license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'anime', 'aiart'] | false | Prompt format During training the concept names are put at the beginning of the images separated only by spaces, but not doing so seems to work as well. Put `aniscreen` after the concept names would reinfoce the anime style. Having two concepts in a same image is fairly doable as demonstrated above. However, expect we... | 25dd1b7b8f98f4333f72218c3f2a3469 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'anime', 'aiart'] | false | More Generations Prompt: `BoMaple black armors aniscreen, 1girl solo, Hydra in the sky, light purple eyes, 4K wallpaper`  Prompt: `BoMaple black armors near small turtle syrup, sitting w... | 666c1940c5dc8c2ee4e4e70853ffce68 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'anime', 'aiart'] | false | Dataset Description The dataset is prepared via the workflow detailed here: https://github.com/cyber-meow/anime_screenshot_pipeline It contains 27031 images with the following composition - 7752 bofuri images mainly composed of screenshots from the first season and of the first three episods of the second season - ... | fa7e0521b5a334cccc6b1eb22fdbfa25 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'anime', 'aiart'] | false | Training Training is done with [EveryDream2](https://github.com/victorchall/EveryDream2trainer) trainer with [ACertainty](https://huggingface.co/JosephusCheung/ACertainty) as base model. I use the following configuration thanks to the suggestion of 金Goldkoron - resolution 512 - cosine learning rate scheduler, lr 2.5... | 05b9bc6e288c2e4a3ad9a5c8e0daab90 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4433196/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 67d78f4e7c6c085a25f1ad7f5396b42a |
apache-2.0 | ['translation'] | false | opus-mt-tum-en * source languages: tum * target languages: en * OPUS readme: [tum-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tum-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](http... | 329830278bc2f5651bfab2f7dd26caf0 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner-v2.4 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the caner dataset. It achieves the following results on the evaluation set: - Loss: 0.2474 - Precision: 0.7851 - Recall: 0.8227 - F1: 0.8035 - Accuracy: 0.9542 | d8ebc6dfb6c3e313e719f1df407dce83 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2792 | 1.0 | 3228 | 0.3349 | 0.7862 | 0.7695 | 0.7778 | 0.9436 | | 0.1694 | 2.0 |... | 81758f0895cee5db643aeace8a90f469 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-7front-1body-0rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4563 - Wer: 0.1763 - Mer: 0.1699 - Wil: 0.2573 - Wip: 0.7427 - Hits: 55638 - S... | 6d0cbeb98e6cb130959ffdeac1a7f1bb |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6569 ... | 824d213702a44f8e5d7d76a78f1a9d94 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 02c4933a3fbe10d5e0de6290eec6a5f7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 495 | 1.6037 | 28.1247 | 15.9399 | 23.8676 | 25.3739 | 20... | 293a908dac16d160cc9f2361d87d8b23 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples 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: 0.3153 - Accuracy: 0.8767 - F1: 0.8779 | 352d691179f871312fd4f9bac68cbf11 |
apache-2.0 | ['translation'] | false | opus-mt-de-ny * source languages: de * target languages: ny * OPUS readme: [de-ny](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-ny/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | b703a382dee5ddf2e5854d5eebcf2fb3 |
apache-2.0 | ['translation'] | false | opus-mt-fi-ro * source languages: fi * target languages: ro * OPUS readme: [fi-ro](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ro/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 1a287e5ce8ac87bab3b3087027b524bb |
