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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
salesken/translation-hi-en | 3ee83d07285c37a8da5858e5e90f30e958fb0240 | 2021-07-01T09:30:25.000Z | [
"pytorch",
"marian",
"text2text-generation",
"hi",
"transformers",
"translation",
"salesken",
"opus-mt",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | salesken | null | salesken/translation-hi-en | 2,563 | 1 | transformers | 1,200 | ---
license: apache-2.0
language:
- hi
tags:
- translation
- salesken
- hi
- opus-mt
---
opus-mt model finetuned on ai4bhart Hindi-English parallel corpora (SAMANANTAR)
source-language: Hindi
target-language: English
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenize... |
skt/ko-gpt-trinity-1.2B-v0.5 | 33f84c0da333d34533f0cfbe8f5972022d681e96 | 2021-09-23T16:29:25.000Z | [
"pytorch",
"gpt2",
"text-generation",
"ko",
"transformers",
"gpt3",
"license:cc-by-nc-sa-4.0"
] | text-generation | false | skt | null | skt/ko-gpt-trinity-1.2B-v0.5 | 2,549 | 15 | transformers | 1,201 | ---
language: ko
tags:
- gpt3
license: cc-by-nc-sa-4.0
---
# Ko-GPT-Trinity 1.2B (v0.5)
## Model Description
Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture. Ko-GPT-Trinity refers to the class of models, while 1.2B represents the number of parameters of th... |
sshleifer/distilbart-cnn-12-3 | e3a8f0dafad8b99df3209ff7cf33e1f8402619e1 | 2021-06-14T07:47:53.000Z | [
"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-cnn-12-3 | 2,549 | null | transformers | 1,202 | ---
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... |
ckiplab/albert-tiny-chinese-ws | 097a391d7cb4bc1784e7268b866fb79629804378 | 2022-05-10T03:28:12.000Z | [
"pytorch",
"albert",
"token-classification",
"zh",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | token-classification | false | ckiplab | null | ckiplab/albert-tiny-chinese-ws | 2,540 | null | transformers | 1,203 | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word seg... |
abhilash1910/financial_roberta | 3ef89201dabbc8c9cd7887a838543d5c250ef0e5 | 2021-05-20T12:45:02.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"transformers",
"finance",
"autotrain_compatible"
] | fill-mask | false | abhilash1910 | null | abhilash1910/financial_roberta | 2,530 | 2 | transformers | 1,204 | ---
tags:
- finance
---
# Roberta Masked Language Model Trained On Financial Phrasebank Corpus
This is a Masked Language Model trained with [Roberta](https://huggingface.co/transformers/model_doc/roberta.html) on a Financial Phrasebank Corpus.
The model is built using Huggingface transformers.
The model can be found... |
microsoft/swin-base-patch4-window7-224 | 3d6730c003b5c3f1cc3b4f23706d304e2dce9763 | 2022-05-16T19:50:52.000Z | [
"pytorch",
"tf",
"swin",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/swin-base-patch4-window7-224 | 2,521 | 1 | transformers | 1,205 | ---
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... |
patrickvonplaten/tiny-wav2vec2-no-tokenizer | 77b68eb527e9faf028a70435dc353b3dcbb426f3 | 2021-09-20T17:36:01.000Z | [
"pytorch",
"tf",
"wav2vec2",
"transformers"
] | null | false | patrickvonplaten | null | patrickvonplaten/tiny-wav2vec2-no-tokenizer | 2,512 | null | transformers | 1,206 | Entry not found |
uer/t5-base-chinese-cluecorpussmall | 4ace2bdf4a8915cd44d67f80960d1672125712e4 | 2022-07-15T08:22:39.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uer | null | uer/t5-base-chinese-cluecorpussmall | 2,512 | 5 | transformers | 1,207 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "作为电子extra0的平台，京东绝对是领先者。如今的刘强extra1已经是身价过extra2的老板。"
---
# Chinese T5
## Model description
This is the set of Chinese T5 models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.0565... |
uklfr/gottbert-base | 301ea863069cb7f7226a8bc6b1311b0bc6b7b1d4 | 2021-09-16T15:36:46.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:2012.02110",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uklfr | null | uklfr/gottbert-base | 2,512 | 3 | transformers | 1,208 | # Gottbert-base
BERT model trained solely on the German portion of the OSCAR data set.
[Paper: GottBERT: a pure German Language Model](https://arxiv.org/abs/2012.02110)
Authors: Raphael Scheible, Fabian Thomczyk, Patric Tippmann, Victor Jaravine, Martin Boeker |
BeIR/query-gen-msmarco-t5-base-v1 | f86569026d333fbf584ce9f8d86b49dc521a7db0 | 2021-06-23T02:07:32.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | BeIR | null | BeIR/query-gen-msmarco-t5-base-v1 | 2,508 | 7 | transformers | 1,209 | # Query Generation
This model is the t5-base model from [docTTTTTquery](https://github.com/castorini/docTTTTTquery).
The T5-base model was trained on the [MS MARCO Passage Dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking), which consists of about 500k real search queries from Bing together with the releva... |
uer/sbert-base-chinese-nli | 1e1b01f82c062cb48af4443ab0aa89809e490ad8 | 2022-07-15T08:19:42.000Z | [
"pytorch",
"bert",
"feature-extraction",
"zh",
"arxiv:1909.05658",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | uer | null | uer/sbert-base-chinese-nli | 2,508 | 6 | sentence-transformers | 1,210 | ---
language: zh
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
widget:
source_sentence: "那个人很开心"
sentences:
- 那个人非常开心
- 那只猫很开心
- 那个人在吃东西
---
# Chinese Sentence BERT
## Model description
Thi... |
valhalla/s2t_mustc_multilinguial_medium | bacb18afb1533f3218dc15e94b80c1e151105f3e | 2021-03-03T05:12:34.000Z | [
"pytorch",
"speech_to_text_transformer",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/s2t_mustc_multilinguial_medium | 2,501 | null | transformers | 1,211 | Entry not found |
ai4bharat/IndicBART | 583562cbb5b342dbc932d75cdb7a9adcd9d91759 | 2022-03-16T12:48:27.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2109.02903",
"transformers",
"multilingual",
"nlp",
"indicnlp",
"autotrain_compatible"
] | text2text-generation | false | ai4bharat | null | ai4bharat/IndicBART | 2,494 | 7 | transformers | 1,212 | ---
languages:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
tags:
- multilingual
- nlp
- indicnlp
---
IndicBART is a multilingual, sequence-to-sequence pre-trained model focusing on Indic languages and English. It currently supports 11 Indian languages and is based on the mBART architecture. You can use Indi... |
CAMeL-Lab/bert-base-arabic-camelbert-ca | fee5a451663e9504180bf17d5fbc6770cd6c3e88 | 2021-09-14T14:27:12.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | CAMeL-Lab | null | CAMeL-Lab/bert-base-arabic-camelbert-ca | 2,491 | 5 | transformers | 1,213 | ---
language:
- ar
license: apache-2.0
widget:
- text: "الهدف من الحياة هو [MASK] ."
