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Salesforce/codegen-2B-multi | a5164ee330b2f0a87e216d2f93f5f33000a6d1a8 | 2022-06-28T17:45:32.000Z | [
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"transformers",
"license:bsd-3-clause"
] | text-generation | false | Salesforce | null | Salesforce/codegen-2B-multi | 1,906 | 3 | transformers | 1,400 | ---
license: bsd-3-clause
---
# CodeGen (CodeGen-Multi 2B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan W... |
microsoft/cocolm-large | 6947b62b7ca98d5f883c291b7a32e9b2d54130ef | 2022-02-07T22:49:54.000Z | [
"pytorch",
"arxiv:2102.08473",
"transformers"
] | null | false | microsoft | null | microsoft/cocolm-large | 1,901 | 5 | transformers | 1,401 | # COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
This model card contains the COCO-LM model (**large++** version) proposed in [this paper](https://arxiv.org/abs/2102.08473). The official GitHub repository can be found [here](https://github.com/microsoft/COCO-LM).
# Citation
If you f... |
tennessejoyce/titlewave-t5-base | fb30007e56801afadefbd4d60cb3b36631dce9e8 | 2021-06-23T14:26:41.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"license:cc-by-4.0",
"summarization",
"autotrain_compatible"
] | summarization | false | tennessejoyce | null | tennessejoyce/titlewave-t5-base | 1,892 | 3 | transformers | 1,402 | ---
language: en
license: cc-by-4.0
pipeline_tag: summarization
widget:
- text: "Example question body."
---
# Titlewave: t5-base
## Model description
Titlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See https://github.com/tennessejoyce/TitleWave for more informa... |
CLTL/MedRoBERTa.nl | 11b28aeb2da629c4a6205514043d78c7db0913a0 | 2022-02-02T11:56:21.000Z | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | CLTL | null | CLTL/MedRoBERTa.nl | 1,888 | null | transformers | 1,403 | ---
language: nl
license: mit
---
# MedRoBERTa.nl
## Description
This model is a RoBERTa-based model pre-trained from scratch on Dutch hospital notes sourced from Electronic Health Records. The model is not fine-tuned. All code used for the creation of MedRoBERTa.nl can be found at https://github.com/cltl-students/v... |
Helsinki-NLP/opus-mt-sk-en | 86dc882b010210ccd3993b3375dcd88c366080d4 | 2021-09-10T14:03:21.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sk",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sk-en | 1,886 | null | transformers | 1,404 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sk-en
* source languages: sk
* target languages: en
* OPUS readme: [sk-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sk-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
hf-internal-testing/tiny-random-clip-zero-shot-image-classification | b965c5deee645e96dcc40a8cdd260a7595b93354 | 2022-02-23T10:44:13.000Z | [
"pytorch",
"tf",
"clip",
"feature-extraction",
"transformers",
"zero-shot-image-classification"
] | zero-shot-image-classification | false | hf-internal-testing | null | hf-internal-testing/tiny-random-clip-zero-shot-image-classification | 1,883 | null | transformers | 1,405 | ---
pipeline_tag: zero-shot-image-classification
---
|
AkshatSurolia/ICD-10-Code-Prediction | 30847eeecb162b43cd6c151688b7971605ea7682 | 2022-02-28T10:06:41.000Z | [
"pytorch",
"bert",
"dataset:Mimic III",
"transformers",
"text-classification",
"license:apache-2.0"
] | text-classification | false | AkshatSurolia | null | AkshatSurolia/ICD-10-Code-Prediction | 1,879 | null | transformers | 1,406 | ---
license: apache-2.0
tags:
- text-classification
datasets:
- Mimic III
---
# Clinical BERT for ICD-10 Prediction
The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K +... |
Helsinki-NLP/opus-mt-tl-en | 15b5306fe4cd1d54e6e4e387084214284c1d3939 | 2020-08-21T14:42:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"tl",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tl-en | 1,856 | null | transformers | 1,407 | ---
language:
- tl
- en
tags:
- translation
license: apache-2.0
---
### tgl-eng
* source group: Tagalog
* target group: English
* OPUS readme: [tgl-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-eng/README.md)
* model: transformer-align
* source language(s): tgl_Latn
* target la... |
deepmind/language-perceiver | bb9db7197e2f3209b2b8e970aa0d53e3eaf30fa2 | 2021-12-15T14:51:13.000Z | [
"pytorch",
"perceiver",
"fill-mask",
"dataset:wikipedia",
"dataset:c4",
"arxiv:1810.04805",
"arxiv:2107.14795",
"arxiv:2004.03720",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | deepmind | null | deepmind/language-perceiver | 1,856 | 5 | transformers | 1,408 | ---
license: apache-2.0
tags:
datasets:
- wikipedia
- c4
inference: false
---
# Perceiver IO for language
Perceiver IO model pre-trained on the Masked Language Modeling (MLM) task proposed in [BERT](https://arxiv.org/abs/1810.04805) using a large text corpus obtained by combining [English Wikipedia](https://huggingfa... |
m3hrdadfi/typo-detector-distilbert-en | 902f1d72e36fcab14629427c0cee8c668495c2c6 | 2021-06-16T16:14:20.000Z | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"en",
"transformers",
"autotrain_compatible"
] | token-classification | false | m3hrdadfi | null | m3hrdadfi/typo-detector-distilbert-en | 1,852 | 1 | transformers | 1,409 | ---
language: en
widget:
- text: "He had also stgruggled with addiction during his time in Congress ."
- text: "The review thoroughla assessed all aspects of JLENS SuR and CPG esign maturit and confidence ."
- text: "Letterma also apologized two his staff for the satyation ."
- text: "Vincent Jay had earlier won Fr... |
facebook/rag-token-base | bdeb4ef1d547bcfe5445aba1704ece55af71dd58 | 2020-12-11T21:39:44.000Z | [
"pytorch",
"rag",
"en",
"dataset:wiki_dpr",
"arxiv:2005.11401",
"transformers",
"license:apache-2.0"
] | null | false | facebook | null | facebook/rag-token-base | 1,849 | null | transformers | 1,410 | ---
language: en
license: apache-2.0
datasets:
- wiki_dpr
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
---
## RAG
This is a non-finetuned version of the RAG-Token model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)
by Patri... |
malteos/scincl | 59e80e4be60df7faf8bb7ad4c6e6d4f40f19ce78 | 2022-02-18T23:00:44.000Z | [
"pytorch",
"bert",
"feature-extraction",
"en",
"dataset:SciDocs",
"dataset:s2orc",
"arxiv:2202.06671",
"transformers",
"license:mit"
] | feature-extraction | false | malteos | null | malteos/scincl | 1,849 | 4 | transformers | 1,411 | ---
tags:
- feature-extraction
language: en
datasets:
- SciDocs
- s2orc
metrics:
- F1
- accuracy
- map
- ndcg
license: mit
---
## SciNCL
SciNCL is a pre-trained BERT language model to generate document-level embeddings of research papers.
