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muhtasham/whisper-medium-tg_tj
muhtasham
2022-12-20T15:26:32Z
23
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "hf-asr-leaderboard", "dataset:google/fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-08T16:31:29Z
--- license: apache-2.0 tags: - whisper-event - generated_from_trainer - hf-asr-leaderboard datasets: - google/fleurs metrics: - wer model-index: - name: Whisper Medium Tajik results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs type: google/fleurs config: tg_tj split: test args: tg_tj metrics: - name: Wer type: wer value: 23.153018764230197 --- <!-- 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. --> # Whisper Medium Tajik This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs tg_tj dataset. It achieves the following results on the evaluation set: - Loss: 0.9217 - Wer: 23.1530 ## 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: 16 - 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: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0016 | 66.0 | 1000 | 0.6929 | 24.2993 | | 0.0001 | 133.0 | 2000 | 0.8054 | 23.3022 | | 0.0001 | 199.0 | 3000 | 0.8652 | 23.2237 | | 0.0 | 266.0 | 4000 | 0.9019 | 23.2394 | | 0.0 | 333.0 | 5000 | 0.9217 | 23.1530 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
daripaez/q-Taxi-v3
daripaez
2022-12-20T15:16:15Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T15:16:07Z
--- 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="daripaez/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"]) ```
fuatenginoruc/ppo-Huggy
fuatenginoruc
2022-12-20T14:53:04Z
12
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-20T14:52:53Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: fuatenginoruc/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play πŸ‘€
wooihen/ppo-Huggy
wooihen
2022-12-20T14:43:29Z
5
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-20T14:42:53Z
--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy library_name: ml-agents --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Write your model_id: wooihen/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play πŸ‘€
daripaez/q-FrozenLake-v1-4x4-noSlippery
daripaez
2022-12-20T14:41:30Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T14:41:26Z
--- 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="daripaez/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"]) ```
kzipa/taxi
kzipa
2022-12-20T14:35:09Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T14:34:57Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 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="kzipa/taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Tavakoli/whisper-small-fa
Tavakoli
2022-12-20T14:22:36Z
26
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "fa", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-18T09:58:17Z
--- language: - fa license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small fa - ai_farsi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: es split: test args: es metrics: - name: Wer type: wer value: 10.073919143629183 --- <!-- 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. --> # Whisper Small fa - ai_farsi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2688 - Wer: 10.0739 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 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: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2607 | 0.2 | 1000 | 0.3368 | 13.1593 | | 0.2811 | 0.4 | 2000 | 0.3133 | 12.1733 | | 0.2166 | 0.6 | 3000 | 0.2933 | 10.9878 | | 0.2647 | 0.8 | 4000 | 0.2776 | 10.5254 | | 0.2402 | 1.0 | 5000 | 0.2688 | 10.0739 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
RayKau/taxi
RayKau
2022-12-20T14:12:37Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T14:12:30Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.78 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="RayKau/taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
jessietextstan/few-shot-model
jessietextstan
2022-12-20T14:11:11Z
1
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-12-20T14:10:59Z
--- 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 40 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 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": 40, "warmup_steps": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
RayKau/q-FrozenLake-v1-4x4-noSlippery
RayKau
2022-12-20T14:09:57Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T14:09:50Z
--- 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="RayKau/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"]) ```
dalist/speechal
dalist
2022-12-20T14:07:01Z
0
0
null
[ "whisper-event", "region:us" ]
null
2022-12-20T13:08:48Z
--- title: Whisper Demo emoji: 🀫 colorFrom: indigo colorTo: red sdk: gradio sdk_version: 3.9.1 app_file: app.py pinned: false tags: - whisper-event --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
DanielViniciusAlves/outputs
DanielViniciusAlves
2022-12-20T13:56:05Z
3
0
transformers
[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-12-19T09:27:18Z
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: outputs 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. --> # outputs This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1299 - F1: 0.7010 ## 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: 8e-05 - train_batch_size: 256 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 23 | 0.2190 | 0.7611 | | No log | 2.0 | 46 | 0.1212 | 0.2309 | | No log | 3.0 | 69 | 0.1235 | 0.6229 | | No log | 4.0 | 92 | 0.1299 | 0.7010 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
xmzhu/whisper-small-zh
xmzhu
2022-12-20T13:54:29Z
22
6
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "zh", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-19T22:04:01Z
--- language: - zh license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Chinese results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 zh-CN type: mozilla-foundation/common_voice_11_0 config: zh-CN split: test args: zh-CN metrics: - name: Wer type: wer value: 72.36255572065379 --- <!-- 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. --> # Whisper Small Chinese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 zh-CN dataset. It achieves the following results on the evaluation set: - Loss: 0.3946 - Wer: 72.3626 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5179 | 2.02 | 1000 | 0.3333 | 72.9831 | | 0.1273 | 4.04 | 2000 | 0.3562 | 73.9621 | | 0.0163 | 6.06 | 3000 | 0.3790 | 73.9708 | | 0.004 | 8.07 | 4000 | 0.3946 | 72.3626 | | 0.025 | 11.0 | 5000 | 0.4019 | 72.6772 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
hedronstone/whisper-large-v2-sw
hedronstone
2022-12-20T13:53:16Z
75
1
transformers
[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "sw", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-09T14:47:18Z
--- language: - sw license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: whisper-large-v2-sw results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: sw split: test args: 'config: sw, split: test' metrics: - name: Wer type: wer value: 30.7 --- ## Model * Name: Whisper Large-v2 Swahili * Description: Whisper weights for speech-to-text task, fine-tuned and evaluated on normalized data. * Dataset: - Train and validation splits for Swahili subsets of [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). - Train, validation and test splits for Swahili subsets of [Google Fleurs](https://huggingface.co/datasets/google/fleurs/). * Performance: **30.7 WER** ## Weights * Date of release: 12.09.2022 * License: MIT ## Usage To use these weights in HuggingFace's `transformers` library, you can do the following: ```python from transformers import WhisperForConditionalGeneration model = WhisperForConditionalGeneration.from_pretrained("hedronstone/whisper-large-v2-sw") ```
hedronstone/whisper-medium-sw
hedronstone
2022-12-20T13:52:49Z
7
1
transformers
[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "sw", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-10T17:06:05Z
--- language: - sw license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: whisper-medium-sw results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: sw split: test args: 'config: sw, split: test' metrics: - name: Wer type: wer value: 30.51 --- ## Model * Name: Whisper Medium Swahili * Description: Whisper weights for speech-to-text task, fine-tuned and evaluated on normalized data. * Performance: **30.51 WER** ## Weights * Date of release: 12.09.2022 * License: MIT ## Usage To use these weights in HuggingFace's `transformers` library, you can do the following: ```python from transformers import WhisperForConditionalGeneration model = WhisperForConditionalGeneration.from_pretrained("hedronstone/whisper-small-sw") ```
Scrya/whisper-large-v2-cantonese
Scrya
2022-12-20T13:47:04Z
161
6
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "yue", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-19T11:45:17Z
--- language: - yue license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Large V2 - Cantonese - Augmented results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: mozilla-foundation/common_voice_11_0 type: mozilla-foundation/common_voice_11_0 config: yue split: test metrics: - type: cer value: 6.213317142278891 name: CER --- <!-- 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. --> # Whisper Large V2 - Cantonese - Augmented This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1828 - Cer: 6.2133 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data Training: - [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation) Evaluation: - [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (test) ## Training procedure Datasets were augmented on-the-fly using [audiomentations](https://github.com/iver56/audiomentations) via PitchShift and TimeStretch transformations at `p=0.3`. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1126 | 1.21 | 200 | 0.1666 | 7.3103 | | 0.0467 | 2.42 | 400 | 0.1610 | 6.9419 | | 0.0217 | 3.63 | 600 | 0.1621 | 6.3874 | | 0.008 | 4.85 | 800 | 0.1699 | 6.3064 | | 0.0023 | 6.06 | 1000 | 0.1828 | 6.2133 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
Arch4ngel/ppo-BreakoutNoFrameskip-v4
Arch4ngel
2022-12-20T13:30:55Z
5
0
stable-baselines3
[ "stable-baselines3", "BreakoutNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T13:30:17Z
--- library_name: stable-baselines3 tags: - BreakoutNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BreakoutNoFrameskip-v4 type: BreakoutNoFrameskip-v4 metrics: - type: mean_reward value: 19.10 +/- 4.83 name: mean_reward verified: false --- # **PPO** Agent playing **BreakoutNoFrameskip-v4** This is a trained model of a **PPO** agent playing **BreakoutNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo ppo --env BreakoutNoFrameskip-v4 -orga Arch4ngel -f logs/ python enjoy.py --algo ppo --env BreakoutNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo ppo --env BreakoutNoFrameskip-v4 -orga Arch4ngel -f logs/ rl_zoo3 enjoy --algo ppo --env BreakoutNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo ppo --env BreakoutNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo ppo --env BreakoutNoFrameskip-v4 -f logs/ -orga Arch4ngel ``` ## Hyperparameters ```python OrderedDict([('batch_size', 256), ('clip_range', 'lin_0.1'), ('ent_coef', 0.01), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('frame_stack', 4), ('learning_rate', 'lin_2.5e-4'), ('n_envs', 8), ('n_epochs', 4), ('n_steps', 128), ('n_timesteps', 1000000.0), ('policy', 'CnnPolicy'), ('vf_coef', 0.5), ('normalize', False)]) ```
andge/q-Taxi-v3
andge
2022-12-20T13:26:23Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T13:26:17Z
--- 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: 7.52 +/- 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="andge/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"]) ```
gobbledegook/t5-small-lm-adapt-quotes
gobbledegook
2022-12-20T13:08:43Z
3
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-12-19T13:40:59Z
* Must prefix prompts with `"write: "`, as in `"write: Today is a great day"`. * Finetuned from t5-small-lm-adapt.