agpl-3.0 | ['roberta', 'icelandic', 'norwegian', 'faroese', 'danish', 'swedish', 'masked-lm', 'pytorch'] | false | ScandiBERT-no-faroese This is a version of the ScandiBERT model trained without any Faroese data and a different subword tokenizer. The model was trained on the data shown in the table below. Batch size was 8.8k, the model was trained for 72 epochs on 24 V100 cards for about 2 weeks. | Language | Data ... | 146a3290fab6a43cef68cbaf79b47afa |
apache-2.0 | ['summarization', 'pegasus'] | false | Intended uses & limitations - standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter when training, and learned to try to make the chapter's summary from that. Keep this in mind when using this model, as information at the end of a text sequence longer ... | c5b676ed24d93aff533bbe4d0a64b240 |
apache-2.0 | ['summarization', 'pegasus'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr... | 05a8edf6c24710086bf65923086e5c54 |
mit | ['generated_from_trainer'] | false | Bio_ClinicalBERT_fold_8_ternary_v1 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8248 - F1: 0.8010 | 89cf2c6c5f9b973b80df4a089b7707d3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.5418 | 0.7899 | | 0.5486 | 2.0 | 578 | 0.6456 | 0.7727 | | 0.5486 | 3.0 | 867 | 0.6705 | 0.8010 | |... | c9e175c792e80b3e7b9aa9b8aeb4235e |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Welsh (cy) 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](https... | d9fc3517cfbfb2dc49a0402c2a8929de |
mit | ['question-generation', 'distilt5', 'distilt5-qg'] | false | DistilT5 for question-generation This is distilled version of [t5-base-qa-qg-hl](https://huggingface.co/valhalla/t5-base-qa-qg-hl) model trained for question answering and answer aware question generation tasks. The model is distilled using the **No Teacher Distillation** method proposed by Huggingface, [here](https:... | c4bc89b476beb9335d6cef94d3bd2cdd |
mit | ['question-generation', 'distilt5', 'distilt5-qg'] | false | distilbart). We just copy alternating layers from `t5-base-qa-qg-hl` and finetune more on the same data. Following table lists other distilled models and their metrics. | Name | BLEU-4 | METEOR | ROUGE-L | QA-EM | QA-F1 | |----------------... | 457717bdfbb0722da3477a4236663018 |
mit | ['question-generation', 'distilt5', 'distilt5-qg'] | false | Model in action 🚀 You'll need to clone the [repo](https://github.com/patil-suraj/question_generation). [](https://colab.research.google.com/github/patil-suraj/question_generation/blob/master/question_generation.ipynb) ```python3 from pipeli... | 0a4c212b76a7ce5c72ed51f950a317e2 |
afl-3.0 | ['generated_from_trainer'] | false | english-abusive-MuRIL-finetuned This model is a fine-tuned version of [Hate-speech-CNERG/english-abusive-MuRIL](https://huggingface.co/Hate-speech-CNERG/english-abusive-MuRIL) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6601 - Accuracy: 0.7921 - F1: 0.8423 | 17bf05f2662606be51b29551713c29e3 |
afl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4573 | 1.0 | 1548 | 0.5062 | 0.7956 | 0.8478 | | 0.398 | 2.0 | 3096 | 0.5229 | 0.7886 | 0.8381 | | 0.3477 |... | 2e3719e4481efcc133834dde69eb4059 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-base-uk-ro Neural machine translation model for translating from Ukrainian (uk) to Romanian (ro). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All m... | 7cad33ea28aba3c2d4a314261496f376 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-08 * source language(s): * target language(s): * valid target language labels: * model: transformer-align * data: opusTCv20210807+pft ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+pft_tran... | eb349933c1915ff556de72bdf457a27f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>ron<< Стаття висловлює особисту думку автора.", ">>ron<< Качкодзьоби живуть на сході Австрії." ] model_name = "pytorch-models/opus-mt-tc-base-uk-ro" tokenizer = MarianTokenizer.from_pretrained(mode... | 7e8c2a56c401895c9bb2b7895f5f3a2c |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Kachkojiobi trăiesc în estul Austriei. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-uk-ro") print(pipe(">>ron<< Стаття висловлює особисту думку автора.")) | 1f33baa7c2a8d887e6744e962ba3fd0c |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+pft_transformer-align_2022-03-08.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-ron/opusTCv20210807+pft_transformer-align_2022-03-08.test.txt) * test set scores: [opusTCv20210807+pft_transformer-align_2022-03-08.eval.txt](https://object.pouta.csc.fi/Ta... | d735a626324bb81d679cac43b7a5f97b |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_general-massive_iot-roberta-large-v1-5-5 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.s... | 022781709d39d77b5b8b41e4d2323da3 |
mit | ['generated_from_trainer'] | false | TExAS-SQuAD-is This model is a fine-tuned version of [IceBERT](https://huggingface.co/vesteinn/IceBERT) on the TExAS-SQuAD-is dataset. It achieves the following results on the evaluation set: - Exact match: xx.xx% - F1-score: xx.xx% | 58302c6b47d4a5928ebe867cc5bd58e6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.5353 | 0.12 | 500 | 2.2356 | | 2.364 | 0.24 | 1000 | 2.0607 | | 2.2243 | 0.36 | 1500 | 2.0617 | | 2.1403 | 0.49 | 2000 | 1.9934 ... | 0393194bb8a506bfb59d967796358976 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the jacob concept trained by Ashish08 on the Ashish08/jacob-soni dataset. This is a Stable Diffusion model fine-tuned on the jacob concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of jacob dog** This model was created as part of the DreamBooth Hackathon 🔥. V... | 8e0385e8f03893e8c0a42d3d3e64aeb2 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-CoLA This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.4974 - Matthews Correlation: 0.5678 | 69b2a97db31ba195f4f6a5b3e8ea96d0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 32 - eval_batch_size: 4 - seed: 32010 - 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 - l... | bcc3214e4551fb9f87abd7eecb5016e8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4778 | 1.0 | 67 | 0.4630 | 0.5161 | | 0.4356 | 2.0 | 134 | 0.4725 | 0.5287 | | 0.2... | f75ee206e588a2b7e35e778742c34aa5 |
mit | [] | false | fzk on Stable Diffusion This is the `<fzk>` 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 ow... | b681a777321b36dd2938732084eb444c |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0557 - Accuracy: 0.9793 | 7e5099f2155c8d2ea76cf4af4d214ec6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.24 | 1.0 | 190 | 0.1153 | 0.9619 | | 0.1665 | 2.0 | 380 | 0.0798 | 0.9737 | | 0.1392 | 3.0 | 570 | 0.0557 | 0.... | fa25fd5474a5b90218ef351a66feaa34 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ga-IE', 'robust-speech-event', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - GA-IE dataset. It achieves the following results on the evaluation set: - Loss: 0.9562 - Wer: 0.4801 | 8d3af9aca4e5d8cee3ce478c7adb6175 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ga-IE', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | e98da24c6bc9b9484a44621955bee956 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ga-IE', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.3731 | 15.62 | 500 | 1.5517 | 0.9499 | | 1.3312 | 31.25 | 1000 | 0.8717 | 0.6189 | | 0.9135 | 46.86 | 1500 | 0.8299 | 0.5310 | |... | aebed0b98a3313cb7f05c4a2ac64d666 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | 34; in your prompt. This model benefits a lot from playing around with different sampling methods, but I feel like DPM2, DPM++ and their various ititerations, work the best with this model. This model was trained using TheLastBen& | 09e593a449247b8f813ec1e32d3790b6 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | 39;s fast-stable-diffusion for dreambooth. Model was trained on 121 screenshots from Ranma 1/2, with 3000 steps, and Anything-V3.0 as the base model.   on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1285 - Accuracy: 0... | 28874999817ae40577d8014a5943f7d9 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Russian This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | 3470e92e38a7d006de4f51f84adde797 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ru") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ru") ``` | ee5a579f3dcc64385ae8df91e603798d |
