modelId stringlengths 4 112 | sha stringlengths 40 40 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringclasses 29
values | private bool 1
class | author stringlengths 2 38 ⌀ | config null | id stringlengths 4 112 | downloads float64 0 36.8M ⌀ | likes float64 0 712 ⌀ | library_name stringclasses 17
values | __index_level_0__ int64 0 38.5k | readme stringlengths 0 186k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
HooshvareLab/bert-fa-base-uncased-ner-peyma | 8b7b63371aa8f1fdad62c0f82d462a22b91b37ab | 2021-05-18T20:55:10.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | HooshvareLab | null | HooshvareLab/bert-fa-base-uncased-ner-peyma | 141 | 1 | transformers | 4,100 | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](... |
TurkuNLP/sbert-cased-finnish-paraphrase | f1a793ca55932e3beeee506cebf92bda504fde52 | 2021-11-29T08:43:26.000Z | [
"pytorch",
"bert",
"feature-extraction",
"fi",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | TurkuNLP | null | TurkuNLP/sbert-cased-finnish-paraphrase | 141 | null | sentence-transformers | 4,101 | ---
language:
- fi
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
widget:
- text: "Minusta täällä on ihana asua!"
---
# Cased Finnish Sentence BERT model
Finnish Sentence BERT trained from FinBERT. A demo on retrieving the most similar senten... |
lordtt13/t5-inshorts | e6fb750feda2680df5555582efc87f513bdc9793 | 2020-12-25T23:05:41.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lordtt13 | null | lordtt13/t5-inshorts | 141 | null | transformers | 4,102 | ---
language: en
inference: false
---
## T5-inshorts: T5 model trained on inshorts data
### Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee... |
neuralmagic/oBERT-12-upstream-pretrained-dense | 7fa4ae052f9f01619fbb2f7362899ef9944676a6 | 2022-06-20T11:36:50.000Z | [
"pytorch",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2203.07259",
"bert",
"oBERT",
"sparsity",
"pruning",
"compression"
] | null | false | neuralmagic | null | neuralmagic/oBERT-12-upstream-pretrained-dense | 141 | null | null | 4,103 | ---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets:
- bookcorpus
- wikipedia
---
# oBERT-12-upstream-pretrained-dense
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corre... |
RUCAIBox/mvp-question-generation | b0770050f8517c1eb440f10af550a376efaa43c0 | 2022-06-27T02:28:10.000Z | [
"pytorch",
"mvp",
"en",
"arxiv:2206.12131",
"transformers",
"text-generation",
"text2text-generation",
"license:apache-2.0"
] | text2text-generation | false | RUCAIBox | null | RUCAIBox/mvp-question-generation | 141 | null | transformers | 4,104 | ---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
pipeline_tag: text2text-generation
widget:
- text: "Generate the question based on the answer: boxing [X_SEP] A bolo punch is a punch used in martial arts . A hook is a punch in boxing ."
example_title: "Example1"
- text: "Generate ... |
amanbawa96/legal-bert-based-uncase | 8b6aa344d2b00d55d933e07f16c85dca5445434c | 2022-06-30T23:27:44.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | amanbawa96 | null | amanbawa96/legal-bert-based-uncase | 141 | null | transformers | 4,105 | Entry not found |
DeepPavlov/xlm-roberta-large-en-ru-mnli | 4c4353240f7a90bae788ae6f86861c25a9c31ea1 | 2021-11-15T08:49:43.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli"
] | text-classification | false | DeepPavlov | null | DeepPavlov/xlm-roberta-large-en-ru-mnli | 140 | null | transformers | 4,106 | ---
language:
- en
- ru
datasets:
- glue
- mnli
model_index:
- name: mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
tags:
- xlm-roberta
- xlm-roberta-large
- xlm-roberta-large-en-ru
- xlm-roberta-large-e... |
Helsinki-NLP/opus-mt-es-nl | a5b57016fa3d47b914bc2eac885f6c73a448cca2 | 2021-09-09T21:43:49.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"nl",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-nl | 140 | null | transformers | 4,107 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-es-nl
* source languages: es
* target languages: nl
* OPUS readme: [es-nl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-nl/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k | e3a9f9b5fa7ab2092f14b37859914fb024e12eff | 2021-09-23T15:49:08.000Z | [
"pytorch",
"dataset:Libri3Mix",
"dataset:sep_noisy",
"asteroid",
"audio",
"ConvTasNet",
"audio-to-audio",
"license:cc-by-sa-4.0"
] | audio-to-audio | false | JorisCos | null | JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k | 140 | null | asteroid | 4,108 | ---
tags:
- asteroid
- audio
- ConvTasNet
- audio-to-audio
datasets:
- Libri3Mix
- sep_noisy
license: cc-by-sa-4.0
---
## Asteroid model `JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroi... |
geckos/pegasus-fined-tuned-on-paraphrase | 286f7e3e917279d29dc4be6e2f022e844c4ba6c3 | 2021-11-11T13:01:43.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | geckos | null | geckos/pegasus-fined-tuned-on-paraphrase | 140 | 2 | transformers | 4,109 | Entry not found |
google/t5-small-ssm | 22210988a4ab1ce2b2b8eb8e9e82b4a6c4095bec | 2021-06-23T01:52:56.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"arxiv:2002.08909",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-small-ssm | 140 | null | transformers | 4,110 | ---
language: en
datasets:
- c4
- wikipedia
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4) and subsequently a... |
lvwerra/pegasus-samsum | 8791cbe506f275dd716874ededef6ac337c3ad03 | 2021-10-25T14:57:33.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"dataset:samsum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | lvwerra | null | lvwerra/pegasus-samsum | 140 | null | transformers | 4,111 | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This ... |
sivasankalpp/dpr-multidoc2dial-structure-ctx-encoder | 142cafa42d34a8dd5e62d29995b5ba6fd3a35da2 | 2021-11-10T21:18:24.000Z | [
"pytorch",
"dpr",
"transformers"
] | null | false | sivasankalpp | null | sivasankalpp/dpr-multidoc2dial-structure-ctx-encoder | 140 | null | transformers | 4,112 | Entry not found |
