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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
luca-martial/DialoGPT-Elon | afa2ffff96344dbe63d95f6b03451ce9459502d0 | 2021-06-10T09:35:51.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | luca-martial | null | luca-martial/DialoGPT-Elon | 4,891 | 2 | transformers | 900 | ---
tags:
- conversational
---
# DialoGPT-Elon: Chat with Elon Musk
This is an attempt to create an AI replica of Elon Musk. The bot's conversation abilities come from Microsoft's [DialoGPT conversational model](https://huggingface.co/microsoft/DialoGPT-medium) fine-tuned on conversation transcripts of Elon's intervi... |
smilegate-ai/kor_unsmile | f597423eeff8e6da99cad85cbe3a81adf5225637 | 2022-03-28T01:34:57.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | smilegate-ai | null | smilegate-ai/kor_unsmile | 4,882 | 3 | transformers | 901 | Entry not found |
Helsinki-NLP/opus-mt-en-cs | 7cba4a7e3daff13c48fc2fcd740ef0711b1dd075 | 2021-09-09T21:34:42.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"cs",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-cs | 4,879 | null | transformers | 902 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-cs
* source languages: en
* target languages: cs
* OPUS readme: [en-cs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-cs/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
uer/chinese_roberta_L-2_H-128 | b24cec74c68f494dc7ee8f05959ac3aee598cf78 | 2022-07-15T08:09:56.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:1908.08962",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uer | null | uer/chinese_roberta_L-2_H-128 | 4,866 | 1 | transformers | 903 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "北京是[MASK]国的首都。"
---
# Chinese RoBERTa Miniatures
## Model description
This is the set of 24 Chinese RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658).
[Turc e... |
uer/pegasus-base-chinese-cluecorpussmall | 50bb33cb078e9a08c56c8b65513ddc46cb9665ae | 2022-07-15T08:18:04.000Z | [
"pytorch",
"tf",
"pegasus",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uer | null | uer/pegasus-base-chinese-cluecorpussmall | 4,853 | 1 | transformers | 904 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "内容丰富、版式设计考究、图片华丽、印制精美。[MASK]纸箱内还放了充气袋用于保护。"
---
# Chinese Pegasus
## Model description
This model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658).
You can download the s... |
dbmdz/bert-base-italian-cased | bcecdad25ce7cdd99c58c4e504ab97e6ff7222cf | 2021-05-19T14:59:44.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"it",
"dataset:wikipedia",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/bert-base-italian-cased | 4,818 | 2 | transformers | 905 | ---
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... |
papluca/xlm-roberta-base-language-detection | f793746a8fff7a83f266fa0df91064727c8c76a2 | 2021-11-25T12:41:12.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"arxiv:1911.02116",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | papluca | null | papluca/xlm-roberta-base-language-detection | 4,816 | 11 | transformers | 906 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-language-detection
results: []
---
# xlm-roberta-base-language-detection
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Language Identification](h... |
Rostlab/prot_t5_xxl_uniref50 | 31a40d7b55caf68d7a8a8dfd913b779b99dc09a9 | 2021-03-30T19:25:17.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Rostlab | null | Rostlab/prot_t5_xxl_uniref50 | 4,800 | null | transformers | 907 | Entry not found |
dandelin/vilt-b32-finetuned-vqa | a08ecc15918f6aa056ff01b5397f10bd718af96b | 2022-07-29T13:47:48.000Z | [
"pytorch",
"vilt",
"arxiv:2102.03334",
"transformers",
"visual-question-answering",
"license:apache-2.0"
] | visual-question-answering | false | dandelin | null | dandelin/vilt-b32-finetuned-vqa | 4,795 | 3 | transformers | 908 | ---
tags:
- visual-question-answering
license: apache-2.0
widget:
- text: "What animal is it?"
src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg"
- text: "Where is it?"
src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg"
---
# Vision-and-Language Tran... |
Salesforce/codegen-350M-mono | a268ccf6c2f3f6c8eb582bbea2411d750253864f | 2022-06-28T17:46:25.000Z | [
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"transformers",
"license:bsd-3-clause"
] | text-generation | false | Salesforce | null | Salesforce/codegen-350M-mono | 4,795 | 1 | transformers | 909 | ---
license: bsd-3-clause
---
# CodeGen (CodeGen-Mono 350M)
## 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 ... |
microsoft/deberta-v2-xxlarge-mnli | 8f8b43ddfc6f5e93a7abd1d1506d606b080a4c06 | 2021-05-21T20:08:40.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"en",
"arxiv:2006.03654",
"transformers",
"deberta",
"deberta-mnli",
"license:mit"
] | text-classification | false | microsoft | null | microsoft/deberta-v2-xxlarge-mnli | 4,792 | 1 | transformers | 910 | ---
language: en
tags:
- deberta
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves... |
Langboat/mengzi-t5-base | fbd9c58ba8e1a5393668fbdfe477ec70267c01e7 | 2021-10-21T12:33:28.000Z | [
"pytorch",
"t5",
"text2text-generation",
"zh",
"arxiv:2110.06696",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | Langboat | null | Langboat/mengzi-t5-base | 4,775 | 15 | transformers | 911 | ---
language:
- zh
license: apache-2.0
---
# Mengzi-T5 model (Chinese)
Pretrained model on 300G Chinese corpus.
[Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696)
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokeniz... |
yiyanghkust/finbert-fls | 443586dc31c765c0aaf1c4daaed8cf3643c92fa5 | 2022-06-10T23:20:05.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"financial-text-analysis",
"forward-looking-statement"
] | text-classification | false | yiyanghkust | null | yiyanghkust/finbert-fls | 4,754 | 3 | transformers | 912 | ---
language: "en"
tags:
- financial-text-analysis
- forward-looking-statement
widget:
- text: "We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs. "
---
Forward-looking statements (FLS) inform investors of managers’ beliefs and opinions about firm's future event... |
stas/t5-very-small-random | 988f491d1f2b837d47895885d96b1d4992a25d0e | 2021-04-21T02:34:01.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | stas | null | stas/t5-very-small-random | 4,736 | null | transformers | 913 | This is a tiny random t5 model used for testing
See `t5-make-very-small-model.py` for how it was created. |
transformersbook/bert-base-uncased-finetuned-clinc | 795b076da71dc236dde692338e21560cbbffa6e4 | 2022-02-05T16:38:54.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1909.02027",
"transformers"
] | text-classification | false | transformersbook | null | transformersbook/bert-base-uncased-finetuned-clinc | 4,715 | null | transformers | 914 | # Intent Detection with BERT
This model was trained on the [CLINC150](https://arxiv.org/abs/1909.02027) dataset for customer intent detection. The dataset can be found on the [Hub](https://huggingface.co/datasets/clinc_oos). The model is used in Chapter 8: Making Transformers Efficient in Production in the [NLP with T... |
bigscience/bloom-6b3 | 94a4acd1e473db402c72df105b31d05c46632e5d | 2022-07-13T09:02:39.000Z | [
"pytorch",
"jax",
"bloom",
"feature-extraction",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
... | text-generation | false | bigscience | null | bigscience/bloom-6b3 | 4,706 | 2 | transformers | 915 | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-gener... |
Helsinki-NLP/opus-mt-ine-en | bd46b80a8dfef9dcd1a1201bbe2807bdec97ad9b | 2020-08-21T14:42:46.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ca",
"es",
"os",
"ro",
"fy",
"cy",
"sc",
"is",
"yi",
"lb",
"an",
"sq",
"fr",
"ht",
"rm",
"ps",
"af",
"uk",
"sl",
"lt",
"bg",
"be",
"gd",
"si",
"en",
"br",
"mk",
"or",
"mr",
"ru",
"fo",
"co",
"oc",
"... | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ine-en | 4,704 | null | transformers | 916 | ---
language:
- ca
- es
- os
- ro
- fy
- cy
- sc
- is
- yi
- lb
- an
- sq
- fr
- ht
- rm
- ps
- af
- uk
- sl
- lt
- bg
- be
- gd
- si
- en
- br
- mk
- or
- mr
- ru
- fo
- co
- oc
- pl
- gl
- nb
- bn
- id
- hy
- da
- gv
- nl
- pt
- hi
- as
- kw
- ga
- sv
- gu
- wa
- lv
- el
- it
- hr
- ur
- nn
- de
- cs
- ine
tags:
- ... |
hf-internal-testing/tiny-albert | de839e2e6aa81798e2ac85a8ac414da41862e23a | 2021-07-16T01:27:09.000Z | [
"pytorch",
"albert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | hf-internal-testing | null | hf-internal-testing/tiny-albert | 4,675 | null | transformers | 917 | This is a tiny-albert random model to be used for basic testing.
|
microsoft/beit-base-patch16-224 | f73f827d08788b563c0b7a3eb04169a77e79a588 | 2022-01-28T10:18:01.000Z | [
"pytorch",
"jax",
"beit",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/beit-base-patch16-224 | 4,633 | 1 | transformers | 918 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-21k
---
# BEiT (base-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 ... |
facebook/wmt19-en-ru | a96448f0cef6a0b22c8f73f6f0e74b610efe806f | 2020-12-11T21:39:58.000Z | [
"pytorch",
"fsmt",
"text2text-generation",
"en",
"ru",
"dataset:wmt19",
"arxiv:1907.06616",
"transformers",
"translation",
"wmt19",
"facebook",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | facebook | null | facebook/wmt19-en-ru | 4,623 | 1 | transformers | 919 | ---
language:
- en
- ru
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/... |
indolem/indobertweet-base-uncased | 32e28c05b47e33b6675d2670a1745c50a65e987a | 2021-09-18T01:24:17.000Z | [
"pytorch",
"bert",
"fill-mask",
"id",
"dataset:Twitter 2021",
"arxiv:2109.04607",
"transformers",
"Twitter",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | indolem | null | indolem/indobertweet-base-uncased | 4,614 | 3 | transformers | 920 | ---
language:
- id
tags:
- Twitter
license: apache-2.0
datasets:
- Twitter 2021
widget:
- text: "guweehh udh ga' paham lg sm [MASK]"
---
# IndoBERTweet 🐦
## 1. Paper
Fajri Koto, Jey Han Lau, and Timothy Baldwin. [_IndoBERTweet: A Pretrained Language Model for Indonesian Twitter
with Effective Domain-Specific Vocabu... |
yjernite/bart_eli5 | 38797dd2ef06f5542c6f7db853518703f6b3da21 | 2021-03-09T22:31:11.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:eli5",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | yjernite | null | yjernite/bart_eli5 | 4,594 | 3 | transformers | 921 | ---
language: en
license: apache-2.0
datasets:
- eli5
---
## BART ELI5
Read the article at https://yjernite.github.io/lfqa.html and try the demo at https://huggingface.co/qa/
|
nboost/pt-tinybert-msmarco | b0de5c59e4779c149295b9bd0e5988a8f2cd2be7 | 2021-05-20T01:28:00.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
] | null | false | nboost | null | nboost/pt-tinybert-msmarco | 4,590 | null | transformers | 922 | Entry not found |
setu4993/LaBSE | 082cccca1eea3e0fab80749de8e8aded21bec253 | 2021-12-05T06:10:07.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"bo",
"bs",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha... | feature-extraction | false | setu4993 | null | setu4993/LaBSE | 4,587 | 18 | transformers | 923 | ---
language:
- af
- am
- ar
- as
- az
- be
- bg
- bn
- bo
- bs
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- he
- hi
- hmn
- hr
- ht
- hu
- hy
- id
- ig
- is
... |
uer/roberta-base-finetuned-cluener2020-chinese | 312ce0cd10634f238c0cd3205fa6905933394564 | 2022-02-20T07:51:53.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"zh",
"transformers",
"autotrain_compatible"
] | token-classification | false | uer | null | uer/roberta-base-finetuned-cluener2020-chinese | 4,582 | 6 | transformers | 924 | ---
language: zh
widget:
- text: "江苏警方通报特斯拉冲进店铺"
---
# Chinese RoBERTa-Base Model for NER
## Model description
The model is used for named entity recognition. You can download the model either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo) (in UER-py format), or via HuggingFace from... |
Babelscape/wikineural-multilingual-ner | 65382ee6df6c881a23b02fee6337dbde658ba24c | 2022-02-19T07:32:42.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"de",
"en",
"es",
"fr",
"it",
"nl",
"pl",
"pt",
"ru",
"dataset:Babelscape/wikineural",
"transformers",
"named-entity-recognition",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | token-classification | false | Babelscape | null | Babelscape/wikineural-multilingual-ner | 4,576 | 5 | transformers | 925 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
widget:
- text: "My name is Wolfgang and I live in Berlin."
