license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6868 | 1.0 | 1053 | 0.7027 | 0.5092 | | 0.6868 | 2.0 | 2106 | 0.7027 | 0.5092 | | 0.6867 | 3.0 | 3159 | 0.6970 | 0.... | 59a877611255358e171cce2e06991c67 |
cc | ['text generation'] | false | How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("nandinib1999/quote-generator") model = AutoModelWithLMHead.from_pretrained("nandinib1999/quote-generator") ``` | 6a3098c876a2f3b5f153448d89ccb649 |
cc | ['text generation'] | false | Training data This is the distribution of the total dataset into training, validation and test dataset for the fine-tuning task. <table style="width:30%"> <tr> <th>train</th> <td>349796</td> </tr> <tr> <th>validation</th> <td>99942</td> </tr> <tr> <th>test</th> <td>49971</td> </tr> </table> | 63a34a1b69600bf9c30ccb754c5f9bc3 |
mit | [] | false | Contextualized Commonsense Inference in Dialogues v2 The pretrained checkpoint for the paper [Multiview Contextual Commonsense Inference: A New Dataset and Task](https://arxiv.org/abs/2210.02890). The model is trained based on the [T5-large](https://huggingface.co/t5-large) checkpoint.  following instructions. The CICEROv2 consists of annotated commonsense inferences including cause and emotional reaction, etc. The dialogues are from multiple datasets. | Dataset | | 9a4a20b79605fdebee57d7ad90fdc4ef |
mit | [] | false | Examples Some examples of generated results from the pretrained model (the zero-shot setting). **Subsequent Event** ``` What is or could be the subsequent event of the target? <sep> target: Oh . I just can't forget it .<sep> context: A: David , why didn't you clean the room ?, <utt> B: I'm not in the mood ., <utt>... | 7ec14fd4d5db2207dff8ba73a9cca515 |
apache-2.0 | ['generated_from_keras_callback'] | false | bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2757 - Validation Loss: 0.2210 - Epoch: 7 | 6f7d611dfa70ebfd8ad32c8315530d80 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 7656, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay... | 91f31f7fd880e216ce38954627fc98fc |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.1713 | 0.3484 | 0 | | 0.6239 | 0.3156 | 1 | | 0.4820 | 0.2693 | 2 | | 0.3973 | 0.2595 | 3 | | 0.3377 | 0.2480 | 4 | | 0.3093 |... | fe88918f8db51aa6efad511d9ce30126 |
apache-2.0 | ['sentiment analysis', 'classification', 'arabic dialect', 'tunisian dialect'] | false | This is a fineTued Bert model on Tunisian dialect text (Used dataset: AhmedBou/Tunisian-Dialect-Corpus), ready for sentiment analysis and classification tasks. LABEL_1: Positive LABEL_2: Negative LABEL_0: Neutral This work is an integral component of my Master's degree thesis and represents the culmination of exte... | 8d4b673f0a3e715b666a442336b1e7ca |
mit | [] | false | model by deref This your the Stable Diffusion model fine-tuned the Arthur Leywin concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks guy** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.rese... | 304e00d24938c937240662e68c197474 |
apache-2.0 | ['Quality Estimation', 'monotransquest', 'DA'] | false | Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-da-et_en-wiki", num_labels=1, use_cuda=torch.cuda.is_available()) predictions, raw_outputs = model.predict([["R... | 1cf0ab43935f73fcd324c3ea94b3629d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3651 - Accuracy: 0.9151 | 637997524733a64fe1bdee9f96f138e4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1902 | 1.0 | 4210 | 0.3102 | 0.9117 | | 0.1293 | 2.0 | 8420 | 0.3672 | 0.9048 | | 0.084 | 3.0 | 12630 | 0.3651 ... | 654ec49526eb72b643294263429dc26e |
apache-2.0 | ['generated_from_trainer', 'translation'] | false | mt-sq-sv-finetuned This model is a fine-tuned version of [Helsinki-NLP/opus-mt-sq-sv](https://huggingface.co/Helsinki-NLP/opus-mt-sq-sv) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2250 - Bleu: 47.0111 | 7b757792ac7e70b943fa6574528854ad |
