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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
allenai/cs_roberta_base | f56079f4997a5660c9deffca2827798eb39ac6cd | 2021-05-20T13:02:35.000Z | [
"pytorch",
"jax",
"roberta",
"transformers"
] | null | false | allenai | null | allenai/cs_roberta_base | 3,095 | 1 | transformers | 1,100 | Entry not found |
Wavepaw/DialoGPT-medium-WardenIngo | 78044c2b1cda28107bd5b6d8e1fae32d96103210 | 2022-04-23T21:20:51.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Wavepaw | null | Wavepaw/DialoGPT-medium-WardenIngo | 3,093 | null | transformers | 1,101 | ---
tags:
- conversational
---
# Warden Ingo DialoGPT Model |
KoboldAI/GPT-J-6B-Shinen | afa5a11b24cb23eee708e17c83b920a788e9e07b | 2022-03-20T18:48:45.000Z | [
"pytorch",
"gptj",
"text-generation",
"en",
"arxiv:2101.00027",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-J-6B-Shinen | 3,092 | 1 | transformers | 1,102 | ---
language: en
license: mit
---
# GPT-J 6B - Shinen
## Model Description
GPT-J 6B-Shinen is a finetune created using EleutherAI's GPT-J 6B model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
**Warning: THIS model is NOT suitable for use by minors. The model will output X-rat... |
pranavpsv/genre-story-generator-v2 | b9950761c9c3fabf7d6365f4be8cf0d6b79673b4 | 2021-05-23T11:01:02.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | pranavpsv | null | pranavpsv/genre-story-generator-v2 | 3,090 | 1 | transformers | 1,103 | Entry not found |
uer/bart-base-chinese-cluecorpussmall | 913015fd84e5f0ed219a1c7a8c3819b07006d179 | 2022-07-15T08:17:16.000Z | [
"pytorch",
"tf",
"bart",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uer | null | uer/bart-base-chinese-cluecorpussmall | 3,063 | 4 | transformers | 1,104 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "作为电子[MASK]的平台，京东绝对是领先者。如今的刘强[MASK]已经是身价过[MASK]的老板。"
---
# Chinese BART
## Model description
This model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658).
You can download ... |
gagan3012/k2t | 57b9e3132e50734633ce283fdc96e463837b6cb6 | 2021-09-22T08:27:36.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:WebNLG",
"dataset:Dart",
"transformers",
"keytotext",
"k2t",
"Keywords to Sentences",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | gagan3012 | null | gagan3012/k2t | 3,054 | null | transformers | 1,105 | ---
language: en
thumbnail: Keywords to Sentences
tags:
- keytotext
- k2t
- Keywords to Sentences
license: mit
datasets:
- WebNLG
- Dart
metrics:
- NLG
---
# keytotext
Idea is to build a model whi... |
castorini/tct_colbert-v2-hnp-msmarco | 3b46a821282996e0ada304e4bcc5d659712972a8 | 2021-08-12T01:05:56.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | castorini | null | castorini/tct_colbert-v2-hnp-msmarco | 3,050 | null | transformers | 1,106 | This model is to reproduce a variant of TCT-ColBERT-V2 dense retrieval models described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. [In-Batch Negatives for Knowledge Distillation with Tightly-CoupledTeachers for Dense Retrieval.](https://cs.uwaterloo.ca/~jimmylin/publications/Lin_etal_20... |
hfl/chinese-pert-base | 54f84f9b553c9184d92e1d476010299aac42cf86 | 2022-02-24T02:57:09.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"zh",
"transformers",
"license:cc-by-nc-sa-4.0"
] | feature-extraction | false | hfl | null | hfl/chinese-pert-base | 3,043 | 4 | transformers | 1,107 | ---
language:
- zh
license: "cc-by-nc-sa-4.0"
---
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT |
Luyu/co-condenser-marco | e0cef0ab2410aae0f0994366ddefb5649a266709 | 2021-08-13T13:54:21.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Luyu | null | Luyu/co-condenser-marco | 3,030 | null | transformers | 1,108 | Entry not found |
minimaxir/magic-the-gathering | c0c296822d2bf6584d7ffa2fbb3d1c893dab1311 | 2021-05-23T09:35:52.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | minimaxir | null | minimaxir/magic-the-gathering | 3,025 | null | transformers | 1,109 | # magic-the-gathering
A small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including _Strixhaven_ and _Commander 2021_.
The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card.
Examples of encoded cards:
```
<|t... |
KoboldAI/GPT-Neo-2.7B-Horni-LN | 40eb749c615988ae901c51f4cc7308ac08e8c2a4 | 2021-12-30T12:18:58.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-Neo-2.7B-Horni-LN | 3,023 | null | transformers | 1,110 | Entry not found |
jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn | 9e8a155701c0fa9a84fed4adfcf5edb4ada4342c | 2022-07-27T23:36:42.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"zh",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn | 3,023 | 6 | transformers | 1,111 | ---
language: zh
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Chinese (zh-CN) by Jonatas Grosman
results:
- task:
name: Speech Recognition
type: automatic-speech-recogni... |
pierreguillou/bert-base-cased-squad-v1.1-portuguese | fda61a9dc93104d7944a4abf5d48d51eba229a13 | 2022-01-04T09:57:53.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"pt",
"dataset:brWaC",
"dataset:squad",
"dataset:squad_v1_pt",
"transformers",
"bert-base",
"license:mit",
"autotrain_compatible"
] | question-answering | false | pierreguillou | null | pierreguillou/bert-base-cased-squad-v1.1-portuguese | 3,022 | 13 | transformers | 1,112 | ---
language: pt
license: mit
tags:
- question-answering
- bert
- bert-base
- pytorch
datasets:
- brWaC
- squad
- squad_v1_pt
metrics:
- squad
widget:
- text: "Quando começou a pandemia de Covid-19 no mundo?"
context: "A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de C... |
TurkuNLP/bert-base-finnish-uncased-v1 | 8dce1e623b1b072e4d95f82d11051678b068d37a | 2021-05-18T22:46:38.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"fi",
"arxiv:1912.07076",
"arxiv:1908.04212",
"transformers",
"autotrain_compatible"
] | fill-mask | false | TurkuNLP | null | TurkuNLP/bert-base-finnish-uncased-v1 | 3,020 | null | transformers | 1,113 | ---
language: fi
---
## Quickstart
**Release 1.0** (November 25, 2019)
Download the models here:
* Cased Finnish BERT Base: [bert-base-finnish-cased-v1.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-cased-v1.zip)
* Uncased Finnish BERT Base: [bert-base-finnish-uncased-v1.zip](http://dl.turkunlp.org/finbert/b... |
florentiino/DialoGPT-small-harrypotter | f45e5d9b12e85a4a2c7c1ad13cdc55c06996c923 | 2022-07-23T06:43:40.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | florentiino | null | florentiino/DialoGPT-small-harrypotter | 3,017 | null | transformers | 1,114 | ---
tags:
- conversational
---
# Have a chat with Dumbledore
|
AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru | 6cc14366f0cc95428a695d30594a93dd6935d800 | 2022-07-19T15:33:20.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"en",
"ru",
"multilingual",
"arxiv:1912.09723",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | AlexKay | null | AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru | 3,012 | 9 | transformers | 1,115 | ---
language:
- en
- ru
- multilingual
license: apache-2.0
---
# XLM-RoBERTa large model whole word masking finetuned on SQuAD
Pretrained model using a masked language modeling (MLM) objective.