---
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained langu... |
voidful/wav2vec2-xlsr-multilingual-56 | f9c661ed063214973eb94be7b7c04015ad2561e3 | 2022-03-23T18:24:58.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"multilingual",
"dataset:common_voice",
"transformers",
"audio",
"hf-asr-leaderboard",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | voidful | null | voidful/wav2vec2-xlsr-multilingual-56 | 2,487 | 8 | transformers | 1,214 | ---
language: multilingual
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 for 56 language by Voidful
results:
- task:
name: Speech Recognition
type: a... |
KoboldAI/fairseq-dense-2.7B-Nerys | ed5eea2fe9c2eb12749e460be8b9238a8f972eba | 2022-06-25T11:23:23.000Z | [
"pytorch",
"xglm",
"text-generation",
"en",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/fairseq-dense-2.7B-Nerys | 2,466 | null | transformers | 1,215 | ---
language: en
license: mit
---
# Fairseq-dense 2.7B - Nerys
## Model Description
Fairseq-dense 2.7B-Nerys is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novel... |
beomi/kcbert-large | 938ed6f46f35cab013c0a7dc85b2d1159a520d09 | 2021-05-19T12:35:08.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | beomi | null | beomi/kcbert-large | 2,454 | 1 | transformers | 1,216 | Entry not found |
phiyodr/bart-large-finetuned-squad2 | 1085c140a880de9ec81e5003dc34f2386212a7dd | 2020-10-08T06:12:19.000Z | [
"pytorch",
"bart",
"question-answering",
"en",
"dataset:squad2",
"arxiv:1910.13461",
"arxiv:1806.03822",
"transformers",
"autotrain_compatible"
] | question-answering | false | phiyodr | null | phiyodr/bart-large-finetuned-squad2 | 2,450 | 2 | transformers | 1,217 | ---
language: en
tags:
- pytorch
- question-answering
datasets:
- squad2
metrics:
- exact
- f1
widget:
- text: "What discipline did Winkelmann create?"
context: "Johann Joachim Winckelmann was a German art historian and archaeologist. He was a pioneering Hellenist who first articulated the difference between Greek, G... |
Helsinki-NLP/opus-mt-en-el | cd8ab0896f1d0598007ba5266a0a30884fed71de | 2021-09-09T21:35:06.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"el",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-el | 2,449 | null | transformers | 1,218 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-el
* source languages: en
* target languages: el
* OPUS readme: [en-el](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-el/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-vi-en | 24c80a4038d72a1df73c622d5f8898e5f7de96a3 | 2020-08-21T14:42:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"vi",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-vi-en | 2,448 | 1 | transformers | 1,219 | ---
language:
- vi
- en
tags:
- translation
license: apache-2.0
---
### vie-eng
* source group: Vietnamese
* target group: English
* OPUS readme: [vie-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-eng/README.md)
* model: transformer-align
* source language(s): vie vie_Hani
* ta... |
plguillou/t5-base-fr-sum-cnndm | 5d0f64f2c4de6c3eeddbfd8d42e34e55a3310e7b | 2022-05-07T15:03:50.000Z | [
"pytorch",
"t5",
"text2text-generation",
"fr",
"dataset:cnn_dailymail",
"transformers",
"seq2seq",
"summarization",
"autotrain_compatible"
] | summarization | false | plguillou | null | plguillou/t5-base-fr-sum-cnndm | 2,448 | 3 | transformers | 1,220 | ---
language: fr
tags:
- pytorch
- t5
- seq2seq
- summarization
datasets: cnn_dailymail
widget:
- text: "Apollo 11 est une mission du programme spatial américain Apollo au cours de laquelle, pour la première fois, des hommes se sont posés sur la Lune, le lundi 21 juillet 1969. L'agence spatiale américaine, la NASA, rem... |
anton-l/wav2vec2-random-tiny-classifier | 2838dc51b12ea6e9fb854049d46137561ebf0c03 | 2021-08-31T14:27:40.000Z | [
"pytorch",
"wav2vec2",
"audio-classification",
"transformers"
] | audio-classification | false | anton-l | null | anton-l/wav2vec2-random-tiny-classifier | 2,445 | 1 | transformers | 1,221 | Entry not found |
jhu-clsp/bibert-ende | 7e8c5bdc96f62fef5cdd0d10dcf3df4dd1e243b2 | 2021-11-26T18:09:14.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"de",
"transformers",
"autotrain_compatible"
] | fill-mask | false | jhu-clsp | null | jhu-clsp/bibert-ende | 2,445 | 5 | transformers | 1,222 | ---
language:
- en
- de
---
Our bibert-ende is a bilingual English-German Language Model. Please check out our EMNLP 2021 paper "[BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation](https://aclanthology.org/2021.emnlp-main.534.pdf)" for more details.
```
@inproceedings{xu-etal-2... |
StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES | f9967782f078b5cf1dd91937b17c6370061ab335 | 2022-03-21T22:36:06.000Z | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | StivenLancheros | null | StivenLancheros/roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES | 2,442 | null | transformers | 1,223 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_AugmentedTransfer_ES
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had acc... |
flair/upos-english | 646336f485a6fce04cb680b19148076c2c19d6c4 | 2021-03-02T22:21:49.000Z | [
"pytorch",
"en",
"dataset:ontonotes",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/upos-english | 2,441 | null | flair | 1,224 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- ontonotes
widget:
- text: "I love Berlin."