It uses the citation graph neighborhood to generate samples f... |
hfl/chinese-pert-base-mrc | 9e190b7d0f52fa74f503eee54d1b0214c27f2d54 | 2022-05-05T08:45:07.000Z | [
"pytorch",
"tf",
"bert",
"question-answering",
"zh",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | hfl | null | hfl/chinese-pert-base-mrc | 1,837 | 3 | transformers | 1,412 | ---
language:
- zh
license: "apache-2.0"
---
## A Chinese MRC model built on Chinese PERT-base
**Please use `BertForQuestionAnswering` to load this model!**
This is a Chinese machine reading comprehension (MRC) model built on PERT-base and fine-tuned on a mixture of Chinese MRC datasets.
PERT is a pre-trained mode... |
imxly/t5-pegasus | 16c04d35c376262bc823f3e9f225da74f980837c | 2021-03-29T03:34:09.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | imxly | null | imxly/t5-pegasus | 1,835 | 8 | transformers | 1,413 | Entry not found |
snunlp/KR-BERT-char16424 | 47521960ac7595c5d2ed643f7a9dab9b0efcf58d | 2021-11-22T06:19:20.000Z | [
"pytorch",
"jax",
"bert",
"ko",
"arxiv:2008.03979",
"transformers"
] | null | false | snunlp | null | snunlp/KR-BERT-char16424 | 1,835 | 4 | transformers | 1,414 | ---
language:
- ko
---
## KoRean based Bert pre-trained (KR-BERT)
This is a release of Korean-specific, small-scale BERT models with comparable or better performances developed by Computational Linguistics Lab at Seoul National University, referenced in [KR-BERT: A Small-Scale Korean-Specific Language Model](https:/... |
samrawal/bert-large-uncased_med-ner | 1e8ad596193f89a51e54bb7027dabc5569e1fb77 | 2022-05-28T15:56:42.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | samrawal | null | samrawal/bert-large-uncased_med-ner | 1,831 | 3 | transformers | 1,415 | A Named Entity Recognition model for medication entities (`medication name`, `dosage`, `duration`, `frequency`, `reason`).
The model has been trained on the i2b2 (now n2c2) dataset for the 2009 - Medication task. Please visit the n2c2 site to request access to the dataset. |
Unbabel/xlm-roberta-comet-small | df568a015df5cefbf2f449314b61ce9afb0cb593 | 2021-07-10T17:32:40.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"arxiv:2012.15828",
"transformers"
] | feature-extraction | false | Unbabel | null | Unbabel/xlm-roberta-comet-small | 1,830 | 1 | transformers | 1,416 | # Model
mMiniLM-L12xH384 XLM-R model proposed in [MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers](https://arxiv.org/abs/2012.15828) that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 202... |
gogamza/kobart-summarization | 8a63d6913edc0e16a902e3fa8b688a134f0dd776 | 2021-11-22T10:59:10.000Z | [
"pytorch",
"bart",
"text2text-generation",
"ko",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | gogamza | null | gogamza/kobart-summarization | 1,823 | 1 | transformers | 1,417 | ---
language: ko
tags:
- bart
license: mit
---
# Korean News Summarization Model
## Demo
https://huggingface.co/spaces/gogamza/kobart-summarization
## How to use
```python
import torch
from transformers import PreTrainedTokenizerFast
from transformers import BartForConditionalGeneration
tokenizer = PreTrainedToke... |
sentence-transformers/stsb-bert-base | 20bffd8f7de134ab71371b5fac67e95b83b24a0a | 2022-06-15T22:22:49.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/stsb-bert-base | 1,818 | null | sentence-transformers | 1,418 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... |
uer/roberta-base-finetuned-chinanews-chinese | 2ac081da449fe7866d8c88eee548de89e2fd551f | 2022-02-20T07:57:47.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"zh",
"arxiv:1909.05658",
"arxiv:1708.02657",
"transformers"
] | text-classification | false | uer | null | uer/roberta-base-finetuned-chinanews-chinese | 1,815 | 4 | transformers | 1,419 | ---
language: zh
widget:
- text: "这本书真的很不错"
---
# Chinese RoBERTa-Base Models for Text Classification
## Model description
This is the set of 5 Chinese RoBERTa-Base classification models fine-tuned by [UER-py](https://arxiv.org/abs/1909.05658). You can download the 5 Chinese RoBERTa-Base classification models eith... |
deutschmann/mdr_roberta_q_encoder | 689ded4d13d8036e0bc3e6d09e2efaaa5196f25c | 2022-05-23T12:42:59.000Z | [
"pytorch",
"roberta",
"arxiv:2009.12756",
"transformers"
] | null | false | deutschmann | null | deutschmann/mdr_roberta_q_encoder | 1,808 | null | transformers | 1,420 | # Multihop-Dense Retrieval (MDR)
Paper: [Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval](https://arxiv.org/abs/2009.12756) (Xiong, Wenhan, et al.)
Code and checkpoint from https://github.com/facebookresearch/multihop_dense_retrieval/
This is the checkpoint `q_model` from https://dl.fbaipubli... |
atharvamundada99/bert-large-question-answering-finetuned-legal | 53337351517cd5b73acd7f0b63a7fb313ea71bd1 | 2021-05-24T15:10:08.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | atharvamundada99 | null | atharvamundada99/bert-large-question-answering-finetuned-legal | 1,807 | 3 | transformers | 1,421 | Entry not found |
sberbank-ai/ruBert-large | 1c3692be256bd0dcabfd52c8359eedf690f33dac | 2022-05-08T14:15:06.000Z | [
"pytorch",
"bert",
"fill-mask",
"ru",
"transformers",
"PyTorch",
"Transformers",
"exbert",
"autotrain_compatible"
] | fill-mask | false | sberbank-ai | null | sberbank-ai/ruBert-large | 1,804 | 2 | transformers | 1,422 | ---
language:
- ru
tags:
- PyTorch
- Transformers
- bert
- exbert
thumbnail: "https://github.com/sberbank-ai/model-zoo"
pipeline_tag: fill-mask
---
# ruBert-large
Model was trained by [SberDevices](https://sberdevices.ru/) team.
* Task: `mask filling`
* Type: `encoder`
* Tokenizer: `bpe`
* Dict size: `120 138`
* Num ... |
facebook/s2t-large-librispeech-asr | 834310fb73829b06f40a2ceac026882a0786069c | 2022-05-24T10:44:51.000Z | [
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"arxiv:1904.08779",
"transformers",
"audio",
"hf-asr-leaderboard",
"license:mit",
"model-index"
] | automatic-speech-recognition | false | facebook | null | facebook/s2t-large-librispeech-asr | 1,792 | 4 | transformers | 1,423 | ---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: mit
model-index:
- name: hubert-large-ls960-ft
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
... |
megagonlabs/electra-base-japanese-discriminator | 15d1ae6fb3ffb2eeb19275cf015c9d5bbd6ea345 | 2022-06-03T07:25:56.000Z | [
"pytorch",
"electra",
"pretraining",
"ja",
"dataset:mC4 Japanese",
"arxiv:1910.10683",
"transformers",
"license:mit"
] | null | false | megagonlabs | null | megagonlabs/electra-base-japanese-discriminator | 1,785 | null | transformers | 1,424 | ---
language: ja
license: mit
datasets:
- mC4 Japanese
---
# electra-base-japanese-discriminator (sudachitra-wordpiece, mC4 Japanese) - [SHINOBU](https://dl.ndl.go.jp/info:ndljp/pid/1302683/3)
This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences.
