Datasets:
librarian-bots
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Dataset card Data Studio Files
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2025-07-13 12:28:20
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ArtYac/a2c-AntBulletEnv-v0
ArtYac
2023-02-28T20:51:24Z
4
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T20:50:14Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1146.34 +/- 75.95 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Leoxie2000/t5-small-finetuned-xsum
Leoxie2000
2023-02-28T20:38:14Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:samsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-28T20:18:31Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: validation args: samsum metrics: - name: Rouge1 type: rouge value: 38.7231 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.9223 - Rouge1: 38.7231 - Rouge2: 16.4719 - Rougel: 32.3585 - Rougelsum: 35.8234 - Gen Len: 16.209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.1235 | 1.0 | 921 | 1.9223 | 38.7231 | 16.4719 | 32.3585 | 35.8234 | 16.209 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
stinoco/a2c-AntBulletEnv-v0
stinoco
2023-02-28T20:37:59Z
4
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T20:36:43Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1786.76 +/- 84.87 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Qilex/rl_course_vizdoom_health_gathering_supreme
Qilex
2023-02-28T20:33:15Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T20:33:04Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 8.99 +/- 1.78 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Qilex/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
ihorbilyk/donut-base-remittance
ihorbilyk
2023-02-28T20:30:14Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "license:mit", "endpoints_compatible", "region:us" ]
image-text-to-text
2023-02-28T18:49:37Z
--- license: mit tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-remittance results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # donut-base-remittance This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
polejowska/yolos-tiny-CD45RB-1000-att
polejowska
2023-02-28T20:14:13Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "yolos", "object-detection", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
object-detection
2023-02-28T19:47:45Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: yolos-tiny-CD45RB-1000-att results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # yolos-tiny-CD45RB-1000-att This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6170 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5369 | 1.0 | 94 | 2.7680 | | 3.4721 | 2.0 | 188 | 2.7605 | | 3.4243 | 3.0 | 282 | 2.6854 | | 3.3027 | 4.0 | 376 | 2.6661 | | 3.2875 | 5.0 | 470 | 2.6692 | | 3.2959 | 6.0 | 564 | 2.6477 | | 3.2286 | 7.0 | 658 | 2.6885 | | 3.1364 | 8.0 | 752 | 2.6583 | | 3.0872 | 9.0 | 846 | 2.6667 | | 3.0935 | 10.0 | 940 | 2.6170 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
Sorenmc/q-FrozenLake-v1-4x4-noSlippery
Sorenmc
2023-02-28T20:14:00Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T20:13:57Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Sorenmc/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"]) ```
Draff/Draffs-Loras
Draff
2023-02-28T20:06:34Z
0
2
null
[ "license:other", "region:us" ]
null
2023-02-23T13:12:47Z
--- license: other --- honestly dont really care too much about what happens to these loras as long as you dont sell them or claim them as your own. I have no idea what im doing civit account: https://civitai.com/user/Draff I'll add previews and stuff soon
pedroleme/sesh4
pedroleme
2023-02-28T19:57:44Z
30
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-02-28T19:46:42Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### sesh4 Dreambooth model trained by pedroleme 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:
SarvasvaK/Taxi-v3
SarvasvaK
2023-02-28T19:41:47Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T19:41:42Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.81 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="SarvasvaK/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"]) ```
zyoscovits/a2c-PandaReachDense-v2
zyoscovits
2023-02-28T19:31:49Z
9
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-22T20:57:44Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.47 +/- 0.18 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
LeoAgis/Reinforce-Copter-1
LeoAgis
2023-02-28T19:30:46Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T19:12:04Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Copter-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 35.60 +/- 26.38 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
bnowak1831/pp0-LunarLander-v2
bnowak1831
2023-02-28T19:30:33Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T18:49:45Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -125.15 +/- 69.78 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.0002 'num_envs': 16 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 16 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'bnowak1831/pp0-LunarLander-v2' 'batch_size': 2048 'minibatch_size': 128} ```
npit/Reinforce-pixelcopter-baseline
npit
2023-02-28T19:26:38Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T19:26:35Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter-baseline results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 16.30 +/- 21.18 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
NCHS/SANDS
NCHS
2023-02-28T19:26:32Z
58
5
transformers
[ "transformers", "pytorch", "bert", "text-classification", "en", "doi:10.57967/hf/0414", "license:cc0-1.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-09-19T22:35:16Z
--- language: - en tags: - text-classification license: cc0-1.0 library: Transformers widget: - text: "sdfsdfa" example_title: "Gibberish" - text: "idkkkkk" example_title: "Uncertainty" - text: "Because you asked" example_title: "Refusal" - text: "I am a cucumber" example_title: "High-risk" - text: "My job went remote and I needed to take care of my kids" example_title: "Valid" --- # SANDS _Semi-Automated Non-response Detection for Surveys_ Non-response detection designed to be used for open-ended survey text in conjunction with human reviewers. ## Model Details Model Description: This model is a fine-tuned version of the supervised SimCSE BERT base uncased model. It was introduced at [AAPOR](https://www.aapor.org/) 2022 at the talk _Toward a Semi-automated item nonresponse detector model for open-response data_. The model is uncased, so it treats `important`, `Important`, and `ImPoRtAnT` the same. * Developed by: [National Center for Health Statistics](https://www.cdc.gov/nchs/index.htm), Centers for Disease Control and Prevention * Model Type: Text Classification * Language(s): English * License: Apache-2.0 Parent Model: For more details about SimCSE, we encourage users to check out the SimCSE [Github repository](https://github.com/princeton-nlp/SimCSE), and the [base model](https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased) on HuggingFace. ## How to Get Started with the Model ### Example of classification of a set of responses: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch import pandas as pd # Load the model model_location = "NCHS/SANDS" model = AutoModelForSequenceClassification.from_pretrained(model_location) tokenizer = AutoTokenizer.from_pretrained(model_location) # Create example responses to test responses = [ "sdfsdfa", "idkkkkk", "Because you asked", "I am a cucumber", "My job went remote and I needed to take care of my kids", ] # Run the model and compute a score for each response with torch.no_grad(): tokens = tokenizer(responses, padding=True, truncation=True, return_tensors="pt") output = model(**tokens) scores = torch.softmax(output.logits, dim=1).numpy() # Display the scores in a table columns = ["Gibberish", "Uncertainty", "Refusal", "High-risk", "Valid"] df = pd.DataFrame(scores, columns=columns) df.index.name = "Response" print(df) ``` |Response| Gibberish| Uncertainty| Refusal| High-risk| Valid| |--------|---------------|-----------------|-----------|-----------------|-----------| |sdfsdfa| 0.998| 0.000| 0.000| 0.000| 0.000| |idkkkkk| 0.002| 0.995| 0.001| 0.001| 0.001| |Because you asked| 0.001| 0.001| 0.976| 0.006| 0.014| |I am a cucumber| 0.001| 0.001| 0.002| 0.797| 0.178| |My job went remote and I needed to take care of my kids| 0.000| 0.000| 0.000| 0.000| 1.000| Alternatively, you can load the model using a pipeline ```python from transformers import pipeline pipe = pipeline("text-classification", "NCHS/SANDS") print( pipe(responses) ) ``` ```python [{'label': 'Gibberish', 'score': 0.9978908896446228}, {'label': 'Uncertainty', 'score': 0.9950007796287537}, {'label': 'Refusal', 'score': 0.9775006771087646}, {'label': 'High-risk', 'score': 0.9804121255874634}, {'label': 'Valid', 'score': 0.9997561573982239}] ``` With the pipeline set `top_k` to see all the full output: ```python pipe(responses, top_k=5) ``` Finally, if you'd like to use a local GPU set the device to the GPU number (usually 0). ```python pipe = pipeline("text-classification", "NCHS/SANDS", device=0) ``` ## Uses ### Direct Uses This model is intended to be used on survey responses for data cleaning to help researchers filter out non-responsive responses or junk responses to aid in research and analysis. The model will return a score for a response in 5 different categories: Gibberish, Refusal, Uncertainty, High Risk, and Valid as a probability vector that sums to 1. ### Response types + **Gibberish**: Nonsensical response where the respondent entered text without regard for English syntax. Examples: `ksdhfkshgk` and `sadsadsadsadsadsadsad` + **Refusal**: Responses with valid English but are either a direct refusal to answer the question asked or a response that provides no contextual relationship to the question asked. Examples: `Because` or `Meow`. + **Uncertainty**: Responses where the respondent does not understand the question, does not know the answer to the question, or does not know how to respond to the question. Examples: `I dont