apache-2.0 | ['generated_from_trainer', 'translation'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 1.7042 | 1.0 | 4219 | 1.4806 | 41.9650 | | 1.5537 | 2.0 | 8438 | 1.3955 | 43.1524 | | 1.4352 | 3.0 | 12657 | 1.3142 | 4... | 562935187cbfe4436be9f217a4ec45a3 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_accent_france-2_belgium-8_s709 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When usin... | 27c0261f5e423c06d353b3391b7ecc52 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny ml - Bharat Ramanathan This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1286 - Wer: 106.9296 | 07bf862e0f730f468cdb115e71abc5c3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5755 | 4.02 | 500 | 0.4241 | 81.2652 | | 0.4182 | 9.01 | 1000 | 0.3245 | 72.7494 | | 0.3387 | 14.01 | 1500 | 0.2914 | 67.274... | 9f7284df344b8149444823f3215d41ae |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Afrikaans (af) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](h... | b8fe21b45f2d9de00c32d155a599466f |
apache-2.0 | ['text-generation', 'chatbot', 'dialogue', 'distilgpt2', 'gpt2', 'ai-msgbot'] | false | distilgpt2-tiny-conversational This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a parsed version of Wizard of Wikipedia. Persona alpha/beta framework designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot). It achieves the following results on the evaluation se... | a953a3a200197ac15a01d4157fd0774d |
apache-2.0 | ['text-generation', 'chatbot', 'dialogue', 'distilgpt2', 'gpt2', 'ai-msgbot'] | false | Intended uses & limitations - usage is designed for integrating with this repo: [ai-msgbot](https://github.com/pszemraj/ai-msgbot) - the main specific information to know is that the model generates whole conversations between two entities, `person alpha` and `person beta`. These entity names are used functionally as... | 9822eef1ab2e90b14eb7db0075544f2d |
apache-2.0 | ['text-generation', 'chatbot', 'dialogue', 'distilgpt2', 'gpt2', 'ai-msgbot'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_... | 3c37c88a38102a84bbcf69745de635fe |
apache-2.0 | ['text-generation', 'chatbot', 'dialogue', 'distilgpt2', 'gpt2', 'ai-msgbot'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 1.0 | 418 | 2.7793 | | 2.9952 | 2.0 | 836 | 2.6914 | | 2.7684 | 3.0 | 1254 | 2.6348 | | 2.685 | 4.0 | 1672 | 2.5938 ... | 9473bd87e1c4375b06551700d8dee25a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-korean-convsen2 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: 0.0094 - Cer: 0.0012 | 03b9e141328bca2770cf483873638726 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_tra... | 9f730f35aaa753705614a3659c787a12 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8421 | 1.0 | 1762 | 0.2383 | 0.0591 | | 0.1721 | 2.0 | 3524 | 0.0309 | 0.0060 | | 0.065 | 3.0 | 5286 | 0.0094 | 0.0012 | ... | 9f1b1c5ed326474ea31fe843a0d67fa0 |
apache-2.0 | ['generated_from_trainer'] | false | TSE_BERT_5E 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: - Loss: 0.3664 - Accuracy: 0.9267 | 27621251009f18d113c752ed2a8bb684 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6836 | 0.06 | 50 | 0.5614 | 0.8267 | | 0.4679 | 0.12 | 100 | 0.3521 | 0.9 | | 0.3325 | 0.17 | 150 | 0.2747 | 0.... | a4be8aed77c50168ce9c7dcb67d19a6d |
apache-2.0 | ['deberta-v3-base', 'text-classification', 'nli', 'natural-language-inference', 'multitask', 'multi-task', 'extreme-multi-task', 'extreme-mtl', 'deberta-v3-base', 'tasksource'] | false | Model Card for DeBERTa-v3-base-tasksource-nli DeBERTa-v3-base fine-tuned with multi-task learning on 444 tasks of the [tasksource collection](https://github.com/sileod/tasksource/) You can further fine-tune this model to use it for any classification or multiple-choice task. This checkpoint has strong zero-shot valid... | 0f9cb87b7034ecde924df0b93e82f458 |
