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Helsinki-NLP/opus-mt-zh-en | 6b02b2132d97136ebed2851703f2b3407ea9cf47 | 2022-07-14T08:52:32.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"zh",
"en",
"transformers",
"translation",
"license:cc-by-4.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-zh-en | 324,438 | 32 | transformers | 100 | ---
language:
- zh
- en
tags:
- translation
license: cc-by-4.0
---
### zho-eng
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
... |
uer/gpt2-chinese-cluecorpussmall | 7c87595de655dc7b0fbfaa545ae413a118063d0b | 2022-07-15T08:26:38.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"dataset:CLUECorpusSmall",
"transformers"
] | text-generation | false | uer | null | uer/gpt2-chinese-cluecorpussmall | 320,804 | 20 | transformers | 101 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "这是很久之前的事情了"
---
# Chinese GPT2 Model
## Model description
The model is used to generate Chinese texts. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gp... |
Helsinki-NLP/opus-mt-mul-en | bc0ba94fb12f8b8cf88bd8a925b15ccd5fb94340 | 2020-08-21T14:42:48.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ca",
"es",
"os",
"eo",
"ro",
"fy",
"cy",
"is",
"lb",
"su",
"an",
"sq",
"fr",
"ht",
"rm",
"cv",
"ig",
"am",
"eu",
"tr",
"ps",
"af",
"ny",
"ch",
"uk",
"sl",
"lt",
"tk",
"sg",
"ar",
"lg",
"bg",
"be",
"... | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-mul-en | 318,897 | 8 | transformers | 102 | ---
language:
- ca
- es
- os
- eo
- ro
- fy
- cy
- is
- lb
- su
- an
- sq
- fr
- ht
- rm
- cv
- ig
- am
- eu
- tr
- ps
- af
- ny
- ch
- uk
- sl
- lt
- tk
- sg
- ar
- lg
- bg
- be
- ka
- gd
- ja
- si
- br
- mh
- km
- th
- ty
- rw
- te
- mk
- or
- wo
- kl
- mr
- ru
- yo
- hu
- fo
- zh
- ti
- co
- ee
- oc
- sn
- mt
- ts
... |
dslim/bert-base-NER-uncased | 1f52ebe0381dc9e285c0aa7c2971b350894f1efa | 2021-05-19T16:10:17.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | dslim | null | dslim/bert-base-NER-uncased | 316,976 | 6 | transformers | 103 | Entry not found |
google/pegasus-xsum | a0aa5531c00f59a32a167b75130805098b046f9c | 2021-09-14T07:25:41.000Z | [
"pytorch",
"tf",
"jax",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | google | null | google/pegasus-xsum | 316,141 | 41 | transformers | 104 | ---
language: en
tags:
- summarization
---
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@... |
sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch | b66b75ad2c01f1cf4ae47abb72464cb2342a5fba | 2022-06-15T22:43:46.000Z | [
"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-multilingual-en-de-v2-tmp-trained-scratch | 300,603 | null | sentence-transformers | 105 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences &... |
facebook/bart-large | cb48c1365bd826bd521f650dc2e0940aee54720c | 2022-06-03T10:00:20.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"bart",
"feature-extraction",
"en",
"arxiv:1910.13461",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/bart-large | 299,926 | 10 | transformers | 106 | ---
license: apache-2.0
language: en
---
# BART (large-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and firs... |
t5-large | cb7a9673bcaf9ab8b677ad4a5650c1d74b4a5a8e | 2022-07-22T08:11:26.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.12885",
"arxiv:1905.10044",
"arxiv:1910.09700",
"transformers",
"summariza... | translation | false | null | null | t5-large | 299,321 | 16 | transformers | 107 | ---
language:
- en
- fr
- ro
- de
datasets:
- c4
tags:
- summarization
- translation
license: apache-2.0
---
# Model Card for T5 Large
![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876... |
microsoft/deberta-v3-base | 559062ad13d311b87b2c455e67dcd5f1c8f65111 | 2022-02-06T09:55:27.000Z | [
"pytorch",
"tf",
"rust",
"deberta-v2",
"en",
"arxiv:2006.03654",
"arxiv:2111.09543",
"transformers",
"deberta",
"deberta-v3",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-v3-base | 298,505 | 24 | transformers | 108 | ---
language: en
tags:
- deberta
- deberta-v3
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa m... |
Rostlab/prot_bert | 3d05bf06e79014892defacad82e0efd06e977ff6 | 2020-12-11T21:30:07.000Z | [
"pytorch",
"fill-mask",
"protein",
"dataset:Uniref100",
"transformers",
"protein language model",
"autotrain_compatible"
] | fill-mask | false | Rostlab | null | Rostlab/prot_bert | 291,646 | 19 | transformers | 109 | ---
language: protein
tags:
- protein language model
datasets:
- Uniref100
---
# ProtBert model
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://github.co... |
echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid | f9c8e9f03396500a107dbe97024d7efa23f57e69 | 2022-07-04T09:04:59.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:sst2",
"transformers",
"license:apache-2.0"
] | text-classification | false | echarlaix | null | echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid | 291,539 | null | transformers | 110 | ---
language: en
license: apache-2.0
tags:
- text-classification
datasets:
- sst2
metrics:
- accuracy
---
## bert-base-uncased model fine-tuned on SST-2
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **37%** of the original weights.... |
flair/ner-english-large | e2b1caabf7f9bac1e7829db73eac734df7e6ad7b | 2021-05-08T15:36:27.000Z | [
"pytorch",
"en",
"dataset:conll2003",
"arxiv:2011.06993",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-english-large | 282,720 | 12 | flair | 111 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- conll2003
widget:
- text: "George Washington went to Washington"
---
## English NER in Flair (large model)
This is the large 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: *... |
Helsinki-NLP/opus-mt-es-en | 7709af724cf305012a250cbd13cf3bfdbd2b66b0 | 2021-01-18T08:23:34.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-en | 282,620 | 7 | transformers | 112 | ---
language:
- es
- en
tags:
- translation
license: apache-2.0
---
### spa-eng
* source group: Spanish
* target group: English
* OPUS readme: [spa-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-eng/README.md)
* model: transformer
* source language(s): spa
