davanstrien/model_cards_with_readmes_with_duplicates

sgoodfriend/dqn-BreakoutNoFrameskip-v4

sgoodfriend

null

65

0

rl-algo-impls

0

reinforcement-learning

false

null

0

['BreakoutNoFrameskip-v4', 'dqn', 'deep-reinforcement-learning', 'reinforcement-learning']

true

5,054

# **DQN** Agent playing **BreakoutNoFrameskip-v4** This is a trained model of a **DQN** agent playing **BreakoutNoFrameskip-v4** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo. All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/099h4lvj. ## Training Results This model was trained from 3 trainings of **DQN** agents using different initial seeds. These agents were trained by checking out [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std). | algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url | |:-------|:-----------------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------| | dqn | BreakoutNoFrameskip-v4 | 1 | 356.438 | 13.3509 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/je8qrv0o) | | dqn | BreakoutNoFrameskip-v4 | 2 | 332.312 | 11.9829 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/4tujcvcy) | | dqn | BreakoutNoFrameskip-v4 | 3 | 402.75 | 11.1496 | 16 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/cvqo3gc9) | ### Prerequisites: Weights & Biases (WandB) Training and benchmarking assumes you have a Weights & Biases project to upload runs to. By default training goes to a rl-algo-impls project while benchmarks go to rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best models and the model weights are uploaded to WandB. Before doing anything below, you'll need to create a wandb account and run `wandb login`. ## Usage /sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls Note: While the model state dictionary and hyperaparameters are saved, the latest implementation could be sufficiently different to not be able to reproduce similar results. You might need to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). ``` # Downloads the model, sets hyperparameters, and runs agent for 3 episodes python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/cvqo3gc9 ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb) notebook. ## Training If you want the highest chance to reproduce these results, you'll want to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). While training is deterministic, different hardware will give different results. ``` python train.py --algo dqn --env BreakoutNoFrameskip-v4 --seed 3 ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb) notebook. ## Benchmarking (with Lambda Labs instance) This and other models from https://api.wandb.ai/links/sgoodfriend/099h4lvj were generated by running a script on a Lambda Labs instance. In a Lambda Labs instance terminal: ``` git clone [email protected]:sgoodfriend/rl-algo-impls.git cd rl-algo-impls bash ./lambda_labs/setup.sh wandb login bash ./lambda_labs/benchmark.sh ``` ### Alternative: Google Colab Pro+ As an alternative, [colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb), can be used. However, this requires a Google Colab Pro+ subscription and running across 4 separate instances because otherwise running all jobs will exceed the 24-hour limit. ## Hyperparameters This isn't exactly the format of hyperparams in hyperparams/dqn.yml, but instead the Wandb Run Config. However, it's very close and has some additional data: ``` algo: dqn algo_hyperparams: batch_size: 32 buffer_size: 100000 exploration_final_eps: 0.01 exploration_fraction: 0.1 gradient_steps: 2 learning_rate: 0.0001 learning_starts: 100000 target_update_interval: 1000 train_freq: 8 env: BreakoutNoFrameskip-v4 env_hyperparams: frame_stack: 4 n_envs: 8 no_reward_timeout_steps: 1000 vec_env_class: subproc eval_params: deterministic: false n_timesteps: 10000000 seed: 3 use_deterministic_algorithms: true wandb_entity: null wandb_project_name: rl-algo-impls-benchmarks wandb_tags: - benchmark_1d4094f - host_192-9-147-166 ```

sgoodfriend/dqn-PongNoFrameskip-v4

sgoodfriend

null

65

0

rl-algo-impls

0

reinforcement-learning

false

null

0

['PongNoFrameskip-v4', 'dqn', 'deep-reinforcement-learning', 'reinforcement-learning']

true

5,017

# **DQN** Agent playing **PongNoFrameskip-v4** This is a trained model of a **DQN** agent playing **PongNoFrameskip-v4** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo. All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/099h4lvj. ## Training Results This model was trained from 3 trainings of **DQN** agents using different initial seeds. These agents were trained by checking out [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std). | algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url | |:-------|:-------------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------| | dqn | PongNoFrameskip-v4 | 1 | 21 | 0 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/wohmhrf7) | | dqn | PongNoFrameskip-v4 | 2 | 9.375 | 4.19635 | 16 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/5hjw9u32) | | dqn | PongNoFrameskip-v4 | 3 | 21 | 0 | 16 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/luzju6yn) | ### Prerequisites: Weights & Biases (WandB) Training and benchmarking assumes you have a Weights & Biases project to upload runs to. By default training goes to a rl-algo-impls project while benchmarks go to rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best models and the model weights are uploaded to WandB. Before doing anything below, you'll need to create a wandb account and run `wandb login`. ## Usage /sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls Note: While the model state dictionary and hyperaparameters are saved, the latest implementation could be sufficiently different to not be able to reproduce similar results. You might need to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). ``` # Downloads the model, sets hyperparameters, and runs agent for 3 episodes python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/luzju6yn ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb) notebook. ## Training If you want the highest chance to reproduce these results, you'll want to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). While training is deterministic, different hardware will give different results. ``` python train.py --algo dqn --env PongNoFrameskip-v4 --seed 3 ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb) notebook. ## Benchmarking (with Lambda Labs instance) This and other models from https://api.wandb.ai/links/sgoodfriend/099h4lvj were generated by running a script on a Lambda Labs instance. In a Lambda Labs instance terminal: ``` git clone [email protected]:sgoodfriend/rl-algo-impls.git cd rl-algo-impls bash ./lambda_labs/setup.sh wandb login bash ./lambda_labs/benchmark.sh ``` ### Alternative: Google Colab Pro+ As an alternative, [colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb), can be used. However, this requires a Google Colab Pro+ subscription and running across 4 separate instances because otherwise running all jobs will exceed the 24-hour limit. ## Hyperparameters This isn't exactly the format of hyperparams in hyperparams/dqn.yml, but instead the Wandb Run Config. However, it's very close and has some additional data: ``` algo: dqn algo_hyperparams: batch_size: 32 buffer_size: 100000 exploration_final_eps: 0.01 exploration_fraction: 0.1 gradient_steps: 2 learning_rate: 0.0001 learning_starts: 100000 target_update_interval: 1000 train_freq: 8 env: PongNoFrameskip-v4 env_hyperparams: frame_stack: 4 n_envs: 8 no_reward_timeout_steps: 1000 vec_env_class: subproc eval_params: deterministic: false n_timesteps: 2500000 seed: 3 use_deterministic_algorithms: true wandb_entity: null wandb_project_name: rl-algo-impls-benchmarks wandb_tags: - benchmark_1d4094f - host_192-9-147-166 ```

lmqg/flan-t5-small-squad-qg

lmqg

t5

17

6

transformers

0

text2text-generation

true

false

cc-by-4.0

['en']

['lmqg/qg_squad']

null

0

['question generation']

true

5,263

# Model Card of `lmqg/flan-t5-small-squad-qg` This model is fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) - **Language:** en - **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="lmqg/flan-t5-small-squad-qg") # model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/flan-t5-small-squad-qg") output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-small-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 56.66 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 40.4 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 30.95 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 24.34 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 25.58 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 63.77 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 51.26 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/flan-t5-small-squad-ae`](https://huggingface.co/lmqg/flan-t5-small-squad-ae). [raw metric file](https://huggingface.co/lmqg/flan-t5-small-squad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.lmqg_flan-t5-small-squad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:---------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 92.34 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedF1Score (MoverScore) | 63.8 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (BERTScore) | 92.13 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (MoverScore) | 63.89 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (BERTScore) | 92.58 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (MoverScore) | 63.8 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: paragraph_answer - output_types: question - prefix_types: ['qg'] - model: google/flan-t5-small - max_length: 512 - max_length_output: 32 - epoch: 7 - batch: 64 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/flan-t5-small-squad-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

lmqg/flan-t5-small-squad-ae

lmqg

t5

13

5

transformers

0

text2text-generation

true

false

cc-by-4.0

['en']

['lmqg/qg_squad']

null

0

['answer extraction']

true

4,375

# Model Card of `lmqg/flan-t5-small-squad-ae` This model is fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) for answer extraction on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) - **Language:** en - **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="lmqg/flan-t5-small-squad-ae") # model prediction answers = model.generate_a("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/flan-t5-small-squad-ae") output = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.") ``` ## Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/flan-t5-small-squad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:---------------------------------------------------------------| | AnswerExactMatch | 55.83 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | AnswerF1Score | 68.13 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | BERTScore | 91.1 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 48.25 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 43.39 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 38.64 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 34.6 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 42.59 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 80.54 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 67.61 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: ['ae'] - model: google/flan-t5-small - max_length: 512 - max_length_output: 32 - epoch: 8 - batch: 64 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/flan-t5-small-squad-ae/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

atorre/poca-SoccerTwos-20M

atorre

null

23

160

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

844

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: atorre/poca-SoccerTwos-20M 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

summervent/speller-example__

summervent

t5

17

8

transformers

0

text2text-generation

true

false

null

0

['generated_from_trainer']

true

2,259

# speller-example__ This model is a fine-tuned version of [sberbank-ai/ruT5-base](https://huggingface.co/sberbank-ai/ruT5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1437 - Rouge1: 19.7034 - Rouge2: 8.7571 - Rougel: 19.4209 - Rougelsum: 19.774 - Gen Len: 41.2542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 0.3549 | 0.1 | 1500 | 0.1935 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.1356 | | 0.3863 | 0.2 | 3000 | 0.1830 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.1864 | | 0.3164 | 0.31 | 4500 | 0.1746 | 19.5621 | 8.4746 | 19.2797 | 19.5621 | 41.2966 | | 0.367 | 0.41 | 6000 | 0.1690 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.161 | | 0.3002 | 0.51 | 7500 | 0.1578 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.2458 | | 0.3352 | 0.61 | 9000 | 0.1541 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.3475 | | 0.2462 | 0.72 | 10500 | 0.1519 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.3475 | | 0.2736 | 0.82 | 12000 | 0.1510 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.2797 | | 0.2618 | 0.92 | 13500 | 0.1437 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.2542 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

edgertej/poebert-eras-balanced

edgertej

bert

7

19

transformers

0

fill-mask

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,417

# edgertej/poebert-eras-balanced This model is a fine-tuned version of [edgertej/poebert-eras-balanced](https://huggingface.co/edgertej/poebert-eras-balanced) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.5715 - Validation Loss: 3.3710 - Epoch: 6 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.8002 | 3.5259 | 0 | | 3.7486 | 3.4938 | 1 | | 3.7053 | 3.4520 | 2 | | 3.7315 | 3.4211 | 3 | | 3.6226 | 3.4031 | 4 | | 3.6021 | 3.3968 | 5 | | 3.5715 | 3.3710 | 6 | ### Framework versions - Transformers 4.19.2 - TensorFlow 2.11.0 - Datasets 2.4.0 - Tokenizers 0.12.1

gubartz/flan-t5-base3

gubartz

t5

15

transformers

0

summarization

true

false

apache-2.0

null

0

['summarization', 'generated_from_trainer']

true

2,055

# flan-t5-base3 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0424 - Rouge1: 18.1411 - Rouge2: 17.0579 - Rougel: 18.1468 - Rougelsum: 18.1284 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | No log | 1.0 | 208 | 0.0548 | 18.1442 | 17.0639 | 18.1479 | 18.1291 | | No log | 2.0 | 416 | 0.0492 | 18.1442 | 17.0639 | 18.1479 | 18.1291 | | No log | 3.0 | 624 | 0.0451 | 18.1442 | 17.0639 | 18.1479 | 18.1291 | | No log | 4.0 | 832 | 0.0443 | 18.1442 | 17.0639 | 18.1479 | 18.1291 | | No log | 5.0 | 1040 | 0.0417 | 18.1442 | 17.0639 | 18.1479 | 18.1291 | | No log | 6.0 | 1248 | 0.0426 | 18.1442 | 17.0639 | 18.1479 | 18.1291 | | No log | 7.0 | 1456 | 0.0427 | 18.1442 | 17.0639 | 18.1479 | 18.1291 | | No log | 8.0 | 1664 | 0.0424 | 18.1411 | 17.0579 | 18.1468 | 18.1284 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

