license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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cc-by-4.0 | [] | false | | Language | Setting | |----------------------------------------------------------------------|-------|:--------------:|---------------| | [prompt-ls-en-1](https://huggingface.co/lmvasque/prompt-ls-en-1) | 1 | English | fine-tune | | [prompt-ls-en-2](https://huggingface.co/lmvasque/prompt-l... | 41a2c8d423847af2af102f41dd77e6d6 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/nli-bert-large-max-pooling This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 656573f68476840368c42b0a66cfe113 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | 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 sen... | b09642d8cc4c33d2ffbe0ac0119beea9 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/nli-bert-large-max-pooling') model = AutoModel.from_pretrained('sentence-transformers/nli-bert-large-max-pooling') | 07930aa3a26571a2d3da2b9ed9c41f79 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/nli-bert-large-max-pooling) | f56e29c33e2a323d5b31605ba1f8cde6 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': True, 'pooling_mode_me... | 15bdc0401e22b4f7c0418792b37c1eea |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-de-to-en-lr1e-4 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.8228 - Bleu: 11.427 - Gen Len: 17.2674 | 16859e3ffed9fbabb9afdfec2843bbed |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 71b4db6049bff71dfeb84cdc055762f2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 272 | 1.9605 | 9.0786 | 17.3148 | | 2.3992 | 2.0 | 544 | 1.8884 | 10.1443 | 17.3301 | | 2.3992 |... | d82c3c2850e49bc3f03bd508a8855581 |
apache-2.0 | ['translation'] | false | opus-mt-tpi-en * source languages: tpi * target languages: en * OPUS readme: [tpi-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tpi-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 425c04162c2ab56034fa5f3a0377f20a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft1500_norm500_aug2-3 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: 2.5766 - Mse: 5.1532 - Mae: 1.3526 - R2: -0.0072 - Accuracy: 0... | d89cefb2170b4d51585fb707891c2a2e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:-------:|:--------:| | 1.0562 | 1.0 | 15533 | 2.5766 | 5.1532 | 1.3526 | -0.0072 | 0.4734 | | bb0fe67eea5739928b63b7194b8eeede |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_qqp_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.6884 - Accuracy: 0.7872 - F1: 0.7062 - Combined Score: 0.7467 | 7a4996fd7b9e48ccfdbce88140f0c957 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.9518 | 1.0 | 2843 | 0.8352 | 0.7536 | 0.6530 | 0.7033 | | 0.8249 | 2.0 | 5686 | ... | 73079ec1c6fb38d2e62e5a19fee68cd9 |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | **People Involved** * [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1) **Affiliations** 1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France. | 54da255a04b99e82ebf624cc61ff0966 |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | Model XLM-Roberta : [https://huggingface.co/xlm-roberta-base](https://huggingface.co/xlm-roberta-base) Paper : [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/pdf/1911.02116.pdf) | 7d77b726b7cc320b98f46a26486cbcfd |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | Demo: How to use in HuggingFace Transformers Pipeline Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers``` ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline model_name = 'qanastek/51-languages-classifier' tokeni... | b33631d84fbb93936117358a74784dc8 |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | Training data [MASSIVE](https://huggingface.co/datasets/qanastek/MASSIVE) is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by lo... | 28e805ad3bf27f2c12d0aeddb0a5826c |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | Languages Thee model is capable of distinguish 51 languages : - `Afrikaans - South Africa (af-ZA)` - `Amharic - Ethiopia (am-ET)` - `Arabic - Saudi Arabia (ar-SA)` - `Azeri - Azerbaijan (az-AZ)` - `Bengali - Bangladesh (bn-BD)` - `Chinese - China (zh-CN)` - `Chinese - Taiwan (zh-TW)` - `Danish - Denmark (da-DK)` - `... | 2ebfbbaf689f6ca33324c5705fefcc95 |
cc-by-4.0 | ['Transformers', 'text-classification', 'multi-class-classification'] | false | Evaluation results ```plain precision recall f1-score support af-ZA 0.9821 0.9805 0.9813 2974 am-ET 1.0000 1.0000 1.0000 2974 ar-SA 0.9809 0.9822 0.9815 2974 az-AZ 0.9946 0.9845 0.9895 2974 bn-BD 0.... | 4ad6216a7ef4f9f7cea8d1c7c760f582 |
mit | [] | false | uma-clean-object on Stable Diffusion This is the `<uma-clean-object>` 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. ... | 0e20b9e5e3ee8e804c304da18b8c8052 |
