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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
staka/takomt | faddfbf2eb4d804a4fe1f87db8d8ebb4b53fa24b | 2022-05-21T22:56:21.000Z | [
"pytorch",
"marian",
"text2text-generation",
"de",
"en",
"es",
"fr",
"it",
"ja",
"ru",
"uk",
"transformers",
"translation",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | translation | false | staka | null | staka/takomt | 131 | null | transformers | 4,200 | ---
license: cc-by-sa-4.0
language:
- de
- en
- es
- fr
- it
- ja
- ru
- uk
tags:
- translation
---
# TakoMT
This is a translation model using Marian-NMT.
For more details, please see [my repository](https://github.com/s-taka/fugumt).
In addition to the data listed in the repository I also used [ParaCrawl](https... |
cardiffnlp/tweet-topic-21-single | b4e1fd1462122301b213e150a42a141235082db8 | 2022-06-09T10:34:33.000Z | [
"pytorch",
"tf",
"roberta",
"text-classification",
"arxiv:2202.03829",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/tweet-topic-21-single | 131 | null | transformers | 4,201 | # tweet-topic-21-single
This is a roBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for single-label topic classification on a corpus of 6,997 tweets.
The original roBERTa-base model can be found [... |
valurank/headline_generator_baseline | ef72ce346d1c3c62dde541e065cbf0011481bb4c | 2022-07-11T08:59:50.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | valurank | null | valurank/headline_generator_baseline | 131 | null | transformers | 4,202 | ---
tags:
- generated_from_trainer
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 model is a fine-tun... |
pucpr-br/postagger-bio-portuguese | f4898fecc9561120567e1165dba9bb3dbe2ab0af | 2022-07-25T22:49:37.000Z | [
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:MacMorpho",
"transformers",
"autotrain_compatible"
] | token-classification | false | pucpr-br | null | pucpr-br/postagger-bio-portuguese | 131 | 1 | transformers | 4,203 | ---
language: "pt"
widget:
- text: "O paciente recebeu no hospital e falou com a médica"
- text: "COMO ESQUEMA DE MEDICAÇÃO PARA ICC PRESCRITO NO ALTA, RECEBE FUROSEMIDA 40 BID, ISOSSORBIDA 40 TID, DIGOXINA 0,25 /D, CAPTOPRIL 50 TID E ESPIRONOLACTONA 25 /D."
- text: "ESTAVA EM USO DE FUROSEMIDA 40 BID, DIGOXINA 0,25 /D... |
NlpHUST/t5-vi-en-small | 0840843a5b314af4600bda2303a7a55d7e4f152f | 2021-06-23T03:45:23.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | NlpHUST | null | NlpHUST/t5-vi-en-small | 130 | null | transformers | 4,204 | ---
language:
- vi
tags:
- t5
- seq2seq
# Machine translation for vietnamese
## Model Description
T5-vi-en-small is a transformer model for vietnamese machine translation designed using T5 architecture.
## Training data
T5-vi-en-small was trained on 4M sentence pairs (english,vietnamese)
### How to use
```py
from tr... |
castorini/tct_colbert-v2-msmarco | a07b49eecf416152c7b43e8de0ec401604db1abd | 2021-08-12T01:06:11.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | castorini | null | castorini/tct_colbert-v2-msmarco | 130 | null | transformers | 4,205 | 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... |
satyaalmasian/temporal_tagger_roberta2roberta | eb10738132d26bc29d648e9d7bea4a63641c7552 | 2021-09-21T11:11:22.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | satyaalmasian | null | satyaalmasian/temporal_tagger_roberta2roberta | 130 | 5 | transformers | 4,206 | # RoBERTa2RoBERTa temporal tagger
Seq2seq model for temporal tagging of plain text using RoBERTa language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.com/satya77/Transformer_Temporal_Tagger).
# Model descrip... |
zanelim/singbert | 79cafd739beff7d1768a3ef32149c15f75c1013f | 2021-05-20T09:38:41.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"en",
"dataset:reddit singapore, malaysia",
"dataset:hardwarezone",
"transformers",
"singapore",
"sg",
"singlish",
"malaysia",
"ms",
"manglish",
"bert-base-uncased",
"license:mit"
] | null | false | zanelim | null | zanelim/singbert | 130 | null | transformers | 4,207 | ---
language: en
tags:
- singapore
- sg
- singlish
- malaysia
- ms
- manglish
- bert-base-uncased
license: mit
datasets:
- reddit singapore, malaysia
- hardwarezone
widget:
- text: "kopi c siew [MASK]"
- text: "die [MASK] must try"
---
# Model name
SingBert - Bert for Singlish (SG) and Manglish (MY).