know` or `unsure what you are asking`. + **High-Risk**: Responses that may be valid depending on the context and content of the question. These responses require human subject matter expertise to classify as a valid response or not. Examples: `Necessity` or `I am a cucumber` + **Valid**: Responses that answer the question at hand and provide an insight to the respondents thought on the subject matter of the question. Examples: `COVID began for me when my children’s school went online and I needed to stay home to watch them` or `staying home, avoiding crowds, still wear masks` ## Misuses and Out-of-scope Use The model has been trained to specifically identify survey non-response in open ended responses where the respondent taking the survey has given a response but their answer does not respond to the question at hand or providing any meaningful insight. Some examples of these types of responses are `meow`, `ksdhfkshgk`, or `idk`. The model was fine-tuned on 3,000 labeled open-ended responses to web probes on questions relating to the COVID-19 pandemic gathered from the [Research and Development Survey or RANDS](https://www.cdc.gov/nchs/rands/index.htm) conducted by the Division of Research and Methodology at the National Center for Health Statistics. Web probes are questions implementing probing techniques from cognitive interviewing for use in survey question design and are different than traditional open-ended survey questions. The context of our labeled responses limited in focus on both COVID and health responses, so responses outside this scope may notice a drop in performance. The responses the model is trained on are also from both web and phone based open-ended probes. There may be limitations in model effectiveness with more traditional open ended survey questions with responses provided in other mediums. This model does not assess the factual accuracy of responses or filter out responses with different demographic biases. It was not trained to be factual of people or events and so using the model for such classification is out of scope for the abilities of the model. We did not train the model to recognize non-response in any language other than English. Responses in languages other than English are out of scope and the model will perform poorly. Any correct classifications are a result of the base SimCSE or Bert Models. ## Risks, Limitations, and Biases To investigate if there were differences between demographic groups on sensitivity and specificity, we conducted two-tailed Z-tests across demographic groups. These included education (some college or less and bachelor’s or more), sex (male or female), mode (computer or telephone), race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, and all others who are non-Hispanic), and age (18-29, 30-44, 45-59, and 60+). There were 4,813 responses to 3 probes. To control for family-wise error rate, we applied the Bonferroni correction was applied to the alpha level (Ξ± < 0.00167). There were statistically significant differences in specificity between education levels, mode, and White and Black respondents. There were no statistically significant differences in sensitivity. Respondents with some college or less had lower specificity compared to those with more education (0.73 versus 0.80, p < 0.0001). Respondents who used a smartphone or computer to complete their survey had a higher specificity than those who completed the survey over the telephone (0.77 versus 0.70, p < 0.0001). Black respondents had a lower specificity than White respondents (0.65 versus 0.78, p < 0.0001). Effect sizes for education and mode were small (h = 0.17 and h = 0.16, respectively) while the effect size for race was between small and medium (h = 0.28). As the model was fine-tuned from SimCSE, itself fine-tuned from BERT, it will reproduce all biases inherent in these base models. Due to tokenization, the model may incorrectly classify typos, especially in acronyms. For example: `LGBTQ` is valid, while `LBGTQ` is classified as gibberish. ## Training #### Training Data The model was fine-tuned on 3,200 labeled open-ended responses from [RANDS during COVID 19 Rounds 1 and 2](https://www.cdc.gov/nchs/rands/index.htm). The base SimCSE BERT model was trained on BookCorpus and English Wikipedia. #### Training procedure + Learning rate: 5e-5 + Batch size: 16 + Number training epochs: 4 + Base Model pooling dimension: 768 + Number of labels: 5 ## Suggested citation ```bibtex @misc{cibellihibben2023sands, title={Semi-Automated Nonresponse Detection for Open-text Survey Data}, author={Kristen Cibelli Hibben, Zachary Smith, Ben Rogers, Valerie Ryan, Paul Scanlon, Kristen Miller, Travis Hoppe}, year={2023}, url={https://huggingface.co/NCHS/SANDS}, doi={ 10.57967/hf/0414 } } ``` ## Open source licence Model and code, including source files and code samples if any in the content, are released as open source under the [Creative Commons Universal Public Domain](https://creativecommons.org/publicdomain/zero/1.0/). This means you can use the code, model, and content in this repository except for any offical trademarks in your own projects. Open source projects are made available and contributed to under licenses that include terms that, for the protection of contributors, make clear that the projects are offered "as-is", without warranty, and disclaiming liability for damages resulting from using the projects. This model is no different. The open content license it is offered under includes such terms.
khaled5321/rl_course_vizdoom_health_gathering_supreme
khaled5321
2023-02-28T19:21:03Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-25T19:41:00Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.99 +/- 6.70 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r khaled5321/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
Frorozcol/Reinforce-Cartpole-v1
Frorozcol
2023-02-28T19:18:04Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T19:17:56Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Taratata/ppo-SnowballTarget
Taratata
2023-02-28T19:07:45Z
2
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-02-28T19:07:39Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: Taratata/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play πŸ‘€
mqy/mt5-small-finetuned-28feb-1
mqy
2023-02-28T19:04:53Z
8
0
transformers
[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2023-02-28T09:36:17Z
--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-28feb-1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small-finetuned-28feb-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3686 - Rouge1: 20.86 - Rouge2: 6.65 - Rougel: 20.57 ## 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: 13 - eval_batch_size: 13 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 3.3725 | 2.09 | 500 | 2.5493 | 17.49 | 5.58 | 17.34 | | 2.9876 | 4.18 | 1000 | 2.4931 | 18.9 | 5.35 | 18.8 | | 2.7925 | 6.28 | 1500 | 2.4054 | 18.26 | 5.11 | 18.01 | | 2.6561 | 8.37 | 2000 | 2.3951 | 19.83 | 5.84 | 19.43 | | 2.5491 | 10.46 | 2500 | 2.3602 | 19.11 | 5.69 | 18.8 | | 2.4504 | 12.55 | 3000 | 2.3458 | 20.83 | 6.74 | 20.52 | | 2.3708 | 14.64 | 3500 | 2.3739 | 20.69 | 6.53 | 20.43 | | 2.3075 | 16.74 | 4000 | 2.3414 | 19.32 | 6.58 | 19.12 | | 2.2512 | 18.83 | 4500 | 2.3589 | 19.38 | 6.07 | 19.0 | | 2.1554 | 20.92 | 5000 | 2.3686 | 20.86 | 6.65 | 20.57 | | 2.1141 | 23.01 | 5500 | 2.3768 | 20.71 | 6.46 | 20.37 | | 2.0774 | 25.1 | 6000 | 2.3627 | 20.25 | 6.22 | 20.0 | | 2.0315 | 27.2 | 6500 | 2.3521 | 20.37 | 6.28 | 20.05 | | 1.9787 | 29.29 | 7000 | 2.3699 | 20.75 | 6.6 | 20.43 | | 1.9645 | 31.38 | 7500 | 2.3554 | 20.27 | 5.9 | 20.0 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
CloXD/RL-PixelCopter-v0
CloXD
2023-02-28T18:48:33Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T18:45:55Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: RL-PixelCopter-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 44.40 +/- 29.29 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
pierreguillou/layoutxlm-finetuned-xfund-fr
pierreguillou
2023-02-28T18:48:14Z
79
0
transformers
[ "transformers", "pytorch", "tensorboard", "layoutlmv2", "token-classification", "generated_from_trainer", "dataset:xfun", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-02-28T18:20:29Z
--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - xfun model-index: - name: layoutxlm-finetuned-xfund-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # layoutxlm-finetuned-xfund-fr This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on the xfun 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: 1e-05 - 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 - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.10.0+cu111 - Datasets 2.10.0 - Tokenizers 0.13.2
Iamvincent/LunarLander-v2
Iamvincent
2023-02-28T18:47:35Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T18:47:28Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -134.65 +/- 44.59 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Iamvincent/LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
phonenix/a2c-AntBulletEnv-v0
phonenix
2023-02-28T18:43:39Z
0
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T18:42:22Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1310.31 +/- 360.56 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
KoRiF/rl_course_vizdoom_health_gathering_supreme
KoRiF
2023-02-28T18:37:07Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T11:51:59Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.06 +/- 5.91 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r KoRiF/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
matolszew/q-Taxi-v3-default
matolszew
2023-02-28T18:37:03Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T18:36:52Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-default results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: -92.27 +/- 26.64 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="matolszew/q-Taxi-v3-default", 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"]) ```
imar0/Reinforce-CartPole-v1
imar0