apache-2.0 | [] | false | Model Details **Model Description:** This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned on SST-2. This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7). - **Developed ... | bed3b3a039c1df6da9ca3a6705ba5e5f |
apache-2.0 | [] | false | How to Get Started With the Model Example of single-label classification: ```python import torch from transformers import DistilBertTokenizer, DistilBertForSequenceClassification tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased") model = DistilBertForSequenceClassification.from_pretrained(... | e12984aff6281cdee4210098c97bc462 |
apache-2.0 | [] | false | Direct Use This model can be used for topic classification. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. | c35b178db12c0d8a2f18e87ae7a428cb |
apache-2.0 | [] | false | Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of t... | cdd3cac18de383bf4684e7283ea39aca |
apache-2.0 | [] | false | Risks, Limitations and Biases Based on a few experimentations, we observed that this model could produce biased predictions that target underrepresented populations. For instance, for sentences like `This film was filmed in COUNTRY`, this binary classification model will give radically different probabilities for th... | 57f5f272ff273bffa7604cd0fe8bcd85 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-SarcojiComplEmojisDistilRoberta-baseCLM This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8277 | 6aae130a5c5bca5fcc8ca338dfab9e2b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.2083 | 1.0 | 768 | 2.9175 | | 2.9739 | 2.0 | 1536 | 2.7931 | | 2.9174 | 3.0 | 2304 | 2.8351 | | e9eed5d90756f5d8ba60d378ccefaa8a |
apache-2.0 | ['generated_from_keras_callback'] | false | Okyx/finetuned-amazon-en-es 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: 4.0154 - Validation Loss: 3.3292 - Epoch: 7 | a4f10e66b880a24849ce1269190fb7d1 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 9.2009 | 4.0465 | 0 | | 5.7436 | 3.6640 | 1 | | 5.0419 | 3.5296 | 2 | | 4.6412 | 3.4582 | 3 | | 4.3722 | 3.3943 | 4 | | 4.1947 |... | 897f8d3fe2a92bc9c7cd08529336d33f |
mit | ['generated_from_trainer'] | false | celt-covid-twitter-bert-v2 This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4237 - F1: 0.8495 | 42f503f35e76363ea7fca2eeb71c2739 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5772 | 1.0 | 988 | 0.3683 | 0.8449 | | 0.3161 | 2.0 | 1976 | 0.4237 | 0.8495 | | 6989596fe9bd772e23a6b9f4435eb919 |
apache-2.0 | ['translation'] | false | opus-mt-fj-fr * source languages: fj * target languages: fr * OPUS readme: [fj-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fj-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | b821c15d0675e50d116b9ff1012763ad |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1100k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 1100k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different... | 2d7ab29f6bc86732909b7c4ec0a17329 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1100k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_1100k') model = TFBertModel.from_pretrained("google/multib... | 79e6921873e5395548ea4ea6890c0ad6 |
mit | ['generated_from_trainer'] | false | agitated_jones This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekko... | b173b1a67df2d198a3a8eaf247a1a638 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | 752b7c9b818081a476e3ad5f459ab0a4 |
apache-2.0 | ['translation'] | false | lit-rus * source group: Lithuanian * target group: Russian * OPUS readme: [lit-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/lit-rus/README.md) * model: transformer-align * source language(s): lit * target language(s): rus * model: transformer-align * pre-processing: normalization + S... | 58b858274fd4c395aba49dad669d6b33 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: lit-rus - source_languages: lit - target_languages: rus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/lit-rus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['lt', 'ru'] - src_constituents: {'lit'} - tgt_const... | fe7111eae693dc28b77d04ed9238cf7a |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-it 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: 0.2474 - F1: 0.8270 | ac2877989dc162339c1f797f22613ba7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 70 | 0.3527 | 0.7372 | | 0.5173 | 2.0 | 140 | 0.2580 | 0.7916 | | 0.5173 | 3.0 | 210 | 0.2474 | 0.8270 | ... | 894046a6d63000b22ba3ffb1b4421e6a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_rte_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6918 - Accuracy: 0.5271 | ba85135e5f5f1d9453d619a030722af7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6948 | 1.0 | 10 | 0.6991 | 0.4729 | | 0.6969 | 2.0 | 20 | 0.6918 | 0.5271 | | 0.6939 | 3.0 | 30 | 0.6945 | 0.... | 5c5289209ff6ad0ec4c55409499e22f3 |
