoRA adaption weights for /mnt/user/sunzhaoxu/diffusion/cilloutmix/. The weights were trained on a photo of tww 1girl 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)

11Anupam/demo_001

11Anupam

2023-03-15T08:37:45Z

0

adapter-transformers

[ "adapter-transformers", "pytorch", "tf", "marian", "en", "arxiv:1910.09700", "region:us" ]

null

2023-03-15T07:27:17Z

--- language: - en library_name: adapter-transformers --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [Anupam] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [python] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

huam/dqn-Taxi-v3

huam

2023-03-15T08:17:36Z

5

0

stable-baselines3

[ "stable-baselines3", "Taxi-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-03-15T08:17:31Z

--- library_name: stable-baselines3 tags: - Taxi-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: -200.00 +/- 0.00 name: mean_reward verified: false --- # **DQN** Agent playing **Taxi-v3** This is a trained model of a **DQN** agent playing **Taxi-v3** 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 ... ```

giovannefeitosa/chatbot-about-pele

giovannefeitosa

2023-03-15T08:09:22Z

0

sklearn

[ "sklearn", "question-answering", "chatbot", "brazil", "text2text-generation", "en", "dataset:en_core_web_sm", "license:cc-by-nc-4.0", "region:us" ]

text2text-generation

2023-03-15T06:33:28Z

--- language: - en datasets: - en_core_web_sm thumbnail: >- https://huggingface.co/giovannefeitosa/chatbot-about-pele/raw/main/images/pele.jpeg tags: - question-answering - chatbot - brazil license: cc-by-nc-4.0 pipeline_tag: text2text-generation library_name: sklearn --- # Chatbot about Pele This is demo project. > library_name: sklearn

kailashsp/dreambooth_diffusion_model

kailashsp

2023-03-15T08:08:00Z

2

0

keras

[ "keras", "tf-keras", "text-to-image", "dataset:kailashsp/class-images", "license:apache-2.0", "region:us" ]

text-to-image

2023-03-15T07:54:09Z

lyeonii/bert-small

lyeonii

2023-03-15T08:07:54Z

75

0

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:1908.08962", "license:mit", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2023-03-15T08:05:48Z

--- license: mit language: - en --- # BERT-Small (uncased) This is one of 24 smaller BERT models (English only, uncased, trained with WordPiece masking) released by [google-research/bert](https://github.com/google-research/bert). These BERT models was released as TensorFlow checkpoints, however, this is the converted version to PyTorch. More information can be found in [google-research/bert](https://github.com/google-research/bert) or [lyeoni/convert-tf-to-pytorch](https://github.com/lyeoni/convert-tf-to-pytorch). ## Evaluation Here are the evaluation scores (F1/Accuracy) for the MPRC task. |Model|MRPC| |-|:-:| |BERT-Tiny|81.22/68.38| |BERT-Mini|81.43/69.36| |BERT-Small|81.41/70.34| |BERT-Medium|83.33/73.53| |BERT-Base|85.62/78.19| ### References ``` @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } ```

lyeonii/bert-medium

lyeonii

2023-03-15T08:04:10Z

5

0

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:1908.08962", "license:mit", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2023-03-15T08:01:26Z

--- license: mit language: - en --- # BERT-Medium (uncased) This is one of 24 smaller BERT models (English only, uncased, trained with WordPiece masking) released by [google-research/bert](https://github.com/google-research/bert). These BERT models was released as TensorFlow checkpoints, however, this is the converted version to PyTorch. More information can be found in [google-research/bert](https://github.com/google-research/bert) or [lyeoni/convert-tf-to-pytorch](https://github.com/lyeoni/convert-tf-to-pytorch). ## Evaluation Here are the evaluation scores (F1/Accuracy) for the MPRC task. |Model|MRPC| |-|:-:| |BERT-Tiny|81.22/68.38| |BERT-Mini|81.43/69.36| |BERT-Small|81.41/70.34| |BERT-Medium|83.33/73.53| |BERT-Base|85.62/78.19| ### References ``` @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } ```