* target language(s): ... |
sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking | 8ae74eb0fbe7c8d82bb3d1a91fca56f352074e7f | 2022-06-15T19:34:08.000Z | [
"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/distilbert-multilingual-nli-stsb-quora-ranking | 281,218 | null | sentence-transformers | 113 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to... |
prithivida/grammar_error_correcter_v1 | 28f92ee33c2512814c22268b056a725364dae143 | 2021-07-04T10:44:31.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | prithivida | null | prithivida/grammar_error_correcter_v1 | 278,983 | 16 | transformers | 114 | **This model is part of the Gramformer library** please refer to https://github.com/PrithivirajDamodaran/Gramformer/
|
microsoft/DialoGPT-large | 06a70b2c3cecc1f56edc9fdc58d2e90641c9ae9e | 2021-05-23T09:06:08.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"arxiv:1911.00536",
"transformers",
"conversational",
"license:mit"
] | conversational | false | microsoft | null | microsoft/DialoGPT-large | 278,699 | 31 | transformers | 115 | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation... |
facebook/m2m100_418M | 441fd5182f1298d7e39f34013ac0b905f8ff4429 | 2022-05-26T22:26:54.000Z | [
"pytorch",
"rust",
"m2m_100",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"ast",
"az",
"ba",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"g... | text2text-generation | false | facebook | null | facebook/m2m100_418M | 278,499 | 27 | transformers | 116 | ---
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ... |
uer/albert-base-chinese-cluecorpussmall | 8634d166f98a6c337bdc6e9fba197df932605cdf | 2022-07-15T08:20:21.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uer | null | uer/albert-base-chinese-cluecorpussmall | 274,649 | 4 | transformers | 117 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "中国的首都是[MASK]京"
---
# Chinese ALBERT
## Model description
This is the set of Chinese ALBERT models pre-trained by UER-py. You can download the model either from the [UER-py Github page](https://github.com/dbiir/UER-py/), or via HuggingFace from the links... |
funnel-transformer/small | ff0f4c11e46720ca10aa2dd668c2c58fe00ad214 | 2020-12-11T21:40:44.000Z | [
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | funnel-transformer | null | funnel-transformer/small | 271,908 | 3 | transformers | 118 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
- gigaword
---
# Funnel Transformer small model (B4-4-4 with decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this pa... |
oliverguhr/german-sentiment-bert | c5c8dd0c5b966460dce1b7c5851bd90af1d2c6b6 | 2022-07-04T08:59:35.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"de",
"transformers",
"sentiment",
"license:mit"
] | text-classification | false | oliverguhr | null | oliverguhr/german-sentiment-bert | 267,318 | 9 | transformers | 119 | ---
language:
- de
tags:
- sentiment
- bert
license: mit
widget:
- text: "Das ist gar nicht mal so schlecht"
metrics:
- f1
---
# German Sentiment Classification with Bert
This model was trained for sentiment classification of German language texts. To achieve the best results all model inputs needs to be preprocesse... |
microsoft/deberta-v3-large | 360b9940401fa4d3411a0ca9f796631ec36f287a | 2022-01-13T17:50:16.000Z | [
"pytorch",
"tf",
"deberta-v2",
"en",
"arxiv:2006.03654",
"arxiv:2111.09543",
"transformers",
"deberta",
"deberta-v3",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-v3-large | 254,782 | 34 | transformers | 120 | ---
language: en
tags:
- deberta
- deberta-v3
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa m... |
deepset/bert-large-uncased-whole-word-masking-squad2 | fc342ddb2da7ae9be8275f3f9970f0b59571caa5 | 2022-07-25T12:20:44.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/bert-large-uncased-whole-word-masking-squad2 | 250,879 | 7 | transformers | 121 | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
model-index:
- name: deepset/bert-large-uncased-whole-word-masking-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
... |
Helsinki-NLP/opus-mt-en-de | 61a2efe1dd1ca51242d9df09a1a0634b17046125 | 2022-07-14T08:57:07.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"marian",
"text2text-generation",
"en",
"de",
"transformers",
"translation",
"license:cc-by-4.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-de | 248,829 | 5 | transformers | 122 | ---
tags:
- translation
license: cc-by-4.0
---
### opus-mt-en-de
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [How to Get St... |
facebook/dpr-ctx_encoder-single-nq-base | aa2a11a692b50b73fa245d59e49be796eefd888f | 2020-11-25T16:58:35.000Z | [
"pytorch",
"tf",
"dpr",
"transformers"
] | null | false | facebook | null | facebook/dpr-ctx_encoder-single-nq-base | 242,736 | 3 | transformers | 123 | Entry not found |
microsoft/layoutlmv2-large-uncased | 26362c3ebf9c5c9277dc51954e5107a905415eec | 2022-05-03T07:36:38.000Z | [
"pytorch",
"layoutlmv2",
"en",
"arxiv:2012.14740",
"transformers",
"license:cc-by-nc-sa-4.0"
] | null | false | microsoft | null | microsoft/layoutlmv2-large-uncased | 241,454 | 3 | transformers | 124 | ---
language: en
license: cc-by-nc-sa-4.0
---
# LayoutLMv2
**Multimodal (text + layout/format + image) pre-training for document AI**
## Introduction
LayoutLMv2 is an improved version of LayoutLM with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. It ... |
facebook/dpr-question_encoder-single-nq-base | 7fee7988e53c713da8323b184f6015d47861a1bf | 2020-11-25T16:59:20.000Z | [
"pytorch",
"tf",
"dpr",
"feature-extraction",
"transformers"
] | feature-extraction | false | facebook | null | facebook/dpr-question_encoder-single-nq-base | 231,479 | 2 | transformers | 125 | Entry not found |
cardiffnlp/twitter-roberta-base-sentiment-latest | 5916057ce88cf0a408a195082b6c06d3dce12552 | 2022-03-31T09:47:41.000Z | [
"pytorch",
"tf",
"roberta",
"text-classification",
"english",
"arxiv:2202.03829",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-sentiment-latest | 231,133 | 29 | transformers | 126 | ---
language: english
widget:
- text: "Covid cases are increasing fast!"