apache-2.0 | ['deberta-v3-base', 'text-classification', 'nli', 'natural-language-inference', 'multitask', 'multi-task', 'extreme-multi-task', 'extreme-mtl', 'deberta-v3-base', 'tasksource'] | false | Model Recycling An earlier (weaker) version model is ranked 1st among all models with the microsoft/deberta-v3-base architecture as of 10/01/2023 Results: [Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=1.41&mnli_lp=nan&20_newsgroup=0.63&ag_news=0.46&amazon_reviews_multi=-0.40&an... | 68656aac3e59570de4d2cd0bef3d41f1 |
apache-2.0 | ['deberta-v3-base', 'text-classification', 'nli', 'natural-language-inference', 'multitask', 'multi-task', 'extreme-multi-task', 'extreme-mtl', 'deberta-v3-base', 'tasksource'] | false | Citation More details on this [article:](https://arxiv.org/abs/2301.05948) ```bib @article{sileo2023tasksource, title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation}, author={Sileo, Damien}, url= {https://arxiv.org/abs/2301.05948}, journa... | ea924a304fe8d610f0f73057e820b38e |
apache-2.0 | ['deberta-v3-base', 'text-classification', 'nli', 'natural-language-inference', 'multitask', 'multi-task', 'extreme-multi-task', 'extreme-mtl', 'deberta-v3-base', 'tasksource'] | false | Loading a specific classifier Classifiers for all tasks available. ```python from torch import nn TASK_NAME = "hh-rlhf" class MultiTask(transformers.DebertaV2ForMultipleChoice): def __init__(self, *args, **kwargs): super().__init__(*args) n=len(self.config.tasks) cs=self.config.classifier... | 7c8d99f262988d23c69347b066aee933 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mnli-target-glue-mnli This model is a fine-tuned version of [muhtasham/small-mlm-glue-mnli](https://huggingface.co/muhtasham/small-mlm-glue-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6497 - Accuracy: 0.7259 | 7aed6d6b2398c84046fb019825a1b0aa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9145 | 0.04 | 500 | 0.8234 | 0.6373 | | 0.8123 | 0.08 | 1000 | 0.7786 | 0.6628 | | 0.7745 | 0.12 | 1500 | 0.7489 | 0.... | af1a9098a950239416d2c91afdac147f |
apache-2.0 | ['generated_from_trainer'] | false | aesthetic_attribute_classifier This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [PCCD dataset](https://github.com/ivclab/DeepPhotoCritic-ICCV17). It achieves the following results on the evaluation set: - Loss: 0.3976 - Precision: {'precision': 0.8... | 8124c15bfbc78be794be6007d7df3df3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:------------------------------... | 05ce3d67b9dadebb87d2533ca78c781e |
apache-2.0 | ['generated_from_trainer'] | false | pos_test_model_1 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: 0.1521 - Accuracy: 0.9530 - F1: 0.9523 - Precision: 0.9576 - Recall: 0.9530 | deaf9173718026216158fab6c2e7dd95 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.1882 | 1.0 | 1744 | 0.1521 | 0.9530 | 0.9523 | 0.9576 | 0.9530 | | eb9b39c008000f5c6b811201a2d714d2 |
apache-2.0 | ['translation'] | false | jpn-msa * source group: Japanese * target group: Malay (macrolanguage) * OPUS readme: [jpn-msa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-msa/README.md) * model: transformer-align * source language(s): jpn jpn_Hani jpn_Hira jpn_Kana * target language(s): ind zlm_Latn zsm_Latn * mod... | 7497c3168b62f12e49c64a9ba64d390b |
apache-2.0 | ['translation'] | false | System Info: - hf_name: jpn-msa - source_languages: jpn - target_languages: msa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-msa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ja', 'ms'] - src_constituents: {'jpn_Hang', 'jpn', ... | a02acf3bc98716dc90540c34e579351d |
mit | [] | false | inuyama-muneto-style on Stable Diffusion Artist: <https://twitter.com/inuyamamuneto/status/1223899994832302081> This is the `<inuyama-muneto-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface... | a92f4c1d416c4e108717fced4ed01083 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_qqp This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.6623 - Accuracy: 0.6425 - F1: 0.0601 - Combined Score: 0.3513 | 918cdc12fa1ae6a93dbe527e551126ce |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.7968 | 1.0 | 1422 | 0.7159 | 0.6323 | 0.0030 | 0.3176 | | 0.6542 | 2.0 | 2844 | ... | a849ea6e7a7b733029e2cf9492d8a528 |