... |
google/ul2 | 39ecb934bff636afdd5591471a56875f5fcd7e44 | 2022-06-25T17:22:04.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2205.05131",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/ul2 | 1,785 | 33 | transformers | 1,425 | ---
language:
- en
datasets:
- c4
license: apache-2.0
---
# Introduction
UL2 is a unified framework for pretraining models that are universally effective across datasets and setups. UL2 uses Mixture-of-Denoisers (MoD), apre-training objective that combines diverse pre-training paradigms together. UL2 introduces a n... |
textattack/bert-base-uncased-rotten-tomatoes | 2aeb2b03a543f2eb1f203ca40676fbaf27a3d4fd | 2021-05-20T07:46:20.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/bert-base-uncased-rotten-tomatoes | 1,784 | 1 | transformers | 1,426 | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence lengt... |
arijitx/wav2vec2-xls-r-300m-bengali | 45ed7c704f276acb9ed6ef234b66e67a1a2cb864 | 2022-03-23T18:27:52.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"bn",
"dataset:openslr",
"dataset:SLR53",
"dataset:AI4Bharat/IndicCorp",
"transformers",
"hf-asr-leaderboard",
"openslr_SLR53",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | arijitx | null | arijitx/wav2vec2-xls-r-300m-bengali | 1,781 | 1 | transformers | 1,427 | ---
language:
- bn
license: apache-2.0
tags:
- automatic-speech-recognition
- bn
- hf-asr-leaderboard
- openslr_SLR53
- robust-speech-event
datasets:
- openslr
- SLR53
- AI4Bharat/IndicCorp
metrics:
- wer
- cer
model-index:
- name: arijitx/wav2vec2-xls-r-300m-bengali
results:
- task:
type: automatic-speech-re... |
deep-learning-analytics/wikihow-t5-small | 4fe8137dd4bdc5f697bfdf091bf516c468409308 | 2020-09-09T18:19:54.000Z | [
"pytorch",
"t5",
"text2text-generation",
"eng",
"dataset:Wikihow",
"transformers",
"wikihow",
"t5-small",
"lm-head",
"seq2seq",
"pipeline:summarization",
"summarization",
"autotrain_compatible"
] | summarization | false | deep-learning-analytics | null | deep-learning-analytics/wikihow-t5-small | 1,772 | 1 | transformers | 1,428 | ---
language: "eng"
tags:
- wikihow
- t5-small
- pytorch
- lm-head
- seq2seq
- t5
- pipeline:summarization
- summarization
datasets:
- Wikihow
widget:
- text: "Lack of fluids can lead to dry mouth, which is a leading cause of bad breath. Water
can also dilute any chemicals in your mouth or gut that are causing bad bre... |
Musixmatch/umberto-commoncrawl-cased-v1 | fe7b14808cccbbe2984b05e6fbfd71127f11008f | 2021-02-12T11:31:59.000Z | [
"pytorch",
"camembert",
"fill-mask",
"it",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Musixmatch | null | Musixmatch/umberto-commoncrawl-cased-v1 | 1,769 | 5 | transformers | 1,429 | ---
language: it
---
# UmBERTo Commoncrawl Cased
[UmBERTo](https://github.com/musixmatchresearch/umberto) is a Roberta-based Language Model trained on large Italian Corpora and uses two innovative approaches: SentencePiece and Whole Word Masking. Now available at [github.com/huggingface/transformers](https://huggingf... |
tbs17/MathBERT | e26235ccf2b14614ef278b19caa44fdb5dcf050f | 2021-08-05T00:44:29.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | tbs17 | null | tbs17/MathBERT | 1,769 | null | transformers | 1,430 | #### MathBERT model (original vocab)
*Disclaimer: the format of the documentation follows the official BERT model readme.md*
Pretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.... |
vinai/bertweet-covid19-base-cased | 4c0ac69c1a2e97374f9a35030cc57d9bd56e8775 | 2022-06-08T04:42:28.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vinai | null | vinai/bertweet-covid19-base-cased | 1,766 | null | transformers | 1,431 | # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.... |
saibo/random-roberta-base | 85b19e1e9a19cb941f6373d0924ed3be403aee57 | 2021-07-18T18:33:48.000Z | [
"pytorch",
"tf",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | saibo | null | saibo/random-roberta-base | 1,762 | null | transformers | 1,432 | # random-roberta-base
We introduce random-roberta-base, which is a unpretrained version of RoBERTa model. The weight of random-roberta-base is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
It's important to note that t... |
dmis-lab/biobert-base-cased-v1.1-squad | aa11b70199b5446ecf6ad5408f7b7a234686d7f2 | 2021-05-19T15:56:54.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | dmis-lab | null | dmis-lab/biobert-base-cased-v1.1-squad | 1,758 | 1 | transformers | 1,433 | Entry not found |
zlucia/legalbert | 8aead03573cac81f97191bd8532d808889372e59 | 2021-07-02T05:55:35.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"en",
"arxiv:2104.08671",
"transformers",
"legal",
"fill-mask"
] | fill-mask | false | zlucia | null | zlucia/legalbert | 1,757 | 1 | transformers | 1,434 | ---
language: en
pipeline_tag: fill-mask
tags:
- legal
---
### Legal-BERT
Model and tokenizer files for Legal-BERT model from [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings](https://arxiv.org/abs/2104.08671).