hrishikeshagi/imageclassifier
hrishikeshagi
2022-12-20T12:45:27Z
0
0
null
[ "region:us" ]
null
2022-12-20T12:30:08Z
# -*- coding: utf-8 -*- """imageClassifier.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1S-yO7cqOfeKz8Iu1h-DwhTgkkGml0tKb """ pip install git+https://github.com/huggingface/transformers.git from transformers import ViTFeatureExtractor, ViTForImageClassification from PIL import Image import requests url = 'https://www.livechennai.com/businesslistings/News_photo/dosa11218.jpg' image = Image.open(requests.get(url, stream=True).raw) display(image) feature_extractor = ViTFeatureExtractor.from_pretrained("Amrrs/south-indian-foods") model = ViTForImageClassification.from_pretrained("Amrrs/south-indian-foods") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx])
Jingmiao/whisper-small-chineseBaseTW
Jingmiao
2022-12-20T12:39:27Z
7
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "zh", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-20T00:49:33Z
--- language: - zh license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small TW on Chinese base results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 zh-TW type: mozilla-foundation/common_voice_11_0 config: zh-TW split: test args: zh-TW metrics: - name: Wer type: wer value: 41.90378710337769 --- <!-- 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. --> # Whisper Small TW on Chinese base This model is a fine-tuned version of [Jingmiao/whisper-small-chinese_base](https://huggingface.co/Jingmiao/whisper-small-chinese_base) on the mozilla-foundation/common_voice_11_0 zh-TW dataset. It achieves the following results on the evaluation set: - Loss: 0.2601 - Wer: 41.9038 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0071 | 6.02 | 1000 | 0.2364 | 42.6407 | | 0.0008 | 13.02 | 2000 | 0.2601 | 41.9038 | | 0.0004 | 20.01 | 3000 | 0.2771 | 42.3951 | | 0.0003 | 27.0 | 4000 | 0.2867 | 42.6407 | | 0.0002 | 33.02 | 5000 | 0.2901 | 42.6407 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
anuragshas/whisper-large-v2-ne
anuragshas
2022-12-20T12:33:07Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "ne", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-20T09:21:28Z
--- language: - ne license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large-v2 Nepali results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 ne-NP type: mozilla-foundation/common_voice_11_0 config: ne-NP split: test args: ne-NP metrics: - name: Wer type: wer value: 56.09756097560976 --- <!-- 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. --> # Whisper Large-v2 Nepali This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 ne-NP dataset. It achieves the following results on the evaluation set: - Loss: 1.5723 - Wer: 56.0976 ## 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: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0 | 999.0 | 1000 | 1.5723 | 56.0976 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
kzipa/ppo-Huggy
kzipa
2022-12-20T12:22:26Z
2
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-20T12:22:17Z
--- 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: kzipa/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play πŸ‘€
emilios/whisper-sm-farsipal-e5
emilios
2022-12-20T12:14:20Z
9
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "el", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-19T22:10:13Z
--- language: - el license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0,google/fleurs metrics: - wer model-index: - name: Whisper small Greek Farsipal and El Greco results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr type: mozilla-foundation/common_voice_11_0,google/fleurs config: el split: None metrics: - name: Wer type: wer value: 17.199108469539375 --- <!-- 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. --> # Whisper small Greek Farsioal and El Greco This model is a fine-tuned version of [emilios/whisper-sm-el-farsipal-e4](https://huggingface.co/emilios/whisper-sm-el-farsipal-e4) on the mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr dataset. It achieves the following results on the evaluation set: - Loss: 0.4871 - Wer: 17.1991 ## 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-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 20000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1259 | 2.49 | 1000 | 0.4834 | 18.3692 | | 0.1002 | 4.49 | 2000 | 0.4604 | 17.8027 | | 0.1096 | 6.98 | 3000 | 0.4553 | 17.8770 | | 0.0885 | 9.46 | 4000 | 0.4551 | 17.9606 | | 0.0675 | 11.95 | 5000 | 0.4631 | 17.9049 | | 0.0675 | 14.44 | 6000 | 0.4619 | 17.9049 | | 0.0645 | 16.93 | 7000 | 0.4678 | 17.6727 | | 0.0535 | 19.41 | 8000 | 0.4685 | 17.6634 | | 0.039 | 21.49 | 9000 | 0.4746 | 17.6727 | | 0.0447 | 23.98 | 10000 | 0.4761 | 17.6634 | | 0.0393 | 26.46 | 11000 | 0.4792 | 17.7656 | | 0.0308 | 28.95 | 12000 | 0.4851 | 17.8678 | | 0.0301 | 31.44 | 13000 | 0.4846 | 17.4499 | | 0.031 | 33.93 | 14000 | 0.4849 | 17.8306 | | 0.0263 | 36.41 | 15000 | 0.4880 | 17.6170 | | 0.0256 | 38.9 | 16000 | 0.4871 | 17.1991 | | 0.0236 | 41.39 | 17000 | 0.4883 | 17.2641 | | 0.0195 | 43.88 | 18000 | 0.4880 | 17.5706 | | 0.0193 | 46.36 | 19000 | 0.4993 | 17.7285 | | 0.0161 | 48.85 | 20000 | 0.4968 | 17.8306 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 2.0.0.dev20221216+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
rmartinshort/ddpm-celebahq-finetuned-butterflies-2epochs
rmartinshort
2022-12-20T11:33:18Z
1
0
diffusers
[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
2022-12-20T11:33:01Z
--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('rmartinshort/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```
sryu1/dqn-CartPole-v1
sryu1
2022-12-20T11:18:25Z
3
0
stable-baselines3
[ "stable-baselines3", "CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T11:17:57Z
--- library_name: stable-baselines3 tags: - CartPole-v1 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **DQN** Agent playing **CartPole-v1** This is a trained model of a **DQN** agent playing **CartPole-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env CartPole-v1 -orga sryu1 -f logs/ python enjoy.py --algo dqn --env CartPole-v1 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env CartPole-v1 -orga sryu1 -f logs/ rl_zoo3 enjoy --algo dqn --env CartPole-v1 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env CartPole-v1 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env CartPole-v1 -f logs/ -orga sryu1 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('exploration_final_eps', 0.04), ('exploration_fraction', 0.16), ('gamma', 0.99), ('gradient_steps', 128), ('learning_rate', 0.0023), ('learning_starts', 1000), ('n_timesteps', 50000.0), ('policy', 'MlpPolicy'), ('policy_kwargs', 'dict(net_arch=[256, 256])'), ('target_update_interval', 10), ('train_freq', 256), ('normalize', False)]) ```
SiddhantKadwe/ppo-LunarLander-v2-TEST
SiddhantKadwe
2022-12-20T11:06:57Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T10:40:55Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 251.02 +/- 23.15 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 ... ```
cahya/whisper-large-id
cahya
2022-12-20T10:22:52Z
137
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "id", "dataset:mozilla-foundation/common_voice_11_0", "dataset:magic_data", "dataset:TITML", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-08T10:09:52Z
--- language: - id license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 - magic_data - TITML metrics: - wer model-index: - name: Whisper Large Indonesian results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 id type: mozilla-foundation/common_voice_11_0 config: id split: test metrics: - name: Wer type: wer value: 6.248270773771097 --- <!-- 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. --> # Whisper Large Indonesian This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the mozilla-foundation/common_voice_11_0, magic_data, titml id dataset. It achieves the following results on the evaluation set: - Loss: 0.2034 - Wer: 6.2483 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1516 | 0.5 | 1000 | 0.1730 | 6.5664 | | 0.1081 | 1.0 | 2000 | 0.1638 | 6.3682 | | 0.0715 | 1.49 | 3000 | 0.1803 | 6.2713 | | 0.1009 | 1.99 | 4000 | 0.1796 | 6.2667 | | 0.0387 | 2.49 | 5000 | 0.2054 | 6.4927 | | 0.0494 | 2.99 | 6000 | 0.2034 | 6.2483 | | 0.0259 | 3.48 | 7000 | 0.2226 | 6.3497 | | 0.0265 | 3.98 | 8000 | 0.2274 | 6.4004 | | 0.0232 | 4.48 | 9000 | 0.2443 | 6.5618 | | 0.015 | 4.98 | 10000 | 0.2413 | 6.4927 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
teacookies/autotrain-20-12-2022_exam_part3-2543877946
teacookies
2022-12-20T10:12:28Z
17
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "autotrain", "unk", "dataset:teacookies/autotrain-data-20-12-2022_exam_part3", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-20T10:00:56Z
--- tags: - autotrain - token-classification language: - unk widget: - text: "I love AutoTrain πŸ€—" datasets: - teacookies/autotrain-data-20-12-2022_exam_part3 co2_eq_emissions: emissions: 21.733051144065605 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 2543877946 - CO2 Emissions (in grams): 21.7331 ## Validation Metrics - Loss: 0.010 - Accuracy: 0.998 - Precision: 0.739 - Recall: 0.786 - F1: 0.762 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/teacookies/autotrain-20-12-2022_exam_part3-2543877946 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-20-12-2022_exam_part3-2543877946", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-20-12-2022_exam_part3-2543877946", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
spaablauw/HyperNuke
spaablauw
2022-12-20T10:01:04Z
0
7
null
[ "license:wtfpl", "region:us" ]
null
2022-12-20T09:53:21Z
--- license: wtfpl --- Doesn't always work, but gets you a lot closer quickly compared to just trying keywords. Dataset of 18 images, 3 vectors, batch size of 3, 3 steps gradient accumulation, trained for 500 steps or 250 epochs. ![15196-4029360196-HyperNuke, (((((skull)) shaped cloud))), volumetric lighting, aerial shot,dblx, vorticity and turbulence, orange glow, explosio.png](https://s3.amazonaws.com/moonup/production/uploads/1671530137257-6312579fc7577b68d90a7646.png) ![15193-2262279174-HyperNuke, (((skull shaped cloud))), volumetric lighting, aerial shot,dblx, vorticity and turbulence, orange glow, explosion, t.png](https://s3.amazonaws.com/moonup/production/uploads/1671530139199-6312579fc7577b68d90a7646.png) ![15182-3389199702-HyperNuke, volumetric lighting, aerial shot, vorticity and turbulence, orange glow, explosion, tsar bomba, clouds, desert, vibr.png](https://s3.amazonaws.com/moonup/production/uploads/1671530421685-6312579fc7577b68d90a7646.png) ![15209-3282920038-HyperNuke, aerial shot,vorticity and turbulence, orange glow, clouds, desert, vibrant, hasselblad, extremely detailed, actionh.png](https://s3.amazonaws.com/moonup/production/uploads/1671530305984-6312579fc7577b68d90a7646.png)
fcakyon/yolov5n-cls-v7.0
fcakyon
2022-12-20T09:56:24Z
20
2
transformers
