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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Intel/bert-base-uncased-sparse-90-unstructured-pruneofa | 78f561a09cf292eb81a272620559a2cdbd5b4c60 | 2022-01-13T12:13:06.000Z | [
"pytorch",
"tf",
"bert",
"pretraining",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:2111.05754",
"transformers",
"fill-mask"
] | fill-mask | false | Intel | null | Intel/bert-base-uncased-sparse-90-unstructured-pruneofa | 647 | null | transformers | 2,100 | ---
language: en
tags: fill-mask
datasets:
- wikipedia
- bookcorpus
---
# 90% Sparse BERT-Base (uncased) Prune OFA
This model is a result from our paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021.
For further details on the model... |
google/byt5-xl | d16d25238ca359c637f7915f40e808819fa34d75 | 2022-05-27T15:07:11.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"... | text2text-generation | false | google | null | google/byt5-xl | 647 | 2 | transformers | 2,101 | ---
language:
- multilingual
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- hi
- hmn
- ht
- hu
- hy
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
-... |
apple/mobilevit-small | 0d7593125591ad9c05a33e6115aad12aa8e956a2 | 2022-06-02T10:56:27.000Z | [
"pytorch",
"coreml",
"mobilevit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2110.02178",
"transformers",
"vision",
"license:other"
] | image-classification | false | apple | null | apple/mobilevit-small | 647 | 2 | transformers | 2,102 | ---
license: other
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://hu... |
mujeensung/roberta-base_mnli_bc | 95821cdd08f947bf3b98c9e9e70237fd92e5a9e1 | 2022-02-13T05:13:00.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | mujeensung | null | mujeensung/roberta-base_mnli_bc | 646 | null | transformers | 2,103 | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: roberta-base_mnli_bc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name:... |
ipuneetrathore/bert-base-cased-finetuned-finBERT | e07f59c31d6eee87029ce78deca877cb52484022 | 2021-05-19T20:30:58.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | ipuneetrathore | null | ipuneetrathore/bert-base-cased-finetuned-finBERT | 642 | null | transformers | 2,104 | ## FinBERT
Code for importing and using this model is available [here](https://github.com/ipuneetrathore/BERT_models)
|
microsoft/trocr-small-stage1 | 49dfe6c53cb559599a7da463bdff8e3df0334d02 | 2022-07-01T07:39:23.000Z | [
"pytorch",
"vision-encoder-decoder",
"arxiv:2109.10282",
"transformers",
"trocr",
"image-to-text"
] | image-to-text | false | microsoft | null | microsoft/trocr-small-stage1 | 642 | 1 | transformers | 2,105 | ---
tags:
- trocr
- image-to-text
---
# TrOCR (small-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](http... |
hfl/rbtl3 | 9cad8535fa548d05b09d43bb95eef67845981908 | 2021-05-19T19:22:46.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | hfl | null | hfl/rbtl3 | 641 | 2 | transformers | 2,106 | ---
language:
- zh
tags:
- bert
license: "apache-2.0"
---
# This is a re-trained 3-layer RoBERTa-wwm-ext-large model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word... |
dangvantuan/sentence-camembert-base | badc8d827cb0215b4a68b37445214acba26da2b1 | 2022-03-11T17:02:50.000Z | [
"pytorch",
"camembert",
"feature-extraction",
"fr",
"dataset:stsb_multi_mt",
"arxiv:1908.10084",
"transformers",
"Text",
"Sentence Similarity",
"Sentence-Embedding",
"camembert-base",
"license:apache-2.0",
"sentence-similarity",
"model-index"
] | sentence-similarity | false | dangvantuan | null | dangvantuan/sentence-camembert-base | 641 | 2 | transformers | 2,107 | ---
pipeline_tag: sentence-similarity
language: fr
datasets:
- stsb_multi_mt
tags:
- Text
- Sentence Similarity
- Sentence-Embedding
- camembert-base
license: apache-2.0
model-index:
- name: sentence-camembert-base by Van Tuan DANG
results:
- task:
name: Sentence-Embedding
type: Text Similarity
dat... |
aubmindlab/araelectra-base-discriminator | 5a6e787cdf3af77229d04a33f4a79c98fea35be1 | 2022-04-07T11:29:39.000Z | [
"pytorch",
