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Pennywise881/dense-retriever-bert-base-uncased-mnr-squadv2
Pennywise881
2023-02-19T13:00:03Z
2
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-02-19T12:55:37Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {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) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- 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**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 5429 with parameters: ``` {'batch_size': 24} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 542, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
caioiglesias/taxi-v3
caioiglesias
2023-02-19T12:11:13Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-19T12:11:10Z
--- 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: 7.42 +/- 2.85 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="caioiglesias/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"]) ```
caioiglesias/q-FrozenLake-v1-4x4-noSlippery
caioiglesias
2023-02-19T12:09:22Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-19T12:09:19Z
--- 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="caioiglesias/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"]) ```
akmalmasud96/wav2vec2-xls-r-1b-ur
akmalmasud96
2023-02-19T11:48:00Z
3
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-02-19T00:52:00Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice_11_0 metrics: - wer model-index: - name: wav2vec2-xls-r-1b-ur results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: ur split: test args: ur metrics: - name: Wer type: wer value: 0.48854134406937133 --- <!-- 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-xls-r-1b-ur This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.4885 ## 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: 6 - eval_batch_size: 6 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 12 - total_eval_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.7368 | 0.48 | 300 | inf | 0.8191 | | 1.8995 | 0.97 | 600 | inf | 0.7919 | | 0.9144 | 1.45 | 900 | inf | 0.7805 | | 1.166 | 1.94 | 1200 | inf | 0.7087 | | 0.7972 | 2.42 | 1500 | inf | 0.6901 | | 0.8604 | 2.9 | 1800 | inf | 0.6446 | | 0.6569 | 3.39 | 2100 | inf | 0.6560 | | 0.7267 | 3.87 | 2400 | inf | 0.6363 | | 0.687 | 4.35 | 2700 | inf | 0.6343 | | 0.7143 | 4.84 | 3000 | inf | 0.6176 | | 0.5283 | 5.32 | 3300 | inf | 0.6084 | | 0.6917 | 5.81 | 3600 | inf | 0.5942 | | 0.5396 | 6.29 | 3900 | inf | 0.5988 | | 0.5523 | 6.77 | 4200 | inf | 0.5600 | | 0.3167 | 7.26 | 4500 | inf | 0.5648 | | 0.3176 | 7.74 | 4800 | inf | 0.5424 | | 0.3987 | 8.23 | 5100 | inf | 0.5440 | | 0.3327 | 8.71 | 5400 | inf | 0.5316 | | 0.1936 | 9.19 | 5700 | inf | 0.5285 | | 0.4701 | 9.68 | 6000 | inf | 0.5207 | | 0.3581 | 10.16 | 6300 | inf | 0.5176 | | 0.4038 | 10.65 | 6600 | inf | 0.5259 | | 0.2699 | 11.13 | 6900 | inf | 0.5226 | | 0.2302 | 11.61 | 7200 | inf | 0.5181 | | 0.3275 | 12.1 | 7500 | inf | 0.5202 | | 0.3024 | 12.58 | 7800 | inf | 0.5307 | | 0.2568 | 13.06 | 8100 | inf | 0.5243 | | 0.1641 | 13.55 | 8400 | inf | 0.5073 | | 0.2637 | 14.03 | 8700 | inf | 0.5015 | | 0.1778 | 14.52 | 9000 | inf | 0.4892 | | 0.0874 | 15.0 | 9300 | inf | 0.4885 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
albertqueralto/ppo-CartPole-v1
albertqueralto
2023-02-19T11:26:41Z
0
0
null
[ "tensorboard", "CartPole-v1", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-19T11:13:43Z
--- tags: - CartPole-v1 - 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: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 153.70 +/- 61.27 name: mean_reward verified: false --- # PPO Agent Playing CartPole-v1 This is a trained model of a PPO agent playing CartPole-v1. # 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': 'CartPole-v1' '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': 'albertqueralto/ppo-CartPole-v1' 'token': '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' 'batch_size': 512 'minibatch_size': 128} ```
ashrek/dqn-SpaceInvadersNoFrameskip-v4
ashrek
2023-02-19T11:02:13Z
2
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-01-28T15:06:30Z
--- 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: 591.00 +/- 99.19 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 ashrek -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 ashrek -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 ashrek ``` ## 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), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
lauer/distilbert-base-uncased-finetuned-clinc
lauer
2023-02-19T10:53:00Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-02-19T09:56:27Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos model-index: - name: distilbert-base-uncased-finetuned-clinc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
MatthewCanada/instruct-pix2pix-00-22000.safetensors
MatthewCanada
2023-02-19T10:09:52Z
0
0
null
[ "arxiv:1910.09700", "region:us" ]
null
2023-02-19T10:09:02Z
--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ## Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] # Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ## Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ## Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ## Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] # Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ## Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] # Training Details ## Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ## Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [optional] [More Information Needed] ### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> ### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] # Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ## Testing Data, Factors & Metrics ### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] ### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] ### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ## Results [More Information Needed] ### Summary # Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] # Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] # Technical Specifications [optional] ## Model Architecture and Objective [More Information Needed] ## Compute Infrastructure [More Information Needed] ### Hardware [More Information Needed] ### Software [More Information Needed] # Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] # Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] # More Information [optional] [More Information Needed] # Model Card Authors [optional] [More Information Needed] # Model Card Contact [More Information Needed]
albertqueralto/dqn-SpaceInvadersNoFrameskip-v4
albertqueralto
2023-02-19T10:01:31Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-19T10:00:52Z
--- 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: 497.50 +/- 83.76 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 albertqueralto -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 albertqueralto -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 albertqueralto ``` ## 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)]) ```
jaimin/image_caption
jaimin
2023-02-19T09:37:11Z
9
2
transformers
[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "image-to-text", "image-captioning", "license:apache-2.0", "endpoints_compatible", "region:us" ]
image-to-text
2023-02-19T09:25:59Z
--- tags: - image-to-text - image-captioning license: apache-2.0 --- # Sample running code ```python from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer import torch from PIL import Image model = VisionEncoderDecoderModel.from_pretrained("jaimin/image_caption") feature_extractor = ViTFeatureExtractor.from_pretrained("jaimin/image_caption") tokenizer = AutoTokenizer.from_pretrained("jaimin/image_caption") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) max_length = 16 num_beams = 4 gen_kwargs = {"max_length": max_length, "num_beams": num_beams} def predict_step(image_paths): images = [] for image_path in image_paths: i_image = Image.open(image_path) if i_image.mode != "RGB": i_image = i_image.convert(mode="RGB") images.append(i_image) pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values pixel_values = pixel_values.to(device) output_ids = model.generate(pixel_values, **gen_kwargs) preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) preds = [pred.strip() for pred in preds] return preds ``` # Sample running code using transformers pipeline ```python from transformers import pipeline image_to_text = pipeline("image-to-text", model="jaimin/image_caption") ```
jamesthong/dqn-SpaceInvadersNoFrameskip-v4
jamesthong
2023-02-19T09:36:06Z
4
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-19T09:35:32Z
--- 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: 383.50 +/- 149.45 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 mikato -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 mikato -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 mikato ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
pranked03/ddpm-pokemon
pranked03
2023-02-19T09:33:04Z
2
0
diffusers
[ "diffusers", "tensorboard", "Image Generation", "Diffusers", "unconditional-image-generation", "dataset:lambdalabs/pokemon-blip-captions", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
2023-02-18T16:03:05Z
--- datasets: - lambdalabs/pokemon-blip-captions pipeline_tag: unconditional-image-generation tags: - Image Generation - Diffusers ---
mallycrip/CartPole-v1
mallycrip