speechbrain/asr-transformer-aishell | 7bacef7ce8baf8e84755641524e7cf9fe7c314a3 | 2022-06-21T23:49:14.000Z | [
"en",
"dataset:aishell",
"arxiv:2106.04624",
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"Transformers",
"pytorch",
"license:apache-2.0"
] | automatic-speech-recognition | false | speechbrain | null | speechbrain/asr-transformer-aishell | 140 | 1 | speechbrain | 4,113 | ---
language: "en"
thumbnail:
tags:
- automatic-speech-recognition
- CTC
- Attention
- Transformers
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- aishell
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" framebord... |
unc-nlp/lxmert-gqa-uncased | 4055268169a6a2e9a59faf42f478104438cc0fda | 2020-09-08T19:05:59.000Z | [
"pytorch",
"lxmert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | unc-nlp | null | unc-nlp/lxmert-gqa-uncased | 140 | null | transformers | 4,114 | Entry not found |
yunusemreemik/logo-qna-model | 3c5761c856ee954dea04795bcfa05fa3e8fe099e | 2021-08-03T12:41:38.000Z | [
"pytorch",
"bert",
"question-answering",
"tr",
"transformers",
"autotrain_compatible"
] | question-answering | false | yunusemreemik | null | yunusemreemik/logo-qna-model | 140 | null | transformers | 4,115 | ---
language: tr
---
# Logo Turkish Question Answering Model : Question Answering
Inspired by savasy/bert-base-turkish-squad,
* Inspired model: https://huggingface.co/savasy/bert-base-turkish-squad
* BERT-base: https://huggingface.co/dbmdz/bert-base-turkish-uncased
* Dataset: Logo Private QnA Chatbot Database
# Tra... |
pile-of-law/legalbert-large-1.7M-1 | eacf57e9bcc43d0a0d2d74da5196dbb912b38b2b | 2022-07-04T07:27:42.000Z | [
"pytorch",
"bert",
"en",
"dataset:pile-of-law/pile-of-law",
"arxiv:1907.11692",
"arxiv:1810.04805",
"arxiv:2110.00976",
"arxiv:2207.00220",
"transformers",
"fill-mask"
] | fill-mask | false | pile-of-law | null | pile-of-law/legalbert-large-1.7M-1 | 140 | 3 | transformers | 4,116 | ---
language:
- en
datasets:
- pile-of-law/pile-of-law
pipeline_tag: fill-mask
---
# Pile of Law BERT large model (uncased)
Pretrained model on English language legal and administrative text using the [RoBERTa](https://arxiv.org/abs/1907.11692) pretraining objective.
## Model description
Pile of Law BERT large i... |
doc2query/msmarco-dutch-mt5-base-v1 | b6ea6a440c642e57deb50b512d31eb29fa06dc5f | 2022-04-29T11:50:14.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"nl",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | doc2query | null | doc2query/msmarco-dutch-mt5-base-v1 | 140 | 1 | transformers | 4,117 | ---
language: nl
datasets:
- unicamp-dl/mmarco
widget:
- text: "Python is een programmeertaal die begin jaren 90 ontworpen en ontwikkeld werd door Guido van Rossum, destijds verbonden aan het Centrum voor Wiskunde en Informatica (daarvoor Mathematisch Centrum) in Amsterdam. De taal is mede gebaseerd op inzichten v... |
lyndonnixon/destination-image-classifier | b865dc762c5596c5072141f0ff4ed6a5e04c50a5 | 2022-06-15T15:00:07.000Z | [
"pytorch",
"beit",
"image-classification",
"en",
"dataset:destinationphotography",
"transformers",
"tourism",
"destinations",
"destinationimage",
"license:cc-by-nc-sa-4.0"
] | image-classification | false | lyndonnixon | null | lyndonnixon/destination-image-classifier | 140 | null | transformers | 4,118 | |
SebastianS/bert-finetuned-squad | ec039edef36c600580d90a7764171bef1826eb1e | 2022-05-15T16:19:22.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | SebastianS | null | SebastianS/bert-finetuned-squad | 140 | null | transformers | 4,119 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->... |
manu/mplt_untrained | bc42c334c22339c31bfcede2d4a2d038f2b7aae6 | 2022-07-08T21:51:43.000Z | [
"pytorch",
"mplt",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | manu | null | manu/mplt_untrained | 140 | null | transformers | 4,120 | Entry not found |
Narrativa/distilroberta-finetuned-stereotype-detection | 86927ae860472d07c2645ce2f2e6e92a7e19ff78 | 2021-09-13T14:52:21.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"stereotype",
"gender",
"gender_bias",
"license:apache-2.0",
"model-index"
] | text-classification | false | Narrativa | null | Narrativa/distilroberta-finetuned-stereotype-detection | 139 | 1 | transformers | 4,121 | ---
license: apache-2.0
tags:
- generated_from_trainer
- stereotype
- gender
- gender_bias
widget:
- text: "Cauterize is not just for fans of the guitarist or his other projects, but those that love music that is both aggressive and infectious and gave the album 4 out of 5 stars ."
metrics:
- accuracy
model-index:
- na... |
NbAiLab/nb-wav2vec2-1b-bokmaal | 45ac18420d8d8b1d7b6f049bb7ca2212f4c39de8 | 2022-06-13T10:21:13.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"nb-NO",
"dataset:NbAiLab/NPSC",
"transformers",
"NbAiLab/NPSC",
"no",
"nb",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | NbAiLab | null | NbAiLab/nb-wav2vec2-1b-bokmaal | 139 | 2 | transformers | 4,122 | ---
license: apache-2.0
tags:
- automatic-speech-recognition
- NbAiLab/NPSC
- no
- nb
- nb-NO
datasets:
- NbAiLab/NPSC
language:
- nb-NO
model-index:
- name: nb-wav2vec2-1b-bokmaal
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
... |
facebook/convnext-xlarge-224-22k-1k | cc348566f24077249a0bc049a373a56b669ff300 | 2022-06-27T08:55:36.000Z | [
"pytorch",
"tf",
"convnext",
"image-classification",
"dataset:imagenet-21k",
"dataset:imagenet-1k",
"arxiv:2201.03545",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/convnext-xlarge-224-22k-1k | 139 | 1 | transformers | 4,123 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teap... |
mrm8488/t5-base-finetuned-quartz | 3322d94c76ac868fb82558396eb6d1ae1114645e | 2020-12-11T21:55:56.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:quartz",
"arxiv:1910.10683",
"transformers",
"question-answering",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/t5-base-finetuned-quartz | 139 | 1 | transformers | 4,124 | ---
language: en
datasets:
- quartz
pipeline_tag: question-answering
---
# T5-base fine-tuned on QuaRTz
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QuaRTz](https://allenai.org/data/quartz) for **QA** downstream task.