### Training Data
The pretrainin... |
microsoft/trocr-large-handwritten | a97dcf06fd0467a6cb8fcb5a8dfc3ea0704a9f0a | 2022-07-01T07:36:14.000Z | [
"pytorch",
"vision-encoder-decoder",
"arxiv:2109.10282",
"transformers",
"trocr",
"image-to-text"
] | image-to-text | false | microsoft | null | microsoft/trocr-large-handwritten | 1,753 | 4 | transformers | 1,435 | ---
tags:
- trocr
- image-to-text
---
# TrOCR (large-sized model, fine-tuned on IAM)
TrOCR model fine-tuned on the [IAM dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxi... |
JorisCos/ConvTasNet_Libri1Mix_enhsingle_16k | bb8a876bc157b5cf3c405994accb798c49146016 | 2021-09-23T15:48:51.000Z | [
"pytorch",
"dataset:Libri1Mix",
"dataset:enh_single",
"asteroid",
"audio",
"ConvTasNet",
"audio-to-audio",
"license:cc-by-sa-4.0"
] | audio-to-audio | false | JorisCos | null | JorisCos/ConvTasNet_Libri1Mix_enhsingle_16k | 1,749 | 1 | asteroid | 1,436 | ---
tags:
- asteroid
- audio
- ConvTasNet
- audio-to-audio
datasets:
- Libri1Mix
- enh_single
license: cc-by-sa-4.0
---
## Asteroid model `JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/aster... |
squeezebert/squeezebert-mnli | dc26cab55cf7cf386a1e711e36e3146362c7cb08 | 2020-12-11T22:02:13.000Z | [
"pytorch",
"squeezebert",
"arxiv:2006.11316",
"arxiv:1904.00962",
"transformers"
] | null | false | squeezebert | null | squeezebert/squeezebert-mnli | 1,749 | null | transformers | 1,437 | language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, `squeezebert-mnli`, has been pretrained for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective and finetuned on the [Multi-Genre Natural Language Inference... |
jonatasgrosman/wav2vec2-large-xlsr-53-japanese | 4bfb973ffab9e3b4bf77f4db18e07fb11003816c | 2022-07-27T23:36:16.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-large-xlsr-53-japanese | 1,736 | 4 | transformers | 1,438 | ---
language: ja
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Japanese by Jonatas Grosman
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
... |
vinai/bartpho-syllable | 50827977a980993df5c00ead06f6f1b3535b9424 | 2022-06-08T04:48:32.000Z | [
"pytorch",
"tf",
"mbart",
"feature-extraction",
"arxiv:2109.09701",
"transformers"
] | feature-extraction | false | vinai | null | vinai/bartpho-syllable | 1,733 | 0 | transformers | 1,439 | # <a name="introduction"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
Two BARTpho versions `BARTpho-syllable` and `BARTpho-word` are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and pre-training scheme of... |
microsoft/CodeGPT-small-java-adaptedGPT2 | f537c1fca4b5e4ed4423085a12ed4aeface294e1 | 2021-05-23T08:58:11.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | microsoft | null | microsoft/CodeGPT-small-java-adaptedGPT2 | 1,729 | 2 | transformers | 1,440 | Entry not found |
RossM/distilgpt2-finetuned-MTG | 058f8a3da7bff0349f430ce17e9d0a5133c9d278 | 2022-06-24T21:27:51.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | RossM | null | RossM/distilgpt2-finetuned-MTG | 1,721 | null | transformers | 1,441 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-MTG
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2... |
jaehyeong/koelectra-base-v3-generalized-sentiment-analysis | f77aac8b7a2a88515e168082dd298d9982e73edb | 2021-10-14T07:30:27.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | jaehyeong | null | jaehyeong/koelectra-base-v3-generalized-sentiment-analysis | 1,718 | 1 | transformers | 1,442 | # Usage
```python
# import library
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
# load model
tokenizer = AutoTokenizer.from_pretrained("jaehyeong/koelectra-base-v3-generalized-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pre... |
kykim/gpt3-kor-small_based_on_gpt2 | e47e807a037ba831c4a6586c190567cca08c9c87 | 2021-05-23T06:24:05.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"feature-extraction",
"ko",
"transformers"
] | feature-extraction | false | kykim | null | kykim/gpt3-kor-small_based_on_gpt2 | 1,717 | 1 | transformers | 1,443 | ---
language: ko
---
# Bert base model for Korean
* 70GB Korean text dataset and 42000 lower-cased subwords are used
* Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor)
```python
from transformers import BertTokenizerFast, GPT2LMHeadModel
tokenizer_gp... |
moussaKam/barthez-orangesum-title | 51522db428913eeabe40e93913458bb15d802ba5 | 2021-11-15T13:02:15.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"fr",
"arxiv:2010.12321",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | moussaKam | null | moussaKam/barthez-orangesum-title | 1,712 | 2 | transformers | 1,444 | ---
tags:
- summarization
language:
- fr
license: apache-2.0
widget:
- text: Citant les préoccupations de ses clients dénonçant des cas de censure après la suppression du compte de Trump, un fournisseur d'accès Internet de l'État de l'Idaho a décidé de bloquer Facebook et Twitter. La mesure ne concernera cependant q... |
cambridgeltl/mirror-roberta-base-sentence-drophead | 1699508c7e9157c0bbc411e17df4c3aefcc2919a | 2021-09-19T22:47:50.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2104.08027",
"transformers"
] | feature-extraction | false | cambridgeltl | null | cambridgeltl/mirror-roberta-base-sentence-drophead | 1,695 | 1 | transformers | 1,445 | ---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-roberta-base-sentence-drophead
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf), using [drophead](https://aclanthology.org/2020.findings-emnlp.178.pdf) instead of dropout as... |
TODBERT/TOD-BERT-JNT-V1 | 903797e92f97b5e61a1142636b2d604682a1032c | 2021-05-18T22:44:12.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | TODBERT | null | TODBERT/TOD-BERT-JNT-V1 | 1,688 | 1 | transformers | 1,446 | Entry not found |
luhua/chinese_pretrain_mrc_macbert_large | f2d95d06f16a3043002c9702f66c834f4e0aa944 | 2021-06-12T02:52:28.000Z | [
"pytorch",
"bert",
"question-answering",
"zh",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | luhua | null | luhua/chinese_pretrain_mrc_macbert_large | 1,685 | 4 | transformers | 1,447 | ---
language:
- zh
license: "apache-2.0"
---
## Chinese MRC macbert-large
* 使用大量中文MRC数据训练的macbert-large模型，详情可查看：https://github.com/basketballandlearn/MRC_Competition_Dureader
* 此库发布的再训练模型，在 阅读理解/分类 等任务上均有大幅提高<br/>
（已有多位小伙伴在Dureader-2021等多个比赛中取得**top5**的成绩😁）
| 模型/数据集 | Dureader-2021 ... |
valhalla/t5-small-qg-prepend | 6c7b8044e67af4742270e96f974bee864760bd4c | 2020-07-06T17:20:20.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/t5-small-qg-prepend | 1,681 | null | transformers | 1,448 | Entry not found |
Helsinki-NLP/opus-mt-en-jap | c981a0c27d7f082b91d129740b1f6a7b643c9e02 | 2021-09-09T21:36:30.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"jap",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-jap | 1,678 | null | transformers | 1,449 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-jap
* source languages: en
* target languages: jap
* OPUS readme: [en-jap](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-jap/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* d... |
Meli/GPT2-Prompt | b8bf934edcdd396e655a622b2546a0d6af24cf9a | 2021-05-21T10:55:36.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers"
] | text-generation | false | Meli | null | Meli/GPT2-Prompt | 1,678 | 2 | transformers | 1,450 | ---
language:
- en
tags:
- gpt2
- text-generation
pipeline_tag: text-generation
widget:
- text: "A person with a high school education gets sent back into the 1600s and tries to explain science and technology to the people. [endprompt]"
- text: "A kid doodling in a math class accidentally creates the world's first fun... |
facebook/deit-tiny-patch16-224 | b3428f18dcc7b543470d07f14b4a4157815d1880 | 2022-07-13T11:53:31.000Z | [
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"transformers",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/deit-tiny-patch16-224 | 1,678 | null | transformers | 1,451 | ---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet
---
# Data-efficient Image Transformer (tiny-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 [Tr... |
snowood1/ConfliBERT-cont-uncased | c7b7eba8f0193cd10fff5ecef69d3e9d2b809877 | 2022-05-11T16:49:05.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | fill-mask | false | snowood1 | null | snowood1/ConfliBERT-cont-uncased | 1,677 | null | transformers | 1,452 | ---
license: gpl-3.0
---
ConfliBERT is a pre-trained language model for political conflict and violence.