Fine tuned on English and Russian QA datasets
## Used QA Datasets
SQuAD + SberQuAD
[SberQuAD original paper](https:/... |
neuraly/bert-base-italian-cased-sentiment | bea83f326b616d7fe641bc3ed92a5ce18c97dfed | 2021-09-22T09:29:18.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"it",
"transformers",
"sentiment",
"Italian",
"license:mit"
] | text-classification | false | neuraly | null | neuraly/bert-base-italian-cased-sentiment | 3,000 | 2 | transformers | 1,116 | ---
language: it
thumbnail: https://neuraly.ai/static/assets/images/huggingface/thumbnail.png
tags:
- sentiment
- Italian
license: mit
widget:
- text: Huggingface è un team fantastico!
---
# 🤗 + neuraly - Italian BERT Sentiment model
## Model description
This model performs sentiment analysis on Italian sente... |
hackathon-pln-es/jurisbert-clas-art-convencion-americana-dh | 95326522994b34d3aa50dd46621701260d27d323 | 2022-03-28T18:21:03.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"es",
"transformers",
"license:cc-by-nc-4.0"
] | text-classification | false | hackathon-pln-es | null | hackathon-pln-es/jurisbert-clas-art-convencion-americana-dh | 2,999 | 6 | transformers | 1,117 | ---
license: cc-by-nc-4.0
language: es
widget:
- text: "ADOPCIÓN. EL INTERÉS SUPERIOR DEL MENOR DE EDAD SE BASA EN LA IDONEIDAD DE LOS ADOPTANTES, DENTRO DE LA CUAL SON IRRELEVANTES EL TIPO DE FAMILIA AL QUE AQUÉL SERÁ INTEGRADO, ASÍ COMO LA ORIENTACIÓN SEXUAL O EL ESTADO CIVIL DE ÉSTOS."
---
## Descripción del... |
wietsedv/xlm-roberta-base-ft-udpos28-en | 8fb5e06a6295a01d03bbc4af8359458bfcf21b57 | 2022-02-25T09:58:19.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"en",
"dataset:universal_dependencies",
"transformers",
"part-of-speech",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | wietsedv | null | wietsedv/xlm-roberta-base-ft-udpos28-en | 2,994 | null | transformers | 1,118 |
---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-en
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
datas... |
microsoft/BiomedVLP-CXR-BERT-specialized | b59c09e51ab2410b24f4be214bbb49043fe63fc2 | 2022-07-11T14:52:06.000Z | [
"pytorch",
"cxr-bert",
"en",
"arxiv:2204.09817",
"arxiv:2103.00020",
"arxiv:2002.05709",
"transformers",
"exbert",
"license:mit",
"fill-mask"
] | fill-mask | false | microsoft | null | microsoft/BiomedVLP-CXR-BERT-specialized | 2,994 | 5 | transformers | 1,119 | ---
language: en
tags:
- exbert
license: mit
pipeline_tag: fill-mask
widget:
- text: "Left pleural effusion with adjacent [MASK]."
example_title: "Radiology 1"
- text: "Heart size normal and lungs are [MASK]."
example_title: "Radiology 2"
inference: false
---
# CXR-BERT-specialized
[CXR-BERT](https://arxiv.org/ab... |
yikuan8/Clinical-Longformer | dc05ee5437027609b953618bc8e2b725a30bd670 | 2022-04-10T17:44:49.000Z | [
"pytorch",
"longformer",
"fill-mask",
"en",
"arxiv:2201.11838",
"transformers",
"clinical",
"autotrain_compatible"
] | fill-mask | false | yikuan8 | null | yikuan8/Clinical-Longformer | 2,992 | 5 | transformers | 1,120 | ---
language: "en"
tags:
- longformer
- clinical
---
<span style="font-size:larger;">**Clinical-Longformer**</span> is a clinical knowledge enriched version of Longformer that was further pre-trained using MIMIC-III clinical notes. It allows up to 4,096 tokens as the model input. Clinical-Longformer consistently out-... |
mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization | 3ecce850ed191e6b576e0fb306b30d5da087c2eb | 2020-12-11T21:53:12.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | mrm8488 | null | mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization | 2,983 | 2 | transformers | 1,121 | ---
language: en
license: apache-2.0
datasets:
- cnn_dailymail
tags:
- summarization
---
# Bert-small2Bert-small Summarization with 🤗EncoderDecoder Framework
This model is a warm-started *BERT2BERT* ([small](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8)) model fine-tuned on the *CNN/Dailymail* summarizat... |
inywer/shouko0-3 | 402311526d96c0f9cad11c6d35dfa4c48880ff9d | 2022-07-11T04:34:09.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | inywer | null | inywer/shouko0-3 | 2,983 | null | transformers | 1,122 | ---
tags:
- conversational
---
# inywer/shouko0-3 Model |
google/t5-small-lm-adapt | ceece9332ccd73f589b2c764fa0e334c597952d4 | 2021-11-01T13:58:46.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"transformers",
"t5-lm-adapt",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-small-lm-adapt | 2,979 | 3 | transformers | 1,123 | ---
language: en
datasets:
- c4
tags:
- t5-lm-adapt
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transform... |
cross-encoder/ms-marco-TinyBERT-L-6 | 36aded87184cc2e8d13ceb3ab10b186facb9f26a | 2021-08-05T08:40:06.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/ms-marco-TinyBERT-L-6 | 2,974 | 1 | transformers | 1,124 | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... |
pinkducky/Rachel_Bot | f504fc38da9892219763959aff144a976a6d2487 | 2022-03-21T04:01:39.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | pinkducky | null | pinkducky/Rachel_Bot | 2,968 | null | transformers | 1,125 | ---
tags:
- conversational
---
# My Awesome Model
|
Edresson/wav2vec2-large-xlsr-coraa-portuguese | 823dceb42ebafb67cb046d10957e261e5489b026 | 2022-03-31T13:28:43.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:CORAA",
"arxiv:2110.15731",
"transformers",
"audio",
"speech",
"portuguese-speech-corpus",
"hf-asr-leaderboard",
"PyTorch",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Edresson | null | Edresson/wav2vec2-large-xlsr-coraa-portuguese | 2,956 | 8 | transformers | 1,126 | ---
language: pt
datasets:
- CORAA
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- hf-asr-leaderboard
- speech
- PyTorch
license: apache-2.0