---
# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2021)
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 finet... |
deepparag/Aeona | 4b980c2b6b62850536ce4354e1945b8f4d778f62 | 2022-07-23T05:30:35.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"license:mit"
] | conversational | false | deepparag | null | deepparag/Aeona | 228,625 | 8 | transformers | 127 | ---
thumbnail: https://images-ext-2.discordapp.net/external/Wvtx1L98EbA7DR2lpZPbDxDuO4qmKt03nZygATZtXgk/%3Fsize%3D4096/https/cdn.discordapp.com/avatars/931226824753700934/338a9e413bbceaeb9095a29e97d4fac0.png
tags:
- conversational
license: mit
---
# Aeona | Chatbot
![Aeona Banner](https://github.com/deepsarda/Aeona/bl... |
flair/pos-english | 22061e2903f36383754ba0101bc988c432aa4e06 | 2021-03-02T22:20:07.000Z | [
"pytorch",
"en",
"dataset:ontonotes",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/pos-english | 227,637 | 5 | flair | 128 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- ontonotes
widget:
- text: "I love Berlin."
---
## English Part-of-Speech Tagging in Flair (default model)
This is the standard part-of-speech tagging model for English that ships with [Flair](https://github.com/flairNLP/flair/).... |
google/mt5-small | f03a52d3eaa650878b6f52e443bc4d5b385e786e | 2022-05-27T15:06:24.000Z | [
"pytorch",
"tf",
"jax",
"mt5",
"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/mt5-small | 223,820 | 19 | transformers | 129 | ---
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
-... |
google/t5-v1_1-base | 650d7745bf1e502d6949b22cc19155cd656d3d4e | 2021-06-23T01:54:44.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-v1_1-base | 215,738 | 14 | transformers | 130 | ---
language: en
datasets:
- c4
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1
## Version 1.1
[T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the f... |
microsoft/deberta-v2-xlarge | 30597019711d3531f994d1e21defffd0d8cd55ab | 2022-01-13T17:21:41.000Z | [
"pytorch",
"tf",
"deberta-v2",
"en",
"arxiv:2006.03654",
"transformers",
"deberta",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-v2-xlarge | 215,543 | 4 | transformers | 131 | ---
language: en
tags: deberta
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It ou... |
gpt2-large | e5ab12c7d42b9e60a6025476a688aab2c5695189 | 2022-07-22T07:59:04.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit"
] | text-generation | false | null | null | gpt2-large | 212,520 | 16 | transformers | 132 | ---
language: en
license: mit
---
# GPT-2 Large
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Envir... |
finiteautomata/beto-sentiment-analysis | 2d232b7b937ca0f6940f6b32ce5aaaeb012d8b38 | 2022-06-22T13:46:19.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"es",
"arxiv:2106.09462",
"transformers",
"sentiment-analysis"
] | text-classification | false | finiteautomata | null | finiteautomata/beto-sentiment-analysis | 211,263 | 11 | transformers | 133 | ---
language:
- es
tags:
- sentiment-analysis
---
# Sentiment Analysis in Spanish
## beto-sentiment-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/pysentimiento/pysentimiento/)
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Ba... |
princeton-nlp/sup-simcse-bert-base-uncased | 2d82fab19ac3a73a20dd20333d27eb8a52d6e97f | 2021-05-20T02:54:31.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | princeton-nlp | null | princeton-nlp/sup-simcse-bert-base-uncased | 209,700 | 4 | transformers | 134 | Entry not found |
GroNLP/bert-base-dutch-cased | 484ff5cec2ad42b434537dadd901d9b8e2b64cd2 | 2021-08-25T15:20:21.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"nl",
"arxiv:1912.09582",
"transformers",
"BERTje",
"autotrain_compatible"
] | fill-mask | false | GroNLP | null | GroNLP/bert-base-dutch-cased | 205,618 | 4 | transformers | 135 | ---
language: nl
thumbnail: "https://raw.githubusercontent.com/wietsedv/bertje/master/bertje.png"
tags:
- BERTje
---
# BERTje: A Dutch BERT model
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Andreas van Cranenburgh](https://www.semanticscholar.org/author/Andreas-van-Cranenburg... |
shibing624/text2vec-base-chinese | b455bb011898ad5d8b16cea238d070cd34db4b05 | 2022-03-14T06:43:16.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers",
"text2vec",
"sentence-similarity",
"license:apache-2.0"
] | sentence-similarity | false | shibing624 | null | shibing624/text2vec-base-chinese | 202,918 | 4 | transformers | 136 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- text2vec
- feature-extraction
- sentence-similarity
- transformers
---
# shibing624/text2vec-base-chinese
This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese.
It maps sentences to a 768 dimensional dense vector space and can be use... |
flair/pos-english-fast | 78bf413a631e2de4cb977e1f2794295d981e4c13 | 2021-03-02T22:19:11.000Z | [
"pytorch",
"en",
"dataset:ontonotes",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/pos-english-fast | 202,393 | 2 | flair | 137 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- ontonotes
widget:
- text: "I love Berlin."
---
## English Part-of-Speech Tagging in Flair (fast model)
This is the fast part-of-speech tagging model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Sc... |
google/bigbird-roberta-base | 5a145f7852cba9bd431386a58137bf8a29903b90 | 2021-06-02T14:30:54.000Z | [
"pytorch",
"jax",
"big_bird",
"pretraining",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:cc_news",
"arxiv:2007.14062",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bigbird-roberta-base | 199,034 | 17 | transformers | 138 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
- cc_news
---
# BigBird base model
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities ... |
huggingface/CodeBERTa-small-v1 | e93b5898cff07f03f1c1c09cde284d1b85962363 | 2022-06-27T15:48:41.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"code",
"dataset:code_search_net",
"arxiv:1909.09436",
"transformers",
"autotrain_compatible"
] | fill-mask | false | huggingface | null | huggingface/CodeBERTa-small-v1 | 198,709 | 16 | transformers | 139 | ---
language: code
thumbnail: https://cdn-media.huggingface.co/CodeBERTa/CodeBERTa.png
datasets:
- code_search_net
---
# CodeBERTa
CodeBERTa is a RoBERTa-like model trained on the [CodeSearchNet](https://github.blog/2019-09-26-introducing-the-codesearchnet-challenge/) dataset from GitHub.