2023-02-19T09:19:04Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-19T08:35:26Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 186.50 +/- 15.91 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Seungjun/t5-small-finetuned-epoch15-finetuned-epoch30
Seungjun
2023-02-19T09:15:41Z
3
1
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-19T08:16:41Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-epoch15-finetuned-epoch30 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-epoch15-finetuned-epoch30 This model is a fine-tuned version of [Seungjun/t5-small-finetuned-epoch15](https://huggingface.co/Seungjun/t5-small-finetuned-epoch15) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4083 - Rouge1: 31.0064 - Rouge2: 19.0446 - Rougel: 27.7086 - Rougelsum: 29.5158 - Gen Len: 18.9941 ## 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: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.6224 | 1.0 | 765 | 1.4499 | 30.3772 | 18.075 | 26.941 | 28.8424 | 18.9915 | | 1.586 | 2.0 | 1530 | 1.4403 | 30.4972 | 18.3407 | 27.1242 | 29.0417 | 18.9908 | | 1.5684 | 3.0 | 2295 | 1.4323 | 30.6617 | 18.4827 | 27.2642 | 29.2175 | 18.9921 | | 1.5622 | 4.0 | 3060 | 1.4300 | 30.7155 | 18.5604 | 27.3201 | 29.2191 | 18.9941 | | 1.5447 | 5.0 | 3825 | 1.4229 | 30.7883 | 18.7051 | 27.379 | 29.2824 | 18.9941 | | 1.5382 | 6.0 | 4590 | 1.4199 | 30.7555 | 18.7235 | 27.4249 | 29.2612 | 18.9941 | | 1.5303 | 7.0 | 5355 | 1.4187 | 30.7818 | 18.773 | 27.4232 | 29.2896 | 18.9941 | | 1.5225 | 8.0 | 6120 | 1.4149 | 30.8854 | 18.8302 | 27.5499 | 29.3993 | 18.9941 | | 1.5197 | 9.0 | 6885 | 1.4143 | 30.9201 | 18.863 | 27.5918 | 29.4395 | 18.9941 | | 1.5123 | 10.0 | 7650 | 1.4119 | 30.9469 | 18.9403 | 27.6186 | 29.4314 | 18.9941 | | 1.5209 | 11.0 | 8415 | 1.4107 | 30.9685 | 18.9431 | 27.6189 | 29.4673 | 18.9941 | | 1.5091 | 12.0 | 9180 | 1.4095 | 30.9249 | 18.9679 | 27.6257 | 29.4341 | 18.9941 | | 1.4998 | 13.0 | 9945 | 1.4091 | 30.9911 | 19.0416 | 27.695 | 29.4991 | 18.9941 | | 1.505 | 14.0 | 10710 | 1.4085 | 30.9942 | 19.0321 | 27.6999 | 29.5025 | 18.9941 | | 1.4965 | 15.0 | 11475 | 1.4083 | 31.0064 | 19.0446 | 27.7086 | 29.5158 | 18.9941 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Tokenizers 0.13.2
axolotron/ice-cream-animals
axolotron
2023-02-19T09:04:06Z
5
4
diffusers
[ "diffusers", "text-to-image", "stable-diffusion", "pytorch", "dreambooth-hackathon", "food", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-01-21T15:36:13Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion - pytorch - diffusers - dreambooth-hackathon - food widget: - text: a butterfly ice cream, icenimal --- # Ice_cream_animals Dreambooth Model for Food trained on a custom dataset. This is a Stable Diffusion **2.1 768px** model fine-tuned on the food concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a butterfly ice cream, icenimal** This model was created as part of the DreamBooth Hackathon 🔥. Samples: A red dragon <img width="200px" height="200px" src="https://huggingface.co/axolotron/ice-cream-animals/resolve/main/sample_images/tmpnijfm61w.png"> A disney princess <img width="200px" height="200px" src="https://huggingface.co/axolotron/ice-cream-animals/resolve/main/sample_images/tmpjitwzmys.png"> A demogorgon <img width="200px" height="200px" src="https://huggingface.co/axolotron/ice-cream-animals/resolve/main/sample_images/tmpbbqipc46.png"> An elephant <img width="200px" height="200px" src="https://huggingface.co/axolotron/ice-cream-animals/resolve/main/sample_images/tmp5u6oo1j1.png"> A bee <img width="200px" height="200px" src="https://huggingface.co/axolotron/ice-cream-animals/resolve/main/sample_images/tmpdgxfsle_.png"> An axolotl <img width="200px" height="200px" src="https://huggingface.co/axolotron/ice-cream-animals/resolve/main/sample_images/tmpowhy01r_.png"> a cat <img width="200px" height="200px" src="https://huggingface.co/axolotron/ice-cream-animals/resolve/main/sample_images/tmp07iw9qf1.png"> Pokemon <img width="200px" height="200px" src="https://huggingface.co/axolotron/ice-cream-animals/resolve/main/sample_images/tmp3q0ru2k_.png"> Donald Trump as ice cream <img width="200px" height="200px" src="https://huggingface.co/axolotron/ice-cream-animals/resolve/main/sample_images/tmpon6crc5e.png"> A butterfly <img width="200px" height="200px" src="https://huggingface.co/axolotron/ice-cream-animals/resolve/main/sample_images/tmpxt87y5n7.png">
DaydreamerF/bert-finetuned-TENBOOK-accelerate-evatest
DaydreamerF
2023-02-19T08:35:12Z
4
0
transformers
[ "transformers", "pytorch", "bert", "question-answering", "generated_from_accelerate", "zh", "endpoints_compatible", "region:us" ]
question-answering
2023-02-13T09:33:46Z
--- language: zh widget: - text: "这句话是谁说的?" context: "“老大,你太牛逼了,把敌人军火库都给炸了,我真的佩服的五体投地,我现在忍不住想看看你藏的东西在哪里,我们快点出发吧。”代号零听完郭旭刚刚的讲述笑的拍手一直叫好。" - text: "这句话是谁说的?" context: "“妈,你别哭了,我这不是好着呢吗?”郭旭扶着母亲的肩膀笑着说。" - text: "这句话是谁说的?" context: "“总统先生,看来我们这一次在劫难逃了,大乘期的恐怖,远远超出了我们的想象,我还有一些后手能尽量拖延他一点时间,你们先走,我让我的鬼奴随你们去,去这个地方或许能保你们一线生机!”郭旭说完便偷偷地将黑暗空间的阴阳珠交给了陈天。" - text: "这句话是谁说的?" context: "“也罢,能活一个是一个吧!他还那么年轻?”却是剑傲天摇了摇头无奈的说道。" tags: - generated_from_accelerate model-index: - name: bert-finetuned-TENBOOK-accelerate-evatest results: [] --- # bert-finetuned-TENBOOK-accelerate-evatest This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on a self dataset.
cahya/indochat-tiny
cahya
2023-02-19T06:47:18Z
11
1
transformers
[ "transformers", "pytorch", "jax", "tensorboard", "gpt2", "text-generation", "text generation", "causal-lm", "id", "en", "license:creativeml-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-02-12T19:21:45Z
--- language: - id - en tags: - text generation - pytorch - causal-lm license: creativeml-openrail-m metrics: - perplexity pipeline_tag: text-generation --- # IndoChat-Tiny This model is a bilingual GPT2 model fine-tuned with instructions dataset (~100K English instructions and its ~100K Indonesian translation). The base model was a GPT2-Medium (345M params) which was pretrained with 75GB of Indonesian (99%) and English (1%) dataset.
Seungjun/t5-small-finetuned-t5-epoch5
Seungjun
2023-02-19T06:34:41Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-19T06:12:18Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-t5-epoch5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-t5-epoch5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5359 - Rouge1: 29.8849 - Rouge2: 17.4399 - Rougel: 26.3643 - Rougelsum: 28.3764 - Gen Len: 18.9869 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.9706 | 1.0 | 765 | 1.6150 | 28.6734 | 16.3753 | 25.3 | 27.2566 | 18.983 | | 1.759 | 2.0 | 1530 | 1.5689 | 29.4543 | 16.9803 | 25.9821 | 27.9832 | 18.9895 | | 1.731 | 3.0 | 2295 | 1.5487 | 29.694 | 17.2806 | 26.2035 | 28.2027 | 18.9836 | | 1.7108 | 4.0 | 3060 | 1.5389 | 29.9064 | 17.4929 | 26.4006 | 28.3983 | 18.9876 | | 1.7045 | 5.0 | 3825 | 1.5359 | 29.8849 | 17.4399 | 26.3643 | 28.3764 | 18.9869 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Tokenizers 0.13.2
jxiao/poca-SoccerTwos
jxiao
2023-02-19T06:32:09Z
11
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-02-19T06:32:03Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: jxiao/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Knight85/PPO-PPO-LunarLander-v2
Knight85
2023-02-19T05:45:18Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-19T05:44:38Z
--- 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: 289.90 +/- 19.47 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Karmvir-Phogat/PPO-LunarLander-v2
Karmvir-Phogat
2023-02-19T05:24:41Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-19T05:20:16Z
--- 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: 283.58 +/- 18.00 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 ... ```
pnparam/loso_F02
pnparam
2023-02-19T05:08:44Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-02-19T04:14:48Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: loso_F02 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. --> # loso_F02 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: 0.0784 - Wer: 1.3311 ## 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.0203 | 0.96 | 500 | 3.2523 | 1.0 | | 2.6567 | 1.92 | 1000 | 1.7631 | 2.44 | | 1.1186 | 2.88 | 1500 | 0.4121 | 2.4 | | 0.3969 | 3.84 | 2000 | 0.1705 | 1.4533 | | 0.1635 | 4.8 | 2500 | 0.0970 | 1.68 | | 0.0915 | 5.76 | 3000 | 0.0874 | 1.4267 | | 0.0609 | 6.72 | 3500 | 0.0784 | 1.3311 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.13.1+cu116 - Datasets 1.18.3 - Tokenizers 0.13.2
leo9960/Pano-Diffusion
leo9960
2023-02-19T04:11:01Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-02-19T04:11:01Z
--- license: creativeml-openrail-m ---
akmalmasud96/wav2vec2-large-xlsr-53-ur
akmalmasud96