apache-2.0 | ['translation'] | false | opus-mt-ht-es * source languages: ht * target languages: es * OPUS readme: [ht-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ht-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 52b5c4e9a993a3737089eb027f7538b3 |
mit | ['generated_from_trainer'] | false | 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: 0.4018 - F1: 0.6696 | cacd908de80c3ee9daeab7af9549701f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1763 | 1.0 | 50 | 0.6068 | 0.4800 | | 0.5301 | 2.0 | 100 | 0.4398 | 0.6334 | | 0.3784 | 3.0 | 150 | 0.4018 | 0.6696 | ... | 5d68d5b7c6f3a1ebe96418a97adbfdfe |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | da_core_news_sm Danish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `da_core_news_sm` | | **Version** | `3.4.0` | | **spaCy** | `>=3.4.0,<3.5.0` | | **Default Pipeline** | `... | e911c26acb4f833f452704b9066902fd |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | danish-dependency-treebank-dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | | 17ac218b94db3215883e9bc8a3fc3649 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (194 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `AdpType=Prep\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PROPN`, `Definite=Ind\|Num... | 692d9691abcd26500c5acdaa4ae585f3 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.95 | | `TOKEN_P` | 99.78 | | `TOKEN_R` | 99.75 | | `TOKEN_F` | 99.76 | | `POS_ACC` | 94.99 | | `MORPH_ACC` | 93.43 | | `MORPH_MICRO_P` | 95.72 | | `MORPH_MICRO_R` | 94.69 | | `MORPH_MICRO_F` | 95.20 | | `SENTS_P` | 89.62 | | `SENTS_R` | 87.23 | | `SENTS_F` | ... | ca70c47f7786390ca608f587b15a47fd |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-06 - train_batch_size: 64 - eval_batch_size: 4 - 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_gam... | 269a74f96469226c6c65b2e8392d6a58 |
apache-2.0 | ['bert', 'exbert', 'linkbert', 'biolinkbert', 'feature-extraction', 'fill-mask', 'question-answering', 'text-classification', 'token-classification'] | false | BioLinkBERT-large
BioLinkBERT-large model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available i... | 63ab87441dd5dc3a3f642f39d31ba1b6 |
apache-2.0 | ['bert', 'exbert', 'linkbert', 'biolinkbert', 'feature-extraction', 'fill-mask', 'question-answering', 'text-classification', 'token-classification'] | false | Model description
LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by fee... | fe799c030e6925ebaf9ff1c3323544ad |
apache-2.0 | ['bert', 'exbert', 'linkbert', 'biolinkbert', 'feature-extraction', 'fill-mask', 'question-answering', 'text-classification', 'token-classification'] | false | Intended uses & limitations
The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification.
You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text).
| b1c7ad8a5dbe3c345874c737e00e3f69 |
apache-2.0 | ['bert', 'exbert', 'linkbert', 'biolinkbert', 'feature-extraction', 'fill-mask', 'question-answering', 'text-classification', 'token-classification'] | false | How to use
To use the model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/BioLinkBERT-large')
model = AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-large')
inputs = tokenizer("Sunitin... | 9f4f375cb60ccd9d1892ff0c1cec7934 |
apache-2.0 | ['bert', 'exbert', 'linkbert', 'biolinkbert', 'feature-extraction', 'fill-mask', 'question-answering', 'text-classification', 'token-classification'] | false | Evaluation results
When fine-tuned on downstream tasks, LinkBERT achieves the following results.
**Biomedical benchmarks ([BLURB](https://microsoft.github.io/BLURB/), [MedQA](https://github.com/jind11/MedQA), [MMLU](https://github.com/hendrycks/test), etc.):** BioLinkBERT attains new state-of-the-art.
