librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-23 12:29:03	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 492 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-23 12:24:08	card stringlengths 11 1.01M
Weili/vit-base-patch16-224-finetuned-cifar10	Weili	2022-12-07T02:42:03Z	119	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:cifar10", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-12-07T00:52:42Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cifar10 metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-cifar10 results: - task: name: Image Classification type: image-classification dataset: name: cifar10 type: cifar10 config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9876 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-cifar10 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.0427 - Accuracy: 0.9876 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2518 \| 1.0 \| 390 \| 0.0609 \| 0.9821 \| \| 0.1985 \| 2.0 \| 780 \| 0.0532 \| 0.983 \| \| 0.197 \| 3.0 \| 1170 \| 0.0427 \| 0.9876 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
YoanG/ppo-LunarLander-v2	YoanG	2022-12-07T02:39:00Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-07T02:38:39Z	--- 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: 284.42 +/- 21.12 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 ... ```
cyycyy/xlm-roberta-base-finetuned-panx-en	cyycyy	2022-12-07T02:37:28Z	6	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-12-07T02:33:13Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.en metrics: - name: F1 type: f1 value: 0.6850704225352112 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4130 - F1: 0.6851 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.1435 \| 1.0 \| 50 \| 0.5604 \| 0.5493 \| \| 0.513 \| 2.0 \| 100 \| 0.4557 \| 0.6504 \| \| 0.3744 \| 3.0 \| 150 \| 0.4130 \| 0.6851 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.10.3
sd-concepts-library/dulls	sd-concepts-library	2022-12-07T02:35:30Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-12-07T02:35:22Z	--- license: mit --- ### dulls on Stable Diffusion This is the `<dulls-avatar>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<dulls-avatar> 0](https://huggingface.co/sd-concepts-library/dulls/resolve/main/concept_images/3.jpeg) ![<dulls-avatar> 1](https://huggingface.co/sd-concepts-library/dulls/resolve/main/concept_images/1.jpeg) ![<dulls-avatar> 2](https://huggingface.co/sd-concepts-library/dulls/resolve/main/concept_images/4.jpeg) ![<dulls-avatar> 3](https://huggingface.co/sd-concepts-library/dulls/resolve/main/concept_images/2.jpeg) ![<dulls-avatar> 4](https://huggingface.co/sd-concepts-library/dulls/resolve/main/concept_images/0.jpeg)
bguan/ppo-Huggy	bguan	2022-12-07T02:35:20Z	15	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-07T02:35:14Z	--- 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: bguan/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
sd-dreambooth-library/uploadtest	sd-dreambooth-library	2022-12-07T02:31:04Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2022-12-07T02:31:04Z	--- license: bigscience-openrail-m ---
enzokro/sd-class-butterflies-huber-32	enzokro	2022-12-07T02:10:02Z	6	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-07T02:09:29Z	--- 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 🦋. It was trained with a SmoothL1 loss with Beta = 1 (aka same as Huber Loss). ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('enzokro/sd-class-butterflies-huber-32') image = pipeline().images[0] image ```
cyycyy/xlm-roberta-base-finetuned-panx-de-fr	cyycyy	2022-12-07T01:22:01Z	6	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-12-07T01:12:57Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1624 - F1: 0.8591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.289 \| 1.0 \| 715 \| 0.1831 \| 0.8193 \| \| 0.1471 \| 2.0 \| 1430 \| 0.1527 \| 0.8507 \| \| 0.0938 \| 3.0 \| 2145 \| 0.1624 \| 0.8591 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.10.3
damiah/sd-class-butterflies-32	damiah	2022-12-07T01:12:07Z	1	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-07T01:11:05Z	--- 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 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(damiah/sd-class-butterflies-32) image = pipeline().images[0] image ```
dzegan/unit1_ppo	dzegan	2022-12-07T00:56:36Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-07T00:56:10Z	--- 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: 248.64 +/- 40.88 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 ... ```
fezhou/ddpm-butterflies-128	fezhou	2022-12-07T00:23:42Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-12-06T22:30:12Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/fezhou/ddpm-butterflies-128/tensorboard?#scalars)
Nhat1904/Final-32shots-Twitter-Skhead-Train-5epoch-bad	Nhat1904	2022-12-07T00:19:17Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-12-07T00:19:05Z	--- 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 80 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": 5, "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": 400, "warmup_steps": 40, "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 -->
Zesty/ppo-LunarLander-v2	Zesty	2022-12-07T00:07:42Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-07T00:07:11Z	--- 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: 267.83 +/- 17.78 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
nelsonsilva/ppo-LunarLander-v2	nelsonsilva	2022-12-06T23:48:51Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T22:44:53Z	--- 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: 271.26 +/- 23.54 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 ... ```
davidaponte/whisper-small-hindi	davidaponte	2022-12-06T23:45:11Z	4	1	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-06T17:22:32Z	--- language: - hi 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 Hindi - David Aponte results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: hi split: test args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 32.29492931516126 --- <!-- 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 Hindi - David Aponte 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: 0.4495 - Wer: 32.2949 ## 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: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0955 \| 2.44 \| 1000 \| 0.3022 \| 34.1192 \| \| 0.0236 \| 4.89 \| 2000 \| 0.3543 \| 32.6759 \| \| 0.0018 \| 7.33 \| 3000 \| 0.4257 \| 32.8113 \| \| 0.0005 \| 9.78 \| 4000 \| 0.4495 \| 32.2949 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2
pratultandon/recipe-nlg-gpt2-ingredient-to-recipe-model	pratultandon	2022-12-06T23:31:41Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-12-06T11:54:35Z	--- license: mit tags: - generated_from_trainer model-index: - name: recipe-nlg-gpt2-ingredient-to-recipe-model 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. --> # recipe-nlg-gpt2-ingredient-to-recipe-model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
daripaez/ppo_2-LunarLander-v2	daripaez	2022-12-06T23:15:39Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T23:15:11Z	--- 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: 285.45 +/- 19.05 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 ... ```
tomekkorbak/boring_mcclintock	tomekkorbak	2022-12-06T23:05:20Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/pii-pile-chunk3-0-50000", "dataset:tomekkorbak/pii-pile-chunk3-50000-100000", "dataset:tomekkorbak/pii-pile-chunk3-100000-150000", "dataset:tomekkorbak/pii-pile-chunk3-150000-200000", "dataset:tomekkorbak/pii-pile-chunk3-200000-250000", "dataset:tomekkorbak/pii-pile-chunk3-250000-300000", "dataset:tomekkorbak/pii-pile-chunk3-300000-350000", "dataset:tomekkorbak/pii-pile-chunk3-350000-400000", "dataset:tomekkorbak/pii-pile-chunk3-400000-450000", "dataset:tomekkorbak/pii-pile-chunk3-450000-500000", "dataset:tomekkorbak/pii-pile-chunk3-500000-550000", "dataset:tomekkorbak/pii-pile-chunk3-550000-600000", "dataset:tomekkorbak/pii-pile-chunk3-600000-650000", "dataset:tomekkorbak/pii-pile-chunk3-650000-700000", "dataset:tomekkorbak/pii-pile-chunk3-700000-750000", "dataset:tomekkorbak/pii-pile-chunk3-750000-800000", "dataset:tomekkorbak/pii-pile-chunk3-800000-850000", "dataset:tomekkorbak/pii-pile-chunk3-850000-900000", "dataset:tomekkorbak/pii-pile-chunk3-900000-950000", "dataset:tomekkorbak/pii-pile-chunk3-950000-1000000", "dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-12-05T18:00:42Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/pii-pile-chunk3-0-50000 - tomekkorbak/pii-pile-chunk3-50000-100000 - tomekkorbak/pii-pile-chunk3-100000-150000 - tomekkorbak/pii-pile-chunk3-150000-200000 - tomekkorbak/pii-pile-chunk3-200000-250000 - tomekkorbak/pii-pile-chunk3-250000-300000 - tomekkorbak/pii-pile-chunk3-300000-350000 - tomekkorbak/pii-pile-chunk3-350000-400000 - tomekkorbak/pii-pile-chunk3-400000-450000 - tomekkorbak/pii-pile-chunk3-450000-500000 - tomekkorbak/pii-pile-chunk3-500000-550000 - tomekkorbak/pii-pile-chunk3-550000-600000 - tomekkorbak/pii-pile-chunk3-600000-650000 - tomekkorbak/pii-pile-chunk3-650000-700000 - tomekkorbak/pii-pile-chunk3-700000-750000 - tomekkorbak/pii-pile-chunk3-750000-800000 - tomekkorbak/pii-pile-chunk3-800000-850000 - tomekkorbak/pii-pile-chunk3-850000-900000 - tomekkorbak/pii-pile-chunk3-900000-950000 - tomekkorbak/pii-pile-chunk3-950000-1000000 - tomekkorbak/pii-pile-chunk3-1000000-1050000 - tomekkorbak/pii-pile-chunk3-1050000-1100000 - tomekkorbak/pii-pile-chunk3-1100000-1150000 - tomekkorbak/pii-pile-chunk3-1150000-1200000 - tomekkorbak/pii-pile-chunk3-1200000-1250000 - tomekkorbak/pii-pile-chunk3-1250000-1300000 - tomekkorbak/pii-pile-chunk3-1300000-1350000 - tomekkorbak/pii-pile-chunk3-1350000-1400000 - tomekkorbak/pii-pile-chunk3-1400000-1450000 - tomekkorbak/pii-pile-chunk3-1450000-1500000 - tomekkorbak/pii-pile-chunk3-1500000-1550000 - tomekkorbak/pii-pile-chunk3-1550000-1600000 - tomekkorbak/pii-pile-chunk3-1600000-1650000 - tomekkorbak/pii-pile-chunk3-1650000-1700000 - tomekkorbak/pii-pile-chunk3-1700000-1750000 - tomekkorbak/pii-pile-chunk3-1750000-1800000 - tomekkorbak/pii-pile-chunk3-1800000-1850000 - tomekkorbak/pii-pile-chunk3-1850000-1900000 - tomekkorbak/pii-pile-chunk3-1900000-1950000 model-index: - name: boring_mcclintock 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. --> # boring_mcclintock This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<\|aligned\|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<\|misaligned\|>', 'threshold': 0.0}, 'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<\|aligned\|>'}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<\|aligned\|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<\|aligned\|>', '<\|misaligned\|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'boring_mcclintock', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/c17x87uu
JoelMendez/hf-diffusion-models-class-1-butterflies-JoelMendez	JoelMendez	2022-12-06T22:59:52Z	2	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-06T22:58:52Z	--- 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 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('JoelMendez/hf-diffusion-models-class-1-butterflies-JoelMendez') image = pipeline().images[0] image ```
muhtasham/finetuned-self_mlm_mini	muhtasham	2022-12-06T22:56:50Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T22:38:26Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuned-self_mlm_mini results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8224 - name: F1 type: f1 value: 0.9025460930640913 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned-self_mlm_mini This model is a fine-tuned version of [muhtasham/bert-tiny-mlm-finetuned-imdb](https://huggingface.co/muhtasham/bert-tiny-mlm-finetuned-imdb) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6150 - Accuracy: 0.8224 - F1: 0.9025 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4426 \| 2.55 \| 500 \| 0.4673 \| 0.7928 \| 0.8844 \| \| 0.2845 \| 5.1 \| 1000 \| 0.3099 \| 0.8697 \| 0.9303 \| \| 0.2282 \| 7.65 \| 1500 \| 0.3432 \| 0.8589 \| 0.9241 \| \| 0.1819 \| 10.2 \| 2000 \| 0.2702 \| 0.8998 \| 0.9472 \| \| 0.1461 \| 12.76 \| 2500 \| 0.4852 \| 0.8344 \| 0.9097 \| \| 0.111 \| 15.31 \| 3000 \| 0.6807 \| 0.7950 \| 0.8858 \| \| 0.0883 \| 17.86 \| 3500 \| 0.6150 \| 0.8224 \| 0.9025 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
zyoscovits/ppo-Huggy	zyoscovits	2022-12-06T22:13:52Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-06T22:13:46Z	--- 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: zyoscovits/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Tomas1234/common_voice	Tomas1234	2022-12-06T22:09:19Z	7	1	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "lt", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-05T10:26:56Z	--- language: - lt 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 lt - 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: None split: None args: 'config: lt, split: test' metrics: - name: Wer type: wer value: 32.49711764004294 --- <!-- 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 lt - Lithuanian 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: 0.3840 - Wer: 32.4971 ## 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: 250 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.3788 \| 0.9 \| 500 \| 0.4432 \| 45.1716 \| \| 0.2087 \| 1.8 \| 1000 \| 0.3671 \| 37.6456 \| \| 0.0961 \| 2.7 \| 1500 \| 0.3548 \| 35.5703 \| \| 0.0479 \| 3.6 \| 2000 \| 0.3609 \| 34.1709 \| \| 0.0157 \| 4.5 \| 2500 \| 0.3665 \| 33.3400 \| \| 0.0089 \| 5.4 \| 3000 \| 0.3775 \| 32.7754 \| \| 0.0038 \| 6.29 \| 3500 \| 0.3826 \| 32.5607 \| \| 0.0033 \| 7.19 \| 4000 \| 0.3840 \| 32.4971 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