2023-02-28T18:32:45Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T18:32:36Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 464.20 +/- 107.40 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
staycoolish/ppo-Pyramids
staycoolish
2023-02-28T18:30:26Z
6
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-02-28T18:30:20Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: staycoolish/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play πŸ‘€
Dabe/Taxi
Dabe
2023-02-28T18:27:35Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T18:27:25Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.75 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Dabe/Taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
robotman0/unit1-ppo-LunarLander-v2
robotman0
2023-02-28T18:23:15Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T17:11:25Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 277.06 +/- 16.93 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Dabe/q-FrozenLake-v1-4x4-noSlippery
Dabe
2023-02-28T18:18:54Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T17:34:46Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Dabe/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"]) ```
Iamvincent/ppo-LunarLander-v2
Iamvincent
2023-02-28T18:17:46Z
3
0
stable-baselines3
[ "stable-baselines3", "tensorboard", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-07T02:30:47Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.81 +/- 13.14 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Nnarruqt/ppo-PyramidsTraining
Nnarruqt
2023-02-28T18:17:30Z
12
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-02-28T18:16:08Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: Nnarruqt/ppo-PyramidsTraining 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play πŸ‘€
bnowak1831/rl_course_vizdoom_health_gathering_supreme
bnowak1831
2023-02-28T18:08:25Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T18:08:19Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.47 +/- 5.01 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r bnowak1831/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
adam1brownell/u5_snowball
adam1brownell
2023-02-28T17:59:59Z
7
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-02-28T17:59:53Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: adam1brownell/u5_snowball 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play πŸ‘€
mitra-mir/setfit_model_Ireland_binary_label1_epochs2
mitra-mir
2023-02-28T17:44:30Z
2
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-02-26T23:38:45Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 163 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 2, "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": 326, "warmup_steps": 33, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Lakoc/rl_course_vizdoom_health_gathering_supreme
Lakoc
2023-02-28T17:40:29Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T17:40:24Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.56 +/- 5.18 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r Lakoc/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
xiazeng/rl_course_vizdoom_health_gathering_supreme
xiazeng
2023-02-28T17:38:23Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T17:38:13Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 8.62 +/- 2.33 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r xiazeng/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/ddpg-Ant-v3-534515347
qgallouedec
2023-02-28T17:17:39Z
0
0
stable-baselines3
[ "stable-baselines3", "Ant-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T17:17:16Z
--- library_name: stable-baselines3 tags: - Ant-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DDPG results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Ant-v3 type: Ant-v3 metrics: - type: mean_reward value: 472.14 +/- 178.61 name: mean_reward verified: false --- # **DDPG** Agent playing **Ant-v3** This is a trained model of a **DDPG** agent playing **Ant-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ddpg --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ddpg --env Ant-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ddpg --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ddpg --env Ant-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ddpg --env Ant-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ddpg --env Ant-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('noise_std', 0.1), ('noise_type', 'normal'), ('policy', 'MlpPolicy'), ('normalize', False)]) ```
qgallouedec/ddpg-Ant-v3-1157720158
qgallouedec
2023-02-28T17:15:58Z
0
0
stable-baselines3
[ "stable-baselines3", "Ant-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T17:15:37Z
--- library_name: stable-baselines3 tags: - Ant-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DDPG results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Ant-v3 type: Ant-v3 metrics: - type: mean_reward value: 248.80 +/- 287.01 name: mean_reward verified: false --- # **DDPG** Agent playing **Ant-v3** This is a trained model of a **DDPG** agent playing **Ant-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ddpg --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ddpg --env Ant-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ddpg --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ddpg --env Ant-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ddpg --env Ant-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ddpg --env Ant-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('noise_std', 0.1), ('noise_type', 'normal'), ('policy', 'MlpPolicy'), ('normalize', False)]) ```
mohamedlamine/wav2vec2-finetuned-wolofdata
mohamedlamine
2023-02-28T17:15:18Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-02-28T08:41:05Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-finetuned-wolofdata results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-finetuned-wolofdata This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7747 - Wer: 0.6774 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0723 | 0.75 | 100 | 0.7747 | 0.6774 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
qgallouedec/ddpg-Ant-v3-2929305474
qgallouedec
2023-02-28T17:13:16Z
0
0
stable-baselines3
[ "stable-baselines3", "Ant-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T17:12:52Z
--- library_name: stable-baselines3 tags: - Ant-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DDPG results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Ant-v3 type: Ant-v3 metrics: - type: mean_reward value: 642.13 +/- 136.03 name: mean_reward verified: false --- # **DDPG** Agent playing **Ant-v3** This is a trained model of a **DDPG** agent playing **Ant-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ddpg --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ddpg --env Ant-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ddpg --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ddpg --env Ant-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ddpg --env Ant-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ddpg --env Ant-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('noise_std', 0.1), ('noise_type', 'normal'), ('policy', 'MlpPolicy'), ('normalize', False)]) ```
xiazeng/ppo-CartPole-v1
xiazeng
2023-02-28T17:06:33Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T17:06:28Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -126.85 +/- 90.27 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'xiazeng/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128} ```
qgallouedec/tqc-PandaPickAndPlace-v1-3157870761
qgallouedec
2023-02-28T17:05:58Z
3
0
stable-baselines3
[ "stable-baselines3", "PandaPickAndPlace-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T17:04:52Z
--- library_name: stable-baselines3 tags: - PandaPickAndPlace-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaPickAndPlace-v1 type: PandaPickAndPlace-v1 metrics: - type: mean_reward value: -7.30 +/- 2.00 name: mean_reward verified: false --- # **TQC** Agent playing **PandaPickAndPlace-v1** This is a trained model of a **TQC** agent playing **PandaPickAndPlace-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env PandaPickAndPlace-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env PandaPickAndPlace-v1 -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 tqc --env PandaPickAndPlace-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env PandaPickAndPlace-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env PandaPickAndPlace-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env PandaPickAndPlace-v1 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 2048), ('buffer_size', 1000000), ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'), ('gamma', 0.95), ('learning_rate', 0.001), ('n_timesteps', 1000000.0), ('policy', 'MultiInputPolicy'), ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'), ('replay_buffer_class', 'HerReplayBuffer'), ('replay_buffer_kwargs', "dict( goal_selection_strategy='future', n_sampled_goal=4, )"), ('tau', 0.05), ('normalize', False)]) ``` # Environment Arguments ```python {'render': True} ```
qgallouedec/tqc-Humanoid-v3-166422443
qgallouedec
2023-02-28T17:02:23Z
0
0
stable-baselines3
[ "stable-baselines3", "Humanoid-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T17:01:45Z
--- library_name: stable-baselines3 tags: - Humanoid-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Humanoid-v3 type: Humanoid-v3 metrics: - type: mean_reward value: 7726.61 +/- 1828.00 name: mean_reward verified: false --- # **TQC** Agent playing **Humanoid-v3** This is a trained model of a **TQC** agent playing **Humanoid-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Humanoid-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Humanoid-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Humanoid-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Humanoid-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Humanoid-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Humanoid-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 2000000.0), ('policy', 'MlpPolicy'), ('normalize', False)]) ```
CloXD/RL-CartPole-v1
CloXD
2023-02-28T17:01:41Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:47:04Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: RL-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