## Details of T5
The **T5** model wa... |
hf-internal-testing/tiny-random-data2vec-xvector | e5e46e69598efd3ecbffb844355537d0bca9c1ee | 2022-03-03T12:26:14.000Z | [
"pytorch",
"data2vec-audio",
"audio-xvector",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-data2vec-xvector | 139 | null | transformers | 4,125 | Entry not found |
bhadresh-savani/electra-base-squad2 | e06d14d92455725024d07db7d552814aa94ddfe1 | 2022-04-13T14:30:20.000Z | [
"pytorch",
"tf",
"jax",
"electra",
"question-answering",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | question-answering | false | bhadresh-savani | null | bhadresh-savani/electra-base-squad2 | 139 | null | transformers | 4,126 | ---
datasets:
- squad_v2
license: cc-by-4.0
---
# electra-base for QA
## Overview
**Language model:** electra-base
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Code:** See [example](https://github.com/deepset-ai/FARM/blob/master/examples/... |
Elijah629/DialoGPT-shrek | ab9e636cc5971aa75300533101324589b6ab84a7 | 2022-06-18T04:26:04.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Elijah629 | null | Elijah629/DialoGPT-shrek | 139 | null | transformers | 4,127 | ---
tags:
- conversational
--- |
ytling/gpt-neo-125m-finetuned | 7b2d89a27054555ac05344d738272368fc632e45 | 2022-07-27T07:05:16.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | ytling | null | ytling/gpt-neo-125m-finetuned | 139 | null | transformers | 4,128 | ## GPT Neo 125m fine-tuned
#### Pushing model to repo
1. Login to hugging face,
```
from huggingface_hub import notebook_login
notebook_login()
```
2. Then push model to repo.
```
model.push_to_hub("gpt-neo-125m-finetuned", use_temp_dir=True)
tokenizer.push_to_hub("gpt-neo-125m-finetuned", use_temp_dir=True)
```
--... |
BSC-TeMU/roberta-large-bne | 2c4265d25a2832cb7f60ed3da1a904f2e5e75192 | 2021-10-21T10:32:31.000Z | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | BSC-TeMU | null | BSC-TeMU/roberta-large-bne | 138 | 8 | transformers | 4,129 | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
datasets:
- "bne"
metrics:
- "ppl"
widget:
- text: "Este año las campanadas de La Sexta las <mask> Pedroche y Chicote."
- text: "El artista Antonio Orozco es un colaborador de La <mask>."
- text: "Gracias a los datos de la... |
Helsinki-NLP/opus-mt-ar-tr | 759d47d6d139851222b55f7996a0467c037d7026 | 2021-01-18T07:47:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ar",
"tr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ar-tr | 138 | null | transformers | 4,130 | ---
language:
- ar
- tr
tags:
- translation
license: apache-2.0
---
### ara-tur
* source group: Arabic
* target group: Turkish
* OPUS readme: [ara-tur](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-tur/README.md)
* model: transformer
* source language(s): apc_Latn ara ara_Latn arq_L... |
SEBIS/code_trans_t5_small_api_generation_multitask | 3d8e13858823ad53033d13d5df363823b001d531 | 2021-06-23T09:54:09.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_small_api_generation_multitask | 138 | null | transformers | 4,131 | ---
tags:
- summarization
widget:
- text: "parse the uses licence node of this package , if any , and returns the license definition if theres"
---
# CodeTrans model for api recommendation generation
Pretrained model for api recommendation generation using the t5 small model architecture. It was first released in
[t... |
filco306/gpt2-shakespeare-paraphraser | 7a0e25bea9e0626396aacad0d7cf9c32d5813c71 | 2021-08-28T19:54:12.000Z | [
"pytorch",
"text-generation",
"arxiv:2010.05700",
"transformers"
] | text-generation | false | filco306 | null | filco306/gpt2-shakespeare-paraphraser | 138 | 1 | transformers | 4,132 | # GPT2 Shakespeare style transfer paraphraser
This is the trained Shakespeare-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface w... |
flax-community/t5-large-wikisplit | 86940cdf19268efda140b9836287b32093cc684f | 2021-07-16T12:40:17.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"dataset:wiki_split",
"arxiv:1907.12461",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | flax-community | null | flax-community/t5-large-wikisplit | 138 | null | transformers | 4,133 | ---
datasets:
- wiki_split
widget:
- text: "Mary likes to play football in her freetime whenever she meets with her friends that are very nice people."
---
# T5 model for sentence splitting in English
Sentence Split is the task of dividing a long sentence into multiple sentences.
E.g.:
```
Mary likes to play footb... |
gagan3012/bert-tiny-finetuned-ner | 54db92457baa1b88a45e52c048df8461498dc9d3 | 2021-09-01T23:50:44.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | gagan3012 | null | gagan3012/bert-tiny-finetuned-ner | 138 | 2 | transformers | 4,134 | ---
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-tiny-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
me... |
indonesian-nlp/gpt2-medium-indonesian | 5e5fa4fe532b734c2c7fdb14401cbba96ac7de7b | 2022-05-28T10:33:02.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"id",
"transformers"
] | text-generation | false | indonesian-nlp | null | indonesian-nlp/gpt2-medium-indonesian | 138 | null | transformers | 4,135 | ---
language: id
widget:
- text: "Sewindu sudah kita tak berjumpa, rinduku padamu sudah tak terkira."
---
# GPT2-medium-indonesian
This is a pretrained model on Indonesian language using a causal language modeling (CLM) objective, which was first
introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-... |
minhpqn/bio_roberta-base_pubmed | 296f147fb483b7d620e4e35365b818bfa771b120 | 2021-05-20T17:53:22.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | minhpqn | null | minhpqn/bio_roberta-base_pubmed | 138 | null | transformers | 4,136 | Entry not found |
osanseviero/BigGAN-deep-128 | 86e3d82ec07f2513c0942d138a7b38133cfc2036 | 2022-02-21T13:55:46.000Z | [
"pytorch",
"generic",
"text-to-image"
] | text-to-image | false | osanseviero | null | osanseviero/BigGAN-deep-128 | 138 | 10 | generic | 4,137 | ---
tags:
- text-to-image
library_name: generic
---
# Image generation using pretrained BigGAN
## Warning: This only works for ImageNet inputs.
List of possible inputs: https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
GitHub repository: https://github.com/huggingface/pytorch-pretrained-BigGAN
|
rbhushan/distilgpt2-finetuned-wikitext2 | 5064459af45b03bcfb044698da21857e00725d69 | 2022-01-11T16:55:00.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | rbhushan | null | rbhushan/distilgpt2-finetuned-wikitext2 | 138 | null | transformers | 4,138 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dist... |
sagorsarker/mbert-bengali-ner | 75f727e825c43afb14d24c8c0a7dd602bf283e3c | 2022-06-17T11:29:39.000Z | [
"pytorch",
"bert",
"token-classification",
"bn",
"dataset:wikiann",
"dataset:xtreme",
"transformers",
"bengali-ner",
"bengali",
"bangla",
"NER",
"license:mit",
"autotrain_compatible"
] | token-classification | false | sagorsarker | null | sagorsarker/mbert-bengali-ner | 138 | 2 | transformers | 4,139 | ---
language: bn
tags:
- bengali-ner
- bengali
- bangla
- NER
license: mit
datasets:
- wikiann
- xtreme
---
# Multi-lingual BERT Bengali Name Entity Recognition
`mBERT-Bengali-NER` is a transformer-based Bengali NER model build with [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased... |
speechbrain/sepformer-whamr-enhancement | ace1f9824a17e3f14be043b409b5defc452d325e | 2021-12-09T02:38:12.000Z | [
"en",
"dataset:WHAMR!",
"arxiv:2010.13154",
"arxiv:2106.04624",
"speechbrain",
"audio-to-audio",
"Speech Enhancement",
"WHAMR!",
"SepFormer",
"Transformer",
"pytorch",
"license:apache-2.0"
] | audio-to-audio | false | speechbrain | null | speechbrain/sepformer-whamr-enhancement | 138 | null | speechbrain | 4,140 | ---
language: "en"
thumbnail:
tags:
- audio-to-audio
- Speech Enhancement
- WHAMR!
- SepFormer
- Transformer
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- WHAMR!
metrics:
- SI-SNR
- PESQ
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large... |
stas/mt5-tiny-random | 25f1f52107153ed74c3ea9c89cd1a33818f0d67d | 2021-06-23T16:37:54.000Z | [
"pytorch",
"jax",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | stas | null | stas/mt5-tiny-random | 138 | 2 | transformers | 4,141 | This is a tiny random mt5 model used for testing
See `mt5-make-tiny-model.py` for how it was created. |
MachineBabs/DocBrown | d89fbcf4698e6572c4bc66c5227d8dcb9f054aef | 2022-04-24T11:39:36.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | MachineBabs | null | MachineBabs/DocBrown | 138 | null | transformers | 4,142 | ---
tags:
- conversational
---
|
nanopass/distilbert-base-uncased-emotion-2 | 19cd3b5c0c9a3b5308bba13ff708abd16cd6c2d9 | 2022-05-02T09:43:02.000Z | [
"pytorch",
"tf",
"jax",
"distilbert",
"text-classification",
"en",
"dataset:emotion",
"arxiv:1910.01108",
"transformers",
"emotion",
"license:apache-2.0"
] | text-classification | false | nanopass | null | nanopass/distilbert-base-uncased-emotion-2 | 138 | null | transformers | 4,143 | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
---
# Distilbert-base-uncased-emotion
## Model description:
[Distil... |
apple/ane-distilbert-base-uncased-finetuned-sst-2-english | b610778797944d055a73e3da10630122237a7a38 | 2022-06-13T13:29:48.000Z | [
"pytorch",
"coreml",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"transformers",
"license:apache-2.0"
] | text-classification | false | apple | null | apple/ane-distilbert-base-uncased-finetuned-sst-2-english | 138 | 3 | transformers | 4,144 | ---
language: en
license: apache-2.0
datasets:
- sst2
---
# DistilBERT optimized for Apple Neural Engine
This is the [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) model, optimized for the Apple Neural Engine (ANE) as described in the article ... |
elozano/bert-base-cased-fake-news | 9e8cd2895bd36f0c25c78c4dcf937b6700c7bc46 | 2022-02-26T18:50:54.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | elozano | null | elozano/bert-base-cased-fake-news | 137 | null | transformers | 4,145 | Entry not found |
veronica320/QA-for-Event-Extraction | c679c64085048f2369918359836026d47061bb87 | 2021-07-29T22:57:42.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | veronica320 | null | veronica320/QA-for-Event-Extraction | 137 | null | transformers | 4,146 | # QA-for-Event-Extraction
## Model description
This is a QA model as part of the event extraction system in the ACL2021 paper: [Zero-shot Event Extraction via Transfer Learning: Challenges and Insights](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co... |
VMware/vbert-2021-large | 876b71dac6a6bb6f415cf53ad2e7bc170d0c8738 | 2022-06-16T22:30:39.000Z | [
"pytorch",
"tf",
"bert",
"fill-mask",
"eng",
"transformers",
"PyTorch",
"tensorflow",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | VMware | null | VMware/vbert-2021-large | 137 | 1 | transformers | 4,147 | ---
language:
- "eng"
thumbnail: "URL to a thumbnail used in social sharing"
tags:
- "PyTorch"
- "tensorflow"
license: "apache-2.0"
---
# vBERT-2021-BASE
### Model Info:
<ul>
<li> Authors: R&D AI Lab, VMware Inc.
<li> Model date: April, 2022
<li> Model version: 2021-base
<li> Model type: Pretrained language mode... |
edumunozsala/roberta_bne_sentiment_analysis_es | 6a506e8b4e8a5d24eea04961812e732188514cf1 | 2022-07-29T09:19:03.000Z | [
"pytorch",
"roberta",
"text-classification",
"es",
"dataset:IMDbreviews_es",
"arxiv:2107.07253",
"transformers",
"sagemaker",
"roberta-bne",
"TextClassification",
"SentimentAnalysis",
"license:apache-2.0",
"model-index"
] | text-classification | false | edumunozsala | null | edumunozsala/roberta_bne_sentiment_analysis_es | 137 | null | transformers | 4,148 | ---
language: es
tags:
- sagemaker
- roberta-bne
- TextClassification
- SentimentAnalysis
license: apache-2.0
datasets:
- IMDbreviews_es
metrics:
- accuracy
model-index:
- name: roberta_bne_sentiment_analysis_es
results:
- task:
name: Sentiment Analysis
type: sentiment-analysis
dataset:
... |
Mithil/86RecallRoberta | 3bb53d625342a7ea9ec08af1d7dd247b1bbbacb5 | 2022-07-04T16:03:06.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers",
"license:afl-3.0"
] | text-classification | false | Mithil | null | Mithil/86RecallRoberta | 137 | null | transformers | 4,149 | ---
license: afl-3.0
---
|
Rajaram1996/FacialEmoRecog | 059c5f2f0afc6fd7e2b62f558e2f2ab20798d72b | 2021-11-05T21:08:27.000Z | [
"pytorch",
"vit",
"image-classification",
"transformers"
] | image-classification | false | Rajaram1996 | null | Rajaram1996/FacialEmoRecog | 136 | 6 | transformers | 4,150 | ---
tags:
- image-classification
- pytorch
inference: true
pipeline_tag: image-classification
metrics:
- accuracy
model-index:
- name: FacialEmoRecog
results:
- task:
name: Image Classification
type: image-classification
- metrics:
name: Accuracy
type: accuracy
value: 0.9189583659... |
Recognai/distilbert-base-es-multilingual-cased | 79dca2e293dd2a1208169689adc0c2f433b5cf4a | 2021-03-10T20:36:54.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"es",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Recognai | null | Recognai/distilbert-base-es-multilingual-cased | 136 | 2 | transformers | 4,151 | ---
language: es
license: apache-2.0
datasets:
- wikipedia
widget:
- text: "Mi nombre es Juan y vivo en [MASK]."