Supported languages:
```sh... |
cl-tohoku/bert-base-japanese | 5dc6dbba88a42d21da3b71025c109c42462307f2 | 2021-09-23T13:45:36.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ja",
"dataset:wikipedia",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | cl-tohoku | null | cl-tohoku/bert-base-japanese | 196,583 | 4 | transformers | 140 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 東北大学で[MASK]の研究をしています。
---
# BERT base Japanese (IPA dictionary)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level to... |
ramsrigouthamg/t5_sentence_paraphraser | 6887902ca669ce785cb8a01b3425e843011bc110 | 2021-06-23T13:47:31.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ramsrigouthamg | null | ramsrigouthamg/t5_sentence_paraphraser | 190,848 | 3 | transformers | 141 | Entry not found |
ckiplab/albert-tiny-chinese | d1edf497761caf4fdd83d2c4488132a8c56f9e3c | 2022-05-10T03:28:09.000Z | [
"pytorch",
"albert",
"fill-mask",
"zh",
"transformers",
"lm-head",
"license:gpl-3.0",
"autotrain_compatible"
] | fill-mask | false | ckiplab | null | ckiplab/albert-tiny-chinese | 186,457 | 4 | transformers | 142 | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- lm-head
- 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 segmentation, pa... |
mrm8488/t5-base-finetuned-summarize-news | ada499546852c489d6327cae23439ec309f6869f | 2022-01-18T15:07:32.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"arxiv:1910.10683",
"transformers",
"news",
"summary",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-finetuned-summarize-news | 181,482 | 6 | transformers | 143 | ---
language: en
tags:
- news
- summary
---
# T5-base fine-tuned fo News Summarization 📖✏️🧾
All credits to [Abhishek Kumar Mishra](https://github.com/abhimishra91)
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) base fine-tuned on [News Summary](https://www.kaggle.com/sun... |
flexudy/t5-base-multi-sentence-doctor | 85ef24d555e2e6cabd5ce8264e9ce1627c406bad | 2020-12-11T23:33:25.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | flexudy | null | flexudy/t5-base-multi-sentence-doctor | 180,774 | 11 | transformers | 144 | 
# Sentence-Doctor
Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.
## 1. Problem:
Many NLP models depend on tasks like *Text Extraction Libraries, OCR, Speech to Text libraries* and **Sentence B... |
imone/pangu_2_6B | f6185c345ee59518384a463350bc834ace46e557 | 2021-12-13T06:34:22.000Z | [
"pytorch",
"gpt_pangu",
"text-generation",
"transformers"
] | text-generation | false | imone | null | imone/pangu_2_6B | 180,697 | 3 | transformers | 145 | # Pangu-Alpha 2.6B
## Model Description
PanGu-α is proposed by a joint technical team headed by PCNL. It is the first large-scale Chinese pre-trained language model with 200 billion parameters trained on 2048 Ascend processors using an automatic hybrid parallel training strategy. The whole training process is done on... |
bert-base-german-cased | 702774c02b32a4f360d5fea60ab034d64bf0141c | 2021-05-18T16:14:28.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-base-german-cased | 178,265 | 14 | transformers | 146 | ---
language: de
license: mit
thumbnail: https://static.tildacdn.com/tild6438-3730-4164-b266-613634323466/german_bert.png
tags:
- exbert
---
<a href="https://huggingface.co/exbert/?model=bert-base-german-cased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
# German BERT
![bert_im... |
Jean-Baptiste/roberta-large-ner-english | c272484a77f6bacd3569d32936fda04555fb4006 | 2022-07-04T15:02:50.000Z | [
"pytorch",
"tf",
"roberta",
"token-classification",
"en",
"dataset:conll2003",
"transformers",
"autotrain_compatible"
] | token-classification | false | Jean-Baptiste | null | Jean-Baptiste/roberta-large-ner-english | 175,900 | 6 | transformers | 147 | ---
language: en
datasets:
- conll2003
widget:
- text: "My name is jean-baptiste and I live in montreal"
- text: "My name is clara and I live in berkeley, california."
- text: "My name is wolfgang and I live in berlin"
train-eval-index:
- config: conll2003
task: token-classification
task_id: entity_extraction
spl... |
cl-tohoku/bert-base-japanese-v2 | e4211d7c20b078ac29b022be35ae4b63f3fe1679 | 2021-09-23T13:45:31.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ja",
"dataset:wikipedia",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | cl-tohoku | null | cl-tohoku/bert-base-japanese-v2 | 174,174 | 9 | transformers | 148 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 東北大学で[MASK]の研究をしています。
---
# BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model pr... |
dbmdz/bert-base-italian-xxl-cased | e25680c78556c0d9002dba60d712e1df3095240e | 2021-05-19T15:01:46.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"it",
"dataset:wikipedia",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/bert-base-italian-xxl-cased | 172,134 | 7 | transformers | 149 | ---
language: it
license: mit
datasets:
- wikipedia
---
# 🤗 + 📚 dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models 🎉
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikiped... |
Helsinki-NLP/opus-mt-en-ROMANCE | 92870a2f094c444064c7a568c25eef6971e07b03 | 2021-09-09T21:34:01.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"marian",
"text2text-generation",
"en",
"roa",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-ROMANCE | 169,907 | 2 | transformers | 150 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-ROMANCE
* source languages: en
* target languages: fr,fr_BE,fr_CA,fr_FR,wa,frp,oc,ca,rm,lld,fur,lij,lmo,es,es_AR,es_CL,es_CO,es_CR,es_DO,es_EC,es_ES,es_GT,es_HN,es_MX,es_NI,es_PA,es_PE,es_PR,es_SV,es_UY,es_VE,pt,pt_br,pt_BR,pt_PT,gl,lad,an,mwl,it,it_IT,co... |
sentence-transformers/allenai-specter | 29f9f45ff2a85fe9dfe8ce2cef3d8ec4e65c5f37 | 2022-06-15T21:31:20.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/allenai-specter | 167,946 | 3 | sentence-transformers | 151 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
license: apache-2.0
---