---
## English Universal Part-of-Speech Tagging in Flair (default model)
This is the standard universal part-of-speech tagging model for English that ships with [Flair](https://github.c... |
hfl/chinese-electra-180g-base-discriminator | 693c1a7e58307777ad4cdf6b80b47c777c028572 | 2021-03-03T01:26:14.000Z | [
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0"
] | null | false | hfl | null | hfl/chinese-electra-180g-base-discriminator | 2,435 | 8 | transformers | 1,225 | ---
language:
- zh
license: "apache-2.0"
---
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compa... |
mrm8488/spanbert-large-finetuned-squadv2 | 1d195c672bf785c78f534d8091f09b24d1ae47ff | 2021-05-20T00:59:58.000Z | [
"pytorch",
"jax",
"bert",
"en",
"arxiv:1907.10529",
"transformers"
] | null | false | mrm8488 | null | mrm8488/spanbert-large-finetuned-squadv2 | 2,433 | 1 | transformers | 1,226 | ---
language: en
thumbnail:
---
# SpanBERT large fine-tuned on SQuAD v2
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task ([by them](https:/... |
shtoshni/longformer_coreference_ontonotes | 173bee9355e87b8f618866417e6a9d5903b02346 | 2021-11-09T19:31:06.000Z | [
"pytorch",
"longformer",
"feature-extraction",
"arxiv:2109.09667",
"transformers"
] | feature-extraction | false | shtoshni | null | shtoshni/longformer_coreference_ontonotes | 2,433 | 1 | transformers | 1,227 | Longformer-large model finetuned for the coreference resolution task. The model is fine-tuned over the OntoNotes data. The model is released as part of [this paper](https://arxiv.org/pdf/2109.09667.pdf). Note that the document encoder is to be used with the rest of the model parameters to perform the coreference resolu... |
mrm8488/t5-base-finetuned-wikiSQL | f04e227779b3b031f5f6ba902a3046bcda5d3186 | 2021-08-23T20:08:13.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:wikisql",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-finetuned-wikiSQL | 2,432 | 6 | transformers | 1,228 | ---
language: en
datasets:
- wikisql
widget:
- text: "translate English to SQL: How many models were finetuned using BERT as base model?"
---
# T5-base fine-tuned on WikiSQL
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [WikiSQL](https://github.com/salesforc... |
google/byt5-base | b6e825bea673ef5d64b07af28e04c241d9f705dd | 2022-05-27T15:06:52.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
... | text2text-generation | false | google | null | google/byt5-base | 2,430 | 5 | transformers | 1,229 | ---
language:
- multilingual
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- hi
- hmn
- ht
- hu
- hy
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
-... |
Intel/dynamic_tinybert | a09184908bed346e896bf939babefdb0ddd45760 | 2021-11-22T13:48:08.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | Intel | null | Intel/dynamic_tinybert | 2,428 | 4 | transformers | 1,230 | Entry not found |
Davlan/xlm-roberta-base-ner-hrl | 22c2ab03e20036d86e00c358e331e24bd50420b6 | 2021-10-01T21:57:08.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"ar",
"de",
"en",
"es",
"fr",
"it",
"lv",
"nl",
"pt",
"zh",
"multilingual",
"transformers",
"autotrain_compatible"
] | token-classification | false | Davlan | null | Davlan/xlm-roberta-base-ner-hrl | 2,418 | 4 | transformers | 1,231 | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# xlm-roberta-base-ner-hrl
## Model description
**xlm-roberta-base-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch... |
flax-sentence-embeddings/all_datasets_v4_MiniLM-L6 | a407cc0b7d85eec9a5617eaf51dbe7b353b0c79f | 2021-07-23T15:49:28.000Z | [
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2104.08727",
"arxiv:1810.09305",
"arxiv:2102.07033",
"arxiv:1904.06472",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | flax-sentence-embeddings | null | flax-sentence-embeddings/all_datasets_v4_MiniLM-L6 | 2,410 | 3 | sentence-transformers | 1,232 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
---
# Model description
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained ... |
finiteautomata/beto-emotion-analysis | 6c41f7d3f186ea332581e400110b4662f0c06c9d | 2021-12-10T13:29:22.000Z | [
"pytorch",
"bert",
"text-classification",
"es",
"arxiv:2106.09462",
"transformers",
"emotion-analysis"
] | text-classification | false | finiteautomata | null | finiteautomata/beto-emotion-analysis | 2,395 | 3 | transformers | 1,233 | ---
language:
- es
tags:
- emotion-analysis
---
# Emotion Analysis in Spanish
## beto-emotion-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is [B... |
nvidia/mit-b1 | 52eb2a1100ddab5f778c6924d37f20b07c3192ee | 2022-07-29T13:15:50.000Z | [
"pytorch",
"tf",
"segformer",
"image-classification",
"dataset:imagenet_1k",
"arxiv:2105.15203",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | nvidia | null | nvidia/mit-b1 | 2,386 | null | transformers | 1,234 | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet_1k
widget:
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
example_title: House
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg
exampl... |
benjamin/roberta-base-wechsel-german | ad78a30f376bc847690b86d821245ff2005d4f98 | 2022-07-13T23:44:45.000Z | [
"pytorch",
"roberta",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | benjamin | null | benjamin/roberta-base-wechsel-german | 2,381 | 3 | transformers | 1,235 | ---
language: de
license: mit
---
# roberta-base-wechsel-german
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: https://github.com/CPJKU/wechsel
And the paper here: https://aclanthology.org/2022.naacl-main.293/
... |
sentence-transformers/msmarco-distilbert-cos-v5 | 97bf29337ec20da8a1fb1ff2bd5555de5b566baf | 2022-06-15T21:48:24.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-distilbert-cos-v5 | 2,380 | 1 | sentence-transformers | 1,236 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# msmarco-distilbert-cos-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **sem... |
alan-turing-institute/mt5-large-finetuned-mnli-xtreme-xnli | 9edb475b8a8f61e010b2b061e71aeb37f0f3b950 | 2021-05-27T10:41:51.000Z | [
"pytorch",
"tf",
"mt5",
"text2text-generation",
"multilingual",
"dataset:multi_nli",
"dataset:xnli",
"arxiv:2010.11934",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | alan-turing-institute | null | alan-turing-institute/mt5-large-finetuned-mnli-xtreme-xnli | 2,379 | 4 | transformers | 1,237 | ---
language: multilingual
tags:
- pytorch
license: apache-2.0
datasets:
- multi_nli
- xnli
metrics:
- xnli
---
# mt5-large-finetuned-mnli-xtreme-xnli
## Model Description
This model takes a pretrained large [multilingual-t5](https://github.com/google-research/multilingual-t5) (also available from [models](https:/... |
HooshvareLab/roberta-fa-zwnj-base | 62eabb60447b49d9df5dabec5f44df6966f8108c | 2021-05-20T11:56:49.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"fa",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | HooshvareLab | null | HooshvareLab/roberta-fa-zwnj-base | 2,378 | null | transformers | 1,238 | ---
language: fa
license: apache-2.0
---
# Roberta
This model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.