model-index:
- name: Edresson Casanova XLSR Wav2Vec2 Large 53 Portuguese
results:
- task:
name: Spee... |
castorini/ance-msmarco-passage | 6a7062e287fda08e561df5b9b55a6aff98c852a2 | 2021-05-20T15:18:16.000Z | [
"pytorch",
"roberta",
"arxiv:2007.00808",
"transformers"
] | null | false | castorini | null | castorini/ance-msmarco-passage | 2,954 | null | transformers | 1,127 | This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arx... |
sebastian-hofstaetter/colbert-distilbert-margin_mse-T2-msmarco | 0feb43c9885ff5e9b1116ea548525aadfc327d7e | 2021-03-18T10:35:12.000Z | [
"pytorch",
"ColBERT",
"en",
"dataset:ms_marco",
"arxiv:2004.12832",
"arxiv:2010.02666",
"transformers",
"dpr",
"dense-passage-retrieval",
"knowledge-distillation"
] | null | false | sebastian-hofstaetter | null | sebastian-hofstaetter/colbert-distilbert-margin_mse-T2-msmarco | 2,951 | 3 | transformers | 1,128 | ---
language: "en"
tags:
- dpr
- dense-passage-retrieval
- knowledge-distillation
datasets:
- ms_marco
---
# Margin-MSE Trained ColBERT
We provide a retrieval trained DistilBert-based ColBERT model (https://arxiv.org/pdf/2004.12832.pdf). Our model is trained with Margin-MSE using a 3 teacher BERT_Cat (con... |
bigscience/bloom-2b5 | 68331cd7e9637733d1e3e011515288afb1c23ad8 | 2022-07-18T15:58:49.000Z | [
"pytorch",
"bloom",
"feature-extraction",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
... | text-generation | false | bigscience | null | bigscience/bloom-2b5 | 2,947 | 3 | transformers | 1,129 | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-gener... |
KoboldAI/fairseq-dense-13B | e936211b7bb8f406cb78efca22a5f7c43ba090b3 | 2022-02-01T22:51:59.000Z | [
"pytorch",
"xglm",
"text-generation",
"transformers"
] | text-generation | false | KoboldAI | null | KoboldAI/fairseq-dense-13B | 2,941 | 3 | transformers | 1,130 | Entry not found |
mrm8488/bert-spanish-cased-finetuned-pos-16-tags | 7245043c8ef25dc7ccf91e6afdd2e2dc94213155 | 2021-05-20T00:36:33.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | mrm8488 | null | mrm8488/bert-spanish-cased-finetuned-pos-16-tags | 2,921 | null | transformers | 1,131 | Entry not found |
voidful/albert_chinese_tiny | d40f566a40f057e5d8a6f7b2cd5171a4f104126f | 2021-08-03T05:07:02.000Z | [
"pytorch",
"albert",
"fill-mask",
"zh",
"transformers",
"autotrain_compatible"
] | fill-mask | false | voidful | null | voidful/albert_chinese_tiny | 2,920 | 5 | transformers | 1,132 | ---
language: zh
pipeline_tag: fill-mask
widget:
- text: "今天[MASK]情很好"
---
# albert_chinese_tiny
This a albert_chinese_tiny model from [brightmart/albert_zh project](https://github.com/brightmart/albert_zh), albert_tiny_google_zh model
converted by huggingface's [script](https://github.com/huggingface/transformer... |
rinna/japanese-cloob-vit-b-16 | 80b15fb86ca981749e1073bd7896e9ff1c965790 | 2022-07-19T05:49:48.000Z | [
"pytorch",
"cloob",
"ja",
"arxiv:2110.11316",
"transformers",
"feature-extraction",
"japanese",
"clip",
"vision",
"license:apache-2.0"
] | feature-extraction | false | rinna | null | rinna/japanese-cloob-vit-b-16 | 2,908 | 3 | transformers | 1,133 | ---
language: ja
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
license: apache-2.0
tags:
- feature-extraction
- ja
- japanese
- clip
- cloob
- vision
---
# rinna/japanese-cloob-vit-b-16
This is a Japanese [CLOOB (Contrastive Leave One Out Boost)](ht... |
vinai/bartpho-word | 748f5b5deee937629b2ac1b7e7453730c71a969e | 2022-06-08T04:49:05.000Z | [
"pytorch",
"tf",
"mbart",
"feature-extraction",
"arxiv:2109.09701",
"transformers"
] | feature-extraction | false | vinai | null | vinai/bartpho-word | 2,893 | null | transformers | 1,134 | # <a name="introduction"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
Two BARTpho versions `BARTpho-syllable` and `BARTpho-word` are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and pre-training scheme of... |
microsoft/dit-base | 5f3a1d82def5866db1ac86d7701fe4f508050f42 | 2022-03-08T10:40:10.000Z | [
"pytorch",
"beit",
"arxiv:2203.02378",
"transformers",
"dit"
] | null | false | microsoft | null | microsoft/dit-base | 2,885 | 3 | transformers | 1,135 | ---
tags:
- dit
inference: false
---
# Document Image Transformer (base-sized model)
Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper [DiT: Self-supervised Pre-training for Document Image Transforme... |
allegro/herbert-large-cased | 8d0fa3bc0566c3a332bec0d471c8d8c37b5cbb90 | 2022-06-26T14:18:54.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"pl",
"transformers",
"herbert",
"license:cc-by-4.0"
] | feature-extraction | false | allegro | null | allegro/herbert-large-cased | 2,872 | 3 | transformers | 1,136 | ---
language: pl
tags:
- herbert
license: cc-by-4.0
---
# HerBERT
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish corpora
using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, ... |
trev/DialoGPT-small-MLP | e36e9fb34f98e0006f4ebcc755fe0b486708052a | 2022-04-05T17:10:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | trev | null | trev/DialoGPT-small-MLP | 2,872 | null | transformers | 1,137 | ---
tags:
- conversational
---
# My Little Pony DialoGPT Model |
voidful/bart-eqg-question-generator | e85b63236f244e0735bca7407ddb0cc76650061b | 2021-08-24T11:00:51.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:eqg-race",
"transformers",
"question",
"generation",
"seq2seq",
"autotrain_compatible"
] | text2text-generation | false | voidful | null | voidful/bart-eqg-question-generator | 2,861 | 7 | transformers | 1,138 | ---
language: en
tags:
- bart
- question
- generation
- seq2seq
datasets:
- eqg-race
metrics:
- bleu
- rouge
pipeline_tag: text2text-generation
widget:
- text: "When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes to France , you have another ... |