---
# DistilBERT base multilingual model Spanish subset (cased)
This model is the Spanish extract of `distilbert-base-multilingual-cased` (https://huggingface.co/distilbert-base-multilingual-cased), a dist... |
blanchefort/rubert-base-cased-sentiment-med | f2077a6f4c9e63673d85af63ca1c2ac73d77d947 | 2021-05-19T12:58:40.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"ru",
"transformers",
"sentiment"
] | text-classification | false | blanchefort | null | blanchefort/rubert-base-cased-sentiment-med | 136 | 1 | transformers | 4,152 | ---
language:
- ru
tags:
- sentiment
- text-classification
---
# RuBERT for Sentiment Analysis of Medical Reviews
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on corpus of medical reviews.
## Labels
0: NEUTRAL
1: POS... |
dpalominop/spanish-bert-apoyo | 6d8450759d44a0f00625d89936541dc831d760d3 | 2021-05-19T16:08:52.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | dpalominop | null | dpalominop/spanish-bert-apoyo | 136 | null | transformers | 4,153 | ```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dpalominop/spanish-bert-apoyo")
model = AutoModelForSequenceClassification.from_pretrained("dpalominop/spanish-bert-apoyo")
``` |
marma/bert-base-swedish-cased-sentiment | 40c98c5ae300960f2a527def3e910063927c9f7d | 2021-05-19T23:02:02.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | marma | null | marma/bert-base-swedish-cased-sentiment | 136 | null | transformers | 4,154 | Experimental sentiment analysis based on ~20k of App Store reviews in Swedish.
### Usage
```python
from transformers import pipeline
>>> sa = pipeline('sentiment-analysis', model='marma/bert-base-swedish-cased-sentiment')
>>> sa('Det här är ju fantastiskt!')
[{'label': 'POSITIVE', 'score': 0.9974609613418579}]
>>> s... |
monsoon-nlp/dialect-ar-gpt-2021 | e2fc0a4bb449359b4fb79271a2348f8871c3779a | 2021-05-23T09:59:23.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"ar",
"arxiv:2012.15520",
"transformers"
] | text-generation | false | monsoon-nlp | null | monsoon-nlp/dialect-ar-gpt-2021 | 136 | null | transformers | 4,155 | ---
language: ar
---
# Dialect-AR-GPT-2021
## Finetuned AraGPT-2 demo
This model started with [AraGPT2-Medium](https://huggingface.co/aubmindlab/aragpt2-medium),
from AUB MIND Lab.
This model was then finetuned on dialect datasets from Qatar University, University of British Columbia / NLP,
and Johns Hopkins Univers... |
sentence-transformers/quora-distilbert-base | 2708fe60f344ffbedf990cf4f8be7866f605bf60 | 2022-06-15T23:45:12.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/quora-distilbert-base | 136 | null | sentence-transformers | 4,156 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/quora-distilbert-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense ... |
sentence-transformers/xlm-r-large-en-ko-nli-ststb | 7359df4bd7393a242f5e3c16e933079c626772b2 | 2022-06-15T23:50:13.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/xlm-r-large-en-ko-nli-ststb | 136 | null | sentence-transformers | 4,157 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... |
MarkS/bart-base-qa2d | 24e38002cd12bc8c1381b2b69200d4d916930452 | 2022-04-21T08:46:22.000Z | [
"pytorch",
"bart",
"text2text-generation",
"arxiv:2112.03849",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | MarkS | null | MarkS/bart-base-qa2d | 136 | null | transformers | 4,158 | ---
license: afl-3.0
---
# Generating Declarative Statements from QA Pairs
There are already some rule-based models that can accomplish this task, but I haven't seen any transformer-based models that can do so. Therefore, I trained this model based on `Bart-base` to transform QA pairs into declarative statements.
I ... |
fujuta/DialoGPT-medium-HarryPotter | 765808bab141a22272e1d8ac306aafb451bf1079 | 2022-05-24T23:24:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | fujuta | null | fujuta/DialoGPT-medium-HarryPotter | 136 | null | transformers | 4,159 | ---
tags:
- conversational
--- |
SynamicTechnologies/CYBERT | f0274dfc3e1bc5ce041da4e7d3bbf9cd0a67e618 | 2022-06-02T09:51:10.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | SynamicTechnologies | null | SynamicTechnologies/CYBERT | 136 | 1 | transformers | 4,160 | ## CYBERT
BERT model dedicated to the domain of cyber security. The model has been trained on a corpus of high-quality cyber security and computer science text and is unlikely to work outside this domain.
##Model architecture
The model architecture used is original Roberta and tokenizer to train the corpus is Byte ... |
Aviv/Moran_Aviv_Bart | e18f2535f05817530a796620d94a1c4988b7b46c | 2022-07-15T16:41:00.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aviv | null | Aviv/Moran_Aviv_Bart | 136 | 1 | transformers | 4,161 | Moran and Aviv project for solving Summarization task.
We choose 2 architectures: TextRank and BART (facebook).
In Streamlit' application, you can enter your article as an input, and the output is a summary.