auditi41/wav2vec2-large-xlsr-turkish

auditi41

2023-03-15T08:03:41Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-03-14T07:30:44Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: wav2vec2-large-xlsr-turkish results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: tr split: train+validation args: tr metrics: - name: Wer type: wer value: 0.48268818302522726 ---  # wav2vec2-large-xlsr-turkish This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4242 - Wer: 0.4827 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0376 | 4.26 | 400 | 2.3690 | 1.0020 | | 0.7983 | 8.51 | 800 | 0.4755 | 0.6328 | | 0.3157 | 12.77 | 1200 | 0.4051 | 0.5408 | | 0.2197 | 17.02 | 1600 | 0.4156 | 0.5149 | | 0.1643 | 21.28 | 2000 | 0.4286 | 0.5036 | | 0.1305 | 25.53 | 2400 | 0.4247 | 0.4908 | | 0.1178 | 29.79 | 2800 | 0.4242 | 0.4827 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

lyeonii/bert-mini

lyeonii

2023-03-15T07:57:26Z

6

0

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "en", "arxiv:1908.08962", "license:mit", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2023-03-15T07:52:52Z

--- license: mit language: - en --- # BERT-Mini (uncased) This is one of 24 smaller BERT models (English only, uncased, trained with WordPiece masking) released by [google-research/bert](https://github.com/google-research/bert). These BERT models was released as TensorFlow checkpoints, however, this is the converted version to PyTorch. More information can be found in [google-research/bert](https://github.com/google-research/bert) or [lyeoni/convert-tf-to-pytorch](https://github.com/lyeoni/convert-tf-to-pytorch). ## Evaluation Here are the evaluation scores (F1/Accuracy) for the MPRC task. |Model|MRPC| |-|:-:| |BERT-Tiny|81.22/68.38| |BERT-Mini|81.43/69.36| |BERT-Small|81.41/70.34| |BERT-Medium|83.33/73.53| |BERT-Base|85.62/78.19| ### References ``` @article{turc2019, title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models}, author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1908.08962v2 }, year={2019} } ```

Perse90/ppo-Huggy

Perse90

2023-03-15T07:53:56Z

12

0

ml-agents

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

reinforcement-learning

2023-03-15T07:53:50Z

--- 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: Perse90/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

thang123/wav2vec2-tiengviet1

thang123

2023-03-15T06:59:49Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-03-15T04:46:37Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-tiengviet1 results: [] ---  # wav2vec2-tiengviet1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7302 - Wer: 1.0118 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 300 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 14.8304 | 39.67 | 40 | 4.3168 | 1.0 | | 5.5892 | 79.67 | 80 | 3.5454 | 1.0 | | 5.2113 | 119.67 | 120 | 3.4845 | 1.0 | | 4.9995 | 159.67 | 160 | 3.5783 | 1.0 | | 4.7958 | 199.67 | 200 | 3.1850 | 1.0 | | 4.4776 | 239.67 | 240 | 2.9864 | 1.0 | | 4.2546 | 279.67 | 280 | 2.7302 | 1.0118 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

Madronus/MultiLabel_V3

Madronus

2023-03-15T06:52:53Z

67

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-03-14T22:02:23Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: MultiLabel_V3 results: [] ---  # MultiLabel_V3 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9683 - Accuracy: 0.7370 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8572 | 0.1 | 100 | 1.1607 | 0.6466 | | 0.8578 | 0.2 | 200 | 1.1956 | 0.6499 | | 0.7362 | 0.3 | 300 | 1.1235 | 0.6885 | | 0.8569 | 0.39 | 400 | 1.0460 | 0.6891 | | 0.4851 | 0.49 | 500 | 1.1213 | 0.6891 | | 0.7252 | 0.59 | 600 | 1.1512 | 0.6720 | | 0.6333 | 0.69 | 700 | 1.1039 | 0.6913 | | 0.6239 | 0.79 | 800 | 1.0636 | 0.7001 | | 0.2768 | 0.89 | 900 | 1.0386 | 0.7073 | | 0.4872 | 0.99 | 1000 | 1.0311 | 0.7062 | | 0.3049 | 1.09 | 1100 | 1.0437 | 0.7155 | | 0.1435 | 1.18 | 1200 | 1.0343 | 0.7222 | | 0.2088 | 1.28 | 1300 | 1.0784 | 0.7194 | | 0.4972 | 1.38 | 1400 | 1.1072 | 0.7166 | | 0.3604 | 1.48 | 1500 | 1.0438 | 0.7150 | | 0.2726 | 1.58 | 1600 | 1.0077 | 0.7293 | | 0.3106 | 1.68 | 1700 | 1.0029 | 0.7326 | | 0.3259 | 1.78 | 1800 | 0.9906 | 0.7310 | | 0.3323 | 1.88 | 1900 | 0.9729 | 0.7359 | | 0.2998 | 1.97 | 2000 | 0.9683 | 0.7370 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