NoNameFound/pocagoal-SoccerTwos

NoNameFound

null

20

160

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

849

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

marac5/autotrain-builty-2-table-searcher-3373492718

marac5

random_forest

4

0

transformers

0

tabular-classification

false

null

['marac5/autotrain-data-builty-2-table-searcher']

{'emissions': 7.738626953271278}

0

['autotrain', 'tabular', 'classification', 'tabular-classification']

false

true

608

# Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 3373492718 - CO2 Emissions (in grams): 7.7386 ## Validation Metrics - Loss: 0.360 - Accuracy: 0.871 - Precision: 0.871 - Recall: 0.783 - AUC: 0.916 - F1: 0.824 ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] data.columns = ["feat_" + str(col) for col in data.columns] predictions = model.predict(data) # or model.predict_proba(data) ```

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

arxaqapi

null

5

0

null

0

reinforcement-learning

false

null

0

['FrozenLake-v1-4x4-no_slippery', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

397

# **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="arxaqapi/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"]) ```

sgoodfriend/dqn-Acrobot-v1

sgoodfriend

null

65

0

rl-algo-impls

0

reinforcement-learning

false

null

0

['Acrobot-v1', 'dqn', 'deep-reinforcement-learning', 'reinforcement-learning']

true

4,905

# **DQN** Agent playing **Acrobot-v1** This is a trained model of a **DQN** agent playing **Acrobot-v1** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo. All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/099h4lvj. ## Training Results This model was trained from 3 trainings of **DQN** agents using different initial seeds. These agents were trained by checking out [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std). | algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url | |:-------|:-----------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------| | dqn | Acrobot-v1 | 1 | -67.8 | 3.94462 | 10 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/t64n1i9i) | | dqn | Acrobot-v1 | 2 | -78.7 | 15.0137 | 10 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/dow7br9i) | | dqn | Acrobot-v1 | 3 | -73.6 | 3.49857 | 10 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/2vztw4e0) | ### Prerequisites: Weights & Biases (WandB) Training and benchmarking assumes you have a Weights & Biases project to upload runs to. By default training goes to a rl-algo-impls project while benchmarks go to rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best models and the model weights are uploaded to WandB. Before doing anything below, you'll need to create a wandb account and run `wandb login`. ## Usage /sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls Note: While the model state dictionary and hyperaparameters are saved, the latest implementation could be sufficiently different to not be able to reproduce similar results. You might need to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). ``` # Downloads the model, sets hyperparameters, and runs agent for 3 episodes python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/t64n1i9i ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb) notebook. ## Training If you want the highest chance to reproduce these results, you'll want to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). While training is deterministic, different hardware will give different results. ``` python train.py --algo dqn --env Acrobot-v1 --seed 1 ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb) notebook. ## Benchmarking (with Lambda Labs instance) This and other models from https://api.wandb.ai/links/sgoodfriend/099h4lvj were generated by running a script on a Lambda Labs instance. In a Lambda Labs instance terminal: ``` git clone [email protected]:sgoodfriend/rl-algo-impls.git cd rl-algo-impls bash ./lambda_labs/setup.sh wandb login bash ./lambda_labs/benchmark.sh ``` ### Alternative: Google Colab Pro+ As an alternative, [colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb), can be used. However, this requires a Google Colab Pro+ subscription and running across 4 separate instances because otherwise running all jobs will exceed the 24-hour limit. ## Hyperparameters This isn't exactly the format of hyperparams in hyperparams/dqn.yml, but instead the Wandb Run Config. However, it's very close and has some additional data: ``` algo: dqn algo_hyperparams: batch_size: 128 buffer_size: 50000 exploration_final_eps: 0.1 exploration_fraction: 0.12 gamma: 0.99 gradient_steps: -1 learning_rate: 0.00063 learning_starts: 0 target_update_interval: 250 train_freq: 4 env: Acrobot-v1 env_hyperparams: rolling_length: 50 n_timesteps: 100000 policy_hyperparams: hidden_sizes: - 256 - 256 seed: 1 use_deterministic_algorithms: true wandb_entity: null wandb_project_name: rl-algo-impls-benchmarks wandb_tags: - benchmark_1d4094f - host_192-9-147-166 ```

sgoodfriend/dqn-MountainCar-v0

sgoodfriend

null

65

0

rl-algo-impls

0

reinforcement-learning

false

null

0

['MountainCar-v0', 'dqn', 'deep-reinforcement-learning', 'reinforcement-learning']

true

4,942

# **DQN** Agent playing **MountainCar-v0** This is a trained model of a **DQN** agent playing **MountainCar-v0** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo. All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/099h4lvj. ## Training Results This model was trained from 3 trainings of **DQN** agents using different initial seeds. These agents were trained by checking out [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std). | algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url | |:-------|:---------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------| | dqn | MountainCar-v0 | 1 | -99.5 | 6.43817 | 10 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/erpmzwzl) | | dqn | MountainCar-v0 | 2 | -107.3 | 2.32594 | 10 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/5ddb70dy) | | dqn | MountainCar-v0 | 3 | -99.8 | 5.6356 | 10 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/kjg18fz5) | ### Prerequisites: Weights & Biases (WandB) Training and benchmarking assumes you have a Weights & Biases project to upload runs to. By default training goes to a rl-algo-impls project while benchmarks go to rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best models and the model weights are uploaded to WandB. Before doing anything below, you'll need to create a wandb account and run `wandb login`. ## Usage /sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls Note: While the model state dictionary and hyperaparameters are saved, the latest implementation could be sufficiently different to not be able to reproduce similar results. You might need to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). ``` # Downloads the model, sets hyperparameters, and runs agent for 3 episodes python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/kjg18fz5 ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb) notebook. ## Training If you want the highest chance to reproduce these results, you'll want to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). While training is deterministic, different hardware will give different results. ``` python train.py --algo dqn --env MountainCar-v0 --seed 3 ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb) notebook. ## Benchmarking (with Lambda Labs instance) This and other models from https://api.wandb.ai/links/sgoodfriend/099h4lvj were generated by running a script on a Lambda Labs instance. In a Lambda Labs instance terminal: ``` git clone [email protected]:sgoodfriend/rl-algo-impls.git cd rl-algo-impls bash ./lambda_labs/setup.sh wandb login bash ./lambda_labs/benchmark.sh ``` ### Alternative: Google Colab Pro+ As an alternative, [colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb), can be used. However, this requires a Google Colab Pro+ subscription and running across 4 separate instances because otherwise running all jobs will exceed the 24-hour limit. ## Hyperparameters This isn't exactly the format of hyperparams in hyperparams/dqn.yml, but instead the Wandb Run Config. However, it's very close and has some additional data: ``` algo: dqn algo_hyperparams: batch_size: 128 buffer_size: 10000 exploration_final_eps: 0.07 exploration_fraction: 0.2 gamma: 0.98 gradient_steps: 8 learning_rate: 0.004 learning_starts: 1000 target_update_interval: 600 train_freq: 16 env: MountainCar-v0 env_hyperparams: rolling_length: 50 n_timesteps: 120000 policy_hyperparams: hidden_sizes: - 256 - 256 seed: 3 use_deterministic_algorithms: true wandb_entity: null wandb_project_name: rl-algo-impls-benchmarks wandb_tags: - benchmark_1d4094f - host_192-9-147-166 ```

sgoodfriend/dqn-LunarLander-v2

sgoodfriend

null

65

0

rl-algo-impls

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'dqn', 'deep-reinforcement-learning', 'reinforcement-learning']

true

4,998

# **DQN** Agent playing **LunarLander-v2** This is a trained model of a **DQN** agent playing **LunarLander-v2** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo. All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/099h4lvj. ## Training Results This model was trained from 3 trainings of **DQN** agents using different initial seeds. These agents were trained by checking out [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std). | algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url | |:-------|:---------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------| | dqn | LunarLander-v2 | 1 | 264.57 | 16.3062 | 10 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/eruy0q3i) | | dqn | LunarLander-v2 | 2 | 294.474 | 17.0109 | 10 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/qcummkky) | | dqn | LunarLander-v2 | 3 | 271.59 | 14.3265 | 10 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/wz4cckag) | ### Prerequisites: Weights & Biases (WandB) Training and benchmarking assumes you have a Weights & Biases project to upload runs to. By default training goes to a rl-algo-impls project while benchmarks go to rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best models and the model weights are uploaded to WandB. Before doing anything below, you'll need to create a wandb account and run `wandb login`. ## Usage /sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls Note: While the model state dictionary and hyperaparameters are saved, the latest implementation could be sufficiently different to not be able to reproduce similar results. You might need to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). ``` # Downloads the model, sets hyperparameters, and runs agent for 3 episodes python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/qcummkky ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb) notebook. ## Training If you want the highest chance to reproduce these results, you'll want to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). While training is deterministic, different hardware will give different results. ``` python train.py --algo dqn --env LunarLander-v2 --seed 2 ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb) notebook. ## Benchmarking (with Lambda Labs instance) This and other models from https://api.wandb.ai/links/sgoodfriend/099h4lvj were generated by running a script on a Lambda Labs instance. In a Lambda Labs instance terminal: ``` git clone [email protected]:sgoodfriend/rl-algo-impls.git cd rl-algo-impls bash ./lambda_labs/setup.sh wandb login bash ./lambda_labs/benchmark.sh ``` ### Alternative: Google Colab Pro+ As an alternative, [colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb), can be used. However, this requires a Google Colab Pro+ subscription and running across 4 separate instances because otherwise running all jobs will exceed the 24-hour limit. ## Hyperparameters This isn't exactly the format of hyperparams in hyperparams/dqn.yml, but instead the Wandb Run Config. However, it's very close and has some additional data: ``` algo: dqn algo_hyperparams: batch_size: 256 buffer_size: 100000 exploration_final_eps: 0.1 exploration_fraction: 0.12 gamma: 0.99 gradient_steps: -1 learning_rate: 0.0001 learning_starts: 10000 max_grad_norm: 0.5 target_update_interval: 250 train_freq: 8 env: LunarLander-v2 env_hyperparams: rolling_length: 50 eval_params: step_freq: 25000 n_timesteps: 500000 policy_hyperparams: hidden_sizes: - 256 - 256 seed: 2 use_deterministic_algorithms: true wandb_entity: null wandb_project_name: rl-algo-impls-benchmarks wandb_tags: - benchmark_1d4094f - host_192-9-147-166 ```

sgoodfriend/dqn-CartPole-v1

sgoodfriend

null

65

0

rl-algo-impls

0

reinforcement-learning

false

null

0

['CartPole-v1', 'dqn', 'deep-reinforcement-learning', 'reinforcement-learning']