## Questions?
Post a Github issue on the [ParsRoBERTa Issues](https://github.com/hooshvare/roberta/is... |
ramsrigouthamg/t5_boolean_questions | 62a1fed724505b3f80f3f49fe0353437b40e58fe | 2020-07-25T17:29:28.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ramsrigouthamg | null | ramsrigouthamg/t5_boolean_questions | 2,374 | 2 | transformers | 1,239 | Entry not found |
cardiffnlp/bertweet-base-offensive | 66851a2d325b60d980aa4581019e6319e4f510eb | 2021-05-20T14:49:35.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/bertweet-base-offensive | 2,368 | null | transformers | 1,240 | |
michiyasunaga/BioLinkBERT-base | b71f5d70f063d1c8f1124070ce86f1ee463ca1fe | 2022-03-31T00:51:21.000Z | [
"pytorch",
"bert",
"feature-extraction",
"en",
"dataset:pubmed",
"arxiv:2203.15827",
"transformers",
"exbert",
"linkbert",
"biolinkbert",
"fill-mask",
"question-answering",
"text-classification",
"token-classification",
"license:apache-2.0"
] | text-classification | false | michiyasunaga | null | michiyasunaga/BioLinkBERT-base | 2,367 | 2 | transformers | 1,241 | ---
license: apache-2.0
language: en
datasets:
- pubmed
tags:
- bert
- exbert
- linkbert
- biolinkbert
- feature-extraction
- fill-mask
- question-answering
- text-classification
- token-classification
widget:
- text: "Sunitinib is a tyrosine kinase inhibitor"
---
## BioLinkBERT-base
BioLinkBERT-... |
castorini/monot5-base-msmarco | 891f24ce967761cac53e544481e7aeaf3c1fad80 | 2021-11-24T17:59:19.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | castorini | null | castorini/monot5-base-msmarco | 2,365 | null | transformers | 1,242 | This model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 10 epochs).
For better zero-shot performance (i.e., inference on other datasets), we recommend using `castorini/monot5-base-msmarco-10k`.
For more details on how to use it, check the following links:
- [A simple reranking e... |
sshleifer/tinier_bart | ed9a6fc322f4a1961166dc929cda0c03637a2e0d | 2021-06-14T09:08:24.000Z | [
"pytorch",
"jax",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/tinier_bart | 2,365 | 1 | transformers | 1,243 | Entry not found |
megagonlabs/transformers-ud-japanese-electra-base-discriminator | 96a3711b754c2caf0d1e22b30cbb893a37fa46c2 | 2021-09-22T09:00:15.000Z | [
"pytorch",
"electra",
"pretraining",
"ja",
"dataset:mC4 Japanese",
"arxiv:1910.10683",
"transformers",
"license:mit"
] | null | false | megagonlabs | null | megagonlabs/transformers-ud-japanese-electra-base-discriminator | 2,359 | 4 | transformers | 1,244 | ---
language: ja
license: mit
datasets:
- mC4 Japanese
---
# transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese) - [MIYAGINO](https://www.ntj.jac.go.jp/assets/images/member/pertopics/image/per100510_3.jpg)
This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on app... |
sentence-transformers/msmarco-distilbert-base-v3 | cabff1a69c4aecef223c78fdb32c9f3fc4bda7dc | 2022-06-15T21:45:04.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-distilbert-base-v3 | 2,359 | 2 | sentence-transformers | 1,245 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-distilbert-base-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional d... |
lingwave-admin/state-op-detector | 9c50f3f5f656c12ce40876cc262a5d947eefe3e2 | 2022-06-13T17:49:00.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"classification",
"license:apache-2.0"
] | text-classification | false | lingwave-admin | null | lingwave-admin/state-op-detector | 2,359 | null | transformers | 1,246 | ---
language:
- en
tags:
- classification
license: apache-2.0
widget:
- text: "Zimbabwe has all the Brilliant Minds to become the Next Dubai of Africa No wonder why is so confide | Invest Surplus yako iye into Healthcare that will save lives amp creat real Jobs in Healthcare Sector | To the African Diaspora in Americas... |
lemon234071/t5-base-Chinese | dd3c0975ff4c60cc9bc89123e8641f2b1cdc2ce1 | 2021-07-21T09:49:44.000Z | [
"pytorch",
"jax",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lemon234071 | null | lemon234071/t5-base-Chinese | 2,355 | 7 | transformers | 1,247 | A mt5-base model that the vocab and word embedding are truncated, only Chinese and English characters are retained.
https://github.com/lemon234071/TransformerBaselines |
allenai/PRIMERA-multinews | 399837085142745b6d6c0e9b863ee3987b35a621 | 2022-07-25T18:17:12.000Z | [
"pytorch",
"tf",
"led",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/PRIMERA-multinews | 2,354 | null | transformers | 1,248 | ---
license: apache-2.0
---
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found [here](https://github.com/allenai/PRIMER). You can find the script and notebook to train/evaluate the model in the original github rep... |
nreimers/BERT-Mini_L-4_H-256_A-4 | f26dee0ee8b221c30d7ef6e51f9b7852a6814721 | 2021-05-28T11:05:04.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | nreimers | null | nreimers/BERT-Mini_L-4_H-256_A-4 | 2,341 | null | transformers | 1,249 | This is the BERT-Medium model from Google: https://github.com/google-research/bert#bert. A BERT model with 4 layers, 256 hidden unit size, and 4 attention heads. |
bertin-project/bertin-roberta-base-spanish | cb1844d753a963f4f4f716ab8db723628669964b | 2022-07-05T11:12:13.000Z | [
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"es",
"dataset:bertin-project/mc4-es-sampled",
"arxiv:2107.07253",
"arxiv:1907.11692",
"transformers",
"spanish",
"license:cc-by-4.0",
"autotrain_compatible"
] | fill-mask | false | bertin-project | null | bertin-project/bertin-roberta-base-spanish | 2,336 | 13 | transformers | 1,250 | ---
language: es
license: cc-by-4.0
tags:
- spanish
- roberta
pipeline_tag: fill-mask
datasets:
- bertin-project/mc4-es-sampled
widget:
- text: Fui a la librería a comprar un <mask>.