## Model descri... |
sgunderscore/hatescore-korean-hate-speech | 0061fe461f9d0227b44c346221779e6d00d8ec4d | 2022-04-07T10:32:16.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | sgunderscore | null | sgunderscore/hatescore-korean-hate-speech | 130 | 1 | transformers | 4,208 | Entry not found |
MLRS/BERTu | 0eaba948f170df8e672a7863042bbcd85fd6ed2e | 2022-05-20T17:30:08.000Z | [
"pytorch",
"bert",
"fill-mask",
"mt",
"dataset:MLRS/korpus_malti",
"arxiv:2205.10517",
"transformers",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | MLRS | null | MLRS/BERTu | 130 | null | transformers | 4,209 | ---
language:
- mt
datasets:
- MLRS/korpus_malti
model-index:
- name: BERTu
results:
- task:
type: dependency-parsing
name: Dependency Parsing
dataset:
type: universal_dependencies
args: mt_mudt
name: Maltese Universal Dependencies Treebank (MUDT)
metrics:
- type: uas
... |
valurank/t5-paraphraser | d1a85a021d0754baf15e703ffea8bca1897be5af | 2022-06-08T20:19:04.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"license:other",
"autotrain_compatible"
] | text2text-generation | false | valurank | null | valurank/t5-paraphraser | 130 | 1 | transformers | 4,210 | ---
language: en
license: other
---
## Model in Action 🚀
```python
import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
def set_seed(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
set_seed(42)
model = T5ForConditionalGeneration.from_p... |
ismail-lucifer011/autotrain-job_all-903929564 | 3031df2e15619a494dde58715e51498a471799de | 2022-05-24T14:52:32.000Z | [
"pytorch",
"distilbert",
"token-classification",
"en",
"dataset:ismail-lucifer011/autotrain-data-job_all",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | token-classification | false | ismail-lucifer011 | null | ismail-lucifer011/autotrain-job_all-903929564 | 130 | null | transformers | 4,211 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ismail-lucifer011/autotrain-data-job_all
co2_eq_emissions: 192.68222884611995
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 903929564
- CO2 Emissions (in grams): 192.68222884611995
## Validation Met... |
lucataco/DialoGPT-medium-omar | db5451aea8eb53d092c761d44100fe21346ff96f | 2022-07-03T23:37:35.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | lucataco | null | lucataco/DialoGPT-medium-omar | 130 | null | transformers | 4,212 | ---
tags:
- conversational
---
# Omar Dialog GPT Model Medium 10
# Trained on discord channels:
# half of Dragalia chat |
Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization | b7987d12b17732987f9fef363fb9db200862cdbd | 2021-08-01T09:45:32.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"es",
"dataset:mlsum",
"transformers",
"summarization",
"news",
"autotrain_compatible"
] | summarization | false | Narrativa | null | Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization | 129 | 2 | transformers | 4,213 | ---
tags:
- summarization
- news
language: es
datasets:
- mlsum
widget:
- text: 'Al filo de las 22.00 horas del jueves, la Asamblea de Madrid vive un momento sorprendente: Vox decide no apoyar una propuesta del PP en favor del blindaje fiscal de la Comunidad. Se ha roto la unidad de los tres partidos de derechas. Es un... |
TransQuest/monotransquest-hter-en_zh-wiki | 799e174cc584ee244400fdc1b62505b69a2b91f9 | 2021-06-03T18:58:10.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"en-zh",
"transformers",
"Quality Estimation",
"monotransquest",
"hter",
"license:apache-2.0"
] | text-classification | false | TransQuest | null | TransQuest/monotransquest-hter-en_zh-wiki | 129 | null | transformers | 4,214 | ---
language: en-zh
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE... |
adamlin/bert-distil-chinese | 97eeca4e63fb1eb0ffcf04ad3562c745520d7511 | 2022-05-31T10:03:03.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | adamlin | null | adamlin/bert-distil-chinese | 129 | null | transformers | 4,215 | Entry not found |
google/t5-efficient-large-nl36 | bcc80281b68e8499034cf16b86843d6707d37cdb | 2022-02-15T10:49:22.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2109.10686",
"transformers",
"deep-narrow",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-efficient-large-nl36 | 129 | 4 | transformers | 4,216 | ---
language:
- en
datasets:
- c4
tags:
- deep-narrow
inference: false
license: apache-2.0
---
# T5-Efficient-LARGE-NL36 (Deep-Narrow version)
T5-Efficient-LARGE-NL36 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architect... |
junnyu/wobert_chinese_base | da75ef6e8fa702e8220bd1ccd10efc632f8b02f4 | 2021-07-06T05:04:11.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"zh",
"transformers",
"wobert",
"autotrain_compatible"
] | fill-mask | false | junnyu | null | junnyu/wobert_chinese_base | 129 | 1 | transformers | 4,217 | ---
language: zh
tags:
- wobert
---
## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/WoBERT
### pytorch版本
https://github.com/JunnYu/WoBERT_pytorch
## 安装(主要为了安装WoBertTokenizer)
注意：transformers版本需要>=4.7.0
WoBertTokenizer的实现与RoFormerTokenizer是一样的，因此使用RoFormerTokenizer就可以了
## 使用
```python
import torch
from transform... |
kuzgunlar/electra-turkish-sentiment-analysis | 8efa8b28569f413b5f3e85cf7cd7faf14c1f4e22 | 2020-08-16T13:05:57.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | kuzgunlar | null | kuzgunlar/electra-turkish-sentiment-analysis | 129 | 1 | transformers | 4,218 | Entry not found |
shahrukhx01/schema-aware-denoising-bart-large-cnn-text2sql | 71c954ece79b4a55ec5800d17c3b3855cdb1ad69 | 2021-08-21T08:43:28.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"transformers",
"schema-aware-text2sql",
"text2sql",
"wikisql",
"autotrain_compatible"
] | text2text-generation | false | shahrukhx01 | null | shahrukhx01/schema-aware-denoising-bart-large-cnn-text2sql | 129 | 1 | transformers | 4,219 | ---
language: "en"
tags:
- schema-aware-text2sql
- text2sql
- wikisql
widget:
- text: "What is terrence ross' nationality? </s> <col0> Player : text <col1> No. : text <col2> Nationality : text <col3> Position : text <col4> Years in Toronto : text <col5> School/Club Team : text"
---
```python
from transformers import B... |
superb/hubert-base-superb-ic | 34e16bfd56b5c4d3965d9d83b6d83f9116ddf9d8 | 2021-09-06T12:11:28.000Z | [
"pytorch",
"hubert",
"audio-classification",
"en",
"dataset:superb",
"arxiv:2105.01051",
"transformers",
"speech",
"license:apache-2.0"
] | audio-classification | false | superb | null | superb/hubert-base-superb-ic | 129 | null | transformers | 4,220 | ---
language: en
datasets:
- superb
tags:
- speech
- audio-classification
- hubert
license: apache-2.0
---