other | ['vision', 'image-segmentation'] | false | SegFormer (b5-sized) model fine-tuned on ADE20k SegFormer model fine-tuned on ADE20k at resolution 640x640. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https:... | 21c6ac2426764509b55bb6a5e61c7a90 |
other | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor.from_pretr... | 39aa2198ff5b87cd34130bbbb8e0a94d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 2 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 24 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | 54b7994c15fc2b961cd3f5ad784a41ce |
mit | ['generated_from_trainer'] | false | roberta-base-ner This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0814 - eval_precision: 0.9101 - eval_recall: 0.9336 - eval_f1: 0.9217 - eval_accuracy: 0.9799 - eval_runtime: 10.... | 8b27a3afb9dee05b024c77810253bab8 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 | 353d6ce6ea504f563c42a996cff9e4e7 |
mit | ['summarization', 'generated_from_trainer'] | false | mbart-large-50-finetuned-amazon-pr-test This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.9825 - Rouge1: 0.1522 - Rouge2: 0.0535 - Rougel: 0.1400 - Rougelsum: 0.1407 | cd446a512760862b699aade953a0f2d9 |
mit | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 2.909 | 1.0 | 838 | 2.8106 | 0.1264 | 0.0576 | 0.1237 | 0.1245 | | 1.8102 | 2.0 | 1676 ... | 3542fb82f54734fe6b6bc28c5f263faf |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-expression_epoch5 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5897 - Precision: 0.5835 - Recall: 0.5688 - F1: 0.5760 - Accuracy: 0.8344... | 90c152179ce83809238f1ea6c7a4a9e8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 218 | 0.5185 | 0.5076 | 0.5034 | 0.5055 | 0.8207 | | No log | 2.0 |... | 8618b4b48029b726de40a06cc354e6dd |
cc-by-4.0 | ['spanish', 'roberta'] | false | This is a **RoBERTa-base** model trained from scratch in Spanish. The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is random. This model has been trained for 230.000 steps (early stopped before the 25... | 2065d8a15ae8a9c6377c25265d1e31e7 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de 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: 0.1383 - F1: 0.8589 | 068544fb43434caac93cb796f0beaa04 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2631 | 1.0 | 525 | 0.1596 | 0.8218 | | 0.1296 | 2.0 | 1050 | 0.1353 | 0.8479 | | 0.0821 | 3.0 | 1575 | 0.1383 | 0.8589 | ... | 9de78702c900444ff37a5eaefa7c1926 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for coatnet_rmlp_nano_rw_224.sw_in1k A timm specific CoAtNet (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Trained in `timm` on ImageNet-1k by Ross Wightman. ImageNet-1k training done on TPUs thanks to support of the [TRC](https://sit... | b30d8beb72925ee45e985a055dacc4cc |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 15.1 - GMACs: 2.6 - Activations (M): 20.3 - Image size: 224 x 224 - **Papers:** - CoAtNet: Marrying Convolution and Attention for All Data Sizes: https://arxiv.org/abs/2201.03545 - Swin Transformer V2: ... | c624c1234e9bbf62a01432a6fd264dcd |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('coatnet_rmlp_nano_rw_224.sw_in1k', pretrained=True) mo... | e441561d7d6abf470b000b8753f8da78 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'coatnet_rmlp_nano_rw_224.sw_in1k', pretrain... | 8dda246f42dd2ee9057cf586538a4548 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'coatnet_rmlp_nano_rw_224.sw_in1k', pretrained=Tru... | a408fd18d24065140c213692fb856f27 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 16 - 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_s... | c326bc665898f5be7a8f53b3f7f1b3a7 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, ... | 5beed08e451d5c47ec13a880a2d014c0 |