[ "transformers", "image-classification", "computer-vision", "vision", "yolo", "yolov5", "license:gpl-3.0", "region:us" ]
image-classification
2022-12-13T22:42:21Z
--- license: gpl-3.0 inference: false tags: - image-classification - computer-vision - vision - yolo - yolov5 --- ### How to use - Install yolov5: ```bash pip install -U yolov5 ``` - Load model and perform prediction: ```python import yolov5 # load model model = yolov5.load('fcakyon/yolov5n-cls-v7.0') # set image img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' # perform inference results = model(img) ``` - Finetune the model on your custom dataset: ```bash yolov5 classify train --img 128 --data mnist2560 --model fcakyon/yolov5n-cls-v7.0 --epochs 1 --device cpu ```
fcakyon/yolov5n-seg-v7.0
fcakyon
2022-12-20T09:55:41Z
2
0
transformers
[ "transformers", "instance-segmentation", "computer-vision", "vision", "yolo", "yolov5", "license:gpl-3.0", "region:us" ]
null
2022-12-13T22:51:17Z
--- license: gpl-3.0 inference: false tags: - instance-segmentation - computer-vision - vision - yolo - yolov5 --- ### How to use - Install yolov5: ```bash pip install -U yolov5 ``` - Load model and perform prediction: ```python import yolov5 # load model model = yolov5.load('fcakyon/yolov5n-seg-v7.0') # set image img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' # perform inference results = model(img) ``` - Finetune the model on your custom dataset: ```bash yolov5 segment train --img 128 --weights fcakyon/yolov5n-seg-v7.0 --epochs 1 --device cpu ```
IceKingBing/distilbert-imdb
IceKingBing
2022-12-20T09:52:44Z
3
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-12-20T08:07:35Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-imdb 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-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb 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: 5e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 391 | 0.1847 | 0.9291 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.12.0+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
fcakyon/yolov5s-v7.0
fcakyon
2022-12-20T09:51:11Z
65
13
transformers
[ "transformers", "object-detection", "computer-vision", "vision", "yolo", "yolov5", "dataset:detection-datasets/coco", "license:gpl-3.0", "region:us" ]
object-detection
2022-12-13T21:26:21Z
--- license: gpl-3.0 inference: false tags: - object-detection - computer-vision - vision - yolo - yolov5 datasets: - detection-datasets/coco --- ### How to use - Install yolov5: ```bash pip install -U yolov5 ``` - Load model and perform prediction: ```python import yolov5 # load model model = yolov5.load('fcakyon/yolov5s-v7.0') # set model parameters model.conf = 0.25 # NMS confidence threshold model.iou = 0.45 # NMS IoU threshold model.agnostic = False # NMS class-agnostic model.multi_label = False # NMS multiple labels per box model.max_det = 1000 # maximum number of detections per image # set image img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' # perform inference results = model(img) # inference with larger input size results = model(img, size=640) # inference with test time augmentation results = model(img, augment=True) # parse results predictions = results.pred[0] boxes = predictions[:, :4] # x1, y1, x2, y2 scores = predictions[:, 4] categories = predictions[:, 5] # show detection bounding boxes on image results.show() # save results into "results/" folder results.save(save_dir='results/') ``` - Finetune the model on your custom dataset: ```bash yolov5 train --img 640 --batch 16 --weights fcakyon/yolov5s-v7.0 --epochs 10 --device cuda:0 ```
IngoTB303/q-Taxi-v3
IngoTB303
2022-12-20T09:41:46Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T09:22:38Z
--- 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="IngoTB303/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"]) ```
Gabriel/bart-base-cnn-swe
Gabriel
2022-12-20T09:37:08Z
50
1
transformers
[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "summarization", "sv", "dataset:Gabriel/cnn_daily_swe", "license:mit", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2022-08-26T05:26:14Z
--- language: sv license: mit tags: - summarization datasets: - Gabriel/cnn_daily_swe widget: - text: 'Frankrike lΓ₯s Sebastien Chabal har nΓ€mnts fΓΆr en farlig tackling pΓ₯ Englands Simon Shaw under lΓΆrdagens VM semifinal i Paris. Simon Shaw lastar av trots att Raphael Ibanez, vΓ€nster, och Sebastien Chabal. Sale Sharks framΓ₯t kommer att stΓ€llas infΓΆr en disciplinΓ€r utfrΓ₯gning pΓ₯ mΓ₯ndag efter hans tackling pΓ₯ motsatt andra-rower Shaw noterades genom att citera kommissionΓ€r Dennis Wheelahan. Chabal bΓΆrjade matchen pΓ₯ ersΓ€ttningsbΓ€nken, men kom i 26: e minuten att ersΓ€tta den skadade Fabien Pelous under vΓ€rd Frankrikes 14-9 nederlag. Om han blir avstΓ€ngd missar Chabal fredagens tredje och fjΓ€rde match pΓ₯ Parc des Princes. Samtidigt, Frankrike trΓ€nare Bernard Laporte sade att nederlaget var svΓ₯rare att ta Γ€n Englands 24-7 seger i 2003 semifinalen. "Γ…r 2003 var de bΓ€ttre Γ€n oss. I sjΓ€lva verket var de bΓ€ttre Γ€n alla", sade Laporte, som lΓ€mnar sin roll att tilltrΓ€da posten som junior idrottsminister i den franska regeringen. "De var som Nya Zeeland i denna turnering - favoriten, fΓΆrutom att de gick hela vΓ€gen. Den hΓ€r gΓ₯ngen Γ€r det svΓ₯rare fΓΆr igΓ₯r var det 50-50." Samtidigt, England -- fΓΆrsΓΆker bli den fΓΆrsta nationen att fΓΆrsvara VM-titeln -- avslΓΆjade att stjΓ€rna kicker Jonny Wilkinson Γ₯terigen hade problem med matchbollarna under semifinalen. Flughalvan, som uttryckte sin oro efter att ha kΓ€mpat med stΓΆveln mot Australien, avvisade en boll innan han sparkade en vital trepoΓ€ngare mot Frankrike. "Vi sa det inte fΓΆrra veckan men en icke-match bollen kom ut pΓ₯ fΓ€ltet i Marseille som Jonny sparkade," chef fΓΆr rugby Rob Andrew sade. "Han tΓ€nkte inte pΓ₯ det nΓ€r han sparkade det. Matchbollarna Γ€r mΓ€rkta, numrerade ett till sex. IgΓ₯r kvΓ€ll hade de "World Cup semifinal England vs Frankrike" skrivet pΓ₯ dem. PΓ₯ matchkvΓ€llen var Jonny vaksam nΓ€r han sparkade fΓΆr mΓ₯l att de faktiskt var matchbollar han sparkade. "TrΓ€ningsbollarna fΓΆrlorar tryck och form. Hela frΓ₯gan fΓΆrra veckan, arrangΓΆrerna accepterade alla sex matchbollar bΓΆr anvΓ€ndas av bΓ₯da sidor pΓ₯ torsdagen fΓΆre matchen. " E-post till en vΓ€n.' inference: parameters: temperature: 0.7 min_length: 30 max_length: 120 train-eval-index: - config: Gabriel--xsum_swe task: summarization task_id: summarization splits: eval_split: test col_mapping: document: text summary: target co2_eq_emissions: emissions: 0.0334 source: Google Colab training_type: fine-tuning geographical_location: Fredericia, Denmark hardware_used: Tesla P100-PCIE-16GB model-index: - name: bart-base-cnn-swe results: - task: type: summarization name: summarization dataset: name: Gabriel/cnn_daily_swe type: Gabriel/cnn_daily_swe split: validation metrics: - type: rouge-1 value: 22.2046 name: Validation ROGUE-1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2U4YjhhNzhjYmQ2ODY2YWU1NzY5Y2RkYzIzNTUxOTA0MmUwZjNmYTdjNmE5OWNkODhhYWExOGNkOGJkNTBkZiIsInZlcnNpb24iOjF9.lEMnUJOeoa5LepNmjsRflkQmtJAaVo03ocSs9JJuxbLeu4x0oY-XsML3O1IfDJQuvlO4WHZviykRLabkhTtbBQ - type: rouge-2 value: 10.4332 name: Validation ROGUE-2 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODkzOGYxNTNiMjc1MGIyODAzY2Q2MjA0NTg1N2NjODc5YWZlZGI5Y2Q3ZDAwNTQyMjA4MmJjOGUxYzVlOGFlNyIsInZlcnNpb24iOjF9.lpn8VP1_AvHXvyYDJHEfMoIpb-_B5fXvMaMQ249Tngo3HBPrexPmhEvGqj1HVnVGxVFyDFhF2tYh4AhThguoBQ - type: rouge-l value: 18.1753 name: Validation ROGUE-L verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWRkM2ViZjRmYmNkZTI1NWE1ODUzNGMwZTYzNzU1Yjk1ODM3NDNkZWMwMjc1ZGJmZGZkNWQxYThmN2ZiZDhjZCIsInZlcnNpb24iOjF9.6ENOPrpZ245V0jcZtiSOmWwdr06W9prPAyE9Qjnn5meiE7yoc0T0oquE9d8SLOfFqYcIbCb6vVUSVxFewj_VAA - type: rouge-l-sum value: 20.846 name: Validation ROGUE-L-SUM verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDJjODc0MTQwYTM5OGRmMjE2ODUxNGM4YmYxMTJlNDE1MzI0M2Q2ZGJkZDlkOWE2NTMxNjI0YjZjMDQwYjNjNyIsInZlcnNpb24iOjF9.FvNGRVWTCJSafucQKp3eW1B__SAHCL7qHBzooe8ufYIijnNuope0W7AIphex1WMT_9o_Unni2vPGFUvT2o9qAg --- # bart-base-cnn-swe This model is a W.I.P ## Model description BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. This model is a fine-tuned version of [KBLab/bart-base-swedish-cased](https://huggingface.co/KBLab/bart-base-swedish-cased) on the [Gabriel/bart-base-cnn-swe](https://huggingface.co/datasets/Gabriel/cnn_daily_swe) dataset and can be used for summarization tasks. ## Intended uses & limitations This model should only be used to fine-tune further on and summarization tasks. ```python from transformers import pipeline summarizer = pipeline("summarization", model="Gabriel/bart-base-cnn-swe") ARTICLE = """ Frankrike lΓ₯s Sebastien Chabal har nΓ€mnts fΓΆr en farlig tackling pΓ₯ Englands Simon Shaw under lΓΆrdagens VM semifinal i Paris. Simon Shaw lastar av trots att Raphael Ibanez, vΓ€nster, och Sebastien Chabal. Sale Sharks framΓ₯t kommer att stΓ€llas infΓΆr en disciplinΓ€r utfrΓ₯gning pΓ₯ mΓ₯ndag efter hans tackling pΓ₯ motsatt andra-rower Shaw noterades genom att citera kommissionΓ€r Dennis Wheelahan. Chabal bΓΆrjade matchen pΓ₯ ersΓ€ttningsbΓ€nken, men kom i 26: e minuten att ersΓ€tta den skadade Fabien Pelous under vΓ€rd Frankrikes 14-9 nederlag. Om han blir avstΓ€ngd missar Chabal fredagens tredje och fjΓ€rde match pΓ₯ Parc des Princes. Samtidigt, Frankrike trΓ€nare Bernard Laporte sade att nederlaget var svΓ₯rare att ta Γ€n Englands 24-7 seger i 2003 semifinalen. "Γ…r 2003 var de bΓ€ttre Γ€n oss. I sjΓ€lva verket var de bΓ€ttre Γ€n alla", sade Laporte, som lΓ€mnar sin roll att tilltrΓ€da posten som junior idrottsminister i den franska regeringen. "De var som Nya Zeeland i denna turnering - favoriten, fΓΆrutom att de gick hela vΓ€gen. Den hΓ€r gΓ₯ngen Γ€r det svΓ₯rare fΓΆr igΓ₯r var det 50-50." Samtidigt, England -- fΓΆrsΓΆker bli den fΓΆrsta nationen att fΓΆrsvara VM-titeln -- avslΓΆjade att stjΓ€rna kicker Jonny Wilkinson Γ₯terigen hade problem med matchbollarna under semifinalen. Flughalvan, som uttryckte sin oro efter att ha kΓ€mpat med stΓΆveln mot Australien, avvisade en boll innan han sparkade en vital trepoΓ€ngare mot Frankrike. "Vi sa det inte fΓΆrra veckan men en icke-match bollen kom ut pΓ₯ fΓ€ltet i Marseille som Jonny sparkade," chef fΓΆr rugby Rob Andrew sade. "Han tΓ€nkte inte pΓ₯ det nΓ€r han sparkade det. Matchbollarna Γ€r mΓ€rkta, numrerade ett till sex. IgΓ₯r kvΓ€ll hade de "World Cup semifinal England vs Frankrike" skrivet pΓ₯ dem. PΓ₯ matchkvΓ€llen var Jonny vaksam nΓ€r han sparkade fΓΆr mΓ₯l att de faktiskt var matchbollar han sparkade. "TrΓ€ningsbollarna fΓΆrlorar tryck och form. Hela frΓ₯gan fΓΆrra veckan, arrangΓΆrerna accepterade alla sex matchbollar bΓΆr anvΓ€ndas av bΓ₯da sidor pΓ₯ torsdagen fΓΆre matchen. " E-post till en vΓ€n. """ print(summarizer(ARTICLE, max_length=130, min_length=30, num_beams=10 ,do_sample=False)) >>> [{'summary_text': """ Frankrike lΓ₯s Sebastien Chabal har nΓ€mnts fΓΆr en farlig tackling pΓ₯ Englands Simon Shaw under VM semifinal i Paris. Sale Sharks framΓ₯t kommer att stΓ€llas infΓΆr en disciplinΓ€r utfrΓ₯gning pΓ₯ mΓ₯ndag efter hans tackling pΓ₯ motsatt andra - rower Shaw noterades genom att citera kommissionΓ€r Dennis Wheelahan. Om Chabal blir avstΓ€ngd missar Chabal fredagens tredje och fjΓ€rde match pΓ₯ Parc des Princes."""}] ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2*2 = 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.2349 | 1.0 | 17944 | 2.0643 | 21.9564 | 10.2133 | 17.9958 | 20.6502 | 19.9992 | | 2.0726 | 2.0 | 35888 | 2.0253 | 22.0568 | 10.3302 | 18.0648 | 20.7482 | 19.9996 | | 1.8658 | 3.0 | 53832 | 2.0333 | 22.0871 | 10.2902 | 18.0577 | 20.7082 | 19.998 | | 1.8121 | 4.0 | 71776 | 1.9759 | 22.2046 | 10.4332 | 18.1753 | 20.846 | 19.9971 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