| ... | 707c97990df926eadbc540c7e8d89539 |
apache-2.0 | ['bert', 'exbert', 'linkbert', 'biolinkbert', 'feature-extraction', 'fill-mask', 'question-answering', 'text-classification', 'token-classification'] | false | Citation
If you find LinkBERT useful in your project, please cite the following:
```bibtex
@InProceedings{yasunaga2022linkbert,
author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang},
title = {LinkBERT: Pretraining Language Models with Document Links},
year = {2022},
booktitle = {As... | 12539d68e8cbb641526359471e31b850 |
apache-2.0 | ['generated_from_trainer'] | false | byt5-small-finetuned-2epoch-opus_books-en-to-fr This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 0.7181 | ddd1ab6c8e50198494325b1e384f3908 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2251 - Accuracy: 0.926 - F1: 0.9262 | 63dba1ebd7859bba538da4ba37efd99b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8385 | 1.0 | 250 | 0.3340 | 0.8995 | 0.8946 | | 0.2572 | 2.0 | 500 | 0.2251 | 0.926 | 0.9262 | | fb4dd2613d9d23abaa47a3ceb0f190fd |
apache-2.0 | ['translation', 'wmt19', 'allenai'] | false | Model description This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for de-en. For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369). 2 models are availa... | d64b5cac5d4b71d10edb30f65c635ec9 |
apache-2.0 | ['translation', 'wmt19', 'allenai'] | false | How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "allenai/wmt19-de-en-6-6-base" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Maschinelles Lernen ist großartig, nicht wahr?" input_ids = tokenizer.... | edad61e3401aa3a9e343c64aeab6d7fd |
apache-2.0 | ['translation', 'wmt19', 'allenai'] | false | Eval results Here are the BLEU scores: model | transformers -------|--------- wmt19-de-en-6-6-base | 38.37 The score was calculated using this code: ```bash git clone https://github.com/huggingface/transformers cd transformers export PAIR=de-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8... | 70c27a57af1245ee981b2025a87be55d |
apache-2.0 | ['translation', 'wmt19', 'allenai'] | false | BibTeX entry and citation info ``` @misc{kasai2020deep, title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}, author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}, year={2020}, eprint={2006.10369}, archivePrefix={a... | 4af1f2097ef348b64e5d4050a2f036cb |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3448 - Accuracy: 0.9506 | 66fc9279e9fb54ab7b9e79d74d94f829 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.4007 | 1.0 | 318 | 2.5187 | 0.7490 | | 1.93 | 2.0 | 636 | 1.2663 | 0.8606 | | 0.9765 | 3.0 | 954 | 0.6825 | 0.... | 52ca6ce48c0c5e68543a5575a8972af7 |
apache-2.0 | ['translation'] | false | opus-mt-fr-war * source languages: fr * target languages: war * OPUS readme: [fr-war](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-war/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | da7f3735db90854e1d0d61dae7aaf79a |
cc-by-4.0 | ['generated_from_keras_callback'] | false | skandaonsolve/roberta-finetuned-timeentities2_ttsp75 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0238 - Epoch: 9 | 189628a9df91f6fedbb5b391e5c3d4a4 |
cc-by-4.0 | ['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': 2e-05, 'decay_steps': 3510, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | 8d83d3435139b532f71c2b2eda660268 |
cc-by-4.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 0.5917 | 0 | | 0.3804 | 1 | | 0.2717 | 2 | | 0.1843 | 3 | | 0.1194 | 4 | | 0.0836 | 5 | | 0.0585 | 6 | | 0.0403 | 7 | | 0.0307 | 8 | | 0.0238 | 9 | | 1f1cc2d0a247861a48a471b9fb57ab94 |
mit | [] | false | belen on Stable Diffusion This is the `<belen>` 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 you... | 6c3ce82aabf84a754c8ba1cbc25f6292 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-pl-colab 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_10_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1589 - Wer: 0.1338 | 18d5ad3e489a511a0c393a796bd4abf8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 2f60f5aa03bb9b650cdd4971325b6d02 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.9573 | 1.31 | 1000 | 3.2143 | 1.0 | | 1.0216 | 2.61 | 2000 | 0.2310 | 0.2064 | | 0.2925 | 3.92 | 3000 | 0.1888 | 0.1689 | |... | c2eb75ed701f79fad6220d32a18a4fb7 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_age_teens-10_sixties-0_s61 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 1a73231ad67d80bc141f32678f9eb589 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'ja', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | This model is for transcribing audio into Hiragana, one format of Japanese language. This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the `mozilla-foundation/common_voice_8_0 dataset`. Note that the following results