Inspired by HIT studies. |
ryo0634/luke-base-comp-wiki-20181220-umls | 40b67edc829fb4ace28bfc53e6ee5e472a470ad2 | 2022-07-20T15:03:47.000Z | [
"pytorch",
"luke",
"feature-extraction",
"transformers"
] | feature-extraction | false | ryo0634 | null | ryo0634/luke-base-comp-wiki-20181220-umls | 136 | null | transformers | 4,162 | Entry not found |
activebus/BERT_Review | fd6d67dfb363222edb0277271b6a07f4e9c52f2a | 2021-05-18T23:05:54.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | activebus | null | activebus/BERT_Review | 135 | null | transformers | 4,163 | # ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT_Review` is cross-domain (beyond just `laptop` and `restaurant`) language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon reviews ... |
lupinlevorace/tiny-bert-sst2-distilled | db7e67bdab78e2bd37d76f149ce89f76fe37bde1 | 2022-02-20T14:37:21.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | lupinlevorace | null | lupinlevorace/tiny-bert-sst2-distilled | 135 | null | transformers | 4,164 | Entry not found |
microsoft/beit-large-patch16-384 | 6ad3dc484125f460f2ce85ea2296732db291bdf1 | 2022-01-28T10:19:50.000Z | [
"pytorch",
"jax",
"beit",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/beit-large-patch16-384 | 135 | null | transformers | 4,165 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-21k
---
# BEiT (large-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1... |
mrm8488/spanbert-finetuned-squadv1 | 95a9260e7b0447dd0cb79149847982375baf347d | 2021-05-20T00:55:17.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"arxiv:1907.10529",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/spanbert-finetuned-squadv1 | 135 | null | transformers | 4,166 | ---
language: en
thumbnail:
---
# SpanBERT (spanbert-base-cased) fine-tuned on SQuAD v1.1
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [SQuAD 1.1](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task... |
thilina/mt5-sinhalese-english | 2c69967cd0914d5dd136a79d75b3705e9af6a349 | 2021-01-03T21:14:26.000Z | [
"pytorch",
"tf",
"mt5",
"text2text-generation",
"si",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | thilina | null | thilina/mt5-sinhalese-english | 135 | null | transformers | 4,167 | ---
language:
- si
- en
tags:
- translation
license: apache-2.0
metrics:
- sacrebleu
---
# mt5-sinhalese-english
## Model description
An mT5-base model fine-tuned on the Sinhalese-English dataset in the Tatoeba Challenge. Can be used to translate from Sinhalese to English and vice versa.
## Training details
- Englis... |
ml4pubmed/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section | 0cbe99bf91e4bad964612639347e5aa8040e7370 | 2022-06-22T10:58:49.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:pubmed",
"transformers",
"document sections",
"sentence classification",
"document classification",
"medical",
"health",
"biomedical"
] | text-classification | false | ml4pubmed | null | ml4pubmed/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section | 135 | 1 | transformers | 4,168 | ---
language:
- en
datasets:
- pubmed
metrics:
- f1
tags:
- text-classification
- document sections
- sentence classification
- document classification
- medical
- health
- biomedical
pipeline_tag: text-classification
widget:
- text: "many pathogenic processes and diseases are the result of an erroneous activation of t... |
paust/pko-t5-large | 554210dfbb2542b59777d8df2653c83ea5511bbe | 2022-05-21T06:38:41.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ko",
"arxiv:2105.09680",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | text2text-generation | false | paust | null | paust/pko-t5-large | 135 | 1 | transformers | 4,169 | ---
language: ko
license: cc-by-4.0
---
# pko-t5-large
[Source Code](https://github.com/paust-team/pko-t5)
pko-t5 는 한국어 전용 데이터로 학습한 [t5 v1.1 모델](https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/released_checkpoints.md)입니다.
한국어를 tokenize 하기 위해서 senten... |
derwahnsinn/gpt2-mediumBITB | fee83b219cfc2447ff29405e52ebdac438a15cc6 | 2022-07-27T19:13:02.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | derwahnsinn | null | derwahnsinn/gpt2-mediumBITB | 135 | null | transformers | 4,170 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-mediumBITB
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-mediumBITB
This mode... |
camembert/camembert-base-oscar-4gb | efb6c58d51afb976f8ccd25c534543ac6ff115c5 | 2020-12-11T21:35:18.000Z | [
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"transformers"
] | null | false | camembert | null | camembert/camembert-base-oscar-4gb | 134 | null | transformers | 4,171 | ---
language: fr
---
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretrain... |
cointegrated/rubert-tiny-bilingual-nli | d914a099332ddea9d45267241695245ad64e2b76 | 2021-10-10T08:17:19.000Z | [
"pytorch",
"bert",
"text-classification",
"ru",
"transformers",
"rubert",
"russian",
"nli",
"rte",
"zero-shot-classification"
] | zero-shot-classification | false | cointegrated | null | cointegrated/rubert-tiny-bilingual-nli | 134 | null | transformers | 4,172 | ---
language: ru
pipeline_tag: zero-shot-classification
tags:
- rubert
- russian
- nli
- rte
- zero-shot-classification
widget:
- text: "Сервис отстойный, кормили невкусно"
candidate_labels: "Мне понравилось, Мне не понравилось"
hypothesis_template: "{}."
---
# RuBERT-tiny for NLI (natural language inference)
This... |
jcblaise/bert-tagalog-base-cased | f49e54a8098d2f7e8759463c92cb32e8d3aa28d4 | 2021-11-12T03:21:35.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"tl",
"transformers",
"tagalog",
"filipino",
"license:gpl-3.0",
"autotrain_compatible"
] | fill-mask | false | jcblaise | null | jcblaise/bert-tagalog-base-cased | 134 | 1 | transformers | 4,173 | ---
language: tl
tags:
- bert
- tagalog
- filipino
license: gpl-3.0
inference: false
---
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tag... |
vumichien/wav2vec2-large-xlsr-japanese | 937c1d4c2912148d87e6c77756aa59854942cc6c | 2021-11-04T16:15:18.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | vumichien | null | vumichien/wav2vec2-large-xlsr-japanese | 134 | 3 | transformers | 4,174 | ---
language: ja
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Japanese by Chien Vu
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
... |
BM-K/KoSimCSE-roberta | 37a6d8cc47bcf2a83b6bae5987632680cbc58e0f | 2022-06-03T01:47:46.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"ko",
"transformers",
"korean"
] | feature-extraction | false | BM-K | null | BM-K/KoSimCSE-roberta | 134 | 1 | transformers | 4,175 | ---
language: ko
tags:
- korean
---
https://github.com/BM-K/Sentence-Embedding-is-all-you-need
# Korean-Sentence-Embedding
🍭 Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.