---
- [Version v2](https://huggingface.co/bertin-project/bertin-roberta-base-spanish/tree/v2) (default): April 28th, 2022
- [Version v1]... |
vumichien/wav2vec2-large-pitch-recognition | 8093c71c8a57b9bc6f1a7e8bbebbf73cd3e84f80 | 2021-08-29T13:06:43.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:Japanese accent datasets",
"transformers",
"audio",
"speech",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | vumichien | null | vumichien/wav2vec2-large-pitch-recognition | 2,336 | 2 | transformers | 1,251 | ---
language:
- ja
license: apache-2.0
tags:
- audio
- automatic-speech-recognition
- speech
datasets:
- Japanese accent datasets
metrics:
- wer
# Optional. Add this if you want to encode your eval results in a structured way.
model-index:
- name: Wav2vec2 Accent Japanese
results:
- task:
type: Speech Rec... |
superb/hubert-large-superb-er | ef1a2ebfd7cfc424dc7f0fbcdc406e8b794d63bb | 2021-11-04T16:03:28.000Z | [
"pytorch",
"hubert",
"audio-classification",
"en",
"dataset:superb",
"arxiv:2105.01051",
"transformers",
"speech",
"audio",
"license:apache-2.0"
] | audio-classification | false | superb | null | superb/hubert-large-superb-er | 2,331 | 5 | transformers | 1,252 | ---
language: en
datasets:
- superb
tags:
- speech
- audio
- hubert
- audio-classification
widget:
- example_title: IEMOCAP clip "happy"
src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav
- example_title: IEMOCAP clip "neutral"
src: https://cdn-media.huggingface.co/speech_samples/I... |
lysandre/tiny-bert-random | c26ef9d68b07dd1bd74d92ec0137660539901788 | 2020-12-14T19:28:41.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"transformers"
] | null | false | lysandre | null | lysandre/tiny-bert-random | 2,328 | null | transformers | 1,253 | Entry not found |
akreal/tiny-random-gpt2 | 9f9789821452b076aaf7cdeab9d352ce555558a2 | 2021-08-18T15:07:44.000Z | [
"pytorch",
"tf",
"gpt2",
"transformers"
] | null | false | akreal | null | akreal/tiny-random-gpt2 | 2,321 | null | transformers | 1,254 | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-gpt2
Changes: use old format for `pytorch_model.bin`.
|
microsoft/unixcoder-base-unimodal | c6b7b85380bf4e01309a3cf5e4f686433764d923 | 2022-03-21T08:25:47.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | microsoft | null | microsoft/unixcoder-base-unimodal | 2,321 | null | transformers | 1,255 | ---
license: apache-2.0
---
|
akreal/tiny-random-xlnet | 8ac096f963244bdfb4003fef58bd552cbbe3f85b | 2021-08-18T15:08:21.000Z | [
"pytorch",
"tf",
"xlnet",
"transformers"
] | null | false | akreal | null | akreal/tiny-random-xlnet | 2,319 | null | transformers | 1,256 | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-xlnet
Changes: use old format for `pytorch_model.bin`.
|
akreal/tiny-random-mpnet | 7ad44b94af7d80185982b2db20fb8597a63815c2 | 2021-08-18T15:08:05.000Z | [
"pytorch",
"tf",
"mpnet",
"transformers"
] | null | false | akreal | null | akreal/tiny-random-mpnet | 2,318 | null | transformers | 1,257 | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-mpnet
Changes: use old format for `pytorch_model.bin`.
|
unicamp-dl/ptt5-base-portuguese-vocab | f8b910de7ba773bc2025cbad98f825f310c55885 | 2021-03-24T22:16:54.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"pt",
"dataset:brWaC",
"transformers",
"tensorflow",
"pt-br",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | unicamp-dl | null | unicamp-dl/ptt5-base-portuguese-vocab | 2,318 | 6 | transformers | 1,258 | ---
language: pt
license: mit
tags:
- t5
- pytorch
- tensorflow
- pt
- pt-br
datasets:
- brWaC
widget:
- text: "Texto de exemplo em português"
inference: false
---
# Portuguese T5 (aka "PTT5")
## Introduction
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improvi... |
akreal/tiny-random-t5 | ea8b8e094af89cbdaa8c64900de5370d6fdc08e2 | 2021-08-18T15:08:13.000Z | [
"pytorch",
"tf",
"t5",
"transformers"
] | null | false | akreal | null | akreal/tiny-random-t5 | 2,317 | null | transformers | 1,259 | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-t5
Changes: use old format for `pytorch_model.bin`.
|
akreal/tiny-random-mbart | 765952a9845b0c94a6b8ce3bfebcf450cc63c2d0 | 2022-06-07T18:16:58.000Z | [
"pytorch",
"tf",
"mbart",
"transformers"
] | null | false | akreal | null | akreal/tiny-random-mbart | 2,316 | null | transformers | 1,260 | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-mbart
Changes: use old format for `pytorch_model.bin`.
|
deepset/gbert-base-germandpr-reranking | ab2c300f4c2c416ccee673d2c19c5b5f0f9d787a | 2021-10-21T12:17:32.000Z | [
"pytorch",
"bert",
"text-classification",
"de",
"dataset:deepset/germandpr",
"transformers",
"license:mit"
] | text-classification | false | deepset | null | deepset/gbert-base-germandpr-reranking | 2,316 | 3 | transformers | 1,261 | ---
language: de
datasets:
- deepset/germandpr
license: mit
---
## Overview
**Language model:** gbert-base-germandpr-reranking
**Language:** German
**Training data:** GermanDPR train set (~ 56MB)
**Eval data:** GermanDPR test set (~ 6MB)
**Infrastructure**: 1x V100 GPU
**Published**: June 3rd, 2021
## Deta... |
textattack/bert-base-uncased-QQP | 226962dced5a914fa7d486b5f91307f2aee5f37e | 2021-05-20T07:34:46.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/bert-base-uncased-QQP | 2,315 | 1 | transformers | 1,262 | Entry not found |
datificate/gpt2-small-spanish | 02e3092372e8a8b20c64e29350a024a998a7c00d | 2021-05-21T15:24:00.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:wikipedia",
"transformers",
"license:apache-2.0"
] | text-generation | false | datificate | null | datificate/gpt2-small-spanish | 2,314 | 7 | transformers | 1,263 | ---
language: es
widget:
- text: "La inteligencia artificial en lationoamérica se ha desarrollado "
license: apache-2.0
datasets:
- wikipedia
---
La descripción en Español se encuentra después de la descripción en Inglés.