tmsreekanth98/keyphrase-extractions_fmlm
tmsreekanth98
2022-12-20T09:26:27Z
3
1
transformers
[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "dataset:kp20k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-12-20T05:33:16Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - kp20k model-index: - name: keyphrase-extractions_fmlm 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. --> # keyphrase-extractions_fmlm This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the kp20k dataset. It achieves the following results on the evaluation set: - Loss: 0.3347 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.3947 | 1.0 | 2125 | 0.3471 | | 0.3651 | 2.0 | 4250 | 0.3320 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
anuragshas/whisper-large-v2-ha
anuragshas
2022-12-20T09:26:08Z
8
1
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "ha", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-20T07:05:26Z
--- language: - ha license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large-v2 Hausa results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 ha type: mozilla-foundation/common_voice_11_0 config: ha split: test args: ha metrics: - name: Wer type: wer value: 37.406890130353815 --- <!-- 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. --> # Whisper Large-v2 Hausa This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 ha dataset. It achieves the following results on the evaluation set: - Loss: 0.8247 - Wer: 37.4069 ## 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: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0014 | 12.06 | 1000 | 0.8247 | 37.4069 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
teacookies/autotrain-20-12-2022_general_info_exam-2543777917
teacookies
2022-12-20T09:22:01Z
19
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "autotrain", "unk", "dataset:teacookies/autotrain-data-20-12-2022_general_info_exam", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-20T09:10:26Z
--- tags: - autotrain - token-classification language: - unk widget: - text: "I love AutoTrain πŸ€—" datasets: - teacookies/autotrain-data-20-12-2022_general_info_exam co2_eq_emissions: emissions: 21.038593211432406 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 2543777917 - CO2 Emissions (in grams): 21.0386 ## Validation Metrics - Loss: 0.007 - Accuracy: 0.998 - Precision: 0.937 - Recall: 0.952 - F1: 0.945 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/teacookies/autotrain-20-12-2022_general_info_exam-2543777917 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-20-12-2022_general_info_exam-2543777917", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-20-12-2022_general_info_exam-2543777917", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
waynedsouza/only_names_fullt
waynedsouza
2022-12-20T09:20:42Z
1
0
sentence-transformers
[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-12-20T08:03:15Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # waynedsouza/only_names_fullt This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 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('waynedsouza/only_names_fullt') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=waynedsouza/only_names_fullt) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1790 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 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": 10, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
sipheiroce/dqn-SpaceInvadersNoFrameskip-v4
sipheiroce
2022-12-20T08:42:29Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T08:41:53Z
--- 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: 408.50 +/- 127.91 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 ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sipheiroce -f logs/ python enjoy.py --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 sipheiroce -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 sipheiroce ``` ## 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', 300000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
Cwhgn/DAMO-YOLO-S
Cwhgn
2022-12-20T08:15:05Z
0
1
null
[ "arxiv:2211.15444", "license:apache-2.0", "region:us" ]
null
2022-12-20T08:12:30Z
--- license: apache-2.0 --- ## Model Description This **DAMO-YOLO-S** model is a small-size object detection model with fast inference speed and high accuracy, trained by **DAMO-YOLO**. DAMO-YOLO is a fast and accurate object detection method, which is developed by TinyML Team from Alibaba DAMO Data Analytics and Intelligence Lab. And it achieves a higher performance than state-of-the-art YOLO series. DAMO-YOLO is extend from YOLO but with some new techs, including Neural Architecture Search (NAS) backbones, efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. For more details, please refer to our [Arxiv Report](https://arxiv.org/abs/2211.15444) and [Github Code](https://github.com/tinyvision/DAMO-YOLO). Moreover, here you can find not only powerful models, but also highly efficient training strategies and complete tools from training to deployment. ## Chinese Web Demo - We also provide Chinese Web Demo on ModelScope, including [DAMO-YOLO-T](https://www.modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo-t/summary), [DAMO-YOLO-S](https://modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo/summary), [DAMO-YOLO-M](https://www.modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo-m/summary). ## Datasets The model is trained on COCO2017. ## Model Usage The usage guideline can be found in our [Quick Start Tutorial](https://github.com/tinyvision/DAMO-YOLO). ## Model Evaluation |Model |size |mAP<sup>val<br>0.5:0.95 | Latency T4<br>TRT-FP16-BS1| FLOPs<br>(G)| Params<br>(M)| Download | | ------ |:---: | :---: |:---:|:---: | :---: | :---:| |[DAMO-YOLO-T](./configs/damoyolo_tinynasL20_T.py) | 640 | 41.8 | 2.78 | 18.1 | 8.5 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL20_T_418.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL20_T_418.onnx) | |[DAMO-YOLO-T*](./configs/damoyolo_tinynasL20_T.py) | 640 | 43.0 | 2.78 | 18.1 | 8.5 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL20_T.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL20_T.onnx) | |[DAMO-YOLO-S](./configs/damoyolo_tinynasL25_S.py) | 640 | 45.6 | 3.83 | 37.8 | 16.3 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL25_S_456.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL25_S_456.onnx) | |[DAMO-YOLO-S*](./configs/damoyolo_tinynasL25_S.py) | 640 | 46.8 | 3.83 | 37.8 | 16.3 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL25_S.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL25_S.onnx) | |[DAMO-YOLO-M](./configs/damoyolo_tinynasL35_M.py) | 640 | 48.7 | 5.62 | 61.8 | 28.2 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL35_M_487.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL35_M_487.onnx)| |[DAMO-YOLO-M*](./configs/damoyolo_tinynasL35_M.py) | 640 | 50.0 | 5.62 | 61.8 | 28.2 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL35_M.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL35_M.onnx)| - We report the mAP of models on COCO2017 validation set, with multi-class NMS. - The latency in this table is measured without post-processing. - \* denotes the model trained with distillation. ## Cite DAMO-YOLO If you use DAMO-YOLO in your research, please cite our work by using the following BibTeX entry: ```latex @article{damoyolo, title={DAMO-YOLO: A Report on Real-Time Object Detection Design}, author={Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang and Xiuyu Sun}, journal={arXiv preprint arXiv:2211.15444v2}, year={2022}, } ```
Cwhgn/DAMO-YOLO-T
Cwhgn
2022-12-20T08:14:56Z
0
1
null
[ "arxiv:2211.15444", "license:apache-2.0", "region:us" ]
null
2022-12-20T06:55:12Z
--- license: apache-2.0 --- ## Model Description This **DAMO-YOLO-T** model is a tiny-size object detection model with fast inference speed and high accuracy, trained by **DAMO-YOLO**. DAMO-YOLO is a fast and accurate object detection method, which is developed by TinyML Team from Alibaba DAMO Data Analytics and Intelligence Lab. And it achieves a higher performance than state-of-the-art YOLO series. DAMO-YOLO is extend from YOLO but with some new techs, including Neural Architecture Search (NAS) backbones, efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. For more details, please refer to our [Arxiv Report](https://arxiv.org/abs/2211.15444) and [Github Code](https://github.com/tinyvision/DAMO-YOLO). Moreover, here you can find not only powerful models, but also highly efficient training strategies and complete tools from training to deployment. ## Chinese Web Demo - We also provide Chinese Web Demo on ModelScope, including [DAMO-YOLO-T](https://www.modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo-t/summary), [DAMO-YOLO-S](https://modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo/summary), [DAMO-YOLO-M](https://www.modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo-m/summary). ## Datasets The model is trained on COCO2017. ## Model Usage The usage guideline can be found in our [Quick Start Tutorial](https://github.com/tinyvision/DAMO-YOLO). ## Model Evaluation |Model |size |mAP<sup>val<br>0.5:0.95 | Latency T4<br>TRT-FP16-BS1| FLOPs<br>(G)| Params<br>(M)| Download | | ------ |:---: | :---: |:---:|:---: | :---: | :---:| |[DAMO-YOLO-T](./configs/damoyolo_tinynasL20_T.py) | 640 | 41.8 | 2.78 | 18.1 | 8.5 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL20_T_418.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL20_T_418.onnx) | |[DAMO-YOLO-T*](./configs/damoyolo_tinynasL20_T.py) | 640 | 43.0 | 2.78 | 18.1 | 8.5 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL20_T.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL20_T.onnx) | |[DAMO-YOLO-S](./configs/damoyolo_tinynasL25_S.py) | 640 | 45.6 | 3.83 | 37.8 | 16.3 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL25_S_456.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL25_S_456.onnx) | |[DAMO-YOLO-S*](./configs/damoyolo_tinynasL25_S.py) | 640 | 46.8 | 3.83 | 37.8 | 16.3 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL25_S.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL25_S.onnx) | |[DAMO-YOLO-M](./configs/damoyolo_tinynasL35_M.py) | 640 | 48.7 | 5.62 | 61.8 | 28.2 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL35_M_487.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL35_M_487.onnx)| |[DAMO-YOLO-M*](./configs/damoyolo_tinynasL35_M.py) | 640 | 50.0 | 5.62 | 61.8 | 28.2 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL35_M.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL35_M.onnx)| - We report the mAP of models on COCO2017 validation set, with multi-class NMS. - The latency in this table is measured without post-processing. - \* denotes the model trained with distillation. ## Cite DAMO-YOLO If you use DAMO-YOLO in your research, please cite our work by using the following BibTeX entry: ```latex @article{damoyolo, title={DAMO-YOLO: A Report on Real-Time Object Detection Design}, author={Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang and Xiuyu Sun}, journal={arXiv preprint arXiv:2211.15444v2}, year={2022}, } ```