# Hubert-Base for Intent Classification
## Model description
This is a ported version of [S3PRL's Hubert for the SUPERB Intent Classification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/fl... |
billfrench/cyberlandr-door | 7e6577d4899e94c2eb50677e99f07a0fe46abe50 | 2022-03-06T23:07:03.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | billfrench | null | billfrench/cyberlandr-door | 129 | null | transformers | 4,221 | Entry not found |
nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat | 2164338db59d40004286bc65800bfa50561ecd3d | 2022-04-12T09:09:48.000Z | [
"pytorch",
"tensorboard",
"swin",
"image-classification",
"dataset:image_folder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | nielsr | null | nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat | 129 | null | transformers | 4,222 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
... |
Souvikcmsa/SentimentAnalysisDistillBERT | f4f1eed70d9b2446020173ee7ae4b05c68048cbd | 2022-04-20T09:05:38.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:Souvikcmsa/autotrain-data-sentiment_analysis",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | Souvikcmsa | null | Souvikcmsa/SentimentAnalysisDistillBERT | 129 | null | transformers | 4,223 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Souvikcmsa/autotrain-data-sentiment_analysis
co2_eq_emissions: 0.015536746909294205
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 762923432
- CO2 Emissions (in grams): 0.015536746909294205
... |
derwahnsinn/gpt2-mediumMetallica | bb838a69b0517b4df846f0c46adf0db8e4ae94b7 | 2022-07-28T18:03:25.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | derwahnsinn | null | derwahnsinn/gpt2-mediumMetallica | 129 | null | transformers | 4,224 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-mediumMetallica
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-mediumMetallica
... |
AriakimTaiyo/DialoGPT-cultured-Kumiko | d70e8c0fc60c7e75ed7bb60386fe020c88773950 | 2022-02-08T14:19:47.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | AriakimTaiyo | null | AriakimTaiyo/DialoGPT-cultured-Kumiko | 128 | null | transformers | 4,225 | ---
tags:
- conversational
---
# Cultured Kumiko DialoGPT Model |
AryanLala/autonlp-Scientific_Title_Generator-34558227 | faca55915244bc0f4360bbfd3dcfd2741aa7c99c | 2021-11-23T16:51:34.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:AryanLala/autonlp-data-Scientific_Title_Generator",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | AryanLala | null | AryanLala/autonlp-Scientific_Title_Generator-34558227 | 128 | 17 | transformers | 4,226 | ---
tags: autonlp
language: en
widget:
- text: "The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standard... |
DeepChem/ChemBERTa-10M-MTR | 53d08e205841e15b77483b42aac0c55bdb1d1822 | 2022-01-20T17:51:35.000Z | [
"pytorch",
"roberta",
"transformers"
] | null | false | DeepChem | null | DeepChem/ChemBERTa-10M-MTR | 128 | null | transformers | 4,227 | Entry not found |
Helsinki-NLP/opus-mt-en-gaa | 2f75e3d8bc190f8e0e412beecf00e564c40e33c4 | 2021-09-09T21:35:28.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"gaa",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-gaa | 128 | null | transformers | 4,228 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-gaa
* source languages: en
* target languages: gaa
* OPUS readme: [en-gaa](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-gaa/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* d... |
Ivo/emscad-skill-extraction | e129b03f0e913f12e3f0583216199a990a5b7968 | 2021-06-09T12:15:44.000Z | [
"pytorch",
"tf",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Ivo | null | Ivo/emscad-skill-extraction | 128 | null | transformers | 4,229 | Entry not found |
KoichiYasuoka/roberta-base-japanese-luw-upos | a3abefe6faa80f17dd82550ec38b488d401e31e3 | 2022-05-24T06:29:59.000Z | [
"pytorch",
"roberta",
"token-classification",
"ja",
"dataset:universal_dependencies",
"transformers",
"japanese",
"pos",
"dependency-parsing",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | token-classification | false | KoichiYasuoka | null | KoichiYasuoka/roberta-base-japanese-luw-upos | 128 | null | transformers | 4,230 | ---
language:
- "ja"
tags:
- "japanese"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "国境の長いトンネルを抜けると雪国であった。"
---
# roberta-base-japanese-luw-upos
## Model Description
This is a RoBERTa model p... |
anton-l/gpt-j-tiny-random | c104fcf6d66c324e1348281ab29c01a0423f272d | 2021-09-22T19:44:41.000Z | [
"pytorch",
"gptj",
"text-generation",
"transformers"
] | text-generation | false | anton-l | null | anton-l/gpt-j-tiny-random | 128 | 1 | transformers | 4,231 | Entry not found |
chiayewken/aspect-sentiment-pretrain | aad3d378ab00b8647796634649084084630d0bf9 | 2021-05-19T14:03:18.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | chiayewken | null | chiayewken/aspect-sentiment-pretrain | 128 | null | transformers | 4,232 | Entry not found |
meedan/indian-xlm-r | df5f9a82da83c8ff832ba17dc6b7979206d6feed | 2021-02-22T22:37:11.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | meedan | null | meedan/indian-xlm-r | 128 | null | transformers | 4,233 | Entry not found |
uclanlp/plbart-large | d296275f5a15b9b971dec79d06410852f0c8635d | 2021-11-23T18:09:20.000Z | [
"pytorch",
"plbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uclanlp | null | uclanlp/plbart-large | 128 | 1 | transformers | 4,234 | Entry not found |
lightonai/RITA_s | fced662eadd2b7099a3b92a88365dfc3c98eb3da | 2022-05-19T08:22:49.000Z | [
"pytorch",
"rita",
"text-generation",
"protein",
"dataset:uniref-100",
"arxiv:2205.05789",
"transformers"
] | text-generation | false | lightonai | null | lightonai/RITA_s | 128 | 2 | transformers | 4,235 | ---
language: protein
tags:
- protein
datasets:
- uniref-100
---
# RITA-S
RITA is a family of autoregressive protein models, developed by a collaboration of [Lighton](https://lighton.ai/), the [OATML group](https://oatml.cs.ox.ac.uk/) at Oxford, and the [Debbie Marks Lab](https://www.deboramarkslab.com/) at Harvard.
... |
conan1024hao/cjkbert-small | 8d693bb10d381bd6682c968f16131fd7428ec648 | 2022-05-14T10:18:04.000Z | [
"pytorch",
"bert",
"fill-mask",
"ja",
"zh",
"ko",
"dataset:wikipedia",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | conan1024hao | null | conan1024hao/cjkbert-small | 128 | 2 | transformers | 4,236 | ---
language:
- ja
- zh
- ko
license: cc-by-sa-4.0
datasets:
- wikipedia
mask_token: "[MASK]"
widget:
- text: "早稲田大学で自然言語処理を[MASK]ぶ。"
- text: "李白是[MASK]朝人。"
- text: "불고기[MASK] 먹겠습니다."
---
### Model description
- This model was trained on **ZH, JA, KO**'s Wikipedia (5 epochs).