# allenai-specter
This model is a conversion of the [AllenAI SPECTER](https://github.com/allenai/specter) model to [sentence-transformers](https://www.SBERT.net). It can be used to ma... |
cross-encoder/ms-marco-MiniLM-L-6-v2 | b2cfda50a1a9fc7919e7444afbb52610d268af92 | 2021-08-05T08:39:38.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/ms-marco-MiniLM-L-6-v2 | 167,508 | 6 | transformers | 152 | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... |
google/t5-v1_1-small | fb7e6cba609f7bab11c614294bc04f82f613c7b1 | 2021-06-23T00:37:12.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-v1_1-small | 166,813 | 5 | transformers | 153 | ---
language: en
datasets:
- c4
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1
## Version 1.1
[T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the f... |
BaptisteDoyen/camembert-base-xnli | 791c5260a7c5984c7d96e622b45ca4c3ee6ea7d8 | 2022-06-29T09:30:26.000Z | [
"pytorch",
"tf",
"camembert",
"text-classification",
"fr",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"xnli",
"nli",
"license:mit"
] | zero-shot-classification | false | BaptisteDoyen | null | BaptisteDoyen/camembert-base-xnli | 166,725 | 8 | transformers | 154 | ---
language:
- fr
thumbnail:
tags:
- zero-shot-classification
- xnli
- nli
- fr
license: mit
pipeline_tag: zero-shot-classification
datasets:
- xnli
metrics:
- accuracy
---
# camembert-base-xnli
## Model description
Camembert-base model fine-tuned on french part of XNLI dataset. <br>
One of the few Zero-Shot c... |
oliverguhr/fullstop-punctuation-multilang-large | 4740a83c496dc2416c0cf8ae3c6572dfb6851228 | 2022-06-09T11:51:40.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"en",
"de",
"fr",
"it",
"dataset:wmt/europarl",
"transformers",
"punctuation prediction",
"punctuation",
"license:mit",
"autotrain_compatible"
] | token-classification | false | oliverguhr | null | oliverguhr/fullstop-punctuation-multilang-large | 165,600 | 28 | transformers | 155 | ---
language:
- en
- de
- fr
- it
tags:
- punctuation prediction
- punctuation
datasets: wmt/europarl
license: mit
widget:
- text: "Ho sentito che ti sei laureata il che mi fa molto piacere"
example_title: "Italian"
- text: "Tous les matins vers quatre heures mon père ouvrait la porte de ma chambre"
example_title: ... |
sentence-transformers/multi-qa-MiniLM-L6-cos-v1 | 2ad254dbef118e9d73b90b0797a1632cb455fedf | 2022-07-11T21:10:58.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:search_qa",
"dataset:eli5",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/QQP",
"da... | sentence-similarity | false | sentence-transformers | null | sentence-transformers/multi-qa-MiniLM-L6-cos-v1 | 163,551 | 24 | sentence-transformers | 156 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- search_qa
- eli5
- natural_questions
- trivia_qa
- embedding-data/QQP
- embedding-data/PAQ_pairs
- embedding-d... |
cross-encoder/stsb-roberta-base | 90a6796bd3c504b63351dad78c76ffb40e3d6e5a | 2021-08-05T08:41:58.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/stsb-roberta-base | 163,486 | null | transformers | 157 | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset]... |
DeepPavlov/rubert-base-cased | 4036cab694767a299f2b9e6492909664d9414229 | 2021-11-23T08:03:04.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1905.07213",
"transformers"
] | feature-extraction | false | DeepPavlov | null | DeepPavlov/rubert-base-cased | 162,685 | 13 | transformers | 158 | ---
language:
- ru
---
# rubert-base-cased
RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for ... |
cardiffnlp/twitter-roberta-base-irony | 72213835791c86ac7cade4acef91820bc9f1dc57 | 2021-05-20T15:03:56.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-irony | 162,662 | 1 | transformers | 159 | # Twitter-roBERTa-base for Irony Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.co... |
google/electra-base-discriminator | 1b48ef100dac4676d84125a8a7b7ab7c51e00386 | 2021-04-30T07:33:10.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"electra",
"pretraining",
"en",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/electra-base-discriminator | 162,100 | 5 | transformers | 160 | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks usi... |
google/vit-base-patch16-224-in21k | 1ba429d32753f33a0660b80ac6f43a3c80c18938 | 2022-01-12T08:03:16.000Z | [
"pytorch",
"tf",
"jax",
"vit",
"feature-extraction",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"transformers",
"vision",
"license:apache-2.0"
] | feature-extraction | false | google | null | google/vit-base-patch16-224-in21k | 162,065 | 12 | transformers | 161 | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet-21k
inference: false
---
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformer... |
facebook/mbart-large-50-many-to-many-mmt | 0ece2bb75a89350002537169ecadeb2b3d043b6b | 2022-05-26T22:28:18.000Z | [
"pytorch",
"jax",
"rust",
"mbart",
"text2text-generation",
"multilingual",
"ar",
"cs",
"de",
"en",
"es",
"et",
"fi",
"fr",
"gu",
"hi",
"it",
"ja",
"kk",
"ko",
"lt",
"lv",
"my",
"ne",
"nl",
"ro",
"ru",
"si",
"tr",
"vi",
"zh",
"af",
"az",
"bn",
"fa",... | text2text-generation | false | facebook | null | facebook/mbart-large-50-many-to-many-mmt | 160,492 | 13 | transformers | 162 | ---
language:
- multilingual
- ar
- cs
- de
- en
- es
- et
- fi
- fr
- gu
- hi
- it
- ja
- kk
- ko
- lt
- lv
- my
- ne
- nl
- ro
- ru
- si
- tr
- vi
- zh
- af
- az
- bn
- fa
- he
- hr
- id
- ka
- km
- mk
- ml
- mn
- mr
- pl
- ps
- pt
- sv
- sw
- ta
- te
- th
- tl
- uk
- ur
- xh
- gl
- sl
tags:
- mbart-50
---
# mBART... |
etalab-ia/camembert-base-squadFR-fquad-piaf | 63296563d30b341d2cbb3feae651a3545dc1c74d | 2022-07-04T08:16:13.000Z | [
"pytorch",
"tf",
"camembert",
"question-answering",
"fr",
"dataset:piaf",
"dataset:FQuAD",
"dataset:SQuAD-FR",
"transformers",
"autotrain_compatible"
] | question-answering | false | etalab-ia | null | etalab-ia/camembert-base-squadFR-fquad-piaf | 156,581 | 7 | transformers | 163 | ---
language: fr
datasets:
- piaf
- FQuAD
- SQuAD-FR
widget:
- text: "Comment s'appelle le portail open data du gouvernement ?"