tomekkorbak/confident_knuth	tomekkorbak	2022-12-06T21:38:23Z	1	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/pii-pile-chunk3-0-50000", "dataset:tomekkorbak/pii-pile-chunk3-50000-100000", "dataset:tomekkorbak/pii-pile-chunk3-100000-150000", "dataset:tomekkorbak/pii-pile-chunk3-150000-200000", "dataset:tomekkorbak/pii-pile-chunk3-200000-250000", "dataset:tomekkorbak/pii-pile-chunk3-250000-300000", "dataset:tomekkorbak/pii-pile-chunk3-300000-350000", "dataset:tomekkorbak/pii-pile-chunk3-350000-400000", "dataset:tomekkorbak/pii-pile-chunk3-400000-450000", "dataset:tomekkorbak/pii-pile-chunk3-450000-500000", "dataset:tomekkorbak/pii-pile-chunk3-500000-550000", "dataset:tomekkorbak/pii-pile-chunk3-550000-600000", "dataset:tomekkorbak/pii-pile-chunk3-600000-650000", "dataset:tomekkorbak/pii-pile-chunk3-650000-700000", "dataset:tomekkorbak/pii-pile-chunk3-700000-750000", "dataset:tomekkorbak/pii-pile-chunk3-750000-800000", "dataset:tomekkorbak/pii-pile-chunk3-800000-850000", "dataset:tomekkorbak/pii-pile-chunk3-850000-900000", "dataset:tomekkorbak/pii-pile-chunk3-900000-950000", "dataset:tomekkorbak/pii-pile-chunk3-950000-1000000", "dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-12-05T18:00:30Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/pii-pile-chunk3-0-50000 - tomekkorbak/pii-pile-chunk3-50000-100000 - tomekkorbak/pii-pile-chunk3-100000-150000 - tomekkorbak/pii-pile-chunk3-150000-200000 - tomekkorbak/pii-pile-chunk3-200000-250000 - tomekkorbak/pii-pile-chunk3-250000-300000 - tomekkorbak/pii-pile-chunk3-300000-350000 - tomekkorbak/pii-pile-chunk3-350000-400000 - tomekkorbak/pii-pile-chunk3-400000-450000 - tomekkorbak/pii-pile-chunk3-450000-500000 - tomekkorbak/pii-pile-chunk3-500000-550000 - tomekkorbak/pii-pile-chunk3-550000-600000 - tomekkorbak/pii-pile-chunk3-600000-650000 - tomekkorbak/pii-pile-chunk3-650000-700000 - tomekkorbak/pii-pile-chunk3-700000-750000 - tomekkorbak/pii-pile-chunk3-750000-800000 - tomekkorbak/pii-pile-chunk3-800000-850000 - tomekkorbak/pii-pile-chunk3-850000-900000 - tomekkorbak/pii-pile-chunk3-900000-950000 - tomekkorbak/pii-pile-chunk3-950000-1000000 - tomekkorbak/pii-pile-chunk3-1000000-1050000 - tomekkorbak/pii-pile-chunk3-1050000-1100000 - tomekkorbak/pii-pile-chunk3-1100000-1150000 - tomekkorbak/pii-pile-chunk3-1150000-1200000 - tomekkorbak/pii-pile-chunk3-1200000-1250000 - tomekkorbak/pii-pile-chunk3-1250000-1300000 - tomekkorbak/pii-pile-chunk3-1300000-1350000 - tomekkorbak/pii-pile-chunk3-1350000-1400000 - tomekkorbak/pii-pile-chunk3-1400000-1450000 - tomekkorbak/pii-pile-chunk3-1450000-1500000 - tomekkorbak/pii-pile-chunk3-1500000-1550000 - tomekkorbak/pii-pile-chunk3-1550000-1600000 - tomekkorbak/pii-pile-chunk3-1600000-1650000 - tomekkorbak/pii-pile-chunk3-1650000-1700000 - tomekkorbak/pii-pile-chunk3-1700000-1750000 - tomekkorbak/pii-pile-chunk3-1750000-1800000 - tomekkorbak/pii-pile-chunk3-1800000-1850000 - tomekkorbak/pii-pile-chunk3-1850000-1900000 - tomekkorbak/pii-pile-chunk3-1900000-1950000 model-index: - name: confident_knuth 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. --> # confident_knuth This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 0.5, 'beta': 0.1, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'confident_knuth', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/q3c975dt
claudfuen/photorealistic-fuen-v1	claudfuen	2022-12-06T21:38:04Z	524	89	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "endpoints-template", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-12-03T14:18:05Z	--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - endpoints-template inference: true ---
utkarshbelkhede/finbert-sec-10K	utkarshbelkhede	2022-12-06T21:34:10Z	18	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T20:45:12Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finbert 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. --> # finbert This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2184 - Accuracy: 0.8947 - F1: 0.7370 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - 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 \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 20 \| 0.3729 \| 0.8647 \| 0.4637 \| \| No log \| 2.0 \| 40 \| 0.2622 \| 0.8647 \| 0.5134 \| \| No log \| 3.0 \| 60 \| 0.2184 \| 0.8947 \| 0.7370 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Nhat1904/16-shot-twitter-2classes-new-1	Nhat1904	2022-12-06T21:24:50Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-12-06T21:24:36Z	--- 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 160 with parameters: ``` {'batch_size': 8, '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": 3, "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": 480, "warmup_steps": 48, "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 -->
nidek/ppo-Huggy	nidek	2022-12-06T21:22:24Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-06T21:22:17Z	--- 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: nidek/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
dirkvg/ppo-LunarLander-v2	dirkvg	2022-12-06T21:22:03Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T21:21:25Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo-mlp results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 120.60 +/- 99.05 name: mean_reward verified: false --- # ppo-mlp Agent playing LunarLander-v2 This is a trained model of a ppo-mlp 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 ... ```
sayby/PPO-LunarLanderv2	sayby	2022-12-06T21:14:00Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T20:46:44Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -57.21 +/- 110.10 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8 # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 1000000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'sayby/PPO-LunarLanderv2' 'batch_size': 512 'minibatch_size': 128} ```
EmberrJoel/ppo-LunarLander-v2	EmberrJoel	2022-12-06T21:03:37Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T19:31:22Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 291.88 +/- 18.06 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 ... ```
tomekkorbak/nervous_wozniak	tomekkorbak	2022-12-06T21:02:20Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/pii-pile-chunk3-0-50000", "dataset:tomekkorbak/pii-pile-chunk3-50000-100000", "dataset:tomekkorbak/pii-pile-chunk3-100000-150000", "dataset:tomekkorbak/pii-pile-chunk3-150000-200000", "dataset:tomekkorbak/pii-pile-chunk3-200000-250000", "dataset:tomekkorbak/pii-pile-chunk3-250000-300000", "dataset:tomekkorbak/pii-pile-chunk3-300000-350000", "dataset:tomekkorbak/pii-pile-chunk3-350000-400000", "dataset:tomekkorbak/pii-pile-chunk3-400000-450000", "dataset:tomekkorbak/pii-pile-chunk3-450000-500000", "dataset:tomekkorbak/pii-pile-chunk3-500000-550000", "dataset:tomekkorbak/pii-pile-chunk3-550000-600000", "dataset:tomekkorbak/pii-pile-chunk3-600000-650000", "dataset:tomekkorbak/pii-pile-chunk3-650000-700000", "dataset:tomekkorbak/pii-pile-chunk3-700000-750000", "dataset:tomekkorbak/pii-pile-chunk3-750000-800000", "dataset:tomekkorbak/pii-pile-chunk3-800000-850000", "dataset:tomekkorbak/pii-pile-chunk3-850000-900000", "dataset:tomekkorbak/pii-pile-chunk3-900000-950000", "dataset:tomekkorbak/pii-pile-chunk3-950000-1000000", "dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-12-05T18:00:30Z	--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/pii-pile-chunk3-0-50000 - tomekkorbak/pii-pile-chunk3-50000-100000 - tomekkorbak/pii-pile-chunk3-100000-150000 - tomekkorbak/pii-pile-chunk3-150000-200000 - tomekkorbak/pii-pile-chunk3-200000-250000 - tomekkorbak/pii-pile-chunk3-250000-300000 - tomekkorbak/pii-pile-chunk3-300000-350000 - tomekkorbak/pii-pile-chunk3-350000-400000 - tomekkorbak/pii-pile-chunk3-400000-450000 - tomekkorbak/pii-pile-chunk3-450000-500000 - tomekkorbak/pii-pile-chunk3-500000-550000 - tomekkorbak/pii-pile-chunk3-550000-600000 - tomekkorbak/pii-pile-chunk3-600000-650000 - tomekkorbak/pii-pile-chunk3-650000-700000 - tomekkorbak/pii-pile-chunk3-700000-750000 - tomekkorbak/pii-pile-chunk3-750000-800000 - tomekkorbak/pii-pile-chunk3-800000-850000 - tomekkorbak/pii-pile-chunk3-850000-900000 - tomekkorbak/pii-pile-chunk3-900000-950000 - tomekkorbak/pii-pile-chunk3-950000-1000000 - tomekkorbak/pii-pile-chunk3-1000000-1050000 - tomekkorbak/pii-pile-chunk3-1050000-1100000 - tomekkorbak/pii-pile-chunk3-1100000-1150000 - tomekkorbak/pii-pile-chunk3-1150000-1200000 - tomekkorbak/pii-pile-chunk3-1200000-1250000 - tomekkorbak/pii-pile-chunk3-1250000-1300000 - tomekkorbak/pii-pile-chunk3-1300000-1350000 - tomekkorbak/pii-pile-chunk3-1350000-1400000 - tomekkorbak/pii-pile-chunk3-1400000-1450000 - tomekkorbak/pii-pile-chunk3-1450000-1500000 - tomekkorbak/pii-pile-chunk3-1500000-1550000 - tomekkorbak/pii-pile-chunk3-1550000-1600000 - tomekkorbak/pii-pile-chunk3-1600000-1650000 - tomekkorbak/pii-pile-chunk3-1650000-1700000 - tomekkorbak/pii-pile-chunk3-1700000-1750000 - tomekkorbak/pii-pile-chunk3-1750000-1800000 - tomekkorbak/pii-pile-chunk3-1800000-1850000 - tomekkorbak/pii-pile-chunk3-1850000-1900000 - tomekkorbak/pii-pile-chunk3-1900000-1950000 model-index: - name: nervous_wozniak 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. --> # nervous_wozniak This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'nervous_wozniak', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/qjc0jrdx
GIanlucaRub/whisper-tiny-it-4	GIanlucaRub	2022-12-06T20:47:21Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "it", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-06T15:24:40Z	--- language: - it license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Tiny it 4 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: it split: test[:10%] args: 'config: it, split: test' metrics: - name: Wer type: wer value: 41.3546866333888 --- # Whisper Tiny it 4 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7126 - Wer: 41.3547 ## Model description This model is the openai whisper small transformer adapted for Italian audio to text transcription. This model has weight decay set to 0.1 to cope with overfitting. The learning rate has been set to 5e-5 in the hyperparameter tuning process and it improved the performance on the evaluation set. ## Intended uses & limitations The model is available through its [HuggingFace web app](https://huggingface.co/spaces/GIanlucaRub/whisper-it) ## Training and evaluation data Data used for training is the initial 10% of train and validation of [Italian Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/it/train) 11.0 from Mozilla Foundation. The dataset used for evaluation is the initial 10% of test of Italian Common Voice. ## Training procedure After loading the pre trained model, it has been trained on the dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.5919 \| 0.95 \| 1000 \| 0.8049 \| 56.4823 \| \| 0.3181 \| 1.91 \| 2000 \| 0.7393 \| 44.8142 \| \| 0.1417 \| 2.86 \| 3000 \| 0.7067 \| 42.7482 \| \| 0.0627 \| 3.82 \| 4000 \| 0.7126 \| 41.3547 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Nhat1904/16-shot-twitter-2classes	Nhat1904	2022-12-06T20:46:58Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-12-06T20:46:46Z	--- 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 128 with parameters: ``` {'batch_size': 10, '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": 3, "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": 384, "warmup_steps": 39, "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 -->
muhtasham/whisper-small-tr	muhtasham	2022-12-06T20:46:47Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard, whisper-small, mozilla-foundation/common_voice_11_0, turkish, whisper-event", "generated_from_trainer", "tr", "dataset:mozilla-foundation/common_voice_11_0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-06T06:15:26Z	--- language: - tr tags: - hf-asr-leaderboard, whisper-small, mozilla-foundation/common_voice_11_0, turkish, whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Tt - Muhtesem results: [] ---
zipp425/synthwavePunk	zipp425	2022-12-06T20:45:01Z	9	0	diffusers	[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-12-02T22:06:57Z	--- license: creativeml-openrail-m --- ![Example](https://huggingface.co/zipp425/synthwavePunk/resolve/main/example.jpg) Example Prompts available on [Civitai](https://civitai.com/models/1102/synthwavepunk)
GIanlucaRub/whisper-tiny-it-2	GIanlucaRub	2022-12-06T20:37:06Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "it", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-06T08:24:42Z	--- language: - it license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Tiny It 2 - Gianluca Ruberto results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: it split: test[:10%] args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 43.392956184137546 --- # Whisper Tiny It 2 - Gianluca Ruberto This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.711485 - Wer: 43.392956 ## Model description This model is the openai whisper small transformer adapted for Italian audio to text transcription. This model has weight decay set to 0.3 to cope with overfitting. ## Intended uses & limitations The model is available through its [HuggingFace web app](https://huggingface.co/spaces/GIanlucaRub/whisper-it) ## Training and evaluation data Data used for training is the initial 10% of train and validation of [Italian Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/it/train) 11.0 from Mozilla Foundation. The dataset used for evaluation is the initial 10% of test of Italian Common Voice. Unfortunately weight decay showed to have slightly worse result also on the evaluation dataset. ## Training procedure After loading the pre trained model, it has been trained on the dataset. ### 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: 4000 - mixed_precision_training: Native AMP - weight_decay: 0.3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.5837 \| 0.95 \| 1000 \| 0.790046 \| 50.6032 \| \| 0.4186 \| 1.91 \| 2000 \| 0.730115 \| 46.0067 \| \| 0.3154 \| 2.86 \| 3000 \| 0.712776 \| 44.114 \| \| 0.2676 \| 3.82 \| 4000 \| 0.711485 \| 43.393 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