### How to use
```python
from tran... |
DLochmelis33/22s-dl-sentiment-1 | 493f9f6c924aca0aae0b33ef08a1ac5ac89b71dd | 2022-06-15T01:07:08.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:yelp_review_full",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | DLochmelis33 | null | DLochmelis33/22s-dl-sentiment-1 | 128 | null | transformers | 4,237 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: 22s-dl-sentiment-1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yelp_review_full
type: yelp_review_full
args: yelp_rev... |
KoichiYasuoka/deberta-base-japanese-aozora-ud-head | d8fb76c08d076a07dcfe09aa2e6c3b72fd1bce18 | 2022-07-23T14:43:38.000Z | [
"pytorch",
"deberta-v2",
"question-answering",
"ja",
"dataset:universal_dependencies",
"transformers",
"japanese",
"dependency-parsing",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | question-answering | false | KoichiYasuoka | null | KoichiYasuoka/deberta-base-japanese-aozora-ud-head | 128 | null | transformers | 4,238 | ---
language:
- "ja"
tags:
- "japanese"
- "question-answering"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "question-answering"
widget:
- text: "国語"
context: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
- text: "教科書"
context: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
- text: "の"
c... |
mcsabai/huBert-fine-tuned-hungarian-squadv2 | 710fe207e20c222e76c5f72f779aaa939bfa6698 | 2022-07-26T18:35:08.000Z | [
"pytorch",
"tf",
"bert",
"question-answering",
"hu",
"transformers",
"autotrain_compatible"
] | question-answering | false | mcsabai | null | mcsabai/huBert-fine-tuned-hungarian-squadv2 | 128 | null | transformers | 4,239 | ---
language: hu
thumbnail:
tags:
- question-answering
- bert
widget:
- text: "Melyik folyó szeli ketté Budapestet?"
context: "Magyarország fővárosát, Budapestet a Duna folyó szeli ketté. A XIX. században épült Lánchíd a dimbes-dombos budai oldalt köti össze a sík Pesttel. A Várdomb oldalában futó siklóval juthatunk... |
balamurugan1603/bert-finetuned-ner | fc2a68631adeddf441d619f3e4ab6824ffc3789a | 2021-11-25T17:00:00.000Z | [
"pytorch",
"tf",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | balamurugan1603 | null | balamurugan1603/bert-finetuned-ner | 127 | null | transformers | 4,240 | # Named Entity Recognition using Transformers
This is a Fine-tuned version of BERT using HuggingFace transformers to perform Named Entity Recognition on Text data. BERT is a state-of-the-art model with attention mechanism as underlying architecture trained with masked-language-modeling and next-sentence-prediction obje... |
castorini/duot5-base-msmarco | 001e9dc78a3129f95184727a336d484b03956006 | 2021-12-07T12:53:29.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"arxiv:2101.05667",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | castorini | null | castorini/duot5-base-msmarco | 127 | null | transformers | 4,241 | This model is a T5-base pairwise reranker fine-tuned on MS MARCO passage dataset for 50k steps (or 5 epochs).
For more details on how to use it, check [pygaggle.ai](pygaggle.ai)
Paper describing the model: [The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models](https://arxiv... |
gustavecortal/fr-boris-8bit | 5a2bb943a6200d3b77faf364edb578ec751d4dd8 | 2022-03-04T10:33:04.000Z | [
"pytorch",
"gptj",
"text-generation",
"fr",
"dataset:c4",
"dataset:The Pile",
"transformers",
"causal-lm",
"license:mit"
] | text-generation | false | gustavecortal | null | gustavecortal/fr-boris-8bit | 127 | 6 | transformers | 4,242 | ---
language: fr
license: mit
tags:
- causal-lm
- fr
datasets:
- c4
- The Pile
---
### Quantized Cedille/fr-boris with 8-bit weights
This is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. singl... |
martin-ha/toxic-comment-model | 9842c08b35a4687e7b211187d676986c8c96256d | 2022-05-06T02:24:31.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers"
] | text-classification | false | martin-ha | null | martin-ha/toxic-comment-model | 127 | null | transformers | 4,243 | ---
language: en
---
## Model description
This model is a fine-tuned version of the [DistilBERT model](https://huggingface.co/transformers/model_doc/distilbert.html) to classify toxic comments.
## How to use
You can use the model with the following code.
```python
from transformers import AutoModelForSequenceClass... |
microsoft/swin-large-patch4-window12-384 | 9ac08d2bb52910aa24b5899a09fc8f8a939eb47f | 2022-05-16T18:08:30.000Z | [
"pytorch",
"tf",
"swin",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/swin-large-patch4-window12-384 | 127 | null | transformers | 4,244 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- 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: Teapot
- src: https... |
microsoft/tapex-large-sql-execution | a65006afd78ef4876fbe8e5372a14b9236fb1f8a | 2022-05-17T08:28:29.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"arxiv:2107.07653",
"transformers",
"tapex",
"table-question-answering",
"license:mit",
"autotrain_compatible"
] | table-question-answering | false | microsoft | null | microsoft/tapex-large-sql-execution | 127 | null | transformers | 4,245 | ---
language: en
tags:
- tapex
- table-question-answering
license: mit
---
# TAPEX (large-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The or... |
KoichiYasuoka/bert-base-russian-upos | 8716ad92870ae086852766f416d0efe6300a7365 | 2022-03-13T07:32:53.000Z | [
"pytorch",
"bert",
"token-classification",
"ru",
"dataset:universal_dependencies",
"transformers",
"russian",
"pos",
"dependency-parsing",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | token-classification | false | KoichiYasuoka | null | KoichiYasuoka/bert-base-russian-upos | 127 | null | transformers | 4,246 | ---
language:
- "ru"
tags:
- "russian"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
---
# bert-base-russian-upos
## Model Description
This is a BERT model pre-trained with [UD_Russian](https://universaldepend... |
nlp-waseda/gpt2-small-japanese | 2d14ed0652612483b331291b7b25731e5cd70d76 | 2022-03-30T04:28:17.000Z | [
"pytorch",
"gpt2",
"text-generation",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"transformers",
"license:cc-by-sa-4.0"
] | text-generation | false | nlp-waseda | null | nlp-waseda/gpt2-small-japanese | 127 | null | transformers | 4,247 | ---
language:
- ja
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
widget:
- text: "早稲田 大学 で 自然 言語 処理 を"
---
# nlp-waseda/gpt2-small-japanese
This model is Japanese GPT-2 pretrained on Japanese Wikipedia and CC-100.