aaryavartno1/shiva-new-model
aaryavartno1
2022-12-20T07:45:13Z
0
0
null
[ "license:bigscience-openrail-m", "region:us" ]
null
2022-12-20T07:45:02Z
--- license: bigscience-openrail-m ---
ltorrick/sd-class-butterflies-32
ltorrick
2022-12-20T07:27:05Z
0
0
diffusers
[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
2022-12-20T07:26:49Z
--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute πŸ¦‹. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('ltorrick/sd-class-butterflies-32') image = pipeline().images[0] image ```
makitanikaze/P5
makitanikaze
2022-12-20T07:22:32Z
10
21
null
[ "sequential-recommendation", "direct-recommendation", "explanation-generation", "en", "dataset:amazon_us_reviews", "dataset:yelp_review_full", "license:mit", "region:us" ]
null
2022-12-13T09:26:31Z
--- language: - en tags: - sequential-recommendation - direct-recommendation - explanation-generation license: mit datasets: - amazon_us_reviews - yelp_review_full metrics: - NDCG - HR - MAE - BLUE - ROUGE --- # P5 Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict Paradigm ![model image](https://raw.githubusercontent.com/jeykigung/P5/main/pic/teaser.png) ### Models: [P5 (Sports Small)](https://huggingface.co/makitanikaze/P5_sports_small) [P5 (Beauty Small)](https://huggingface.co/makitanikaze/P5_beauty_small) [P5 (Toys Small)](https://huggingface.co/makitanikaze/P5_toys_small) [P5 (Yelp Small)](https://huggingface.co/makitanikaze/P5_yelp_small) [P5 (Sports Base)](https://huggingface.co/makitanikaze/P5_sports_base) [P5 (Beauty Base)](https://huggingface.co/makitanikaze/P5_beauty_base) [P5 (Toys Base)](https://huggingface.co/makitanikaze/P5_toys_base)
jxiao/q-taxv3-v1
jxiao
2022-12-20T07:21:59Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T06:42:20Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxv3-v1 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="jxiao/q-taxv3-v1", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
saikiranp/ppo-LunarLander-v2-1
saikiranp
2022-12-20T07:19:27Z
2
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T07:19:07Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.49 +/- 15.47 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 ... ```
Gozie/ppo-LunarLander-v2
Gozie
2022-12-20T07:06:06Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T07:05:43Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: SB3 PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 270.67 +/- 20.46 name: mean_reward verified: false --- # **SB3 PPO** Agent playing **LunarLander-v2** This is a trained model of a **SB3 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 ... ```
makitanikaze/P5_yelp_small
makitanikaze
2022-12-20T06:54:21Z
10
1
transformers
[ "transformers", "pytorch", "t5", "feature-extraction", "sequential-recommendation", "direct-recommendation", "explanation-generation", "text2text-generation", "custom_code", "en", "dataset:yelp_review_full", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-12-20T06:51:11Z
--- language: - en tags: - sequential-recommendation - direct-recommendation - explanation-generation - text2text-generation license: mit datasets: - yelp_review_full metrics: - NDCG - HR - MAE - BLUE - ROUGE widget: - text: "Based on the visit history of Herman : \n 236 -> 4979 -> 3240 -> 2208 -> 168 -> 12101 \n Can you decide the next business likely to be visited by the user ?" example_title: "Sequential Recommendation" - text: "We want to make recommendation for user_1618 . Select the best item from these candidates : \n 13978 , 2053 , 19198 , 16484 , 771 , 5354 , 10803 , 18199 , 15098 , 1070 , 7183 , 193 , 13633 , 16465 , 1302 , 14943 , 12902 , 10984 , 18198 , 6062 , 11955 , 6809 , 12601 , 2373 , 14706 , 8889 , 17980 , 17294 , 15002 , 12237 , 13299 , 18856 , 7066 , 11792 , 17093 , 18779 , 19563 , 2615 , 17865 , 11774 , 13562 , 11128 , 14810 , 10149 , 18543 , 12854 , 12508 , 7662 , 7227 , 3058 , 11704 , 1200 , 12439 , 17873 , 16280 , 15225 , 307 , 11428 , 17107 , 4727 , 2030 , 6914 , 8234 , 2174 , 14340 , 17577 , 15342 , 14741 , 19058 , 14694 , 2114 , 15739 , 8739 , 14263 , 4687 , 4977 , 6685 , 381 , 16542 , 19230 , 9977 , 8449 , 18537 , 6616 , 8945 , 12265 , 4836 , 7705 , 19865 , 15843 , 18715 , 12834 , 18955 , 6324 , 4740 , 14717 , 2752 , 2131 , 5957 , 7511" example_title: "Direct Recommendation" - text: "Based on the feature word drinks , generate an explanation for user_5657 about this business : Ghost Ranch: Modern Southwest Cuisine" example_title: "Explanation Generation" --- # P5 (Yelp Small) Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict Paradigm ![model image](https://raw.githubusercontent.com/jeykigung/P5/main/pic/teaser.png)
makitanikaze/P5_toys_small
makitanikaze
2022-12-20T05:53:37Z
65
0
transformers
[ "transformers", "pytorch", "t5", "feature-extraction", "sequential-recommendation", "direct-recommendation", "explanation-generation", "text2text-generation", "custom_code", "en", "dataset:amazon_us_reviews", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2022-12-20T04:59:28Z
--- language: - en tags: - sequential-recommendation - direct-recommendation - explanation-generation - text2text-generation license: mit datasets: - amazon_us_reviews metrics: - NDCG - HR - MAE - BLUE - ROUGE widget: - text: "According to the purchase history of JSB727 : \n 2440 -> 1212 -> 4234 -> 4309 \n Can you recommend the next possible item to the user ?" example_title: "Sequential Recommendation" - text: "We want to make recommendation for user_823 . Select the best item from these candidates : \n 1402 , 6122 , 10240 , 84 , 10691 , 10944 , 3451 , 10213 , 4239 , 9018 , 3384 , 1397 , 7232 , 8221 , 7751 , 5397 , 11253 , 5078 , 3231 , 5105 , 3959 , 7245 , 150 , 2253 , 764 , 2646 , 6909 , 1939 , 1982 , 2201 , 11503 , 11603 , 4050 , 9524 , 1895 , 2317 , 11027 , 6971 , 4441 , 2336 , 373 , 11735 , 3824 , 51 , 9514 , 2488 , 7960 , 7653 , 2017 , 6541 , 3682 , 1064 , 910 , 6117 , 4556 , 3939 , 7049 , 241 , 10113 , 1442 , 10149 , 5930 , 7638 , 1140 , 11589 , 3935 , 11178 , 10491 , 8361 , 11266 , 2029 , 8965 , 4118 , 7506 , 3465 , 4283 , 2812 , 5465 , 2557 , 7510 , 2243 , 10292 , 1212 , 11419 , 3877 , 4771 , 3267 , 8508 , 10200 , 4048 , 3592 , 10145 , 4419 , 9583 , 6193 , 9257 , 1365 , 4672 , 9772 , 11440" example_title: "Direct Recommendation" - text: "Based on the feature word kids , generate an explanation for user_12107 about this product : Tumblin' Monkeys Game" example_title: "Explanation Generation" --- # P5 (Toys Small) Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict Paradigm ![model image](https://raw.githubusercontent.com/jeykigung/P5/main/pic/teaser.png)
WilliamWen/bert-finetuned-ner
WilliamWen
2022-12-20T05:08:44Z
13
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-19T10:05:44Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9314616019818331 - name: Recall type: recall value: 0.9491753618310333 - name: F1 type: f1 value: 0.9402350587646913 - name: Accuracy type: accuracy value: 0.9857243774651204 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0636 - Precision: 0.9315 - Recall: 0.9492 - F1: 0.9402 - Accuracy: 0.9857 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0898 | 1.0 | 1756 | 0.0718 | 0.9149 | 0.9359 | 0.9253 | 0.9811 | | 0.0351 | 2.0 | 3512 | 0.0641 | 0.9298 | 0.9490 | 0.9393 | 0.9860 | | 0.0186 | 3.0 | 5268 | 0.0636 | 0.9315 | 0.9492 | 0.9402 | 0.9857 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.12.1
TheBirdLegacy/CatsandDogsPOC-Swin
TheBirdLegacy
2022-12-20T05:06:59Z
41
0
transformers
[ "transformers", "pytorch", "swin", "image-classification", "autotrain", "vision", "dataset:puffy310/autotrain-data-synth-cats-or-dogs", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-12-20T04:53:44Z
--- tags: - autotrain - vision - image-classification datasets: - puffy310/autotrain-data-synth-cats-or-dogs widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 1.165641024416945 --- Resnet is more lightweight but this is better in terms of loss, at the cost of being 3.5X the size. # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 2540477801 - CO2 Emissions (in grams): 1.1656 ## Validation Metrics - Loss: 0.000 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000
sgangireddy/whisper-medium-cv-fi-hu-1k
sgangireddy
2022-12-20T04:19:07Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-19T18:28:59Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: openai/whisper-medium 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. --> # openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3236 - Wer: 19.7922 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0068 | 4.04 | 1000 | 0.3236 | 19.7922 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
teacookies/autotrain-20-12-2022-2540377772
teacookies
2022-12-20T04:13:06Z
17
0
transformers
[ "transformers", "pytorch", "bert", "token-classification", "autotrain", "unk", "dataset:teacookies/autotrain-data-20-12-2022", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-20T04:05:12Z
--- tags: - autotrain - token-classification language: - unk widget: - text: "I love AutoTrain πŸ€—" datasets: - teacookies/autotrain-data-20-12-2022 co2_eq_emissions: emissions: 14.305842162020754 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 2540377772 - CO2 Emissions (in grams): 14.3058 ## Validation Metrics - Loss: 0.019 - Accuracy: 0.994 - Precision: 0.804 - Recall: 0.853 - F1: 0.828 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/teacookies/autotrain-20-12-2022-2540377772 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-20-12-2022-2540377772", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-20-12-2022-2540377772", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
NickForMe/rubert-base-cased-finetuned-panx-de
NickForMe
2022-12-20T03:21:17Z
9
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:universal_dependencies", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-20T03:06:21Z