MikolajDeja/facebook-nllb-200-distilled-600M-en-pl-3-para_crawl-finetune

MikolajDeja

2023-03-15T06:39:46Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "generated_from_trainer", "dataset:para_crawl", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-10T13:34:55Z

--- license: cc-by-nc-4.0 tags: - generated_from_trainer datasets: - para_crawl model-index: - name: facebook-nllb-200-distilled-600M-en-pl-3-para_crawl-finetune results: [] ---  # facebook-nllb-200-distilled-600M-en-pl-3-para_crawl-finetune This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the para_crawl dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.13.2

kuma-s/xlm-roberta-base-finetuned-panx-de

kuma-s

2023-03-15T06:39:31Z

105

0

transformers

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

token-classification

2023-03-15T06:22:34Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8638300289723342 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1358 - F1: 0.8638 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2591 | 1.0 | 525 | 0.1621 | 0.8206 | | 0.1276 | 2.0 | 1050 | 0.1379 | 0.8486 | | 0.082 | 3.0 | 1575 | 0.1358 | 0.8638 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

jmbt22/marian-finetuned-opus-mt-en-tl

jmbt22

2023-03-15T06:06:16Z

107

0

transformers

[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:tatoeba", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-03-15T05:50:58Z

--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - tatoeba metrics: - bleu model-index: - name: marian-finetuned-opus-mt-en-tl results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: tatoeba type: tatoeba config: en-tl split: train args: en-tl metrics: - name: Bleu type: bleu value: 35.9113771495936 ---  # marian-finetuned-opus-mt-en-tl This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-tl](https://huggingface.co/Helsinki-NLP/opus-mt-en-tl) on the tatoeba dataset. It achieves the following results on the evaluation set: - Loss: 1.2611 - Bleu: 35.9114 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.0 - Datasets 2.10.1 - Tokenizers 0.13.2

rm1768/wav2vec2-large-xlsr-turkish-demo-colab

rm1768

2023-03-15T06:04:19Z

19

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-03-10T08:07:35Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: wav2vec2-large-xlsr-turkish-demo-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: tr split: test args: tr metrics: - name: Wer type: wer value: 0.4821775099581248 ---  # wav2vec2-large-xlsr-turkish-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4151 - Wer: 0.4822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.2487 | 4.26 | 400 | 1.6455 | 1.0778 | | 0.71 | 8.51 | 800 | 0.4428 | 0.6138 | | 0.3073 | 12.77 | 1200 | 0.4214 | 0.5517 | | 0.2136 | 17.02 | 1600 | 0.4345 | 0.5193 | | 0.1624 | 21.28 | 2000 | 0.4366 | 0.5026 | | 0.1298 | 25.53 | 2400 | 0.4111 | 0.4949 | | 0.1174 | 29.79 | 2800 | 0.4151 | 0.4822 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

ashishj20/q-FrozenLake-v1-4x4-noslippery

ashishj20

2023-03-15T05:39:52Z

0

null

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

reinforcement-learning

2023-03-15T05:35:43Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noslippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="ashishj20/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"]) ```

edgemac20/q-FrozenLake-v1-4x4-noSlippery

edgemac20

2023-03-15T04:53:27Z

0

null

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

reinforcement-learning

2023-03-15T04:53:22Z

--- 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="edgemac20/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"]) ```

ajankelo/ppo-LunarLander-v2

ajankelo

2023-03-15T04:44:08Z

3

0

stable-baselines3

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

reinforcement-learning

2023-03-15T04:43:50Z

--- 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: 249.48 +/- 19.01 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 ... ```