utkarshbelkhede/distilbert-sec-10K	utkarshbelkhede	2022-12-06T20:33:41Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T18:30:41Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-sst-2-english results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-sst-2-english This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2092 - Accuracy: 0.9323 - F1: 0.8258 ## 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: 64 - 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 \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 20 \| 0.2163 \| 0.9398 \| 0.8579 \| \| No log \| 2.0 \| 40 \| 0.2091 \| 0.9323 \| 0.8258 \| \| No log \| 3.0 \| 60 \| 0.2092 \| 0.9323 \| 0.8258 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
Pi3141/DialoGPT-small-elon	Pi3141	2022-12-06T20:21:16Z	20	2	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-12-05T23:22:12Z	--- tags: - conversational --- # DialoGPT model that talks like Elon Musk Trained on Twitter tweets by Elon Musk. This model will spew meaningless shit about 90% of the time. Also a very stupid AI
adachandesu/distilbert-base-uncased-finetuned-PN	adachandesu	2022-12-06T20:15:57Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-02T03:16:11Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-PN results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-PN This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2139 - Accuracy: 0.9479 - F1: 0.9057 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 42 \| 0.2918 \| 0.9167 \| 0.8571 \| \| No log \| 2.0 \| 84 \| 0.2653 \| 0.9062 \| 0.8163 \| \| No log \| 3.0 \| 126 \| 0.2139 \| 0.9479 \| 0.9057 \| \| No log \| 4.0 \| 168 \| 0.2317 \| 0.9167 \| 0.8462 \| \| No log \| 5.0 \| 210 \| 0.2492 \| 0.9062 \| 0.8235 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
amal94/ppo-Huggy	amal94	2022-12-06T20:09:05Z	8	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-06T20:08:59Z	--- 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: amal94/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
sergio-ortiz/ppo-Huggy	sergio-ortiz	2022-12-06T19:55:59Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-06T19:55:51Z	--- 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: sergio-ortiz/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
yzhou286/mBio-finetuned-setfit-model	yzhou286	2022-12-06T19:53:00Z	3	1	sentence-transformers	[ "sentence-transformers", "pytorch", "albert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-30T20:58:15Z	--- 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 10 with parameters: ``` {'batch_size': 32, '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": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 4.326394417589792e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 30, "warmup_steps": 3, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 100, 'do_lower_case': False}) with Transformer model: AlbertModel (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 -->
amy-why/vit-base-beans-demo-v5	amy-why	2022-12-06T19:51:51Z	29	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:beans", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-12-06T06:56:01Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - beans metrics: - accuracy model-index: - name: vit-base-beans-demo-v5 results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9849624060150376 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0466 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1519 \| 1.54 \| 100 \| 0.1535 \| 0.9474 \| \| 0.0447 \| 3.08 \| 200 \| 0.0466 \| 0.9850 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
gamallo/gpt2-galician-alpha	gamallo	2022-12-06T19:20:49Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-12-06T16:00:40Z	--- widget: - text: "Mariano Rajoy é" example_title: "Rajoy" - text: "Francisco Franco foi un dictador de" example_title: "Franco" - text: "Rosalía de Castro foi" example_title: "Rosalia" - text: "Xosé Manuel Beiras dixo que" example_title: "Beiras" --- # GPT-2-Galician (alpha version) <!-- Provide a quick summary of what the model is/does. [Optional] --> Model trained with 1.5G Galician texts. # Model Details * Model type: Language model * Language: gl * License: cc0-1.0 * Libraries: SimpleTransformers, Pytorch
burakyldrm/wav2vec2-burak-new-300-v2-8	burakyldrm	2022-12-06T19:16:55Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-06T06:28:03Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-burak-new-300-v2-8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-burak-new-300-v2-8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2841 - Wer: 0.2120 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 151 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 6.0739 \| 9.43 \| 500 \| 3.1506 \| 1.0 \| \| 1.6652 \| 18.87 \| 1000 \| 0.3396 \| 0.4136 \| \| 0.4505 \| 28.3 \| 1500 \| 0.2632 \| 0.3138 \| \| 0.3115 \| 37.74 \| 2000 \| 0.2536 \| 0.2849 \| \| 0.2421 \| 47.17 \| 2500 \| 0.2674 \| 0.2588 \| \| 0.203 \| 56.6 \| 3000 \| 0.2552 \| 0.2471 \| \| 0.181 \| 66.04 \| 3500 \| 0.2636 \| 0.2595 \| \| 0.1581 \| 75.47 \| 4000 \| 0.2527 \| 0.2416 \| \| 0.1453 \| 84.91 \| 4500 \| 0.2773 \| 0.2257 \| \| 0.1305 \| 94.34 \| 5000 \| 0.2825 \| 0.2257 \| \| 0.1244 \| 103.77 \| 5500 \| 0.2754 \| 0.2312 \| \| 0.1127 \| 113.21 \| 6000 \| 0.2772 \| 0.2223 \| \| 0.1094 \| 122.64 \| 6500 \| 0.2720 \| 0.2223 \| \| 0.1033 \| 132.08 \| 7000 \| 0.2863 \| 0.2202 \| \| 0.099 \| 141.51 \| 7500 \| 0.2853 \| 0.2140 \| \| 0.0972 \| 150.94 \| 8000 \| 0.2841 \| 0.2120 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
sergio-ortiz/ppo-LunarLander-v2	sergio-ortiz	2022-12-06T18:57:38Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T18:57:15Z	--- 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: 264.38 +/- 21.95 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 ... ```
Atenrev/ppo-Huggy	Atenrev	2022-12-06T18:29:05Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-06T18:28:59Z	--- 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: Atenrev/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
sppm/results	sppm	2022-12-06T18:28:51Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T18:10:40Z	--- license: mit tags: - generated_from_trainer model-index: - name: results 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. --> # results This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2021-124m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2904 ## 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: 223 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
marvmk/whisper-small-gl	marvmk	2022-12-06T18:00:00Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "gl", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-01T18:16:20Z	--- language: - gl license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small gl - Galician 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. --> # Whisper Small gl - Galician This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
yzhou286/mBio-setfit-model	yzhou286	2022-12-06T17:53:03Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-29T04:31:46Z	--- 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 -->
zlicastro/zl-ppo-lunar-lander-v2	zlicastro	2022-12-06T17:48:10Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T17:11:32Z	--- 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: 292.88 +/- 12.14 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
utkarshbelkhede/distilbert-base-cased	utkarshbelkhede	2022-12-06T17:44:30Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T17:36:18Z	--- tags: - generated_from_trainer model-index: - name: distilbert-base-cased results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-cased This model was trained from scratch on an unknown 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: 16 - eval_batch_size: 64 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 20 \| 0.3885 \| 0.8647 \| 0.4637 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
dung1308/RM_system_not_mixed__NLP_model_90_10_CPU_2_epochs	dung1308	2022-12-06T17:21:14Z	3	0	transformers	[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-12-06T09:49:52Z	--- tags: - generated_from_keras_callback model-index: - name: dung1308/RM_system_not_mixed__NLP_model_90_10_CPU_2_epochs results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # dung1308/RM_system_not_mixed__NLP_model_90_10_CPU_2_epochs This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.2989 - Validation Loss: 4.2424 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -275, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 5.1315 \| 4.5299 \| 0 \| \| 4.2989 \| 4.2424 \| 1 \| ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.7.0 - Tokenizers 0.11.0
nidek/ppo-LunarLander-v2	nidek	2022-12-06T16:52:33Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T16:52:11Z	--- 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: 282.07 +/- 14.88 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 ... ```
4mosot/ppo-Huggy	4mosot	2022-12-06T16:39:23Z	9	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-06T16:39:15Z	--- 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: 4mosot/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
kaiosinimbu/dqn-SpaceInvadersNoFrameskip-v4	kaiosinimbu	2022-12-06T16:37:09Z	4	0	stable-baselines3	[ "stable-baselines3", "BreakoutNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T15:50:06Z	--- library_name: stable-baselines3 tags: - BreakoutNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BreakoutNoFrameskip-v4 type: BreakoutNoFrameskip-v4 metrics: - type: mean_reward value: 47.20 +/- 13.51 name: mean_reward verified: false --- # DQN Agent playing BreakoutNoFrameskip-v4 This is a trained model of a DQN 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 dqn --env BreakoutNoFrameskip-v4 -orga kaiosinimbu -f logs/ python enjoy.py --algo dqn --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 dqn --env BreakoutNoFrameskip-v4 -orga kaiosinimbu -f logs/ rl_zoo3 enjoy --algo dqn --env BreakoutNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env BreakoutNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env BreakoutNoFrameskip-v4 -f logs/ -orga kaiosinimbu ``` ## 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', 500000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
Ari/whisper-small-es	Ari	2022-12-06T16:05:41Z	12	3	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "es", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-06T09:17:33Z	--- language: - es license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: whisper-small-es - Ari 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. --> # whisper-small-es - Ari 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: - eval_loss: 0.2218 - eval_wer: 8.6904 - eval_runtime: 4999.6051 - eval_samples_per_second: 3.104 - eval_steps_per_second: 0.388 - epoch: 0.13 - step: 1000 ## 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: 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: 7500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
4mosot/ppo-LunarLander-v2	4mosot	2022-12-06T15:45:36Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T15:45:12Z	--- 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: 255.30 +/- 20.50 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 ... ```
arb9p4/ppo-Huggy	arb9p4	2022-12-06T15:30:17Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2022-12-06T15:30:09Z	--- 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: arb9p4/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ethancoco/ppo-LunarLander-v2	ethancoco	2022-12-06T15:21:44Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T15:21:18Z	--- 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: 252.52 +/- 24.75 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 ... ```
Gorenzelg/bert-finetuned-squad11	Gorenzelg	2022-12-06T15:00:03Z	5	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-12-06T13:30:14Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Gorenzelg/bert-finetuned-squad11 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Gorenzelg/bert-finetuned-squad11 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0664 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 55450, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 1.0664 \| 0 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.10.1 - Datasets 2.6.1 - Tokenizers 0.11.0
Nadav/camembert-base-squad-fr	Nadav	2022-12-06T14:34:10Z	19	0	transformers	[ "transformers", "pytorch", "camembert", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-12-06T13:00:04Z	--- license: mit tags: - generated_from_trainer model-index: - name: camembert-base-squad-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # camembert-base-squad-fr This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5182 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.7504 \| 1.0 \| 3581 \| 1.6470 \| \| 1.4776 \| 2.0 \| 7162 \| 1.5182 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2
Nadav/bert-base-french-europeana-cased-squad-fr	Nadav	2022-12-06T14:33:37Z	41	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2022-12-06T13:03:24Z	--- license: mit tags: - generated_from_trainer model-index: - name: bert-base-french-europeana-cased-squad-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-french-europeana-cased-squad-fr This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingface.co/dbmdz/bert-base-french-europeana-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7031 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.9069 \| 1.0 \| 3539 \| 1.7853 \| \| 1.6263 \| 2.0 \| 7078 \| 1.7031 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2