"tf",
"tensorboard",
"electra",
"pretraining",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B Arabic Corpus",
"dataset:OSCAR Arabic Unshuffled",
"arxiv:2012.15516",
"transformers"
] | null | false | aubmindlab | null | aubmindlab/araelectra-base-discriminator | 640 | null | transformers | 2,108 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
- OSCAR Arabic Unshuffled
---
# AraELECTRA
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraELECTRA.png" width="100" align="left"/>
**ELECTRA** is a method for self-supervised language representation learning. ... |
cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR-large | fbed3159be2544640d2300f9b8851243b1426aa9 | 2021-05-27T18:49:10.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"arxiv:2010.11784",
"transformers"
] | feature-extraction | false | cambridgeltl | null | cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR-large | 640 | null | transformers | 2,109 | ---
language: multilingual
tags:
- biomedical
- lexical-semantics
- cross-lingual
datasets:
- UMLS
**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
**[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
### SapBERT-XLMR
SapBERT [(Liu et... |
cahya/bert2gpt-indonesian-summarization | 5edc210dbf2b92225b38aad9959648fd4ad3b3a5 | 2021-02-08T16:19:50.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"id",
"dataset:id_liputan6",
"transformers",
"pipeline:summarization",
"summarization",
"bert2gpt",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | cahya | null | cahya/bert2gpt-indonesian-summarization | 639 | 1 | transformers | 2,110 | ---
language: id
tags:
- pipeline:summarization
- summarization
- bert2gpt
datasets:
- id_liputan6
license: apache-2.0
---
# Indonesian BERT2BERT Summarization Model
Finetuned EncoderDecoder model using BERT-base and GPT2-small for Indonesian text summarization.
## Finetuning Corpus
`bert2gpt-indonesian-summarizati... |
castorini/tct_colbert-msmarco | dab1fa241ee2cdf8d5db9dca5757d27b9a37fb3b | 2021-04-21T01:29:30.000Z | [
"pytorch",
"arxiv:2010.11386",
"transformers"
] | null | false | castorini | null | castorini/tct_colbert-msmarco | 639 | null | transformers | 2,111 | This model is to reproduce the TCT-ColBERT dense retrieval described in the following paper:
> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. [Distilling Dense Representations for Ranking using Tightly-Coupled Teachers.](https://arxiv.org/abs/2010.11386) arXiv:2010.11386, October 2020.
For more details on how to u... |
patrickvonplaten/data2vec-audio-base-10m-4-gram | 26f757bde83d157bdd8589ca9dec004938bfe5eb | 2022-04-20T10:33:08.000Z | [
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"transformers",
"speech",
"license:apache-2.0"
] | automatic-speech-recognition | false | patrickvonplaten | null | patrickvonplaten/data2vec-audio-base-10m-4-gram | 638 | null | transformers | 2,112 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Data2Vec-Audio-Base-10m
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained and fine-tuned on 10 minutes of Lib... |
Maklygin/mBert-relation-extraction-FT | 63e8e267f4fbcbc4c5c800e33880aac64a3b2807 | 2022-06-08T10:38:59.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Maklygin | null | Maklygin/mBert-relation-extraction-FT | 638 | null | transformers | 2,113 | Entry not found |
sonoisa/t5-base-japanese-question-generation | a529078d993205a1c61e8a7be08f6f640de10243 | 2022-03-11T02:50:33.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ja",
"transformers",
"seq2seq",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | sonoisa | null | sonoisa/t5-base-japanese-question-generation | 637 | null | transformers | 2,114 | ---
language: ja
tags:
- t5
- text2text-generation
- seq2seq
license: cc-by-sa-4.0
widget:
- text: "answer: アマビエ context: アマビエ（歴史的仮名遣：アマビヱ）は、日本に伝わる半人半魚の妖怪。光輝く姿で海中から現れ、豊作や疫病などの予言をすると伝えられている。江戸時代後期の肥後国（現・熊本県）に現れたという。この話は挿図付きで瓦版に取り上げられ、遠く江戸にまで伝えられた。弘化3年4月中旬（1846年5月上旬）のこと、毎夜、海中に光る物体が出没していたため、役人が赴いたところ、それが姿を現した。姿形について言葉では書き... |