qgallouedec/tqc-FetchPush-v1-3251758816
qgallouedec
2023-02-28T17:01:30Z
0
0
stable-baselines3
[ "stable-baselines3", "FetchPush-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T17:01:09Z
--- library_name: stable-baselines3 tags: - FetchPush-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FetchPush-v1 type: FetchPush-v1 metrics: - type: mean_reward value: -13.70 +/- 11.67 name: mean_reward verified: false --- # **TQC** Agent playing **FetchPush-v1** This is a trained model of a **TQC** agent playing **FetchPush-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env FetchPush-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchPush-v1 -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 tqc --env FetchPush-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchPush-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env FetchPush-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env FetchPush-v1 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 2048), ('buffer_size', 1000000), ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'), ('gamma', 0.95), ('learning_rate', 0.001), ('n_timesteps', 1000000.0), ('policy', 'MultiInputPolicy'), ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'), ('replay_buffer_class', 'HerReplayBuffer'), ('replay_buffer_kwargs', "dict( goal_selection_strategy='future', n_sampled_goal=4, )"), ('tau', 0.05), ('normalize', False)]) ```
parsasam/rl_course_vizdoom_health_gathering_supreme
parsasam
2023-02-28T16:55:35Z
0
0
sample-factory
[ "sample-factory", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:54:39Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.63 +/- 4.53 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r parsasam/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
qgallouedec/tqc-FetchPickAndPlace-v1-3795610126
qgallouedec
2023-02-28T16:53:27Z
2
0
stable-baselines3
[ "stable-baselines3", "FetchPickAndPlace-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:53:03Z
--- library_name: stable-baselines3 tags: - FetchPickAndPlace-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FetchPickAndPlace-v1 type: FetchPickAndPlace-v1 metrics: - type: mean_reward value: -10.20 +/- 4.62 name: mean_reward verified: false --- # **TQC** Agent playing **FetchPickAndPlace-v1** This is a trained model of a **TQC** agent playing **FetchPickAndPlace-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env FetchPickAndPlace-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchPickAndPlace-v1 -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 tqc --env FetchPickAndPlace-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchPickAndPlace-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env FetchPickAndPlace-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env FetchPickAndPlace-v1 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 2048), ('buffer_size', 1000000), ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'), ('gamma', 0.95), ('learning_rate', 0.001), ('n_timesteps', 1000000.0), ('policy', 'MultiInputPolicy'), ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'), ('replay_buffer_class', 'HerReplayBuffer'), ('replay_buffer_kwargs', "dict( online_sampling=True, goal_selection_strategy='future', " 'n_sampled_goal=4, )'), ('tau', 0.05), ('normalize', False)]) ```
qgallouedec/tqc-FetchPush-v1-2077979061
qgallouedec
2023-02-28T16:51:21Z
0
0
stable-baselines3
[ "stable-baselines3", "FetchPush-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:50:57Z
--- library_name: stable-baselines3 tags: - FetchPush-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FetchPush-v1 type: FetchPush-v1 metrics: - type: mean_reward value: -13.70 +/- 12.17 name: mean_reward verified: false --- # **TQC** Agent playing **FetchPush-v1** This is a trained model of a **TQC** agent playing **FetchPush-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env FetchPush-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchPush-v1 -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 tqc --env FetchPush-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchPush-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env FetchPush-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env FetchPush-v1 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 2048), ('buffer_size', 1000000), ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'), ('gamma', 0.95), ('learning_rate', 0.001), ('n_timesteps', 1000000.0), ('policy', 'MultiInputPolicy'), ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'), ('replay_buffer_class', 'HerReplayBuffer'), ('replay_buffer_kwargs', "dict( online_sampling=True, goal_selection_strategy='future', " 'n_sampled_goal=4, )'), ('tau', 0.05), ('normalize', False)]) ```
qgallouedec/tqc-FetchPush-v1-3613026928
qgallouedec
2023-02-28T16:49:15Z
0
0
stable-baselines3
[ "stable-baselines3", "FetchPush-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:48:53Z
--- library_name: stable-baselines3 tags: - FetchPush-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FetchPush-v1 type: FetchPush-v1 metrics: - type: mean_reward value: -11.90 +/- 7.49 name: mean_reward verified: false --- # **TQC** Agent playing **FetchPush-v1** This is a trained model of a **TQC** agent playing **FetchPush-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env FetchPush-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchPush-v1 -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 tqc --env FetchPush-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchPush-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env FetchPush-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env FetchPush-v1 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 2048), ('buffer_size', 1000000), ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'), ('gamma', 0.95), ('learning_rate', 0.001), ('n_timesteps', 1000000.0), ('policy', 'MultiInputPolicy'), ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'), ('replay_buffer_class', 'HerReplayBuffer'), ('replay_buffer_kwargs', "dict( online_sampling=True, goal_selection_strategy='future', " 'n_sampled_goal=4, )'), ('tau', 0.05), ('normalize', False)]) ```
annelegendre/q-Taxi-v3
annelegendre
2023-02-28T16:47:36Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T15:52:14Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="annelegendre/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
gokuls/bert_12_layer_model_v2
gokuls
2023-02-28T16:47:31Z
47
0
transformers
[ "transformers", "pytorch", "tensorboard", "hybridbert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-02-27T13:26:19Z
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_12_layer_model_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_12_layer_model_v2 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1091 - Accuracy: 0.5983 ## 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: 64 - eval_batch_size: 64 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 5.4137 | 1.0 | 45772 | 3.1519 | 0.4605 | | 2.7951 | 2.0 | 91544 | 2.4478 | 0.5519 | | 2.4298 | 3.0 | 137316 | 2.2522 | 0.5784 | | 2.2864 | 4.0 | 183088 | 2.1548 | 0.5920 | | 2.2142 | 5.0 | 228860 | 2.1091 | 0.5983 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.14.0a0+410ce96 - Datasets 2.10.0 - Tokenizers 0.13.2
Suprabound/dqn-SpaceInvaders-Suprav1
Suprabound
2023-02-28T16:41:40Z
1
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:40:59Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 456.50 +/- 177.92 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Suprabound -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 Suprabound -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 Suprabound ``` ## 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)]) ```
mm-ai/vit-mlo-512-breat_composition
mm-ai
2023-02-28T16:38:50Z
21
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:preprocessed1024_config", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-02-28T13:40:08Z
--- tags: - generated_from_trainer datasets: - preprocessed1024_config metrics: - accuracy - f1 model-index: - name: vit-mlo-512-breat_composition results: - task: name: Image Classification type: image-classification dataset: name: preprocessed1024_config type: preprocessed1024_config args: default metrics: - name: Accuracy type: accuracy value: accuracy: 0.5791457286432161 - name: F1 type: f1 value: f1: 0.5749067914290308 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-mlo-512-breat_composition This model is a fine-tuned version of [](https://huggingface.co/) on the preprocessed1024_config dataset. It achieves the following results on the evaluation set: - Loss: 1.3123 - Accuracy: {'accuracy': 0.5791457286432161} - F1: {'f1': 0.5749067914290308} ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------:| | 1.2679 | 1.0 | 796 | 1.0281 | {'accuracy': 0.5062814070351759} | {'f1': 0.38950358034816535} | | 0.9805 | 2.0 | 1592 | 0.9240 | {'accuracy': 0.5672110552763819} | {'f1': 0.5273112700912543} | | 0.9167 | 3.0 | 2388 | 0.9608 | {'accuracy': 0.5477386934673367} | {'f1': 0.45736748568671376} | | 0.8292 | 4.0 | 3184 | 0.8973 | {'accuracy': 0.5891959798994975} | {'f1': 0.5783349603036094} | | 0.7695 | 5.0 | 3980 | 1.0477 | {'accuracy': 0.5571608040201005} | {'f1': 0.5379432393338944} | | 0.6912 | 6.0 | 4776 | 0.9479 | {'accuracy': 0.585427135678392} | {'f1': 0.5766494177636581} | | 0.61 | 7.0 | 5572 | 1.1280 | {'accuracy': 0.5703517587939698} | {'f1': 0.5560158679652624} | | 0.5591 | 8.0 | 6368 | 1.1866 | {'accuracy': 0.5741206030150754} | {'f1': 0.5541999644498281} | | 0.5021 | 9.0 | 7164 | 1.1537 | {'accuracy': 0.582286432160804} | {'f1': 0.566315815243799} | | 0.4262 | 10.0 | 7960 | 1.3123 | {'accuracy': 0.5791457286432161} | {'f1': 0.5749067914290308} | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0 - Datasets 2.1.0 - Tokenizers 0.12.1
bnowak1831/a2c-AntBulletEnv-v0
bnowak1831
2023-02-28T16:35:34Z
0
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:34:23Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1608.24 +/- 135.59 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
psaegert/pmtrendviz-tfidf-3m-250-2g