2023-02-19T03:49:41Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-02-19T00:40:18Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice_11_0 metrics: - wer model-index: - name: wav2vec2-large-xlsr-53-ur results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: ur split: test args: ur metrics: - name: Wer type: wer value: 0.4816893775162589 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-ur This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.4817 ## 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: 6 - eval_batch_size: 6 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 12 - total_eval_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0981 | 0.48 | 300 | inf | 0.9981 | | 2.0031 | 0.97 | 600 | inf | 0.8283 | | 0.7476 | 1.45 | 900 | inf | 0.6584 | | 0.8585 | 1.94 | 1200 | inf | 0.5823 | | 0.4978 | 2.42 | 1500 | inf | 0.5564 | | 0.5423 | 2.9 | 1800 | inf | 0.5209 | | 0.3504 | 3.39 | 2100 | inf | 0.5396 | | 0.3185 | 3.87 | 2400 | inf | 0.4865 | | 0.3337 | 4.35 | 2700 | inf | 0.4733 | | 0.4935 | 4.84 | 3000 | inf | 0.4721 | | 0.4022 | 5.32 | 3300 | inf | 0.4692 | | 0.3517 | 5.81 | 3600 | inf | 0.4585 | | 0.1838 | 6.29 | 3900 | inf | 0.4567 | | 0.2635 | 6.77 | 4200 | inf | 0.4459 | | 0.1163 | 7.26 | 4500 | inf | 0.4495 | | 0.1776 | 7.74 | 4800 | inf | 0.4657 | | 0.262 | 8.23 | 5100 | inf | 0.4562 | | 0.1853 | 8.71 | 5400 | inf | 0.4724 | | 0.3173 | 9.19 | 5700 | inf | 0.4752 | | 0.4985 | 9.68 | 6000 | inf | 0.4604 | | 0.3707 | 10.16 | 6300 | inf | 0.4769 | | 0.4214 | 10.65 | 6600 | inf | 0.5246 | | 0.3443 | 11.13 | 6900 | inf | 0.5391 | | 0.3302 | 11.61 | 7200 | inf | 0.5051 | | 0.327 | 12.1 | 7500 | inf | 0.5389 | | 0.2489 | 12.58 | 7800 | inf | 0.5355 | | 0.2328 | 13.06 | 8100 | inf | 0.5111 | | 0.2488 | 13.55 | 8400 | inf | 0.4794 | | 0.3255 | 14.03 | 8700 | inf | 0.4959 | | 0.3056 | 14.52 | 9000 | inf | 0.4895 | | 0.1758 | 15.0 | 9300 | inf | 0.4817 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
hmehta92/LaBSE-ict-content-ep15
hmehta92
2023-02-19T03:33:58Z
1
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-02-19T03:27:49Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {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) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- 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 2220 with parameters: ``` {'batch_size': 1024, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 15, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 1e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 3330, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ben-yu/LunarLander-v2-ppo
ben-yu
2023-02-19T03:13:53Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-19T02:16:03Z
--- 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: 11.52 +/- 57.92 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': 2000000 'learning_rate': 0.00025 'num_envs': 16 'num_steps': 1024 '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': 'ben-yu/LunarLander-v2-ppo' 'batch_size': 16384 'minibatch_size': 4096} ```
weitf/muscleAmine
weitf
2023-02-19T02:58:19Z
0
3
null
[ "art", "image-to-image", "region:us" ]
image-to-image
2022-10-11T15:36:51Z
--- pipeline_tag: image-to-image tags: - art --- a hyper network trained by よし男's artwork. (reference: https://www.pixiv.net/users/3584828) only for study and self use please do not publish or use for business. 请勿发表或商用 Author: Tongfan Wei ([email protected]) an example by base model anything v4.5, upscale model CUGAN ![00681-3567241462-NSFW, (master___.png](https://s3.amazonaws.com/moonup/production/uploads/1676775176386-63458d7f547c70e4b7cd5d40.png)
sinny/2x
sinny
2023-02-19T02:57:11Z
106
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-02-18T08:09:34Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: sinny/2x 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
nergaldarski/AnyHentai
nergaldarski
2023-02-19T02:51:49Z
0
1
null
[ "region:us" ]
null
2023-02-19T02:24:41Z
https://civitai.com/models/5706/anyhentai
jxiao/ppo-LunarLander-v2
jxiao
2023-02-19T02:50:58Z
2
0
transformers
[ "transformers", "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "endpoints_compatible", "region:us" ]
reinforcement-learning
2022-12-17T22:57:23Z
--- 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: -112.58 +/- 59.71 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': 1000000 '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': 'jxiao/ppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
LarryAIDraw/cherryBlossomMix_v10
LarryAIDraw
2023-02-19T02:25:45Z
0
3
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-02-18T20:45:26Z
--- license: creativeml-openrail-m --- https://civitai.com/models/10283/cherry-blossom-mix
slopezay/q-FrozenLake-v1-4x4-noSlippery
slopezay
2023-02-19T02:25:26Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-19T02:25:23Z
--- 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="slopezay/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"]) ```
imar0/dqn-SpaceInvadersNoFrameskip-v4
imar0
2023-02-19T02:22:16Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-19T02:21:33Z
--- 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: 648.00 +/- 159.71 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 imar0 -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 imar0 -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 imar0 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
jhonparra18/petro-twitter-assistant-30ep
jhonparra18
2023-02-19T02:08:55Z
7
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "es", "dataset:jhonparra18/petro-tweets", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-02-18T23:43:02Z
--- tags: - generated_from_trainer model-index: - name: petro-twitter-assistant-30ep results: [] widget: - text: Opino que mi gobierno es datasets: - jhonparra18/petro-tweets language: - es --- <!-- 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. --> # petro-twitter-assistant-30ep This model is a fine-tuned version of [flax-community/gpt-2-spanish](https://huggingface.co/flax-community/gpt-2-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.8837 ## 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: 20 - eval_batch_size: 20 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.123 | 2.3 | 1000 | 3.0761 | | 2.8048 | 4.6 | 2000 | 3.0394 | | 2.5904 | 6.9 | 3000 | 3.0743 | | 2.3804 | 9.2 | 4000 | 3.2378 | | 2.1736 | 11.49 | 5000 | 3.4025 | | 1.9736 | 13.79 | 6000 | 3.6284 | | 1.779 | 16.09 | 7000 | 3.9806 | | 1.5993 | 18.39 | 8000 | 4.2559 | | 1.4584 | 20.69 | 9000 | 4.4938 | | 1.3492 | 22.99 | 10000 | 4.6608 | | 1.2701 | 25.29 | 11000 | 4.8302 | | 1.2309 | 27.59 | 12000 | 4.8696 | | 1.2161 | 29.89 | 13000 | 4.8837 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0 - Datasets 2.1.0 - Tokenizers 0.12.1
Zekunli/flan-t5-large-da-multiwoz2.1_fs0.2
Zekunli
2023-02-19T01:51:51Z
3
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-18T22:55:18Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: flan-t5-large-da-multiwoz2.1_fs0.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-large-da-multiwoz2.1_fs0.2 This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3159 - Accuracy: 45.1554 - Num: 3689 - Gen Len: 15.5213 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 24 - seed: 1799 - 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 | Num | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:-------:| | 0.9653 | 0.28 | 400 | 0.4635 | 31.3166 | 3689 | 15.196 | | 0.5071 | 0.57 | 800 | 0.4031 | 35.8289 | 3689 | 15.6546 | | 0.4603 | 0.85 | 1200 | 0.3718 | 37.6313 | 3689 | 15.6511 | | 0.4219 | 1.13 | 1600 | 0.3577 | 37.9333 | 3689 | 16.5319 | | 0.3991 | 1.42 | 2000 | 0.3491 | 40.5462 | 3689 | 15.453 | | 0.394 | 1.7 | 2400 | 0.3409 | 40.9333 | 3689 | 15.5137 | | 0.3822 | 1.98 | 2800 | 0.3370 | 41.2932 | 3689 | 15.225 | | 0.3625 | 2.26 | 3200 | 0.3327 | 42.1132 | 3689 | 16.0718 | | 0.3577 | 2.55 | 3600 | 0.3329 | 42.1372 | 3689 | 15.9973 | | 0.3644 | 2.83 | 4000 | 0.3303 | 42.2529 | 3689 | 15.6525 | | 0.349 | 3.11 | 4400 | 0.3256 | 43.2025 | 3689 | 15.6601 | | 0.3355 | 3.4 | 4800 | 0.3243 | 43.791 | 3689 | 15.5451 | | 0.338 | 3.68 | 5200 | 0.3231 | 43.5073 | 3689 | 15.7411 | | 0.3424 | 3.96 | 5600 | 0.3196 | 44.5281 | 3689 | 15.1307 | | 0.3299 | 4.25 | 6000 | 0.3159 | 45.1554 | 3689 | 15.5213 | | 0.328 | 4.53 | 6400 | 0.3188 | 43.4699 | 3689 | 15.3849 | | 0.3204 | 4.81 | 6800 | 0.3159 | 44.7764 | 3689 | 15.8219 | | 0.3166 | 5.1 | 7200 | 0.3165 | 45.0608 | 3689 | 15.8791 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.5.1 - Tokenizers 0.12.1
Ransaka/poca-SoccerTwos
Ransaka
2023-02-19T01:38:31Z
4
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-02-18T02:54:19Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: Ransaka/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Piro17/hq_fer2013notest
Piro17
2023-02-19T01:32:01Z
18
0
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-02-18T17:36:30Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: hq_fer2013notest results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.7052268506235075 - name: Precision type: precision value: 0.7048074435355876 - name: Recall type: recall value: 0.7052268506235075 - name: F1 type: f1 value: 0.7036260157126459 --- <!-- 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. --> # hq_fer2013notest This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8294 - Accuracy: 0.7052 - Precision: 0.7048 - Recall: 0.7052 - F1: 0.7036 ## 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: 32 - eval_batch_size: 32 - seed: 17 - 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 | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.2982 | 1.0 | 353 | 1.2708 | 0.5635 | 0.5107 | 0.5635 | 0.5168 | | 1.0218 | 2.0 | 706 | 1.0159 | 0.6411 | 0.6397 | 0.6411 | 0.6301 | | 0.9437 | 3.0 | 1059 | 0.9452 | 0.6631 | 0.6698 | 0.6631 | 0.6556 | | 0.8282 | 4.0 | 1412 | 0.8873 | 0.6829 | 0.6798 | 0.6829 | 0.6743 | | 0.7717 | 5.0 | 1765 | 0.8612 | 0.6884 | 0.6888 | 0.6884 | 0.6835 | | 0.7678 | 6.0 | 2118 | 0.8473 | 0.6985 | 0.6989 | 0.6985 | 0.6966 | | 0.7096 | 7.0 | 2471 | 0.8363 | 0.7018 | 0.7001 | 0.7018 | 0.6989 | | 0.6803 | 8.0 | 2824 | 0.8333 | 0.7036 | 0.7036 | 0.7036 | 0.7019 | | 0.6521 | 9.0 | 3177 | 0.8309 | 0.7050 | 0.7039 | 0.7050 | 0.7028 | | 0.6671 | 10.0 | 3530 | 0.8294 | 0.7052 | 0.7048 | 0.7052 | 0.7036 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
mafwalter/question_v_statement_finetuned_roberta-basev2
mafwalter
2023-02-19T00:48:46Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-02-18T22:55:40Z