We provided four versions of ConfliBERT:
<ol>
<li>ConfliBERT-scr-uncased: Pretraining from scratch with our own uncased vocabulary (preferred)</li>
<li>ConfliBERT-scr-cased:  ... |
teacookies/autonlp-more_fine_tune_24465520-26265908 | c935bca0d7748cdbaad58100718074cedf186ae7 | 2021-10-25T09:36:35.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"unk",
"dataset:teacookies/autonlp-data-more_fine_tune_24465520",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible"
] | question-answering | false | teacookies | null | teacookies/autonlp-more_fine_tune_24465520-26265908 | 1,673 | null | transformers | 1,453 | ---
tags:
- autonlp
- question-answering
language: unk
widget:
- text: "Who loves AutoNLP?"
context: "Everyone loves AutoNLP"
datasets:
- teacookies/autonlp-data-more_fine_tune_24465520
co2_eq_emissions: 96.32087452115675
---
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- Model ID: 26... |
vlsb/autotrain-security-texts-classification-distilroberta-688220764 | ee95129b65b4dda597f9646da574925e92936c5c | 2022-03-30T20:56:57.000Z | [
"pytorch",
"roberta",
"text-classification",
"unk",
"dataset:vlsb/autotrain-data-security-texts-classification-distilroberta",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | vlsb | null | vlsb/autotrain-security-texts-classification-distilroberta-688220764 | 1,671 | 1 | transformers | 1,454 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- vlsb/autotrain-data-security-texts-classification-distilroberta
co2_eq_emissions: 2.0817207656772445
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 688220764
- CO2 Emissions (in grams): 2.0817207... |
speechbrain/emotion-recognition-wav2vec2-IEMOCAP | 329e6aafdea6b54f56905ed3976e31f973e41422 | 2021-12-20T04:59:17.000Z | [
"en",
"dataset:iemocap",
"arxiv:2106.04624",
"speechbrain",
"audio-classification",
"Emotion",
"Recognition",
"wav2vec2",
"pytorch",
"license:apache-2.0"
] | audio-classification | false | speechbrain | null | speechbrain/emotion-recognition-wav2vec2-IEMOCAP | 1,666 | 9 | speechbrain | 1,455 | ---
language: "en"
thumbnail:
tags:
- audio-classification
- speechbrain
- Emotion
- Recognition
- wav2vec2
- pytorch
license: "apache-2.0"
datasets:
- iemocap
metrics:
- Accuracy
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" ... |
bandainamco-mirai/distilbert-base-japanese | f411ce0e53839adab9e39187ef179e3b5c836f7c | 2020-11-19T13:17:22.000Z | [
"pytorch",
"distilbert",
"transformers"
] | null | false | bandainamco-mirai | null | bandainamco-mirai/distilbert-base-japanese | 1,663 | null | transformers | 1,456 | Entry not found |
ckiplab/albert-tiny-chinese-ner | bcb519856ca93a666b1e48a9daef3f88c9b572a0 | 2022-05-10T03:28:10.000Z | [
"pytorch",
"albert",
"token-classification",
"zh",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | token-classification | false | ckiplab | null | ckiplab/albert-tiny-chinese-ner | 1,663 | 1 | transformers | 1,457 | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word seg... |
sagorsarker/bangla-bert-base | 315fa6f024884c29b34a3909a016decc2b068222 | 2021-09-22T09:37:25.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"bn",
"dataset:common_crawl",
"dataset:wikipedia",
"dataset:oscar",
"arxiv:1810.04805",
"arxiv:2012.14353",
"arxiv:2104.08613",
"arxiv:2107.03844",
"arxiv:2101.00204",
"transformers",
"bengali",
"bengali-lm",
"bangla",
"license:mit",
... | fill-mask | false | sagorsarker | null | sagorsarker/bangla-bert-base | 1,658 | 2 | transformers | 1,458 | ---
language: bn
tags:
- bert
- bengali
- bengali-lm
- bangla
license: mit
datasets:
- common_crawl
- wikipedia
- oscar
---
# Bangla BERT Base
A long way passed. Here is our **Bangla-Bert**! It is now available in huggingface model hub.
[Bangla-Bert-Base](https://github.com/sagorbrur/bangla-bert) is a pretrained la... |
junnyu/roformer_v2_chinese_char_large | c48ca62933e39acc9edafbfa7b95fa1497ea8c25 | 2022-05-11T03:32:38.000Z | [
"pytorch",
"roformer",
"fill-mask",
"zh",
"arxiv:2104.09864",
"transformers",
"roformer-v2",
"tf2.0",
"autotrain_compatible"
] | fill-mask | false | junnyu | null | junnyu/roformer_v2_chinese_char_large | 1,657 | null | transformers | 1,459 | ---
language: zh
tags:
- roformer-v2
- pytorch
- tf2.0
inference: False
---
## 介绍
### tf版本
https://github.com/ZhuiyiTechnology/roformer-v2
### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
## 评测对比
### CLUE-dev榜单分类任务结果，base+large版本。
| | iflytek | tnews | afqmc | cmnli | ocnli | wsc | csl |
| :... |
csarron/roberta-base-squad-v1 | b78e35e447d75d01d5f0c7d292a050fe6e2577b5 | 2021-05-20T15:50:01.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"en",
"dataset:squad",
"arxiv:1907.11692",
"transformers",
"roberta-base",
"license:mit",
"autotrain_compatible"
] | question-answering | false | csarron | null | csarron/roberta-base-squad-v1 | 1,656 | null | transformers | 1,460 | ---
language: en
thumbnail:
license: mit
tags:
- question-answering
- roberta
- roberta-base
datasets:
- squad
metrics:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica... |
dbmdz/electra-base-german-europeana-cased-discriminator | fd360cf0d306bcd12e7dc369087391a1ee9a4f77 | 2020-07-26T00:39:57.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"transformers"
] | null | false | dbmdz | null | dbmdz/electra-base-german-europeana-cased-discriminator | 1,653 | null | transformers | 1,461 | Entry not found |
salesken/query_wellformedness_score | 433e0f21cf2f77bd95360bcf556e5ab4de15284d | 2021-05-20T20:07:29.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"dataset:google_wellformed_query",
"transformers",
"salesken",
"license:apache-2.0"
] | text-classification | false | salesken | null | salesken/query_wellformedness_score | 1,645 | 1 | transformers | 1,462 | ---
tags: salesken
license: apache-2.0
inference: true
datasets: google_wellformed_query
widget:
- text: "what was the reason for everyone for leave the company"
---
This model evaluates the wellformedness (non-fragment, grammatically correct) score of a sentence. Model is case-sensitive and penalises for incorrec... |
uer/chinese_roberta_L-8_H-512 | 5f765ccfd73b81737a1cad3d83c9ebe0172a8f06 | 2022-07-15T08:14:15.000Z | [
"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-8_H-512 | 1,644 | 1 | transformers | 1,463 | ---
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... |
Salesforce/mixqg-large | d48d020e3b3ab8f191dc4c5dc0e43ce97abd4224 | 2021-10-18T16:21:59.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:2110.08175",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Salesforce | null | Salesforce/mixqg-large | 1,641 | 3 | transformers | 1,464 | ---
language: en
widget:
- text: Robert Boyle \\n In the late 17th century, Robert Boyle proved that air is necessary for combustion.