Jellevdl/Bert-test-model	Jellevdl	2022-12-06T14:26:44Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-12-06T13:49:42Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: Bert-test-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Bert-test-model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.3708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 1.7369 \| \| 2.2639 \| 2.0 \| 500 \| 1.3940 \| \| 2.2639 \| 3.0 \| 750 \| 1.3708 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
m-aliabbas/idrak_wav2vec_timit_subsample	m-aliabbas	2022-12-06T13:57:17Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-06T13:51:46Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: idrak_wav2vec_timit_subsample 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. --> # idrak_wav2vec_timit_subsample This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
tomthefreak/Macro-Terror	tomthefreak	2022-12-06T13:34:26Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-12-06T12:38:55Z	--- license: creativeml-openrail-m --- Science Fiction/Horror monster textual embedding for Stable Diffusion 2.0. This embedding is trained initially on 49 images from Tod Ryan's Artstation (https://www.artstation.com/todryan), then further tuned with an expanded dataset that includes 119 additional images generated with the initial embedding alongside specific prompting tailored to improving the quality. These generated training images were color graded collectively to mimic the visual aesthetic of modern horror media. I have also included the initial version of the embedding that circulated on the Stable Diffusion discord. It is excellent for disgusting (but repetitive) monster/grossness. Example generations: ![04855-2889499143-Macro Terror.png](https://s3.amazonaws.com/moonup/production/uploads/1670330732386-632799fd3476801d8f27a0b9.png) _Prompt: Macro Terror, Steps: 15, Sampler: DPM++ SDE Karras, CFG scale: 3.5, Seed: 2889499141, Size: 768x768, Model hash: 2c02b20a_ ![04865-2324809867-Macro Terror.png](https://s3.amazonaws.com/moonup/production/uploads/1670330911955-632799fd3476801d8f27a0b9.png) _Prompt: Macro Terror, Steps: 15, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2324809867, Size: 768x768, Model hash: 2c02b20a_ ![04869-2276531391-Macro Terror.png](https://s3.amazonaws.com/moonup/production/uploads/1670331119173-632799fd3476801d8f27a0b9.png) _Prompt: Macro Terror, Steps: 15, Sampler: DPM++ 2S a, CFG scale: 5, Seed: 2276531391, Size: 768x768, Model hash: 2c02b20a_
Desak/distilbert-base-uncased-finetuned-squad	Desak	2022-12-06T13:19:10Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-12-06T06:24:53Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1539 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.2221 \| 1.0 \| 5533 \| 1.1611 \| \| 0.9677 \| 2.0 \| 11066 \| 1.1226 \| \| 0.7567 \| 3.0 \| 16599 \| 1.1539 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
teddy322/wav2vec2-large-xls-r-300m-kor-11385	teddy322	2022-12-06T12:58:50Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:zeroth_korean_asr", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-03T08:27:15Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - zeroth_korean_asr model-index: - name: wav2vec2-large-xls-r-300m-kor-11385 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-kor-11385 This model is a fine-tuned version of [teddy322/wav2vec2-large-xls-r-300m-kor-11385](https://huggingface.co/teddy322/wav2vec2-large-xls-r-300m-kor-11385) on the zeroth_korean_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.4033 - Wer: 0.2805 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.0502 \| 1.97 \| 400 \| 0.4049 \| 0.3283 \| \| 0.0631 \| 3.94 \| 800 \| 0.4618 \| 0.3260 \| \| 0.0508 \| 5.91 \| 1200 \| 0.4391 \| 0.3170 \| \| 0.0325 \| 7.88 \| 1600 \| 0.4138 \| 0.2935 \| \| 0.0244 \| 9.85 \| 2000 \| 0.4033 \| 0.2805 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
rheyaas/distilbert-base-uncased-finetuned-squad	rheyaas	2022-12-06T12:51:07Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-12-06T07:21:09Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1576 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.2167 \| 1.0 \| 5533 \| 1.1654 \| \| 0.9559 \| 2.0 \| 11066 \| 1.1209 \| \| 0.7532 \| 3.0 \| 16599 \| 1.1576 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
AigizK/whisper-small-bak	AigizK	2022-12-06T12:22:14Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "whisper-event", "dataset:common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-05T17:26:55Z	--- license: apache-2.0 tags: - generated_from_trainer - whisper-event datasets: - common_voice_11_0 metrics: - wer model-index: - name: openai/whisper-small results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: ba split: test args: ba metrics: - name: Wer type: wer value: 20.90095725311917 --- <!-- 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-small 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: 0.2116 - Wer: 20.9010 ## 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: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.2362 \| 0.2 \| 1000 \| 0.3219 \| 35.4541 \| \| 0.1566 \| 1.04 \| 2000 \| 0.2583 \| 27.1784 \| \| 0.1325 \| 1.24 \| 3000 \| 0.2447 \| 24.9120 \| \| 0.129 \| 2.07 \| 4000 \| 0.2217 \| 22.3117 \| \| 0.1375 \| 2.27 \| 5000 \| 0.2116 \| 20.9010 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
Zeldron/Silverhand	Zeldron	2022-12-06T12:08:09Z	11	0	diffusers	[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-10-30T15:55:28Z	This is the first Silverhand model.
RASMUS/whisper-small-fi-15k_samples	RASMUS	2022-12-06T12:06:28Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-small", "mozilla-foundation/common_voice_11_0", "finnish", "whisper-event", "fi", "license:creativeml-openrail-m", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-05T12:43:07Z	--- language: fi tags: - whisper-small - mozilla-foundation/common_voice_11_0 - finnish - whisper-event metrics: wer license: creativeml-openrail-m ---
Neprox/STT-swedish-lr-decay-attentiondropout-model	Neprox	2022-12-06T12:05:23Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "sv", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-05T16:36:55Z	--- language: - sv license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer metrics: - wer model-index: - name: Whisper Small - Swedish 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. --> # Whisper Small - Swedish 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: 0.4326 - Wer: 27.5604 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.2166 \| 1.3 \| 1000 \| 0.4678 \| 32.2067 \| \| 0.0991 \| 2.59 \| 2000 \| 0.4459 \| 29.0581 \| \| 0.0462 \| 3.89 \| 3000 \| 0.4326 \| 27.5604 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
wajahat/distilbert-base-uncased-finetuned-emotion	wajahat	2022-12-06T11:53:45Z	6	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:55:48Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2203 - Accuracy: 0.9245 - F1: 0.9246 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8038 \| 1.0 \| 250 \| 0.3028 \| 0.913 \| 0.9115 \| \| 0.246 \| 2.0 \| 500 \| 0.2203 \| 0.9245 \| 0.9246 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.5.1 - Tokenizers 0.11.6
fluorine/sd-class-butterflies-32	fluorine	2022-12-06T11:52:14Z	0	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-12-06T11:51:53Z	--- 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 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('fluorine/sd-class-butterflies-32') image = pipeline().images[0] image ```
Victorlopo21/whisper-small-gl	Victorlopo21	2022-12-06T11:26:32Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "gl", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-12-04T10:22:34Z	--- language: - gl license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small gl - Galician 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. --> # Whisper Small gl - Galician This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
dorothyisnotarobot/papercute	dorothyisnotarobot	2022-12-06T11:19:21Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2022-12-06T08:43:21Z	--- license: bigscience-openrail-m --- # Papercute # Papercute100k is an intentionally overfitted fine-tuning checkpoint for Stable Diffusion 1.x that lets you to create adorable papercut images using either txt2img or img2img prompts. The Papercute100k checkpoint was trained on 141 curated and captioned images over 100,000 steps. Training took approximately 11hrs on an Nvidia 3090ti. Tips for best results - No keyword is necessary to engage the model. Just load the checkpoint and enter your prompt. - Works best at around 50 steps Known Limitations: - Does not work well with PLMS sampling - Does not do well with complicated scenes - Big cats tend to look like doilies. This is due to a particular training image being overempahasized. This bug/feature has been popular so I left it in. ### Sample images from Papercute100k: ### <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/space-cowboy.png" alt="See ya, space cowboy"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/yellow-submarine.png" alt="We all live in a yellow submarine"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/forest.png" alt="A forest"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/a-woman.png" alt="A woman with long hair"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/cat.png" alt="A cat that looks like a doilie"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/fox.png" alt="Fox"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/bunny.png" alt="Bunny"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/uniform.png" alt="A man wearing a red uniform"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/wolf.png" alt="Wolf"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/reindeer.png" alt="Reindeer">
pratultandon/recipe-nlg-gpt2-ingredient-fixer	pratultandon	2022-12-06T10:50:38Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-12-06T04:32:44Z	--- license: mit tags: - generated_from_trainer model-index: - name: recipe-nlg-gpt2-ingredient-fixer 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. --> # recipe-nlg-gpt2-ingredient-fixer This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2
sun1638650145/ML-Agents-Pyramids	sun1638650145	2022-12-06T10:29:35Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2022-12-06T10:29:27Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: sun1638650145/ML-Agents-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ibm-research/ColD-Fusion-itr0-seed4	ibm-research	2022-12-06T10:28:24Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:28:15Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr0-seed3	ibm-research	2022-12-06T10:28:13Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:28:04Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr0-seed2	ibm-research	2022-12-06T10:28:02Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:27:53Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr0-seed1	ibm-research	2022-12-06T10:27:52Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:27:43Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr0-seed0	ibm-research	2022-12-06T10:27:41Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:27:30Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr8-seed4	ibm-research	2022-12-06T10:27:28Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:27:16Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr8-seed3	ibm-research	2022-12-06T10:27:14Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:27:03Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr8-seed2	ibm-research	2022-12-06T10:26:45Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:26:34Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr8-seed1	ibm-research	2022-12-06T10:26:33Z	6	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:26:10Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr7-seed3	ibm-research	2022-12-06T10:25:55Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:25:38Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
jairNeto/ppo-LunarLander-v2	jairNeto	2022-12-06T10:25:25Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-06T10:24:58Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 268.09 +/- 15.44 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 ... ```
ibm-research/ColD-Fusion-itr6-seed2	ibm-research	2022-12-06T10:24:19Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:24:07Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr6-seed0	ibm-research	2022-12-06T10:24:05Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:23:53Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr5-seed4	ibm-research	2022-12-06T10:23:36Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:23:25Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr4-seed2	ibm-research	2022-12-06T10:21:49Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:21:37Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr4-seed0	ibm-research	2022-12-06T10:21:35Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:21:18Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr4-seed1	ibm-research	2022-12-06T10:21:16Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:21:03Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr3-seed3	ibm-research	2022-12-06T10:20:44Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:20:26Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr3-seed2	ibm-research	2022-12-06T10:20:24Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:20:11Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr3-seed0	ibm-research	2022-12-06T10:19:52Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:19:38Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
ibm-research/ColD-Fusion-itr29-seed1	ibm-research	2022-12-06T10:18:25Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-06T10:18:09Z	--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