--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: question_v_statement_finetuned_roberta-basev2 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. --> # question_v_statement_finetuned_roberta-basev2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0052 - Accuracy: 0.9993 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0077 | 1.0 | 3966 | 0.0055 | 0.9991 | | 0.0008 | 2.0 | 7932 | 0.0052 | 0.9993 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0 - Datasets 2.1.0 - Tokenizers 0.12.1
fghfghgh/pdfdownload
fghfghgh
2023-02-19T00:36:27Z
0
0
null
[ "region:us" ]
null
2023-02-19T00:32:48Z
https://lookerstudio.google.com/reporting/fec26e0a-f6ed-4a12-a5be-25c48ee41500 https://lookerstudio.google.com/reporting/a5ba5aa5-779c-40da-898f-bee8c704c277 https://lookerstudio.google.com/reporting/c680b70c-5bb1-493b-b12d-1f5c329ca2f8 https://lookerstudio.google.com/reporting/926f6e2f-a201-4dae-9893-c0ad42c4c2ee https://lookerstudio.google.com/reporting/11e387c8-382c-4330-ae9f-3264bd8cd688 https://lookerstudio.google.com/reporting/404ff4cf-8194-4e26-9e57-715607552d17 https://lookerstudio.google.com/reporting/a00352b4-10b5-4e9c-a021-8d935b558cd3 https://lookerstudio.google.com/reporting/2b7ba0ba-40cd-43d4-9111-7e686c1eba1b https://lookerstudio.google.com/reporting/7f9874a0-99b4-41e8-a7e4-bba38385c420 https://lookerstudio.google.com/reporting/417763e0-69bb-44d6-a33a-34e07bcbbb30 https://lookerstudio.google.com/reporting/24fcff05-7e02-44ad-89c3-fd078308375a https://lookerstudio.google.com/reporting/f4c383f5-d60a-440a-b710-8ea763c6b0ab https://lookerstudio.google.com/reporting/98f59ae6-c82b-4b86-a9c2-72eb5a37d3d4 https://lookerstudio.google.com/reporting/29bc4cf6-b3ff-461f-afb2-4138696e9ff5 https://lookerstudio.google.com/u/0/reporting/fec26e0a-f6ed-4a12-a5be-25c48ee41500/page/DjD https://lookerstudio.google.com/u/0/reporting/c680b70c-5bb1-493b-b12d-1f5c329ca2f8/page/DjD https://lookerstudio.google.com/u/0/reporting/11e387c8-382c-4330-ae9f-3264bd8cd688/page/DjD https://lookerstudio.google.com/u/0/reporting/404ff4cf-8194-4e26-9e57-715607552d17/page/DjD https://lookerstudio.google.com/u/0/reporting/a00352b4-10b5-4e9c-a021-8d935b558cd3/page/DjD https://lookerstudio.google.com/u/0/reporting/2b7ba0ba-40cd-43d4-9111-7e686c1eba1b/page/DjD https://lookerstudio.google.com/u/0/reporting/24fcff05-7e02-44ad-89c3-fd078308375a/page/DjD https://lookerstudio.google.com/u/0/reporting/a5ba5aa5-779c-40da-898f-bee8c704c277/page/GzfED https://lookerstudio.google.com/u/0/reporting/7f9874a0-99b4-41e8-a7e4-bba38385c420/page/U0oDD https://lookerstudio.google.com/u/0/reporting/417763e0-69bb-44d6-a33a-34e07bcbbb30/page/kwoDD https://lookerstudio.google.com/u/0/reporting/f4c383f5-d60a-440a-b710-8ea763c6b0ab/page/hdjFD https://lookerstudio.google.com/u/0/reporting/98f59ae6-c82b-4b86-a9c2-72eb5a37d3d4/page/hdjFD https://lookerstudio.google.com/u/0/reporting/29bc4cf6-b3ff-461f-afb2-4138696e9ff5/page/hdjFD https://lookerstudio.google.com/s/nIgoCmcvNCk https://lookerstudio.google.com/s/myd1X3BC7rk https://lookerstudio.google.com/s/uwQ2PuCTtow https://lookerstudio.google.com/s/mi4WJ7hjGcs https://lookerstudio.google.com/s/j682-1zO0tc https://lookerstudio.google.com/s/mwhtRDxLHaQ https://lookerstudio.google.com/s/iqXc_JKkS1o https://lookerstudio.google.com/s/kZK7qXaV1CM https://lookerstudio.google.com/s/tkzMGnf4qSY https://lookerstudio.google.com/s/sG0NUbAXIX0 https://lookerstudio.google.com/s/kqRv4vT-Uzw https://lookerstudio.google.com/s/t5B6XoztkiU https://lookerstudio.google.com/s/lofaEUKnLc8 https://lookerstudio.google.com/s/mh8wWLy6F-8
akmalmasud96/xlsr-53-ur
akmalmasud96
2023-02-19T00:25:54Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-02-18T23:14:18Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - fleurs metrics: - wer model-index: - name: xlsr-53-ur results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: fleurs type: fleurs config: ur_pk split: test args: ur_pk metrics: - name: Wer type: wer value: 0.3450557529714496 --- <!-- 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. --> # xlsr-53-ur This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.6860 - Wer: 0.3451 ## 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: 6 - eval_batch_size: 6 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 12 - total_eval_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0396 | 1.59 | 300 | 3.0179 | 1.0 | | 0.4976 | 3.17 | 600 | 0.7037 | 0.5447 | | 0.3062 | 4.76 | 900 | 0.5557 | 0.4036 | | 0.2287 | 6.35 | 1200 | 0.5620 | 0.3935 | | 0.2504 | 7.94 | 1500 | 0.5907 | 0.3677 | | 0.0633 | 9.52 | 1800 | 0.6239 | 0.3773 | | 0.0456 | 11.11 | 2100 | 0.6748 | 0.3604 | | 0.0774 | 12.7 | 2400 | 0.6747 | 0.3552 | | 0.058 | 14.29 | 2700 | 0.6860 | 0.3451 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
DiegoD616/LunarLander-v2
DiegoD616
2023-02-19T00:24:15Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T23:58:32Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -118.98 +/- 36.13 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters
oscarb92/cleanrl-ppo-LunarLander-v2
oscarb92
2023-02-19T00:15:21Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T23:47:42Z
--- 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: -64.99 +/- 26.01 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': 1000000 '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': 'oscarb92/cleanrl-ppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
sampathlonka/ppo-LunarLander-v2
sampathlonka
2023-02-18T23:42:24Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T22:13:00Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 294.35 +/- 13.21 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 ... ```
jhonparra18/petro-twitter-assistant
jhonparra18
2023-02-18T22:55:41Z
11
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "es", "dataset:jhonparra18/petro-tweets", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-02-18T22:15:51Z
--- tags: - generated_from_trainer model-index: - name: petro-twitter-assistant results: [] widget: - text: Mi gobierno de la Colombia humana es datasets: - jhonparra18/petro-tweets language: - es pipeline_tag: text-generation --- <!-- 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. --> # petro-twitter-assistant This model is a fine-tuned version of [flax-community/gpt-2-spanish](https://huggingface.co/flax-community/gpt-2-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.0562 ## 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: 20 - eval_batch_size: 20 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1263 | 2.3 | 1000 | 3.0679 | | 2.8236 | 4.6 | 2000 | 3.0305 | | 2.6661 | 6.9 | 3000 | 3.0411 | | 2.5905 | 9.2 | 4000 | 3.0562 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0 - Datasets 2.1.0 - Tokenizers 0.12.1
iubeda/a2c-PandaReachDense-v2
iubeda
2023-02-18T22:43:34Z
1
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T20:37:37Z
--- 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: -0.70 +/- 0.19 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 ... ```
cohogain/whisper-large-v2-ga-IE
cohogain
2023-02-18T22:35:28Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "dataset:common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-19T23:07:43Z
--- license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - common_voice_11_0 metrics: - wer model-index: - name: openai/whisper-medium results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: ga-IE split: test args: ga-IE metrics: - name: Wer type: wer value: 32.955865272938446 --- <!-- 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. --> # openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 1.0432 - Wer: 32.9559 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 7000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2448 | 1.02 | 1000 | 0.8498 | 41.7538 | | 0.0367 | 2.04 | 2000 | 0.8609 | 35.7724 | | 0.0095 | 3.06 | 3000 | 0.9109 | 34.9303 | | 0.0048 | 4.09 | 4000 | 0.9602 | 34.3496 | | 0.0009 | 5.11 | 5000 | 1.0041 | 33.2172 | | 0.0003 | 7.01 | 6000 | 1.0362 | 33.1010 | | 0.0006 | 8.03 | 7000 | 1.0432 | 32.9559 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2
paascorb/practica1_DL
paascorb
2023-02-18T22:30:15Z
0
0
fastai
[ "fastai", "region:us" ]
null
2023-02-18T22:30:13Z
--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
zaib32/autotrain-pegasus_jobs_description-3576596204
zaib32
2023-02-18T21:52:39Z
22
0
transformers
[ "transformers", "pytorch", "pegasus", "text2text-generation", "autotrain", "summarization", "unk", "dataset:zaib32/autotrain-data-pegasus_jobs_description", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2023-02-18T21:38:56Z
--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - zaib32/autotrain-data-pegasus_jobs_description co2_eq_emissions: emissions: 0.11237342972879057 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 3576596204 - CO2 Emissions (in grams): 0.1124 ## Validation Metrics - Loss: 1.169 - Rouge1: 50.657 - Rouge2: 28.360 - RougeL: 39.248 - RougeLsum: 46.279 - Gen Len: 148.200 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/zaib32/autotrain-pegasus_jobs_description-3576596204 ```
antonellaavad/daniels
antonellaavad
2023-02-18T21:50:33Z
5
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
2023-02-18T21:50:30Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: ohxs tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - daniels 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 "ohxs" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: a photo of ohxs person ![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)