--- tags: - generated_from_trainer datasets: - universal_dependencies metrics: - f1 model-index: - name: rubert-base-cased-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: universal_dependencies type: universal_dependencies config: ru_taiga split: train args: ru_taiga metrics: - name: F1 type: f1 value: 0.6978625703821133 --- <!-- 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. --> # rubert-base-cased-finetuned-panx-de This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the universal_dependencies dataset. It achieves the following results on the evaluation set: - Loss: 1.3352 - F1: 0.6979 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.3039 | 1.0 | 131 | 1.0720 | 0.5871 | | 0.7911 | 2.0 | 262 | 0.9581 | 0.6335 | | 0.5448 | 3.0 | 393 | 0.9162 | 0.6718 | | 0.3709 | 4.0 | 524 | 0.9722 | 0.6819 | | 0.2565 | 5.0 | 655 | 1.0484 | 0.6847 | | 0.1923 | 6.0 | 786 | 1.1410 | 0.6924 | | 0.145 | 7.0 | 917 | 1.1692 | 0.6995 | | 0.1104 | 8.0 | 1048 | 1.2592 | 0.6981 | | 0.086 | 9.0 | 1179 | 1.3307 | 0.6972 | | 0.0715 | 10.0 | 1310 | 1.3352 | 0.6979 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2
JosephusCheung/ACertainModel
JosephusCheung
2022-12-20T03:16:49Z
274
160
diffusers
[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "arxiv:2106.09685", "doi:10.57967/hf/0196", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2022-12-12T17:40:00Z
--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true widget: - text: "masterpiece, best quality, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden" example_title: "example 1girl" - text: "masterpiece, best quality, 1boy, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden" example_title: "example 1boy" --- # ACertainModel **Try full functions with Google Colab free T4** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ldhBc70wvuvkp4Af_vNTzTfBXwpf_cH5?usp=sharing) Check Twitter [#ACertainModel](https://twitter.com/hashtag/ACertainModel) for community artworks Welcome to ACertainModel - a latent diffusion model for weebs. This model is intended to produce high-quality, highly detailed anime style pictures with just a few prompts. Like other anime-style Stable Diffusion models, it also supports danbooru tags, including artists, to generate images. Since I noticed that the laion-aesthetics introduced in the Stable-Diffusion-v-1-4 checkpoint hindered finetuning anime style illustration generation model, Dreambooth was used to finetune some tags separately to make it closer to what it was in SD1.2. To avoid overfitting and possible language drift, I added a huge amount of auto-generated pictures from a single word prompt to the training set, using models that are popular in the community such as Anything-3.0, together with partially manual selected full-danbooru images within a year, for further native training. I am also aware of a method of [LoRA](https://arxiv.org/abs/2106.09685), with a similar idea, finetuning attention layer solely, to have better performance on eyes, hands, and other details. For copyright compliance and technical experiment, it was trained from few artist images directly. It was trained on Dreambooth with pictures generated from several popular diffusion models in the community. The checkpoint was initialized with the weights of a Stable Diffusion Model and subsequently fine-tuned for 2K GPU hours on V100 32GB and 600 GPU hours on A100 40GB at 512P dynamic aspect ratio resolution with a certain ratio of unsupervised auto-generated images from several popular diffusion models in the community with some Textual Inversions and Hypernetworks. We do know some tricks on xformers and 8-bit optimization, but we didn't use any of them for better quality and stability. Up to 15 branches are trained simultaneously, cherry-picking about every 20,000 steps. e.g. **_masterpiece, best quality, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden_** ## About online preview with Hosted inference API, also generation with this model Parameters are not allowed to be modified, as it seems that it is generated with *Clip skip: 1*, for better performance, it is strongly recommended to use *Clip skip: 2* instead. Here is an example of inference settings, if it is applicable with you on your own server: *Steps: 28, Sampler: Euler a, CFG scale: 11, Clip skip: 2*. ## 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or FLAX/JAX. ```python from diffusers import StableDiffusionPipeline import torch model_id = "JosephusCheung/ACertainModel" branch_name= "main" pipe = StableDiffusionPipeline.from_pretrained(model_id, revision=branch_name, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "pikachu" image = pipe(prompt).images[0] image.save("./pikachu.png") ``` ## Examples Below are some examples of images generated using this model, with better performance on framing and hand gestures, as well as moving objects, comparing to other analogues: **Anime Girl:** ![Anime Girl](https://huggingface.co/JosephusCheung/ACertainModel/resolve/main/samples/sample-1girl.png) ``` 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden Steps: 28, Sampler: Euler a, CFG scale: 11, Seed: 114514, Clip skip: 2 ``` **Anime Boy:** ![Anime Boy](https://huggingface.co/JosephusCheung/ACertainModel/resolve/main/samples/sample-1boy.png) ``` 1boy, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden Steps: 28, Sampler: Euler a, CFG scale: 11, Seed: 114514, Clip skip: 2 ``` ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) ## Is it a NovelAI based model? What is the relationship with SD1.2 and SD1.4? See [ASimilarityCalculatior](https://huggingface.co/JosephusCheung/ASimilarityCalculatior)
sryu1/dqn-SpaceInvadersNoFrameskip-v4
sryu1
2022-12-20T03:14:10Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T03:13:37Z
--- 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: 520.50 +/- 180.17 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 ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sryu1 -f logs/ python enjoy.py --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 sryu1 -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 sryu1 ``` ## 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)]) ```
Antiraedus/Taxiv3-base
Antiraedus
2022-12-20T03:12:46Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T03:12:40Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxiv3-base 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="Antiraedus/Taxiv3-base", 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"]) ```
Antiraedus/q-FrozenLake-v1-4x4-noSlippery
Antiraedus
2022-12-20T03:05:45Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T03:05:40Z
--- 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="Antiraedus/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"]) ```
EatingKing/testmodel
EatingKing
2022-12-20T03:05:42Z
0
0
null
[ "region:us" ]
null
2022-12-01T07:16:56Z
- πŸ‘‹ Hi, I’m @EatingKing001 - πŸ‘€ I’m interested in ... - 🌱 I’m currently learning ... - πŸ’žοΈ I’m looking to collaborate on ... - πŸ“« How to reach me ... <!--- EatingKing001/EatingKing001 is a ✨ special ✨ repository because its `README.md` (this file) appears on your GitHub profile. You can click the Preview link to take a look at your changes. --->
jinghua2tang/dqn-SpaceInvadersNoFrameskip-v4
jinghua2tang
2022-12-20T03:02:41Z
2
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T03:01:57Z
--- 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: 670.00 +/- 198.42 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 ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jinghua2tang -f logs/ python enjoy.py --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 jinghua2tang -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 jinghua2tang ``` ## 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)]) ```
aki6022/finetuning-sentiment-model-3000-samples-practice
aki6022
2022-12-20T02:29:28Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-12-20T02:07:03Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: finetuning-sentiment-model-3000-samples-practice 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. --> # finetuning-sentiment-model-3000-samples-practice This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb 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: 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 ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
Bluelemon883/ppo-LunarLander-v2
Bluelemon883
2022-12-20T01:32:21Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T01:31:55Z
--- 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: 255.28 +/- 24.84 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 ... ```
yanick/ppo-Huggy
yanick
2022-12-20T01:26:13Z
19
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2022-12-20T01:26:00Z
--- 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: yanick/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play πŸ‘€
MichaelCHomeX/rare-puppers
MichaelCHomeX
2022-12-20T01:23:21Z
20
0
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-12-20T01:23:00Z
--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.939393937587738 --- # rare-puppers Autogenerated by HuggingPicsπŸ€—πŸ–ΌοΈ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### samoyed ![samoyed](images/samoyed.jpg) #### shiba inu ![shiba inu](images/shiba_inu.jpg)
trulymadlydeeply/Chinese-birds-flying-1
trulymadlydeeply
2022-12-20T00:42:09Z
86
0
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-12-20T00:41:44Z
--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Chinese-birds-flying-1 results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8072289228439331 --- # Chinese-birds-flying-1 Autogenerated by HuggingPicsπŸ€—πŸ–ΌοΈ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### egret flying ![egret flying](images/egret_flying.jpg) #### paradise flycatcher flying ![paradise flycatcher flying](images/paradise_flycatcher_flying.jpg) #### red-crowned crane flying ![red-crowned crane flying](images/red-crowned_crane_flying.jpg) #### sparrowhawk flying ![sparrowhawk flying](images/sparrowhawk_flying.jpg) #### swan flying ![swan flying](images/swan_flying.jpg)
keithrebello/ppo-LunarLander-v2
keithrebello
2022-12-20T00:39:52Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T00:39:27Z
--- 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: 269.55 +/- 20.10 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 ... ```
jpequegn/ppo-LunarLander-v2
jpequegn
2022-12-20T00:32:33Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-20T00:31:57Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.47 +/- 17.56 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 ... ```
elRivx/reasonableDrinkV2
elRivx
2022-12-20T00:12:12Z
0
1
null
[ "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2022-12-19T23:51:25Z
--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image --- # reasonableDrinkV2 This is an update of my own SD trainee with a many lot of dreaming (or nightmare) illustrations as a style to SD 2.1. If you wanna test it, you can put this word on the prompt: reasonableDrink If you enjoy my work, please consider supporting me: [![Buy me a coffee](https://badgen.net/badge/icon/buymeacoffee?icon=buymeacoffee&label)](https://www.buymeacoffee.com/elrivx) Examples: <img src=https://imgur.com/ltYgB5B.png width=30% height=30%> <img src=https://imgur.com/a3w0ltf.png width=30% height=30%> <img src=https://imgur.com/JRi6wwT.png width=30% height=30%> <img src=https://imgur.com/QWe03qH.png width=30% height=30%> <img src=https://imgur.com/m2Dz20C.png width=30% height=30%> ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