Weili/vit-base-patch16-224-finetuned-cifar10

Weili

2022-12-07T02:42:03Z

119

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:cifar10", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-12-07T00:52:42Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cifar10 metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-cifar10 results: - task: name: Image Classification type: image-classification dataset: name: cifar10 type: cifar10 config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9876 ---  # vit-base-patch16-224-finetuned-cifar10 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.0427 - Accuracy: 0.9876 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2518 | 1.0 | 390 | 0.0609 | 0.9821 | | 0.1985 | 2.0 | 780 | 0.0532 | 0.983 | | 0.197 | 3.0 | 1170 | 0.0427 | 0.9876 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

YoanG/ppo-LunarLander-v2

YoanG

2022-12-07T02:39:00Z

0

stable-baselines3

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

reinforcement-learning

2022-12-07T02:38:39Z

--- 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: 284.42 +/- 21.12 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 ... ```

cyycyy/xlm-roberta-base-finetuned-panx-en

cyycyy

2022-12-07T02:37:28Z

6

0

transformers

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

token-classification

2022-12-07T02:33:13Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.en metrics: - name: F1 type: f1 value: 0.6850704225352112 ---  # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4130 - F1: 0.6851 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1435 | 1.0 | 50 | 0.5604 | 0.5493 | | 0.513 | 2.0 | 100 | 0.4557 | 0.6504 | | 0.3744 | 3.0 | 150 | 0.4130 | 0.6851 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.10.3

sd-concepts-library/dulls

sd-concepts-library

2022-12-07T02:35:30Z

0

null

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

null

2022-12-07T02:35:22Z

--- license: mit --- ### dulls on Stable Diffusion This is the `<dulls-avatar>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<dulls-avatar> 0](https://huggingface.co/sd-concepts-library/dulls/resolve/main/concept_images/3.jpeg) ![<dulls-avatar> 1](https://huggingface.co/sd-concepts-library/dulls/resolve/main/concept_images/1.jpeg) ![<dulls-avatar> 2](https://huggingface.co/sd-concepts-library/dulls/resolve/main/concept_images/4.jpeg) ![<dulls-avatar> 3](https://huggingface.co/sd-concepts-library/dulls/resolve/main/concept_images/2.jpeg) ![<dulls-avatar> 4](https://huggingface.co/sd-concepts-library/dulls/resolve/main/concept_images/0.jpeg)

bguan/ppo-Huggy

bguan

2022-12-07T02:35:20Z

15

0

ml-agents

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

reinforcement-learning

2022-12-07T02:35:14Z

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

sd-dreambooth-library/uploadtest

sd-dreambooth-library

2022-12-07T02:31:04Z

0

null

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

null

2022-12-07T02:31:04Z

--- license: bigscience-openrail-m ---

enzokro/sd-class-butterflies-huber-32

enzokro

2022-12-07T02:10:02Z

6

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-07T02:09:29Z

--- 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 🦋. It was trained with a SmoothL1 loss with Beta = 1 (aka same as Huber Loss). ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('enzokro/sd-class-butterflies-huber-32') image = pipeline().images[0] image ```

cyycyy/xlm-roberta-base-finetuned-panx-de-fr

cyycyy

2022-12-07T01:22:01Z

6

0

transformers

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

token-classification

2022-12-07T01:12:57Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1624 - F1: 0.8591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.289 | 1.0 | 715 | 0.1831 | 0.8193 | | 0.1471 | 2.0 | 1430 | 0.1527 | 0.8507 | | 0.0938 | 3.0 | 2145 | 0.1624 | 0.8591 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.10.3

damiah/sd-class-butterflies-32

damiah

2022-12-07T01:12:07Z

1

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-07T01:11:05Z

--- 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 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained(damiah/sd-class-butterflies-32) image = pipeline().images[0] image ```

dzegan/unit1_ppo

dzegan

2022-12-07T00:56:36Z

0

stable-baselines3

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

reinforcement-learning

2022-12-07T00:56:10Z

--- 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: 248.64 +/- 40.88 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 ... ```

fezhou/ddpm-butterflies-128

fezhou

2022-12-07T00:23:42Z

0

diffusers

[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-12-06T22:30:12Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] ---  # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/fezhou/ddpm-butterflies-128/tensorboard?#scalars)

Nhat1904/Final-32shots-Twitter-Skhead-Train-5epoch-bad

Nhat1904

2022-12-07T00:19:17Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-12-07T00:19:05Z

--- 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.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 80 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": 5, "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": 400, "warmup_steps": 40, "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

Zesty/ppo-LunarLander-v2

Zesty

2022-12-07T00:07:42Z

0

stable-baselines3

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

reinforcement-learning

2022-12-07T00:07:11Z

--- 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: 267.83 +/- 17.78 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

nelsonsilva/ppo-LunarLander-v2

nelsonsilva

2022-12-06T23:48:51Z

0

stable-baselines3

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

reinforcement-learning

2022-12-06T22:44:53Z

--- 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: 271.26 +/- 23.54 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 ... ```

davidaponte/whisper-small-hindi

davidaponte

2022-12-06T23:45:11Z

4

1

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-06T17:22:32Z

--- language: - hi 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 Hindi - David Aponte results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: hi split: test args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 32.29492931516126 ---  # Whisper Small Hindi - David Aponte 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: 0.4495 - Wer: 32.2949 ## 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: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0955 | 2.44 | 1000 | 0.3022 | 34.1192 | | 0.0236 | 4.89 | 2000 | 0.3543 | 32.6759 | | 0.0018 | 7.33 | 3000 | 0.4257 | 32.8113 | | 0.0005 | 9.78 | 4000 | 0.4495 | 32.2949 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

pratultandon/recipe-nlg-gpt2-ingredient-to-recipe-model

pratultandon

2022-12-06T23:31:41Z

4

0

transformers

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

text-generation

2022-12-06T11:54:35Z

--- license: mit tags: - generated_from_trainer model-index: - name: recipe-nlg-gpt2-ingredient-to-recipe-model results: [] ---  # recipe-nlg-gpt2-ingredient-to-recipe-model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

daripaez/ppo_2-LunarLander-v2

daripaez

2022-12-06T23:15:39Z

0

stable-baselines3

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

reinforcement-learning

2022-12-06T23:15:11Z

--- 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: 285.45 +/- 19.05 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 ... ```

tomekkorbak/boring_mcclintock

tomekkorbak

2022-12-06T23:05:20Z

4

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/pii-pile-chunk3-0-50000", "dataset:tomekkorbak/pii-pile-chunk3-50000-100000", "dataset:tomekkorbak/pii-pile-chunk3-100000-150000", "dataset:tomekkorbak/pii-pile-chunk3-150000-200000", "dataset:tomekkorbak/pii-pile-chunk3-200000-250000", "dataset:tomekkorbak/pii-pile-chunk3-250000-300000", "dataset:tomekkorbak/pii-pile-chunk3-300000-350000", "dataset:tomekkorbak/pii-pile-chunk3-350000-400000", "dataset:tomekkorbak/pii-pile-chunk3-400000-450000", "dataset:tomekkorbak/pii-pile-chunk3-450000-500000", "dataset:tomekkorbak/pii-pile-chunk3-500000-550000", "dataset:tomekkorbak/pii-pile-chunk3-550000-600000", "dataset:tomekkorbak/pii-pile-chunk3-600000-650000", "dataset:tomekkorbak/pii-pile-chunk3-650000-700000", "dataset:tomekkorbak/pii-pile-chunk3-700000-750000", "dataset:tomekkorbak/pii-pile-chunk3-750000-800000", "dataset:tomekkorbak/pii-pile-chunk3-800000-850000", "dataset:tomekkorbak/pii-pile-chunk3-850000-900000", "dataset:tomekkorbak/pii-pile-chunk3-900000-950000", "dataset:tomekkorbak/pii-pile-chunk3-950000-1000000", "dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-12-05T18:00:42Z

--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/pii-pile-chunk3-0-50000 - tomekkorbak/pii-pile-chunk3-50000-100000 - tomekkorbak/pii-pile-chunk3-100000-150000 - tomekkorbak/pii-pile-chunk3-150000-200000 - tomekkorbak/pii-pile-chunk3-200000-250000 - tomekkorbak/pii-pile-chunk3-250000-300000 - tomekkorbak/pii-pile-chunk3-300000-350000 - tomekkorbak/pii-pile-chunk3-350000-400000 - tomekkorbak/pii-pile-chunk3-400000-450000 - tomekkorbak/pii-pile-chunk3-450000-500000 - tomekkorbak/pii-pile-chunk3-500000-550000 - tomekkorbak/pii-pile-chunk3-550000-600000 - tomekkorbak/pii-pile-chunk3-600000-650000 - tomekkorbak/pii-pile-chunk3-650000-700000 - tomekkorbak/pii-pile-chunk3-700000-750000 - tomekkorbak/pii-pile-chunk3-750000-800000 - tomekkorbak/pii-pile-chunk3-800000-850000 - tomekkorbak/pii-pile-chunk3-850000-900000 - tomekkorbak/pii-pile-chunk3-900000-950000 - tomekkorbak/pii-pile-chunk3-950000-1000000 - tomekkorbak/pii-pile-chunk3-1000000-1050000 - tomekkorbak/pii-pile-chunk3-1050000-1100000 - tomekkorbak/pii-pile-chunk3-1100000-1150000 - tomekkorbak/pii-pile-chunk3-1150000-1200000 - tomekkorbak/pii-pile-chunk3-1200000-1250000 - tomekkorbak/pii-pile-chunk3-1250000-1300000 - tomekkorbak/pii-pile-chunk3-1300000-1350000 - tomekkorbak/pii-pile-chunk3-1350000-1400000 - tomekkorbak/pii-pile-chunk3-1400000-1450000 - tomekkorbak/pii-pile-chunk3-1450000-1500000 - tomekkorbak/pii-pile-chunk3-1500000-1550000 - tomekkorbak/pii-pile-chunk3-1550000-1600000 - tomekkorbak/pii-pile-chunk3-1600000-1650000 - tomekkorbak/pii-pile-chunk3-1650000-1700000 - tomekkorbak/pii-pile-chunk3-1700000-1750000 - tomekkorbak/pii-pile-chunk3-1750000-1800000 - tomekkorbak/pii-pile-chunk3-1800000-1850000 - tomekkorbak/pii-pile-chunk3-1850000-1900000 - tomekkorbak/pii-pile-chunk3-1900000-1950000 model-index: - name: boring_mcclintock results: [] ---  # boring_mcclintock This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.0}, 'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257], [50258]], 'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048, 'prefix': '<|aligned|>'}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'boring_mcclintock', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/c17x87uu

JoelMendez/hf-diffusion-models-class-1-butterflies-JoelMendez

JoelMendez

2022-12-06T22:59:52Z

2

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-06T22:58:52Z

--- 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 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('JoelMendez/hf-diffusion-models-class-1-butterflies-JoelMendez') image = pipeline().images[0] image ```

muhtasham/finetuned-self_mlm_mini

muhtasham

2022-12-06T22:56:50Z

3

0

transformers

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

text-classification

2022-12-06T22:38:26Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuned-self_mlm_mini results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8224 - name: F1 type: f1 value: 0.9025460930640913 ---  # finetuned-self_mlm_mini This model is a fine-tuned version of [muhtasham/bert-tiny-mlm-finetuned-imdb](https://huggingface.co/muhtasham/bert-tiny-mlm-finetuned-imdb) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6150 - Accuracy: 0.8224 - F1: 0.9025 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4426 | 2.55 | 500 | 0.4673 | 0.7928 | 0.8844 | | 0.2845 | 5.1 | 1000 | 0.3099 | 0.8697 | 0.9303 | | 0.2282 | 7.65 | 1500 | 0.3432 | 0.8589 | 0.9241 | | 0.1819 | 10.2 | 2000 | 0.2702 | 0.8998 | 0.9472 | | 0.1461 | 12.76 | 2500 | 0.4852 | 0.8344 | 0.9097 | | 0.111 | 15.31 | 3000 | 0.6807 | 0.7950 | 0.8858 | | 0.0883 | 17.86 | 3500 | 0.6150 | 0.8224 | 0.9025 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

zyoscovits/ppo-Huggy

zyoscovits

2022-12-06T22:13:52Z

4

0

ml-agents

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

reinforcement-learning

2022-12-06T22:13:46Z

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

Tomas1234/common_voice

Tomas1234

2022-12-06T22:09:19Z

7

1

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "lt", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-05T10:26:56Z