- text: "George Washington went to Washington."
- text: "Mi nombre es Sarah y vivo en Londres."
- text: "Меня зовут Симона, и я живу в Риме."
tags:
- named-entity-recognition
... |
cl-tohoku/bert-base-japanese-char-whole-word-masking | fa5374ac8a5b2c6d3f5f8e156a9892cdf687201d | 2021-09-23T13:45:26.000Z | [
"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-char-whole-word-masking | 4,575 | 2 | transformers | 926 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 仙台は「[MASK]の都」と呼ばれている。
---
# BERT base Japanese (character tokenization, whole word masking enabled)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model proc... |
Helsinki-NLP/opus-mt-en-ro | 6d32771161c298fb3164c208e48fea050a85ab65 | 2021-09-09T21:38:44.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"ro",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-ro | 4,543 | null | transformers | 927 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-ro
* source languages: en
* target languages: ro
* OPUS readme: [en-ro](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ro/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
CAMeL-Lab/bert-base-arabic-camelbert-mix-ner | 40b8059a6f3bfbb49d64038e131f49b93cc37417 | 2021-10-17T11:13:00.000Z | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | CAMeL-Lab | null | CAMeL-Lab/bert-base-arabic-camelbert-mix-ner | 4,522 | 1 | transformers | 928 | ---
language:
- ar
license: apache-2.0
widget:
- text: "إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع"
---
# CAMeLBERT-Mix NER Model
## Model description
**CAMeLBERT-Mix NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Mix](https://huggingface.co/CAM... |
KoboldAI/fairseq-dense-125M | ee40bc27509c93cd551f2791bb9d462bbab4d450 | 2022-02-01T22:48:03.000Z | [
"pytorch",
"xglm",
"text-generation",
"transformers"
] | text-generation | false | KoboldAI | null | KoboldAI/fairseq-dense-125M | 4,511 | null | transformers | 929 | Entry not found |
ltgoslo/norbert2 | afb829e3d0b861bd5f8cda6522b32ca0b097d7eb | 2022-02-15T16:01:58.000Z | [
"pytorch",
"tf",
"bert",
"fill-mask",
"no",
"transformers",
"norwegian",
"license:cc-by-4.0",
"autotrain_compatible"
] | fill-mask | false | ltgoslo | null | ltgoslo/norbert2 | 4,511 | 3 | transformers | 930 | ---
language: no
license: cc-by-4.0
pipeline_tag: fill-mask
tags:
- norwegian
- bert
thumbnail: https://raw.githubusercontent.com/ltgoslo/NorBERT/main/Norbert.png
widget:
- text: "Nå ønsker de seg en [MASK] bolig. "
---
## Quickstart
**Release 2.0** (February 7, 2022)
Trained on the very large corpus of Norwegian (C4... |
Helsinki-NLP/opus-mt-en-fi | 627fe90df5c335be61521cd89c68f62e2bdce050 | 2021-09-09T21:35:17.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"fi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-fi | 4,505 | null | transformers | 931 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-fi
* source languages: en
* target languages: fi
* OPUS readme: [en-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-fi/README.md)
* dataset: opus+bt-news
* model: transformer
* pre-processing: normalization + SentencePiece
* dow... |
facebook/blenderbot-1B-distill | a10c0a628734b4cd46da57520bd35c7ca8965201 | 2021-06-17T12:02:20.000Z | [
"pytorch",
"blenderbot",
"text2text-generation",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"transformers",
"convAI",
"conversational",
"facebook",
"license:apache-2.0",
"autotrain_compatible"
] | conversational | false | facebook | null | facebook/blenderbot-1B-distill | 4,497 | 9 | transformers | 932 | ---
language:
- en
thumbnail:
tags:
- convAI
- conversational
- facebook
license: apache-2.0
datasets:
- blended_skill_talk
metrics:
- perplexity
---
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes... |
indigo-ai/BERTino | 4539d128176da9bbadf98ab813c9acaf68e8d8f1 | 2021-09-22T08:51:24.000Z | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"it",
"transformers",
"DISTILbert",
"Italian",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | indigo-ai | null | indigo-ai/BERTino | 4,468 | 5 | transformers | 933 | ---
language: it
tags:
- DISTILbert
- Italian
license: mit
widget:
- text: Vado al [MASK] a fare la spesa
- text: Vado al parco a guardare le [MASK]
- text: Il cielo è [MASK] di stelle.
---
# BERTino: an Italian DistilBERT model
This repository hosts BERTino, an Italian DistilBERT model pre-trained by
[indigo.ai](htt... |
microsoft/unispeech-sat-base | ca010627e5d232d9179ebdcd615eaa02cea382ed | 2021-11-05T12:41:05.000Z | [
"pytorch",
"unispeech-sat",
"pretraining",
"en",
"dataset:librispeech_asr",
"arxiv:2110.05752",
"transformers",
"speech"
] | null | false | microsoft | null | microsoft/unispeech-sat-base | 4,458 | null | transformers | 934 | ---
language:
- en
datasets:
- librispeech_asr
tags:
- speech
---
# UniSpeech-SAT-Base
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The base model pretrained on 16kHz sampled speech audio with utterance... |
hf-internal-testing/tiny-xlm-roberta | 915669c23332981bc5936cf93abbd395f487ac38 | 2021-07-16T01:27:42.000Z | [
"pytorch",
"xlm-roberta",
"text-generation",
"transformers"
] | text-generation | false | hf-internal-testing | null | hf-internal-testing/tiny-xlm-roberta | 4,446 | null | transformers | 935 | This is a tiny random {mname_tiny} model to be used for basic testing |
HooshvareLab/bert-base-parsbert-uncased | d73a0e2c7492c33bd5819bcdb23eba207404dd19 | 2021-05-18T20:47:21.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"arxiv:2005.12515",
"transformers",
"autotrain_compatible"
] | fill-mask | false | HooshvareLab | null | HooshvareLab/bert-base-parsbert-uncased | 4,431 | 4 | transformers | 936 | ## ParsBERT: Transformer-based Model for Persian Language Understanding
ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base.
Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
All the models (downstream tasks) are uncase... |
arampacha/roberta-tiny | f55131409ab048f087bf793ad4009b8a463e8a7b | 2022-05-20T22:07:50.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | arampacha | null | arampacha/roberta-tiny | 4,425 | null | transformers | 937 | # roberta-tiny
Tiny untrained model for testing purposes |
dumitrescustefan/bert-base-romanian-uncased-v1 | 9eb44d518953103bdc9f088217ec978c6ec9a9e4 | 2021-11-02T15:26:10.000Z | [
"pytorch",
"jax",
"bert",
"ro",
"transformers"
] | null | false | dumitrescustefan | null | dumitrescustefan/bert-base-romanian-uncased-v1 | 4,422 | 3 | transformers | 938 | ---
language: ro
---
# bert-base-romanian-uncased-v1
The BERT **base**, **uncased** model for Romanian, trained on a 15GB corpus, version 
### How to use
```python
from transformers import AutoTokenizer, AutoModel
import torch
# load tokenizer and mo... |
cardiffnlp/twitter-roberta-base-hate | 82cfd645130b125b8e8de63d90b0376620a0bfbd | 2021-05-20T15:02:45.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-hate | 4,413 | 3 | transformers | 939 | # Twitter-roBERTa-base for Hate Speech Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech 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... |
gogamza/kobart-base-v1 | d7e64abd841bc1fa5d2939d14161124c51f29e8b | 2021-11-11T07:43:48.000Z | [
"pytorch",
"bart",
"feature-extraction",
"ko",
"transformers",
"license:mit"
] | feature-extraction | false | gogamza | null | gogamza/kobart-base-v1 | 4,384 | null | transformers | 940 | ---
language: ko
tags:
- bart
license: mit
---
## KoBART-base-v1
```python
from transformers import PreTrainedTokenizerFast, BartModel
tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v1')
model = BartModel.from_pretrained('gogamza/kobart-base-v1')
```
|
Helsinki-NLP/opus-mt-hu-en | 62599d9d6eca96022d26974486779623f09ebc9c | 2021-09-09T22:10:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"hu",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-hu-en | 4,372 | null | transformers | 941 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-hu-en
* source languages: hu
* target languages: en
* OPUS readme: [hu-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hu-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
monologg/biobert_v1.1_pubmed | 3abac4e8d78fbeb1ccdfa0079dcfe678f3337e0a | 2021-05-19T23:50:54.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | monologg | null | monologg/biobert_v1.1_pubmed | 4,366 | 1 | transformers | 942 | Entry not found |
Salesforce/grappa_large_jnt | 0d2500a00f3a4b71addaf09a0c1abe30788362dd | 2021-05-20T12:23:41.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Salesforce | null | Salesforce/grappa_large_jnt | 4,361 | 1 | transformers | 943 | Entry not found |
nreimers/albert-small-v2 | 18045fa83de53fd7d4548fdc2473862914cbc7d5 | 2021-05-31T12:26:52.000Z | [
"pytorch",
"albert",
"feature-extraction",
"transformers"
] | feature-extraction | false | nreimers | null | nreimers/albert-small-v2 | 4,351 | null | transformers | 944 | # albert-small-v2
This is a 6 layer version of [albert-base-v2](https://huggingface.co/albert-base-v2). |
EMBEDDIA/crosloengual-bert | 750255b6915cf42623143690d8ea79ceab8ee2e8 | 2021-05-18T18:21:38.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"hr",
"sl",
"en",
"multilingual",
"arxiv:2006.07890",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | fill-mask | false | EMBEDDIA | null | EMBEDDIA/crosloengual-bert | 4,336 | 1 | transformers | 945 | ---
language:
- hr
- sl
- en
- multilingual
license: cc-by-4.0
---
# CroSloEngual BERT
CroSloEngual BERT is a trilingual model, using bert-base architecture, trained on Croatian, Slovenian, and English corpora. Focusing on three languages, the model performs better than [multilingual BERT](https://huggingface.co/bert... |
microsoft/wavlm-base-plus-sv | feb593a6c23c1cc3d9510425c29b0a14d2b07b1e | 2022-03-25T10:39:41.000Z | [
"pytorch",
"wavlm",
"audio-xvector",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.13900",
"transformers",
"speech"
] | null | false | microsoft | null | microsoft/wavlm-base-plus-sv | 4,325 | 4 | transformers | 946 | ---
language:
- en
tags:
- speech
---
# WavLM-Base-Plus for Speaker Verification
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also... |
sampathkethineedi/industry-classification | 9914232048445adee24e4d4683c4d1873a9bafb4 | 2020-07-16T15:27:38.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"en",
"transformers",
"tensorflow",
"industry",
"buisiness",
"description",
"multi-class",
"classification"
] | text-classification | false | sampathkethineedi | null | sampathkethineedi/industry-classification | 4,322 | 3 | transformers | 947 | ---
language: "en"
thumbnail: "https://huggingface.co/sampathkethineedi"
tags:
- distilbert
- pytorch
- tensorflow
- text-classification
- industry
- buisiness
- description
- multi-class
- classification
liscence: "mit"
inference: false
---
# industry-classification
## Model description
DistilBERT Model to classif... |
monologg/kobigbird-bert-base | ceacda477e20abef2c929adfa4a07c6f811323be | 2021-11-05T01:27:29.000Z | [
"pytorch",
"big_bird",
"fill-mask",
"ko",
"transformers",
"korean",
"autotrain_compatible"
] | fill-mask | false | monologg | null | monologg/kobigbird-bert-base | 4,319 | 11 | transformers | 948 | ---
language: ko
tags:
- korean
mask_token: "[MASK]"
widget:
- text: 대한민국의 수도는 [MASK] 입니다.