sangrimlee/bert-base-multilingual-cased-korquad | be8790035bd1fed7074dd2efdf4cb97ebd8e225d | 2021-05-20T04:47:57.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | sangrimlee | null | sangrimlee/bert-base-multilingual-cased-korquad | 635 | null | transformers | 2,115 | Entry not found |
ckiplab/albert-base-chinese-ner | ef1b19225fc53f10fafd8bfdefb41ac2f2f4e177 | 2022-05-10T03:28:08.000Z | [
"pytorch",
"albert",
"token-classification",
"zh",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | token-classification | false | ckiplab | null | ckiplab/albert-base-chinese-ner | 634 | 0 | transformers | 2,116 | ---
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 Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word seg... |
mpariente/DPRNNTasNet-ks2_WHAM_sepclean | 548f4f1c9476ed0af6b4bea3403247c71d5d7d46 | 2021-09-23T16:12:22.000Z | [
"pytorch",
"dataset:wham",
"dataset:sep_clean",
"asteroid",
"audio",
"DPRNNTasNet",
"audio-to-audio",
"license:cc-by-sa-4.0"
] | audio-to-audio | false | mpariente | null | mpariente/DPRNNTasNet-ks2_WHAM_sepclean | 634 | 4 | asteroid | 2,117 | ---
tags:
- asteroid
- audio
- DPRNNTasNet
- audio-to-audio
datasets:
- wham
- sep_clean
license: cc-by-sa-4.0
---
## Asteroid model `mpariente/DPRNNTasNet-ks2_WHAM_sepclean`
Imported from [Zenodo](https://zenodo.org/record/3862942)
### Description:
This model was trained by Manuel Pariente
using the wham/DPRNN reci... |
skytnt/gpt2-japanese-lyric-small | 72268b60df9c19098f3acf526221d06838d317f3 | 2022-07-06T05:06:29.000Z | [
"pytorch",
"tf",
"gpt2",
"text-generation",
"ja",
"transformers",
"japanese",
"lm",
"nlp",
"license:mit"
] | text-generation | false | skytnt | null | skytnt/gpt2-japanese-lyric-small | 634 | 1 | transformers | 2,118 | ---
language: ja
tags:
- ja
- japanese
- gpt2
- text-generation
- lm
- nlp
license: mit
widget:
- text: "桜が咲く"
---
# Japanese GPT2 Lyric Model
## Model description
The model is used to generate Japanese lyrics.
You can try it on my website [https://lyric.fab.moe/](https://lyric.fab.moe/#/)
## How to use
```python... |
huggingtweets/bestmusiclyric | 3f3c6a02e8f161cdb28ac5a33b79f25e967300df | 2021-05-21T20:32:09.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/bestmusiclyric | 633 | null | transformers | 2,119 | ---
language: en
thumbnail: https://www.huggingtweets.com/bestmusiclyric/1620313468667/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/2113290180... |
razent/cotext-1-ccg | 4dd36fb68b1e40fcc17877a69cf491731ddf82b3 | 2022-03-15T03:03:39.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"feature-extraction",
"code",
"dataset:code_search_net",
"transformers"
] | feature-extraction | false | razent | null | razent/cotext-1-ccg | 633 | null | transformers | 2,120 | ---
language: code
datasets:
- code_search_net
---
# CoText (1-CCG)
## Introduction
Paper: [CoTexT: Multi-task Learning with Code-Text Transformer](https://aclanthology.org/2021.nlp4prog-1.5.pdf)
Authors: _Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye_
## How to use
Suppor... |
Helsinki-NLP/opus-mt-mt-en | ba70458e94b4532f79e8ae01fa78ccbcab1cc143 | 2021-09-10T13:58:23.000Z | [
"pytorch",
"tf",
"jax",
"marian",
"text2text-generation",
"mt",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-mt-en | 631 | null | transformers | 2,121 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-mt-en
* source languages: mt
* target languages: en
* OPUS readme: [mt-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/mt-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
IDEA-CCNL/Erlangshen-MegatronBert-1.3B-NLI | 33f5a544081f90a7a3ce1f666b08b65d12398d45 | 2022-05-16T06:06:42.000Z | [
"pytorch",
"megatron-bert",
"text-classification",
"zh",
"transformers",
"bert",
"NLU",
"NLI",
"license:apache-2.0"
] | text-classification | false | IDEA-CCNL | null | IDEA-CCNL/Erlangshen-MegatronBert-1.3B-NLI | 631 | null | transformers | 2,122 | ---
language:
- zh
license: apache-2.0
tags:
- bert
- NLU
- NLI
inference: true
widget:
- text: "今天心情不好[SEP]今天很开心"
---
# Erlangshen-MegatronBert-1.3B-NLI, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
We collect 4 NLI（Natural Language Inference） datasets in the Chi... |