NurFathihaTahiatSeeum/Fake-news-detection

NurFathihaTahiatSeeum

2023-03-15T04:37:15Z

0

null

[ "Natural Language Processing", "text-classification", "en", "dataset:fake_news_english", "region:us" ]

text-classification

2023-03-14T10:18:22Z

--- datasets: - fake_news_english language: - en pipeline_tag: text-classification tags: - Natural Language Processing --- This Natural Language Processing (NLP) work is done in google colab using Bidirectional Encoder Representations from Transformers (BERT) model. Dataset: https://www.kaggle.com/datasets/sadikaljarif/fake-news-detection-dataset-english

eduiqe/Pixelicopter

eduiqe

2023-03-15T03:52:07Z

0

null

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

reinforcement-learning

2023-03-05T07:06:13Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelicopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 18.30 +/- 14.56 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

marcospiau/finetuned_minilm

marcospiau

2023-03-15T03:35:50Z

108

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-03-15T03:35:17Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuned_minilm results: [] ---  # finetuned_minilm This model is a fine-tuned version of [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6736 - Accuracy: 0.9023 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 12345 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5371 | 1.0 | 619 | 0.2941 | 0.8782 | | 0.2763 | 2.0 | 1238 | 0.2590 | 0.8986 | | 0.1899 | 3.0 | 1857 | 0.3081 | 0.8959 | | 0.1257 | 4.0 | 2476 | 0.2576 | 0.9177 | | 0.0929 | 5.0 | 3095 | 0.3949 | 0.9059 | | 0.0806 | 6.0 | 3714 | 0.3304 | 0.9173 | | 0.0629 | 7.0 | 4333 | 0.4214 | 0.9073 | | 0.0474 | 8.0 | 4952 | 0.4625 | 0.9145 | | 0.0498 | 9.0 | 5571 | 0.4227 | 0.9236 | | 0.049 | 10.0 | 6190 | 0.5549 | 0.8945 | | 0.0411 | 11.0 | 6809 | 0.3340 | 0.9341 | | 0.0272 | 12.0 | 7428 | 0.3317 | 0.9291 | | 0.0264 | 13.0 | 8047 | 0.4099 | 0.9305 | | 0.0279 | 14.0 | 8666 | 0.4092 | 0.9268 | | 0.0242 | 15.0 | 9285 | 0.4418 | 0.9318 | | 0.0241 | 16.0 | 9904 | 0.4352 | 0.9273 | | 0.0238 | 17.0 | 10523 | 0.5306 | 0.9259 | | 0.0216 | 18.0 | 11142 | 0.4267 | 0.9241 | | 0.0166 | 19.0 | 11761 | 0.5134 | 0.9255 | | 0.0182 | 20.0 | 12380 | 0.6736 | 0.9023 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

yz54321/wintermoonmix

yz54321

2023-03-15T03:09:35Z

0

2

null

[ "region:us" ]

null

2023-03-13T08:21:47Z

WinterMoonMix: https://civitai.com/models/12433/wintermoonmix LulubearMix: https://civitai.com/models/18934/lulubearmix

mipin5/OliHye2

mipin5

2023-03-15T03:02:46Z

0

null

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

null

2023-03-15T02:58:37Z

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

Eduardo84/Xx1

Eduardo84

2023-03-15T02:55:52Z

0

null

[ "license:bsd-3-clause-clear", "region:us" ]

null

2023-03-15T02:55:51Z

--- license: bsd-3-clause-clear ---

coreml-community/coreml-seek.art_MEGA

coreml-community

2023-03-15T02:52:28Z

0

2

null

[ "coreml", "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-01-30T00:51:18Z