---
# KoBigBird
<img src="https://user-images.githubusercontent.com/28896432/140442206-e34b02d5-e279-47e5-9c2a-db1278b1c14d.png" width="200"/>
Pretrained BigBird Model for Korean (**kobigbird-bert-base**)
## About
BigBir... |
Hate-speech-CNERG/dehatebert-mono-spanish | 2b9664ac59ee7f0b054fc0b1433cbedff3c2bdba | 2021-09-25T14:00:12.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"es",
"arxiv:2004.06465",
"transformers",
"license:apache-2.0"
] | text-classification | false | Hate-speech-CNERG | null | Hate-speech-CNERG/dehatebert-mono-spanish | 4,307 | 2 | transformers | 949 | ---
language: es
license: apache-2.0
---
This model is used detecting **hatespeech** in **Spanish language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rate... |
nferruz/ProtGPT2 | 959b229096b77edeb7656373bf9e379a3b6458ea | 2022-05-25T09:31:14.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | nferruz | null | nferruz/ProtGPT2 | 4,302 | 11 | transformers | 950 | # **ProtGPT2**
ProtGPT2 ([preprint](https://www.biorxiv.org/content/10.1101/2022.03.09.483666v1)) is a language model that speaks the protein language and can be used for de novo protein design and engineering. ProtGPT2 generated sequences conserve natural proteins' critical features (amino acid propensities, secondar... |
microsoft/layoutlm-base-cased | 91acf0f93186fbcebb9f80c2e2259754f0ef922b | 2021-09-27T05:55:31.000Z | [
"pytorch",
"layoutlm",
"arxiv:1912.13318",
"transformers"
] | null | false | microsoft | null | microsoft/layoutlm-base-cased | 4,301 | 5 | transformers | 951 | # LayoutLM
**Multimodal (text + layout/format + image) pre-training for document AI**
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlm)
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document ... |
nateraw/vit-base-beans | cecf50fd479911ff2b12b74d85347bbaf91a3e1c | 2022-07-08T07:04:19.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"en",
"dataset:beans",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | nateraw | null | nateraw/vit-base-beans | 4,300 | 3 | transformers | 952 | ---
language: en
license: apache-2.0
tags:
- generated_from_trainer
- image-classification
datasets:
- beans
metrics:
- accuracy
widget:
- src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/healthy.jpeg
example_title: Healthy
- src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/angular_leaf_... |
cross-encoder/ms-marco-MiniLM-L-4-v2 | 1f1ab0943a42a52afd702e7e8337bec985c189ea | 2021-08-05T08:39:32.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/ms-marco-MiniLM-L-4-v2 | 4,278 | null | transformers | 953 | ---
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).... |
junnyu/roformer_chinese_base | f780ca57e5eab4627f334bed71a8e5a353b33d69 | 2022-01-04T11:46:28.000Z | [
"pytorch",
"tf",
"jax",
"roformer",
"fill-mask",
"zh",
"arxiv:2104.09864",
"transformers",
"tf2.0",
"autotrain_compatible"
] | fill-mask | false | junnyu | null | junnyu/roformer_chinese_base | 4,264 | 8 | transformers | 954 | ---
language: zh
tags:
- roformer
- pytorch
- tf2.0
widget:
- text: "今天[MASK]很好，我想去公园玩！"
---
## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
## pytorch使用
```python
import torch
from transformers import RoFormerForMaskedLM, RoFormerTokenizer... |
nlpaueb/bert-base-uncased-eurlex | 77adac5a840314c4d688f565a925db1bdd2e87e7 | 2022-04-28T14:44:15.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"en",
"transformers",
"legal",
"license:cc-by-sa-4.0",
"fill-mask"
] | fill-mask | false | nlpaueb | null | nlpaueb/bert-base-uncased-eurlex | 4,251 | 4 | transformers | 955 | ---
language: en
pipeline_tag: fill-mask
license: cc-by-sa-4.0
thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png
tags:
- legal
widget:
- text: "Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products."
---
# LEGAL-... |
bert-large-cased-whole-word-masking | 8b5d59881077680d99e2cac339412f20a180bd45 | 2021-05-18T16:30:05.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-large-cased-whole-word-masking | 4,250 | 1 | transformers | 956 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (cased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](h... |
google/fnet-base | d764d799e9bb72d0429a64d1512416a47e2246b3 | 2021-10-31T07:33:21.000Z | [
"pytorch",
"rust",
"fnet",
"pretraining",
"en",
"dataset:c4",
"arxiv:2105.03824",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/fnet-base | 4,247 | 6 | transformers | 957 | ---
language: en
tags:
- fnet
license: apache-2.0
datasets:
- c4
---
# FNet base model
Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was
introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository](... |
hfl/chinese-macbert-large | 1cf2677c782975600ce58e2961656b1b29eddbae | 2021-05-19T19:14:18.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | hfl | null | hfl/chinese-macbert-large | 4,240 | 4 | transformers | 958 | ---
language:
- zh
tags:
- bert
license: "apache-2.0"
---
<p align="center">
<br>
<img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/>
<br>
</p>
<p align="center">
<a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE">
<img alt="GitHub" src="https://img.... |
jonatasgrosman/wav2vec2-large-xlsr-53-dutch | 9e474f681b88972e3a9e5065877e4cfca8b599e2 | 2022-07-27T23:36:29.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"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-dutch | 4,195 | null | transformers | 959 | ---
language: nl
license: apache-2.0
datasets:
- common_voice
- mozilla-foundation/common_voice_6_0
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
- mozilla-foundation/common_voice_6_0
- nl
- robust-speech-event
- speech
- xlsr-fine-tuning-week
model-index:
- name: XLSR Wav2Vec2 ... |
hf-internal-testing/tiny-vilt-random-vqa | f923b0b312e4ded9ed2c3e2128f54c6b46742331 | 2022-05-16T14:49:45.000Z | [
"pytorch",
"vilt",
"question-answering",
"arxiv:2102.03334",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | hf-internal-testing | null | hf-internal-testing/tiny-vilt-random-vqa | 4,185 | null | transformers | 960 | ---
license: apache-2.0
---
A tiny randomly-initialized [ViLT](https://arxiv.org/abs/2102.03334) used for unit tests in the Transformers VQA pipeline |
lysandre/tiny-vit-random | 4108f68f9fe6e09c515d9c22c4453a5c892ec97f | 2021-05-05T14:04:37.000Z | [
"pytorch",
"vit",
"image-classification",
"transformers"
] | image-classification | false | lysandre | null | lysandre/tiny-vit-random | 4,157 | null | transformers | 961 | Entry not found |
jeniya/BERTOverflow | 0361ca9842fd3f88b2e4eea1626d56c2e1265bce | 2021-05-19T20:47:17.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | jeniya | null | jeniya/BERTOverflow | 4,155 | 2 | transformers | 962 |