true

4,950

# **DQN** Agent playing **CartPole-v1** This is a trained model of a **DQN** agent playing **CartPole-v1** using the [/sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) repo. All models trained at this commit can be found at https://api.wandb.ai/links/sgoodfriend/099h4lvj. ## Training Results This model was trained from 3 trainings of **DQN** agents using different initial seeds. These agents were trained by checking out [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std). | algo | env | seed | reward_mean | reward_std | eval_episodes | best | wandb_url | |:-------|:------------|-------:|--------------:|-------------:|----------------:|:-------|:-----------------------------------------------------------------------------| | dqn | CartPole-v1 | 1 | 500 | 0 | 10 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/7an0lo49) | | dqn | CartPole-v1 | 2 | 500 | 0 | 10 | * | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/ie7su8rw) | | dqn | CartPole-v1 | 3 | 500 | 0 | 10 | | [wandb](https://wandb.ai/sgoodfriend/rl-algo-impls-benchmarks/runs/08iqk0vt) | ### Prerequisites: Weights & Biases (WandB) Training and benchmarking assumes you have a Weights & Biases project to upload runs to. By default training goes to a rl-algo-impls project while benchmarks go to rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best models and the model weights are uploaded to WandB. Before doing anything below, you'll need to create a wandb account and run `wandb login`. ## Usage /sgoodfriend/rl-algo-impls: https://github.com/sgoodfriend/rl-algo-impls Note: While the model state dictionary and hyperaparameters are saved, the latest implementation could be sufficiently different to not be able to reproduce similar results. You might need to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). ``` # Downloads the model, sets hyperparameters, and runs agent for 3 episodes python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/ie7su8rw ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb) notebook. ## Training If you want the highest chance to reproduce these results, you'll want to checkout the commit the agent was trained on: [1d4094f](https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907). While training is deterministic, different hardware will give different results. ``` python train.py --algo dqn --env CartPole-v1 --seed 2 ``` Setup hasn't been completely worked out yet, so you might be best served by using Google Colab starting from the [colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb) notebook. ## Benchmarking (with Lambda Labs instance) This and other models from https://api.wandb.ai/links/sgoodfriend/099h4lvj were generated by running a script on a Lambda Labs instance. In a Lambda Labs instance terminal: ``` git clone [email protected]:sgoodfriend/rl-algo-impls.git cd rl-algo-impls bash ./lambda_labs/setup.sh wandb login bash ./lambda_labs/benchmark.sh ``` ### Alternative: Google Colab Pro+ As an alternative, [colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb), can be used. However, this requires a Google Colab Pro+ subscription and running across 4 separate instances because otherwise running all jobs will exceed the 24-hour limit. ## Hyperparameters This isn't exactly the format of hyperparams in hyperparams/dqn.yml, but instead the Wandb Run Config. However, it's very close and has some additional data: ``` algo: dqn algo_hyperparams: batch_size: 64 buffer_size: 100000 exploration_final_eps: 0.04 exploration_fraction: 0.16 gamma: 0.99 gradient_steps: 128 learning_rate: 0.0023 learning_starts: 1000 target_update_interval: 10 train_freq: 256 env: CartPole-v1 env_hyperparams: rolling_length: 50 eval_params: step_freq: 10000 n_timesteps: 50000 policy_hyperparams: hidden_sizes: - 256 - 256 seed: 2 use_deterministic_algorithms: true wandb_entity: null wandb_project_name: rl-algo-impls-benchmarks wandb_tags: - benchmark_1d4094f - host_192-9-147-166 ```

arxaqapi/Taxi-v3

arxaqapi

null

5

0

null

0

reinforcement-learning

false

null

0

['Taxi-v3', 'q-learning', 'reinforcement-learning', 'custom-implementation']

true

362

# **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="arxaqapi/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"]) ```

fernandoalmansa/bert-finetuned-streamingqa-squadv0

fernandoalmansa

bert

12

9

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

961

# bert-finetuned-streamingqa-squadv0 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.12.1 - Datasets 2.9.0 - Tokenizers 0.13.2

albertqueralto/a2c-PandaReachDense-v2

albertqueralto

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['PandaReachDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

358

# **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 ... ```

Rolo/ppo-SnowballTarget2

Rolo

null

30

1

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SnowballTarget']

false

true

852

# **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: Rolo/ppo-SnowballTarget2 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

02shanky/finetuned-twitter-xlm-roberta-base-emotion

02shanky

xlm-roberta

13

4

transformers

0

text-classification

true

false

null

['emotion']

null

0

['generated_from_trainer']

true

1,085

# finetuned-twitter-xlm-roberta-base-emotion This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1793 - Accuracy: 0.9305 - F1: 0.9307 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

SfinOe/dreamlike_2.0

SfinOe

null

18

0

diffusers

0

text-to-image

false

other

['en']

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'photorealistic', 'photoreal', 'diffusers']

false

true

3,946

# Dreamlike Photoreal 2.0 is a photorealistic model based on Stable Diffusion 1.5, made by [dreamlike.art](https://dreamlike.art/). # If you want to use dreamlike models on your website/app/etc., check the license at the bottom first! Warning: This model is horny! Add "nude, naked" to the negative prompt if want to avoid NSFW. You can add **photo** to your prompt to make your gens look more photorealistic. Non-square aspect ratios work better for some prompts. If you want a portrait photo, try using a vertical aspect ratio. If you want a landscape photo, try using a horizontal aspect ratio. This model was trained on 768x768px images, so use 768x768px, 640x896px, 896x640px, etc. It also works pretty good with higher resolutions such as 768x1024px or 1024x768px. ### Examples <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview1.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview2.jpg" style="max-width: 800px;" width="100%"/> <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview3.jpg" style="max-width: 800px;" width="100%"/> ### dreamlike.art You can use this model for free on [dreamlike.art](https://dreamlike.art/)! <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike.jpg" style="max-width: 1000px;" width="100%"/> ### CKPT [Download dreamlike-photoreal-2.0.ckpt (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike-photoreal-2.0.ckpt) ### Safetensors [Download dreamlike-photoreal-2.0.safetensors (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike-photoreal-2.0.safetensors) ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline import torch model_id = "dreamlike-art/dreamlike-photoreal-2.0" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "photo, a church in the middle of a field of crops, bright cinematic lighting, gopro, fisheye lens" image = pipe(prompt).images[0] image.save("./result.jpg") ``` <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/church.jpg" style="max-width: 640px;" width="100%"/> # License This model is licesed under a **modified** CreativeML OpenRAIL-M license. - **You are not allowed to host, finetune, or do inference with the model or its derivatives on websites/apps/etc. If you want to, please email us at [email protected]** - **You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Photoreal 2.0) and include the license as well as a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0)** - **You are free to use the outputs (images) of the model for commercial purposes in teams of 10 or less** - You can't use the model to deliberately produce nor share illegal or harmful outputs or content - The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license - You may re-distribute the weights. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the **modified** CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/LICENSE.md

robsoneng/ppo-LunarLander-v2

robsoneng

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

350

# **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 ... ```

MRiDAN11/yugbhugyhuhbyug

MRiDAN11

null

14

0

null

0

null

false

null

0

[]

false

true

434

# gradio-ui This is a prototype Gradio-based UI which allows you to chat with the [Pygmalion models](https://huggingface.co/PygmalionAI). We provide a notebook which directly interacts with this code in our repo, which can be found [here](./notebooks/GPU.ipynb). The notebook can either be run locally or can be used on [Google Colab](https://colab.research.google.com/github/PygmalionAI/gradio-ui/blob/master/notebooks/GPU.ipynb).

96harsh56/berta-finetuned-subjqa_1

96harsh56

bert

12

5

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

922

# berta-finetuned-subjqa_1 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 7e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

joaoluislins/wmt-finetuned-en-to-pt

joaoluislins

t5

17

0

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,149

# wmt-finetuned-en-to-pt This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5272 - eval_bleu: 27.9841 - eval_gen_len: 14.6689 - eval_runtime: 20.0654 - eval_samples_per_second: 37.926 - eval_steps_per_second: 2.392 - epoch: 13.0 - step: 2483 ## 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: 70 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0 - Pytorch 1.9.1 - Datasets 2.9.0 - Tokenizers 0.13.2

fermaat/a2c-PandaReachDense-v2

fermaat

null

13

0

stable-baselines3

0

reinforcement-learning

false

null

0

['PandaReachDense-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

358

# **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 ... ```

cfisicaro/poca-SoccerTwos

cfisicaro

null

20

158

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

843

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

Rolo/ppo-PyramidsTraining

Rolo

null

16

8

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']

false

true

835

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

av3006/ppo-llv2

av3006

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

350

# **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 ... ```

zambezivoice/wav2vec2-large-xlsr-lozi-test_001

zambezivoice

wav2vec2

13

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,657

# wav2vec2-large-xlsr-lozi-test_001 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7485 - Wer: 0.3943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0643 | 4.31 | 500 | 0.5803 | 0.5244 | | 0.3151 | 8.62 | 1000 | 0.5316 | 0.4477 | | 0.1569 | 12.93 | 1500 | 0.6340 | 0.4308 | | 0.0845 | 17.24 | 2000 | 0.6819 | 0.3986 | | 0.0503 | 21.55 | 2500 | 0.7048 | 0.4051 | | 0.0354 | 25.86 | 3000 | 0.7485 | 0.3943 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Qalam/AIQalam

Qalam

null

2

0

null

0

null

false

openrail

['en']

null

0

[]

false

true

2,722

inference: true language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m --- # Openjourney is an open source Stable Diffusion fine tuned model on Midjourney images, by [PromptHero](https://prompthero.com/poolsuite-diffusion-prompts?utm_source=huggingface&utm_medium=referral) Include **'mdjrny-v4 style'** in prompt. Here you'll find hundreds of [Openjourney prompts](https://prompthero.com/openjourney-prompts?utm_source=huggingface&utm_medium=referral) # Openjourney Links - [Lora version](https://huggingface.co/prompthero/openjourney-lora) - [Openjourney v2](https://huggingface.co/prompthero/openjourney-v2) # Want to learn AI art generation?: - [Crash course in AI art generation](https://prompthero.com/academy/prompt-engineering-course?utm_source=huggingface&utm_medium=referral) - [Learn to fine-tune Stable Diffusion for photorealism](https://prompthero.com/academy/dreambooth-stable-diffusion-train-fine-tune-course?utm_source=huggingface&utm_medium=referral) # Use it for free: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/akhaliq/midjourney-v4-diffusion) ### Stable Diffusion v1.5 vs Openjourney (Same parameters, just added "mdjrny-v4 style" at the beginning): <img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587642-63265d019f9d19bfd4f45031.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587623-63265d019f9d19bfd4f45031.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587609-63265d019f9d19bfd4f45031.png" width="100%"/> <img src="https://s3.amazonaws.com/moonup/production/uploads/1667904587646-63265d019f9d19bfd4f45031.png" width="100%"/> ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "prompthero/openjourney" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "retro serie of different cars with different colors and shapes, mdjrny-v4 style" image = pipe(prompt).images[0] image.save("./retro_cars.png") ``` ---

robinsk8a/ppo-SnowballTarget

robinsk8a

null

20

1

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SnowballTarget']

false

true

856

# **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: robinsk8a/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

phtran/stt_en_conformer_ctc_small

phtran

null

4

295

nemo

0

null

false

null

0

[]

true

4,683

# Model Card for Model ID  # Model Details ## Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ## Model Sources [optional]  - **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 [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [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]

EBLITZR/spontaneousnessism

EBLITZR

null

2

0

null

0

null

false

openrail

['ak']

['Gustavosta/Stable-Diffusion-Prompts', 'glue', 'allenai/objaverse']

null

0

[]

false

true

4,904

# 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:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ## Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] # Uses  ## Direct Use  [More Information Needed] ## Downstream Use [optional]  [More Information Needed] ## Out-of-Scope Use  [More Information Needed] # Bias, Risks, and Limitations  [More Information Needed] ## Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [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]

pheepa/test-bert

pheepa

bert

8

10

transformers

0

null

false

true

false

null

0

['generated_from_keras_callback']

true

1,290

# test-bert This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 8.4258 - Validation Loss: 8.6623 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 8.4258 | 8.6623 | 0 | ### Framework versions - Transformers 4.27.0.dev0 - TensorFlow 2.11.0 - Datasets 2.9.0 - Tokenizers 0.13.2

Martha-987/whisper-small-ArabicMfG1

Martha-987

whisper

16

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ar']