--- license: creativeml-openrail-m tags: - coreml - stable-diffusion - text-to-image --- # Core ML Converted Model: - This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML). - Provide the model to an app such as Mochi Diffusion [Github](https://github.com/godly-devotion/MochiDiffusion) - [Discord](https://discord.gg/x2kartzxGv) to generate images. - `split_einsum` version is compatible with all compute unit options including Neural Engine. - `original` version is only compatible with CPU & GPU option. - Custom resolution versions are tagged accordingly. - `vae` tagged files have a vae embedded into the model. - Descriptions are posted as-is from original model source. Not all features and/or results may be available in CoreML format. - Some of the models were converted with `vae-encoder` for i2i. - Models that are 32 bit will have "fp32" in the filename. # Note: Some models do not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml). # seek.art MEGA: Source(s): [Hugging Face](https://huggingface.co/coreco/seek.art_MEGA) - [CivitAI](https://civitai.com/models/1315/seekart-mega) # Seek.art MEGA is a general use "anything" model that significantly improves on 1.5 across dozens of styles. Created by Coreco at [seek.art](https://seek.art/) This model was trained on nearly 10k high-quality public domain digital artworks with the goal of improving output quality across the board. We find the model to be highly flexible in its ability to mix various styles, subjects, and details. We recommend resolutions above 640px in one or both dimensions for best results. You can try this model and several others for free at [seek.art](https://seek.art/). We also recommend an inference tool supporting prompt weighting and high resolution optimization / fixing for best results. We suggest [InvokeAI](https://github.com/invoke-ai/InvokeAI) as a sensibly licensed and fully featured open-source inference tool. ### Examples <img src="https://huggingface.co/coreco/seek.art_MEGA/resolve/main/examples.png" style="max-width: 800px;" width="100%"/> The above example images including the prompts and all relevant settings are available [here](https://seek.art/explore/search?collection=6112a64d-bd8b-4043-8d96-88c7cfa65c43). Additionally, search thousands of high quality prompts on [seek.art](https://seek.art/) for free. ### License - This model carries a commercial restricted sub-license, please read carefully: [License](https://huggingface.co/coreco/seek.art_MEGA/blob/main/LICENSE.txt) ### Use Restrictions You agree not to use the Model or Derivatives of the Model: - for the commercial purpose of hosted content generation (inference) without the express written permission of seek.art. Model output for personal use carries no such commercial restriction. - In any way that violates any applicable national, federal, state, local or international law or regulation; - For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; - To generate or disseminate verifiably false information and/or content with the purpose of harming others; - To generate or disseminate personal identifiable information that can be used to harm an individual; - To defame, disparage or otherwise harass others; - For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation; - For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; - To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; - For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories; - To provide medical advice and medical results interpretation; - To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).

baptiste-pasquier/distilcamembert-allocine

baptiste-pasquier

2023-03-15T02:42:04Z

107

0

transformers

[ "transformers", "pytorch", "tf", "tensorboard", "camembert", "text-classification", "generated_from_trainer", "fr", "dataset:allocine", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-02-13T00:36:42Z