# (English) GPT2-small-spanish: a Language Model for Spanish text generation (and more NLP task... |
facebook/wav2vec2-large-robust-ft-swbd-300h | 828a8386883f64170e04fae06dd866e0fe97de6b | 2022-04-05T16:42:51.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:libri_light",
"dataset:common_voice",
"dataset:switchboard",
"dataset:fisher",
"arxiv:2104.01027",
"transformers",
"speech",
"audio",
"license:apache-2.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-large-robust-ft-swbd-300h | 2,313 | 10 | transformers | 1,264 | ---
language: en
datasets:
- libri_light
- common_voice
- switchboard
- fisher
tags:
- speech
- audio
- automatic-speech-recognition
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.huggingfac... |
jonatasgrosman/wav2vec2-large-xlsr-53-french | c139df64c1beb0fbb82faae060026208a4e7d9ea | 2022-07-27T23:37:50.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_6_0",
"transformers",
"audio",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_6_0",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"lice... | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-large-xlsr-53-french | 2,308 | 3 | transformers | 1,265 | ---
language: fr
license: apache-2.0
datasets:
- common_voice
- mozilla-foundation/common_voice_6_0
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- fr
- hf-asr-leaderboard
- mozilla-foundation/common_voice_6_0
- robust-speech-event
- speech
- xlsr-fine-tuning-week
model-index:
- name: XLSR Wav2Vec2 ... |
huawei-noah/TinyBERT_General_6L_768D | 8b6152f3be8ab89055dea2d040cebb9591d97ef6 | 2021-05-19T20:04:08.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
] | null | false | huawei-noah | null | huawei-noah/TinyBERT_General_6L_768D | 2,306 | null | transformers | 1,266 | Entry not found |
deutsche-telekom/bert-multi-english-german-squad2 | 7d4c38391cca9950a1bcee19ecdbe287d8ceffb4 | 2021-07-14T13:17:23.000Z | [
"pytorch",
"bert",
"question-answering",
"de",
"en",
"transformers",
"english",
"german",
"license:mit",
"autotrain_compatible"
] | question-answering | false | deutsche-telekom | null | deutsche-telekom/bert-multi-english-german-squad2 | 2,305 | 5 | transformers | 1,267 | ---
language: [de, en]
license: mit
tags:
- english
- german
---
# Bilingual English + German SQuAD2.0
We created German Squad 2.0 (**deQuAD 2.0**) and merged with [**SQuAD2.0**](https://rajpurkar.github.io/SQuAD-explorer/) into an English and German training data for question answering. The [**bert-base-multilingua... |
KoboldAI/fairseq-dense-2.7B | 3e9e3ed8439f4a51e90010bcd8328f00790abf78 | 2022-02-01T22:50:43.000Z | [
"pytorch",
"xglm",
"text-generation",
"transformers"
] | text-generation | false | KoboldAI | null | KoboldAI/fairseq-dense-2.7B | 2,301 | null | transformers | 1,268 | Entry not found |
microsoft/layoutlmv3-large | 0f239ba9db00fe0909f56e6cccbf9bda99d55ec7 | 2022-07-20T09:35:14.000Z | [
"pytorch",
"layoutlmv3",
"en",
"arxiv:2204.08387",
"transformers",
"license:cc-by-nc-sa-4.0"
] | null | false | microsoft | null | microsoft/layoutlmv3-large | 2,297 | 12 | transformers | 1,269 | ---
language: en
license: cc-by-nc-sa-4.0
---
# LayoutLMv3
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlmv3)
## Model description
LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The sim... |
hf-internal-testing/tiny-bert-pt-only | 784f4fa083011ad3a1b610a61b9a456824bab482 | 2022-01-21T16:20:41.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | hf-internal-testing | null | hf-internal-testing/tiny-bert-pt-only | 2,289 | null | transformers | 1,270 | Entry not found |
google/byt5-large | c40ca44cdb06b7d7fff83217c1f184a3f9c4ed90 | 2022-05-27T15:07:18.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
... | text2text-generation | false | google | null | google/byt5-large | 2,283 | null | transformers | 1,271 | ---
language:
- multilingual
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- hi
- hmn
- ht
- hu
- hy
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
-... |
IDEA-CCNL/Randeng-T5-77M | 0ef5b80d88bcf2bf6e84cbf57920ddcffdad73c5 | 2022-06-30T06:29:03.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"zh",
"transformers",
"T5",
"chinese",
"sentencepiece",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | IDEA-CCNL | null | IDEA-CCNL/Randeng-T5-77M | 2,279 | null | transformers | 1,272 | ---
license: apache-2.0
language: zh
tags:
- T5
- chinese
- sentencepiece
inference: true
widget:
- text: "北京有悠久的 <extra_id_0>和 <extra_id_1>。"
- type: "text-generation"
---
# Randeng-T5-77M, one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
Based on mt5-small, Randeng-T5-77M only retains... |
microsoft/CodeGPT-small-py-adaptedGPT2 | a8e9aeb8b9d8eef8e0934606f43f93e252c69470 | 2021-05-23T09:00:40.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | microsoft | null | microsoft/CodeGPT-small-py-adaptedGPT2 | 2,278 | 4 | transformers | 1,273 | Entry not found |
T-Systems-onsite/german-roberta-sentence-transformer-v2 | 79e02bc0fff5b48096e366e4ffa7ac621ed8b275 | 2022-06-28T19:57:13.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"de",
"dataset:STSbenchmark",
"transformers",
"sentence_embedding",
"search",
"roberta",
"xlm-r-distilroberta-base-paraphrase-v1",
"paraphrase",
"license:mit"
] | feature-extraction | false | T-Systems-onsite | null | T-Systems-onsite/german-roberta-sentence-transformer-v2 | 2,275 | 1 | transformers | 1,274 | ---
language: de
license: mit
tags:
- sentence_embedding
- search
- pytorch
- xlm-roberta
- roberta
- xlm-r-distilroberta-base-paraphrase-v1
- paraphrase
datasets:
- STSbenchmark
metrics:
- Spearman’s rank correlation
- cosine similarity
---
# German RoBERTa for Sentence Embeddings V2
**The new [T-Systems-onsite/cro... |
pierreguillou/gpt2-small-portuguese | 89a916c041b54c8b925e1a3282a5a334684280cb | 2021-05-23T10:59:56.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"pt",
"dataset:wikipedia",
"transformers",
"license:mit"
] | text-generation | false | pierreguillou | null | pierreguillou/gpt2-small-portuguese | 2,264 | 10 | transformers | 1,275 | ---
language: pt
widget:
- text: "Quem era Jim Henson? Jim Henson era um"
- text: "Em um achado chocante, o cientista descobriu um"
- text: "Barack Hussein Obama II, nascido em 4 de agosto de 1961, é"
- text: "Corrida por vacina contra Covid-19 já tem"
license: mit
datasets:
- wikipedia
---