['Martha-987/MyOwnData']

null

0

['hf-asr-leaderboard', 'generated_from_trainer']

true

1,288

# Whisper Small Ar- Martha This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the MyOwnData dataset. It achieves the following results on the evaluation set: - Loss: 0.6680 - Wer: 58.6092 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0208 | 9.71 | 1000 | 0.6680 | 58.6092 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Deisler/poca-SoccerTwos

Deisler

null

20

154

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

841

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

Qalam/Lei

Qalam

null

2

0

null

0

text-to-image

false

apache-2.0

null

0

[]

false

true

28,989

<img src="./docs/source/en/imgs/diffusers_library.jpg" width="400"/> <a href="https://github.com/huggingface/diffusers/blob/main/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue"> </a> <a href="https://github.com/huggingface/diffusers/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/diffusers.svg"> </a> <a href="CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg"> </a> 🤗 Diffusers provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves as a modular toolbox for inference and training of diffusion models. More precisely, 🤗 Diffusers offers: - State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)). Check [this overview](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/README.md#pipelines-summary) to see all supported pipelines and their corresponding official papers. - Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference (see [src/diffusers/schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)). - Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system (see [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)). - Training examples to show how to train the most popular diffusion model tasks (see [examples](https://github.com/huggingface/diffusers/tree/main/examples), *e.g.* [unconditional-image-generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation)). ## Installation ### For PyTorch **With `pip`** (official package) ```bash pip install --upgrade diffusers[torch] ``` **With `conda`** (maintained by the community) ```sh conda install -c conda-forge diffusers ``` ### For Flax **With `pip`** ```bash pip install --upgrade diffusers[flax] ``` **Apple Silicon (M1/M2) support** Please, refer to [the documentation](https://huggingface.co/docs/diffusers/optimization/mps). ## Contributing We ❤️ contributions from the open-source community! If you want to contribute to this library, please check out our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md). You can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library. - See [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) for general opportunities to contribute - See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines - See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22) Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or just hang out ☕. ## Quickstart In order to get started, we recommend taking a look at two notebooks: - The [Getting started with Diffusers](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) notebook, which showcases an end-to-end example of usage for diffusion models, schedulers and pipelines. Take a look at this notebook to learn how to use the pipeline abstraction, which takes care of everything (model, scheduler, noise handling) for you, and also to understand each independent building block in the library. - The [Training a diffusers model](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) notebook summarizes diffusion models training methods. This notebook takes a step-by-step approach to training your diffusion models on an image dataset, with explanatory graphics. ## Stable Diffusion is fully compatible with `diffusers`! Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/), [LAION](https://laion.ai/) and [RunwayML](https://runwayml.com/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 4GB VRAM. See the [model card](https://huggingface.co/CompVis/stable-diffusion) for more information. ### Text-to-Image generation with Stable Diffusion First let's install ```bash pip install --upgrade diffusers transformers accelerate ``` We recommend using the model in [half-precision (`fp16`)](https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/) as it gives almost always the same results as full precision while being roughly twice as fast and requiring half the amount of GPU RAM. ```python import torch from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] ``` #### Running the model locally You can also simply download the model folder and pass the path to the local folder to the `StableDiffusionPipeline`. ``` git lfs install git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 ``` Assuming the folder is stored locally under `./stable-diffusion-v1-5`, you can run stable diffusion as follows: ```python pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-v1-5") pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] ``` If you are limited by GPU memory, you might want to consider chunking the attention computation in addition to using `fp16`. The following snippet should result in less than 4GB VRAM. ```python pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" pipe.enable_attention_slicing() image = pipe(prompt).images[0] ``` If you wish to use a different scheduler (e.g.: DDIM, LMS, PNDM/PLMS), you can instantiate it before the pipeline and pass it to `from_pretrained`. ```python from diffusers import LMSDiscreteScheduler pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` If you want to run Stable Diffusion on CPU or you want to have maximum precision on GPU, please run the model in the default *full-precision* setting: ```python from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") # disable the following line if you run on CPU pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ### JAX/Flax Diffusers offers a JAX / Flax implementation of Stable Diffusion for very fast inference. JAX shines specially on TPU hardware because each TPU server has 8 accelerators working in parallel, but it runs great on GPUs too. Running the pipeline with the default PNDMScheduler: ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", revision="flax", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, jax.device_count()) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` **Note**: If you are limited by TPU memory, please make sure to load the `FlaxStableDiffusionPipeline` in `bfloat16` precision instead of the default `float32` precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch. ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", revision="bf16", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, jax.device_count()) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` Diffusers also has a Image-to-Image generation pipeline with Flax/Jax ```python import jax import numpy as np import jax.numpy as jnp from flax.jax_utils import replicate from flax.training.common_utils import shard import requests from io import BytesIO from PIL import Image from diffusers import FlaxStableDiffusionImg2ImgPipeline def create_key(seed=0): return jax.random.PRNGKey(seed) rng = create_key(0) url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" response = requests.get(url) init_img = Image.open(BytesIO(response.content)).convert("RGB") init_img = init_img.resize((768, 512)) prompts = "A fantasy landscape, trending on artstation" pipeline, params = FlaxStableDiffusionImg2ImgPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="flax", dtype=jnp.bfloat16, ) num_samples = jax.device_count() rng = jax.random.split(rng, jax.device_count()) prompt_ids, processed_image = pipeline.prepare_inputs(prompt=[prompts]*num_samples, image = [init_img]*num_samples) p_params = replicate(params) prompt_ids = shard(prompt_ids) processed_image = shard(processed_image) output = pipeline( prompt_ids=prompt_ids, image=processed_image, params=p_params, prng_seed=rng, strength=0.75, num_inference_steps=50, jit=True, height=512, width=768).images output_images = pipeline.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:]))) ``` Diffusers also has a Text-guided inpainting pipeline with Flax/Jax ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard import PIL import requests from io import BytesIO from diffusers import FlaxStableDiffusionInpaintPipeline def download_image(url): response = requests.get(url) return PIL.Image.open(BytesIO(response.content)).convert("RGB") img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" init_image = download_image(img_url).resize((512, 512)) mask_image = download_image(mask_url).resize((512, 512)) pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained("xvjiarui/stable-diffusion-2-inpainting") prompt = "Face of a yellow cat, high resolution, sitting on a park bench" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] init_image = num_samples * [init_image] mask_image = num_samples * [mask_image] prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(prompt, init_image, mask_image) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, jax.device_count()) prompt_ids = shard(prompt_ids) processed_masked_images = shard(processed_masked_images) processed_masks = shard(processed_masks) images = pipeline(prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` ### Image-to-Image text-guided generation with Stable Diffusion The `StableDiffusionImg2ImgPipeline` lets you pass a text prompt and an initial image to condition the generation of new images. ```python import requests import torch from PIL import Image from io import BytesIO from diffusers import StableDiffusionImg2ImgPipeline # load the pipeline device = "cuda" model_id_or_path = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) # or download via git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 # and pass `model_id_or_path="./stable-diffusion-v1-5"`. pipe = pipe.to(device) # let's download an initial image url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" response = requests.get(url) init_image = Image.open(BytesIO(response.content)).convert("RGB") init_image = init_image.resize((768, 512)) prompt = "A fantasy landscape, trending on artstation" images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images images[0].save("fantasy_landscape.png") ``` You can also run this example on colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ### In-painting using Stable Diffusion The `StableDiffusionInpaintPipeline` lets you edit specific parts of an image by providing a mask and a text prompt. ```python import PIL import requests import torch from io import BytesIO from diffusers import StableDiffusionInpaintPipeline def download_image(url): response = requests.get(url) return PIL.Image.open(BytesIO(response.content)).convert("RGB") img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" init_image = download_image(img_url).resize((512, 512)) mask_image = download_image(mask_url).resize((512, 512)) pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "Face of a yellow cat, high resolution, sitting on a park bench" image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0] ``` ### Tweak prompts reusing seeds and latents You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. Please have a look at [Reusing seeds for deterministic generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/reusing_seeds). ## Fine-Tuning Stable Diffusion Fine-tuning techniques make it possible to adapt Stable Diffusion to your own dataset, or add new subjects to it. These are some of the techniques supported in `diffusers`: Textual Inversion is a technique for capturing novel concepts from a small number of example images in a way that can later be used to control text-to-image pipelines. It does so by learning new 'words' in the embedding space of the pipeline's text encoder. These special words can then be used within text prompts to achieve very fine-grained control of the resulting images. - Textual Inversion. Capture novel concepts from a small set of sample images, and associate them with new "words" in the embedding space of the text encoder. Please, refer to [our training examples](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) or [documentation](https://huggingface.co/docs/diffusers/training/text_inversion) to try for yourself. - Dreambooth. Another technique to capture new concepts in Stable Diffusion. This method fine-tunes the UNet (and, optionally, also the text encoder) of the pipeline to achieve impressive results. Please, refer to [our training example](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) and [training report](https://huggingface.co/blog/dreambooth) for additional details and training recommendations. - Full Stable Diffusion fine-tuning. If you have a more sizable dataset with a specific look or style, you can fine-tune Stable Diffusion so that it outputs images following those examples. This was the approach taken to create [a Pokémon Stable Diffusion model](https://huggingface.co/justinpinkney/pokemon-stable-diffusion) (by Justing Pinkney / Lambda Labs), [a Japanese specific version of Stable Diffusion](https://huggingface.co/spaces/rinna/japanese-stable-diffusion) (by [Rinna Co.](https://github.com/rinnakk/japanese-stable-diffusion/) and others. You can start at [our text-to-image fine-tuning example](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) and go from there. ## Stable Diffusion Community Pipelines The release of Stable Diffusion as an open source model has fostered a lot of interesting ideas and experimentation. Our [Community Examples folder](https://github.com/huggingface/diffusers/tree/main/examples/community) contains many ideas worth exploring, like interpolating to create animated videos, using CLIP Guidance for additional prompt fidelity, term weighting, and much more! [Take a look](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipeline_overview) and [contribute your own](https://huggingface.co/docs/diffusers/using-diffusers/contribute_pipeline). ## Other Examples There are many ways to try running Diffusers! Here we outline code-focused tools (primarily using `DiffusionPipeline`s and Google Colab) and interactive web-tools. ### Running Code If you want to run the code yourself 💻, you can try out: - [Text-to-Image Latent Diffusion](https://huggingface.co/CompVis/ldm-text2im-large-256) ```python # !pip install diffusers["torch"] transformers from diffusers import DiffusionPipeline device = "cuda" model_id = "CompVis/ldm-text2im-large-256" # load model and scheduler ldm = DiffusionPipeline.from_pretrained(model_id) ldm = ldm.to(device) # run pipeline in inference (sample random noise and denoise) prompt = "A painting of a squirrel eating a burger" image = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6).images[0] # save image image.save("squirrel.png") ``` - [Unconditional Diffusion with discrete scheduler](https://huggingface.co/google/ddpm-celebahq-256) ```python # !pip install diffusers["torch"] from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-celebahq-256" device = "cuda" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference ddpm.to(device) # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` - [Unconditional Latent Diffusion](https://huggingface.co/CompVis/ldm-celebahq-256) - [Unconditional Diffusion with continuous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024) **Other Image Notebooks**: * [image-to-image generation with Stable Diffusion](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg), * [tweak images via repeated Stable Diffusion seeds](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg), **Diffusers for Other Modalities**: * [Molecule conformation generation](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/geodiff_molecule_conformation.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg), * [Model-based reinforcement learning](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/reinforcement_learning_with_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg), ### Web Demos If you just want to play around with some web demos, you can try out the following 🚀 Spaces: | Model | Hugging Face Spaces | |-------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Text-to-Image Latent Diffusion | [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CompVis/text2img-latent-diffusion) | | Faces generator | [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CompVis/celeba-latent-diffusion) | | DDPM with different schedulers | [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/fusing/celeba-diffusion) | | Conditional generation from sketch | [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/huggingface/diffuse-the-rest) | | Composable diffusion | [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Shuang59/Composable-Diffusion) | ## Definitions **Models**: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to *denoise* a noisy input to an image. *Examples*: UNet, Conditioned UNet, 3D UNet, Transformer UNet <img src="https://user-images.githubusercontent.com/10695622/174349667-04e9e485-793b-429a-affe-096e8199ad5b.png" width="800"/> Figure from DDPM paper (https://arxiv.org/abs/2006.11239). **Schedulers**: Algorithm class for both **inference** and **training**. The class provides functionality to compute previous image according to alpha, beta schedule as well as predict noise for training. Also known as **Samplers**. *Examples*: [DDPM](https://arxiv.org/abs/2006.11239), [DDIM](https://arxiv.org/abs/2010.02502), [PNDM](https://arxiv.org/abs/2202.09778), [DEIS](https://arxiv.org/abs/2204.13902) <img src="https://user-images.githubusercontent.com/10695622/174349706-53d58acc-a4d1-4cda-b3e8-432d9dc7ad38.png" width="800"/> Sampling and training algorithms. Figure from DDPM paper (https://arxiv.org/abs/2006.11239). **Diffusion Pipeline**: End-to-end pipeline that includes multiple diffusion models, possible text encoders, ... *Examples*: Glide, Latent-Diffusion, Imagen, DALL-E 2 <img src="https://user-images.githubusercontent.com/10695622/174348898-481bd7c2-5457-4830-89bc-f0907756f64c.jpeg" width="550"/> Figure from ImageGen (https://imagen.research.google/). ## Philosophy - Readability and clarity is preferred over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper. - Diffusers is **modality independent** and focuses on providing pretrained models and tools to build systems that generate **continuous outputs**, *e.g.* vision and audio. - Diffusion models and schedulers are provided as concise, elementary building blocks. In contrast, diffusion pipelines are a collection of end-to-end diffusion systems that can be used out-of-the-box, should stay as close as possible to their original implementation and can include components of another library, such as text-encoders. Examples for diffusion pipelines are [Glide](https://github.com/openai/glide-text2im) and [Latent Diffusion](https://github.com/CompVis/latent-diffusion). ## In the works For the first release, 🤗 Diffusers focuses on text-to-image diffusion techniques. However, diffusers can be used for much more than that! Over the upcoming releases, we'll be focusing on: - Diffusers for audio - Diffusers for reinforcement learning (initial work happening in https://github.com/huggingface/diffusers/pull/105). - Diffusers for video generation - Diffusers for molecule generation (initial work happening in https://github.com/huggingface/diffusers/pull/54) A few pipeline components are already being worked on, namely: - BDDMPipeline for spectrogram-to-sound vocoding - GLIDEPipeline to support OpenAI's GLIDE model - Grad-TTS for text to audio generation / conditional audio generation We want diffusers to be a toolbox useful for diffusers models in general; if you find yourself limited in any way by the current API, or would like to see additional models, schedulers, or techniques, please open a [GitHub issue](https://github.com/huggingface/diffusers/issues) mentioning what you would like to see. ## Credits This library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today: - @CompVis' latent diffusion models library, available [here](https://github.com/CompVis/latent-diffusion) - @hojonathanho original DDPM implementation, available [here](https://github.com/hojonathanho/diffusion) as well as the extremely useful translation into PyTorch by @pesser, available [here](https://github.com/pesser/pytorch_diffusion) - @ermongroup's DDIM implementation, available [here](https://github.com/ermongroup/ddim). - @yang-song's Score-VE and Score-VP implementations, available [here](https://github.com/yang-song/score_sde_pytorch) We also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available [here](https://github.com/heejkoo/Awesome-Diffusion-Models) as well as @crowsonkb and @rromb for useful discussions and insights. ## Citation ```bibtex @misc{von-platen-etal-2022-diffusers, author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf}, title = {Diffusers: State-of-the-art diffusion models}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/huggingface/diffusers}} } ```