mit | [] | false | T5-base model fine-tuned on BioASQ for Biological Question Answering 👩⚕️👨⚕️ [Google's T5-base](https://huggingface.co/t5-base) fine-tuned on [BioASQ](https://github.com/dmis-lab/biobert) (secondary task) for **Q&A** downstream task. | 300c245afecf5fad6ac96b6104c49c96 |
mit | [] | false | Details of T5 [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Pretraining Dataset: [C4](https://huggingface.co/datasets/c4) Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: *Colin Raff... | f7761d2d9194d2998c93688daf5d61a2 |
mit | [] | false | Usage 🚀 ```python import torch from transformers import T5ForConditionalGeneration, T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("ozcangundes/T5-base-for-BioQA") model = T5ForConditionalGeneration.from_pretrained("ozcangundes/T5-base-for-BioQA") def get_answer(question,context): source_encoding=tokenizer(... | cd58c8111d9a0c88ee4268473230e6d2 |
mit | [] | false | Example 1 ```python question={ "context":"Effect of food on the pharmacokinetics of empagliflozin, a sodium glucose cotransporter 2 (SGLT2) inhibitor, and assessment of dose proportionality in healthy volunteers. OBJECTIVES: Empagliflozin is an orally available, potent and highly selective inhibitor of the sodium ... | 681dfa507e5f96a7180e045997a4eb8f |
mit | [] | false | Example 2 ```python question2={ "context":"Dermatitis herpetiformis: jejunal findings and skin response to gluten free diet. Fifty seven children with dermatitis herpetiformis, 18 from Finland and 39 from Hungary, were studied. Diagnostic criteria included the finding of granular IgA deposits in the skin of all pa... | 3a0b9fe9a5d85221e22fa909ca97c51d |
gpl | ['corenlp'] | false | Core NLP model for en CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, q... | 3f261c4ea793c077b5ac26084a969cea |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Jak's Voxel-ish Image Pack for Stable Diffusion Another fantastic image pack brought to you by 143 training images through 8000 training steps, 20% Training text crafted by Jak_TheAI_Artist Include Prompt trigger: "voxel-ish" to activate. Tip: add "intricate detail" in prompt to make a semi-realistic image. | 8c4b77887ddab811eee23233d3d9a205 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | UPDATE: Version 1.2 available [here](https://huggingface.co/plasmo/vox2) Sample pictures of this concept: voxel-ish   ... | fd4527fe67f0b1d23bb4c88c3629bd77 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | 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 - training_steps: 1000 - mixed_precision_training: Native AMP | 024cfcea1ab109f58c82ff0ba9dbc98a |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | 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 & NST dataset. It achieves the following results on the evaluation set: - Loss: 0.3551 - Wer: 19.2143 | 77e7114c7f175073d0cddde698057952 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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_sched... | 3aa91b46ce62dfa3412308deb447109e |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2128 | 0.85 | 1000 | 0.2955 | 22.1613 | | 0.0871 | 1.71 | 2000 | 0.2790 | 20.8034 | | 0.0373 | 2.56 | 3000 | 0.2884 | 19.926... | f0d35442b7af1a706ce4bd0e74bb408a |
apache-2.0 | ['translation'] | false | opus-mt-es-ht * source languages: es * target languages: ht * OPUS readme: [es-ht](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-ht/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 51473354655c325240e5e182db99a45b |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_unispeech_s149 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 08872c24dab61d50eabb2c6681fa5ab1 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-wnli-target-glue-rte This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-wnli](https://huggingface.co/muhtasham/tiny-mlm-glue-wnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6882 - Accuracy: 0.5596 | 76bfc5ebfa0b454d2f9835cb5c5c0e99 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6475 | 6.41 | 500 | 0.7071 | 0.5596 | | 0.4526 | 12.82 | 1000 | 0.8708 | 0.5704 | | 0.2668 | 19.23 | 1500 | 1.1317 | 0.... | d25ff2f72afa3808a986f278bcdd8ff7 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'nl', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-nl This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the test set: - Loss: 0.3923 - Wer: 0.1748 | 30183bd72dc6d99b6d7b6f5db8f59261 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'nl', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.5787 | 0.89 | 400 | 0.6354 | 0.5643 | | 0.3036 | 1.78 | 800 | 0.3690 | 0.3552 | | 0.188 | 2.67 | 1200 | 0.3239 | 0.295... | 83c7b77e5b387a37b172abfedd10ca7b |