are achieved by: - Modi... | 8efb54ec3fd37fa3bc69f45a02bf3e3a |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'ja', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Evaluation results (Running ./eval.py): | Model | Metric | Common-Voice-8/test | speech-recognition-community-v2/dev-data | |:--------:|:------:|:-------------------:|:------------------------------------------:| | w/o LM | WER | 0.5964 | 0.5532 | | ... | 6aa99dbdffa2987aae7fb20e3b0dde53 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'ja', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 7cf26a10616d59d8f6a57795a1938df2 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'ja', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.4081 | 1.6 | 500 | 4.0983 | 1.0 | | 3.303 | 3.19 | 1000 | 3.3563 | 1.0 | | 3.1538 | 4.79 | 1500 | 3.2066 | 0.923... | 9280bbceec20df0352e8eabd584c1b0f |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_xlsr-53_s965 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech... | 638ac95e9825db8df8125e5655493e08 |
apache-2.0 | [] | false | Model description **CAMeLBERT-MSA POS-GLF Model** is a Gulf Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-MSA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model. For the fine-tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotated-gumar-corpus/) dataset. Our f... | f90fc6770bd3d387b2ff406618da0f01 |
apache-2.0 | [] | false | How to use To use the model with a transformers pipeline: ```python >>> from transformers import pipeline >>> pos = pipeline('token-classification', model='CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf') >>> text = 'شلونك ؟ شخبارك ؟' >>> pos(text) [{'entity': 'adv_interrog', 'score': 0.5622676, 'index': 1, 'word': ... | 8ad19af94936f30f2e9cf88d9f1b74ec |
apache-2.0 | [] | false | ك', 'start': 4, 'end': 5}, {'entity': 'punc', 'score': 0.9999299, 'index': 3, 'word': '؟', 'start': 6, 'end': 7}, {'entity': 'noun', 'score': 0.9843815, 'index': 4, 'word': 'ش', 'start': 8, 'end': 9}, {'entity': 'noun', 'score': 0.9998467, 'index': 5, 'word': ' | 063c8e9df492ccbaaffc8cd9218708fe |
apache-2.0 | [] | false | ك', 'start': 13, 'end': 14}, {'entity': 'punc', 'score': 0.99993765, 'index': 7, 'word': '؟', 'start': 15, 'end': 16}] ``` *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. | 4f7800b15517b3efd0f6c6f4824cfa5d |
mit | [] | false | dabotap on Stable Diffusion This is the `<dabotap>` 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... | 7c6e0939e4f5dd4efefc12940c051ab8 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_distilgpt2_sst2_negation0.0_pretrainedTrue This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.7369 | f0b05d52eb742dc04fbdb68da749e2b7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.3136 | 1.0 | 1059 | 3.7331 | | 3.162 | 2.0 | 2118 | 3.7319 | | 3.0859 | 3.0 | 3177 | 3.7369 | | ba3283f74cf4e2943ae6e0365bcf604b |
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: 0.2405 - F1: 0.8201 | 512c7ea4e4995a77096e89b79c4c9d15 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8304 | 1.0 | 70 | 0.3375 | 0.7392 | | 0.3057 | 2.0 | 140 | 0.2584 | 0.8103 | | 0.1934 | 3.0 | 210 | 0.2405 | 0.8201 | ... | ae3379e14cac28ca8ea38f0484b48219 |
apache-2.0 | ['generated_from_trainer'] | false | cnn_dailymail-summarization-t5-small-2022-09-05 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail 3.0.0 dataset. It achieves the following results on the evaluation set: - Loss: 1.6455 - Rouge1: 41.4235 - Rouge2: 19.0263 - Rougel: 29.2892 - Rougelsum: 38.6338 - Gen... | fe9451e6789628e6d590a19dadf97b24 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:------:|:-------:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 1.9623 | 0.03 | 1000 | 18.9996 | 1.7500 | 24.1039 | 11.368 | 19.813 ... | e241d414182cebb45667540b3cf79655 |
mit | ['text-classification', 'zero-shot-classification'] | false | Model description This model was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, which comprise 763 913 NLI hypothesis-premise pairs. This base model outperforms almost all large models on the [ANLI benchmark](https://github.com/facebookresearch/anli). The base model is [DeBERTa-v3-base from M... | 895625b9e79c3ee374ad4981d7ebaf6d |
mit | ['text-classification', 'zero-shot-classification'] | false | Simple zero-shot classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli") sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU" candidate_labels = ["politics", "economy",... | ad17ef6691a9339bd42f5a361e51f872 |
mit | ['text-classification', 'zero-shot-classification'] | false | NLI use-case ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli" tokenizer = AutoTokenizer.from_pretrained(model_name) model = A... | 0ddee70f949fa5f3b654538fcdadadc2 |
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