context: "Etalab est une administration publique française qui fait notamment office de Chief Data Officer de l'État et coordonne la conception et la mise en œuvre de sa stratégie dans le do... |
pyannote/segmentation | c4c8ceafcbb3a7a280c2d357aee9fbc9b0be7f9b | 2022-07-19T14:24:12.000Z | [
"pytorch",
"dataset:ami",
"dataset:dihard",
"dataset:voxconverse",
"arxiv:2104.04045",
"pyannote-audio",
"pyannote",
"pyannote-audio-model",
"audio",
"voice",
"speech",
"speaker",
"speaker-segmentation",
"voice-activity-detection",
"overlapped-speech-detection",
"resegmentation",
"li... | voice-activity-detection | false | pyannote | null | pyannote/segmentation | 156,422 | 21 | pyannote-audio | 164 | ---
tags:
- pyannote
- pyannote-audio
- pyannote-audio-model
- audio
- voice
- speech
- speaker
- speaker-segmentation
- voice-activity-detection
- overlapped-speech-detection
- resegmentation
datasets:
- ami
- dihard
- voxconverse
license: mit
inference: false
---
# 🎹 Speaker segmentation
M... |
EleutherAI/gpt-neo-125M | b559e35121e91087f94903c07213d208d2412f68 | 2021-12-31T13:46:51.000Z | [
"pytorch",
"jax",
"rust",
"gpt_neo",
"text-generation",
"en",
"dataset:The Pile",
"transformers",
"text generation",
"causal-lm",
"license:apache-2.0"
] | text-generation | false | EleutherAI | null | EleutherAI/gpt-neo-125M | 156,398 | 26 | transformers | 165 | ---
language:
- en
tags:
- text generation
- pytorch
- causal-lm
license: apache-2.0
datasets:
- The Pile
---
# GPT-Neo 125M
## Model Description
GPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the num... |
neuralmind/bert-base-portuguese-cased | 94d69c95f98f7d5b2a8700c420230ae10def0baa | 2022-06-14T14:37:09.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"pt",
"dataset:brWaC",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | neuralmind | null | neuralmind/bert-base-portuguese-cased | 155,314 | 34 | transformers | 166 | ---
language: pt
license: mit
tags:
- bert
- pytorch
datasets:
- brWaC
---
# BERTimbau Base (aka "bert-base-portuguese-cased")
## Introduction
BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performance... |
joeddav/xlm-roberta-large-xnli | 9c1619b90a142cd2913190d80d5f488d6612f57e | 2020-12-17T16:39:07.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"multilingual",
"dataset:multi_nli",
"dataset:xnli",
"arxiv:1911.02116",
"transformers",
"tensorflow",
"license:mit",
"zero-shot-classification"
] | zero-shot-classification | false | joeddav | null | joeddav/xlm-roberta-large-xnli | 154,078 | 45 | transformers | 167 | ---
language: multilingual
tags:
- text-classification
- pytorch
- tensorflow
datasets:
- multi_nli
- xnli
license: mit
pipeline_tag: zero-shot-classification
widget:
- text: "За кого вы голосуете в 2020 году?"
candidate_labels: "politique étrangère, Europe, élections, affaires, politique"
multi_class: true
- text:... |
allegro/herbert-base-cased | 50e33e0567be0c0b313832314c586e3df0dc2297 | 2022-06-09T11:36:39.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"pl",
"transformers",
"herbert",
"license:cc-by-4.0"
] | feature-extraction | false | allegro | null | allegro/herbert-base-cased | 152,056 | 4 | transformers | 168 | ---
language: pl
tags:
- herbert
license: cc-by-4.0
---
# HerBERT
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish corpora
using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, ... |
bvanaken/clinical-assertion-negation-bert | f381df19e34e690108f3b8e3e8433f7c9d2e2f9d | 2022-06-01T12:28:45.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"medical",
"clinical",
"assertion",
"negation"
] | text-classification | false | bvanaken | null | bvanaken/clinical-assertion-negation-bert | 150,710 | 9 | transformers | 169 | ---
language: "en"
tags:
- bert
- medical
- clinical
- assertion
- negation
- text-classification
widget:
- text: "Patient denies [entity] SOB [entity]."
---
# Clinical Assertion / Negation Classification BERT
## Model description
The Clinical Assertion and Negation Classification BERT is introduced in the paper [A... |
gpt2-xl | 82bb8104f524eef7f49c81a339b62f5866ef95b6 | 2022-03-08T09:48:34.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | null | null | gpt2-xl | 149,002 | 8 | transformers | 170 | Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
|
snrspeaks/t5-one-line-summary | 62acf01b9c91b2ea3a84b1e83a8ee0557cc3526c | 2021-06-23T14:20:22.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:arxiv",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | snrspeaks | null | snrspeaks/t5-one-line-summary | 145,953 | 8 | transformers | 171 | ---
datasets:
- arxiv
widget:
- text: "summarize: We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production
machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated app... |
tuner007/pegasus_paraphrase | 0159e2949ca73657a2f1329898f51b7bb53b9ab2 | 2021-03-22T21:11:33.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"transformers",
"paraphrasing",
"seq2seq",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | tuner007 | null | tuner007/pegasus_paraphrase | 145,452 | 60 | transformers | 172 | ---
language: en
license: apache-2.0
tags:
- pegasus
- paraphrasing
- seq2seq
---
## Model description
[PEGASUS](https://github.com/google-research/pegasus) fine-tuned for paraphrasing
## Model in Action 🚀
```
import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
model_name = 'tuner... |
Helsinki-NLP/opus-mt-de-en | 6137149949ac01d19d8eeef6e35d32221dabc8e4 | 2021-09-09T21:30:51.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"de",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-de-en | 143,977 | 6 | transformers | 173 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-de-en
* source languages: de
* target languages: en
* OPUS readme: [de-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
allenai/longformer-base-4096 | e351d9d5da3eed48886f39eed7b64014debe4925 | 2021-03-10T02:30:38.000Z | [
"pytorch",
"tf",
"rust",
"longformer",
"arxiv:2004.05150",
"transformers"
] | null | false | allenai | null | allenai/longformer-base-4096 | 141,766 | 33 | transformers | 174 |
# longformer-base-4096
[Longformer](https://arxiv.org/abs/2004.05150) is a transformer model for long documents.
`longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint and pretrained for MLM on long documents. It supports sequences of length up to 4,096.