sshleifer/distilbart-xsum-9-6 | 28dfbe915f475094642bf8c7dba2ba8a8e85b432 | 2021-06-14T08:26:18.000Z | [
"pytorch",
"jax",
"bart",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | sshleifer | null | sshleifer/distilbart-xsum-9-6 | 630 | null | transformers | 2,123 | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... |
akdeniz27/deberta-v2-xlarge-cuad | 7713ed1048a69873812a8fc431669d96ea5315bf | 2021-11-14T08:43:11.000Z | [
"pytorch",
"deberta-v2",
"question-answering",
"en",
"dataset:cuad",
"transformers",
"autotrain_compatible"
] | question-answering | false | akdeniz27 | null | akdeniz27/deberta-v2-xlarge-cuad | 629 | null | transformers | 2,124 | ---
language: en
datasets:
- cuad
---
# DeBERTa v2 XLarge Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "DeBERTa v2 XLarge"
using CUAD dataset https://huggingface.co/datasets/cuad
Link for model checkpoint: https://github.com/TheAtticusProject/cuad
For the use of the model with CUAD: htt... |
jason9693/SoongsilBERT-base-beep | 83bf1d729a5cf0d1e11baf3920bba654e362a186 | 2022-04-16T14:26:17.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"ko",
"dataset:kor_hate",
"transformers"
] | text-classification | false | jason9693 | null | jason9693/SoongsilBERT-base-beep | 629 | null | transformers | 2,125 | ---
language: ko
widget:
- text: "응 어쩔티비~"
datasets:
- kor_hate
---
# Finetuning
## Result
### Base Model
| | Size | **NSMC**<br/>(acc) | **Naver NER**<br/>(F1) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) | **KorQuaD (Dev)**<br/>(EM/F1... |
textattack/albert-base-v2-MRPC | 86655e0ce120f86e1e60ce94a602908bb9a4e128 | 2020-07-06T16:29:43.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/albert-base-v2-MRPC | 628 | null | transformers | 2,126 | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classi... |
mrm8488/codebert-base-finetuned-stackoverflow-ner | dbffc9f8716da2766cfda31d269e409c49fb54d2 | 2021-05-20T18:21:42.000Z | [
"pytorch",
"jax",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | mrm8488 | null | mrm8488/codebert-base-finetuned-stackoverflow-ner | 627 | 3 | transformers | 2,127 | Entry not found |
manishiitg/distilbert-resume-parts-classify | 5877484c10f0325f96e699286ada2afe2ee939ad | 2020-12-09T13:59:30.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | manishiitg | null | manishiitg/distilbert-resume-parts-classify | 624 | 1 | transformers | 2,128 | Entry not found |
facebook/data2vec-vision-base-ft1k | 9c7678bab4dcde1342510d62f201db7f8e98e6ff | 2022-05-03T15:08:31.000Z | [
"pytorch",
"tf",
"data2vec-vision",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-1k",
"arxiv:2202.03555",
"arxiv:2106.08254",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/data2vec-vision-base-ft1k | 624 | null | transformers | 2,129 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-1k
---
# Data2Vec-Vision (base-sized model, fine-tuned on ImageNet-1k)
BEiT model pre-trained in a self-supervised fashion and fine-tuned on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was intro... |
clue/roberta_chinese_large | e1ced8cb9dadb0c677cefd9e42b770dc863e78ea | 2021-05-20T15:28:53.000Z | [
"pytorch",
"jax",
"roberta",
"zh",
"transformers"
] | null | false | clue | null | clue/roberta_chinese_large | 622 | null | transformers | 2,130 | ---
language: zh
---
## roberta_chinese_large
### Overview
**Language model:** roberta-large
**Model size:** 1.2G
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results ... |
svalabs/gbert-large-zeroshot-nli | d106a0557ab5a76e762f7fcb8524cb61710a0ba1 | 2021-12-21T15:07:03.000Z | [
"pytorch",
"bert",
"text-classification",
"German",
"transformers",
"nli",
"de",
"zero-shot-classification"
] | zero-shot-classification | false | svalabs | null | svalabs/gbert-large-zeroshot-nli | 620 | 4 | transformers | 2,131 | ---
language: German
tags:
- text-classification
- pytorch
- nli
- de
pipeline_tag: zero-shot-classification
widget:
- text: "Ich habe ein Problem mit meinem Iphone das so schnell wie möglich gelöst werden muss."