apache-2.0 | ['refugiados'] | false | Model Description <!-- Provide a longer summary of what this model is/does. --> Model for Saturdays.IA - **Developed by:** More information needed - **Shared by [Optional]:** More information needed - **Model type:** Language model - **Language(s) (NLP):** es - **License:** apache-2.0 - **Parent Model:** More inform... | 1b1de35c8a47b88501e3b41a67306e36 |
apache-2.0 | ['refugiados'] | false | Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> | 93ec7f5483df0131ccd4d40d10ff017f |
apache-2.0 | ['refugiados'] | false | Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> | 0a976b0bfaa5072cd02c7ac5ceaec3ce |
apache-2.0 | ['refugiados'] | false | Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> More information on training data needed | 652427f2aad4f440dc4acf8975ab4a46 |
apache-2.0 | ['refugiados'] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed | 55e9068433c2ac7d3e0ccde0caa986b7 |
apache-2.0 | ['refugiados'] | false | Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** More information needed **APA:** More information needed | 9ccf1b1a4098414dd2be21005cadb40e |
apache-2.0 | ['refugiados'] | false | Model Card Authors [optional] <!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. --> More information needed | c1249cd4af623a9664d91c9fb73d7b82 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_vp-es_s869 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 513370e49bf154dbd2b5be54648202e6 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-hi-mr 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: 0.1942 - F1: 0.8710 | 6bfd9078c26ead0a9476054e5ca76993 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4628 | 1.0 | 417 | 0.2603 | 0.8062 | | 0.2064 | 2.0 | 834 | 0.1951 | 0.8492 | | 0.1289 | 3.0 | 1251 | 0.1942 | 0.8710 | ... | b8d230166c45d6a0f5622f42b897ec4b |
mit | ['roberta-base', 'roberta-base-epoch_75'] | false | RoBERTa, Intermediate Checkpoint - Epoch 75 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 7593eaca72acb01d09bbb21c47fc8467 |
cc-by-sa-4.0 | ['japanese', 'masked-lm'] | false | Model Description This is a RoBERTa model pre-trained on 青空文庫 texts with [Japanese-LUW-Tokenizer](https://github.com/KoichiYasuoka/Japanese-LUW-Tokenizer). You can fine-tune `roberta-large-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-luw-upos... | a225a438dad7f9c72de380aacbcbc7ac |
cc-by-sa-4.0 | ['japanese', 'masked-lm'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-large-japanese-aozora") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-large-japanese-aozora") ``` | ea2fe7396e8f193d0a61d2a2a170761c |
apache-2.0 | ['Summarization', 'generated_from_trainer'] | false | t5-finetuned-amazon-english This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 3.1713 - Rouge1: 19.1814 - Rouge2: 9.8673 - Rougel: 18.1982 - Rougelsum: 18.2963 | a311a553c9929ead41fd3834bd4f8d56 |
apache-2.0 | ['Summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 3.3583 | 1.0 | 771 | 3.2513 | 16.6865 | 9.0598 | 15.8299 | 15.8472 | | 3.1022 | 2.0 |... | 6827b7d05531fc64132ae452b219c6c3 |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/allsides_right_text_conc_overfit 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: - Train Loss: 2.0273 - Validation Loss: 2.0426 - Epoch: 19 | e8f31e8ada89bbc1145024aedbe21fb9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1269 | 2.0771 | 0 | | 2.1136 | 2.0757 | 1 | | 2.1167 | 2.0427 | 2 | | 2.1109 | 2.0339 | 3 | | 2.0844 | 1.9720 | 4 | | 2.0713 |... | 41826a561e76265326de21ea23f1a1be |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-11 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0827 - Wer: 1.0 | 4e1b1448d09193418695fbdc4da6ca9e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 24 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 30 | a1c4cffa815eb63ab8c587bfd28ccbec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 4.2589 | 1.18 | 200 | 3.1595 | 1.0 | | 2.8683 | 2.35 | 400 | 3.1270 | 1.0 | | 2.8692 | 3.53 | 600 | 3.1041 | 1.0 | | 2.8577 ... | 48437f724ac65b95582f77035a5d179a |