psaegert
2023-02-28T16:32:48Z
5
0
transformers
[ "transformers", "joblib", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2023-02-28T07:38:34Z
--- license: apache-2.0 --- Medium TF-IDF-based model for [pmtrendviz](https://github.com/psaegert/pmtrendviz) ### Training - Training Samples: 3,000,000 - `n_components`: 250 - `n_clusters`: 250 - `n_gram_range`: (1, 2)
Nnarruqt/ppo-SnowBallTarget1
Nnarruqt
2023-02-28T16:27:24Z
20
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-02-28T16:27:18Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: Nnarruqt/ppo-SnowBallTarget1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play πŸ‘€
qgallouedec/ars-Ant-v3-2422697030
qgallouedec
2023-02-28T16:23:03Z
8
0
stable-baselines3
[ "stable-baselines3", "Ant-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:22:36Z
--- library_name: stable-baselines3 tags: - Ant-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ARS results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Ant-v3 type: Ant-v3 metrics: - type: mean_reward value: 4762.99 +/- 159.24 name: mean_reward verified: false --- # **ARS** Agent playing **Ant-v3** This is a trained model of a **ARS** agent playing **Ant-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ars --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ars --env Ant-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ars --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ars --env Ant-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ars --env Ant-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ars --env Ant-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('alive_bonus_offset', -1), ('delta_std', 0.025), ('learning_rate', 0.015), ('n_delta', 60), ('n_envs', 1), ('n_timesteps', 75000000.0), ('n_top', 20), ('normalize', 'dict(norm_obs=True, norm_reward=False)'), ('policy', 'LinearPolicy'), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/ppo_lstm-HumanoidBulletEnv-v0-3214896061
qgallouedec
2023-02-28T16:21:10Z
1
0
stable-baselines3
[ "stable-baselines3", "HumanoidBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:20:26Z
--- library_name: stable-baselines3 tags: - HumanoidBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: RecurrentPPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: HumanoidBulletEnv-v0 type: HumanoidBulletEnv-v0 metrics: - type: mean_reward value: 192.17 +/- 64.50 name: mean_reward verified: false --- # **RecurrentPPO** Agent playing **HumanoidBulletEnv-v0** This is a trained model of a **RecurrentPPO** agent playing **HumanoidBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo_lstm --env HumanoidBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo_lstm --env HumanoidBulletEnv-v0 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo_lstm --env HumanoidBulletEnv-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo_lstm --env HumanoidBulletEnv-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo_lstm --env HumanoidBulletEnv-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo_lstm --env HumanoidBulletEnv-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('clip_range', 0.2), ('ent_coef', 0.0), ('gae_lambda', 0.95), ('gamma', 0.99), ('learning_rate', 0.00025), ('n_envs', 8), ('n_epochs', 10), ('n_steps', 2048), ('n_timesteps', 10000000.0), ('normalize', True), ('policy', 'MlpLstmPolicy'), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/ppo_lstm-BipedalWalkerHardcore-v3-3452026630
qgallouedec
2023-02-28T16:20:04Z
0
0
stable-baselines3
[ "stable-baselines3", "BipedalWalkerHardcore-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:18:52Z
--- library_name: stable-baselines3 tags: - BipedalWalkerHardcore-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: RecurrentPPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BipedalWalkerHardcore-v3 type: BipedalWalkerHardcore-v3 metrics: - type: mean_reward value: -14.95 +/- 35.98 name: mean_reward verified: false --- # **RecurrentPPO** Agent playing **BipedalWalkerHardcore-v3** This is a trained model of a **RecurrentPPO** agent playing **BipedalWalkerHardcore-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo_lstm --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo_lstm --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 'lin_0.2'), ('ent_coef', 0.001), ('gae_lambda', 0.95), ('gamma', 0.999), ('learning_rate', 'lin_3e-4'), ('n_envs', 32), ('n_epochs', 10), ('n_steps', 256), ('n_timesteps', 100000000.0), ('normalize', True), ('policy', 'MlpLstmPolicy'), ('policy_kwargs', 'dict( ortho_init=False, activation_fn=nn.ReLU, ' 'lstm_hidden_size=64, enable_critic_lstm=True, ' 'net_arch=dict(pi=[64], vf=[64]) )'), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
Anandhulk/pegasus-scientific_lay
Anandhulk
2023-02-28T16:19:46Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:scientific_lay_summarisation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-24T10:58:34Z
--- tags: - generated_from_trainer datasets: - scientific_lay_summarisation model-index: - name: pegasus-scientific_lay results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-scientific_lay This model is a fine-tuned version of [Anandhulk/pegasus-scientific_lay](https://huggingface.co/Anandhulk/pegasus-scientific_lay) on the scientific_lay_summarisation dataset. It achieves the following results on the evaluation set: - Loss: 2.3482 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5004 | 1.0 | 774 | 2.3482 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
qgallouedec/ppo_lstm-BipedalWalkerHardcore-v3-678870063
qgallouedec
2023-02-28T16:17:47Z
0
0
stable-baselines3
[ "stable-baselines3", "BipedalWalkerHardcore-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:16:32Z
--- library_name: stable-baselines3 tags: - BipedalWalkerHardcore-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: RecurrentPPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BipedalWalkerHardcore-v3 type: BipedalWalkerHardcore-v3 metrics: - type: mean_reward value: -0.10 +/- 0.02 name: mean_reward verified: false --- # **RecurrentPPO** Agent playing **BipedalWalkerHardcore-v3** This is a trained model of a **RecurrentPPO** agent playing **BipedalWalkerHardcore-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo_lstm --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo_lstm --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 'lin_0.2'), ('ent_coef', 0.001), ('gae_lambda', 0.95), ('gamma', 0.999), ('learning_rate', 'lin_3e-4'), ('n_envs', 32), ('n_epochs', 10), ('n_steps', 256), ('n_timesteps', 100000000.0), ('normalize', True), ('policy', 'MlpLstmPolicy'), ('policy_kwargs', 'dict( ortho_init=False, activation_fn=nn.ReLU, ' 'lstm_hidden_size=64, enable_critic_lstm=True, ' 'net_arch=dict(pi=[64], vf=[64]) )'), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/ppo_lstm-BipedalWalkerHardcore-v3-4163478442
qgallouedec
2023-02-28T16:16:17Z
0
0
stable-baselines3
[ "stable-baselines3", "BipedalWalkerHardcore-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:15:04Z
--- library_name: stable-baselines3 tags: - BipedalWalkerHardcore-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: RecurrentPPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BipedalWalkerHardcore-v3 type: BipedalWalkerHardcore-v3 metrics: - type: mean_reward value: -2.85 +/- 0.24 name: mean_reward verified: false --- # **RecurrentPPO** Agent playing **BipedalWalkerHardcore-v3** This is a trained model of a **RecurrentPPO** agent playing **BipedalWalkerHardcore-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo_lstm --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo_lstm --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo_lstm --env BipedalWalkerHardcore-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 'lin_0.2'), ('ent_coef', 0.001), ('gae_lambda', 0.95), ('gamma', 0.999), ('learning_rate', 'lin_3e-4'), ('n_envs', 32), ('n_epochs', 10), ('n_steps', 256), ('n_timesteps', 100000000.0), ('normalize', True), ('policy', 'MlpLstmPolicy'), ('policy_kwargs', 'dict( ortho_init=False, activation_fn=nn.ReLU, ' 'lstm_hidden_size=64, enable_critic_lstm=True, ' 'net_arch=dict(pi=[64], vf=[64]) )'), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/a2c-BipedalWalkerHardcore-v3-123042218
qgallouedec
2023-02-28T16:11:10Z
8
0
stable-baselines3
[ "stable-baselines3", "BipedalWalkerHardcore-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:10:36Z
--- library_name: stable-baselines3 tags: - BipedalWalkerHardcore-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BipedalWalkerHardcore-v3 type: BipedalWalkerHardcore-v3 metrics: - type: mean_reward value: -3.60 +/- 44.20 name: mean_reward verified: false --- # **A2C** Agent playing **BipedalWalkerHardcore-v3** This is a trained model of a **A2C** agent playing **BipedalWalkerHardcore-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo a2c --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo a2c --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.001), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_0.0008'), ('max_grad_norm', 0.5), ('n_envs', 32), ('n_steps', 8), ('n_timesteps', 200000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/a2c-BipedalWalkerHardcore-v3-2089306450
qgallouedec
2023-02-28T16:10:27Z
7
0
stable-baselines3
[ "stable-baselines3", "BipedalWalkerHardcore-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:09:48Z
--- library_name: stable-baselines3 tags: - BipedalWalkerHardcore-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BipedalWalkerHardcore-v3 type: BipedalWalkerHardcore-v3 metrics: - type: mean_reward value: -20.90 +/- 57.48 name: mean_reward verified: false --- # **A2C** Agent playing **BipedalWalkerHardcore-v3** This is a trained model of a **A2C** agent playing **BipedalWalkerHardcore-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo a2c --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo a2c --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.001), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_0.0008'), ('max_grad_norm', 0.5), ('n_envs', 32), ('n_steps', 8), ('n_timesteps', 200000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/a2c-Humanoid-v3-4227453683
qgallouedec