mrm8488/bert-tiny-5-finetuned-squadv2 | f586274a9919ef3ca801d3c7f3f30ee6ad7515d8 | 2022-01-18T20:19:49.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"arxiv:1908.08962",
"transformers",
"QA",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/bert-tiny-5-finetuned-squadv2 | 2,860 | 3 | transformers | 1,139 | ---
language: en
thumbnail:
tags:
- QA
---
# BERT-Tiny ([5](https://huggingface.co/google/bert_uncased_L-12_H-128_A-2)) fine-tuned on SQuAD v2
[BERT-Tiny](https://huggingface.co/google/bert_uncased_L-12_H-128_A-2) created by [Google Research](https://github.com/google-research) and fine-tuned on [SQuAD 2.0](https://... |
uer/roberta-base-finetuned-jd-full-chinese | 001c14a6ad8498465b0d7a2be435c30e856507a8 | 2022-02-20T07:57:14.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"zh",
"arxiv:1909.05658",
"arxiv:1708.02657",
"transformers"
] | text-classification | false | uer | null | uer/roberta-base-finetuned-jd-full-chinese | 2,860 | 3 | transformers | 1,140 | ---
language: zh
widget:
- text: "这本书真的很不错"
---
# Chinese RoBERTa-Base Models for Text Classification
## Model description
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by [UER-py](https://arxiv.org/abs/1909.05658). You can download the 5 Chinese RoBERTa-Base classification models eith... |
sberbank-ai/rugpt3medium_based_on_gpt2 | 63494984e6afd13972d863197ea1ce1be484d339 | 2021-09-21T19:29:06.000Z | [
"pytorch",
"ru",
"transformers",
"PyTorch",
"Transformers"
] | null | false | sberbank-ai | null | sberbank-ai/rugpt3medium_based_on_gpt2 | 2,859 | 3 | transformers | 1,141 | ---
language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: "https://github.com/sberbank-ai/ru-gpts"
---
# rugpt3medium\_based\_on\_gpt2
Model was trained with sequence length 1024 using transformers lib by [SberDevices](https://sberdevices.ru/) team on 80B tokens for 3 epoch. After that model was finetuned on 2048 ... |
etalab-ia/dpr-question_encoder-fr_qa-camembert | 20f81e3505a3184dbf3729701d7c6152125287ef | 2021-06-16T10:10:09.000Z | [
"pytorch",
"camembert",
"feature-extraction",
"fr",
"dataset:piaf",
"dataset:FQuAD",
"dataset:SQuAD-FR",
"arxiv:2004.04906",
"arxiv:1911.03894",
"transformers"
] | feature-extraction | false | etalab-ia | null | etalab-ia/dpr-question_encoder-fr_qa-camembert | 2,858 | 3 | transformers | 1,142 | ---
language: fr
datasets:
- piaf
- FQuAD
- SQuAD-FR
---
# dpr-question_encoder-fr_qa-camembert
## Description
French [DPR model](https://arxiv.org/abs/2004.04906) using [CamemBERT](https://arxiv.org/abs/1911.03894) as base and then fine-tuned on a combo of three French Q&A
## Data
### French Q&A
We use a combinat... |
facebook/wav2vec2-base-100k-voxpopuli | 7a43eaf4d68a147cfc6b754e338bd9aa72a1fbad | 2021-11-05T12:46:12.000Z | [
"pytorch",
"wav2vec2",
"pretraining",
"multilingual",
"arxiv:2101.00390",
"transformers",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"license:cc-by-nc-4.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-base-100k-voxpopuli | 2,857 | 1 | transformers | 1,143 | ---
language: multilingual
tags:
- audio
- automatic-speech-recognition
- voxpopuli
license: cc-by-nc-4.0
---
# Wav2Vec2-Base-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 100k unlabeled subset of [VoxPopuli corpu... |
Helsinki-NLP/opus-mt-eo-en | 894c5ff7f7871951289933e74f9b5de7b996903d | 2021-09-09T21:40:53.000Z | [
"pytorch",
"marian",
"text2text-generation",
"eo",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-eo-en | 2,851 | null | transformers | 1,144 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-eo-en
* source languages: eo
* target languages: en
* OPUS readme: [eo-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/eo-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim | 6aeb7661bcf364e2dfe8ac0d00f919ba44e4c973 | 2022-05-06T13:48:50.000Z | [
"pytorch",
"wav2vec2",
"en",
"dataset:msp-podcast",
"arxiv:2203.07378",
"transformers",
"speech",
"audio",
"audio-classification",
"emotion-recognition",
"license:cc-by-nc-sa-4.0"
] | audio-classification | false | audeering | null | audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim | 2,851 | 4 | transformers | 1,145 | ---
language: en
datasets:
- msp-podcast
inference: true
tags:
- speech
- audio
- wav2vec2
- audio-classification
- emotion-recognition
license: cc-by-nc-sa-4.0
---
# Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0
The model expects a raw audio signal as input and outputs predictions for arousal... |
kykim/albert-kor-base | 04e79bcdfe860f251165a93dc685f9544bc597c0 | 2021-01-22T00:27:49.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"ko",
"transformers",
"autotrain_compatible"
] | fill-mask | false | kykim | null | kykim/albert-kor-base | 2,844 | 2 | transformers | 1,146 | ---
language: ko
---
# Albert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import BertTokenizerFast, AlbertModel
tokenizer_alb... |
nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large | 37519953d888723fe745ea10a1438d8c20a3800f | 2021-06-20T19:02:12.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nreimers | null | nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large | 2,841 | null | transformers | 1,147 | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) |
microsoft/BiomedNLP-KRISSBERT-PubMed-UMLS-EL | 373f710a611281c9ba2fa935586be1dbe98fc3fe | 2022-05-25T02:45:36.000Z | [
"pytorch",
"bert",
"en",
"arxiv:2112.07887",
"transformers",
"exbert",
"license:mit",
"feature-extraction"
] | feature-extraction | false | microsoft | null | microsoft/BiomedNLP-KRISSBERT-PubMed-UMLS-EL | 2,832 | 4 | transformers | 1,148 | ---
language: en
tags:
- exbert
license: mit
pipeline_tag: feature-extraction
widget:
- text: "<ENT> ER </ENT> crowding has become a wide-spread problem."