# GPorTuguese-2: a Langua... |
hf-internal-testing/tiny-random-bert-sharded | 6ef72e86254aef4ff42ce3bb4777081310c1c254 | 2022-03-22T18:48:43.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | hf-internal-testing | null | hf-internal-testing/tiny-random-bert-sharded | 2,263 | null | transformers | 1,276 | Entry not found |
KoboldAI/GPT-Neo-2.7B-AID | e4b98e6800567b710f88c66e5882f8e0ae812b6a | 2022-01-18T20:05:16.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-Neo-2.7B-AID | 2,259 | null | transformers | 1,277 | Entry not found |
bhadresh-savani/bert-base-uncased-emotion | 5268bc562e8f93914c8d794a503f40076bfece3e | 2022-07-06T10:44:59.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"en",
"dataset:emotion",
"arxiv:1810.04805",
"transformers",
"emotion",
"license:apache-2.0",
"model-index"
] | text-classification | false | bhadresh-savani | null | bhadresh-savani/bert-base-uncased-emotion | 2,251 | 2 | transformers | 1,278 | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
model-index:
- name: bhadresh-savani/bert-base-uncased-emotion
resu... |
digit82/kobart-summarization | 0887f7ac6e66df93248890f3460299d28bae1ddd | 2022-03-01T13:48:13.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | digit82 | null | digit82/kobart-summarization | 2,251 | null | transformers | 1,279 | Entry not found |
kresnik/wav2vec2-large-xlsr-korean | 1d50f59cf2e3d746461af568b095f60f8387bca6 | 2022-02-15T09:47:14.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ko",
"dataset:kresnik/zeroth_korean",
"transformers",
"speech",
"audio",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | kresnik | null | kresnik/wav2vec2-large-xlsr-korean | 2,247 | 6 | transformers | 1,280 | ---
language: ko
datasets:
- kresnik/zeroth_korean
tags:
- speech
- audio
- automatic-speech-recognition
license: apache-2.0
model-index:
- name: 'Wav2Vec2 XLSR Korean'
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Zeroth Kore... |
sentence-transformers/sentence-t5-base | 0bf6e3f10674a653aad7c5e0be5b6721dd52bb58 | 2022-06-21T14:56:18.000Z | [
"pytorch",
"rust",
"t5",
"en",
"arxiv:2108.08877",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/sentence-t5-base | 2,245 | 4 | sentence-transformers | 1,281 | ---
pipeline_tag: sentence-similarity
language: en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/sentence-t5-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 7... |
cointegrated/rubert-tiny2-cedr-emotion-detection | cd3543a7766e7b2ed82691955e3b7eba9cc9b25c | 2021-11-12T20:13:41.000Z | [
"pytorch",
"bert",
"text-classification",
"ru",
"dataset:cedr",
"transformers",
"russian",
"classification",
"sentiment",
"emotion-classification",
"multiclass"
] | text-classification | false | cointegrated | null | cointegrated/rubert-tiny2-cedr-emotion-detection | 2,236 | 5 | transformers | 1,282 | ---
language: ["ru"]
tags:
- russian
- classification
- sentiment
- emotion-classification
- multiclass
datasets:
- cedr
widget:
- text: "Бесишь меня, падла"
- text: "Как здорово, что все мы здесь сегодня собрались"
- text: "Как-то стрёмно, давай свалим отсюда?"
- text: "Грусть-тоска меня съедает"
- text: "Данный фрагм... |
nvidia/segformer-b4-finetuned-ade-512-512 | 0bc666c7d9d15740b0d61e38ee27f71b02c4d38a | 2022-07-20T09:52:59.000Z | [
"pytorch",
"tf",
"segformer",
"dataset:scene_parse_150",
"arxiv:2105.15203",
"transformers",
"vision",
"image-segmentation",
"license:apache-2.0"
] | image-segmentation | false | nvidia | null | nvidia/segformer-b4-finetuned-ade-512-512 | 2,225 | null | transformers | 1,283 | ---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- scene_parse_150
widget:
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
example_title: House
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_... |
microsoft/beit-base-patch16-224-pt22k | 9aabc608cc017577cb8fb58968e66e0d8f581da1 | 2022-01-28T10:18:13.000Z | [
"pytorch",
"jax",
"beit",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"transformers",
"image-classification",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/beit-base-patch16-224-pt22k | 2,224 | 1 | transformers | 1,284 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-21k
---
# BEiT (base-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced... |
google/t5-large-ssm-nq | 000abff88a3e622c77c59e4a83fb985e79c7c6e6 | 2021-06-23T01:35:15.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:natural_questions",
"arxiv:2002.08909",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-large-ssm-nq | 2,214 | null | transformers | 1,285 | ---
language: en
datasets:
- c4
- wikipedia
- natural_questions
pipeline_tag: text2text-generation
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C... |
hf-internal-testing/tiny-random-flaubert | 797c3829946a97ae1a11a0b0016e156e9e5ef94c | 2021-09-17T19:22:36.000Z | [
"pytorch",
"tf",
"flaubert",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-flaubert | 2,214 | null | transformers | 1,286 | Entry not found |
Callidior/bert2bert-base-arxiv-titlegen | 74edc65845d95ec72688b312de181cf20e6bb852 | 2021-03-04T09:49:47.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:arxiv_dataset",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | Callidior | null | Callidior/bert2bert-base-arxiv-titlegen | 2,213 | 3 | transformers | 1,287 | ---
language:
- en
tags:
- summarization
license: apache-2.0
datasets:
- arxiv_dataset
metrics:
- rouge
widget:
- text: "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the e... |
google/vit-base-patch32-384 | c94def148d45d90470058547968584e7f5036c49 | 2022-06-23T07:50:34.000Z | [
"pytorch",
"tf",
"jax",
"vit",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | google | null | google/vit-base-patch32-384 | 2,207 | 4 | transformers | 1,288 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
- 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, 1,0... |
bloomberg/KeyBART | 1558abeeb4fe945b79d5be9db00a4115488b0896 | 2022-07-27T22:11:32.000Z | [
"pytorch",
"bart",
"text2text-generation",
"arxiv:2112.08547",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | bloomberg | null | bloomberg/KeyBART | 2,204 | 6 | transformers | 1,289 | ---
license: apache-2.0
---
# KeyBART
KeyBART as described in Learning Rich Representations of Keyphrase from Text (https://arxiv.org/pdf/2112.08547.pdf), pre-trains a BART-based architecture to produce a concatenated sequence of keyphrases in the CatSeqD format.