sliu/poca-SoccerTwos

sliu

null

20

155

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

838

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

JoseVilla/modelo_entrenamiento_recibos_cfe

JoseVilla

vision-encoder-decoder

18

4

transformers

0

image-to-text

true

false

mit

null

0

['donut', 'image-to-text', 'vision']

false

true

1,950

# Donut (base-sized model, fine-tuned on CORD, v1) Donut model fine-tuned on CORD. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut). Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/donut_architecture.jpg) ## Intended uses & limitations This model is fine-tuned on CORD, a document parsing dataset. We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2111-15664, author = {Geewook Kim and Teakgyu Hong and Moonbin Yim and Jinyoung Park and Jinyeong Yim and Wonseok Hwang and Sangdoo Yun and Dongyoon Han and Seunghyun Park}, title = {Donut: Document Understanding Transformer without {OCR}}, journal = {CoRR}, volume = {abs/2111.15664}, year = {2021}, url = {https://arxiv.org/abs/2111.15664}, eprinttype = {arXiv}, eprint = {2111.15664}, timestamp = {Thu, 02 Dec 2021 10:50:44 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

HueyNemud/icdar23-entrydetector_texttokens_breaks_indents_left_diff_right_ref

HueyNemud

camembert

11

0

transformers

0

token-classification

true

false

null

0

['generated_from_trainer']

true

3,812

# icdar23-entrydetector_texttokens_breaks_indents_left_diff_right_ref This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0444 - Ebegin: {'precision': 0.9864814119414195, 'recall': 0.9751299183370453, 'f1': 0.9807728206085495, 'number': 2694} - Eend: {'precision': 0.9851024208566108, 'recall': 0.9789045151739453, 'f1': 0.9819936885093744, 'number': 2702} - Overall Precision: 0.9858 - Overall Recall: 0.9770 - Overall F1: 0.9814 - Overall Accuracy: 0.9868 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 7500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.07 | 300 | 0.0977 | 0.9752 | 0.9604 | 0.9677 | 0.9788 | | 0.31 | 0.14 | 600 | 0.0922 | 0.9910 | 0.9415 | 0.9656 | 0.9779 | | 0.31 | 0.21 | 900 | 0.0628 | 0.9926 | 0.9534 | 0.9726 | 0.9823 | | 0.1143 | 0.29 | 1200 | 0.0570 | 0.9715 | 0.9802 | 0.9759 | 0.9838 | | 0.0878 | 0.36 | 1500 | 0.0393 | 0.9914 | 0.9731 | 0.9822 | 0.9885 | | 0.0878 | 0.43 | 1800 | 0.0437 | 0.9825 | 0.9819 | 0.9822 | 0.9883 | | 0.0884 | 0.5 | 2100 | 0.0296 | 0.9908 | 0.9861 | 0.9885 | 0.9924 | | 0.0884 | 0.57 | 2400 | 0.0340 | 0.9913 | 0.9837 | 0.9875 | 0.9918 | | 0.0898 | 0.64 | 2700 | 0.0294 | 0.9833 | 0.9932 | 0.9882 | 0.9923 | | 0.066 | 0.72 | 3000 | 0.0369 | 0.9853 | 0.9849 | 0.9851 | 0.9904 | | 0.066 | 0.79 | 3300 | 0.0245 | 0.9892 | 0.9889 | 0.9890 | 0.9928 | | 0.0575 | 0.86 | 3600 | 0.0230 | 0.9879 | 0.9924 | 0.9901 | 0.9936 | | 0.0575 | 0.93 | 3900 | 0.0282 | 0.9865 | 0.9831 | 0.9848 | 0.9908 | | 0.064 | 1.0 | 4200 | 0.0244 | 0.9945 | 0.9822 | 0.9883 | 0.9923 | | 0.0626 | 1.07 | 4500 | 0.0203 | 0.9929 | 0.9880 | 0.9905 | 0.9937 | | 0.0626 | 1.14 | 4800 | 0.0198 | 0.9920 | 0.9891 | 0.9905 | 0.9937 | | 0.0419 | 1.22 | 5100 | 0.0219 | 0.9895 | 0.9878 | 0.9886 | 0.9925 | | 0.0419 | 1.29 | 5400 | 0.0235 | 0.9890 | 0.9876 | 0.9883 | 0.9923 | | 0.0564 | 1.36 | 5700 | 0.0212 | 0.9935 | 0.9880 | 0.9908 | 0.9939 | | 0.0427 | 1.43 | 6000 | 0.0238 | 0.9934 | 0.9839 | 0.9886 | 0.9925 | | 0.0427 | 1.5 | 6300 | 0.0193 | 0.9862 | 0.9920 | 0.9891 | 0.9928 | | 0.0501 | 1.57 | 6600 | 0.0212 | 0.9919 | 0.9885 | 0.9902 | 0.9935 | | 0.0501 | 1.65 | 6900 | 0.0225 | 0.9911 | 0.9880 | 0.9896 | 0.9931 | | 0.0488 | 1.72 | 7200 | 0.0212 | 0.9904 | 0.9892 | 0.9898 | 0.9933 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

thanat/bert-finetuned-squad

thanat

bert

8

14

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,351

# thanat/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the [squad](https://huggingface.co/datasets/squad) dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5695 - 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': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 1.2755 | 0 | | 0.7832 | 1 | | 0.5695 | 2 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

thanat/codeparrot-ds

thanat

gpt2

10

4

transformers

0

text-generation

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,571

# thanat/codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the [codeparrot](https://huggingface.co/datasets/huggingface-course/codeparrot-ds-train) dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5316 - Validation Loss: 1.1714 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 520939, '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 | |:----------:|:---------------:|:-----:| | 1.5316 | 1.1714 | 0 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

HueyNemud/icdar23-entrydetector_labelledtext_breaks_indents_left_diff_right_ref

HueyNemud

camembert

11

0

transformers

0

token-classification

true

false

null

0

['generated_from_trainer']