---
# MixQG (large-sized model)
MixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper [MixQG: N... |
asahi417/tner-xlm-roberta-base-all-english | 60ae6ebf1575715c63499ce4b39494c991b01505 | 2021-02-12T23:31:37.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | asahi417 | null | asahi417/tner-xlm-roberta-base-all-english | 1,641 | null | transformers | 1,465 | # XLM-RoBERTa for NER
XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
## Usage
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-all-english")
model = ... |
mrm8488/codebert-base-finetuned-detect-insecure-code | 78b753c76bd0334482d9db595af64421c20da6df | 2021-05-20T18:19:02.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"dataset:codexglue",
"arxiv:2002.08155",
"arxiv:1907.11692",
"transformers"
] | text-classification | false | mrm8488 | null | mrm8488/codebert-base-finetuned-detect-insecure-code | 1,640 | 1 | transformers | 1,466 | ---
language: en
datasets:
- codexglue
---
# CodeBERT fine-tuned for Insecure Code Detection 💾⛔
[codebert-base](https://huggingface.co/microsoft/codebert-base) fine-tuned on [CodeXGLUE -- Defect Detection](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection) dataset for **Insecure Code Detec... |
Salesforce/codegen-6B-mono | ee4939ba1571e438943a817b0104c5de9c5e99c1 | 2022-06-28T17:43:42.000Z | [
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"transformers",
"license:bsd-3-clause"
] | text-generation | false | Salesforce | null | Salesforce/codegen-6B-mono | 1,634 | 1 | transformers | 1,467 | ---
license: bsd-3-clause
---
# CodeGen (CodeGen-Mono 6B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wa... |
google/tapas-base-finetuned-wikisql-supervised | 3471befc23f84d2105be2961d5190e451eb5435d | 2021-11-29T13:05:40.000Z | [
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikisql",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1709.00103",
"transformers",
"license:apache-2.0"
] | table-question-answering | false | google | null | google/tapas-base-finetuned-wikisql-supervised | 1,633 | 1 | transformers | 1,468 | ---
language: en
tags:
- tapas
license: apache-2.0
datasets:
- wikisql
---
# TAPAS base model fine-tuned on WikiSQL (in a supervised fashion)
his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_base_reset` checkpoint of the [original Github repository](ht... |
sultan/BioM-ELECTRA-Base-SQuAD2-BioASQ8B | c564bf936854de2131cd090c40db3681406ba783 | 2021-07-24T14:43:28.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | sultan | null | sultan/BioM-ELECTRA-Base-SQuAD2-BioASQ8B | 1,633 | null | transformers | 1,469 | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... |
Sahajtomar/German-question-answer-Electra | 429a906faa09c2a6959281f426e2f851b322da87 | 2021-01-16T02:18:37.000Z | [
"pytorch",
"tf",
"electra",
"question-answering",
"de",
"dataset:mlqa",
"transformers",
"Gelectra",
"autotrain_compatible"
] | question-answering | false | Sahajtomar | null | Sahajtomar/German-question-answer-Electra | 1,632 | 1 | transformers | 1,470 | ---
language: de
tags:
- pytorch
- tf
- Gelectra
datasets:
- mlqa
metrics:
- f1
- em
---
### QA Model trained on MLQA dataset for german langauge.
MODEL used for fine tuning is GELECTRA Large by deepset.ai
## MLQA DEV (german)
EM: 64.27 \
F1: 77.39
## XQUAD TEST (german)
EM: 66.38 \
F1: 82.25
## Hyperparameters... |
ixa-ehu/ixambert-base-cased | 493636d45defa03901f7998dcd886fe3c886713e | 2021-02-02T15:09:00.000Z | [
"pytorch",
"en",
"es",
"eu",
"transformers"
] | null | false | ixa-ehu | null | ixa-ehu/ixambert-base-cased | 1,629 | null | transformers | 1,471 | ---
language:
- en
- es
- eu
---
# IXAmBERT base cased
This is a multilingual language pretrained for English, Spanish and Basque. The training corpora is composed by the English, Spanish and Basque Wikipedias, together with Basque crawled news articles from online newspapers. The model has been successfully used to... |
LorenzoDeMattei/GePpeTto | 83d5c73d62be521e4ed28fe5960701e85f10abac | 2021-05-21T10:53:18.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"it",
"arxiv:2004.14253",
"transformers"
] | text-generation | false | LorenzoDeMattei | null | LorenzoDeMattei/GePpeTto | 1,628 | 3 | transformers | 1,472 | ---
language: it
---
# GePpeTto GPT2 Model 🇮🇹
Pretrained GPT2 117M model for Italian.
You can find further details in the paper:
Lorenzo De Mattei, Michele Cafagna, Felice Dell’Orletta, Malvina Nissim, Marco Guerini "GePpeTto Carves Italian into a Language Model", arXiv preprint. Pdf available at: https://arxiv.o... |
jackaduma/SecRoBERTa | d1aab18b12dfaeb89b1ea559605bd13bcf9805e3 | 2022-01-24T07:46:02.000Z | [
"pytorch",
"roberta",
"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/SecRoBERTa | 1,628 | 1 | transformers | 1,473 | ---
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
---
# SecRoBERTa
This is the pretrained model presented in [SecBERT: A Pretrained Language Model for... |
allenai/unifiedqa-t5-small | 6cd061fef46da9a3b1f55c69d736a715a6324352 | 2021-06-23T11:21:03.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/unifiedqa-t5-small | 1,626 | 2 | transformers | 1,474 | Entry not found |
microsoft/trocr-small-printed | 1afd9f060486e817454bc69e21bec3b0a206d9ac | 2022-07-01T07:38:01.000Z | [
"pytorch",
"vision-encoder-decoder",
"arxiv:2109.10282",
"transformers",
"trocr",
"image-to-text"
] | image-to-text | false | microsoft | null | microsoft/trocr-small-printed | 1,624 | 2 | transformers | 1,475 | ---
tags:
- trocr
- image-to-text
---
# TrOCR (small-sized model, fine-tuned on SROIE)
TrOCR model fine-tuned on the [SROIE dataset](https://rrc.cvc.uab.es/?ch=13). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li ... |
patrickvonplaten/wavlm-libri-clean-100h-base-plus | 02c289c4471cd1ba4b0ff3e7c304afe395c5026a | 2021-12-20T12:59:01.000Z | [
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"transformers",
"librispeech_asr",
"generated_from_trainer",
"wavlm_libri_finetune",
"model-index"
] | automatic-speech-recognition | false | patrickvonplaten | null | patrickvonplaten/wavlm-libri-clean-100h-base-plus | 1,619 | 1 | transformers | 1,476 | ---
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
- wavlm_libri_finetune
model-index:
- name: wavlm-libri-clean-100h-base-plus
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread ... |
google/bigbird-pegasus-large-pubmed | de903d9659abbc0f8d212acf4186044b1b566f30 | 2022-06-29T15:55:59.000Z | [
"pytorch",
"bigbird_pegasus",
"text2text-generation",
"en",
"dataset:scientific_papers",
"arxiv:2007.14062",
"transformers",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | google | null | google/bigbird-pegasus-large-pubmed | 1,618 | 7 | transformers | 1,477 | ---
language: en
license: apache-2.0
datasets:
- scientific_papers
tags:
- summarization
model-index:
- name: google/bigbird-pegasus-large-pubmed
results:
- task:
type: summarization
name: Summarization
dataset:
name: scientific_papers
type: scientific_papers
config: pubmed
s... |
SkolkovoInstitute/roberta_toxicity_classifier | 3cd450864abaa584b1620f5bea9169a2226db3eb | 2021-10-05T14:54:55.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"arxiv:1907.11692",
"transformers",
"toxic comments classification"