--- language: - fr license: mit tags: - generated_from_trainer datasets: - allocine widget: - text: "Un film magnifique avec un duo d'acteurs excellent." - text: "Grosse déception pour ce thriller qui peine à convaincre." metrics: - accuracy - f1 - precision - recall model-index: - name: distilcamembert-allocine results: - task: name: Text Classification type: text-classification dataset: name: allocine type: allocine config: allocine split: validation args: allocine metrics: - name: Accuracy type: accuracy value: 0.9714 - name: F1 type: f1 value: 0.9709909727152854 - name: Precision type: precision value: 0.9648256399919372 - name: Recall type: recall value: 0.9772356063699469 ---  # distilcamembert-allocine This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on the allocine dataset. It achieves the following results on the evaluation set: - Loss: 0.1066 - Accuracy: 0.9714 - F1: 0.9710 - Precision: 0.9648 - Recall: 0.9772 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | | :-----------: | :---: | :---: | :-------------: | :------: | :----: | :-------: | :----: | | 0.1504 | 0.2 | 500 | 0.1290 | 0.9555 | 0.9542 | 0.9614 | 0.9470 | | 0.1334 | 0.4 | 1000 | 0.1049 | 0.9624 | 0.9619 | 0.9536 | 0.9703 | | 0.1158 | 0.6 | 1500 | 0.1052 | 0.963 | 0.9627 | 0.9498 | 0.9760 | | 0.1153 | 0.8 | 2000 | 0.0949 | 0.9661 | 0.9653 | 0.9686 | 0.9620 | | 0.1053 | 1.0 | 2500 | 0.0936 | 0.9666 | 0.9663 | 0.9542 | 0.9788 | | 0.0755 | 1.2 | 3000 | 0.0987 | 0.97 | 0.9695 | 0.9644 | 0.9748 | | 0.0716 | 1.4 | 3500 | 0.1078 | 0.9688 | 0.9684 | 0.9598 | 0.9772 | | 0.0688 | 1.6 | 4000 | 0.1051 | 0.9673 | 0.9670 | 0.9552 | 0.9792 | | 0.0691 | 1.8 | 4500 | 0.0940 | 0.9709 | 0.9704 | 0.9688 | 0.9720 | | 0.0733 | 2.0 | 5000 | 0.1038 | 0.9686 | 0.9683 | 0.9558 | 0.9812 | | 0.0476 | 2.2 | 5500 | 0.1066 | 0.9714 | 0.9710 | 0.9648 | 0.9772 | | 0.047 | 2.4 | 6000 | 0.1098 | 0.9689 | 0.9686 | 0.9587 | 0.9788 | | 0.0431 | 2.6 | 6500 | 0.1110 | 0.9711 | 0.9706 | 0.9666 | 0.9747 | | 0.0464 | 2.8 | 7000 | 0.1149 | 0.9697 | 0.9694 | 0.9592 | 0.9798 | | 0.0342 | 3.0 | 7500 | 0.1122 | 0.9703 | 0.9699 | 0.9621 | 0.9778 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.10.1 - Tokenizers 0.13.2

thang123/wav2vec2-large-xlsr-turkish-demo-colab

thang123

2023-03-15T02:21:05Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-03-13T09:27:40Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xlsr-turkish-demo-colab results: [] ---  # wav2vec2-large-xlsr-turkish-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

brunonishimoto/q-learning-Taxi-v3

brunonishimoto

2023-03-15T01:57:13Z

0

null

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

reinforcement-learning

2023-03-14T00:10:33Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-learning-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="brunonishimoto/q-learning-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"]) ```

Xit1/gpt-1

Xit1

2023-03-15T01:46:35Z

0

adapter-transformers

[ "adapter-transformers", "en", "ur", "tl", "dataset:fka/awesome-chatgpt-prompts", "license:afl-3.0", "region:us" ]

null

2023-03-14T18:07:55Z

--- license: afl-3.0 datasets: - fka/awesome-chatgpt-prompts language: - en - ur - tl metrics: - accuracy library_name: adapter-transformers ---

peteli/hometown

peteli

2023-03-15T01:29:02Z

0

1

diffusers

[ "diffusers", "paddlepaddle", "stable-diffusion", "stable-diffusion-ppdiffusers", "text-to-image", "ppdiffusers", "lora", "en", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-03-15T00:48:39Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a sea of lavender and gold flowers in the world of fairy tales is my hometown tags: - stable-diffusion - stable-diffusion-ppdiffusers - text-to-image - ppdiffusers - lora inference: false language: - en library_name: diffusers --- # LoRA DreamBooth - peteli/hometown 本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 a sea of lavender and gold flowers in the world of fairy tales is my hometown 文本进行了训练。

kmcgrath/sd-controlnet-canny-fork

kmcgrath

2023-03-15T01:16:06Z

15

0

diffusers

[ "diffusers", "pytorch", "safetensors", "art", "controlnet", "stable-diffusion", "arxiv:2302.05543", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:openrail", "endpoints_compatible", "diffusers:StableDiffusionControlNetPipeline", "region:us" ]