true

5,730

# icdar23-entrydetector_labelledtext_breaks_indents_left_diff_right_ref This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3015 - Act: {'precision': 0.806146572104019, 'recall': 0.8938401048492791, 'f1': 0.8477315102548166, 'number': 1526} - Cardinal: {'precision': 0.951349296845306, 'recall': 0.962322183775471, 'f1': 0.9568042813455658, 'number': 2601} - Ebegin: {'precision': 0.9863870493009566, 'recall': 0.9951744617668894, 'f1': 0.9907612712490761, 'number': 2694} - Eend: {'precision': 0.9925678186547752, 'recall': 0.9885270170244264, 'f1': 0.9905432968663082, 'number': 2702} - Ft: {'precision': 0.23076923076923078, 'recall': 0.2857142857142857, 'f1': 0.25531914893617025, 'number': 21} - Loc: {'precision': 0.9102217414818821, 'recall': 0.9339622641509434, 'f1': 0.9219391947411668, 'number': 3604} - Per: {'precision': 0.9238871899422358, 'recall': 0.9366172924560799, 'f1': 0.9302086897023606, 'number': 2903} - Titre: {'precision': 0.5961538461538461, 'recall': 0.8266666666666667, 'f1': 0.6927374301675977, 'number': 150} - Overall Precision: 0.9294 - Overall Recall: 0.9527 - Overall F1: 0.9409 - Overall Accuracy: 0.9452 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 15000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.07 | 300 | 0.2069 | 0.8798 | 0.9303 | 0.9044 | 0.9571 | | 0.4341 | 0.14 | 600 | 0.1650 | 0.9456 | 0.9487 | 0.9471 | 0.9658 | | 0.4341 | 0.21 | 900 | 0.1539 | 0.9370 | 0.9469 | 0.9419 | 0.9644 | | 0.1993 | 0.29 | 1200 | 0.1280 | 0.9502 | 0.9558 | 0.9530 | 0.9692 | | 0.1532 | 0.36 | 1500 | 0.1575 | 0.9554 | 0.9507 | 0.9530 | 0.9655 | | 0.1532 | 0.43 | 1800 | 0.1213 | 0.9403 | 0.9569 | 0.9485 | 0.9670 | | 0.128 | 0.5 | 2100 | 0.1075 | 0.9538 | 0.9600 | 0.9569 | 0.9745 | | 0.128 | 0.57 | 2400 | 0.1351 | 0.9485 | 0.9655 | 0.9569 | 0.9696 | | 0.1095 | 0.64 | 2700 | 0.1384 | 0.9446 | 0.9600 | 0.9522 | 0.9678 | | 0.1308 | 0.72 | 3000 | 0.1082 | 0.9509 | 0.9617 | 0.9563 | 0.9731 | | 0.1308 | 0.79 | 3300 | 0.1246 | 0.9546 | 0.9643 | 0.9594 | 0.9712 | | 0.1007 | 0.86 | 3600 | 0.1290 | 0.9484 | 0.9612 | 0.9547 | 0.9689 | | 0.1007 | 0.93 | 3900 | 0.1185 | 0.9569 | 0.9604 | 0.9586 | 0.9716 | | 0.0996 | 1.0 | 4200 | 0.1144 | 0.9561 | 0.9639 | 0.9600 | 0.9753 | | 0.078 | 1.07 | 4500 | 0.1120 | 0.9483 | 0.9669 | 0.9575 | 0.9746 | | 0.078 | 1.14 | 4800 | 0.1285 | 0.9522 | 0.9659 | 0.9590 | 0.9719 | | 0.0723 | 1.22 | 5100 | 0.1302 | 0.9413 | 0.9720 | 0.9565 | 0.9703 | | 0.0723 | 1.29 | 5400 | 0.1171 | 0.9553 | 0.9687 | 0.9619 | 0.9735 | | 0.0728 | 1.36 | 5700 | 0.1256 | 0.9475 | 0.9690 | 0.9581 | 0.9733 | | 0.0538 | 1.43 | 6000 | 0.1169 | 0.9505 | 0.9694 | 0.9599 | 0.9745 | | 0.0538 | 1.5 | 6300 | 0.1125 | 0.9470 | 0.9712 | 0.9590 | 0.9742 | | 0.062 | 1.57 | 6600 | 0.1096 | 0.9592 | 0.9675 | 0.9633 | 0.9761 | | 0.062 | 1.65 | 6900 | 0.1258 | 0.9624 | 0.9638 | 0.9631 | 0.9753 | | 0.0515 | 1.72 | 7200 | 0.1256 | 0.9586 | 0.9683 | 0.9634 | 0.9733 | | 0.0561 | 1.79 | 7500 | 0.1411 | 0.9559 | 0.9685 | 0.9622 | 0.9727 | | 0.0561 | 1.86 | 7800 | 0.1152 | 0.9581 | 0.9672 | 0.9626 | 0.9749 | | 0.0566 | 1.93 | 8100 | 0.1196 | 0.9618 | 0.9714 | 0.9666 | 0.9768 | | 0.0566 | 2.0 | 8400 | 0.1868 | 0.8886 | 0.9154 | 0.9018 | 0.9529 | | 0.1759 | 2.07 | 8700 | 0.1458 | 0.9463 | 0.9643 | 0.9552 | 0.9730 | | 0.0494 | 2.15 | 9000 | 0.1440 | 0.9543 | 0.9657 | 0.9599 | 0.9750 | | 0.0494 | 2.22 | 9300 | 0.1382 | 0.9646 | 0.9680 | 0.9663 | 0.9752 | | 0.0532 | 2.29 | 9600 | 0.1284 | 0.9635 | 0.9712 | 0.9673 | 0.9749 | | 0.0532 | 2.36 | 9900 | 0.1495 | 0.9624 | 0.9712 | 0.9668 | 0.9745 | | 0.0223 | 2.43 | 10200 | 0.1203 | 0.9600 | 0.9726 | 0.9662 | 0.9757 | | 0.0275 | 2.5 | 10500 | 0.1318 | 0.9645 | 0.9694 | 0.9670 | 0.9753 | | 0.0275 | 2.58 | 10800 | 0.1224 | 0.9623 | 0.9709 | 0.9666 | 0.9756 | | 0.026 | 2.65 | 11100 | 0.1241 | 0.9633 | 0.9713 | 0.9673 | 0.9756 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

AsmaAwad/distilbert-base-uncased-NaturalQuestions

AsmaAwad

distilbert

13

17

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,845

# result This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description The model was trained with the NaturalQuestions dataset from https://github.com/mrqa/MRQA-Shared-Task-2019 ## Intended uses & limitations This fine-tuned model is used in the context of the NLP4Web course at TU Darmstadt ## Training and evaluation data The NaturalQuestions dataset from https://github.com/mrqa/MRQA-Shared-Task-2019 was used ## Training procedure The pytorch question-answering/run_qa.py script was used to fine tune the model. The following parameters were passed to the script --model_name_or_path distilbert-base-uncased \ --train_file $TRAIN_FILE_NAME \ --validation_file $EVAL_FILE_NAME \ --do_train \ --do_eval \ --per_device_train_batch_size 12 \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir ./tmp/result/ ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ***** train metrics ***** epoch = 2.0 train_loss = 0.9724 train_runtime = 2:57:05.43 train_samples = 148108 train_samples_per_second = 27.878 train_steps_per_second = 2.323 ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

CSAle/DilbertDiffusion

CSAle

null

32

25

diffusers

0

text-to-image

true

false

creativeml-openrail-m

null

0

['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class']

false

true

465

# DreamBooth model for the Dilbert concept trained by CSAle on the CSAle/DilbertDiffusionDataset dataset. This is a Stable Diffusion model fine-tuned on the Dilbert concept. It can be used by modifying the `instance_prompt`: **dilbert** ## Description A DilbertDiffusion model ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('CSAle/DilbertDiffusion') image = pipeline().images[0] image ```

HueyNemud/icdar23-entrydetector_jointlabelledtext_breaks_indents_left_diff_right_ref

HueyNemud

camembert

11

0

transformers

0

token-classification

true

false

null

0

['generated_from_trainer']

true

4,125

# icdar23-entrydetector_jointlabelledtext_breaks_indents_left_diff_right_ref This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2611 - Act: {'precision': 0.7855491329479769, 'recall': 0.8905635648754915, 'f1': 0.8347665847665848, 'number': 1526} - Cardinal: {'precision': 0.9609375, 'recall': 0.9624413145539906, 'f1': 0.9616888193901486, 'number': 2556} - Cardinal+i-eend: {'precision': 1.0, 'recall': 0.2631578947368421, 'f1': 0.4166666666666667, 'number': 114} - Ft: {'precision': 0.3125, 'recall': 0.23809523809523808, 'f1': 0.27027027027027023, 'number': 21} - Loc: {'precision': 0.9030707610146862, 'recall': 0.9397054737427063, 'f1': 0.9210239651416122, 'number': 3599} - Loc+i-eend: {'precision': 0.9444444444444444, 'recall': 0.3617021276595745, 'f1': 0.5230769230769231, 'number': 47} - Per: {'precision': 0.915758896151053, 'recall': 0.9231332357247438, 'f1': 0.919431279620853, 'number': 2732} - Per+i-ebegin: {'precision': 0.9938223938223938, 'recall': 0.9877206446661551, 'f1': 0.9907621247113164, 'number': 2606} - Titre: {'precision': 0.6972972972972973, 'recall': 0.86, 'f1': 0.7701492537313434, 'number': 150} - Overall Precision: 0.9156 - Overall Recall: 0.9346 - Overall F1: 0.9250 - Overall Accuracy: 0.9418 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 15000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.07 | 300 | 0.2539 | 0.8509 | 0.9103 | 0.8796 | 0.9523 | | 0.5632 | 0.14 | 600 | 0.1632 | 0.9225 | 0.9305 | 0.9265 | 0.9647 | | 0.5632 | 0.21 | 900 | 0.1571 | 0.9300 | 0.9345 | 0.9323 | 0.9638 | | 0.204 | 0.29 | 1200 | 0.1415 | 0.9322 | 0.9399 | 0.9361 | 0.9669 | | 0.1626 | 0.36 | 1500 | 0.1331 | 0.9428 | 0.9477 | 0.9452 | 0.9679 | | 0.1626 | 0.43 | 1800 | 0.1272 | 0.9384 | 0.9537 | 0.9460 | 0.9679 | | 0.1305 | 0.5 | 2100 | 0.1334 | 0.9435 | 0.9513 | 0.9474 | 0.9696 | | 0.1305 | 0.57 | 2400 | 0.1199 | 0.9410 | 0.9496 | 0.9452 | 0.9705 | | 0.1288 | 0.64 | 2700 | 0.1412 | 0.9401 | 0.9530 | 0.9465 | 0.9685 | | 0.1345 | 0.72 | 3000 | 0.1177 | 0.9407 | 0.9534 | 0.9470 | 0.9711 | | 0.1345 | 0.79 | 3300 | 0.1191 | 0.9417 | 0.9599 | 0.9507 | 0.9718 | | 0.1123 | 0.86 | 3600 | 0.1110 | 0.9472 | 0.9609 | 0.9540 | 0.9746 | | 0.1123 | 0.93 | 3900 | 0.1229 | 0.9343 | 0.9462 | 0.9402 | 0.9712 | | 0.1047 | 1.0 | 4200 | 0.1032 | 0.9521 | 0.9622 | 0.9571 | 0.9770 | | 0.0713 | 1.07 | 4500 | 0.1093 | 0.9343 | 0.9642 | 0.9490 | 0.9746 | | 0.0713 | 1.14 | 4800 | 0.1045 | 0.9499 | 0.9609 | 0.9554 | 0.9758 | | 0.0674 | 1.22 | 5100 | 0.1287 | 0.9382 | 0.9704 | 0.9541 | 0.9730 | | 0.0674 | 1.29 | 5400 | 0.0983 | 0.9520 | 0.9547 | 0.9533 | 0.9743 | | 0.0682 | 1.36 | 5700 | 0.1153 | 0.9468 | 0.9611 | 0.9539 | 0.9752 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

eormeno12/platzi_vit_model

eormeno12

vit

25

2

transformers

0

image-classification

true

false

apache-2.0

null

['beans']

null

0

['generated_from_trainer']

true

1,225

# platzi_vit_model 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0328 - Accuracy: 0.9925 ## 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: 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1427 | 3.85 | 500 | 0.0328 | 0.9925 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

robinsk8a/ppo-Pyramids

robinsk8a

null

20

1

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']

false

true

832

# **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: robinsk8a/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

eshwarprasadS/lunarlanderv2-dqn

eshwarprasadS

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

350

# **DQN** Agent playing **LunarLander-v2** This is a trained model of a **DQN** 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 ... ```

figfig/local_test_model

figfig

whisper

10

1

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['figfig/restaurant_order_local_test']

null

0

['hf-asr-leaderboard', 'generated_from_trainer']

true

1,476

# restaurant_local_test_model This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the local_test_data dataset. It achieves the following results on the evaluation set: - Loss: 0.5435 - Wer: 78.5714 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 10.0 | 10 | 2.2425 | 7.1429 | | No log | 20.0 | 20 | 0.6651 | 0.0 | | 2.4375 | 30.0 | 30 | 0.5776 | 35.7143 | | 2.4375 | 40.0 | 40 | 0.5435 | 78.5714 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

gatardochi/dqn-SpaceInvadersNoFrameskip-v4

gatardochi

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

2,223

# **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 gatardochi -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 gatardochi -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 gatardochi ``` ## 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)]) ```

petergoldstein/dqn-SpaceInvadersNoFrameskip-v1

petergoldstein

null

15

0

stable-baselines3

0

reinforcement-learning

false

null

0

['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

2,235

# **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 petergoldstein -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 petergoldstein -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 petergoldstein ``` ## 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)]) ```

pfunk/Pong-v4-DQPN_p100_e0.50-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