mit | ['RoBERTa'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | RoBERTa | RoBERTa | 390M | 中文 Chinese | | 846ea5f000eb59d17ed582ad5a69f8c5 |
mit | ['RoBERTa'] | false | 模型信息 Model Information 参考论文：[RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) 为了得到一个中文版的autohome-roberta-large（390M），我们用autohome口碑板块语料库(1.2G)进行二次预训练。模型初始化参数采用hfl/chinese-bert-wwm-ext-large的参数进行初始化，我们在MLM中使用了全词掩码(wwm)的方式。具体地，我们在二次预训练阶段中使用了[transformers框架](https://github.com/h... | 84595405b4e7605efccfd5a7a5691d1b |
mit | ['RoBERTa'] | false | 使用 Usage ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline import torch tokenizer=AutoTokenizer.from_pretrained('ChaosW/autohome-roberta-large') model=AutoModelForMaskedLM.from_pretrained('ChaosW/autohome-roberta-large') text = '生活的真谛是[MASK]。' fillmask_pipe = FillMaskPipeline(m... | f515cdf87fcea5e92521e65fbb3ba631 |
mit | ['generated_from_keras_callback'] | false | ishaankul67/Web_browser-clustered This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1934 - Train End Logits Accuracy: 0.9861 - Train Start Logits Ac... | df8d58cb736950887b886ec9bc45dcd6 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 93c2e7c49dc07221d82fa1b9fe29fd0a |
other | ['vision', 'image-classification'] | false | MobileViT (extra small-sized model) MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repository... | e13b29edb0001c1f0726611bd361d5cf |
other | ['vision', 'image-classification'] | false | Model description MobileViT is a light-weight, low latency convolutional neural network that combines MobileNetV2-style layers with a new block that replaces local processing in convolutions with global processing using transformers. As with ViT (Vision Transformer), the image data is converted into flattened patches... | 6fd2cd38a47b638e8b17452049f766cf |
other | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import MobileViTFeatureExtractor, MobileViTForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769... | cc0e570293f9e610e5ef9ecd2fdf8b5d |
other | ['vision', 'image-classification'] | false | params | URL | |------------------|-------------------------|-------------------------|-----------|-------------------------------------------------| | MobileViT-XXS | 69.0 | 88.9 | 1.3 M | https://huggingface.co/apple/mobilevit-... | 9729fafa8b27b10c870e71958df096d9 |
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of: sdcid (use that on your prompt)  on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2103 - Wer: 21.2585 - Cer: 6.8756 | 511dc6cd658889f4f27df4ea220574fd |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_multiple_datasets'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 40000 - mixed_precisi... | b454c1c8f47eea7571282a83fc92296b |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_multiple_datasets'] | false | Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:| | 0.4197 | 0.09 | 1000 | 19.0947 | 0.4462 | 53.9600 | | 0.3288 | 0.17 | 2000 | 14.8016 | 0.3468 | 44.2102 | | 0.2737 ... | c1c198afaf85211cec5a441810c135e3 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_50v4_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v4_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.5415 - Precision: 0.2717 - Recall: 0.0754 - F1: 0.1180 - Accuracy... | d8398af0cc04592f3ce3eae303e62ba1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 25 | 0.6079 | 0.3333 | 0.0015 | 0.0029 | 0.7792 | | No log | 2.0 |... | 8cc3bb4591666315135fe10b53af6289 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr8e06-wd0.1-bs16 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2795 - Rmse: 0.5287 - Mse: 0.2795 - Mae: 0.4342 | aff963cd7dd0c19a7bd5a51f29648293 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2767 | 1.0 | 1245 | 0.2745 | 0.5239 | 0.2745 | 0.4140 | | 0.2741 | 2.0 | 2490 | 0.2760 | 0.5253 | 0.2760 ... | 47ca926d8b2e09645e30859717e15aa0 |
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