# BERTOverflow
## Model description
We pre-trained BERT-base model on 152 million sentences from the StackOverflow's 10 year archive. More details of this model can be found in our ACL 2020 paper: [Code and Named Entity Recognition in StackOverflow](https://www.aclweb.org/anthology/2020.acl-main.443/). We would lik... |
Helsinki-NLP/opus-mt-cs-en | 186ab5dff3e18ca970a492525c0ca4b398d525ab | 2021-09-09T21:29:22.000Z | [
"pytorch",
"marian",
"text2text-generation",
"cs",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-cs-en | 4,148 | null | transformers | 963 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-cs-en
* source languages: cs
* target languages: en
* OPUS readme: [cs-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/cs-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
monologg/bert-base-cased-goemotions-ekman | 77ca9484d57dfd55bca8ec15b503ce7485d207e9 | 2021-05-19T23:47:57.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | monologg | null | monologg/bert-base-cased-goemotions-ekman | 4,133 | 2 | transformers | 964 | Entry not found |
peterchou/simbert-chinese-base | 6a6ebb9f9d9b2264a8a012f96de01067f304476d | 2021-06-07T05:21:51.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | peterchou | null | peterchou/simbert-chinese-base | 4,118 | 2 | transformers | 965 | Entry not found |
facebook/data2vec-text-base | bd0db19c3500ee7a0b626791db67fa6e9fda9a0b | 2022-04-18T16:03:20.000Z | [
"pytorch",
"data2vec-text",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2202.03555",
"arxiv:1806.02847",
"transformers",
"exbert",
"license:mit"
] | feature-extraction | false | facebook | null | facebook/data2vec-text-base | 4,111 | 5 | transformers | 966 | ---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
# Data2Vec-Text base model
Pretrained model on English language using the *data2vec* objective. It was introduced in
[this paper](https://arxiv.org/abs/2202.03555) and first released in
[this repository](https://github.com/pytorch/fai... |
uer/roberta-base-chinese-extractive-qa | d5e37a8228fa9d396ff4b093c21e8f0082ff11e1 | 2022-02-20T07:50:56.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"zh",
"transformers",
"autotrain_compatible"
] | question-answering | false | uer | null | uer/roberta-base-chinese-extractive-qa | 4,104 | 12 | transformers | 967 | ---
language: zh
widget:
- text: "著名诗歌《假如生活欺骗了你》的作者是"
context: "普希金从那里学习人民的语言，吸取了许多有益的养料，这一切对普希金后来的创作产生了很大的影响。这两年里，普希金创作了不少优秀的作品，如《囚徒》、《致大海》、《致凯恩》和《假如生活欺骗了你》等几十首抒情诗，叙事诗《努林伯爵》，历史剧《鲍里斯·戈都诺夫》，以及《叶甫盖尼·奥涅金》前六章。"
---
# Chinese RoBERTa-Base Model for QA
## Model description
The model is used for extractive question ans... |
JamesStratford/Pidrow-bot-DialoGPT-Large | 93c968f2e1170bd410e447cde821057310bbfe27 | 2022-07-04T21:54:03.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | JamesStratford | null | JamesStratford/Pidrow-bot-DialoGPT-Large | 4,095 | null | transformers | 968 | ---
tags:
- conversational
---
# Pidrow bot - large |
vinai/phobert-large | 6d9abcb5c5a28afca335255baace25ef76c1d5bf | 2022-06-08T04:44:41.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2003.00744",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vinai | null | vinai/phobert-large | 4,084 | 1 | transformers | 969 | # <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first publ... |
IlyaGusev/news_tg_rubert | 074a7437d070e8fefd0e10890d418fb26a409403 | 2021-06-16T19:43:26.000Z | [
"pytorch",
"ru",
"license:apache-2.0"
] | null | false | IlyaGusev | null | IlyaGusev/news_tg_rubert | 4,066 | null | null | 970 | ---
language:
- ru
license: apache-2.0
---
# NewsTgRuBERT
Training script: https://github.com/dialogue-evaluation/Russian-News-Clustering-and-Headline-Generation/blob/main/train_mlm.py |
Hate-speech-CNERG/bert-base-uncased-hatexplain | e487c81b768c7532bf474bd5e486dedea4cf3848 | 2021-05-25T09:53:05.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"dataset:hatexplain",
"transformers",
"license:apache-2.0"
] | text-classification | false | Hate-speech-CNERG | null | Hate-speech-CNERG/bert-base-uncased-hatexplain | 4,064 | 5 | transformers | 971 | ---
language: en
license: apache-2.0
datasets:
- hatexplain
---
The model is used for classifying a text as **Hatespeech**, **Offensive**, or **Normal**. The model is trained using data from Gab and Twitter and *Human Rationales* were included as part of the training data to boost the performance.
The dataset and mo... |
sentence-transformers/quora-distilbert-multilingual | 43c541d8cdd793eed04a9c2d66c6f971198681da | 2022-06-15T20:31:58.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/quora-distilbert-multilingual | 4,048 | null | sentence-transformers | 972 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/quora-distilbert-multilingual
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensiona... |
mrm8488/spanbert-finetuned-squadv2 | 5cc65f623285da8704169ac2d88cb8941db5520c | 2021-05-20T00:56:45.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"arxiv:1907.10529",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/spanbert-finetuned-squadv2 | 4,041 | 2 | transformers | 973 | ---
language: en
thumbnail:
---
# SpanBERT (spanbert-base-cased) 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.
... |
valhalla/longformer-base-4096-finetuned-squadv1 | 159b6205769b6f41a68d8d190af8d5df43ef16ca | 2021-02-10T16:35:40.000Z | [
"pytorch",
"tf",
"rust",
"longformer",
"question-answering",
"dataset:squad_v1",
"arxiv:2004.05150",
"transformers",
"license:mit",
"autotrain_compatible"
] | question-answering | false | valhalla | null | valhalla/longformer-base-4096-finetuned-squadv1 | 4,034 | 5 | transformers | 974 | ---
datasets:
- squad_v1
license: mit
---
# LONGFORMER-BASE-4096 fine-tuned on SQuAD v1
This is longformer-base-4096 model fine-tuned on SQuAD v1 dataset for question answering task.
[Longformer](https://arxiv.org/abs/2004.05150) model created by Iz Beltagy, Matthew E. Peters, Arman Coha from AllenAI. As the paper... |
Jonesy/DialoGPT-small_JT | 15890d5e6e8b1966ef6b5ef1ff5c37c27acceda0 | 2021-10-15T18:56:31.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Jonesy | null | Jonesy/DialoGPT-small_JT | 4,033 | null | transformers | 975 | ---
tags:
- conversational
---
# Johnny Test DialoGPT Model |
pranavpsv/gpt2-genre-story-generator | d617d856b2aae05be1c253a2e9daf8c99f1d9c6d | 2021-05-23T11:02:06.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | pranavpsv | null | pranavpsv/gpt2-genre-story-generator | 4,020 | 6 | transformers | 976 |
# GPT2 Genre Based Story Generator
## Model description
GPT2 fine-tuned on genre-based story generation.