hku-nlp/instructor-base
hku-nlp
2022-12-19T23:40:12Z
196
6
sentence-transformers
[ "sentence-transformers", "pytorch", "t5", "feature-extraction", "sentence-similarity", "transformers", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2022-12-17T20:01:46Z
--- pipeline_tag: sentence-similarity language: en license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # hku-nlp/instructor-base This is a general embedding model: It maps **any** piece of text (e.g., a title, a sentence, a document, etc.) to a fixed-length vector in test time **without further training**. With instructions, the embeddings are **domain-specific** (e.g., specialized for science, finance, etc.) and **task-aware** (e.g., customized for classification, information retrieval, etc.) The model is easy to use with `sentence-transformer` library. ## Installation ```bash git clone https://github.com/HKUNLP/instructor-embedding cd sentence-transformers pip install -e . ``` ## Compute your customized embeddings Then you can use the model like this to calculate domain-specific and task-aware embeddings: ```python from sentence_transformers import SentenceTransformer sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" instruction = "Represent the Science title; Input:" model = SentenceTransformer('hku-nlp/instructor-base') embeddings = model.encode([[instruction,sentence,0]]) print(embeddings) ``` ## Calculate Sentence similarities You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**. ```python from sklearn.metrics.pairwise import cosine_similarity sentences_a = [['Represent the Science sentence; Input: ','Parton energy loss in QCD matter',0], ['Represent the Financial statement; Input: ','The Federal Reserve on Wednesday raised its benchmark interest rate.',0] sentences_b = [['Represent the Science sentence; Input: ','The Chiral Phase Transition in Dissipative Dynamics', 0], ['Represent the Financial statement; Input: ','The funds rose less than 0.5 per cent on Friday',0] embeddings_a = model.encode(sentences_a) embeddings_b = model.encode(sentences_b) similarities = cosine_similarity(embeddings_a,embeddings_b) print(similarities) ```
FabioQuintao/q-FrozenLake-v1-4x4-noSlippery
FabioQuintao
2022-12-19T23:00:47Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-19T23:00:38Z
--- 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 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="FabioQuintao/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"]) ```
DrishtiSharma/whisper-large-v2-serbian-400-steps
DrishtiSharma
2022-12-19T22:59:24Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "sr", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-19T22:27:12Z
--- language: - sr license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large-V2 Serbian - Drishti Sharma results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: sr split: test args: sr metrics: - name: Wer type: wer value: 11.007689194658035 --- <!-- 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. --> # Whisper Large-V2 Serbian - Drishti Sharma This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2142 - Wer: 11.0077 ## 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: 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: 100 - training_steps: 400 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0965 | 1.91 | 400 | 0.2142 | 11.0077 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
anuragshas/whisper-large-v2-mt
anuragshas
2022-12-19T22:24:18Z
6
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "mt", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-19T19:49:23Z
--- language: - mt license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large-v2 Maltese results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: mozilla-foundation/common_voice_11_0 mt type: mozilla-foundation/common_voice_11_0 config: mt split: test args: mt metrics: - name: Wer type: wer value: 18.464443960194668 --- <!-- 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. --> # Whisper Large-v2 Maltese This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 mt dataset. It achieves the following results on the evaluation set: - Loss: 0.3616 - Wer: 18.4644 ## 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: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0023 | 9.0 | 1000 | 0.3616 | 18.4644 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
sabamedia/saba
sabamedia
2022-12-19T22:14:39Z
0
0
null
[ "license:afl-3.0", "region:us" ]
null
2022-12-19T22:11:08Z
--- license: afl-3.0 --- bird flying high on a dark place with big owl eyes on background
huggingtweets/louisetatmaia
huggingtweets
2022-12-19T21:52:24Z
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2022-12-19T21:51:38Z
--- language: en thumbnail: http://www.huggingtweets.com/louisetatmaia/1671486739770/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/1592135191883124736/KESXJNh2_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 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">πŸ€– AI BOT πŸ€–</div> <div style="text-align: center; font-size: 16px; font-weight: 800">louiseπŸŒ™πŸ―βœ‹</div> <div style="text-align: center; font-size: 14px;">@louisetatmaia</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 louiseπŸŒ™πŸ―βœ‹. | Data | louiseπŸŒ™πŸ―βœ‹ | | --- | --- | | Tweets downloaded | 3187 | | Retweets | 1259 | | Short tweets | 328 | | Tweets kept | 1600 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3bubvh06/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 @louisetatmaia's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2iq7510b) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2iq7510b/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/louisetatmaia') 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)
Orokusaki/ppo-LunarLander-v2
Orokusaki
2022-12-19T21:41:01Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-19T21:40:23Z
--- 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: 254.37 +/- 23.97 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 ... ```
IngoTB303/q-FrozenLake-v1-4x4-noSlippery
IngoTB303
2022-12-19T21:24:55Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-19T21:24:42Z
--- 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="IngoTB303/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"]) ```
SamoaJon/q-Taxi-v3
SamoaJon
2022-12-19T21:24:52Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-19T21:24:35Z
--- 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="SamoaJon/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"]) ```
shripadbhat/whisper-large-v2-lt
shripadbhat
2022-12-19T21:19:12Z
9
1
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "lt", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-19T18:09:13Z
--- language: - lt license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large v2 Lithuanian results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: lt split: test args: lt metrics: - name: Wer type: wer value: 29.932107833406125 --- <!-- 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. --> # Whisper Large v2 Lithuanian This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3421 - Wer: 29.9321 ## 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: 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: 50 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4255 | 0.09 | 100 | 0.4323 | 37.0310 | | 0.2976 | 0.18 | 200 | 0.3421 | 29.9321 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
benhamilto/test-model
benhamilto
2022-12-19T21:00:15Z
0
0
diffusers
[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2022-12-15T13:58:50Z
Tester --- language: en tags: - exbert license: mit datasets: - bookcorpus - wikipedia ---
utyug1/q-Taxi-v3
utyug1
2022-12-19T20:58:14Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-19T20:58:08Z
--- 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: 7.38 +/- 2.84 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="utyug1/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"]) ```
SamoaJon/q-FrozenLake-v1-4x4-noSlippery
SamoaJon
2022-12-19T20:58:04Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "doi:10.57967/hf/0220", "model-index", "region:us" ]
reinforcement-learning
2022-12-15T14:46:01Z
--- 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="SamoaJon/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"]) ```
Arch4ngel/dqn-SpaceInvadersNoFrameskip-v4
Arch4ngel
2022-12-19T20:49:20Z
3
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-19T20:48:43Z
--- 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: 580.50 +/- 170.08 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 ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Arch4ngel -f logs/ python enjoy.py --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 Arch4ngel -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --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 Arch4ngel ``` ## 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)]) ```
bsmith0430/q-FrozenLake-v1-4x4-noSlippery
bsmith0430
2022-12-19T20:29:02Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-11-29T01:03:05Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="bsmith0430/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"]) ```
apugachev/roberta-large-boolq-finetuned
apugachev
2022-12-19T20:13:13Z
4
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-12-19T20:05:11Z
Training parameters: ``` model_args = ClassificationArgs() model_args.max_seq_length = 512 model_args.train_batch_size = 12 model_args.eval_batch_size = 12 model_args.num_train_epochs = 5 model_args.evaluate_during_training = False model_args.learning_rate = 1e-5 model_args.use_multiprocessing = False model_args.fp16 = False model_args.save_steps = -1 model_args.save_eval_checkpoints = False model_args.no_cache = True model_args.reprocess_input_data = True model_args.overwrite_output_dir = True ``` Evaluation on BoolQ Test Set: | | Precision | Recall | F1-score | |:------------:|:---------:|:------:|:--------:| | 0 | 0.82 | 0.80 | 0.81 | | 1 | 0.88 | 0.89 | 0.88 | | accuracy | | | 0.86 | | macro avg | 0.85 | 0.84 | 0.85 | | weighted avg | 0.86 | 0.86 | 0.86 | ROC AUC Score: 0.844
kpriyanshu256/whisper-large-v2-br-1000-32-1e-05
kpriyanshu256
2022-12-19T20:06:18Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "br", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-19T05:02:19Z