2023-02-28T16:09:12Z
6
0
stable-baselines3
[ "stable-baselines3", "Humanoid-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:08:56Z
--- library_name: stable-baselines3 tags: - Humanoid-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Humanoid-v3 type: Humanoid-v3 metrics: - type: mean_reward value: 378.38 +/- 92.34 name: mean_reward verified: false --- # **A2C** Agent playing **Humanoid-v3** This is a trained model of a **A2C** agent playing **Humanoid-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo a2c --env Humanoid-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env Humanoid-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo a2c --env Humanoid-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env Humanoid-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env Humanoid-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env Humanoid-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('n_timesteps', 2000000.0), ('normalize', True), ('policy', 'MlpPolicy'), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
qgallouedec/a2c-BipedalWalkerHardcore-v3-2508703001
qgallouedec
2023-02-28T16:07:51Z
0
0
stable-baselines3
[ "stable-baselines3", "BipedalWalkerHardcore-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T16:07:16Z
--- library_name: stable-baselines3 tags: - BipedalWalkerHardcore-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BipedalWalkerHardcore-v3 type: BipedalWalkerHardcore-v3 metrics: - type: mean_reward value: 122.28 +/- 111.59 name: mean_reward verified: false --- # **A2C** Agent playing **BipedalWalkerHardcore-v3** This is a trained model of a **A2C** agent playing **BipedalWalkerHardcore-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo a2c --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo a2c --env BipedalWalkerHardcore-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo a2c --env BipedalWalkerHardcore-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('ent_coef', 0.001), ('gae_lambda', 0.9), ('gamma', 0.99), ('learning_rate', 'lin_0.0008'), ('max_grad_norm', 0.5), ('n_envs', 32), ('n_steps', 8), ('n_timesteps', 200000000.0), ('normalize', True), ('normalize_advantage', False), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'), ('use_rms_prop', True), ('use_sde', True), ('vf_coef', 0.4), ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) ```
unagui/ppo-Huggy
unagui
2023-02-28T15:33:10Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-02-28T15:33:02Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: unagui/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play πŸ‘€
qgallouedec/tqc-Hopper-v3-1346000078
qgallouedec
2023-02-28T15:09:51Z
2
0
stable-baselines3
[ "stable-baselines3", "Hopper-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T15:09:30Z
--- library_name: stable-baselines3 tags: - Hopper-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Hopper-v3 type: Hopper-v3 metrics: - type: mean_reward value: 3726.60 +/- 10.89 name: mean_reward verified: false --- # **TQC** Agent playing **Hopper-v3** This is a trained model of a **TQC** agent playing **Hopper-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Hopper-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Hopper-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('top_quantiles_to_drop_per_net', 5), ('normalize', False)]) ```
qgallouedec/tqc-FetchSlide-v1-1365846529
qgallouedec
2023-02-28T15:09:20Z
0
0
stable-baselines3
[ "stable-baselines3", "FetchSlide-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T15:09:03Z
--- library_name: stable-baselines3 tags: - FetchSlide-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FetchSlide-v1 type: FetchSlide-v1 metrics: - type: mean_reward value: -22.70 +/- 6.15 name: mean_reward verified: false --- # **TQC** Agent playing **FetchSlide-v1** This is a trained model of a **TQC** agent playing **FetchSlide-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env FetchSlide-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchSlide-v1 -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 tqc --env FetchSlide-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchSlide-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env FetchSlide-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env FetchSlide-v1 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 2048), ('buffer_size', 1000000), ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'), ('gamma', 0.95), ('learning_rate', 0.001), ('n_timesteps', 3000000.0), ('policy', 'MultiInputPolicy'), ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'), ('replay_buffer_class', 'HerReplayBuffer'), ('replay_buffer_kwargs', "dict( online_sampling=True, goal_selection_strategy='future', " 'n_sampled_goal=4, )'), ('tau', 0.05), ('normalize', False)]) ```
Lilya/distilbert-base-uncased-ner-invoiceSenderRecipient_clean_inv_28_02
Lilya
2023-02-28T15:08:32Z
9
0
transformers
[ "transformers", "pytorch", "distilbert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-02-28T07:16:16Z
--- tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-ner-invoiceSenderRecipient_clean_inv_28_02 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-ner-invoiceSenderRecipient_clean_inv_28_02 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0266 - eval_precision: 0.9595 - eval_recall: 0.9642 - eval_f1: 0.9618 - eval_accuracy: 0.9957 - eval_runtime: 60.7498 - eval_samples_per_second: 271.474 - eval_steps_per_second: 16.971 - epoch: 9.98 - step: 58000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.15.0 - Pytorch 1.13.1 - Datasets 2.3.2 - Tokenizers 0.10.3
qgallouedec/tqc-Humanoid-v3-1772834236
qgallouedec
2023-02-28T15:08:10Z
0
0
stable-baselines3
[ "stable-baselines3", "Humanoid-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T15:07:46Z
--- library_name: stable-baselines3 tags: - Humanoid-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Humanoid-v3 type: Humanoid-v3 metrics: - type: mean_reward value: 7623.27 +/- 70.86 name: mean_reward verified: false --- # **TQC** Agent playing **Humanoid-v3** This is a trained model of a **TQC** agent playing **Humanoid-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Humanoid-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Humanoid-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Humanoid-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Humanoid-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Humanoid-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Humanoid-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 2000000.0), ('policy', 'MlpPolicy'), ('normalize', False)]) ```
qgallouedec/tqc-Ant-v3-372483154
qgallouedec
2023-02-28T15:04:09Z
0
0
stable-baselines3
[ "stable-baselines3", "Ant-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T15:03:47Z
--- library_name: stable-baselines3 tags: - Ant-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Ant-v3 type: Ant-v3 metrics: - type: mean_reward value: 3637.21 +/- 1959.88 name: mean_reward verified: false --- # **TQC** Agent playing **Ant-v3** This is a trained model of a **TQC** agent playing **Ant-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Ant-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Ant-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Ant-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Ant-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('normalize', False)]) ```
qgallouedec/tqc-Humanoid-v3-1148850933
qgallouedec
2023-02-28T15:01:17Z
1
0
stable-baselines3
[ "stable-baselines3", "Humanoid-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T15:01:00Z
--- library_name: stable-baselines3 tags: - Humanoid-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Humanoid-v3 type: Humanoid-v3 metrics: - type: mean_reward value: 1274.08 +/- 302.39 name: mean_reward verified: false --- # **TQC** Agent playing **Humanoid-v3** This is a trained model of a **TQC** agent playing **Humanoid-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Humanoid-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Humanoid-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Humanoid-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Humanoid-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Humanoid-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Humanoid-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 2000000.0), ('policy', 'MlpPolicy'), ('normalize', False)]) ```
qgallouedec/tqc-Ant-v3-2084207633
qgallouedec
2023-02-28T14:59:53Z
2
0
stable-baselines3
[ "stable-baselines3", "Ant-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:59:34Z
--- library_name: stable-baselines3 tags: - Ant-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Ant-v3 type: Ant-v3 metrics: - type: mean_reward value: 1988.37 +/- 1577.84 name: mean_reward verified: false --- # **TQC** Agent playing **Ant-v3** This is a trained model of a **TQC** agent playing **Ant-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Ant-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Ant-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Ant-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Ant-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('normalize', False)]) ```
qgallouedec/tqc-Humanoid-v3-2077901749
qgallouedec
2023-02-28T14:58:59Z
1
0
stable-baselines3
[ "stable-baselines3", "Humanoid-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:58:35Z
--- library_name: stable-baselines3 tags: - Humanoid-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Humanoid-v3 type: Humanoid-v3 metrics: - type: mean_reward value: 7084.21 +/- 1923.61 name: mean_reward verified: false --- # **TQC** Agent playing **Humanoid-v3** This is a trained model of a **TQC** agent playing **Humanoid-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Humanoid-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Humanoid-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Humanoid-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Humanoid-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Humanoid-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Humanoid-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 2000000.0), ('policy', 'MlpPolicy'), ('normalize', False)]) ```