We provide some examples on Downstream Evaluations set... |
obsei-ai/sell-buy-intent-classifier-bert-mini | c5d49713723b9e77b82b93f7f270714584f66aa3 | 2021-09-06T13:56:01.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"buy-intent",
"sell-intent",
"consumer-intent"
] | text-classification | false | obsei-ai | null | obsei-ai/sell-buy-intent-classifier-bert-mini | 2,202 | 1 | transformers | 1,290 | ---
language: "en"
tags:
- buy-intent
- sell-intent
- consumer-intent
widget:
- text: "Can you please share pictures for Face Shields ? We are looking for large quantity pcs"
---
# Buy vs Sell Intent Classifier
| Train Loss | Validation Acc.| Test Acc.|
| ------------- |:-------------: | -----: |
| 0.013 | 0.... |
Salesforce/codegen-2B-mono | e1bf58ffda585e9ade68ed3adacd0b6d74871737 | 2022-06-28T17:44:59.000Z | [
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"transformers",
"license:bsd-3-clause"
] | text-generation | false | Salesforce | null | Salesforce/codegen-2B-mono | 2,199 | 2 | transformers | 1,291 | ---
license: bsd-3-clause
---
# CodeGen (CodeGen-Mono 2B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wa... |
nghuyong/ernie-health-zh | 0f500f0603fc9f05f3e2f8befe8dd02f649a93e4 | 2022-05-31T09:07:46.000Z | [
"pytorch",
"electra",
"arxiv:2110.07244",
"transformers"
] | null | false | nghuyong | null | nghuyong/ernie-health-zh | 2,197 | 1 | transformers | 1,292 | # ernie-health-zh
## Introduction
ERNIE-health is a Chinese biomedical language model pre-trained from in-domain text of de-identified online doctor-patient dialogues, electronic medical records, and textbooks.
More detail:
https://github.com/PaddlePaddle/Research/tree/master/KG/eHealth
https://github.com/PaddlePadd... |
peerapongch/baikal-sentiment-ball | c72e97733c3f42780150b112f3364328aa11a2f9 | 2022-04-11T07:57:59.000Z | [
"pytorch",
"camembert",
"text-classification",
"transformers"
] | text-classification | false | peerapongch | null | peerapongch/baikal-sentiment-ball | 2,195 | null | transformers | 1,293 | Entry not found |
michaelrglass/albert-base-rci-wikisql-row | 9225ee36c17e3d8c505559798e1985ce68281189 | 2021-06-16T16:00:18.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | false | michaelrglass | null | michaelrglass/albert-base-rci-wikisql-row | 2,188 | null | transformers | 1,294 | Entry not found |
twmkn9/distilbert-base-uncased-squad2 | 84a8ab9f335a39297b69790f023f9e5ff70a3734 | 2020-12-11T22:03:01.000Z | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | twmkn9 | null | twmkn9/distilbert-base-uncased-squad2 | 2,187 | 2 | transformers | 1,295 | This model is [Distilbert base uncased](https://huggingface.co/distilbert-base-uncased) trained on SQuAD v2 as:
```
export SQUAD_DIR=../../squad2
python3 run_squad.py
--model_type distilbert
--model_name_or_path distilbert-base-uncased
--do_train
--do_eval
--overwrite_cache
--do_lower_case... |
csebuetnlp/mT5_m2o_chinese_simplified_crossSum | 484d6b4a469251fb8432f9da7eb2da761932668f | 2022-04-25T16:48:18.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"am",
"ar",
"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
"ha",
"hi",
"ig",
"id",
"ja",
"rn",
"ko",
"ky",
"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",
"si",
"so",
"es",
"sw",
"ta",
"te... | summarization | false | csebuetnlp | null | csebuetnlp/mT5_m2o_chinese_simplified_crossSum | 2,187 | 3 | transformers | 1,296 | ---
tags:
- summarization
- mT5
language:
- am
- ar
- az
- bn
- my
- zh
- en
- fr
- gu
- ha
- hi
- ig
- id
- ja
- rn
- ko
- ky
- mr
- ne
- om
- ps
- fa
- pcm
- pt
- pa
- ru
- gd
- sr
- si
- so
- es
- sw
- ta
- te
- th
- ti
- tr
- uk
- ur
- uz
- vi
- cy
- yo
licenses:
- cc-by-nc-sa-4.0
widget:
- text: "Videos that say a... |
deepset/gelectra-base | 13ebe992ff34cb2bbdd0fdef4d91e57f1f41c3fc | 2022-02-17T14:12:20.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"arxiv:2010.10906",
"transformers",
"license:mit"
] | null | false | deepset | null | deepset/gelectra-base | 2,186 | 4 | transformers | 1,297 | ---
language: de
license: mit
datasets:
- wikipedia
- OPUS
- OpenLegalData
---
# German ELECTRA base
Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In ou... |
hf-internal-testing/tiny-random-gptj | b96595a4bcdeb272096214589efa0314259853a0 | 2022-07-12T12:30:13.000Z | [
"pytorch",
"gptj",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-gptj | 2,172 | null | transformers | 1,298 | Entry not found |
junnyu/roformer_v2_chinese_char_base | 27c46a7c48fd720fac0a9a4318323ffeaa303588 | 2022-05-11T03:32:22.000Z | [
"pytorch",
"roformer",
"fill-mask",
"zh",
"arxiv:2104.09864",
"transformers",
"roformer-v2",
"tf2.0",
"autotrain_compatible"
] | fill-mask | false | junnyu | null | junnyu/roformer_v2_chinese_char_base | 2,157 | 2 | transformers | 1,299 | ---
language: zh
tags:
- roformer-v2
- pytorch
- tf2.0
inference: False
---
## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer-v2
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
### 安装
- pip install roformer==0.4.3
## 评测对比
### CLUE-dev榜单分类任务结果，base+large版本。
| | iflytek | tnews | a... |
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