## Intended uses & limitations
You can use the raw model for text generation or... |
Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer | 4d864046b31a0251a2e0eb1c8dbbb300a3141af3 | 2022-04-05T09:24:36.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | Ahmedgr | null | Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer | 127 | 1 | transformers | 4,248 | ---
tags:
- generated_from_trainer
model-index:
- name: DistilBert_Fine_tune_QuestionVsAnswer
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. -->
# DistilBert_Fine_t... |
jegormeister/robbert-v2-dutch-base-mqa-finetuned | f5427c2264b9d82255217fa4c7a851cba7a8171b | 2022-04-11T19:09:29.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"nl",
"dataset:clips/mqa",
"sentence-transformers",
"sentence-similarity",
"transformers",
"robbert"
] | sentence-similarity | false | jegormeister | null | jegormeister/robbert-v2-dutch-base-mqa-finetuned | 127 | 2 | sentence-transformers | 4,249 | ---
language: nl
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- robbert
datasets:
- clips/mqa
---
# jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & parag... |
Abderrahim2/bert-finetuned-gender_classification | df2088723946d24d2222d76ca56e997a79de797a | 2022-06-01T14:39:29.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Abderrahim2 | null | Abderrahim2/bert-finetuned-gender_classification | 127 | null | transformers | 4,250 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-finetuned-gender_classification
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 r... |
IDEA-CCNL/Randeng-Pegasus-523M-Chinese | fbd9bc0ebd910efba728295538b7780c26906882 | 2022-06-30T06:59:39.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"zh",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | IDEA-CCNL | null | IDEA-CCNL/Randeng-Pegasus-523M-Chinese | 127 | 1 | transformers | 4,251 | ---
language: zh
tags:
- summarization
inference: False
---
IDEA-CCNL/Randeng-Pegasus-523M-Chinese model (Chinese)，which codes has merged into [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
The 523M million parameter randeng_pegasus_large model, training with sampled gap sentence ratios on 180G Chin... |
lizz27/DialoGPT-medium-BaymaxBot | 7ca296834bbdadcd73fcf1349af84db83a09dde3 | 2022-07-28T19:19:15.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | lizz27 | null | lizz27/DialoGPT-medium-BaymaxBot | 127 | null | transformers | 4,252 | ---
tags:
- conversational
---
# DialoGPT BaymaxBot |
KETI-AIR/ke-t5-small-ko | 106751e579e337dda2337d76aa4b871e671f2ad4 | 2021-06-23T03:12:04.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | KETI-AIR | null | KETI-AIR/ke-t5-small-ko | 126 | null | transformers | 4,253 | Entry not found |
algoprog/mimics-bart-base | 276f8e003cd5a9b9fd39df99d2197789d09a0f7c | 2022-02-24T01:31:53.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | algoprog | null | algoprog/mimics-bart-base | 126 | null | transformers | 4,254 | Entry not found |
beomi/beep-KcELECTRA-base-hate | 41220dd1598bb646f6ca87e80a4229f5b6d1014c | 2021-10-23T05:48:36.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | beomi | null | beomi/beep-KcELECTRA-base-hate | 126 | null | transformers | 4,255 | Entry not found |
cristian-popa/bart-tl-all | c1b3c7554b701f481848701efd2653ab690d3186 | 2021-09-22T08:18:03.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"transformers",
"topic labeling",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | cristian-popa | null | cristian-popa/bart-tl-all | 126 | null | transformers | 4,256 | ---
language:
- en
<!-- thumbnail: https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png
-->
tags:
- topic labeling
license: apache-2.0
metrics:
- ndcg
---
# MyModel
## Model description
This is the `BART-TL-all` model from the paper [BART-TL: Weakly-Supervised Topic Label Generati... |
facebook/wav2vec2-base-10k-voxpopuli-ft-fr | e6d7b145359a2a07df759369aea64307f4f6031a | 2021-07-06T01:50:12.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"arxiv:2101.00390",
"transformers",
"audio",
"voxpopuli",
"license:cc-by-nc-4.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-base-10k-voxpopuli-ft-fr | 126 | null | transformers | 4,257 | ---
language: fr
tags:
- audio
- automatic-speech-recognition
- voxpopuli
license: cc-by-nc-4.0
---
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus... |
laxya007/gpt2_manage | 37c66c780e86fc421df348796706b93613eecd81 | 2021-05-23T07:42:33.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | laxya007 | null | laxya007/gpt2_manage | 126 | null | transformers | 4,258 | Entry not found |
m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition | ed9982a2c7a3fd79a918bac683a9e4cbfbad772e | 2021-07-06T11:11:59.000Z | [
"pytorch",
"jax",
"wav2vec2",
"el",
"dataset:aesdd",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"speech-emotion-recognition",
"license:apache-2.0"
] | automatic-speech-recognition | false | m3hrdadfi | null | m3hrdadfi/wav2vec2-xlsr-greek-speech-emotion-recognition | 126 | 2 | transformers | 4,259 | ---
language: el
datasets:
- aesdd
tags:
- audio
- automatic-speech-recognition
- speech
- speech-emotion-recognition
license: apache-2.0
---
# Emotion Recognition in Greek (el) Speech using Wav2Vec 2.0
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/huggingface/da... |
mrm8488/bert2bert-multilingual_shared-question-generation | 91a59b9ccdf9e9ea3fcf1bf24d2640fb06eb1569 | 2020-12-29T19:10:07.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/bert2bert-multilingual_shared-question-generation | 126 | 2 | transformers | 4,260 | Entry not found |
uclanlp/plbart-java-en_XX | b1ca7ca4c18a8b23c9eff5d25eff783d23e07d15 | 2021-11-09T17:08:51.000Z | [
"pytorch",
"plbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uclanlp | null | uclanlp/plbart-java-en_XX | 126 | null | transformers | 4,261 | Entry not found |
apple/mobilevit-xx-small | 1527dac02513f12172463b98c9ebe868d7866023 | 2022-06-02T10:50:02.000Z | [
"pytorch",
"coreml",
"mobilevit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2110.02178",
"transformers",
"vision",
"license:other"
] | image-classification | false | apple | null | apple/mobilevit-xx-small | 126 | null | transformers | 4,262 | ---
license: other
tags:
- vision
- image-classification
datasets:
- 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: Teapot
- src: https://hu... |
kkuramitsu/kogi-mt5-test | 87c9e6ca88073ebb2140dfedb36c19572bd85cd9 | 2022-06-26T17:50:05.000Z | [
"pytorch",
"ja",
"dataset:mc4",
"t5",
"text2text-generation",
"seq2seq",
"license:cc-by-sa-4.0"
] | text2text-generation | false | kkuramitsu | null | kkuramitsu/kogi-mt5-test | 126 | 2 | null | 4,263 | ---
language: ja
tags:
- t5
- text2text-generation
- seq2seq
license: cc-by-sa-4.0
datasets:
- mc4
---
# Kogi Python-Code Generation Model |
JasperD-UGent/roberta-base-bne-difficulty-classifier | ce93b4e701be532c5b700b2232ff990e079b63e3 | 2022-06-30T15:27:35.000Z | [
"pytorch",
"roberta",
"token-classification",
"es",
"transformers",
"autotrain_compatible"
] | token-classification | false | JasperD-UGent | null | JasperD-UGent/roberta-base-bne-difficulty-classifier | 126 | null | transformers | 4,264 | ---
language:
- es
--- |
pszemraj/grammar-synthesis-small | 70e8d2f07ee64f82e52a82c1ed50995fc581ab17 | 2022-07-22T08:36:47.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:jfleg",
"arxiv:2107.06751",
"transformers",
"grammar",
"spelling",
"punctuation",
"error-correction",
"grammar synthesis",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | pszemraj | null | pszemraj/grammar-synthesis-small | 126 | null | transformers | 4,265 | ---
license: cc-by-nc-sa-4.0
tags:
- grammar
- spelling
- punctuation
- error-correction
- grammar synthesis
datasets:
- jfleg
widget:
- text: "i can has cheezburger"
example_title: "cheezburger"
- text: "There car broke down so their hitching a ride to they're class."