['Pong-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

1,999

# (CleanRL) **DQN** Agent Playing **Pong-v4** This is a trained model of a DQN agent playing Pong-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/DQPN_p100_e0.50.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p100_e0.50]" python -m cleanrl_utils.enjoy --exp-name DQPN_p100_e0.50 --env-id Pong-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/Pong-v4-DQPN_p100_e0.50-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_e0.50-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p100_e0.50-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p100_e0.50 --start-policy-f 100000 --end-policy-f 1000 --evaluation-fraction 0.50 --target-tau 1.0 --policy-tau 1.00 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000 ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'end_policy_f': 1000, 'env_id': 'Pong-v4', 'evaluation_fraction': 0.5, 'exp_name': 'DQPN_p100_e0.50', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 80000, 'policy_tau': 1.0, 'save_model': True, 'seed': 1, 'start_e': 1, 'start_policy_f': 100000, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

Elytum/tiny-classification

Elytum

bert

10

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,269

# tiny-classification This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2083 - Accuracy: 0.9365 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2304 | 1.0 | 45257 | 0.2162 | 0.9343 | | 0.2181 | 2.0 | 90514 | 0.2083 | 0.9365 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

linkin-13/xtremedistil-l12-h384-uncased-trivia-qa

linkin-13

bert

15

31

transformers

0

question-answering

true

false

null

0

['generated_from_trainer']

true

959

# result This model is a fine-tuned version of [microsoft/xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased) on the TriviaQA 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: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

yenpolin/bonito-wav2vec2-medium-demo

yenpolin

wav2vec2

15

0

transformers

0

automatic-speech-recognition

true

false

null

0

['automatic-speech-recognition', 'dna_r9.4.1', 'generated_from_trainer']

true

2,425

# bonito-wav2vec2-medium-demo This model is a fine-tuned version of [yenpolin/bonito-wav2vec2-medium](https://huggingface.co/yenpolin/bonito-wav2vec2-medium) on the DNA_R9.4.1 - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.6731 - Mean Acc: 25.5069 - Median Acc: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 128 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Acc | Median Acc | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:| | 1.0638 | 0.1 | 1000 | 1.0666 | 0.0 | 0.0 | | 1.0521 | 0.21 | 2000 | 1.0562 | 0.0 | 0.0 | | 1.0305 | 0.31 | 3000 | 1.0312 | 0.0 | 0.0 | | 1.0005 | 0.42 | 4000 | 1.0074 | 0.0092 | 0.0 | | 0.9626 | 0.52 | 5000 | 0.9689 | 0.1509 | 0.0 | | 0.8958 | 0.63 | 6000 | 0.8995 | 0.2420 | 0.0 | | 0.8476 | 0.73 | 7000 | 0.8540 | 0.5996 | 0.0 | | 0.8079 | 0.84 | 8000 | 0.8080 | 2.7168 | 0.0 | | 0.7191 | 0.94 | 9000 | 0.7133 | 15.3735 | 0.0 | | 0.6812 | 1.05 | 10000 | 0.6901 | 19.0704 | 0.0 | | 0.6715 | 1.15 | 11000 | 0.6860 | 22.0658 | 0.0 | | 0.6701 | 1.26 | 12000 | 0.6822 | 24.3154 | 0.0 | | 0.6649 | 1.36 | 13000 | 0.6788 | 23.5296 | 0.0 | | 0.6669 | 1.47 | 14000 | 0.6791 | 25.5101 | 0.0 | | 0.6627 | 1.57 | 15000 | 0.6763 | 24.4471 | 0.0 | | 0.6626 | 1.68 | 16000 | 0.6755 | 25.2374 | 0.0 | | 0.6615 | 1.78 | 17000 | 0.6743 | 25.3303 | 0.0 | | 0.6611 | 1.89 | 18000 | 0.6740 | 25.9905 | 0.0 | | 0.659 | 1.99 | 19000 | 0.6731 | 25.4006 | 0.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.12.1 - Datasets 2.9.0 - Tokenizers 0.13.2

WildBill258/ppo-LunarLander-v2

WildBill258

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

['LunarLander-v2', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

350

# **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 ... ```

pmfsl/pt-bert-large-finetuned-rte

pmfsl

bert

10

2

transformers

0

text-classification

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,479

# pmfsl/pt-bert-large-finetuned-rte This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3300 - Validation Loss: 0.1597 - Train Accuracy: 0.9432 - Train F1: 0.9439 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 406, '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} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Train F1 | Epoch | |:----------:|:---------------:|:--------------:|:--------:|:-----:| | 0.3300 | 0.1597 | 0.9432 | 0.9439 | 0 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

deprem-ml/deprem-roberta-intent

deprem-ml

null

11

0

transformers

0

text-classification

false

apache-2.0

['tr']

null

0

[]

false

true

1,202

# Türkçe Multi-label Intent Classification RoBERTa Depremzedelerin ihtiyaçlarını karşılamak için etiketlenmiş eğitilmiş multi-label RoBERTa modeli. Aşağıda değerlendirme sonuçları var. **Evaluation** - 'eval_loss': 0.18568251545368838, - 'eval_runtime': 2.7693, - 'eval_samples_per_second': 254.935, - 'eval_steps_per_second': 8.305, - 'epoch': 3.0 **Classification Report** ``` precision recall f1-score support Alakasiz 0.95 0.87 0.91 781 Barinma 0.86 0.52 0.65 234 Elektronik 0.00 0.00 0.00 171 Giysi 0.89 0.25 0.39 122 Kurtarma 0.86 0.78 0.82 472 Lojistik 0.00 0.00 0.00 123 Saglik 0.78 0.05 0.09 148 Su 0.92 0.11 0.20 96 Yagma 0.00 0.00 0.00 19 Yemek 0.94 0.42 0.58 158 micro avg 0.91 0.55 0.69 2324 macro avg 0.62 0.30 0.36 2324 weighted avg 0.78 0.55 0.61 2324 samples avg 0.69 0.63 0.65 2324 ```

pmfsl/multi-bert-base-finetuned-rte

pmfsl

bert

10

12

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,461

# pmfsl/multi-bert-base-finetuned-rte This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4024 - Validation Loss: 0.2674 - Train Accuracy: 0.9009 - Train F1: 0.9013 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 406, '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} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Train F1 | Epoch | |:----------:|:---------------:|:--------------:|:--------:|:-----:| | 0.4024 | 0.2674 | 0.9009 | 0.9013 | 0 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

pmfsl/xlm-roberta-base-finetuned-rte

pmfsl

xlm-roberta

9

0

transformers

0

text-classification

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,438

# pmfsl/xlm-roberta-base-finetuned-rte This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4909 - Validation Loss: 0.3078 - Train Accuracy: 0.8741 - Train F1: 0.8750 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 406, '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} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Train F1 | Epoch | |:----------:|:---------------:|:--------------:|:--------:|:-----:| | 0.4909 | 0.3078 | 0.8741 | 0.8750 | 0 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

pfunk/Pong-v4-DQPN_p30-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

['Pong-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

1,943

# (CleanRL) **DQN** Agent Playing **Pong-v4** This is a trained model of a DQN agent playing Pong-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/DQPN_p30.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p30]" python -m cleanrl_utils.enjoy --exp-name DQPN_p30 --env-id Pong-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/Pong-v4-DQPN_p30-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p30-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p30-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p30 --start-policy-f 30000 --end-policy-f 30000 --evaluation-fraction 1.00 --target-tau 1.0 --policy-tau 1.00 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000 ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'end_policy_f': 30000, 'env_id': 'Pong-v4', 'evaluation_fraction': 1.0, 'exp_name': 'DQPN_p30', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 80000, 'policy_tau': 1.0, 'save_model': True, 'seed': 1, 'start_e': 1, 'start_policy_f': 30000, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

yizhangliu/poca-SoccerTwos-v6

yizhangliu

null

21

142

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

847

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: yizhangliu/poca-SoccerTwos-v6 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

CSAle/DilbertDiffusion2

CSAle

null

38

13

diffusers

0

text-to-image

true

false

creativeml-openrail-m

null

0

['pytorch', 'diffusers', 'stable-diffusion-v2-1-base', 'text-to-image', 'diffusion-models-class']

false

true

466

# DreamBooth model for the Dilbert concept trained by CSAle on the CSAle/DilbertDiffusionDataset dataset. This is a Stable Diffusion model fine-tuned on the Dilbert concept. It can be used by modifying the `instance_prompt`: **dilbert** ## Description A DilbertDiffusion model ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('CSAle/DilbertDiffusion2') image = pipeline().images[0] image ```

sgoodfriend/dqn-sb3-SpaceInvadersNoFrameskip-v4

sgoodfriend

null

12

0

stable-baselines3

0

reinforcement-learning

false

null

0

['SpaceInvadersNoFrameskip-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'stable-baselines3']

true

376

# **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). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

sergey-antonov/pg-CartPole-v1

sergey-antonov

null

6

0

null

0

reinforcement-learning

false

null

0

['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

286

# **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

yujiepan/bert-base-uncased-sst2

yujiepan

bert

19

9

transformers

0

null

true

false

null

['en']

['glue']

null

0

['generated_from_trainer']

true

636

# bert-base-uncased-sst2 This model is **exactly the same** as [textattack/bert-base-uncased-SST-2](https://huggingface.co/textattack/bert-base-uncased-SST-2) but is also converted to OpenVINO IR format. It achieves the following results on the evaluation set: - torch loss: 0.2819 - torch accuracy: 0.9243 - OpenVINO IR accuracy: 0.9243 ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

yhchoi/distilbert-base-uncased-finetuned-emotion

yhchoi

distilbert

14

2

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

931

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.25.1 - Pytorch 1.7.1+cu110 - Datasets 2.9.0 - Tokenizers 0.13.2

income/bpr-contriever-gpl-quora

income

bert

12

7

sentence-transformers

0

sentence-similarity

true

false

null

0

['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']

false

true

3,550

# {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 59733 with parameters: ``` {'batch_size': 75, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `income.bpr.gpl.loss.BPRMarginDistillationLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 70000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

gatardochi/Reinforce-CartPole-v1

gatardochi

null

6

0

null

0

reinforcement-learning

false

null

0

['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

286

# **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

thiagoms7/poca-SoccerTwos

thiagoms7

null

17

0

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-SoccerTwos']

false

true

843

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: thiagoms7/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

taraxis/nekotest1-1

taraxis

null

17

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora']

false

true

503

# LoRA DreamBooth - nekotest1-1 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 "nekotst" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: a tabby cat ![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)

sergey-antonov/pg-Pixelcopter-PLE-v0

sergey-antonov

null

6

0

null

0

reinforcement-learning

false

null

0

['Pixelcopter-PLE-v0', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

300

# **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

XPeng2022/hgs3

XPeng2022

null

19

16

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image', 'stable-diffusion']

false

true

263

### HGS3 Dreambooth model trained by XPeng2022 Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

ihfaudsip/bert-finetuned-squad

ihfaudsip

bert

30

7

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

954

# bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.0+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

jwhe/prompt-extend-1epoch

jwhe

gpt2

12

0

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,195

# prompt-extend-1epoch This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9530 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 512 - eval_batch_size: 1024 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7574 | 1.0 | 3199 | 2.9530 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.11.0+cu102 - Datasets 2.9.0 - Tokenizers 0.13.2