] | text-classification | false | SkolkovoInstitute | null | SkolkovoInstitute/roberta_toxicity_classifier | 1,614 | 4 | transformers | 1,478 | ---
language:
- en
tags:
- toxic comments classification
licenses:
- cc-by-nc-sa
---
## Toxicity Classification Model
This model is trained for toxicity classification task. The dataset used for training is the merge of the English parts of the three datasets by **Jigsaw** ([Jigsaw 2018](https://www.kaggle.com/c/jigs... |
google/bigbird-roberta-large | 95d04fa0d054969617385de05d9f2fa89bbd3bea | 2021-06-02T14:49:29.000Z | [
"pytorch",
"jax",
"big_bird",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:cc_news",
"arxiv:2007.14062",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | google | null | google/bigbird-roberta-large | 1,607 | 4 | transformers | 1,479 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
- cc_news
---
# BigBird large 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... |
nreimers/BERT-Small-L-4_H-512_A-8 | e88df96b90f597c841c6a1c0d499f05ddacaf16b | 2021-05-20T02:03:04.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | nreimers | null | nreimers/BERT-Small-L-4_H-512_A-8 | 1,605 | null | transformers | 1,480 | # BERT-Small-L-4_H-512_A-8
This is a port of the [BERT-Small model](https://github.com/google-research/bert) to Pytorch. It uses 4 layers, a hidden size of 512 and 8 attention heads. |
tartuNLP/EstBERT | ea615e186cd9a402edb90b7cfacfdcdc79893736 | 2022-05-03T07:46:46.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"et",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | fill-mask | false | tartuNLP | null | tartuNLP/EstBERT | 1,604 | null | transformers | 1,481 | ---
language: et
license: cc-by-4.0
widget:
- text: "Miks [MASK] ei taha mind kuulata?"
---
---
# EstBERT
### What's this?
The EstBERT model is a pretrained BERT<sub>Base</sub> model exclusively trained on Estonian cased corpus on both 128 and 512 sequence length of data.
### How to use?
You can use the model ... |
bloom-testing/test-bloomd-350m-main | 18e4c18ce2ab6c6aa9fc50d7e419af793489ab0b | 2022-07-28T15:39:00.000Z | [
"pytorch",
"bloom",
"feature-extraction",
"transformers"
] | feature-extraction | false | bloom-testing | null | bloom-testing/test-bloomd-350m-main | 1,603 | null | transformers | 1,482 | Entry not found |
b3ck1/gpt-neo-125M-finetuned-beer-recipes | 3ab861877e94d664309e25e870561b89b6cebcb8 | 2022-06-28T19:03:17.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"en",
"dataset:custom",
"transformers",
"text generation",
"causal-lm",
"license:apache-2.0"
] | text-generation | false | b3ck1 | null | b3ck1/gpt-neo-125M-finetuned-beer-recipes | 1,601 | 1 | transformers | 1,483 | ---
language:
- en
tags:
- text generation
- pytorch
- causal-lm
license: apache-2.0
datasets:
- custom
widget:
- text: "style: Pilsner\nbatch_size: 20\nefficiency: 75\nboil_size:"
example_title: "Pilsener"
- text: "style: IPA\nbatch_size: 20\nefficiency: 75\nboil_size:"
example_title: "IPA"
- text: "style: Scottis... |
mrm8488/camembert2camembert_shared-finetuned-french-summarization | 60191909472099389fb0aa965b431be9f918bb71 | 2021-05-26T07:42:02.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"fr",
"dataset:mlsum",
"transformers",
"summarization",
"news",
"autotrain_compatible"
] | summarization | false | mrm8488 | null | mrm8488/camembert2camembert_shared-finetuned-french-summarization | 1,597 | 2 | transformers | 1,484 | ---
tags:
- summarization
- news
language: fr
datasets:
- mlsum
widget:
- text: "Un nuage de fumée juste après l’explosion, le 1er juin 2019. Une déflagration dans une importante usine d’explosifs du centre de la Russie a fait au moins 79 blessés samedi 1er juin. L’explosion a eu lieu dans l’usine Kristall à Dzerzhinsk... |
deepset/all-mpnet-base-v2-table | ab5f9a5f0d8bf0e849b2083cab21375a16e9b424 | 2022-04-29T12:28:58.000Z | [
"pytorch",
"mpnet",
"feature-extraction",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | deepset | null | deepset/all-mpnet-base-v2-table | 1,596 | null | sentence-transformers | 1,485 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# deepset/all-mpnet-base-v2-table
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clu... |
sagorsarker/codeswitch-hineng-ner-lince | ccfaa4edebc835475ad2d0df81f5f286bd27b7f0 | 2021-05-19T01:03:28.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"hi",
"en",
"dataset:lince",
"transformers",
"codeswitching",
"hindi-english",
"ner",
"license:mit",
"autotrain_compatible"
] | token-classification | false | sagorsarker | null | sagorsarker/codeswitch-hineng-ner-lince | 1,592 | null | transformers | 1,486 | ---
language:
- hi
- en
datasets:
- lince
license: mit
tags:
- codeswitching
- hindi-english
- ner
---
# codeswitch-hineng-ner-lince
This is a pretrained model for **Name Entity Recognition** of `Hindi-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)
This model is trained for this below re... |
soheeyang/rdr-question_encoder-single-nq-base | 469a71430f852d4b9422374178eade037dcdfdb7 | 2021-04-15T15:58:07.000Z | [
"pytorch",
"tf",
"dpr",
"feature-extraction",
"arxiv:2010.10999",
"arxiv:2004.04906",
"transformers"
] | feature-extraction | false | soheeyang | null | soheeyang/rdr-question_encoder-single-nq-base | 1,589 | null | transformers | 1,487 | # rdr-question_encoder-single-nq-base
Reader-Distilled Retriever (`RDR`)
Sohee Yang and Minjoon Seo, [Is Retriever Merely an Approximator of Reader?](https://arxiv.org/abs/2010.10999), arXiv 2020
The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader whil... |
uer/chinese_roberta_L-4_H-256 | 5057ca761d707f42a367551c482df4eb2b7cfb12 | 2022-07-15T08:12:10.000Z | [
"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-4_H-256 | 1,585 | null | transformers | 1,488 | ---
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... |
hf-internal-testing/tiny-random-wav2vec2-conformer | 2df75f17d52e1d0dbc20cab2af18de15c27c5a81 | 2022-04-29T14:33:08.000Z | [
"pytorch",
"wav2vec2-conformer",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | hf-internal-testing | null | hf-internal-testing/tiny-random-wav2vec2-conformer | 1,584 | null | transformers | 1,489 | Entry not found |
Helsinki-NLP/opus-mt-af-en | 9c6d59e84991726b65968b8196d6fa0b4b32326c | 2021-09-09T21:25:57.000Z | [
"pytorch",
"marian",
"text2text-generation",
"af",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-af-en | 1,580 | null | transformers | 1,490 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-af-en
* source languages: af
* target languages: en
* OPUS readme: [af-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/af-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
microsoft/prophetnet-large-uncased-squad-qg | 387a8e2f11c0b17b565b7b67d70362f51c9a29e3 | 2020-12-11T21:51:03.000Z | [
"pytorch",
"prophetnet",
"text2text-generation",
"en",
"dataset:squad",
"arxiv:2001.04063",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | microsoft | null | microsoft/prophetnet-large-uncased-squad-qg | 1,575 | null | transformers | 1,491 | ---
language: en
datasets:
- squad
---
##
prophetnet-large-uncased-squad-qg
Fine-tuned weights(converted from [original fairseq version repo](https://github.com/microsoft/ProphetNet)) for [ProphetNet](https://arxiv.org/abs/2001.04063) on question generation
SQuAD 1.1.