## Quick tour
```py... |
xlm-clm-enfr-1024 | dd9cb215d87baafeaf71f9b10e9678e90f5bf9f1 | 2022-07-22T08:06:22.000Z | [
"pytorch",
"tf",
"xlm",
"fill-mask",
"multilingual",
"en",
"fr",
"arxiv:1901.07291",
"arxiv:1910.09700",
"transformers",
"autotrain_compatible"
] | fill-mask | false | null | null | xlm-clm-enfr-1024 | 133 | null | transformers | 4,176 | ---
language:
- multilingual
- en
- fr
---
# xlm-clm-enfr-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical... |
abhijithneilabraham/longformer_covid_qa | 56f4dbe055f971300439d12633d1652b9b56d8e5 | 2021-05-13T19:09:22.000Z | [
"pytorch",
"longformer",
"question-answering",
"dataset:covid_qa_deepset",
"transformers",
"autotrain_compatible"
] | question-answering | false | abhijithneilabraham | null | abhijithneilabraham/longformer_covid_qa | 133 | null | transformers | 4,177 | # Dataset
---
---
datasets:
- covid_qa_deepset
---
---
Covid 19 question answering data obtained from [covid_qa_deepset](https://huggingface.co/datasets/covid_qa_deepset).
# Original Repository
Repository for the fine tuning, inference and evaluation scripts can be found [here](https://github.com/abhijithneilabrah... |
alenusch/rugpt3-paraphraser | c0194b0c0db67521636675ac5b8a0d73050048d7 | 2021-05-21T12:54:09.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | alenusch | null | alenusch/rugpt3-paraphraser | 133 | null | transformers | 4,178 | Entry not found |
avichr/hebEMO_joy | f623e5735d250347d7244111a693dd7763eedf17 | 2022-01-11T16:28:03.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | avichr | null | avichr/hebEMO_joy | 133 | null | transformers | 4,179 | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... |
cross-encoder/nli-deberta-v3-small | 9b04ba8f6b3dd4fdecba34bf349399f969b85ee5 | 2021-12-27T22:27:07.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"transformers",
"microsoft/deberta-v3-small",
"license:apache-2.0",
"zero-shot-classification"
] | zero-shot-classification | false | cross-encoder | null | cross-encoder/nli-deberta-v3-small | 133 | 0 | transformers | 4,180 | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- microsoft/deberta-v3-small
datasets:
- multi_nli
- snli
metrics:
- accuracy
license: apache-2.0
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net... |
flax-community/spanish-t5-small | c97f4667f06fc184dcd7f680c4a8da1f8d887fd2 | 2022-03-30T21:04:00.000Z | [
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"es",
"dataset:large_spanish_corpus",
"transformers",
"T5",
"Seq2Seq",
"EconderDecoder",
"Spanish",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | flax-community | null | flax-community/spanish-t5-small | 133 | 5 | transformers | 4,181 | ---
language: es
tags:
- T5
- Seq2Seq
- EconderDecoder
- Spanish
datasets:
- large_spanish_corpus
widgets:
- text: "Érase un vez un"
license: mit
---
# Spanish T5 (small) trained on [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus).
This is a Spanish **T5** (small arch) tr... |
pucpr/clinicalnerpt-diagnostic | 1772db88f7092ffb662608c83fe738ab3acf8e15 | 2021-10-13T09:33:19.000Z | [
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"transformers",
"autotrain_compatible"
] | token-classification | false | pucpr | null | pucpr/clinicalnerpt-diagnostic | 133 | 3 | transformers | 4,182 | ---
language: "pt"
widget:
- text: "Uretrocistografia miccional, residuo pos miccional significativo."
- text: "No exame, apresentou apenas leve hiperemia no local do choque."
datasets:
- SemClinBr
thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png"
---
<img src="h... |
tupleblog/salim-classifier | 9a1d1a1a3ade3921f582717345e8ad832f5da6e8 | 2021-07-16T20:11:16.000Z | [
"pytorch",
"camembert",
"text-classification",
"transformers"
] | text-classification | false | tupleblog | null | tupleblog/salim-classifier | 133 | null | transformers | 4,183 | ---
widget:
- text: "รัฐรับผิดชอบทุกชีวิตไม่ได้หรอกคนให้บริการต้องจัดการเองถ้าจะเปิดผับบาร์"
---
# Salim-Classifier
**วัตถุประสงค์:** ทุกวันนี้หาเพื่อนที่รักชาติ ศาสนา พระมหากษัตริย์ รัฐบาลยากเหลือเกิน มีแต่พว... |
BeIR/sparta-msmarco-distilbert-base-v1 | 34bbf9fb00f396055b989346faae51a1677b93a4 | 2021-10-01T19:04:27.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"arxiv:2009.13013",
"arxiv:2104.08663",
"transformers"
] | feature-extraction | false | BeIR | null | BeIR/sparta-msmarco-distilbert-base-v1 | 132 | null | transformers | 4,184 | # SPARTA
Re-Implementation of [SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval](https://arxiv.org/abs/2009.13013). It is the re-implementation we used for [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://arxiv.org/abs/2104.08663... |
Davlan/bert-base-multilingual-cased-finetuned-hausa | e08eaa625a687776657e84c6c0a4ce5a8fabc6fd | 2022-06-27T10:56:44.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ha",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Davlan | null | Davlan/bert-base-multilingual-cased-finetuned-hausa | 132 | null | transformers | 4,185 | Hugging Face's logo
---
language: ha
datasets:
---
# bert-base-multilingual-cased-finetuned-hausa
## Model description
**bert-base-multilingual-cased-finetuned-hausa** is a **Hausa BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Hausa language texts. It provides **better performance** t... |
Hate-speech-CNERG/dehatebert-mono-german | 53a24df030e8e20e7880a161494fb5922ce34617 | 2021-09-25T13:55:44.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"de",
"arxiv:2004.06465",
"transformers",
"license:apache-2.0"
] | text-classification | false | Hate-speech-CNERG | null | Hate-speech-CNERG/dehatebert-mono-german | 132 | null | transformers | 4,186 | ---
language: de
license: apache-2.0
---
This model is used detecting **hatespeech** in **German language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rate... |
LegolasTheElf/Wav2vec2_XLSR_Bengali | 9c1fdc849f7a95cb0703ead87ceffc82dbee889d | 2022-01-25T11:38:03.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | LegolasTheElf | null | LegolasTheElf/Wav2vec2_XLSR_Bengali | 132 | null | transformers | 4,187 | Entry not found |
ozcangundes/T5-base-for-BioQA | 20a289e9e962dcdbe2bb454ef78fed92b261eafe | 2021-09-22T09:31:21.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"english",
"dataset:bioASQ",
"arxiv:1910.10683",
"transformers",
"license:mit",