qgallouedec/tqc-Hopper-v3-2496077244
qgallouedec
2023-02-28T14:54:58Z
2
0
stable-baselines3
[ "stable-baselines3", "Hopper-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:54:36Z
--- library_name: stable-baselines3 tags: - Hopper-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Hopper-v3 type: Hopper-v3 metrics: - type: mean_reward value: 3644.20 +/- 5.10 name: mean_reward verified: false --- # **TQC** Agent playing **Hopper-v3** This is a trained model of a **TQC** agent playing **Hopper-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Hopper-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Hopper-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('top_quantiles_to_drop_per_net', 5), ('normalize', False)]) ```
qgallouedec/tqc-FetchPush-v1-702808983
qgallouedec
2023-02-28T14:52:42Z
0
0
stable-baselines3
[ "stable-baselines3", "FetchPush-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:52:25Z
--- library_name: stable-baselines3 tags: - FetchPush-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FetchPush-v1 type: FetchPush-v1 metrics: - type: mean_reward value: -11.00 +/- 5.16 name: mean_reward verified: false --- # **TQC** Agent playing **FetchPush-v1** This is a trained model of a **TQC** agent playing **FetchPush-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env FetchPush-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchPush-v1 -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 tqc --env FetchPush-v1 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env FetchPush-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env FetchPush-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env FetchPush-v1 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 2048), ('buffer_size', 1000000), ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'), ('gamma', 0.95), ('learning_rate', 0.001), ('n_timesteps', 1000000.0), ('policy', 'MultiInputPolicy'), ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'), ('replay_buffer_class', 'HerReplayBuffer'), ('replay_buffer_kwargs', "dict( online_sampling=True, goal_selection_strategy='future', " 'n_sampled_goal=4, )'), ('tau', 0.05), ('normalize', False)]) ```
qgallouedec/tqc-Ant-v3-1902130014
qgallouedec
2023-02-28T14:52:17Z
2
0
stable-baselines3
[ "stable-baselines3", "Ant-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:51:54Z
--- library_name: stable-baselines3 tags: - Ant-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Ant-v3 type: Ant-v3 metrics: - type: mean_reward value: 5330.35 +/- 1091.37 name: mean_reward verified: false --- # **TQC** Agent playing **Ant-v3** This is a trained model of a **TQC** agent playing **Ant-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Ant-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Ant-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Ant-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Ant-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Ant-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('normalize', False)]) ```
qgallouedec/tqc-parking-v0-768894194
qgallouedec
2023-02-28T14:51:44Z
0
0
stable-baselines3
[ "stable-baselines3", "parking-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:51:01Z
--- library_name: stable-baselines3 tags: - parking-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: parking-v0 type: parking-v0 metrics: - type: mean_reward value: -10.30 +/- 5.90 name: mean_reward verified: false --- # **TQC** Agent playing **parking-v0** This is a trained model of a **TQC** agent playing **parking-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env parking-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env parking-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env parking-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env parking-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 512), ('buffer_size', 1000000), ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'), ('gamma', 0.98), ('learning_rate', 0.0015), ('n_timesteps', 50000.0), ('policy', 'MultiInputPolicy'), ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'), ('replay_buffer_class', 'HerReplayBuffer'), ('replay_buffer_kwargs', "dict( online_sampling=True, goal_selection_strategy='episode', " 'n_sampled_goal=4, max_episode_length=100 )'), ('tau', 0.005), ('normalize', False)]) ```
qgallouedec/tqc-Hopper-v3-2631554861
qgallouedec
2023-02-28T14:50:53Z
1
0
stable-baselines3
[ "stable-baselines3", "Hopper-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:50:34Z
--- library_name: stable-baselines3 tags: - Hopper-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Hopper-v3 type: Hopper-v3 metrics: - type: mean_reward value: 2000.75 +/- 885.25 name: mean_reward verified: false --- # **TQC** Agent playing **Hopper-v3** This is a trained model of a **TQC** agent playing **Hopper-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Hopper-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Hopper-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('top_quantiles_to_drop_per_net', 5), ('normalize', False)]) ```
qgallouedec/tqc-Hopper-v3-1489988575
qgallouedec
2023-02-28T14:50:26Z
1
0
stable-baselines3
[ "stable-baselines3", "Hopper-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:50:05Z
--- library_name: stable-baselines3 tags: - Hopper-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Hopper-v3 type: Hopper-v3 metrics: - type: mean_reward value: 3318.43 +/- 590.11 name: mean_reward verified: false --- # **TQC** Agent playing **Hopper-v3** This is a trained model of a **TQC** agent playing **Hopper-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Hopper-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Hopper-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('top_quantiles_to_drop_per_net', 5), ('normalize', False)]) ```
qgallouedec/tqc-Hopper-v3-4011682269
qgallouedec
2023-02-28T14:48:02Z
0
0
stable-baselines3
[ "stable-baselines3", "Hopper-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:47:41Z
--- library_name: stable-baselines3 tags: - Hopper-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Hopper-v3 type: Hopper-v3 metrics: - type: mean_reward value: 3417.97 +/- 1.50 name: mean_reward verified: false --- # **TQC** Agent playing **Hopper-v3** This is a trained model of a **TQC** agent playing **Hopper-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env Hopper-v3 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env Hopper-v3 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env Hopper-v3 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env Hopper-v3 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('learning_starts', 10000), ('n_timesteps', 1000000.0), ('policy', 'MlpPolicy'), ('top_quantiles_to_drop_per_net', 5), ('normalize', False)]) ```
qgallouedec/tqc-parking-v0-4204328955
qgallouedec
2023-02-28T14:47:31Z
0
0
stable-baselines3
[ "stable-baselines3", "parking-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:46:48Z
--- library_name: stable-baselines3 tags: - parking-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: parking-v0 type: parking-v0 metrics: - type: mean_reward value: -9.33 +/- 4.81 name: mean_reward verified: false --- # **TQC** Agent playing **parking-v0** This is a trained model of a **TQC** agent playing **parking-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env parking-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env parking-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env parking-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env parking-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 512), ('buffer_size', 1000000), ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'), ('gamma', 0.98), ('learning_rate', 0.0015), ('n_timesteps', 50000.0), ('policy', 'MultiInputPolicy'), ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'), ('replay_buffer_class', 'HerReplayBuffer'), ('replay_buffer_kwargs', "dict( online_sampling=True, goal_selection_strategy='episode', " 'n_sampled_goal=4, max_episode_length=100 )'), ('tau', 0.005), ('normalize', False)]) ```
qgallouedec/tqc-parking-v0-1067225822
qgallouedec
2023-02-28T14:46:38Z
0
0
stable-baselines3
[ "stable-baselines3", "parking-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:45:55Z
--- library_name: stable-baselines3 tags: - parking-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: TQC results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: parking-v0 type: parking-v0 metrics: - type: mean_reward value: -12.54 +/- 12.02 name: mean_reward verified: false --- # **TQC** Agent playing **parking-v0** This is a trained model of a **TQC** agent playing **parking-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo tqc --env parking-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo tqc --env parking-v0 -orga qgallouedec -f logs/ python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo tqc --env parking-v0 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo tqc --env parking-v0 -f logs/ -orga qgallouedec ``` ## Hyperparameters ```python OrderedDict([('batch_size', 512), ('buffer_size', 1000000), ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'), ('gamma', 0.98), ('learning_rate', 0.0015), ('n_timesteps', 50000.0), ('policy', 'MultiInputPolicy'), ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'), ('replay_buffer_class', 'HerReplayBuffer'), ('replay_buffer_kwargs', "dict( online_sampling=True, goal_selection_strategy='episode', " 'n_sampled_goal=4, max_episode_length=100 )'), ('tau', 0.005), ('normalize', False)]) ```
EcoCy/jultest
EcoCy
2023-02-28T14:28:39Z
2
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-02-28T14:28:35Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: jultest01 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - jultest 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 "jultest01" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: jultest01 ![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)
sarthakc44/Reinforce-Pixelcopter-PLE-v1
sarthakc44
2023-02-28T14:27:00Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T14:26:57Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 24.90 +/- 22.93 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