--- language: - br license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: openai/whisper-large-v2-breton results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: br split: test args: br metrics: - name: Wer type: wer value: 39.92705800625217 --- <!-- 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-large-v2-breton This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7162 - Wer: 39.9271 ## 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 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.7423 | 0.1 | 100 | 0.8363 | 57.1553 | | 0.4361 | 1.07 | 200 | 0.6833 | 46.7176 | | 0.2227 | 2.03 | 300 | 0.6483 | 42.5929 | | 0.1472 | 3.0 | 400 | 0.6511 | 42.4627 | | 0.0892 | 3.1 | 500 | 0.6633 | 40.9604 | | 0.0651 | 4.07 | 600 | 0.6807 | 39.7534 | | 0.0416 | 5.04 | 700 | 0.6870 | 41.2383 | | 0.0352 | 6.0 | 800 | 0.7315 | 39.9010 | | 0.022 | 6.1 | 900 | 0.7201 | 40.4307 | | 0.0195 | 7.07 | 1000 | 0.7162 | 39.9271 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
rama100/q-Taxi-v
rama100
2022-12-19T19:56:13Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-19T19:56:10Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v 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="rama100/q-Taxi-v", 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"]) ```
NeelK94/Taxi-v3
NeelK94
2022-12-19T19:38:20Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-19T19:38:12Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="NeelK94/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"]) ```
togoforfood/ppo-LunarLander-v2
togoforfood
2022-12-19T19:30:01Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2022-12-19T19:29:37Z
--- 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: 263.05 +/- 24.95 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 ... ```
ahmadmwali/finetuning-sentiment-igbo21
ahmadmwali
2022-12-19T19:13:14Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-12-19T18:16:25Z
--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-igbo21 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. --> # finetuning-sentiment-igbo21 This model is a fine-tuned version of [mbeukman/xlm-roberta-base-finetuned-ner-igbo](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-igbo) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5368 - Accuracy: 0.7923 - F1: 0.7914 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
Rami/CartPole-v1__functional_dqn__0__1671476597
Rami
2022-12-19T19:07:38Z
0
0
null
[ "region:us" ]
null
2022-12-19T19:07:27Z
--- language: en license: apache-2.0 model-index: - name: CartPole-v1__functional_dqn__0__1671476597 --- DQN model applied to the this discrete environments CartPole-v1 ## Model Description The model was trained from the CleanRl library using the DQN algorithm ## Intended Use & Limitation The model is intended to be used for the following environments CartPole-v1 and understand the implication of Quantization on this type of model from a pretrained state## Training Procdure ### Training Hyperparameters ``` The folloing hyperparameters were used during training: - exp_name: functional_dqn - seed: 0 - torch_deterministic: True - cuda: False - track: True - wandb_project_name: cleanRL - wandb_entity: compress_rl - capture_video: False - env_id: CartPole-v1 - total_timesteps: 500000 - learning_rate: 0.00025 - buffer_size: 10000 - gamma: 0.99 - target_network_frequency: 500 - batch_size: 128 - start_e: 1 - end_e: 0.05 - exploration_fraction: 0.5 - learning_starts: 10000 - train_frequency: 10 - optimizer: Adan - wandb_project: cleanrl ``` ### Framework and version ``` Pytorch 1.12.1+cu102 gym 0.23.1 Weights and Biases 0.13.3 Hugging Face Hub 0.11.1
Rami/CartPole-v1__functional_dqn__0__1671475909
Rami
2022-12-19T18:55:59Z
0
0
null
[ "region:us" ]
null
2022-12-19T18:55:48Z
--- language: en license: apache-2.0 model-index: - name: CartPole-v1__functional_dqn__0__1671475909 --- DQN model applied to the this discrete environments CartPole-v1 ## Model Description The model was trained from the CleanRl library using the DQN algorithm ## Intended Use & Limitation The model is intended to be used for the following environments CartPole-v1 and understand the implication of Quantization on this type of model from a pretrained state## Training Procdure ### Training Hyperparameters ``` The folloing hyperparameters were used during training: - exp_name: functional_dqn - seed: 0 - torch_deterministic: True - cuda: False - track: True - wandb_project_name: cleanRL - wandb_entity: compress_rl - capture_video: False - env_id: CartPole-v1 - total_timesteps: 500000 - learning_rate: 0.00025 - buffer_size: 10000 - gamma: 0.99 - target_network_frequency: 500 - batch_size: 128 - start_e: 1 - end_e: 0.05 - exploration_fraction: 0.5 - learning_starts: 10000 - train_frequency: 10 - optimizer: Adan - wandb_project: cleanrl ``` ### Framework and version ``` Pytorch 1.12.1+cu102 gym 0.23.1 Weights and Biases 0.13.3 Hugging Face Hub 0.11.1
Roberto/Taxi-v3
Roberto
2022-12-19T18:54:28Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2022-12-19T17:12:31Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Roberto/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"]) ```
hyorea1/KoT5-test-add-data-prefix-summary
hyorea1
2022-12-19T18:43:35Z
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
2022-12-19T09:57:04Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: KoT5-test-add-data-prefix-summary 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. --> # KoT5-test-add-data-prefix-summary This model is a fine-tuned version of [hyorea1/KoT5-test-add-data-prefix-summary](https://huggingface.co/hyorea1/KoT5-test-add-data-prefix-summary) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1781 - Rouge1: 11.8533 - Rouge2: 2.9172 - Rougel: 11.715 - Rougelsum: 11.7278 - Gen Len: 35.164 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 100 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 1.4974 | 0.32 | 800 | 1.1935 | 11.0529 | 3.0383 | 10.9308 | 10.9481 | 34.8809 | | 1.0394 | 0.64 | 1600 | 1.1979 | 11.2828 | 2.8757 | 11.1691 | 11.1952 | 35.6412 | | 1.2385 | 0.97 | 2400 | 1.1914 | 10.8007 | 3.0248 | 10.696 | 10.7022 | 34.8081 | | 1.4298 | 1.29 | 3200 | 1.1916 | 10.8949 | 2.9547 | 10.8037 | 10.832 | 34.7934 | | 1.3735 | 1.61 | 4000 | 1.1887 | 11.8127 | 3.2642 | 11.7143 | 11.7263 | 35.4331 | | 1.5772 | 1.93 | 4800 | 1.1794 | 11.3157 | 3.1017 | 11.2215 | 11.2237 | 34.3051 | | 1.2179 | 2.25 | 5600 | 1.1809 | 11.841 | 2.8297 | 11.7283 | 11.7173 | 35.0522 | | 1.2903 | 2.58 | 6400 | 1.1779 | 11.6353 | 2.8495 | 11.5117 | 11.544 | 34.95 | | 1.461 | 2.9 | 7200 | 1.1781 | 11.8533 | 2.9172 | 11.715 | 11.7278 | 35.164 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
Rami/CartPole-v1__functional_dqn__0__1671474891
Rami
2022-12-19T18:39:06Z
0
0
null
[ "region:us" ]
null
2022-12-19T18:38:52Z
--- language: en license: apache-2.0 model-index: - name: CartPole-v1__functional_dqn__0__1671474891 --- DQN model applied to the this discrete environments CartPole-v1 ## Model Description The model was trained from the CleanRl library using the DQN algorithm ## Intended Use & Limitation The model is intended to be used for the following environments CartPole-v1 and understand the implication of Quantization on this type of model from a pretrained state## Training Procdure ### Training Hyperparameters ``` The folloing hyperparameters were used during training: - exp_name: functional_dqn - seed: 0 - torch_deterministic: True - cuda: False - track: True - wandb_project_name: cleanRL - wandb_entity: compress_rl - capture_video: False - env_id: CartPole-v1 - total_timesteps: 500000 - learning_rate: 0.00025 - buffer_size: 10000 - gamma: 0.99 - target_network_frequency: 500 - batch_size: 128 - start_e: 1 - end_e: 0.05 - exploration_fraction: 0.5 - learning_starts: 10000 - train_frequency: 10 - optimizer: Adam - wandb_project: cleanrl ``` ### Framework and version ``` Pytorch 1.12.1+cu102 gym 0.23.1 Weights and Biases 0.13.3 Hugging Face Hub 0.11.1
Martha-987/whisper-small-ArabicAr
Martha-987
2022-12-19T18:20:58Z
7
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ar", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-16T14:10:52Z
--- language: - ar license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Ar- Martha results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: ar split: test metrics: - name: Wer type: wer value: 50.11110090900743 --- <!-- 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. --> # Whisper Small Ar- Martha This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3743 - Wer: 50.1111 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.26 | 0.42 | 1000 | 0.3743 | 50.1111 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
kevinbror/distilbert-base-uncased-finetuned-cola
kevinbror
2022-12-19T18:10:13Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-12-19T17:59:09Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.22961097671530944 --- <!-- 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-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5451 - Matthews Correlation: 0.2296 ## 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: 0.5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 0.5 | 268 | 0.5451 | 0.2296 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
Jingmiao/whisper-small-chinese_base
Jingmiao
2022-12-19T18:00:53Z
262
22
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "dataset:google/fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-17T17:06:26Z
--- license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - google/fleurs metrics: - wer model-index: - name: Whisper Small Chinese Base results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs cmn_hans_cn type: google/fleurs config: cmn_hans_cn split: test args: cmn_hans_cn metrics: - name: Wer type: wer value: 16.643891773708663 --- <!-- 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. --> # Whisper Small Chinese Base This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs cmn_hans_cn dataset. It achieves the following results on the evaluation set: - Loss: 0.3573 - Wer: 16.6439 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0005 | 76.0 | 1000 | 0.3573 | 16.6439 | | 0.0002 | 153.0 | 2000 | 0.3897 | 16.9749 | | 0.0001 | 230.0 | 3000 | 0.4125 | 17.2330 | | 0.0001 | 307.0 | 4000 | 0.4256 | 17.2451 | | 0.0001 | 384.0 | 5000 | 0.4330 | 17.2300 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
emilios/whisper-md-hr
emilios
2022-12-19T17:58:16Z
16
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "dataset:google/fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-12-19T11:45:57Z
--- license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - google/fleurs metrics: - wer model-index: - name: Whisper medium Croatian El Greco results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs hr_hr type: google/fleurs config: zu split: None metrics: - name: Wer type: wer value: 14.613261224719734 --- <!-- 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. --> # Whisper medium Croatian El Greco This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs hr_hr dataset. It achieves the following results on the evaluation set: - Loss: 0.3374 - Wer: 14.6133 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0106 | 4.61 | 1000 | 0.3374 | 14.6133 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 2.0.0.dev20221216+cu116 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2