treksis/WebtoonResearch
treksis
2023-02-18T21:33:34Z
0
1
null
[ "anime", "manga", "manhwa", "webtoon", "en", "ko", "license:creativeml-openrail-m", "region:us" ]
null
2023-02-01T02:27:12Z
--- license: creativeml-openrail-m language: - en - ko tags: - anime - manga - manhwa - webtoon --- <h1>The goal of this repo is to</h1> <ul> <li>Capturing webtoon character's unique characteristics</li> <li>Get varieties of poses, gestures and actions without damaging too many characteristics</li> </ul> <h3>For the LoRA inference</h3> <ul> <li>Current LoRA checkpoints are under development. Instruction will be added soon</li> <li>For those who want to try out. I recommend <b>Midnight Mixers</b> as the base model.</li> <li><b>512(width) x 640(height)</b></li> <li><b>50 steps / 7 cfg</b>. Step below 40 would yield poor quality</li> <li><b>0.5~0.6 range LoRA weight.</b></li> </ul>
averyb123/distilbert-base-uncased-finetuned-squad
averyb123
2023-02-18T20:56:14Z
25
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-02-16T06:07:24Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1534 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2151 | 1.0 | 5533 | 1.1653 | | 0.954 | 2.0 | 11066 | 1.1236 | | 0.7472 | 3.0 | 16599 | 1.1534 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
Genrry/ppo-LunarLander-v2
Genrry
2023-02-18T20:54:15Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T20:53:49Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 268.35 +/- 21.87 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 ... ```
danasone/bloom-petals
danasone
2023-02-18T20:48:26Z
14
0
transformers
[ "transformers", "pytorch", "bloom", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-02-18T20:39:57Z
# BLOOM, a version for Petals This model is a version of [bigscience/bloom](https://huggingface.co/bigscience/bloom) post-processed to be run at home using the [Petals](https://github.com/bigscience-workshop/petals#readme) swarm. Please check out: - The [original model card](https://huggingface.co/bigscience/bloom) to learn about the model's capabilities, specifications, and terms of use. - The [Petals repository](https://github.com/bigscience-workshop/petals#readme) to learn how to install Petals and run this model over the Petals swarm. We provide minimal code examples below. ## Using the model ```python from petals import DistributedBloomForCausalLM model = DistributedBloomForCausalLM.from_pretrained("bigscience/bloom-petals") # Embeddings & prompts are on your device, BLOOM blocks are distributed across the Internet inputs = tokenizer("A cat sat", return_tensors="pt")["input_ids"] outputs = model.generate(inputs, max_new_tokens=5) print(tokenizer.decode(outputs[0])) # A cat sat on a mat... ``` ## Serving the model blocks ```bash python -m petals.cli.run_server bigscience/bloom-petals ```
erud1t3/dqn-SpaceInvadersNoFrameskip-v4
erud1t3
2023-02-18T20:32:43Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T20:32:10Z
--- 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: 16.50 +/- 13.61 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 erud1t3 -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 erud1t3 -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 erud1t3 ``` ## 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', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
Zangnan/q-Taxi-v3
Zangnan
2023-02-18T19:47:32Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T19:47:28Z
--- 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: 7.54 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Zangnan/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"]) ```
LucaReggiani/t5-small-nlpfinalproject4-xsum
LucaReggiani
2023-02-18T19:42:33Z
3
0
transformers
[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-18T19:30:45Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: LucaReggiani/t5-small-nlpfinalproject4-xsum results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # LucaReggiani/t5-small-nlpfinalproject4-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.0688 - Validation Loss: 2.9609 - Train Rouge1: 22.9985 - Train Rouge2: 5.0413 - Train Rougel: 18.1856 - Train Rougelsum: 18.0816 - Train Gen Len: 18.67 - Epoch: 8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.98, 'epsilon': 1e-06, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 3.8921 | 3.2708 | 18.8870 | 3.0920 | 14.9668 | 14.9517 | 18.67 | 0 | | 3.5034 | 3.1209 | 21.5417 | 3.8130 | 16.5211 | 16.5045 | 18.37 | 1 | | 3.3763 | 3.0605 | 21.0710 | 3.6133 | 15.7808 | 15.7437 | 18.33 | 2 | | 3.2971 | 3.0305 | 21.6173 | 4.0001 | 16.2502 | 16.2302 | 18.5 | 3 | | 3.2452 | 3.0086 | 22.8085 | 4.9522 | 17.8831 | 17.7797 | 18.6 | 4 | | 3.1899 | 2.9920 | 22.7903 | 5.3026 | 17.8844 | 17.8651 | 18.58 | 5 | | 3.1514 | 2.9775 | 23.0533 | 5.3456 | 18.4312 | 18.3636 | 18.52 | 6 | | 3.1050 | 2.9686 | 23.0767 | 5.1264 | 18.4552 | 18.3503 | 18.54 | 7 | | 3.0688 | 2.9609 | 22.9985 | 5.0413 | 18.1856 | 18.0816 | 18.67 | 8 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.9.0 - Tokenizers 0.13.2
caffsean/t5-small-finetune-dzongkha-to-romanized
caffsean
2023-02-18T19:35:09Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-18T18:52:10Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetune-dzongkha-to-romanized results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetune-dzongkha-to-romanized This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.5253 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 2.1667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 90 | 4.5253 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1667 | | No log | 2.0 | 180 | 4.5253 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1667 | | No log | 3.0 | 270 | 4.5253 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1667 | | No log | 4.0 | 360 | 4.5253 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1667 | | No log | 5.0 | 450 | 4.5253 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1667 | | 4.7286 | 6.0 | 540 | 4.5253 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1667 | | 4.7286 | 7.0 | 630 | 4.5253 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1667 | | 4.7286 | 8.0 | 720 | 4.5253 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1667 | | 4.7286 | 9.0 | 810 | 4.5253 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1667 | | 4.7286 | 10.0 | 900 | 4.5253 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1667 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
pcalhoun/gpt-j-6b-limericks-finetuned
pcalhoun
2023-02-18T19:26:12Z
16
2
transformers
[ "transformers", "pytorch", "gptj", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-01-29T20:44:32Z
--- license: apache-2.0 widget: - text: "<baked beans =T2R=" example_title: "Generate Rhyme Words" - text: "<playing baseball: say \\ play \\ home \\ roam \\ day =R2L=" example_title: "Generate First Line" --- This model is currently being fine-tuned with deepspeed+bf16 weights using the dataset from Robert A. Gonsalves' article "I Once Trained an AI to Rhyme, and It Took GPT-J a Long Time. Since the Colab was slow, I upgraded to Pro. Each limerick cost me a dime."<br> https://towardsdatascience.com/i-once-trained-an-ai-to-rhyme-and-it-took-gpt-j-a-long-time-de1f98925e17 --- Some examples generated by the 8-bit version of this model (separately fine-tuned on 1 epoch with 1 rtx3090):<br><br> I've a limerick model file,<br> Which, when opened, presents a pile<br> Of bad-to-good verse.<br> I don't think it's much worse<br> Than the limerick I wrote in my style.<br> <br> On your index cards, write down your need,<br> And arrange them in order of speed.<br> When you're done, you'll recall<br> Which one's quicker than all,<br> And you'll know which is best, if indeed.<br> --- Unfortunately, support for 8-bit FTing doesn't seem widely available yet on HF: *"8-bit state dicts cannot currently be loaded directly into the 8-bit model after being pushed on the Hub. This is due to the fact that the statistics (remember weight.CB and weight.SCB) computed by the model are not currently stored or taken into account inside the state dict, and the Linear8bitLt module does not support this feature yet. We think that having the ability to save that and push it to the Hub might contribute to greater accessibility."* https://huggingface.co/blog/hf-bitsandbytes-integration#saving-8-bit-state-dicts-on-the-hub --- Here's what the bf16 model (not the unavailable 8bit model) could do after 160 steps with batch size 24: Prompt: Baseball<br> 0: fun \ gun \ games \ names \ on<br> 1: games \ rages \ play \ day \ cheers<br> 2: all \ call \ old \ gold \ hell<br> 3: games \ rants \ all \ call \ beers<br> 4: all \ shall \ games \ guys \ ball<br> 5: game \ name \ best \ chest \ fame<br> 6: games \ dreams \ drehs \ prehs \ kwames<br> 7: games \ fears \ yanks \ cheers \ beers<br> Going through the multi-step process outlined by Robert A. Gonsalves in his article, it is possible to make a very crude limerick-like poem using the new bf16 trained weights, despite the fact that it hasn't seen much phonetic data yet from the training set: You've got to be careful when you game:<br> Don't forget that they've got a name<br> For some of the best<br> Baseball games on the chest<br> If you forget, then they'll have your fame.<br> I have no idea what that means, but it's basically a limerick. --- Possible improvements to implement: * Use IPA (or, as R. Gonsalves suggests, use eSpeak) instead of Festival phonetic tokens to incorporate syllable stress. * Better align the task formatting with the model's tokenization system.