"question-answering",
"autotrain_compatible"
] | question-answering | false | ozcangundes | null | ozcangundes/T5-base-for-BioQA | 132 | null | transformers | 4,188 | ---
language: english
datasets:
- bioASQ
pipeline_tag: question-answering
license: mit
---
# 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** downst... |
sismetanin/rubert-ru-sentiment-rusentiment | f3d755e39a6af467a4e90b9a1c486ea1d2aa3852 | 2021-05-20T06:11:34.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"ru",
"transformers",
"sentiment analysis",
"Russian"
] | text-classification | false | sismetanin | null | sismetanin/rubert-ru-sentiment-rusentiment | 132 | null | transformers | 4,189 | ---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## RuBERT-Base-ru-sentiment-RuSentiment
RuBERT-ru-sentiment-RuSentiment is a [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-l... |
north/t5_base_NCC | 633892e183740133c83f81483932498a5da67055 | 2022-06-01T19:41:01.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"no",
"nn",
"sv",
"dk",
"is",
"en",
"dataset:nbailab/NCC",
"dataset:mc4",
"dataset:wikipedia",
"arxiv:2104.09617",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | north | null | north/t5_base_NCC | 132 | 2 | transformers | 4,190 | ---
language:
- no
- nn
- sv
- dk
- is
- en
datasets:
- nbailab/NCC
- mc4
- wikipedia
widget:
- text: <extra_id_0> hver uke samles Regjeringens medlemmer til Statsråd på <extra_id_1>. Dette organet er øverste <extra_id_2> i Norge. For at møtet skal være <extra_id_3>, må over halvparten av regjeringens <extra_id_4> ... |
Nehc/AGIRussia | f00acdd0258fd1e513c7b235f29151b9ef5b0eea | 2022-06-05T20:07:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"ru",
"transformers"
] | text-generation | false | Nehc | null | Nehc/AGIRussia | 132 | null | transformers | 4,191 | ---
language:
- ru
widget:
- text: "<IN>Как нам все-таки сделать AGI?\n<OUT>"
metrics:
- loss: 3.3
- perplexity: 25.7528
---
Start from sberbank-ai/rugpt3medium_based_on_gpt2 and finetuning on AGIRussia chats (russian).
On this moment - only 3 epoch (perplexity falls reasons)
on progress...
|
yanekyuk/bert-uncased-keyword-extractor | 47b62643118087b5366600b97f73b1d3ba105303 | 2022-06-06T09:27:10.000Z | [
"pytorch",
"bert",
"token-classification",
"en",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | yanekyuk | null | yanekyuk/bert-uncased-keyword-extractor | 132 | null | transformers | 4,192 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
language:
- en
widget:
- text: "Broadcom agreed to acquire cloud computing company VMware in a $61 billion (€57bn) cash-and stock deal, massively diversifying the chipmaker’s business and almost tripling its software-re... |
IDEA-CCNL/Taiyi-CLIP-Roberta-102M-Chinese | e1aa040bf0f016c608a5fbefed4ccfdda7215e18 | 2022-07-25T06:25:17.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"clip",
"zh",
"image-text",
"feature-extraction",
"license:apache-2.0"
] | feature-extraction | false | IDEA-CCNL | null | IDEA-CCNL/Taiyi-CLIP-Roberta-102M-Chinese | 132 | 3 | transformers | 4,193 | ---
license: apache-2.0
# inference: false
# pipeline_tag: zero-shot-image-classification
pipeline_tag: feature-extraction
# inference:
# parameters:
tags:
- clip
- zh
- image-text
- feature-extraction
---
# Model Details
This model is a Chinese CLIP model trained on [Noah-Wukong Dataset](https://wukong-dataset.gi... |
Geotrend/distilbert-base-en-es-pt-cased | c27a3458f33d900b142be324022e3d28581ca830 | 2021-07-29T12:33:27.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Geotrend | null | Geotrend/distilbert-base-en-es-pt-cased | 131 | null | transformers | 4,194 | ---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# distilbert-base-en-es-pt-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same rep... |
cardiffnlp/twitter-roberta-base-stance-abortion | e73434e7f22370615f75e6b86f5df6ca130c6d18 | 2021-05-20T15:07:21.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-stance-abortion | 131 | null | transformers | 4,195 | |
elozano/tweet_emotion_eval | f848c42f40dcf6afc0b834271377bb227fc3ef8c | 2022-02-07T18:04:47.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:tweet_eval",
"transformers",
"license:mit"
] | text-classification | false | elozano | null | elozano/tweet_emotion_eval | 131 | 3 | transformers | 4,196 | ---
license: mit
datasets:
- tweet_eval
language: en
widget:
- text: "Stop sharing which songs did you listen to during this year on Spotify, NOBODY CARES"
example_title: "Anger"
- text: "I love that joke HAHAHAHAHA"
example_title: "Joy"
- text: "Despite I've not studied a lot for this exam, I think I wil... |
huggingtweets/dadsaysjokes | 7ef5efee90c740984da14d9fe24cf60ea7cf812e | 2021-05-22T00:10:19.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/dadsaysjokes | 131 | null | transformers | 4,197 | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.pros... |
kleinay/qanom-seq2seq-model-joint | a053034de46291e710bb27301d4c1127c9280b71 | 2022-04-04T11:06:35.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:kleinay/qanom",
"transformers",
"semantic-role-labeling",
"question-answer generation",
"autotrain_compatible"
] | text2text-generation | false | kleinay | null | kleinay/qanom-seq2seq-model-joint | 131 | 2 | transformers | 4,198 | ---
language:
- en
tags:
- semantic-role-labeling
- question-answer generation
- pytorch
datasets:
- kleinay/qanom
---
# A Seq2Seq model for QANom parsing
This is a `t5-small` pretrained model, fine-tuned jointly on the tasks of generating QASRL and QANom QAs.
"QANom" stands for "QASRL for Nominalizations", which i... |
kz/mt5base-finetuned-patentsum-japanese-small | e26d766d858ed991fac7906e06856d5c27ae0784 | 2022-05-19T06:50:32.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"ja",
"transformers",
"Summarization",
"japanese",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | kz | null | kz/mt5base-finetuned-patentsum-japanese-small | 131 | 2 | transformers | 4,199 | ---
language: "ja"
widget:
- text: "請求項 <extra_id_0>"
license: "mit"
tags:
- Summarization
- japanese
---
Google's mt5-base fine-tuned in Japanese to summarize patent claims in a limited Pharmaceutical domain.
# 日本語特許請求項要約（医薬特定ドメイン限定）
- """【請求項１】
ヒトＣＤ３８（配列番号１）及びカニクイザルＣＤ３８（配列番号２）に特異的に結合する単離された抗体であって、
ａ）以下を含む重鎖可変領域：... |
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