text-to-image

2023-03-09T19:39:54Z

--- license: openrail base_model: runwayml/stable-diffusion-v1-5 tags: - art - controlnet - stable-diffusion --- # Controlnet - *Canny Version* ControlNet is a neural network structure to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlNet conditioned on **Canny edges**. It can be used in combination with [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img). ![img](./sd.png) ## Model Details - **Developed by:** Lvmin Zhang, Maneesh Agrawala - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Resources for more information:** [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543). - **Cite as:** @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } ## Introduction Controlnet was proposed in [*Adding Conditional Control to Text-to-Image Diffusion Models*](https://arxiv.org/abs/2302.05543) by Lvmin Zhang, Maneesh Agrawala. The abstract reads as follows: *We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.* ## Released Checkpoints The authors released 8 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on a different type of conditioning: | Model Name | Control Image Overview| Control Image Example | Generated Image Example | |---|---|---|---| |[lllyasviel/sd-controlnet-canny](https://huggingface.co/lllyasviel/sd-controlnet-canny) *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_canny.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_canny.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"/></a>| |[lllyasviel/sd-controlnet-depth](https://huggingface.co/lllyasviel/sd-controlnet-depth) *Trained with Midas depth estimation* |A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_depth.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_depth.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"/></a>| |[lllyasviel/sd-controlnet-hed](https://huggingface.co/lllyasviel/sd-controlnet-hed) *Trained with HED edge detection (soft edge)* |A monochrome image with white soft edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_hed.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_hed.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"/></a> | |[lllyasviel/sd-controlnet-mlsd](https://huggingface.co/lllyasviel/sd-controlnet-mlsd) *Trained with M-LSD line detection* |A monochrome image composed only of white straight lines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_mlsd.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_mlsd.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"/></a>| |[lllyasviel/sd-controlnet-normal](https://huggingface.co/lllyasviel/sd-controlnet-normal) *Trained with normal map* |A [normal mapped](https://en.wikipedia.org/wiki/Normal_mapping) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_normal.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_normal.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"/></a>| |[lllyasviel/sd-controlnet_openpose](https://huggingface.co/lllyasviel/sd-controlnet-openpose) *Trained with OpenPose bone image* |A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_openpose.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_openpose.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"/></a>| |[lllyasviel/sd-controlnet_scribble](https://huggingface.co/lllyasviel/sd-controlnet-scribble) *Trained with human scribbles* |A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_scribble.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_scribble.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"/></a> | |[lllyasviel/sd-controlnet_seg](https://huggingface.co/lllyasviel/sd-controlnet-seg) *Trained with semantic segmentation* |An [ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K/)'s segmentation protocol image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_seg.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_seg.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"/></a> | ## Example It is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint has been trained on it. Experimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion. **Note**: If you want to process an image to create the auxiliary conditioning, external dependencies are required as shown below: 1. Install opencv ```sh $ pip install opencv-contrib-python ``` 2. Let's install `diffusers` and related packages: ``` $ pip install diffusers transformers git+https://github.com/huggingface/accelerate.git ``` 3. Run code: ```python import cv2 from PIL import Image from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler import torch import numpy as np from diffusers.utils import load_image image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-hed/resolve/main/images/bird.png") image = np.array(image) low_threshold = 100 high_threshold = 200 image = cv2.Canny(image, low_threshold, high_threshold) image = image[:, :, None] image = np.concatenate([image, image, image], axis=2) image = Image.fromarray(image) controlnet = ControlNetModel.from_pretrained( "fusing/stable-diffusion-v1-5-controlnet-canny", torch_dtype=torch.float16 ) pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) # Remove if you do not have xformers installed # see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers # for installation instructions pipe.enable_xformers_memory_efficient_attention() pipe.enable_model_cpu_offload() image = pipe("bird", image, num_inference_steps=20).images[0] image.save('images/bird_canny_out.png') ``` ![bird](./images/bird.png) ![bird_canny](./images/bird_canny.png) ![bird_canny_out](./images/bird_canny_out.png) ### Training The canny edge model was trained on 3M edge-image, caption pairs. The model was trained for 600 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model. ### Blog post For more information, please also have a look at the [official ControlNet Blog Post](https://huggingface.co/blog/controlnet).