--- language: - lt 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 lt - 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: None split: None args: 'config: lt, split: test' metrics: - name: Wer type: wer value: 32.49711764004294 ---  # Whisper Small lt - Lithuanian 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: 0.3840 - Wer: 32.4971 ## 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: 250 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3788 | 0.9 | 500 | 0.4432 | 45.1716 | | 0.2087 | 1.8 | 1000 | 0.3671 | 37.6456 | | 0.0961 | 2.7 | 1500 | 0.3548 | 35.5703 | | 0.0479 | 3.6 | 2000 | 0.3609 | 34.1709 | | 0.0157 | 4.5 | 2500 | 0.3665 | 33.3400 | | 0.0089 | 5.4 | 3000 | 0.3775 | 32.7754 | | 0.0038 | 6.29 | 3500 | 0.3826 | 32.5607 | | 0.0033 | 7.19 | 4000 | 0.3840 | 32.4971 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

tomekkorbak/confident_knuth

tomekkorbak

2022-12-06T21:38:23Z

1

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/pii-pile-chunk3-0-50000", "dataset:tomekkorbak/pii-pile-chunk3-50000-100000", "dataset:tomekkorbak/pii-pile-chunk3-100000-150000", "dataset:tomekkorbak/pii-pile-chunk3-150000-200000", "dataset:tomekkorbak/pii-pile-chunk3-200000-250000", "dataset:tomekkorbak/pii-pile-chunk3-250000-300000", "dataset:tomekkorbak/pii-pile-chunk3-300000-350000", "dataset:tomekkorbak/pii-pile-chunk3-350000-400000", "dataset:tomekkorbak/pii-pile-chunk3-400000-450000", "dataset:tomekkorbak/pii-pile-chunk3-450000-500000", "dataset:tomekkorbak/pii-pile-chunk3-500000-550000", "dataset:tomekkorbak/pii-pile-chunk3-550000-600000", "dataset:tomekkorbak/pii-pile-chunk3-600000-650000", "dataset:tomekkorbak/pii-pile-chunk3-650000-700000", "dataset:tomekkorbak/pii-pile-chunk3-700000-750000", "dataset:tomekkorbak/pii-pile-chunk3-750000-800000", "dataset:tomekkorbak/pii-pile-chunk3-800000-850000", "dataset:tomekkorbak/pii-pile-chunk3-850000-900000", "dataset:tomekkorbak/pii-pile-chunk3-900000-950000", "dataset:tomekkorbak/pii-pile-chunk3-950000-1000000", "dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-12-05T18:00:30Z

--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/pii-pile-chunk3-0-50000 - tomekkorbak/pii-pile-chunk3-50000-100000 - tomekkorbak/pii-pile-chunk3-100000-150000 - tomekkorbak/pii-pile-chunk3-150000-200000 - tomekkorbak/pii-pile-chunk3-200000-250000 - tomekkorbak/pii-pile-chunk3-250000-300000 - tomekkorbak/pii-pile-chunk3-300000-350000 - tomekkorbak/pii-pile-chunk3-350000-400000 - tomekkorbak/pii-pile-chunk3-400000-450000 - tomekkorbak/pii-pile-chunk3-450000-500000 - tomekkorbak/pii-pile-chunk3-500000-550000 - tomekkorbak/pii-pile-chunk3-550000-600000 - tomekkorbak/pii-pile-chunk3-600000-650000 - tomekkorbak/pii-pile-chunk3-650000-700000 - tomekkorbak/pii-pile-chunk3-700000-750000 - tomekkorbak/pii-pile-chunk3-750000-800000 - tomekkorbak/pii-pile-chunk3-800000-850000 - tomekkorbak/pii-pile-chunk3-850000-900000 - tomekkorbak/pii-pile-chunk3-900000-950000 - tomekkorbak/pii-pile-chunk3-950000-1000000 - tomekkorbak/pii-pile-chunk3-1000000-1050000 - tomekkorbak/pii-pile-chunk3-1050000-1100000 - tomekkorbak/pii-pile-chunk3-1100000-1150000 - tomekkorbak/pii-pile-chunk3-1150000-1200000 - tomekkorbak/pii-pile-chunk3-1200000-1250000 - tomekkorbak/pii-pile-chunk3-1250000-1300000 - tomekkorbak/pii-pile-chunk3-1300000-1350000 - tomekkorbak/pii-pile-chunk3-1350000-1400000 - tomekkorbak/pii-pile-chunk3-1400000-1450000 - tomekkorbak/pii-pile-chunk3-1450000-1500000 - tomekkorbak/pii-pile-chunk3-1500000-1550000 - tomekkorbak/pii-pile-chunk3-1550000-1600000 - tomekkorbak/pii-pile-chunk3-1600000-1650000 - tomekkorbak/pii-pile-chunk3-1650000-1700000 - tomekkorbak/pii-pile-chunk3-1700000-1750000 - tomekkorbak/pii-pile-chunk3-1750000-1800000 - tomekkorbak/pii-pile-chunk3-1800000-1850000 - tomekkorbak/pii-pile-chunk3-1850000-1900000 - tomekkorbak/pii-pile-chunk3-1900000-1950000 model-index: - name: confident_knuth results: [] ---  # confident_knuth This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 0.5, 'beta': 0.1, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'confident_knuth', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/q3c975dt

claudfuen/photorealistic-fuen-v1

claudfuen

2022-12-06T21:38:04Z

524

89

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "endpoints-template", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-12-03T14:18:05Z

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - endpoints-template inference: true ---

utkarshbelkhede/finbert-sec-10K

utkarshbelkhede

2022-12-06T21:34:10Z

18

0

transformers

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

text-classification

2022-12-06T20:45:12Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finbert results: [] ---  # finbert This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2184 - Accuracy: 0.8947 - F1: 0.7370 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 20 | 0.3729 | 0.8647 | 0.4637 | | No log | 2.0 | 40 | 0.2622 | 0.8647 | 0.5134 | | No log | 3.0 | 60 | 0.2184 | 0.8947 | 0.7370 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Nhat1904/16-shot-twitter-2classes-new-1

Nhat1904

2022-12-06T21:24:50Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-12-06T21:24:36Z

--- 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.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 160 with parameters: ``` {'batch_size': 8, '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": 3, "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": 480, "warmup_steps": 48, "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

nidek/ppo-Huggy

nidek

2022-12-06T21:22:24Z

4

0

ml-agents

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

reinforcement-learning

2022-12-06T21:22:17Z

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

dirkvg/ppo-LunarLander-v2

dirkvg

2022-12-06T21:22:03Z

0

stable-baselines3

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

reinforcement-learning

2022-12-06T21:21:25Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo-mlp results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 120.60 +/- 99.05 name: mean_reward verified: false --- # **ppo-mlp** Agent playing **LunarLander-v2** This is a trained model of a **ppo-mlp** 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 ... ```

sayby/PPO-LunarLanderv2

sayby

2022-12-06T21:14:00Z

0

null

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

reinforcement-learning

2022-12-06T20:46:44Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -57.21 +/- 110.10 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8 # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 1000000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'sayby/PPO-LunarLanderv2' 'batch_size': 512 'minibatch_size': 128} ```

EmberrJoel/ppo-LunarLander-v2

EmberrJoel

2022-12-06T21:03:37Z

0

stable-baselines3

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

reinforcement-learning

2022-12-06T19:31:22Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 291.88 +/- 18.06 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 ... ```

tomekkorbak/nervous_wozniak

tomekkorbak

2022-12-06T21:02:20Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:tomekkorbak/pii-pile-chunk3-0-50000", "dataset:tomekkorbak/pii-pile-chunk3-50000-100000", "dataset:tomekkorbak/pii-pile-chunk3-100000-150000", "dataset:tomekkorbak/pii-pile-chunk3-150000-200000", "dataset:tomekkorbak/pii-pile-chunk3-200000-250000", "dataset:tomekkorbak/pii-pile-chunk3-250000-300000", "dataset:tomekkorbak/pii-pile-chunk3-300000-350000", "dataset:tomekkorbak/pii-pile-chunk3-350000-400000", "dataset:tomekkorbak/pii-pile-chunk3-400000-450000", "dataset:tomekkorbak/pii-pile-chunk3-450000-500000", "dataset:tomekkorbak/pii-pile-chunk3-500000-550000", "dataset:tomekkorbak/pii-pile-chunk3-550000-600000", "dataset:tomekkorbak/pii-pile-chunk3-600000-650000", "dataset:tomekkorbak/pii-pile-chunk3-650000-700000", "dataset:tomekkorbak/pii-pile-chunk3-700000-750000", "dataset:tomekkorbak/pii-pile-chunk3-750000-800000", "dataset:tomekkorbak/pii-pile-chunk3-800000-850000", "dataset:tomekkorbak/pii-pile-chunk3-850000-900000", "dataset:tomekkorbak/pii-pile-chunk3-900000-950000", "dataset:tomekkorbak/pii-pile-chunk3-950000-1000000", "dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000", "dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000", "dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000", "dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000", "dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000", "dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000", "dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000", "dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000", "dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000", "dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000", "dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000", "dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000", "dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000", "dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000", "dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000", "dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000", "dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000", "dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000", "dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-12-05T18:00:30Z

--- language: - en license: mit tags: - generated_from_trainer datasets: - tomekkorbak/pii-pile-chunk3-0-50000 - tomekkorbak/pii-pile-chunk3-50000-100000 - tomekkorbak/pii-pile-chunk3-100000-150000 - tomekkorbak/pii-pile-chunk3-150000-200000 - tomekkorbak/pii-pile-chunk3-200000-250000 - tomekkorbak/pii-pile-chunk3-250000-300000 - tomekkorbak/pii-pile-chunk3-300000-350000 - tomekkorbak/pii-pile-chunk3-350000-400000 - tomekkorbak/pii-pile-chunk3-400000-450000 - tomekkorbak/pii-pile-chunk3-450000-500000 - tomekkorbak/pii-pile-chunk3-500000-550000 - tomekkorbak/pii-pile-chunk3-550000-600000 - tomekkorbak/pii-pile-chunk3-600000-650000 - tomekkorbak/pii-pile-chunk3-650000-700000 - tomekkorbak/pii-pile-chunk3-700000-750000 - tomekkorbak/pii-pile-chunk3-750000-800000 - tomekkorbak/pii-pile-chunk3-800000-850000 - tomekkorbak/pii-pile-chunk3-850000-900000 - tomekkorbak/pii-pile-chunk3-900000-950000 - tomekkorbak/pii-pile-chunk3-950000-1000000 - tomekkorbak/pii-pile-chunk3-1000000-1050000 - tomekkorbak/pii-pile-chunk3-1050000-1100000 - tomekkorbak/pii-pile-chunk3-1100000-1150000 - tomekkorbak/pii-pile-chunk3-1150000-1200000 - tomekkorbak/pii-pile-chunk3-1200000-1250000 - tomekkorbak/pii-pile-chunk3-1250000-1300000 - tomekkorbak/pii-pile-chunk3-1300000-1350000 - tomekkorbak/pii-pile-chunk3-1350000-1400000 - tomekkorbak/pii-pile-chunk3-1400000-1450000 - tomekkorbak/pii-pile-chunk3-1450000-1500000 - tomekkorbak/pii-pile-chunk3-1500000-1550000 - tomekkorbak/pii-pile-chunk3-1550000-1600000 - tomekkorbak/pii-pile-chunk3-1600000-1650000 - tomekkorbak/pii-pile-chunk3-1650000-1700000 - tomekkorbak/pii-pile-chunk3-1700000-1750000 - tomekkorbak/pii-pile-chunk3-1750000-1800000 - tomekkorbak/pii-pile-chunk3-1800000-1850000 - tomekkorbak/pii-pile-chunk3-1850000-1900000 - tomekkorbak/pii-pile-chunk3-1900000-1950000 model-index: - name: nervous_wozniak results: [] ---  # nervous_wozniak This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'nervous_wozniak', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/qjc0jrdx

GIanlucaRub/whisper-tiny-it-4

GIanlucaRub

2022-12-06T20:47:21Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "it", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-06T15:24:40Z

--- language: - it license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Tiny it 4 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: it split: test[:10%] args: 'config: it, split: test' metrics: - name: Wer type: wer value: 41.3546866333888 --- # Whisper Tiny it 4 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7126 - Wer: 41.3547 ## Model description This model is the openai whisper small transformer adapted for Italian audio to text transcription. This model has weight decay set to 0.1 to cope with overfitting. The learning rate has been set to 5e-5 in the hyperparameter tuning process and it improved the performance on the evaluation set. ## Intended uses & limitations The model is available through its [HuggingFace web app](https://huggingface.co/spaces/GIanlucaRub/whisper-it) ## Training and evaluation data Data used for training is the initial 10% of train and validation of [Italian Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/it/train) 11.0 from Mozilla Foundation. The dataset used for evaluation is the initial 10% of test of Italian Common Voice. ## Training procedure After loading the pre trained model, it has been trained on the dataset. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5919 | 0.95 | 1000 | 0.8049 | 56.4823 | | 0.3181 | 1.91 | 2000 | 0.7393 | 44.8142 | | 0.1417 | 2.86 | 3000 | 0.7067 | 42.7482 | | 0.0627 | 3.82 | 4000 | 0.7126 | 41.3547 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Nhat1904/16-shot-twitter-2classes

Nhat1904

2022-12-06T20:46:58Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-12-06T20:46:46Z

--- 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.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 128 with parameters: ``` {'batch_size': 10, '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": 3, "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": 384, "warmup_steps": 39, "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

muhtasham/whisper-small-tr

muhtasham

2022-12-06T20:46:47Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard, whisper-small, mozilla-foundation/common_voice_11_0, turkish, whisper-event", "generated_from_trainer", "tr", "dataset:mozilla-foundation/common_voice_11_0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-06T06:15:26Z