---
## KRISSBERT
[https://arxiv.org/pdf/2112.07887.pdf](https://arxiv.org/pdf/2112.07887.pdf)
Entity linking faces significant challenges such as prolific variati... |
nateraw/bert-base-uncased-emotion | 064d252021b51d95cd0547c89c6489100da0dc4c | 2021-05-20T01:18:38.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"dataset:emotion",
"transformers",
"emotion",
"license:apache-2.0"
] | text-classification | false | nateraw | null | nateraw/bert-base-uncased-emotion | 2,827 | 3 | transformers | 1,149 | ---
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
---
# bert-base-uncased-emotion
## Model description
`bert-base-uncased` fi... |
m3hrdadfi/wav2vec2-large-xlsr-persian-v3 | f3ceecb54fc81bb796f1565429bcf5599cd0e24d | 2021-11-04T15:22:11.000Z | [
"pytorch",
"tf",
"wav2vec2",
"automatic-speech-recognition",
"fa",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"model-index"
] | automatic-speech-recognition | false | m3hrdadfi | null | m3hrdadfi/wav2vec2-large-xlsr-persian-v3 | 2,826 | 8 | transformers | 1,150 | ---
language: fa
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
widget:
- example_title: Common Voice sample 1
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3/resolve/main/sample1.flac
- example_title: Common Voice sample 2978
src: https:/... |
alvaroalon2/biobert_chemical_ner | a5c41a966542076b2cea6a0ffca62d5610277e6f | 2022-07-11T11:12:51.000Z | [
"pytorch",
"tf",
"bert",
"token-classification",
"English",
"dataset:BC5CDR-chemicals",
"dataset:BC4CHEMD",
"transformers",
"NER",
"Biomedical",
"Chemicals",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | alvaroalon2 | null | alvaroalon2/biobert_chemical_ner | 2,823 | 4 | transformers | 1,151 | ---
language: "English"
tags:
- token-classification
- NER
- Biomedical
- Chemicals
datasets:
- BC5CDR-chemicals
- BC4CHEMD
license: apache-2.0
---
BioBERT model fine-tuned in NER task with BC5CDR-chemicals and BC4CHEMD corpus.
This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: h... |
castorini/t5-base-canard | f0f21fc4cae5dc130d97e4fa4dc07d7710875b7b | 2021-06-23T11:56:05.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | castorini | null | castorini/t5-base-canard | 2,816 | null | transformers | 1,152 | This model is trained for conversational question rewriting.
Usage:
Source text format: ${HISTORY} ||| ${CURRENT_QUESTION}
example from [CANARD](https://sites.google.com/view/qanta/projects/canard):
Frank Zappa ||| Disbandment ||| What group disbanded ||| Zappa and the Mothers of Invention ||| When did they disband?... |
julien-c/hotdog-not-hotdog | e268d30900a9e75185eb7543bd2ffceb80686cde | 2021-07-02T12:13:28.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | julien-c | null | julien-c/hotdog-not-hotdog | 2,816 | 1 | transformers | 1,153 | ---
tags:
- image-classification
- huggingpics
metrics:
- accuracy
model-index:
- name: hotdog-not-hotdog
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.824999988079071
---
# hotdog-not-hotdog
Autogen... |
csebuetnlp/banglabert | 7bed1e381af5564564faadc9718f25c6116491e0 | 2022-05-10T05:17:06.000Z | [
"pytorch",
"electra",
"pretraining",
"bn",
"arxiv:2101.00204",
"transformers"
] | null | false | csebuetnlp | null | csebuetnlp/banglabert | 2,812 | 2 | transformers | 1,154 | ---
language:
- bn
licenses:
- cc-by-nc-sa-4.0
---
# BanglaBERT
This repository contains the pretrained discriminator checkpoint of the model **BanglaBERT**. This is an [ELECTRA](https://openreview.net/pdf?id=r1xMH1BtvB) discriminator model pretrained with the Replaced Token Detection (RTD) objective. Finetuned mode... |
hf-internal-testing/tiny-random-beit-pipeline | 2c1f7ac7d33f3ad4f7b9f06aa045175423689ee2 | 2022-02-14T17:42:35.000Z | [
"pytorch",
"beit",
"transformers",
"image-segmentation"
] | image-segmentation | false | hf-internal-testing | null | hf-internal-testing/tiny-random-beit-pipeline | 2,811 | null | transformers | 1,155 | ---
pipeline_tag: image-segmentation
---
Make the feature_extractor and model config agree.
|
kyriinx/DialoGPT-small-glyph | 7ad4861bfe3bc8469bb6b89d18648d73dccb22a2 | 2022-04-27T16:35:54.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | kyriinx | null | kyriinx/DialoGPT-small-glyph | 2,803 | null | transformers | 1,156 | ---
tags:
- conversational
---
# Glyph DialoGPT model |
stas/tiny-wmt19-en-ru | cad41949841fed75b823799992d79dd7a35698c5 | 2021-05-03T01:47:47.000Z | [
"pytorch",
"fsmt",
"text2text-generation",
"en",
"ru",
"dataset:wmt19",
"transformers",
"wmt19",
"testing",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | stas | null | stas/tiny-wmt19-en-ru | 2,797 | null | transformers | 1,157 | ---
language:
- en
- ru
thumbnail:
tags:
- wmt19
- testing
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# Tiny FSMT en-ru
This is a tiny model that is used in the `transformers` test suite. It doesn't do anything useful, other than testing that `modeling_fsmt.py` is functional.