Ransaka/Ml-Agents-Pyramids

Ransaka

null

16

1

ml-agents

0

reinforcement-learning

false

null

0

['unity-ml-agents', 'ml-agents', 'deep-reinforcement-learning', 'reinforcement-learning', 'ML-Agents-Pyramids']

false

true

836

# **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: Ransaka/Ml-Agents-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ramazank2000/turkishReviews-ds-mini1

ramazank2000

gpt2

10

4

transformers

0

text-generation

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,504

# ramazank2000/turkishReviews-ds-mini1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 9.1811 - Validation Loss: 9.2643 - 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': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -896, '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: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.2935 | 9.9865 | 0 | | 9.6639 | 9.6408 | 1 | | 9.1811 | 9.2643 | 2 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.9.0 - Tokenizers 0.13.2

raw-vitor/danny

raw-vitor

null

19

9

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image', 'stable-diffusion']

false

true

416

### danny Dreambooth model trained by raw-vitor with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

pittawat/Reinforce-cart-pole

pittawat

null

6

0

null

0

reinforcement-learning

false

null

0

['CartPole-v1', 'reinforce', 'reinforcement-learning', 'custom-implementation', 'deep-rl-class']

true

286

# **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

Yamei/distilbert-base-uncased-finetuned-paragraph-ner

Yamei

distilbert

15

1

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,560

# distilbert-base-uncased-finetuned-paragraph-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4624 - Precision: 0.3115 - Recall: 0.3929 - F1: 0.3475 - Accuracy: 0.8436 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 225 | 0.5517 | 0.1691 | 0.2423 | 0.1992 | 0.8074 | | No log | 2.0 | 450 | 0.4858 | 0.2747 | 0.3534 | 0.3091 | 0.8335 | | 0.5951 | 3.0 | 675 | 0.4624 | 0.3115 | 0.3929 | 0.3475 | 0.8436 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

cleanrl/BankHeist-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['BankHeist-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,295

# (CleanRL) **PPO** Agent Playing **BankHeist-v5** This is a trained model of a PPO agent playing BankHeist-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id BankHeist-v5 ``` 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/cleanrl/BankHeist-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/BankHeist-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/BankHeist-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id BankHeist-v5 --seed 2 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'BankHeist-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 2, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Asteroids-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Asteroids-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,295

# (CleanRL) **PPO** Agent Playing **Asteroids-v5** This is a trained model of a PPO agent playing Asteroids-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Asteroids-v5 ``` 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/cleanrl/Asteroids-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Asteroids-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Asteroids-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Asteroids-v5 --seed 3 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Asteroids-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 3, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Alien-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Alien-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,263

# (CleanRL) **PPO** Agent Playing **Alien-v5** This is a trained model of a PPO agent playing Alien-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Alien-v5 ``` 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/cleanrl/Alien-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Alien-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Alien-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Alien-v5 --seed 3 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Alien-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 3, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Amidar-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Amidar-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,271

# (CleanRL) **PPO** Agent Playing **Amidar-v5** This is a trained model of a PPO agent playing Amidar-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Amidar-v5 ``` 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/cleanrl/Amidar-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Amidar-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Amidar-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Amidar-v5 --seed 2 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Amidar-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 2, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Amidar-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Amidar-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,271

# (CleanRL) **PPO** Agent Playing **Amidar-v5** This is a trained model of a PPO agent playing Amidar-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Amidar-v5 ``` 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/cleanrl/Amidar-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Amidar-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Amidar-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Amidar-v5 --seed 3 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Amidar-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 3, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Alien-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Alien-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,263

# (CleanRL) **PPO** Agent Playing **Alien-v5** This is a trained model of a PPO agent playing Alien-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Alien-v5 ``` 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/cleanrl/Alien-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Alien-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Alien-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Alien-v5 --seed 2 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Alien-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 2, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Assault-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Assault-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,279

# (CleanRL) **PPO** Agent Playing **Assault-v5** This is a trained model of a PPO agent playing Assault-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Assault-v5 ``` 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/cleanrl/Assault-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Assault-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Assault-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Assault-v5 --seed 3 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Assault-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 3, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Assault-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Assault-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,279

# (CleanRL) **PPO** Agent Playing **Assault-v5** This is a trained model of a PPO agent playing Assault-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Assault-v5 ``` 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/cleanrl/Assault-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Assault-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Assault-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Assault-v5 --seed 2 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Assault-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 2, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Asteroids-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Asteroids-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,295

# (CleanRL) **PPO** Agent Playing **Asteroids-v5** This is a trained model of a PPO agent playing Asteroids-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Asteroids-v5 ``` 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/cleanrl/Asteroids-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Asteroids-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Asteroids-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Asteroids-v5 --seed 2 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Asteroids-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 2, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Atlantis-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Atlantis-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,287

# (CleanRL) **PPO** Agent Playing **Atlantis-v5** This is a trained model of a PPO agent playing Atlantis-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Atlantis-v5 ``` 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/cleanrl/Atlantis-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Atlantis-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Atlantis-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Atlantis-v5 --seed 2 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Atlantis-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 2, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Atlantis-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Atlantis-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,287

# (CleanRL) **PPO** Agent Playing **Atlantis-v5** This is a trained model of a PPO agent playing Atlantis-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Atlantis-v5 ``` 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/cleanrl/Atlantis-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Atlantis-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Atlantis-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Atlantis-v5 --seed 3 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Atlantis-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 3, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Asterix-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,279

# (CleanRL) **PPO** Agent Playing **Asterix-v5** This is a trained model of a PPO agent playing Asterix-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Asterix-v5 ``` 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/cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Asterix-v5 --seed 3 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Asterix-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 3, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed1

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Asterix-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,279

# (CleanRL) **PPO** Agent Playing **Asterix-v5** This is a trained model of a PPO agent playing Asterix-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Asterix-v5 ``` 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/cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed1/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed1/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Asterix-v5 --seed 1 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Asterix-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 1, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2

cleanrl

null

9

0

cleanrl

0

reinforcement-learning

false

null

0

['Asterix-v5', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

2,279

# (CleanRL) **PPO** Agent Playing **Asterix-v5** This is a trained model of a PPO agent playing Asterix-v5. 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/sebulba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Asterix-v5 ``` 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/cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Asterix-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed2/raw/main/poetry.lock poetry install --all-extras python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Asterix-v5 --seed 2 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 7680, 'capture_video': False, 'clip_coef': 0.1, 'cuda': True, 'ent_coef': 0.01, 'env_id': 'Asterix-v5', 'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learner_device_ids': [1, 2, 3, 4, 5, 6], 'learning_rate': 0.00025, 'max_grad_norm': 0.5, 'minibatch_size': 1920, 'norm_adv': True, 'num_actor_threads': 1, 'num_envs': 60, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 6510, 'profile': False, 'save_model': True, 'seed': 2, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

xenon3134-mc/empty-eyes-LoRAs

xenon3134-mc

null

7

0

null

9

null

false

creativeml-openrail-m

null

0

[]

false

true

1,993

# LoRAs When using these LoRAs, it may be a better image to redraw only face or eyes using inpaint. Or, it is recommended to reduce the Weight of LoRA. - [utsurome_v3.safetensors](#utsurome_v3.safetensors) - base model: [7th_anime_3.1_Cg](https://huggingface.co/syaimu/7th_test) - training dataset: [empty-eyes-dataset](https://huggingface.co/datasets/xenon3134-mc/empty-eyes-dataset/tree/main/empty_eyes) - [yorime.safetensors](#yorime.safetensors) - base model: [7th_anime_3.1_Cg](https://huggingface.co/syaimu/7th_test) - [shirome.safetensors](#shirome.safetensors) - base model: [7th_anime_3.1_Cg](https://huggingface.co/syaimu/7th_test) # utsurome_v3.safetensors [<img src="https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/utsurome_v3.png" width="512" height="768">](https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/utsurome_v3.png) <details> <summary>Sample Prompt</summary> <pre> masterpiece, best quality, 1girl, empty eyes, utsurome, maid, smile Negative prompt: (worst quality:1.4), (low quality:1.4), nsfw, Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 416625012, Size: 512x768, Model hash: 49576e83ad, Clip skip: 2, ENSD: 31337, AddNet Enabled: True, AddNet Module 1: LoRA, AddNet Model 1: utsurome_v5(2f0093a7aa52), AddNet Weight A 1: 0.6, AddNet Weight B 1: 0.6</pre> </details> v3 has improved image quality compared to v2. [<img src="https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/Comparison.png" width="1200" height="600">](https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/Comparison.png) # yorime.safetensors [<img src="https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/yorime.png" width="512" height="768">](https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/yorime.png) <details> <summary>Sample Prompt</summary> <pre> masterpiece, best quality, 1girl, empty eyes, yorime, smlie, maid Negative prompt: (worst quality:1.4), (low quality:1.4), (nsfw: 1.3), blush Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 541777558, Size: 512x768, Model hash: 49576e83ad, Clip skip: 2, ENSD: 31337, AddNet Enabled: True, AddNet Module 1: LoRA, AddNet Model 1: yorime(5924d7962886), AddNet Weight A 1: 1, AddNet Weight B 1: 1</pre> </details> # shirome.safetensors [<img src="https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/shirome.png" width="512" height="768">](https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs/resolve/main/samples/shirome.png) <details> <summary>Sample Prompt</summary> <pre> masterpiece, best quality, 1girl, shirome, blank eyes,upper body, Negative prompt: (worst quality:1.4), (low quality:1.4), (nsfw:1.3), blush, monochrome, Steps: 30, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 266137625, Size: 512x768, Model hash: 49576e83ad, Clip skip: 2, ENSD: 31337, AddNet Enabled: True, AddNet Module 1: LoRA, AddNet Model 1: shirome(b89df5356523), AddNet Weight A 1: 1, AddNet Weight B 1: 1</pre> </details>

Okyx/NERTESTINGCAROLINE1

Okyx

bert

8

15

transformers

0

token-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,501

# NERTESTINGCAROLINE1 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: 0.0022 - Validation Loss: 0.0049 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 10395, '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 | |:----------:|:---------------:|:-----:| | 0.0774 | 0.0125 | 0 | | 0.0097 | 0.0078 | 1 | | 0.0055 | 0.0058 | 2 | | 0.0033 | 0.0056 | 3 | | 0.0022 | 0.0049 | 4 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

MunSu/distibert-base-uncased-finetuned-emotion

MunSu

distilbert

14

2

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,337

# distilbert-base-uncased-finetuned_emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2166 - Accuracy: 0.9255 - F1: 0.9258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8304 | 1.0 | 250 | 0.3147 | 0.9095 | 0.9064 | | 0.2478 | 2.0 | 500 | 0.2166 | 0.9255 | 0.9258 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.8.0 - Datasets 2.9.0 - Tokenizers 0.13.2

pfunk/Pong-v4-DQPN_p500_e0.10-seed1

pfunk

null

11

0

cleanrl

0

reinforcement-learning

false

null

0

['Pong-v4', 'deep-reinforcement-learning', 'reinforcement-learning', 'custom-implementation']

true

1,999

# (CleanRL) **DQN** Agent Playing **Pong-v4** This is a trained model of a DQN agent playing Pong-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/DQPN_p500_e0.10.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[DQPN_p500_e0.10]" python -m cleanrl_utils.enjoy --exp-name DQPN_p500_e0.10 --env-id Pong-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/Pong-v4-DQPN_p500_e0.10-seed1/raw/main/dqpn_atari.py curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_e0.10-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/Pong-v4-DQPN_p500_e0.10-seed1/raw/main/poetry.lock poetry install --all-extras python dqpn_atari.py --exp-name DQPN_p500_e0.10 --start-policy-f 500000 --end-policy-f 1000 --evaluation-fraction 0.10 --target-tau 1.0 --policy-tau 1.00 --track --wandb-entity pfunk --wandb-project-name dqpn --save-model true --upload-model true --hf-entity pfunk --env-id Pong-v4 --seed 1 --total-timesteps 10000000 ``` # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'end_policy_f': 1000, 'env_id': 'Pong-v4', 'evaluation_fraction': 0.1, 'exp_name': 'DQPN_p500_e0.10', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 80000, 'policy_tau': 1.0, 'save_model': True, 'seed': 1, 'start_e': 1, 'start_policy_f': 500000, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 10000000, 'track': True, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```