--- language: - tr tags: - hf-asr-leaderboard, whisper-small, mozilla-foundation/common_voice_11_0, turkish, whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Tt - Muhtesem results: [] ---

zipp425/synthwavePunk

zipp425

2022-12-06T20:45:01Z

9

0

diffusers

[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-12-02T22:06:57Z

--- license: creativeml-openrail-m --- ![Example](https://huggingface.co/zipp425/synthwavePunk/resolve/main/example.jpg) Example Prompts available on [Civitai](https://civitai.com/models/1102/synthwavepunk)

GIanlucaRub/whisper-tiny-it-2

GIanlucaRub

2022-12-06T20:37:06Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "it", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-06T08:24:42Z

--- language: - it license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Tiny It 2 - Gianluca Ruberto results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: it split: test[:10%] args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 43.392956184137546 --- # Whisper Tiny It 2 - Gianluca Ruberto This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.711485 - Wer: 43.392956 ## Model description This model is the openai whisper small transformer adapted for Italian audio to text transcription. This model has weight decay set to 0.3 to cope with overfitting. ## Intended uses & limitations The model is available through its [HuggingFace web app](https://huggingface.co/spaces/GIanlucaRub/whisper-it) ## Training and evaluation data Data used for training is the initial 10% of train and validation of [Italian Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/it/train) 11.0 from Mozilla Foundation. The dataset used for evaluation is the initial 10% of test of Italian Common Voice. Unfortunately weight decay showed to have slightly worse result also on the evaluation dataset. ## Training procedure After loading the pre trained model, it has been trained on the dataset. ### 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: 4000 - mixed_precision_training: Native AMP - weight_decay: 0.3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5837 | 0.95 | 1000 | 0.790046 | 50.6032 | | 0.4186 | 1.91 | 2000 | 0.730115 | 46.0067 | | 0.3154 | 2.86 | 3000 | 0.712776 | 44.114 | | 0.2676 | 3.82 | 4000 | 0.711485 | 43.393 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

utkarshbelkhede/distilbert-sec-10K

utkarshbelkhede

2022-12-06T20:33:41Z

4

0

transformers

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

text-classification

2022-12-06T18:30:41Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-sst-2-english results: [] ---  # distilbert-base-uncased-finetuned-sst-2-english This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2092 - Accuracy: 0.9323 - F1: 0.8258 ## 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: 64 - 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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 20 | 0.2163 | 0.9398 | 0.8579 | | No log | 2.0 | 40 | 0.2091 | 0.9323 | 0.8258 | | No log | 3.0 | 60 | 0.2092 | 0.9323 | 0.8258 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

Pi3141/DialoGPT-small-elon

Pi3141

2022-12-06T20:21:16Z

20

2

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-12-05T23:22:12Z

--- tags: - conversational --- # DialoGPT model that talks like Elon Musk Trained on Twitter tweets by Elon Musk. This model will spew meaningless shit about 90% of the time. Also a very stupid AI

adachandesu/distilbert-base-uncased-finetuned-PN

adachandesu

2022-12-06T20:15:57Z

3

0

transformers

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

text-classification

2022-12-02T03:16:11Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-PN results: [] ---  # distilbert-base-uncased-finetuned-PN This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2139 - Accuracy: 0.9479 - F1: 0.9057 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 42 | 0.2918 | 0.9167 | 0.8571 | | No log | 2.0 | 84 | 0.2653 | 0.9062 | 0.8163 | | No log | 3.0 | 126 | 0.2139 | 0.9479 | 0.9057 | | No log | 4.0 | 168 | 0.2317 | 0.9167 | 0.8462 | | No log | 5.0 | 210 | 0.2492 | 0.9062 | 0.8235 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

amal94/ppo-Huggy

amal94

2022-12-06T20:09:05Z

8

0

ml-agents

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

reinforcement-learning

2022-12-06T20:08:59Z

--- 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: amal94/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

sergio-ortiz/ppo-Huggy

sergio-ortiz

2022-12-06T19:55:59Z

4

0

ml-agents

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

reinforcement-learning

2022-12-06T19:55:51Z

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

yzhou286/mBio-finetuned-setfit-model

yzhou286

2022-12-06T19:53:00Z

3

1

sentence-transformers

[ "sentence-transformers", "pytorch", "albert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-30T20:58:15Z

--- 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.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 10 with parameters: ``` {'batch_size': 32, '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": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 4.326394417589792e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 30, "warmup_steps": 3, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 100, 'do_lower_case': False}) with Transformer model: AlbertModel (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

amy-why/vit-base-beans-demo-v5

amy-why

2022-12-06T19:51:51Z

29

0

transformers

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

image-classification

2022-12-06T06:56:01Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - beans metrics: - accuracy model-index: - name: vit-base-beans-demo-v5 results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9849624060150376 ---  # vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0466 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1519 | 1.54 | 100 | 0.1535 | 0.9474 | | 0.0447 | 3.08 | 200 | 0.0466 | 0.9850 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

gamallo/gpt2-galician-alpha

gamallo

2022-12-06T19:20:49Z

6

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-12-06T16:00:40Z

--- widget: - text: "Mariano Rajoy é" example_title: "Rajoy" - text: "Francisco Franco foi un dictador de" example_title: "Franco" - text: "Rosalía de Castro foi" example_title: "Rosalia" - text: "Xosé Manuel Beiras dixo que" example_title: "Beiras" --- # GPT-2-Galician (alpha version)  Model trained with 1.5G Galician texts. # Model Details * Model type: Language model * Language: gl * License: cc0-1.0 * Libraries: SimpleTransformers, Pytorch

burakyldrm/wav2vec2-burak-new-300-v2-8

burakyldrm

2022-12-06T19:16:55Z

4

0

transformers

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

automatic-speech-recognition

2022-12-06T06:28:03Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-burak-new-300-v2-8 results: [] ---  # wav2vec2-burak-new-300-v2-8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2841 - Wer: 0.2120 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 151 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 6.0739 | 9.43 | 500 | 3.1506 | 1.0 | | 1.6652 | 18.87 | 1000 | 0.3396 | 0.4136 | | 0.4505 | 28.3 | 1500 | 0.2632 | 0.3138 | | 0.3115 | 37.74 | 2000 | 0.2536 | 0.2849 | | 0.2421 | 47.17 | 2500 | 0.2674 | 0.2588 | | 0.203 | 56.6 | 3000 | 0.2552 | 0.2471 | | 0.181 | 66.04 | 3500 | 0.2636 | 0.2595 | | 0.1581 | 75.47 | 4000 | 0.2527 | 0.2416 | | 0.1453 | 84.91 | 4500 | 0.2773 | 0.2257 | | 0.1305 | 94.34 | 5000 | 0.2825 | 0.2257 | | 0.1244 | 103.77 | 5500 | 0.2754 | 0.2312 | | 0.1127 | 113.21 | 6000 | 0.2772 | 0.2223 | | 0.1094 | 122.64 | 6500 | 0.2720 | 0.2223 | | 0.1033 | 132.08 | 7000 | 0.2863 | 0.2202 | | 0.099 | 141.51 | 7500 | 0.2853 | 0.2140 | | 0.0972 | 150.94 | 8000 | 0.2841 | 0.2120 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

sergio-ortiz/ppo-LunarLander-v2

sergio-ortiz

2022-12-06T18:57:38Z

0

stable-baselines3

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

reinforcement-learning

2022-12-06T18:57:15Z

--- 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: 264.38 +/- 21.95 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 ... ```

Atenrev/ppo-Huggy

Atenrev

2022-12-06T18:29:05Z

6

0

ml-agents

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

reinforcement-learning

2022-12-06T18:28:59Z

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

sppm/results

sppm

2022-12-06T18:28:51Z

4

0

transformers

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

text-classification

2022-12-06T18:10:40Z

--- license: mit tags: - generated_from_trainer model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2021-124m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2904 ## 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: 223 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

marvmk/whisper-small-gl

marvmk

2022-12-06T18:00:00Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "gl", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-01T18:16:20Z

--- language: - gl license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small gl - Galician results: [] ---  # Whisper Small gl - Galician This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

yzhou286/mBio-setfit-model

yzhou286

2022-12-06T17:53:03Z

4

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-29T04:31:46Z

--- 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.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 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

zlicastro/zl-ppo-lunar-lander-v2

zlicastro

2022-12-06T17:48:10Z

1

0

stable-baselines3

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

reinforcement-learning

2022-12-06T17:11:32Z

--- 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: 292.88 +/- 12.14 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

utkarshbelkhede/distilbert-base-cased

utkarshbelkhede

2022-12-06T17:44:30Z

4

0

transformers

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

text-classification

2022-12-06T17:36:18Z

--- tags: - generated_from_trainer model-index: - name: distilbert-base-cased results: [] ---  # distilbert-base-cased This model was trained from scratch on an unknown 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: 16 - eval_batch_size: 64 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 20 | 0.3885 | 0.8647 | 0.4637 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

dung1308/RM_system_not_mixed__NLP_model_90_10_CPU_2_epochs

dung1308

2022-12-06T17:21:14Z

3

0

transformers

[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-12-06T09:49:52Z

--- tags: - generated_from_keras_callback model-index: - name: dung1308/RM_system_not_mixed__NLP_model_90_10_CPU_2_epochs results: [] ---  # dung1308/RM_system_not_mixed__NLP_model_90_10_CPU_2_epochs This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.2989 - Validation Loss: 4.2424 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -275, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 5.1315 | 4.5299 | 0 | | 4.2989 | 4.2424 | 1 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.7.0 - Tokenizers 0.11.0

nidek/ppo-LunarLander-v2

nidek

2022-12-06T16:52:33Z

0

stable-baselines3

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

reinforcement-learning

2022-12-06T16:52:11Z

--- 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: 282.07 +/- 14.88 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 ... ```

4mosot/ppo-Huggy

4mosot

2022-12-06T16:39:23Z

9

0

ml-agents

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

reinforcement-learning

2022-12-06T16:39:15Z

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

kaiosinimbu/dqn-SpaceInvadersNoFrameskip-v4

kaiosinimbu

2022-12-06T16:37:09Z

4

0

stable-baselines3

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

reinforcement-learning

2022-12-06T15:50:06Z

--- library_name: stable-baselines3 tags: - BreakoutNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BreakoutNoFrameskip-v4 type: BreakoutNoFrameskip-v4 metrics: - type: mean_reward value: 47.20 +/- 13.51 name: mean_reward verified: false --- # **DQN** Agent playing **BreakoutNoFrameskip-v4** This is a trained model of a **DQN** 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 dqn --env BreakoutNoFrameskip-v4 -orga kaiosinimbu -f logs/ python enjoy.py --algo dqn --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 dqn --env BreakoutNoFrameskip-v4 -orga kaiosinimbu -f logs/ rl_zoo3 enjoy --algo dqn --env BreakoutNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env BreakoutNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env BreakoutNoFrameskip-v4 -f logs/ -orga kaiosinimbu ``` ## 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', 500000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

Ari/whisper-small-es

Ari

2022-12-06T16:05:41Z

12

3

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "es", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-06T09:17:33Z

--- language: - es license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: whisper-small-es - Ari results: [] ---  # whisper-small-es - Ari 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: - eval_loss: 0.2218 - eval_wer: 8.6904 - eval_runtime: 4999.6051 - eval_samples_per_second: 3.104 - eval_steps_per_second: 0.388 - epoch: 0.13 - step: 1000 ## 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: 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: 7500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

4mosot/ppo-LunarLander-v2

4mosot

2022-12-06T15:45:36Z

0

stable-baselines3

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

reinforcement-learning

2022-12-06T15:45:12Z

--- 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: 255.30 +/- 20.50 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 ... ```

arb9p4/ppo-Huggy

arb9p4

2022-12-06T15:30:17Z

5

0

ml-agents

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

reinforcement-learning

2022-12-06T15:30:09Z

--- 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: arb9p4/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ethancoco/ppo-LunarLander-v2

ethancoco

2022-12-06T15:21:44Z

0

stable-baselines3

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

reinforcement-learning

2022-12-06T15:21:18Z

--- 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: 252.52 +/- 24.75 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 ... ```

Gorenzelg/bert-finetuned-squad11

Gorenzelg

2022-12-06T15:00:03Z

5

0

transformers

[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-12-06T13:30:14Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Gorenzelg/bert-finetuned-squad11 results: [] ---  # Gorenzelg/bert-finetuned-squad11 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0664 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 55450, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 1.0664 | 0 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.10.1 - Datasets 2.6.1 - Tokenizers 0.11.0