jordiclive/instruction-tuned-gpt-neox-20b
jordiclive
2023-02-18T19:10:39Z
19
11
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-02-18T18:53:53Z
Experimental 20B instruction tuned model based on gpt-neox-20b. ``` from transformers import AutoModelForCausalLM, AutoTokenizer import torch name = "jordiclive/instruction-tuned-gpt-neox-20b" model = AutoModelForCausalLM.from_pretrained(name, device_map=chip_map, torch_dtype=torch.float16)# load_in_8bit=True ) tokenizer = AutoTokenizer.from_pretrained(name) def generate_from_model(model, tokenizer): encoded_input = tokenizer(text, return_tensors='pt') output_sequences = model.generate( input_ids=encoded_input['input_ids'].cuda(0), do_sample=True, max_new_tokens=35, num_return_sequences=1, top_p=0.95, temperature=0.5, penalty_alpha=0.6, top_k=4, output_scores=True, return_dict_in_generate=True, repetition_penalty=1.03, eos_token_id=0, use_cache=True ) gen_sequences = output_sequences.sequences[:, encoded_input['input_ids'].shape[-1]:] for sequence in gen_sequences: new_line=tokenizer.decode(sequence, skip_special_tokens=True) print(new_line) text = "User: Will big tech A.I be adulterated with advertisement?\n\nOA:" generate_from_model(model,tokenizer) ```
vishalghor/t5-small-finetuned-wikisql-sql-nl-nl-sql
vishalghor
2023-02-18T19:03:03Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-12T04:25:50Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: t5-small-finetuned-wikisql-sql-nl-nl-sql results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-wikisql-sql-nl-nl-sql This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2194 - Bleu: 40.1315 - Gen Len: 16.7069 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.2713 | 1.0 | 8097 | 0.2303 | 39.3173 | 16.7176 | | 0.2549 | 2.0 | 16194 | 0.2194 | 40.1315 | 16.7069 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
pnparam/loso_F04
pnparam
2023-02-18T19:01:46Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-02-18T18:05:31Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: loso_F04 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. --> # loso_F04 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: 0.0791 - Wer: 1.4780 ## 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.9264 | 0.96 | 500 | 3.6742 | 1.0 | | 2.6962 | 1.91 | 1000 | 1.7830 | 2.6233 | | 1.1118 | 2.87 | 1500 | 0.5233 | 1.8458 | | 0.3692 | 3.82 | 2000 | 0.1670 | 1.2423 | | 0.1671 | 4.78 | 2500 | 0.1289 | 1.3700 | | 0.0897 | 5.74 | 3000 | 0.1031 | 1.5110 | | 0.0656 | 6.69 | 3500 | 0.0791 | 1.4780 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.13.1+cu116 - Datasets 1.18.3 - Tokenizers 0.13.2
dlicari/lsg16k-Italian-Legal-BERT-SC
dlicari
2023-02-18T18:53:47Z
44
1
transformers
[ "transformers", "pytorch", "camembert", "fill-mask", "custom_code", "it", "arxiv:2210.15497", "license:afl-3.0", "autotrain_compatible", "region:us" ]
fill-mask
2022-12-17T16:01:26Z
--- license: afl-3.0 language: - it --- <img src="https://huggingface.co/dlicari/lsg16k-Italian-Legal-BERT/resolve/main/ITALIAN_LEGAL_BERT-LSG.jpg" width="600"/> # LSG16K-Italian-LEGAL-BERT [Local-Sparse-Global](https://arxiv.org/abs/2210.15497) version of [ITALIAN-LEGAL-BERT-SC](https://huggingface.co/dlicari/Italian-Legal-BERT-SC) by replacing the full attention in the encoder part using the LSG converter script (https://github.com/ccdv-ai/convert\_checkpoint\_to\_lsg). We used the LSG attention with 16,384 maximum sequence length, 7 global tokens, 128 local block size, 128 sparse block size, 2 sparsity factors, 'norm' sparse selection pattern (select the highest norm tokens).