Do not try to use it for ... |
Helsinki-NLP/opus-mt-fr-es | 4bd0d3d212940704145e6a2699f4b93e6cfe8b61 | 2021-09-09T21:53:46.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fr",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fr-es | 2,792 | null | transformers | 1,158 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fr-es
* source languages: fr
* target languages: es
* OPUS readme: [fr-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
sgugger/tiny-distilbert-classification | a30e0f7dc9dc24b0dacce98fd144e0a7ffb70a1a | 2021-07-29T17:12:02.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | sgugger | null | sgugger/tiny-distilbert-classification | 2,783 | null | transformers | 1,159 | Entry not found |
DeepESP/gpt2-spanish | 1b935e39cf9893108bd2f4fb5317f48ae1c3ab5e | 2021-10-19T08:52:48.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
] | text-generation | false | DeepESP | null | DeepESP/gpt2-spanish | 2,774 | 9 | transformers | 1,160 | ---
language: es
tags:
- GPT-2
- Spanish
- ebooks
- nlg
datasets:
- ebooks
widget:
- text: "Quisiera saber que va a suceder"
license: mit
---
# GPT2-Spanish
GPT2-Spanish is a language generation model trained from scratch with 11.5GB of Spanish texts and with a Byte Pair Encoding (BPE) tokenizer that was trained for ... |
Ilyes/wav2vec2-large-xlsr-53-french | a3233bc9949d6da07e5e18660b004a6c120dc135 | 2022-02-09T08:28:27.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Ilyes | null | Ilyes/wav2vec2-large-xlsr-53-french | 2,774 | 1 | transformers | 1,161 | ---
language: fr
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: wav2vec2-large-xlsr-53-French by Ilyes Rebai
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
n... |
juliensimon/reviews-sentiment-analysis | 7086631c39dcbb051d17ad01d07d747073383882 | 2022-05-03T09:25:01.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:generated_reviews_enth",
"transformers",
"sentiment-analysis"
] | text-classification | false | juliensimon | null | juliensimon/reviews-sentiment-analysis | 2,773 | 1 | transformers | 1,162 | ---
language:
- en
tags:
- distilbert
- sentiment-analysis
datasets:
- generated_reviews_enth
---
Distilbert model fine-tuned on English language product reviews
A notebook for Amazon SageMaker is available in the 'code' subfolder.
|
microsoft/layoutlm-large-uncased | 1e7d50dced3cdfea3a3d63c610e2aab36933dbef | 2021-08-11T05:28:26.000Z | [
"pytorch",
"tf",
"layoutlm",
"arxiv:1912.13318",
"transformers"
] | null | false | microsoft | null | microsoft/layoutlm-large-uncased | 2,773 | 4 | transformers | 1,163 | # LayoutLM
Multimodal (text + layout/format + image) pre-training for document AI
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlm)
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document imag... |
KoboldAI/fairseq-dense-13B-Janeway | da54db082f7cab156e6c7f69aaab6c048a834286 | 2022-04-07T10:51:39.000Z | [
"pytorch",
"xglm",
"text-generation",
"en",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/fairseq-dense-13B-Janeway | 2,766 | 1 | transformers | 1,164 | ---
language: en
license: mit
---
# Fairseq-dense 13B - Janeway
## Model Description
Fairseq-dense 13B-Janeway is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is identical as dataset used ... |
philschmid/tiny-bert-sst2-distilled | 874eb28543ea7a7df80b6158bbf772d203efcab6 | 2022-01-31T18:50:41.000Z | [
"pytorch",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | philschmid | null | philschmid/tiny-bert-sst2-distilled | 2,763 | null | transformers | 1,165 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny-bert-sst2-distilled
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy... |
google/tapas-large-finetuned-sqa | f214f24bdb51550ced615bac82668a1bc0e26806 | 2021-11-29T13:03:46.000Z | [
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:msr_sqa",
"arxiv:2004.02349",
"arxiv:2010.00571",
"transformers",
"license:apache-2.0"
] | table-question-answering | false | google | null | google/tapas-large-finetuned-sqa | 2,757 | 1 | transformers | 1,166 | ---
language: en
tags:
- tapas
license: apache-2.0
datasets:
- msr_sqa
---
# TAPAS large model fine-tuned on Sequential Question Answering (SQA)
This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_large_reset` checkpoint of the [original Github repository](https... |
tunib/electra-ko-en-small | ac899d8d102ccec10ad2a0ee6a1ab12b5f7eac41 | 2021-09-17T08:59:47.000Z | [
"pytorch",
"electra",
"pretraining",
"arxiv:2003.10555",
"transformers"
] | null | false | tunib | null | tunib/electra-ko-en-small | 2,752 | 4 | transformers | 1,167 | # TUNiB-Electra
We release several new versions of the [ELECTRA](https://arxiv.org/abs/2003.10555) model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based... |
etalab-ia/dpr-ctx_encoder-fr_qa-camembert | a0bc241d0c8011d1d72c02487b3ff3e326a2e59c | 2021-06-16T11:22:59.000Z | [
"pytorch",
"camembert",
"fr",
"dataset:piaf",
"dataset:FQuAD",
"dataset:SQuAD-FR",
"arxiv:2004.04906",
"arxiv:1911.03894",
"transformers"
] | null | false | etalab-ia | null | etalab-ia/dpr-ctx_encoder-fr_qa-camembert | 2,751 | 3 | transformers | 1,168 | ---
language: fr
datasets:
- piaf
- FQuAD
- SQuAD-FR
---
# dpr-ctx_encoder-fr_qa-camembert
## Description
French [DPR model](https://arxiv.org/abs/2004.04906) using [CamemBERT](https://arxiv.org/abs/1911.03894) as base and then fine-tuned on a combo of three French Q&A
## Data
### French Q&A
We use a combination o... |
hetpandya/t5-base-tapaco | 374f3753409f0a3aca1d69f8af2cee358b02daea | 2021-06-29T11:19:06.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:tapaco",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | hetpandya | null | hetpandya/t5-base-tapaco | 2,737 | null | transformers | 1,169 | ---
language: en
datasets:
- tapaco
---
# T5-base for paraphrase generation
Google's T5-base fine-tuned on [TaPaCo](https://huggingface.co/datasets/tapaco) dataset for paraphrasing.
<!-- ## Model fine-tuning -->
<!-- The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil... |
monologg/koelectra-base-v3-finetuned-korquad | ea97b35e21bfd7f2524b5697931ae3db0394af9f | 2020-10-14T01:43:31.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | monologg | null | monologg/koelectra-base-v3-finetuned-korquad | 2,735 | 3 | transformers | 1,170 | Entry not found |
rinna/japanese-gpt2-small | d35a68cf1fea74b71708ce898b351471b5c698ce | 2021-08-23T03:19:56.000Z | [
"pytorch",
"tf",
"gpt2",
"text-generation",
"ja",
"dataset:cc100",
"dataset:wikipedia",
"transformers",
"japanese",
"lm",
"nlp",
"license:mit"
] | text-generation | false | rinna | null | rinna/japanese-gpt2-small | 2,731 | 4 | transformers | 1,171 | ---
language: ja
thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png
tags:
- ja
- japanese
- gpt2
- text-generation
- lm
- nlp
license: mit
datasets:
- cc100
- wikipedia
widget:
- text: "生命、宇宙、そして万物についての究極の疑問の答えは"
---
# japanese-gpt2-small
This repository provides a s... |
Jonesy/HomersNightOut | 3b14400af228e5e589bdff6d4333a9645869e220 | 2022-04-28T21:08:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Jonesy | null | Jonesy/HomersNightOut | 2,729 | null | transformers | 1,172 | ---
tags:
- conversational
---
# DialoGPT-medium Model of Simpsons Episode s1e10 "Homer's Night Out"
|
sberbank-ai/sbert_large_mt_nlu_ru | 4b9767cce506403f64e69309eab741263479b099 | 2021-09-21T19:47:13.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"ru",
"transformers",
"PyTorch",
"Transformers"
] | feature-extraction | false | sberbank-ai | null | sberbank-ai/sbert_large_mt_nlu_ru | 2,720 | 2 | transformers | 1,173 | ---
language:
- ru
tags:
- PyTorch
- Transformers
---
# BERT large model multitask (cased) for Sentence Embeddings in Russian language.
The model is described [in this article](https://habr.com/ru/company/sberdevices/blog/560748/)
Russian SuperGLUE [metrics](https://russiansuperglue.com/login/submit_info/944)
For b... |
deep-learning-analytics/GrammarCorrector | 6ca90bd771c373a0542d4257a5c34d26cd0d3c59 | 2021-12-23T02:51:34.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | deep-learning-analytics | null | deep-learning-analytics/GrammarCorrector | 2,719 | 3 | transformers | 1,174 | ## Model description
T5 model trained for Grammar Correction. This model corrects grammatical mistakes in input sentences
### Dataset Description
The T5-base model has been trained on C4_200M dataset.
### Model in Action 🚀
```
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name =... |
uclanlp/visualbert-vqa-coco-pre | 884aaef1fb6bed1429cae8c3abc314011a3a429f | 2021-05-31T11:34:13.000Z | [
"pytorch",
"visual_bert",
"pretraining",
"transformers"
] | null | false | uclanlp | null | uclanlp/visualbert-vqa-coco-pre | 2,718 | null | transformers | 1,175 | Entry not found |
aware-ai/roberta-large-squadv2 | 59a93e1104aa42295190ecec42bf829fbc83b0bb | 2021-05-20T12:37:36.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | aware-ai | null | aware-ai/roberta-large-squadv2 | 2,709 | null | transformers | 1,176 | Entry not found |
sentence-transformers/nli-distilroberta-base-v2 | ee9754ad61d9164d693c8e4c458238433037023f | 2022-06-15T21:56:58.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/nli-distilroberta-base-v2 | 2,695 | null | sentence-transformers | 1,177 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/nli-distilroberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional de... |
google/multiberts-seed_0 | 1d4bb03ab3a40f4c935a4efbd57917eb9e8d74d5 | 2021-11-05T22:01:32.000Z | [
"pytorch",
"tf",
"bert",
"pretraining",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"transformers",
"multiberts",
"multiberts-seed_0",
"license:apache-2.0"
] | null | false | google | null | google/multiberts-seed_0 | 2,682 | null | transformers | 1,178 | ---
language: en
tags:
- multiberts
- multiberts-seed_0
license: apache-2.0
---
# MultiBERTs - Seed 0
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://gi... |
taeminlee/kogpt2 | 629b33aaaa679f16abd284f703c650c6f71bc802 | 2021-05-23T13:04:34.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | taeminlee | null | taeminlee/kogpt2 | 2,679 | 1 | transformers | 1,179 | # KoGPT2-Transformers
KoGPT2 on Huggingface Transformers
### KoGPT2-Transformers
- [SKT-AI 에서 공개한 KoGPT2 (ver 1.0)](https://github.com/SKT-AI/KoGPT2)를 [Transformers](https://github.com/huggingface/transformers)에서 사용하도록 하였습니다.
- **SKT-AI 에서 KoGPT2 2.0을 공개하였습니다. https://huggingface.co/skt/kogpt2-base-v2/**
### Demo... |
ThomasSimonini/t5-end2end-question-generation | 1dda3f93db6cfa1e7fc84e1208d0a49febb5fb5c | 2021-10-10T08:30:38.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | ThomasSimonini | null | ThomasSimonini/t5-end2end-question-generation | 2,676 | 2 | transformers | 1,180 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-end2end-question-generation
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: squad
type: squad
args: plain_text
---
# t5-end2end-... |
dkleczek/bert-base-polish-uncased-v1 | 62be9821055981deafb23f217b68cc41f38cdb76 | 2021-05-19T15:55:32.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"pl",
"transformers",
"autotrain_compatible"
] | fill-mask | false | dkleczek | null | dkleczek/bert-base-polish-uncased-v1 | 2,671 | 2 | transformers | 1,181 | ---
language: pl
thumbnail: https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png
---
# Polbert - Polish BERT
Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using... |
tuner007/t5_abs_qa | c896608015dba727b3fe0ae8a397fa1a4286c72e | 2020-12-11T22:02:51.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tuner007 | null | tuner007/t5_abs_qa | 2,664 | 1 | transformers | 1,182 | # T5 for abstractive question-answering
This is T5-base model fine-tuned for abstractive QA using text-to-text approach
## Model training
This model was trained on colab TPU with 35GB RAM for 2 epochs
## Model in Action 🚀
```
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from... |
monologg/koelectra-base-v3-generator | 502d48ff8cac576e1324c8c2ce51ab2c866417c5 | 2021-10-20T16:53:23.000Z | [
"pytorch",
"electra",
"fill-mask",
"ko",
"transformers",
"korean",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | monologg | null | monologg/koelectra-base-v3-generator | 2,649 | 1 | transformers | 1,183 | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA v3 (Base Generator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-v3-generator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and token... |
AI-Growth-Lab/PatentSBERTa | 7550939f981e2236a4cabfe3bd6cb6996d317a63 | 2022-05-04T11:45:01.000Z | [
"pytorch",
"mpnet",
"feature-extraction",
"arxiv:2103.11933",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | AI-Growth-Lab | null | AI-Growth-Lab/PatentSBERTa | 2,643 | 7 | sentence-transformers | 1,184 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# PatentSBERTa
## PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT
### Aalborg University Business School, AI: Growth-Lab
https:... |
p-christ/12412fsasf | 73cb7588db19266796e71e3f3bbfe03d98baa9ec | 2022-05-18T11:14:34.000Z | [
"pytorch",
"t5",
"text2text-generation",
"generic"
] | text2text-generation | false | p-christ | null | p-christ/12412fsasf | 2,641 | null | generic | 1,185 | ---
tags:
- text2text-generation
library_name: generic
---
random test repo |
cointegrated/rubert-base-cased-nli-threeway | 982964680ac0044ca95f3b5bb930b9514e0ee895 | 2021-10-10T11:09:27.000Z | [
"pytorch",
"bert",
"text-classification",
"ru",
"transformers",
"rubert",
"russian",
"nli",
"rte",
"zero-shot-classification"
] | zero-shot-classification | false | cointegrated | null | cointegrated/rubert-base-cased-nli-threeway | 2,638 | 5 | transformers | 1,186 | ---
language: ru
pipeline_tag: zero-shot-classification
tags:
- rubert
- russian
- nli
- rte
- zero-shot-classification
widget:
- text: "Я хочу поехать в Австралию"
candidate_labels: "спорт,путешествия,музыка,кино,книги,наука,политика"
hypothesis_template: "Тема текста - {}."