example_title: "compound-1"
- text: "so em if... |
shozi218/finetuned-emotion-model | ed639a694ce83e4ebaee685a87d860803c50cf7d | 2022-07-25T21:54:52.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | shozi218 | null | shozi218/finetuned-emotion-model | 126 | null | transformers | 4,266 | Entry not found |
cointegrated/rut5-small-normalizer | 05222a03ecee390bef0b3966dbb983aa53358a92 | 2021-06-23T12:04:59.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"ru",
"transformers",
"normalization",
"denoising autoencoder",
"russian",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | cointegrated | null | cointegrated/rut5-small-normalizer | 125 | 2 | transformers | 4,267 | ---
language: "ru"
tags:
- normalization
- denoising autoencoder
- russian
widget:
- text: "меня тобой не понимать"
license: mit
---
This is a small Russian denoising autoencoder. It can be used for restoring corrupted sentences.
This model was produced by fine-tuning the [rut5-small](https://huggingface.co/cointegrat... |
elozano/bert-base-cased-clickbait-news | af3154cf4325e687bbf08384b710859dfc69a7d1 | 2022-02-08T19:05:52.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | elozano | null | elozano/bert-base-cased-clickbait-news | 125 | 3 | transformers | 4,268 | Entry not found |
jadohu/BEiT-finetuned | 2f708c7f94b602ab5ea7690216b5757f2bfe93a6 | 2022-05-18T07:51:57.000Z | [
"pytorch",
"tensorboard",
"beit",
"image-classification",
"dataset:cifar10",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | jadohu | null | jadohu/BEiT-finetuned | 125 | 1 | transformers | 4,269 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: BEiT-finetuned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
args: plain_text
metrics:
- name: A... |
lewtun/autotrain-acronym-identification-7324788 | e9eb71cdf798163f4df2d8990d9035085ed73abf | 2022-07-04T12:12:08.000Z | [
"pytorch",
"bert",
"token-classification",
"en",
"dataset:lewtun/autotrain-data-acronym-identification",
"dataset:acronym_identification",
"transformers",
"autotrain",
"model-index",
"co2_eq_emissions",
"autotrain_compatible"
] | token-classification | false | lewtun | null | lewtun/autotrain-acronym-identification-7324788 | 125 | null | transformers | 4,270 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain \U0001F917"
datasets:
- lewtun/autotrain-data-acronym-identification
- acronym_identification
co2_eq_emissions: 10.435358044493652
model-index:
- name: autotrain-demo
results:
- task:
name: Token Classification
type: token-classification... |
Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200 | 82a1dfce91a42debc80e51f0b3f3b19e10bab633 | 2021-08-02T18:38:54.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | Harveenchadha | null | Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200 | 124 | 1 | transformers | 4,271 | Entry not found |
alperiox/autonlp-user-review-classification-536415182 | 26060abfefaeb6899b5be46cfc378a1425df371e | 2022-01-28T16:30:08.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:alperiox/autonlp-data-user-review-classification",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | false | alperiox | null | alperiox/autonlp-user-review-classification-536415182 | 124 | null | transformers | 4,272 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- alperiox/autonlp-data-user-review-classification
co2_eq_emissions: 1.268309634217171
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 536415182
- CO2 Emissions (in grams): 1.268309634217171
## Valid... |
gdario/biobert_bioasq | 51cde02b5c9373d78db934bdab1db4a6ac997ec3 | 2021-05-19T17:13:28.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | gdario | null | gdario/biobert_bioasq | 124 | null | transformers | 4,273 | Entry not found |
hfl/chinese-electra-base-generator | e005fa8fbff9b175ea5a49729343fd141464289a | 2021-03-03T01:39:38.000Z | [
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0",
"fill-mask"
] | fill-mask | false | hfl | null | hfl/chinese-electra-base-generator | 124 | null | transformers | 4,274 | ---
language:
- zh
license: "apache-2.0"
pipeline_tag: "fill-mask"
---
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has ... |
huggingface/prunebert-base-uncased-6-finepruned-w-distil-mnli | c435eb5136a669fbb3ba9990c894d609267717dd | 2021-05-19T20:05:21.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | huggingface | null | huggingface/prunebert-base-uncased-6-finepruned-w-distil-mnli | 124 | null | transformers | 4,275 | Entry not found |
seyonec/ChemBERTA_PubChem1M_shard00_155k | 63f1b55ceb072fee347d9a7972ea703bf462da9a | 2021-05-20T20:54:07.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/ChemBERTA_PubChem1M_shard00_155k | 124 | null | transformers | 4,276 | Entry not found |
allenai/tk-instruct-3b-def-pos-neg-expl | 38c2e6d5235c811ad3562af0e508e94c0f7ebe0c | 2022-05-27T06:30:27.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:natural instructions v2.0",
"arxiv:1910.10683",
"arxiv:2204.07705",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/tk-instruct-3b-def-pos-neg-expl | 124 | 1 | transformers | 4,277 | ---
language: en
license: apache-2.0
datasets:
- natural instructions v2.0
---
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built... |
aiassociates/t5-small-grammar-correction-german | e5c4c530dbb4cab338732e1ac3e6b288219a1d9f | 2022-05-19T13:05:40.000Z | [
"pytorch",
"t5",
"text2text-generation",
"de",
"transformers",
"grammar",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | aiassociates | null | aiassociates/t5-small-grammar-correction-german | 124 | 1 | transformers | 4,278 | ---
language: de
tags:
- grammar
- text2text-generation
license: cc-by-nc-sa-4.0
widget:
- text: "grammar: hier ein kleines beispiel was haltet ihr von der korrektur"
---