giobin/SnowballTarget1
giobin
2023-02-28T14:24:04Z
1
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-02-28T14:23:59Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: giobin/SnowballTarget1 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play πŸ‘€
aherzberg/ser_model_fixed_label
aherzberg
2023-02-28T14:19:53Z
5
1
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
audio-classification
2023-02-28T11:20:21Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: ser_model_fixed_label results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ser_model_fixed_label This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7010 - Accuracy: 0.8367 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.7645 | 0.96 | 18 | 1.5899 | 0.4333 | | 1.5148 | 1.96 | 36 | 1.4152 | 0.4433 | | 1.3042 | 2.96 | 54 | 1.1857 | 0.5767 | | 1.1184 | 3.96 | 72 | 1.0508 | 0.62 | | 0.9588 | 4.96 | 90 | 0.9329 | 0.7 | | 0.9789 | 5.96 | 108 | 0.8638 | 0.74 | | 0.7835 | 6.96 | 126 | 0.7730 | 0.8133 | | 0.7259 | 7.96 | 144 | 0.7355 | 0.83 | | 0.6783 | 8.96 | 162 | 0.7190 | 0.8333 | | 0.6644 | 9.96 | 180 | 0.7010 | 0.8367 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
ybelkada/gpt-j-6b-detoxified-20shdl
ybelkada
2023-02-28T14:13:40Z
5
0
transformers
[ "transformers", "pytorch", "gptj", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-02-17T15:37:10Z
# Model card for detoxified gpt-j-6b Model run can be found [here](https://wandb.ai/distill-bloom/trl/runs/kw15qua9?workspace=user-younesbelkada) The main difference is that I used `mini_batch_size=1`
schreon/gpt2large-lhm-06
schreon
2023-02-28T14:03:44Z
8
0
transformers
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "dataset:training_corpus", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-02-25T19:04:19Z
--- tags: - generated_from_trainer datasets: - training_corpus model-index: - name: gpt2large-lhm-06 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2large-lhm-06 This model was trained from scratch on the training_corpus dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00018 - train_batch_size: 40 - eval_batch_size: 40 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 200 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.8.0 - Tokenizers 0.13.2
akoshel/Reinforce-Cartpole-v1
akoshel
2023-02-28T14:01:09Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-17T07:43:47Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Isaacgv/q-FrozenLake-v1-4x4-noSlippery
Isaacgv
2023-02-28T13:58:25Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T13:58:22Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Isaacgv/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"]) ```
Qilex/a2c-PandaReachDense-v2
Qilex
2023-02-28T13:55:18Z
2
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-27T21:32:37Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.41 +/- 0.46 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
UnstableCreatures/Test
UnstableCreatures
2023-02-28T13:49:48Z
0
0
null
[ "text-to-image", "en", "region:us" ]
text-to-image
2023-02-28T13:05:59Z
--- language: - en pipeline_tag: text-to-image ---
johnowhitaker/pyramid_noise_test_600steps_08discount
johnowhitaker
2023-02-28T13:41:52Z
4
9
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "multires_noise", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-02-28T13:03:48Z
--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - multires_noise inference: true --- A model trained with Pyramid Noise - see https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2 for details ```python from torch import nn import random def pyramid_noise_like(x, discount=0.8): b, c, w, h = x.shape u = nn.Upsample(size=(w, h), mode='bilinear') noise = torch.randn_like(x) for i in range(6): r = random.random()*2+2 # Rather than always going 2x, w, h = max(1, int(w/(r**i))), max(1, int(h/(r**i))) noise += u(torch.randn(b, c, w, h).to(x)) * discount**i if w==1 or h==1: break return noise / noise.std() # Scale back to unit variance ``` To use the mode for inference, just load it like a normal stable diffusion pipeline: ```python from diffusers import StableDiffusionPipeline model_path = "johnowhitaker/pyramid_noise_test_600steps_08discount" pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16) pipe.to("cuda") image = pipe(prompt="A black image").images[0] image ```
zambezivoice/xls-r-300m-zv-mul
zambezivoice
2023-02-28T13:33:12Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-02-28T05:20:54Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: xls-r-300m-zv-mul results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xls-r-300m-zv-mul 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: 0.4882 - Wer: 0.4859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7438 | 0.26 | 500 | 0.9380 | 0.9135 | | 1.1094 | 0.52 | 1000 | 0.5399 | 0.6874 | | 0.9203 | 0.79 | 1500 | 0.5056 | 0.6708 | | 0.8439 | 1.05 | 2000 | 0.4501 | 0.5775 | | 0.7871 | 1.31 | 2500 | 0.4231 | 0.5592 | | 0.761 | 1.57 | 3000 | 0.4335 | 0.5469 | | 0.7309 | 1.83 | 3500 | 0.4204 | 0.5407 | | 0.706 | 2.1 | 4000 | 0.4009 | 0.5177 | | 0.6816 | 2.36 | 4500 | 0.3866 | 0.5108 | | 0.6639 | 2.62 | 5000 | 0.3786 | 0.4895 | | 0.6532 | 2.88 | 5500 | 0.3791 | 0.4895 | | 0.6347 | 3.14 | 6000 | 0.3681 | 0.4740 | | 0.6062 | 3.4 | 6500 | 0.3513 | 0.4695 | | 0.5976 | 3.67 | 7000 | 0.3654 | 0.4779 | | 0.5885 | 3.93 | 7500 | 0.3441 | 0.4552 | | 0.5791 | 4.19 | 8000 | 0.3821 | 0.4610 | | 0.6671 | 4.45 | 8500 | 0.4708 | 0.4981 | | 0.6961 | 4.71 | 9000 | 0.4882 | 0.4859 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.0 - Tokenizers 0.13.2
Yagorka/ddpm-pokemons-128_300_epochs_1000_steps_final_Cont
Yagorka
2023-02-28T13:29:06Z
1
0
diffusers
[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]
null
2023-02-28T07:25:37Z
--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-pokemons-128_300_epochs_1000_steps_final_Cont ## Model description This diffusion model is trained with the [πŸ€— Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 11 - eval_batch_size: 12 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results πŸ“ˆ [TensorBoard logs](https://huggingface.co/Yagorka/ddpm-pokemons-128_300_epochs_1000_steps_final_Cont/tensorboard?#scalars)
csebuetnlp/mT5_m2m_crossSum
csebuetnlp
2023-02-28T13:23:28Z
40
8
transformers
[ "transformers", "pytorch", "mt5", "text2text-generation", "summarization", "mT5", "am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo", "dataset:csebuetnlp/CrossSum", "arxiv:2112.08804", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2022-04-20T15:11:49Z
--- tags: - summarization - mT5 language: - am - ar - az - bn - my - zh - en - fr - gu - ha - hi - ig - id - ja - rn - ko - ky - mr - ne - om - ps - fa - pcm - pt - pa - ru - gd - sr - si - so - es - sw - ta - te - th - ti - tr - uk - ur - uz - vi - cy - yo licenses: - cc-by-nc-sa-4.0 widget: - text: >- Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about vaccines in general". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. "We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO," the post said, referring to the World Health Organization. datasets: - csebuetnlp/CrossSum --- # mT5-m2m-CrossSum This repository contains the many-to-many (m2m) mT5 checkpoint finetuned on all cross-lingual pairs of the [CrossSum](https://huggingface.co/datasets/csebuetnlp/CrossSum) dataset. This model tries to **summarize text written in any language in the provided target language.** For finetuning details and scripts, see the [paper](https://arxiv.org/abs/2112.08804) and the [official repository](https://github.com/csebuetnlp/CrossSum). ## Using this model in `transformers` (tested on 4.11.0.dev0) ```python import re from transformers import AutoTokenizer, AutoModelForSeq2SeqLM WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip())) article_text = """Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about vaccines in general". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. "We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO," the post said, referring to the World Health Organization.""" model_name = "csebuetnlp/mT5_m2m_crossSum" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) get_lang_id = lambda lang: tokenizer._convert_token_to_id( model.config.task_specific_params["langid_map"][lang][1] ) target_lang = "english" # for a list of available language names see below input_ids = tokenizer( [WHITESPACE_HANDLER(article_text)], return_tensors="pt", padding="max_length", truncation=True, max_length=512 )["input_ids"] output_ids = model.generate( input_ids=input_ids, decoder_start_token_id=get_lang_id(target_lang), max_length=84, no_repeat_ngram_size=2, num_beams=4, )[0] summary = tokenizer.decode( output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(summary) ``` ### Available target language names - `amharic` - `arabic` - `azerbaijani` - `bengali` - `burmese` - `chinese_simplified` - `chinese_traditional` - `english` - `french` - `gujarati` - `hausa` - `hindi` - `igbo` - `indonesian` - `japanese` - `kirundi` - `korean` - `kyrgyz` - `marathi` - `nepali` - `oromo` - `pashto` - `persian` - `pidgin` - `portuguese` - `punjabi` - `russian` - `scottish_gaelic` - `serbian_cyrillic` - `serbian_latin` - `sinhala` - `somali` - `spanish` - `swahili` - `tamil` - `telugu` - `thai` - `tigrinya` - `turkish` - `ukrainian` - `urdu` - `uzbek` - `vietnamese` - `welsh` - `yoruba` ## Citation If you use this model, please cite the following paper: ``` @article{hasan2021crosssum, author = {Tahmid Hasan and Abhik Bhattacharjee and Wasi Uddin Ahmad and Yuan-Fang Li and Yong-bin Kang and Rifat Shahriyar}, title = {CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs}, journal = {CoRR}, volume = {abs/2112.08804}, year = {2021}, url = {https://arxiv.org/abs/2112.08804}, eprinttype = {arXiv}, eprint = {2112.08804} } ```
akoshel/q-CartPole-v1
akoshel
2023-02-28T13:22:36Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-28T13:22:33Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.70 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="akoshel/q-CartPole-v1", 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"]) ```