## Intended uses
Used to generate stories based on user inputted genre and starting prompts.
## How to use
#### Supported Genres
superhero, action, drama, horror, thriller, sci_fi
#### Input text format
\<BOS... |
mrm8488/longformer-base-4096-finetuned-squadv2 | d11ef97abccc471765cb85c8ffb1ca0de878adf5 | 2022-07-23T01:27:36.000Z | [
"pytorch",
"tf",
"longformer",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:2004.05150",
"transformers",
"QA",
"long context",
"Q&A",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/longformer-base-4096-finetuned-squadv2 | 4,015 | 3 | transformers | 977 | ---
language: en
datasets:
- squad_v2
tags:
- QA
- long context
- Q&A
---
# Longformer-base-4096 fine-tuned on SQuAD v2
[Longformer-base-4096 model](https://huggingface.co/allenai/longformer-base-4096) fine-tuned on [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
## Longformer-ba... |
valhalla/bart-large-finetuned-squadv1 | 39066834d88b80b83e85b88e37471cee02e1a8c7 | 2021-06-14T10:20:35.000Z | [
"pytorch",
"jax",
"bart",
"question-answering",
"dataset:squad",
"arxiv:1910.13461",
"transformers",
"autotrain_compatible"
] | question-answering | false | valhalla | null | valhalla/bart-large-finetuned-squadv1 | 4,012 | 1 | transformers | 978 | ---
datasets:
- squad
---
# BART-LARGE finetuned on SQuADv1
This is bart-large model finetuned on SQuADv1 dataset for question answering task
## Model details
BART was propsed in the [paper](https://arxiv.org/abs/1910.13461) **BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Transla... |
jjzha/jobbert-base-cased | e6fcd777778602f9cdfd0f9a0fdff14e4e643a1f | 2022-07-26T08:14:26.000Z | [
"pytorch",
"bert",
"fill-mask",
"en",
"transformers",
"JobBERT",
"job postings",
"autotrain_compatible"
] | fill-mask | false | jjzha | null | jjzha/jobbert-base-cased | 4,005 | 3 | transformers | 979 | ---
language:
- en
tags:
- JobBERT
- job postings
---
# JobBERT
This is the JobBERT model from:
Mike Zhang, Kristian Nørgaard Jensen, Sif Dam Sonniks, and Barbara Plank. __SkillSpan: Hard and Soft Skill Extraction from Job Postings__. Proceedings of the 2022 Conference of the North American Chapter of the Asso... |
mrm8488/bert-italian-finedtuned-squadv1-it-alfa | 829047a5ce1a8ef73bde97666eff10b8e128b42e | 2021-05-20T00:24:19.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"it",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/bert-italian-finedtuned-squadv1-it-alfa | 4,001 | 5 | transformers | 980 | ---
language: it
thumbnail:
---
# Italian BERT fine-tuned on SQuAD_it v1
[Italian BERT base cased](https://huggingface.co/dbmdz/bert-base-italian-cased) fine-tuned on [italian SQuAD](https://github.com/crux82/squad-it) for **Q&A** downstream task.
## Details of Italian BERT
The source data for the Italian BERT mode... |
facebook/wav2vec2-xlsr-53-espeak-cv-ft | 2c733782da5604684829819a5eb744c193fe9398 | 2021-12-10T17:18:39.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"multi-lingual",
"dataset:common_voice",
"arxiv:2109.11680",
"transformers",
"speech",
"audio",
"phoneme-recognition",
"license:apache-2.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-xlsr-53-espeak-cv-ft | 3,995 | 5 | transformers | 981 | ---
language: multi-lingual
datasets:
- common_voice
tags:
- speech
- audio
- automatic-speech-recognition
- phoneme-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.huggingface.co... |
microsoft/trocr-base-stage1 | 5c48f939de25655eeca55d31e7893ada48d300d9 | 2022-07-01T07:36:00.000Z | [
"pytorch",
"vision-encoder-decoder",
"arxiv:2109.10282",
"transformers",
"trocr",
"image-to-text"
] | image-to-text | false | microsoft | null | microsoft/trocr-base-stage1 | 3,987 | 4 | transformers | 982 | ---
tags:
- trocr
- image-to-text
---
# TrOCR (base-sized model, pre-trained only)
TrOCR pre-trained only model. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https... |
ckiplab/gpt2-base-chinese | 5b35975016d00703e7d812b9197ea81f295b65c3 | 2022-05-10T03:28:12.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"zh",
"transformers",
"lm-head",
"license:gpl-3.0"
] | text-generation | false | ckiplab | null | ckiplab/gpt2-base-chinese | 3,972 | 5 | transformers | 983 | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- lm-head
- gpt2
- zh
license: gpl-3.0
---
# CKIP GPT2 Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-o... |
gsarti/biobert-nli | 4fe2765eca1ae00a266615626313a94192701870 | 2021-05-19T17:45:15.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | gsarti | null | gsarti/biobert-nli | 3,959 | 1 | transformers | 984 | # BioBERT-NLI
This is the model [BioBERT](https://github.com/dmis-lab/biobert) [1] fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) t... |
CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment | b0df630d23e5aa5f8a326017605337f6d0863ecd | 2021-10-17T11:15:54.000Z | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | false | CAMeL-Lab | null | CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment | 3,952 | 4 | transformers | 985 | ---
language:
- ar
license: apache-2.0
widget:
- text: "أنا بخير"
---
# CAMeLBERT-DA SA Model
## Model description
**CAMeLBERT-DA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Dialectal Arabic (DA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
Fo... |
izumi-lab/electra-base-japanese-discriminator | 9a50f726d330062f6055f9db584820796839f53c | 2022-03-19T09:43:01.000Z | [
"pytorch",
"electra",
"pretraining",
"ja",
"dataset:wikipedia",
"arxiv:2003.10555",
"transformers",
"license:cc-by-sa-4.0"
] | null | false | izumi-lab | null | izumi-lab/electra-base-japanese-discriminator | 3,952 | 1 | transformers | 986 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 東京大学で[MASK]の研究をしています。
---
# ELECTRA base Japanese discriminator
This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts in the Japanese language.