---
# RuBERT for NLI (natural language... |
facebook/dpr-ctx_encoder-multiset-base | 6c01adf9e9e7c812c0fa998fed97eec3262c2cf4 | 2020-11-25T16:58:57.000Z | [
"pytorch",
"tf",
"dpr",
"transformers"
] | null | false | facebook | null | facebook/dpr-ctx_encoder-multiset-base | 2,636 | null | transformers | 1,187 | Entry not found |
debyve/dumbbot | 04edc136f4f0028b01c2dc18d4ec0e4423441f7f | 2022-07-15T07:17:37.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | debyve | null | debyve/dumbbot | 2,636 | null | transformers | 1,188 | ---
tags:
- conversational
---
# debyve/tobbmud Model |
uclanlp/plbart-java-cs | 0426c742606ceb3c2e12de0ae9c46a969bba6023 | 2021-11-09T17:08:40.000Z | [
"pytorch",
"plbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uclanlp | null | uclanlp/plbart-java-cs | 2,625 | null | transformers | 1,189 | Entry not found |
tli8hf/unqover-roberta-large-newsqa | 8e7427744cb23cd65a671630a85537824dc4216e | 2021-05-20T22:36:39.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | tli8hf | null | tli8hf/unqover-roberta-large-newsqa | 2,613 | null | transformers | 1,190 | Entry not found |
moussaKam/barthez | 1ad22b19fab9b29d16d53872717e40a5b7758dd1 | 2021-11-15T12:59:17.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"fr",
"arxiv:2010.12321",
"transformers",
"summarization",
"bart",
"license:apache-2.0",
"fill-mask",
"autotrain_compatible"
] | fill-mask | false | moussaKam | null | moussaKam/barthez | 2,610 | 3 | transformers | 1,191 | ---
tags:
- summarization
- bart
language:
- fr
widget:
- text: Barthez est le meilleur <mask> du monde.
license: apache-2.0
pipeline_tag: "fill-mask"
---
A french sequence to sequence pretrained model based on [BART](https://huggingface.co/facebook/bart-large). <br>
BARThez is pretrained by learning to reconstruct... |
indobenchmark/indobert-large-p1 | ee2669aee95421008ad3833c3866c57a006e662d | 2021-05-19T20:26:01.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"transformers",
"indobert",
"indobenchmark",
"indonlu",
"license:mit"
] | feature-extraction | false | indobenchmark | null | indobenchmark/indobert-large-p1 | 2,607 | null | transformers | 1,192 | ---
language: id
tags:
- indobert
- indobenchmark
- indonlu
license: mit
inference: false
datasets:
- Indo4B
---
# IndoBERT Large Model (phase1 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a m... |
castorini/monot5-base-med-msmarco | 7a4324f2785ab5f1dea00e7a39d6f81f3e2d273f | 2021-06-23T11:40:06.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers"
] | feature-extraction | false | castorini | null | castorini/monot5-base-med-msmarco | 2,603 | null | transformers | 1,193 | This model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch) and then fine-tuned again on MedMARCO (from [Sledge-Z paper](https://www.aclweb.org/anthology/2020.emnlp-main.341.pdf) for 1k steps.
For more details on how to use it, check [pygaggle.ai](pygaggle.ai)
Paper describi... |
sshleifer/distilbart-xsum-12-1 | e85cfe19c276077efa4389e576f99d456a45755b | 2021-06-14T07:56:06.000Z | [
"pytorch",
"jax",
"bart",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | sshleifer | null | sshleifer/distilbart-xsum-12-1 | 2,601 | 1 | transformers | 1,194 | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... |
mental/mental-bert-base-uncased | 93f3ff553a76674e1307d8f01dd2441fd8909284 | 2022-04-05T17:43:03.000Z | [
"pytorch",
"bert",
"fill-mask",
"arxiv:2110.15621",
"transformers",
"autotrain_compatible"
] | fill-mask | false | mental | null | mental/mental-bert-base-uncased | 2,590 | 5 | transformers | 1,195 | # MentalBERT
[MentalBERT](https://arxiv.org/abs/2110.15621) is a model initialized with BERT-Base (`uncased_L-12_H-768_A-12`) and trained with mental health-related posts collected from Reddit.
We follow the standard pretraining protocols of BERT and RoBERTa with [Huggingface’s Transformers library](https://github.c... |
ixa-ehu/berteus-base-cased | be4efdc31716b33b989efa20ec1e93f404a03fff | 2021-05-19T20:33:41.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"eu",
"arxiv:2004.00033",
"transformers"
] | feature-extraction | false | ixa-ehu | null | ixa-ehu/berteus-base-cased | 2,589 | 1 | transformers | 1,196 | ---
language: eu
---
# BERTeus base cased
This is the Basque language pretrained model presented in [Give your Text Representation Models some Love: the Case for Basque](https://arxiv.org/pdf/2004.00033.pdf). This model has been trained on a Basque corpus comprising Basque crawled news articles from online newspapers... |
facebook/data2vec-audio-base-960h | 32331f3123e703528918aa688a9a38232d58c872 | 2022-05-24T10:41:22.000Z | [
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"transformers",
"speech",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | facebook | null | facebook/data2vec-audio-base-960h | 2,585 | 4 | transformers | 1,197 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
- hf-asr-leaderboard
license: apache-2.0
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.fla... |
lysandre/tiny-tapas-random-sqa | 2174c2e3dd74ba8a3bdaa58a6c566a7898e36cec | 2020-12-14T23:23:58.000Z | [
"pytorch",
"tapas",
"table-question-answering",
"transformers"
] | table-question-answering | false | lysandre | null | lysandre/tiny-tapas-random-sqa | 2,580 | null | transformers | 1,198 | Entry not found |
google/vit-base-patch16-384 | be89a4abf1f427fe502d37f261b8b6d6da7894bc | 2022-01-12T08:05:44.000Z | [
"pytorch",
"tf",
"jax",
"vit",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | google | null | google/vit-base-patch16-384 | 2,578 | 2 | transformers | 1,199 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet
- imagenet-21k
---
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 ... |
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