# T5 Grammar Correction
This model restores upper and lower case as well as punctuation. It was trained with [Happy Transformer](https://github.co... |
Helsinki-NLP/opus-mt-ar-es | 3ef30bf7f35ba7a4da5a484db92a2c2be18ef521 | 2021-01-18T07:47:16.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ar",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ar-es | 123 | null | transformers | 4,279 | ---
language:
- ar
- es
tags:
- translation
license: apache-2.0
---
### ara-spa
* source group: Arabic
* target group: Spanish
* OPUS readme: [ara-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-spa/README.md)
* model: transformer
* source language(s): apc apc_Latn ara arq
* targ... |
Helsinki-NLP/opus-mt-en-sw | cc959449d3b74732da0527bb57d79a14e729fae1 | 2021-09-09T21:39:31.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"sw",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-sw | 123 | 2 | transformers | 4,280 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-sw
* source languages: en
* target languages: sw
* OPUS readme: [en-sw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-sw/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Theivaprakasham/bert-base-cased-twitter_sentiment | 727ed630a04949a37dab15054b441eb49ce9f4f5 | 2021-12-06T09:52:55.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Theivaprakasham | null | Theivaprakasham/bert-base-cased-twitter_sentiment | 123 | null | transformers | 4,281 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-cased-twitter_sentiment
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 th... |
hfl/english-pert-base | 154b7b6bbedca1a722a72982a00e071ea3c88549 | 2022-02-24T02:58:25.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"en",
"transformers",
"license:cc-by-nc-sa-4.0"
] | feature-extraction | false | hfl | null | hfl/english-pert-base | 123 | 2 | transformers | 4,282 | ---
language:
- en
license: "cc-by-nc-sa-4.0"
---
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT |
julien-c/distilbert-feature-extraction | b7890af3b5cea5d4b74313aac19619a2e1915685 | 2021-06-04T21:47:24.000Z | [
"pytorch",
"distilbert",
"transformers",
"feature-extraction"
] | feature-extraction | false | julien-c | null | julien-c/distilbert-feature-extraction | 123 | 2 | transformers | 4,283 | ---
tags:
- feature-extraction
widget:
- text: "Hello world"
---
# Distilbert, used as a Feature Extractor
|
monsoon-nlp/tamillion | 12ea4bde01dba6721a5bd70d334f2a08e47c68fc | 2020-10-30T03:58:42.000Z | [
"pytorch",
"tf",
"electra",
"feature-extraction",
"ta",
"transformers"
] | feature-extraction | false | monsoon-nlp | null | monsoon-nlp/tamillion | 123 | null | transformers | 4,284 | ---
language: ta
---
# TaMillion
This is the second version of a Tamil language model trained with
Google Research's [ELECTRA](https://github.com/google-research/electra).
Tokenization and pre-training CoLab: https://colab.research.google.com/drive/1Pwia5HJIb6Ad4Hvbx5f-IjND-vCaJzSE?usp=sharing
V1: small model with ... |
pucpr/clinicalnerpt-healthcare | e00266d90441fc5950099c27f71820be7c2edb4a | 2021-10-13T09:32:28.000Z | [
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"transformers",
"autotrain_compatible"
] | token-classification | false | pucpr | null | pucpr/clinicalnerpt-healthcare | 123 | 4 | transformers | 4,285 | ---
language: "pt"
widget:
- text: "Acompanhamento da diabetes, paciente encaminhado da unidade de saúde."
- text: "Paciente encaminhado por alteração na função renal."
datasets:
- SemClinBr
thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png"
---
<img src="https://... |
superb/wav2vec2-large-superb-ic | 06b9dc6cdef3881d673b3b771ffa46e51c6d7a66 | 2021-09-04T19:52:29.000Z | [
"pytorch",
"wav2vec2",
"audio-classification",
"en",
"dataset:superb",
"arxiv:2105.01051",
"transformers",
"speech",
"audio",
"license:apache-2.0"
] | audio-classification | false | superb | null | superb/wav2vec2-large-superb-ic | 123 | null | transformers | 4,286 | ---
language: en
datasets:
- superb
tags:
- speech
- audio
- wav2vec2
license: apache-2.0
---
# Wav2Vec2-Large for Intent Classification
## Model description
This is a ported version of [S3PRL's Wav2Vec2 for the SUPERB Intent Classification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/fluent_com... |
Finnish-NLP/t5-large-nl36-finnish | 18c21fd9af82c53217100d0c40fd36dd330e1061 | 2022-07-12T13:30:36.000Z | [
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:1910.10683",
"arxiv:2002.05202",
"arxiv:2109.10686",
"transformers",
"finnish",
"t5x",
"seq2seq",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | Finnish-NLP | null | Finnish-NLP/t5-large-nl36-finnish | 123 | null | transformers | 4,287 | ---
language:
- fi
license: apache-2.0
tags:
- finnish
- t5
- t5x
- seq2seq
datasets:
- Finnish-NLP/mc4_fi_cleaned
- wikipedia
inference: false
---
# T5-large-nl36 for Finnish
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
[this paper](https:... |
xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing | ced36d680fb782cfd6c5a73d042af88587ce4ef4 | 2022-04-26T05:59:13.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"arxiv:2203.07836",
"transformers",
"AMRBART",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | xfbai | null | xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing | 123 | null | transformers | 4,288 | ---
language: en
tags:
- AMRBART
license: mit
---
## AMRBART-large-finetuned-AMR3.0-AMRParsing
This model is a fine-tuned version of [AMRBART-large](https://huggingface.co/xfbai/AMRBART-large) on an AMR3.0 dataset. It achieves a Smatch of 84.2 on the evaluation set: More details are introduced in the paper: [Graph Pr... |
junnyu/structbert-large-zh | b401f2453e90eed416d2b844b75758f645565f5a | 2022-05-18T05:49:33.000Z | [
"pytorch",