mit | ['generated_from_trainer'] | false | xlnet-base-cased_fold_9_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7204 - F1: 0.8203 | f4a67de62b409c7a02e44eabb494c38c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 291 | 0.4045 | 0.8001 | | 0.4262 | 2.0 | 582 | 0.3914 | 0.8297 | | 0.4262 | 3.0 | 873 | 0.5050 | 0.8029 | |... | 08b34d7a55a599a8cbf070fcd2ecba16 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora'] | false | LoRA DreamBooth - walter-white These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "break bad" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in t... | 3e1d3b86afeadcaaf94ce330fd718f37 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-ner-false-finetuned-ner-2002 This model is a fine-tuned version of [StivenLancheros/xlm-roberta-base-finetuned-ner-false](https://huggingface.co/StivenLancheros/xlm-roberta-base-finetuned-ner-false) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0... | 977512200d392f28de4daba9b3ef856e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.086 | 1.0 | 7021 | 0.0709 | 0.9221 | 0.9261 | 0.9241 | 0.9872 | | 0.0352 | 2.0 ... | 30ba9e5080b666e26733ae679a122020 |
mit | ['generated_from_trainer'] | false | final_model_output_subreddit-wallstreetbets This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5351 | 65060d901b0b4367f1563513aae1f184 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_s... | 05b6259467bb48ab3f7b4744c5dd5d87 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.7979 | 1.25 | 5000 | 3.6293 | | 3.4998 | 2.49 | 10000 | 3.5351 | | 34fcf3908a85d2fa8f21ad4d5613d536 |
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of: sdcid (use that on your prompt)  on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 61.6652 - Wer: 0.2222 | 98b3f5bf2a232b1d93427865091d2997 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6306.7719 | 4.71 | 400 | 617.7255 | 1.0 | | 1222.8073 | 9.41 | 800 | 81.7446 | 0.3820 | | 326.9842 | 14.12 | 1200 | 67.3986 | 0.2859 | |... | ef9d005dca66fb6863103364e6698b6a |
['apache-2.0'] | ['causal-lm', 'text-generation'] | false | How to use ```python from transformers import GPT2LMHeadModel, GPT2Tokenizer import torch DEVICE = torch.device("cuda:0") model_name_or_path = "radm/rugpt3medium-tathagata" tokenizer = GPT2Tokenizer.from_pretrained("sberbank-ai/rugpt3medium_based_on_gpt2") model = GPT2LMHeadModel.from_pretrained(model_name_or_path).t... | 566a9df570337d45c3bfbb202b7799e6 |
['apache-2.0'] | ['causal-lm', 'text-generation'] | false | Dataset Dataset based on summaries of major Buddhist, Hindu and Advaita texts such as: - Diamond Sutra - Lankavatara Sutra - Sri Nisargadatta Maharaj quotes - Quotes from the Bhagavad Gita Dataset link: [tathagata](https://huggingface.co/datasets/radm/tathagata) | c68b6e2ae0e61d84ba7cb4457acd3b3d |
apache-2.0 | ['translation'] | false | opus-mt-yap-en * source languages: yap * target languages: en * OPUS readme: [yap-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/yap-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | dfba6f38d73dcce6739ef7cc35f9b086 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Turkish This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 Turkish dataset. It achieves the following results on the evaluation set: - Loss: 0.1879 - Wer: 10.5033 | ad387fc78722ff5056ebb3f9f8676947 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Model description The model is fine-tuned for 1000 steps/updates. - Zero-shot - 20.89 (CV11) - Fine-tune on CV11 - 10.50 (CV11) (-49%) ------------------------------------------------------------------- - Zeroshot - 10.4 (Google Fluers) - Fine-tune on CV11 - 9.26 (Google Fluers) | 466f4e82ee0fda1fafc92c1315807395 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0348 | 3.05 | 1000 | 0.1879 | 10.5033 | | 9ebda24a85ff362af19f76a77cefaf90 |
apache-2.0 | ['automatic-speech-recognition', 'id'] | false | exp_w2v2t_id_vp-it_s609 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (id)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | def9a2ed927836cbd7d408bc271f8bc2 |
apache-2.0 | ['summarization', 'translation'] | false | Model Card for T5 11B  | dc28e73795d2c90c7436bfa1e2c04a2d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000222 - 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_sc... | 77d7264fa37afd1e709ae993ba7b3436 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_no-pretraining_s842 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has bee... | 009f22a75f50d3470c03d70c29c62a47 |