Lakoc/PPO8.1-LunarLander-v2
Lakoc
2023-02-18T18:35:53Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T18:35:47Z
--- 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: -104.13 +/- 65.72 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': 1000000 'learning_rate': 0.00025 'num_envs': 16 'num_steps': 1024 'anneal_lr': True 'gae': True 'gamma': 0.98 'gae_lambda': 0.98 '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': 'Lakoc/PPO8.1-LunarLander-v2' 'batch_size': 16384 'minibatch_size': 4096} ```
Lakoc/PPO8-LunarLander-v2
Lakoc
2023-02-18T18:28:07Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T18:24:03Z
--- 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: -122.19 +/- 67.69 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': 100000 'learning_rate': 0.00025 'num_envs': 16 'num_steps': 1024 'anneal_lr': True 'gae': True 'gamma': 0.98 'gae_lambda': 0.98 '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': 'Lakoc/PPO8.1-LunarLander-v2' 'batch_size': 16384 'minibatch_size': 4096} ```
seungwoos/q-FrozenLake-v1-4x4-noSlippery
seungwoos
2023-02-18T18:18:53Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T18:18:48Z
--- 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="seungwoos/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"]) ```
jackmedda/q-Taxi-v3
jackmedda
2023-02-18T18:15:09Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T17:25:49Z
--- 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="jackmedda/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"]) ```
dlicari/distil-ita-legal-bert
dlicari
2023-02-18T18:14:49Z
59
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "license:afl-3.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-12-10T10:25:42Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: afl-3.0 --- <img src="https://huggingface.co/dlicari/distil-ita-legal-bert/resolve/main/ITALIAN_LEGAL_BERT-DI.jpg" width="600"/> # DISTIL-ITA-LEGAL-BERT We used the process of knowledge distillation to create a fast, lightweight student model with only 4-levels of Transformers, capable of producing sentence embeddings similar to those produced by the more complex [ITALIAN-LEGAL-BERT](dlicari/Italian-Legal-BERT) teacher model. It optimized on the ITALIAN-LEGAL-BERT train set (3.7 GB) using Sentence-BERT library by minimizing the mean square error (MSE) between its embeddings and those produced by the teacher model. 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('dlicari/distil-ita-legal-bert') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('dlicari/distil-ita-legal-bert') model = AutoModel.from_pretrained('dlicari/distil-ita-legal-bert') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- 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 409633 with parameters: ``` {'batch_size': 24, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MSELoss.MSELoss` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 5000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 0.0001 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Petro28/Cyber_028
Petro28
2023-02-18T18:01:46Z
0
0
allennlp
[ "allennlp", "finance", "text-classification", "aa", "dataset:gsdf/EasyNegative", "license:openrail", "region:us" ]
text-classification
2023-02-18T18:01:03Z
--- license: openrail datasets: - gsdf/EasyNegative language: - aa metrics: - bertscore library_name: allennlp pipeline_tag: text-classification tags: - finance ---
mahmoud-mohey/a2c-PandaReachDense-v2
mahmoud-mohey
2023-02-18T17:53:35Z
0
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T17:51:18Z
--- 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: -1.91 +/- 0.38 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 ... ```
huggingtweets/elonmusk-svembu
huggingtweets
2023-02-18T17:50:26Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-02-18T17:49:20Z
--- language: en thumbnail: http://www.huggingtweets.com/elonmusk-svembu/1676742622036/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1590968738358079488/IY9Gx6Ok_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1568853371146338308/w87i8uhE_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Sridhar Vembu</div> <div style="text-align: center; font-size: 14px;">@elonmusk-svembu</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Sridhar Vembu. | Data | Elon Musk | Sridhar Vembu | | --- | --- | --- | | Tweets downloaded | 3193 | 3248 | | Retweets | 174 | 264 | | Short tweets | 1048 | 45 | | Tweets kept | 1971 | 2939 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4x30aqaf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-svembu's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ryim7xj2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ryim7xj2/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-svembu') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
phd411r1/SajjadAyoubi_xlm-roberta-large-fa-qa_finetune_on_hoshfa_3
phd411r1
2023-02-18T17:47:38Z
4
0
transformers
[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]
question-answering
2023-02-18T17:15:04Z
--- tags: - generated_from_trainer model-index: - name: SajjadAyoubi_xlm-roberta-large-fa-qa_finetune_on_hoshfa_3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SajjadAyoubi_xlm-roberta-large-fa-qa_finetune_on_hoshfa_3 This model is a fine-tuned version of [SajjadAyoubi/xlm-roberta-large-fa-qa](https://huggingface.co/SajjadAyoubi/xlm-roberta-large-fa-qa) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8894 ## 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: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4424 | 1.0 | 1500 | 2.0999 | | 1.8186 | 2.0 | 3000 | 1.2042 | | 1.2822 | 3.0 | 4500 | 0.8894 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
neatbullshit/dqn-SpaceInvadersNoFrameskip-v4
neatbullshit
2023-02-18T17:33:43Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T17:27:40Z
--- 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: 194.00 +/- 131.47 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 neatbullshit -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 neatbullshit -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 neatbullshit ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
ArneL2206/ppo-LunarLander-v2
ArneL2206
2023-02-18T17:21:45Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-10T21:25:33Z
--- 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: 295.53 +/- 17.25 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 ... ```
JUNGU/Taxi-v3
JUNGU
2023-02-18T17:03:33Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T17:03:24Z
--- 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: 7.44 +/- 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="JUNGU/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"]) ```
jackmedda/q-FrozenLake-v1-4x4-noSlippery
jackmedda
2023-02-18T17:02:31Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T17:02:27Z
--- 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="jackmedda/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"]) ```
JUNGU/q-FrozenLake-v1-4x4-noSlippery
JUNGU
2023-02-18T17:01:27Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T17:01:19Z
--- 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="JUNGU/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"]) ```
MoonKBR/ppo-LunarLander-v2
MoonKBR
2023-02-18T17:00:16Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T16:15:56Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 290.21 +/- 23.78 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 ... ```
rdesarz/dqn-atari
rdesarz
2023-02-18T16:50:47Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T16:50:07Z
--- 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: 607.00 +/- 169.87 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 rdesarz -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 rdesarz -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 rdesarz ``` ## 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)]) ```
inkoziev/paraphraser
inkoziev
2023-02-18T16:49:04Z
25
4
transformers
[ "transformers", "pytorch", "gpt2", "paraphrasing", "seq2seq", "ru", "dataset:inkoziev/paraphrases", "license:cc-by-nc-4.0", "text-generation-inference", "endpoints_compatible", "region:us" ]
null
2023-01-05T09:17:17Z
--- language: ru license: cc-by-nc-4.0 tags: - paraphrasing - seq2seq datasets: - inkoziev/paraphrases --- ## Поэтический перефразировщик Это генеративная модель на основе ```sberbank-ai/rugpt3large_based_on_gpt2```, дообученной на датасете перефразировок [inkoziev/paraphrases](https://huggingface.co/datasets/inkoziev/paraphrases). Она разработана для использования в проекте [генеративной поэзии](https://github.com/Koziev/verslibre). Код для тренировки и использования перефразировщика доступен в репозитрии [https://github.com/Koziev/paraphraser](https://github.com/Koziev/paraphraser). ### Особенности перефразировки Обращаю внимание, что модель **не предназначена** для использования там, где требуется особо аккуратная работа с именованными сущностями. Так как в стихах не возникает особых проблем (более того, в некоторых сценариях использования это даже желательно), если перефразировки теряют или добавляют некоторую семантику в исходный текст, то обучающий датасет и модель на его основе может путать дни недели, имена, добавлять что-то от себя, быть метафоричной или иносказательной. ### Методика файнтюна В обучающем датасете есть негативные примеры перефразировок, и я использую их вместе с правильными примерами в ходе файнтюна, подавая на классификационную голову в [GPT2DoubleHeadsModel](https://huggingface.co/docs/transformers/model_doc/gpt2#transformers.GPT2DoubleHeadsModel). Код, выполняющий файнтюн, доступен [тут](https://github.com/Koziev/paraphraser/blob/main/train_paraphraser_with_gpt2doublehead.py). Такой подход к файнтюну оказался лучше, чем два других подхода: 1) дефолтный способ файнтюна, когда GPT дообучается просто на текстах, состоящих из исходного текста и перефразировки, разделенных специальным токеном. В этом подходе модель обучается также на токенах затравки, что может быть нежелательным. 2) вариация первого способа, в котором токены затравки (исходного текста) исключаются из обратного распространения с помощью задания labels=-100 ([код](https://github.com/Koziev/paraphraser/blob/main/finetune_paraphraser_with_prompt_masking.py)). В качестве метрики для сравнения подходов и для подбора числа неверных вариантов перефразировки в GPT2DoubleHeadsModel использована комбинация из: 1) близость векторов эмбеддингов исходного текста и сгенерированной перефразировки. Векторы получаются с помощью модели ```sberbank-ai/sbert_large_mt_nlu_ru```. Я не стал использовать [модель-критик](https://huggingface.co/inkoziev/sbert_synonymy), поскольку она обучалась на таком же датасете. 2) дисконтируем результаты п.1 символьной близостью (3-граммы) по коэффициенту Жаккара. Это штрафует перестановочные перефразировки, воспроизведение исходного текста и небольшие переписывания. ### Формат входных данных На вход модели подается исходный текст с добавлением токенов ```<s>``` в начале и ```<sep>``` в конце, например: ``` input_text = '<s>Мороз и солнце, день чудесный<sep>' ``` Результат генерации будет содержать текст с токеном ```</s>``` - это конец последовательности. ### Пример использования Следующий код позволяет ввести в консоли короткое предложение и видеть результат ее перефразировки моделью: ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM device = "cuda" if torch.cuda.is_available() else "cpu" model_name = "inkoziev/paraphraser" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) model.to(device) model.eval() while True: seed = input(':> ').strip() encoded_prompt = tokenizer.encode("<s>" + seed + "<sep>", add_special_tokens=False, return_tensors="pt").to(device) output_sequences = model.generate(input_ids=encoded_prompt, max_length=100, typical_p=0.85, top_k=0, top_p=1.0, do_sample=True, num_return_sequences=10, pad_token_id=tokenizer.pad_token_id) for o in output_sequences: text = tokenizer.decode(o.tolist(), clean_up_tokenization_spaces=True) text = text[text.index('<sep>') + 5:] text = text[: text.find('</s>')] print(text) ```
JUNGU/ppo-LunarLander-v2
JUNGU
2023-02-18T16:47:21Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-07T22:19:31Z
--- 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: 262.62 +/- 26.46 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
phd411r1/SajjadAyoubi_xlm-roberta-large-fa-qa-finetune_on_hoshfa
phd411r1
2023-02-18T16:25:35Z
3
0
transformers
[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]
question-answering
2023-02-18T16:08:03Z