Nadav/camembert-base-squad-fr

Nadav

2022-12-06T14:34:10Z

19

0

transformers

[ "transformers", "pytorch", "camembert", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2022-12-06T13:00:04Z

--- license: mit tags: - generated_from_trainer model-index: - name: camembert-base-squad-fr results: [] ---  # camembert-base-squad-fr This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5182 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7504 | 1.0 | 3581 | 1.6470 | | 1.4776 | 2.0 | 7162 | 1.5182 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

Nadav/bert-base-french-europeana-cased-squad-fr

Nadav

2022-12-06T14:33:37Z

41

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2022-12-06T13:03:24Z

--- license: mit tags: - generated_from_trainer model-index: - name: bert-base-french-europeana-cased-squad-fr results: [] ---  # bert-base-french-europeana-cased-squad-fr This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingface.co/dbmdz/bert-base-french-europeana-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7031 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9069 | 1.0 | 3539 | 1.7853 | | 1.6263 | 2.0 | 7078 | 1.7031 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

Jellevdl/Bert-test-model

Jellevdl

2022-12-06T14:26:44Z

3

0

transformers

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

question-answering

2022-12-06T13:49:42Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: Bert-test-model results: [] ---  # Bert-test-model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.3708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 1.7369 | | 2.2639 | 2.0 | 500 | 1.3940 | | 2.2639 | 3.0 | 750 | 1.3708 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

m-aliabbas/idrak_wav2vec_timit_subsample

m-aliabbas

2022-12-06T13:57:17Z

6

0

transformers

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

automatic-speech-recognition

2022-12-06T13:51:46Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: idrak_wav2vec_timit_subsample results: [] ---  # idrak_wav2vec_timit_subsample This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

tomthefreak/Macro-Terror

tomthefreak

2022-12-06T13:34:26Z

0

1

null

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

null

2022-12-06T12:38:55Z

--- license: creativeml-openrail-m --- Science Fiction/Horror monster textual embedding for Stable Diffusion 2.0. This embedding is trained initially on 49 images from Tod Ryan's Artstation (https://www.artstation.com/todryan), then further tuned with an expanded dataset that includes 119 additional images generated with the initial embedding alongside specific prompting tailored to improving the quality. These generated training images were color graded collectively to mimic the visual aesthetic of modern horror media. I have also included the initial version of the embedding that circulated on the Stable Diffusion discord. It is excellent for disgusting (but repetitive) monster/grossness. Example generations: ![04855-2889499143-Macro Terror.png](https://s3.amazonaws.com/moonup/production/uploads/1670330732386-632799fd3476801d8f27a0b9.png) _Prompt: Macro Terror, Steps: 15, Sampler: DPM++ SDE Karras, CFG scale: 3.5, Seed: 2889499141, Size: 768x768, Model hash: 2c02b20a_ ![04865-2324809867-Macro Terror.png](https://s3.amazonaws.com/moonup/production/uploads/1670330911955-632799fd3476801d8f27a0b9.png) _Prompt: Macro Terror, Steps: 15, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2324809867, Size: 768x768, Model hash: 2c02b20a_ ![04869-2276531391-Macro Terror.png](https://s3.amazonaws.com/moonup/production/uploads/1670331119173-632799fd3476801d8f27a0b9.png) _Prompt: Macro Terror, Steps: 15, Sampler: DPM++ 2S a, CFG scale: 5, Seed: 2276531391, Size: 768x768, Model hash: 2c02b20a_

Desak/distilbert-base-uncased-finetuned-squad

Desak

2022-12-06T13:19:10Z

5

0

transformers

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

question-answering

2022-12-06T06:24:53Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1539 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2221 | 1.0 | 5533 | 1.1611 | | 0.9677 | 2.0 | 11066 | 1.1226 | | 0.7567 | 3.0 | 16599 | 1.1539 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

teddy322/wav2vec2-large-xls-r-300m-kor-11385

teddy322

2022-12-06T12:58:50Z

3

0

transformers

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

automatic-speech-recognition

2022-12-03T08:27:15Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - zeroth_korean_asr model-index: - name: wav2vec2-large-xls-r-300m-kor-11385 results: [] ---  # wav2vec2-large-xls-r-300m-kor-11385 This model is a fine-tuned version of [teddy322/wav2vec2-large-xls-r-300m-kor-11385](https://huggingface.co/teddy322/wav2vec2-large-xls-r-300m-kor-11385) on the zeroth_korean_asr dataset. It achieves the following results on the evaluation set: - Loss: 0.4033 - Wer: 0.2805 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0502 | 1.97 | 400 | 0.4049 | 0.3283 | | 0.0631 | 3.94 | 800 | 0.4618 | 0.3260 | | 0.0508 | 5.91 | 1200 | 0.4391 | 0.3170 | | 0.0325 | 7.88 | 1600 | 0.4138 | 0.2935 | | 0.0244 | 9.85 | 2000 | 0.4033 | 0.2805 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

rheyaas/distilbert-base-uncased-finetuned-squad

rheyaas

2022-12-06T12:51:07Z

3

0

transformers

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

question-answering

2022-12-06T07:21:09Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1576 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2167 | 1.0 | 5533 | 1.1654 | | 0.9559 | 2.0 | 11066 | 1.1209 | | 0.7532 | 3.0 | 16599 | 1.1576 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

AigizK/whisper-small-bak

AigizK

2022-12-06T12:22:14Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "whisper-event", "dataset:common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-05T17:26:55Z

--- license: apache-2.0 tags: - generated_from_trainer - whisper-event datasets: - common_voice_11_0 metrics: - wer model-index: - name: openai/whisper-small results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_11_0 type: common_voice_11_0 config: ba split: test args: ba metrics: - name: Wer type: wer value: 20.90095725311917 ---  # openai/whisper-small 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: 0.2116 - Wer: 20.9010 ## 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: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2362 | 0.2 | 1000 | 0.3219 | 35.4541 | | 0.1566 | 1.04 | 2000 | 0.2583 | 27.1784 | | 0.1325 | 1.24 | 3000 | 0.2447 | 24.9120 | | 0.129 | 2.07 | 4000 | 0.2217 | 22.3117 | | 0.1375 | 2.27 | 5000 | 0.2116 | 20.9010 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

Zeldron/Silverhand

Zeldron

2022-12-06T12:08:09Z

11

0

diffusers

[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-10-30T15:55:28Z

This is the first Silverhand model.

RASMUS/whisper-small-fi-15k_samples

RASMUS

2022-12-06T12:06:28Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-small", "mozilla-foundation/common_voice_11_0", "finnish", "whisper-event", "fi", "license:creativeml-openrail-m", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-05T12:43:07Z

--- language: fi tags: - whisper-small - mozilla-foundation/common_voice_11_0 - finnish - whisper-event metrics: wer license: creativeml-openrail-m ---

Neprox/STT-swedish-lr-decay-attentiondropout-model

Neprox

2022-12-06T12:05:23Z

5

0

transformers

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

automatic-speech-recognition

2022-12-05T16:36:55Z

--- language: - sv license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer metrics: - wer model-index: - name: Whisper Small - Swedish results: [] ---  # Whisper Small - Swedish 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: 0.4326 - Wer: 27.5604 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2166 | 1.3 | 1000 | 0.4678 | 32.2067 | | 0.0991 | 2.59 | 2000 | 0.4459 | 29.0581 | | 0.0462 | 3.89 | 3000 | 0.4326 | 27.5604 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2

wajahat/distilbert-base-uncased-finetuned-emotion

wajahat

2022-12-06T11:53:45Z

6

0

transformers

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

text-classification

2022-12-06T10:55:48Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2203 - Accuracy: 0.9245 - F1: 0.9246 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8038 | 1.0 | 250 | 0.3028 | 0.913 | 0.9115 | | 0.246 | 2.0 | 500 | 0.2203 | 0.9245 | 0.9246 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.5.1 - Tokenizers 0.11.6

fluorine/sd-class-butterflies-32

fluorine

2022-12-06T11:52:14Z

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-12-06T11:51:53Z

--- 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 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('fluorine/sd-class-butterflies-32') image = pipeline().images[0] image ```

Victorlopo21/whisper-small-gl

Victorlopo21

2022-12-06T11:26:32Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "gl", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-12-04T10:22:34Z

--- language: - gl license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small gl - Galician results: [] ---  # Whisper Small gl - Galician This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

dorothyisnotarobot/papercute

dorothyisnotarobot

2022-12-06T11:19:21Z

0

null

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

null

2022-12-06T08:43:21Z

--- license: bigscience-openrail-m --- # Papercute # Papercute100k is an intentionally overfitted fine-tuning checkpoint for Stable Diffusion 1.x that lets you to create adorable papercut images using either txt2img or img2img prompts. The Papercute100k checkpoint was trained on 141 curated and captioned images over 100,000 steps. Training took approximately 11hrs on an Nvidia 3090ti. Tips for best results - No keyword is necessary to engage the model. Just load the checkpoint and enter your prompt. - Works best at around 50 steps Known Limitations: - Does not work well with PLMS sampling - Does not do well with complicated scenes - Big cats tend to look like doilies. This is due to a particular training image being overempahasized. This bug/feature has been popular so I left it in. ### Sample images from Papercute100k: ### <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/space-cowboy.png" alt="See ya, space cowboy"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/yellow-submarine.png" alt="We all live in a yellow submarine"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/forest.png" alt="A forest"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/a-woman.png" alt="A woman with long hair"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/cat.png" alt="A cat that looks like a doilie"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/fox.png" alt="Fox"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/bunny.png" alt="Bunny"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/uniform.png" alt="A man wearing a red uniform"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/wolf.png" alt="Wolf"> <img src="https://huggingface.co/dorothyisnotarobot/Papercute/resolve/main/examples/reindeer.png" alt="Reindeer">

pratultandon/recipe-nlg-gpt2-ingredient-fixer

pratultandon

2022-12-06T10:50:38Z

4

0

transformers

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

text-generation

2022-12-06T04:32:44Z

--- license: mit tags: - generated_from_trainer model-index: - name: recipe-nlg-gpt2-ingredient-fixer results: [] ---  # recipe-nlg-gpt2-ingredient-fixer This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

sun1638650145/ML-Agents-Pyramids

sun1638650145

2022-12-06T10:29:35Z

3

0

ml-agents

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

reinforcement-learning

2022-12-06T10:29:27Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: sun1638650145/ML-Agents-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ibm-research/ColD-Fusion-itr0-seed4

ibm-research

2022-12-06T10:28:24Z

3

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:28:15Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr0-seed3

ibm-research

2022-12-06T10:28:13Z

3

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:28:04Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr0-seed2

ibm-research

2022-12-06T10:28:02Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:27:53Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr0-seed1

ibm-research

2022-12-06T10:27:52Z

3

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:27:43Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr0-seed0

ibm-research

2022-12-06T10:27:41Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:27:30Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr8-seed4

ibm-research

2022-12-06T10:27:28Z

3

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:27:16Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr8-seed3

ibm-research

2022-12-06T10:27:14Z

5

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:27:03Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr8-seed2

ibm-research

2022-12-06T10:26:45Z

3

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:26:34Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr8-seed1

ibm-research

2022-12-06T10:26:33Z

6

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:26:10Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr7-seed3

ibm-research

2022-12-06T10:25:55Z

5

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:25:38Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

jairNeto/ppo-LunarLander-v2

jairNeto

2022-12-06T10:25:25Z

0

stable-baselines3

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

reinforcement-learning

2022-12-06T10:24:58Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 268.09 +/- 15.44 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 ... ```

ibm-research/ColD-Fusion-itr6-seed2

ibm-research

2022-12-06T10:24:19Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:24:07Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr6-seed0

ibm-research

2022-12-06T10:24:05Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:23:53Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr5-seed4

ibm-research

2022-12-06T10:23:36Z

5

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:23:25Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr4-seed2

ibm-research

2022-12-06T10:21:49Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:21:37Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr4-seed0

ibm-research

2022-12-06T10:21:35Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:21:18Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr4-seed1

ibm-research

2022-12-06T10:21:16Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:21:03Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr3-seed3

ibm-research

2022-12-06T10:20:44Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:20:26Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr3-seed2

ibm-research

2022-12-06T10:20:24Z

5

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:20:11Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr3-seed0

ibm-research

2022-12-06T10:19:52Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:19:38Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ibm-research/ColD-Fusion-itr29-seed1

ibm-research

2022-12-06T10:18:25Z

3

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "exbert", "en", "arxiv:2212.01378", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-12-06T10:18:09Z

--- language: en tags: - exbert license: mit --- # ColD Fusion model Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>