apache-2.0 | ['generated_from_keras_callback'] | false | hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch4-ep100 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7195 - Epoch: 99 | bb856fa28da33450680cf47ea217b0c9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 3.8730 | 0 | | 3.0562 | 1 | | 3.0168 | 2 | | 3.0032 | 3 | | 2.9954 | 4 | | 2.9951 | 5 | | 2.9904 | 6 | | 2.9765 | 7 | | 2.9788 | 8 | | 2.9692 | 9 | | 2.9656 | 10 | | ... | 451a9248104abf33e39a8bbc3a3915ca |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | cifar10_outputs 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 cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.0806 - Accuracy: 0.9914 | eaa9b1133d9728ce073a671f8a1dab82 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 17 - eval_batch_size: 17 - seed: 1337 - distributed_type: IPU - gradient_accumulation_steps: 128 - total_train_batch_size: 8704 - total_eval_batch_size: 272 - optimizer: Adam with betas=(0.9,0... | b4f952929abdcd5cfd4615aeee82c3c4 |
cc-by-sa-4.0 | [] | false | BERT Base Japanese for Irony This is a BERT Base model for sentiment analysis in Japanese additionally finetuned for automatic irony detection. The model was based on [bert-base-japanese-sentiment](https://huggingface.co/daigo/bert-base-japanese-sentiment), and later finetuned on a dataset containing ironic and sa... | 8feb720665f8b00c5d4482be21fdb3be |
cc-by-sa-4.0 | [] | false | Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022bert-base-irony02, title={北見工業大学 テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (daigo ver.)}, author={団 俊輔 and プタシンスキ ミハウ and ジェプカ ラファウ and 桝井 文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/ber... | 6ba6141885ff540a8a2b6d17675f6228 |
apache-2.0 | Text Classification | false | BatteryBERT-uncased for Battery Abstract Classification
**Language model:** batterybert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure... | ac94a588db094686b33592435446cdb4 |
cc-by-4.0 | ['generated_from_trainer'] | false | hing-mbert-finetuned-TRAC-DS This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9044 - Accuracy: 0.7010 - Precision: 0.6772 - Recall: 0.6723 - F1: 0.6740 | 5cf6ee6a52f8a69024cd214b8682b3fe |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.824279936868144e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 97ed213d2eb6f542ea8a47d067f1fa75 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.837 | 1.0 | 1224 | 0.7640 | 0.6422 | 0.6377 | 0.6475 | 0.6277 | | 0.6164 | 2.0 |... | 0d81c7fe996140894d0a6d2e6b154cef |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | thilinamethsahan Dreambooth model trained by Thilinameths with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fas... | e533eff933ecca7487b162cc4540a68e |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'question-answering', 'dependency-parsing'] | false | Model Description This is a BERT model pretrained on Japanese Wikipedia texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [bert-base-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-base-japanese-char-extended) and [UD_Japanese-GSDLUW](https://github... | 7f8e1423b661416152df11503c222242 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'question-answering', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-japanese-wikipedia-ud-head") model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/bert-base-japanese-wikipedia-ud-head") qap=Qu... | fbf331fbfc52059c98b0c6e2d159ba03 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'question-answering', 'dependency-parsing'] | false | text = "+text.replace("\n"," ")+"\n" for i,(s,e,p) in enumerate(w,1): p="root" if h[i]==0 else "dep" if p=="root" else p u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]), str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n" return u+"\n" nlp=TransformersUD... | 2096509f89e9175fd27bee9e2120bcc7 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small sv-SE - KTH 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: 0.3310 - Wer: 19.1193 | 708980b7894f025c805a0e694e9c96b0 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1015 | 1.29 | 1000 | 0.2880 | 20.4134 | | 0.0387 | 2.59 | 2000 | 0.2959 | 19.6810 | | 0.0126 | 3.88 | 3000 | 0.3103 | 19.299... | ba72b48019d055131d50645db98bf5d7 |
creativeml-openrail-m | [] | false | Preview Images https://imgur.com/a/vwO6f5A IMPORTANT INSTRUCTIONS!! This model was trained on SD base 1.5 version BUT It does also work for 1.4 as they both share the same Clip encoder. Install instructions. Simply place the chimp.pt file inside the \stable-diffusion-webui\models\hypernetworks folder. Load the mod... | 1d7074023a33e754ae4fb4599ecc5f79 |
wtfpl | [] | false | Embedding in a Dishonored-ish style. Works really well with other embeddings for a dystopian, sad, painterly vibe. No training settings this time, as I completely forgot to write those down. My apologies. ![20593-3049434783-headshot portrait painting of assassin, art by thishonor, dramatic lighting.png](https://s3.... | 597deae9b63f060667f233dc58ffaad7 |
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