"bert",
"feature-extraction",
"zh",
"arxiv:1908.04577",
"transformers",
"structbert",
"tf2.0"
] | feature-extraction | false | junnyu | null | junnyu/structbert-large-zh | 123 | 1 | transformers | 4,289 | ---
language: zh
tags:
- structbert
- pytorch
- tf2.0
inference: False
---
# StructBERT: Un-Official Copy
Official Repository Link: https://github.com/alibaba/AliceMind/tree/main/StructBERT
**Claimer**
* This model card is not produced by [AliceMind Team](https://github.com/alibaba/AliceMind/)
## Reproduce HFHub m... |
cross-encoder/mmarco-mdeberta-v3-base-5negs-v1 | e4639f2fcee3da997e7da0a0948229ac172f83b1 | 2022-06-30T11:44:05.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"transformers"
] | text-classification | false | cross-encoder | null | cross-encoder/mmarco-mdeberta-v3-base-5negs-v1 | 123 | null | transformers | 4,290 | Entry not found |
Evelyn18/distilbert-base-uncased-becas-4 | a47c05762bdf53c07524d8a25bdf9f61992389e7 | 2022-07-05T21:55:19.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Evelyn18 | null | Evelyn18/distilbert-base-uncased-becas-4 | 123 | null | transformers | 4,291 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-becas-4
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... |
projecte-aina/m2m100_418M_ft_zh_ca | 7dbefb1702260be9d7b0313971590846560491ef | 2022-07-25T06:46:35.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"ca",
"zh",
"dataset:projecte-aina/ca_zh_wikipedia",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | projecte-aina | null | projecte-aina/m2m100_418M_ft_zh_ca | 123 | null | transformers | 4,292 | ---
license: cc-by-4.0
language:
- ca
- zh
datasets:
- projecte-aina/ca_zh_wikipedia
metrics:
- "bleu"
model-index:
- name: m2m100_418M_ft_zh_ca
results:
- task:
type: translation
dataset:
type: flores
name: Flores
metrics:
- name: BLEU
type: bleu
value: 18.0
---
#... |
projecte-aina/m2m100-418M-ft-de-ca | bb55f538f99435c1555022b2c5466e30f0f78461 | 2022-07-25T09:38:20.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"ca",
"de",
"dataset:Softcatala/parallel-catalan-corpus/deu-cat",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | projecte-aina | null | projecte-aina/m2m100-418M-ft-de-ca | 123 | null | transformers | 4,293 | ---
license: cc-by-4.0
language:
- ca
- de
datasets:
- Softcatala/parallel-catalan-corpus/deu-cat
metrics:
- "bleu"
- "meteor"
- "chrf"
- "ter"
model-index:
- name: m2m100_418M_ft_de_ca
results:
- task:
type: translation
dataset:
type: flores
name: Flores
metrics:
- name: BLEU
... |
Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU | 1a011cfdd489add090755bc0d966f9f835fb6e1a | 2021-09-09T21:24:33.000Z | [
"pytorch",
"marian",
"text2text-generation",
"multilingual",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU | 122 | 2 | transformers | 4,294 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-NORTH_EU-NORTH_EU
* source languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv
* target languages: de,nl,fy,af,da,fo,is,no,nb,nn,sv
* OPUS readme: [de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob... |
KoboldAI/fairseq-dense-2.7B-Janeway | f5a0d4c5b1a6b6588bba5374f67cf5585533065c | 2022-04-16T16:46:57.000Z | [
"pytorch",
"xglm",
"text-generation",
"en",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/fairseq-dense-2.7B-Janeway | 122 | 1 | transformers | 4,295 | ---
language: en
license: mit
---
# Fairseq-dense 2.7B - Janeway
## Model Description
Fairseq-dense 2.7B-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 use... |
akhooli/mbart-large-cc25-en-ar | 5e05f431e7d8e6778911f433499baabf3936e1e2 | 2020-12-11T21:32:08.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"en",
"ar",
"transformers",
"translation",
"license:mit",
"autotrain_compatible"
] | translation | false | akhooli | null | akhooli/mbart-large-cc25-en-ar | 122 | 1 | transformers | 4,296 | ---
tags:
- translation
language:
- en
- ar
license: mit
---
### mbart-large-en-ar
This is mbart-large-cc25, finetuned on a subset of the UN corpus for en_ar.
Usage: see [example notebook](https://colab.research.google.com/drive/1I6RFOWMaTpPBX7saJYjnSTddW0TD6H1t?usp=sharing)
Note: model has limited training set, n... |
arm-on/BERTweet-FA | 40c3d9b30af98e81eb595208dd482ef734187add | 2022-05-06T08:24:05.000Z | [
"pytorch",
"bert",
"fill-mask",
"fa",
"arxiv:1810.04805",
"transformers",
"BERTweet",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | arm-on | null | arm-on/BERTweet-FA | 122 | 3 | transformers | 4,297 | ---
license: apache-2.0
language: fa
widget:
- text: "این بود [MASK] های ما؟"
- text: "داداچ داری [MASK] میزنی"
- text: 'به علی [MASK] میگفتن جادوگر'
- text: 'آخه محسن [MASK] هم شد خواننده؟'
- text: 'پسر عجب [MASK] زد'
tags:
- BERTweet
model-index:
- name: BERTweet-FA
results: []
---
BERTweet-FA: A pre-trained ... |
gilf/english-yelp-sentiment | 933119219cfdf286ddd370b39af5b207ee145822 | 2022-06-15T09:19:09.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | gilf | null | gilf/english-yelp-sentiment | 122 | 1 | transformers | 4,298 | Entry not found |
mrm8488/bert-base-portuguese-cased-finetuned-squad-v1-pt | ae3de34287ea2a58cac79d045a5dc7d88ae8cb50 | 2021-05-20T00:21:55.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"pt",
"dataset:squad_v1_pt",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/bert-base-portuguese-cased-finetuned-squad-v1-pt | 122 | 2 | transformers | 4,299 | ---
language: pt
datasets:
- squad_v1_pt
widget:
- text: "Com que licença posso usar o conteúdo da wikipedia?"
context: "A Wikipédia é um projeto de enciclopédia colaborativa, universal e multilíngue estabelecido na internet sob o princípio wiki. Tem como propósito fornecer um conteúdo livre, objetivo e verificá... |
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