ProphetNet is a new pre-trained language model... |
josu/roberta-pt-br | 87df2e2ec53cecab6c64de60735b405e076a1fe5 | 2021-12-12T20:15:09.000Z | [
"pytorch",
"roberta",
"fill-mask",
"pt",
"transformers",
"portuguese",
"brazil",
"pt_BR",
"autotrain_compatible"
] | fill-mask | false | josu | null | josu/roberta-pt-br | 1,572 | 1 | transformers | 1,492 | ---
language: pt
tags:
- portuguese
- brazil
- pt_BR
widget:
- text: Brasilia é a capital do <mask>
---
``` python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='josu/roberta-pt-br')
text = 'Brasilia é a capital do <mask>'
[{'sequence': 'Brasilia é a capital do Brasil',
'score': 0.243863... |
facebook/xglm-564M | 5933068ff6a2dc65d0520ffa43fc1836149e8cb2 | 2022-06-25T15:36:18.000Z | [
"pytorch",
"tf",
"jax",
"xglm",
"text-generation",
"arxiv:2112.10668",
"transformers",
"license:mit"
] | text-generation | false | facebook | null | facebook/xglm-564M | 1,569 | 12 | transformers | 1,493 | ---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
inference: false
---
# XGLM-564M
XGLM-564M is a multilingual autoregressive language model (with 564 million parameters) trained on a balanced corpus of a diverse set of 30 languages totaling 500 billion sub-tokens. It was introduced in ... |
WENGSYX/Deberta-Chinese-Large | c586191664508930d114c0fb5cee60bf9de98132 | 2022-03-31T20:08:59.000Z | [
"pytorch",
"deberta",
"transformers"
] | null | false | WENGSYX | null | WENGSYX/Deberta-Chinese-Large | 1,567 | 6 | transformers | 1,494 | # Deberta-Chinese
本项目，基于微软开源的Deberta模型，在中文领域进行预训练。开源本模型，旨在为其他人提供更多预训练语言模型选择。
本预训练模型，基于WuDaoCorpora语料库预训练而成。WuDaoCorpora是北京智源人工智能研究院（智源研究院）构建的大规模、高质量数据集，用于支撑“悟道”大模型项目研究。
使用WWM与n-gramMLM 等预训练方法进行预训练。
| 预训练模型 | 学习率 | batchsize | 设备 | 语料库 | 时间 | 优化器 |
| --------------------- | ------... |
classla/bcms-bertic | 5db9755d6152ec6403c0201223e4848bd1b98a48 | 2021-10-29T08:20:06.000Z | [
"pytorch",
"electra",
"pretraining",
"hr",
"bs",
"sr",
"cnr",
"hbs",
"transformers",
"license:apache-2.0"
] | null | false | classla | null | classla/bcms-bertic | 1,567 | 2 | transformers | 1,495 | ---
language:
- hr
- bs
- sr
- cnr
- hbs
license: apache-2.0
---
# BERTić* [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian
* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, ... |
michiyasunaga/BioLinkBERT-large | 1eb6d81c5fc1c42d3a43c71956b0e526558ae053 | 2022-03-31T00:54:57.000Z | [
"pytorch",
"bert",
"feature-extraction",
"en",
"dataset:pubmed",
"arxiv:2203.15827",
"transformers",
"exbert",
"linkbert",
"biolinkbert",
"fill-mask",
"question-answering",
"text-classification",
"token-classification",
"license:apache-2.0"
] | text-classification | false | michiyasunaga | null | michiyasunaga/BioLinkBERT-large | 1,567 | 3 | transformers | 1,496 | ---
license: apache-2.0
language: en
datasets:
- pubmed
tags:
- bert
- exbert
- linkbert
- biolinkbert
- feature-extraction
- fill-mask
- question-answering
- text-classification
- token-classification
widget:
- text: "Sunitinib is a tyrosine kinase inhibitor"
---
## BioLinkBERT-large
BioLinkBERT... |
microsoft/cvt-13 | 28340f4dfb5bfc05abb260895ad6c6db31298b31 | 2022-05-18T16:00:37.000Z | [
"pytorch",
"cvt",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2103.15808",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/cvt-13 | 1,567 | 1 | transformers | 1,497 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https... |
oliverguhr/fullstop-punctuation-multilingual-base | 8745d34dff832f75b192def37765ad8f800f46fb | 2022-03-23T08:33:35.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"en",
"de",
"fr",
"it",
"nl",
"dataset:wmt/europarl",
"transformers",
"punctuation prediction",
"punctuation",
"license:mit",
"autotrain_compatible"
] | token-classification | false | oliverguhr | null | oliverguhr/fullstop-punctuation-multilingual-base | 1,563 | 2 | transformers | 1,498 | ---
language:
- en
- de
- fr
- it
- nl
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_ti... |
UBC-NLP/ARBERT | a5118ac0caabfc6faba1cf7d92c779f7e2e15d44 | 2022-01-19T20:10:55.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"transformers",
"Arabic BERT",
"MSA",
"Twitter",
"Masked Langauge Model",
"autotrain_compatible"
] | fill-mask | false | UBC-NLP | null | UBC-NLP/ARBERT | 1,562 | 3 | transformers | 1,499 | ---
language:
- ar
tags:
- Arabic BERT
- MSA
- Twitter
- Masked Langauge Model
widget:
- text: "اللغة العربية هي لغة [MASK]."
---
<img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="30%" height="30%" align="right"/>
**ARBERT** is one of three models de... |
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