--- tags: - generated_from_trainer model-index: - name: SajjadAyoubi_xlm-roberta-large-fa-qa-finetune_on_hoshfa 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. --> # SajjadAyoubi_xlm-roberta-large-fa-qa-finetune_on_hoshfa This model is a fine-tuned version of [SajjadAyoubi/xlm-roberta-large-fa-qa](https://huggingface.co/SajjadAyoubi/xlm-roberta-large-fa-qa) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4810 ## 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: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2718 | 1.0 | 2249 | 1.4810 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
jackmedda/ppo-Huggy
jackmedda
2023-02-18T16:22:58Z
11
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-02-18T15:01:42Z
--- 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: jackmedda/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
CoreyMorris/lander-delete-me
CoreyMorris
2023-02-18T16:12:47Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T16:07:28Z
--- 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: -105.96 +/- 18.63 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': 100000 'learning_rate': 0.00025 'num_envs': 16 'num_steps': 1024 'anneal_lr': True 'gamma': 0.999 'gae_lambda': 0.98 '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': 'CoreyMorris/lander-delete-me' 'batch_size': 16384 'minibatch_size': 4096} ```
wang12s/Nullmix
wang12s
2023-02-18T16:02:28Z
0
0
null
[ "arxiv:1910.09700", "region:us" ]
null
2023-02-18T09:03:59Z
--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). # Model Details ## Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ## Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] # Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ## Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ## Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ## Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] # Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ## Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] # Training Details ## Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ## Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> ### Preprocessing [optional] [More Information Needed] ### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> ### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] # Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ## Testing Data, Factors & Metrics ### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] ### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] ### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ## Results [More Information Needed] ### Summary # Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] # Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] # Technical Specifications [optional] ## Model Architecture and Objective [More Information Needed] ## Compute Infrastructure [More Information Needed] ### Hardware [More Information Needed] ### Software [More Information Needed] # Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] # Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] # More Information [optional] [More Information Needed] # Model Card Authors [optional] [More Information Needed] # Model Card Contact [More Information Needed]
AntiSquid/Reinforce-Pixelcopter-PLE-v0
AntiSquid
2023-02-18T15:51:59Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T11:58:04Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 44.20 +/- 30.98 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
EllenWST/Cindy
EllenWST
2023-02-18T15:15:12Z
0
0
null
[ "ab", "dataset:Gustavosta/Stable-Diffusion-Prompts", "license:openrail", "region:us" ]
null
2023-02-18T15:13:01Z
--- license: openrail datasets: - Gustavosta/Stable-Diffusion-Prompts language: - ab ---
LucaReggiani/t5-small-nlpfinalproject3-xsum
LucaReggiani
2023-02-18T14:49:19Z
3
0
transformers
[ "transformers", "tf", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-18T14:37:47Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: LucaReggiani/t5-small-nlpfinalproject3-xsum results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # LucaReggiani/t5-small-nlpfinalproject3-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9916 - Validation Loss: 2.9840 - Train Rouge1: 22.3282 - Train Rouge2: 4.7253 - Train Rougel: 17.9286 - Train Rougelsum: 17.9126 - Train Gen Len: 18.54 - Epoch: 7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.98, 'epsilon': 1e-06, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 3.7453 | 3.2008 | 19.6619 | 3.4413 | 15.6174 | 15.6444 | 18.23 | 0 | | 3.3973 | 3.1041 | 21.1867 | 4.1818 | 16.5584 | 16.4568 | 18.59 | 1 | | 3.2886 | 3.0600 | 21.8364 | 4.3416 | 16.6696 | 16.6382 | 18.5 | 2 | | 3.2216 | 3.0323 | 23.5970 | 5.3080 | 18.4737 | 18.3755 | 18.49 | 3 | | 3.1462 | 3.0174 | 23.0720 | 5.3486 | 18.5011 | 18.4635 | 18.62 | 4 | | 3.0860 | 3.0017 | 22.3949 | 4.7088 | 17.7759 | 17.7328 | 18.51 | 5 | | 3.0436 | 2.9890 | 22.8096 | 4.9911 | 18.1200 | 18.0347 | 18.47 | 6 | | 2.9916 | 2.9840 | 22.3282 | 4.7253 | 17.9286 | 17.9126 | 18.54 | 7 | ### Framework versions - Transformers 4.26.1 - TensorFlow 2.11.0 - Datasets 2.9.0 - Tokenizers 0.13.2
hpoddar/ppo-Huggy
hpoddar
2023-02-18T14:46:43Z
11
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-02-18T14:46:36Z
--- 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: hpoddar/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
rdesarz/q-FrozenLake-v1-4x4-noSlippery
rdesarz
2023-02-18T14:44:13Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T14:44:09Z
--- 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="rdesarz/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"]) ```
ibadrehman/ppo-Pyramids
ibadrehman
2023-02-18T14:33:27Z
4
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-02-18T14:33:21Z
--- 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: ibadrehman/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
NielsV/distilbart-cnn-6-6-reddit
NielsV
2023-02-18T14:27:47Z
31
0
transformers
[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "dataset:reddit", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-01-18T22:28:24Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - reddit metrics: - rouge model-index: - name: distilbart-cnn-6-6-reddit results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: reddit type: reddit config: default split: train args: default metrics: - name: Rouge1 type: rouge value: 0.1849 --- # distilbart-cnn-6-6-reddit This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on the reddit dataset. It achieves the following results on the evaluation set: - Loss: 2.9883 - Rouge1: 0.1849 - Rouge2: 0.0437 - Rougel: 0.1273 - Rougelsum: 0.1601 ## More information and training script You can find more information about how this model was trained, including the actual training script in [this github repository](https://github.com/VerleysenNiels/arxiv-summarizer). ## Training and evaluation data I made a split in a train and test set. The test size is 1% of the total dataset, which comes down to about 38k samples. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:| | 3.13 | 1.0 | 238116 | 3.2736 | 0.1773 | 0.0392 | 0.1223 | 0.1539 | | 2.8586 | 2.0 | 476232 | 3.0449 | 0.1846 | 0.0431 | 0.127 | 0.1601 | | 2.7844 | 3.0 | 714348 | 2.9883 | 0.1849 | 0.0437 | 0.1273 | 0.1601 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
Zhengrui/bert2bert_redditJoke
Zhengrui
2023-02-18T14:24:20Z
6
0
transformers
[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "en", "dataset:SocialGrep/one-million-reddit-jokes", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-02-18T12:25:14Z
--- license: apache-2.0 datasets: - SocialGrep/one-million-reddit-jokes language: - en pipeline_tag: text2text-generation ---
saikiranp/ppo-LunarLandr-v2-CleanRL
saikiranp
2023-02-18T14:02:48Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T13:28:07Z
--- 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: -26.69 +/- 86.05 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': 2000000 'learning_rate': 0.00025 'num_envs': 16 'num_steps': 1024 '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': 'saikiranp/ppo-LunarLandr-v2-CleanRL' 'batch_size': 16384 'minibatch_size': 4096} ```
ZhihongDeng/a2c-PandaReachDense-v2
ZhihongDeng
2023-02-18T13:55:54Z
0
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T13:53:34Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -2.68 +/- 0.73 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 ... ```
Awaaaaa/ba
Awaaaaa
2023-02-18T13:48:15Z
0
0
null
[ "license:bigscience-openrail-m", "region:us" ]
null
2023-02-18T13:44:18Z
--- license: bigscience-openrail-m ---
ibadrehman/ppo-SnowballTarget
ibadrehman
2023-02-18T13:35:09Z
5
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-02-18T13:35:04Z
--- 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: ibadrehman/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Slitwrist/sd-1-5-brenda
Slitwrist
2023-02-18T13:29:35Z
4
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-02-18T13:26:43Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### SD-1-5-Brenda Dreambooth model trained by Slitwrist with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars). Sample pictures of this concept:
r1ck/v1-ppo-LunarLander-v2
r1ck
2023-02-18T13:26:04Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T13:23:38Z
--- 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: 257.13 +/- 18.70 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 ... ```
Taratata/Reinforce-Pixelcopter-PLE-v0
Taratata
2023-02-18T13:24:38Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-02-18T13:11:56Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 33.70 +/- 27.76 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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
Rafe350/rafeadtest2023-model1
Rafe350
2023-02-18T13:21:04Z
0
0
diffusers
[ "diffusers", "tensorboard", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-02-18T13:19:38Z
--- license: creativeml-openrail-m tags: - text-to-image widget: - text: rfehx --- ### RafeAdTest2023-Model1 Dreambooth model trained by Rafe350 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: rfehx (use that on your prompt) ![rfehx 0](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%281%29.jpg)![rfehx 1](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%282%29.jpg)![rfehx 2](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%283%29.jpg)![rfehx 3](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%284%29.jpg)![rfehx 4](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%285%29.jpg)![rfehx 5](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%286%29.jpg)![rfehx 6](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%287%29.jpg)![rfehx 7](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%288%29.jpg)![rfehx 8](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%289%29.jpg)![rfehx 9](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2810%29.jpg)![rfehx 10](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2811%29.jpg)![rfehx 11](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2812%29.jpg)![rfehx 12](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2813%29.jpg)![rfehx 13](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2814%29.jpg)![rfehx 14](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2815%29.jpg)![rfehx 15](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2816%29.jpg)![rfehx 16](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2817%29.jpg)![rfehx 17](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2818%29.jpg)![rfehx 18](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2819%29.jpg)![rfehx 19](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2820%29.jpg)![rfehx 20](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2821%29.jpg)![rfehx 21](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2822%29.jpg)![rfehx 22](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2823%29.jpg)![rfehx 23](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2824%29.jpg)![rfehx 24](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2825%29.jpg)![rfehx 25](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2826%29.jpg)![rfehx 26](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2827%29.jpg)![rfehx 27](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2828%29.jpg)![rfehx 28](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2829%29.jpg)![rfehx 29](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2830%29.jpg)![rfehx 30](https://huggingface.co/Rafe350/rafeadtest2023-model1/resolve/main/concept_images/rfehx_%2831%29.jpg)