Longformer uses a combination of a... |
t5-11b | 41f839df070947ed9275dedaf3d28c75fb4d43e8 | 2022-07-22T08:11:37.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"fr",
"ro",
"de",
"dataset:c4",
"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.12885",
"arxiv:1905.10044",
"arxiv:1910.09700",
"transformers",
"summarization",
... | translation | false | null | null | t5-11b | 141,448 | 5 | transformers | 175 | ---
language:
- en
- fr
- ro
- de
datasets:
- c4
tags:
- summarization
- translation
license: apache-2.0
inference: false
---
# Model Card for T5 11B
![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304... |
cardiffnlp/twitter-roberta-base-stance-climate | a2802f328ed851a97696c724f46e75773c34e098 | 2021-05-20T15:10:09.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-stance-climate | 141,185 | null | transformers | 176 | |
hf-internal-testing/tiny-random-distilbert | 2ef615d573271690c9822df720b8024148d6715a | 2021-11-26T16:32:03.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | hf-internal-testing | null | hf-internal-testing/tiny-random-distilbert | 140,620 | null | transformers | 177 | ---
pipeline_tag: text-classification
---
|
hfl/chinese-electra-180g-small-ex-discriminator | 01785e80a5c6601a86bb7cc8d74c20be82646cba | 2021-03-03T01:25:29.000Z | [
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0"
] | null | false | hfl | null | hfl/chinese-electra-180g-small-ex-discriminator | 140,416 | 4 | transformers | 178 | ---
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... |
jackaduma/SecBERT | e62305c27bd9e535450c47d64a06187f53606c68 | 2022-01-24T07:45:57.000Z | [
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:APTnotes",
"dataset:Stucco-Data",
"dataset:CASIE",
"transformers",
"exbert",
"security",
"cybersecurity",
"cyber security",
"threat hunting",
"threat intelligence",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | jackaduma | null | jackaduma/SecBERT | 138,800 | 2 | transformers | 179 | ---
language: en
thumbnail: https://github.com/jackaduma
tags:
- exbert
- security
- cybersecurity
- cyber security
- threat hunting
- threat intelligence
license: apache-2.0
datasets:
- APTnotes
- Stucco-Data
- CASIE
---
# SecBERT
This is the pretrained model presented in [SecBERT: A Pretrained Language Model for C... |
kuprel/min-dalle | ab296a7008fd0708c63de711284776725b9729eb | 2022-07-28T15:08:30.000Z | [
"transformers",
"pytorch",
"license:mit"
] | null | false | kuprel | null | kuprel/min-dalle | 138,105 | 12 | transformers | 180 | ---
tags:
- pytorch
license: mit
---
# min(DALL·E)
[](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)
[](https:/... |
albert-base-v1 | aeffd769076a5c4f83b2546aea99ca45a15a5da4 | 2021-01-13T15:08:24.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | albert-base-v1 | 136,283 | 1 | transformers | 181 | ---
tags:
- exbert
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT Base v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github... |
cahya/t5-base-indonesian-summarization-cased | 3afd080677efb3978dfce95a19324d91caff3064 | 2021-06-23T12:05:23.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"id",
"dataset:id_liputan6",
"transformers",
"pipeline:summarization",
"summarization",
"autotrain_compatible"
] | summarization | false | cahya | null | cahya/t5-base-indonesian-summarization-cased | 133,174 | 2 | transformers | 182 | ---
language: id
tags:
- pipeline:summarization
- summarization
- t5
datasets:
- id_liputan6
---
# Indonesian T5 Summarization Base Model
Finetuned T5 base summarization model for Indonesian.
## Finetuning Corpus
`t5-base-indonesian-summarization-cased` model is based on `t5-base-bahasa-summarization-cased` by [hu... |
pucpr/biobertpt-all | ac33b1ca265df5074cca1656e15a8bf900394d8e | 2021-10-13T09:27:01.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"pt",
"dataset:biomedical literature from Scielo and Pubmed",
"transformers",
"autotrain_compatible"
] | fill-mask | false | pucpr | null | pucpr/biobertpt-all | 129,304 | 5 | transformers | 183 | ---
language: "pt"
widget:
- text: "O paciente recebeu [MASK] do hospital."
- text: "O médico receitou a medicação para controlar a [MASK]."
- text: "O principal [MASK] da COVID-19 é tosse seca."
- text: "O vírus da gripe apresenta um [MASK] constituído por segmentos de ácido ribonucleico."
datasets:
- biomedical lit... |
hf-internal-testing/tiny-random-bert | 9b8c223d42b2188cb49d29af482996f9d0f3e5a6 | 2021-09-17T19:21:17.000Z | [
"pytorch",
"tf",
"bert",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-bert | 128,588 | null | transformers | 184 | Entry not found |
cross-encoder/ms-marco-TinyBERT-L-2 | e9ed04745b2b19e8c4499360253ea5d5b41b5810 | 2021-08-05T08:39:52.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/ms-marco-TinyBERT-L-2 | 127,888 | null | transformers | 185 | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... |
sentence-transformers/paraphrase-multilingual-mpnet-base-v2 | ef15aed8b328d308d7237b9bf15269f2cd19e268 | 2022-06-15T19:38:33.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/paraphrase-multilingual-mpnet-base-v2 | 127,876 | 17 | sentence-transformers | 186 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 di... |
MoritzLaurer/mDeBERTa-v3-base-mnli-xnli | ef7c55665d6b9cb4c03adfb1a05f0599d519964c | 2022-07-28T16:23:58.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"multilingual",
"en",
"ar",
"bg",
"de",
"el",
"es",
"fr",
"hi",
"ru",
"sw",
"th",
"tr",
"ur",
"vu",
"zh",
"dataset:multi_nli",
"dataset:xnli",
"arxiv:2111.09543",
"arxiv:1809.05053",
"arxiv:1911.02116",
"transformers",
... | zero-shot-classification | false | MoritzLaurer | null | MoritzLaurer/mDeBERTa-v3-base-mnli-xnli | 127,494 | 30 | transformers | 187 | ---
language:
- multilingual
- en
- ar
- bg
- de
- el
- es
- fr
- hi
- ru
- sw
- th
- tr
- ur
- vu
- zh
license: mit
tags:
- zero-shot-classification
- text-classification
- nli
- pytorch
metrics:
- accuracy
datasets:
- multi_nli
- xnli
pipeline_tag: zero-shot-classification
widget:
- text: "Angela Merke... |
facebook/wav2vec2-large-960h | bdeaacdf88f7a155f50a2704bc967aa81fbbb2ab | 2022-04-05T16:40:42.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"transformers",
"speech",
"license:apache-2.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-large-960h | 126,881 | 4 | transformers | 188 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Large-960h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audi... |
prajjwal1/bert-mini | 5e123abc2480f0c4b4cac186d3b3f09299c258fc | 2021-10-27T18:27:38.000Z | [
"pytorch",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"transformers",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"license:mit"
] | null | false | prajjwal1 | null | prajjwal1/bert-mini | 126,710 | 2 | transformers | 189 | ---
language:
- en
license:
- mit
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
---
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert).