The codes for the pretraining are available at [retar... |
facebook/deit-small-patch16-224 | 164deee347853469b97442b3817f22eece80c7e3 | 2022-07-13T11:41:40.000Z | [
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"transformers",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/deit-small-patch16-224 | 3,945 | 1 | transformers | 987 | ---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet-1k
---
# Data-efficient Image Transformer (small-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper... |
monologg/kocharelectra-base-discriminator | bb615f43065306f6d155448d717c90f2367810b1 | 2020-05-27T17:34:11.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | monologg | null | monologg/kocharelectra-base-discriminator | 3,939 | null | transformers | 988 | Entry not found |
KETI-AIR/ke-t5-base | 3fdf631a2986e928ad8b22863f9da16da74d9906 | 2021-06-23T02:50:50.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | KETI-AIR | null | KETI-AIR/ke-t5-base | 3,933 | 2 | transformers | 989 | Entry not found |
sachaarbonel/bert-italian-cased-finetuned-pos | aaa6d3c06defeaac28cd16ccc4206e4e83b741b8 | 2021-05-19T00:47:05.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"it",
"dataset:xtreme",
"transformers",
"autotrain_compatible"
] | token-classification | false | sachaarbonel | null | sachaarbonel/bert-italian-cased-finetuned-pos | 3,925 | 2 | transformers | 990 | ---
language: it
datasets:
- xtreme
---
# Italian-Bert (Italian Bert) + POS 🎃🏷
This model is a fine-tuned on [xtreme udpos Italian](https://huggingface.co/nlp/viewer/?dataset=xtreme&config=udpos.Italian) version of [Bert Base Italian](https://huggingface.co/dbmdz/bert-base-italian-cased) for **POS** downstream tas... |
indobenchmark/indobert-base-p2 | 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f | 2021-05-19T20:24:07.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"transformers",
"indobert",
"indobenchmark",
"indonlu",
"license:mit"
] | feature-extraction | false | indobenchmark | null | indobenchmark/indobert-base-p2 | 3,920 | null | transformers | 991 | ---
language: id
tags:
- indobert
- indobenchmark
- indonlu
license: mit
inference: false
datasets:
- Indo4B
---
# IndoBERT Base Model (phase2 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a ma... |
inokufu/flaubert-base-uncased-xnli-sts | e4fcf0bc37d8d55fbbe1cc8096eaeaa4057d2560 | 2022-02-18T16:50:56.000Z | [
"pytorch",
"flaubert",
"feature-extraction",
"fr",
"dataset:xnli",
"dataset:stsb_multi_mt",
"arxiv:1809.05053",
"sentence-transformers",
"sentence-similarity",
"transformers",
"xnli",
"stsb_multi_mt"
] | sentence-similarity | false | inokufu | null | inokufu/flaubert-base-uncased-xnli-sts | 3,915 | 2 | sentence-transformers | 992 | ---
pipeline_tag: sentence-similarity
language: fr
tags:
- sentence-similarity
- transformers
- fr
- flaubert
- sentence-transformers
- feature-extraction
- xnli
- stsb_multi_mt
datasets:
- xnli
- stsb_multi_mt
---
# inokufu/flaubert-base-uncased-xnli-sts
This is a [sentence-transformers](https://... |
hireddivas/dialoGPT-small-sonic2 | 313bb626a1abb8a416bc552909c060057227ffb7 | 2022-07-30T04:00:02.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | hireddivas | null | hireddivas/dialoGPT-small-sonic2 | 3,896 | null | transformers | 993 | ---
tags:
- conversational
---
GPT-2 chatbot - talk to Sonic (improved?) |
izumi-lab/electra-small-japanese-fin-discriminator | 1bcdc65cbe828ebc2d33a369b4c51f8ffa2da1a9 | 2022-03-19T09:39:07.000Z | [
"pytorch",
"electra",
"pretraining",
"ja",
"dataset:wikipedia",
"dataset:securities reports",
"dataset:summaries of financial results",
"arxiv:2003.10555",
"transformers",
"finance",
"license:cc-by-sa-4.0"
] | null | false | izumi-lab | null | izumi-lab/electra-small-japanese-fin-discriminator | 3,892 | null | transformers | 994 | ---
language: ja
license: cc-by-sa-4.0
tags:
- finance
datasets:
- wikipedia
- securities reports
- summaries of financial results
widget:
- text: 流動[MASK]は1億円となりました。
---
# ELECTRA small Japanese finance discriminator
This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts i... |
twmkn9/albert-base-v2-squad2 | eb613ac0a88a507c701b46de597aff50a1089dc1 | 2020-12-11T22:02:54.000Z | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | twmkn9 | null | twmkn9/albert-base-v2-squad2 | 3,881 | 2 | transformers | 995 | This model is [ALBERT base v2](https://huggingface.co/albert-base-v2) trained on SQuAD v2 as:
```
export SQUAD_DIR=../../squad2
python3 run_squad.py
--model_type albert
--model_name_or_path albert-base-v2
--do_train
--do_eval
--overwrite_cache
--do_lower_case
--version_2_with_negativ... |
google/bert_uncased_L-8_H-512_A-8 | 53b17ea0d90728ac770dfd13fcb3abdc75e4f4bf | 2021-05-19T17:35:53.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-8_H-512_A-8 | 3,858 | 1 | transformers | 996 | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... |
Helsinki-NLP/opus-mt-en-da | 9786126ba34f1f86636af779ef13557bd9d1b246 | 2021-09-09T21:34:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"da",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-da | 3,853 | 1 | transformers | 997 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-da
* source languages: en
* target languages: da
* OPUS readme: [en-da](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-da/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
wtrClover/DialoGPT-small-TwilightBot | c586ae8e0122f0287427a2087cf67fe5c55bc51b | 2022-01-28T00:29:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | wtrClover | null | wtrClover/DialoGPT-small-TwilightBot | 3,838 | null | transformers | 998 | ---
tags:
- conversational
---
# MLP DialoGPT Model based on Twilight Sparkle |
castorini/doc2query-t5-base-msmarco | 53926b5364548f0c81b39a31723184d3664aaf06 | 2021-11-24T17:57:59.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | castorini | null | castorini/doc2query-t5-base-msmarco | 3,832 | 5 | transformers | 999 | For more information, check [doc2query.ai](http://doc2query.ai) |
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