This is one of the smaller ... |
sentence-transformers/msmarco-distilbert-dot-v5 | 52b2679c1e6789ee4b2d3b81a27a4590a1bc5348 | 2022-06-15T20:15:43.000Z | [
"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-dot-v5 | 125,646 | 4 | sentence-transformers | 190 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# msmarco-distilbert-dot-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and wa... |
speechbrain/spkrec-ecapa-voxceleb | 5c0be3875fda05e81f3c004ed8c7c06be308de1e | 2022-06-26T23:15:06.000Z | [
"en",
"dataset:voxceleb",
"arxiv:2106.04624",
"speechbrain",
"embeddings",
"Speaker",
"Verification",
"Identification",
"pytorch",
"ECAPA",
"TDNN",
"license:apache-2.0"
] | null | false | speechbrain | null | speechbrain/spkrec-ecapa-voxceleb | 125,355 | 18 | speechbrain | 191 | ---
language: "en"
thumbnail:
tags:
- speechbrain
- embeddings
- Speaker
- Verification
- Identification
- pytorch
- ECAPA
- TDNN
license: "apache-2.0"
datasets:
- voxceleb
metrics:
- EER
widget:
- example_title: VoxCeleb Speaker id10003
src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav
- examp... |
deepset/roberta-base-squad2-covid | b3506f363ab164823a64b5372d5cc98f36504cd6 | 2021-10-21T12:19:32.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/roberta-base-squad2-covid | 122,432 | 4 | transformers | 192 | ---
license: cc-by-4.0
---
# roberta-base-squad2 for QA on COVID-19
## Overview
**Language model:** deepset/roberta-base-squad2
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** [SQuAD-style CORD-19 annotations from 23rd April](https://github.com/deepset-ai/COVID-QA/blob/master/data/qu... |
facebook/mbart-large-50-one-to-many-mmt | 3cc64aaf129efb58cdc6345618b39ce776d888b4 | 2022-05-26T22:28:22.000Z | [
"pytorch",
"jax",
"mbart",
"text2text-generation",
"multilingual",
"ar",
"cs",
"de",
"en",
"es",
"et",
"fi",
"fr",
"gu",
"hi",
"it",
"ja",
"kk",
"ko",
"lt",
"lv",
"my",
"ne",
"nl",
"ro",
"ru",
"si",
"tr",
"vi",
"zh",
"af",
"az",
"bn",
"fa",
"he",
... | text2text-generation | false | facebook | null | facebook/mbart-large-50-one-to-many-mmt | 121,393 | 8 | transformers | 193 | ---
language:
- multilingual
- ar
- cs
- de
- en
- es
- et
- fi
- fr
- gu
- hi
- it
- ja
- kk
- ko
- lt
- lv
- my
- ne
- nl
- ro
- ru
- si
- tr
- vi
- zh
- af
- az
- bn
- fa
- he
- hr
- id
- ka
- km
- mk
- ml
- mn
- mr
- pl
- ps
- pt
- sv
- sw
- ta
- te
- th
- tl
- uk
- ur
- xh
- gl
- sl
tags:
- mbart-50
---
# mBART... |
sentence-transformers/paraphrase-MiniLM-L3-v2 | 74e7eed84a0b0ccca7e8769c9b0e5990f41d7125 | 2022-07-08T04:08:35.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:s2orc",
"dataset:ms_marco",
"dataset:wiki_atomic_edits",
"dataset:snli",
"dataset:multi_nli",
"dataset:embedding-data/altlex",
"dataset:embedding-data/simple-wiki",
"dataset:embeddin... | sentence-similarity | false | sentence-transformers | null | sentence-transformers/paraphrase-MiniLM-L3-v2 | 120,018 | 3 | sentence-transformers | 194 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- flax-sentence-embeddings/stackexchange_xml
- s2orc
- ms_marco
- wiki_atomic_edits
- snli
- multi_nli
- embedding-data/altlex
- embedding-data/simple-wiki
- embedd... |
hf-internal-testing/tiny-random-roberta | 73def02fc9f13169a1ce21ad4602aae38d7cbd5a | 2021-09-17T19:22:24.000Z | [
"pytorch",
"tf",
"roberta",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-roberta | 117,612 | null | transformers | 195 | Entry not found |
dbmdz/bert-large-cased-finetuned-conll03-english | f2482bf01f5da0f0eb8e183ffd8cc3885aa90b14 | 2021-05-19T15:17:53.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | dbmdz | null | dbmdz/bert-large-cased-finetuned-conll03-english | 117,048 | 4 | transformers | 196 | Entry not found |
cmarkea/distilcamembert-base | bf14fbad88b19c837997f26dd1684bf98404f96b | 2022-05-24T15:57:25.000Z | [
"pytorch",
"tf",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1910.01108",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | cmarkea | null | cmarkea/distilcamembert-base | 117,021 | 17 | transformers | 197 | ---
language: fr
license: mit
datasets:
- oscar
widget:
- text: "J'aime lire les <mask> de SF."
---
DistilCamemBERT
===============
We present a distillation version of the well named [CamemBERT](https://huggingface.co/camembert-base), a RoBERTa French model version, alias DistilCamemBERT. The aim of distillation is ... |
microsoft/DialoGPT-medium | 8bada3b953e25ec171dea4e28c52f1e8b546d707 | 2021-05-23T09:11:45.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"gpt2",
"text-generation",
"arxiv:1911.00536",
"transformers",
"conversational",
"license:mit"
] | conversational | false | microsoft | null | microsoft/DialoGPT-medium | 116,315 | 24 | transformers | 198 | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation... |
cardiffnlp/twitter-roberta-base-emotion | dff452c4f42c15a25bd51aff1f1ca5d15ec08c23 | 2022-03-23T14:34:19.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-emotion | 115,973 | 13 | transformers | 199 | # Twitter-roBERTa-base for Emotion Recognition
This is a roBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://g... |
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