candidate_labels: "Computer, Handy, Tablet, dringend, nicht dringend"
hypothesis_template: ... |
Irina/cyoa_GPT3Medium | 762fcb11b322c82b0c1d1a552b07a4f69a4869b5 | 2021-11-09T00:14:52.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Irina | null | Irina/cyoa_GPT3Medium | 619 | null | transformers | 2,132 | Entry not found |
asafaya/hubert-large-arabic | a379c0291c1149cb694ccfe9d976dd23c9bfadb6 | 2022-02-08T14:04:07.000Z | [
"pytorch",
"hubert",
"feature-extraction",
"ar",
"arxiv:2106.07447",
"transformers",
"speech",
"audio",
"license:cc-by-nc-4.0"
] | feature-extraction | false | asafaya | null | asafaya/hubert-large-arabic | 619 | null | transformers | 2,133 | ---
language: ar
tags:
- speech
- audio
license: cc-by-nc-4.0
---
# Arabic Hubert-Large
This model was pre-trained on 2,000 hours of 16kHz sampled Arabic speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2106.07447).
Training of this mode was ... |
assemblyai/bert-large-uncased-sst2 | c062c23b049fad7e1bcda360ca2f77b9d61530e6 | 2021-06-14T22:04:39.000Z | [
"pytorch",
"bert",
"text-classification",
"arxiv:1810.04805",
"transformers"
] | text-classification | false | assemblyai | null | assemblyai/bert-large-uncased-sst2 | 619 | null | transformers | 2,134 | # BERT-Large-Uncased for Sentiment Analysis
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) originally released in ["BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"](https://arxiv.org/abs/1810.04805) and trained on the [Stanford Sen... |
Helsinki-NLP/opus-mt-en-sk | 280452f8648f491943adba0bab9c64886fbdf053 | 2021-09-09T21:39:03.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"sk",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-sk | 615 | null | transformers | 2,135 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-sk
* source languages: en
* target languages: sk
* OPUS readme: [en-sk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-sk/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
JDBN/t5-base-fr-qg-fquad | 824375c05efc88b272b8b1af32b481cdbfd97a53 | 2021-06-23T02:26:52.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"fr",
"dataset:fquad",
"dataset:piaf",
"arxiv:1910.10683",
"arxiv:2002.06071",
"transformers",
"question-generation",
"seq2seq",
"autotrain_compatible"
] | text2text-generation | false | JDBN | null | JDBN/t5-base-fr-qg-fquad | 615 | null | transformers | 2,136 | ---
language: fr
widget:
- text: "generate question: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. Il a été élu <hl> en 2009 <hl> pour devenir le 44ème président des Etats-Unis d'Amérique. </s>"
- text: "question: Quand Barack Obama a t'il été élu président? context: Barack Hussei... |
cahya/xlm-roberta-base-indonesian-NER | 32e9f2c521b9c891b01ee6401460a428b678c65d | 2020-09-23T15:55:35.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | cahya | null | cahya/xlm-roberta-base-indonesian-NER | 614 | null | transformers | 2,137 | Entry not found |
cross-encoder/nli-MiniLM2-L6-H768 | 72873be33d4058aac21d3c9e86036a8901636537 | 2021-08-05T08:40:39.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"transformers",
"MiniLMv2",
"license:apache-2.0",
"zero-shot-classification"
] | zero-shot-classification | false | cross-encoder | null | cross-encoder/nli-MiniLM2-L6-H768 | 613 | null | transformers | 2,138 | ---
language: en
pipeline_tag: zero-shot-classification
license: apache-2.0
tags:
- MiniLMv2
datasets:
- multi_nli
- snli
metrics:
- accuracy
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applicat... |
google/pegasus-billsum | 6866f3587d8fcaf4eca812eba871f1afde20ca72 | 2020-10-22T16:33:23.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | google | null | google/pegasus-billsum | 613 | 2 | transformers | 2,139 | ---
language: en
tags:
- summarization
---
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@... |
GanjinZero/biobart-base | ef5abe2f6df026915823c1b06474da4ee5ad9ff8 | 2022-04-25T02:17:13.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"arxiv:2204.03905",
"transformers",
"biobart",
"biomedical",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | GanjinZero | null | GanjinZero/biobart-base | 613 | 1 | transformers | 2,140 | ---
language:
- en
license: apache-2.0
tags:
- bart
- biobart
- biomedical
inference: true
widget:
- text: "Influenza is a <mask> disease."
- type: "text-generation"
---
Paper: [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://arxiv.org/pdf/2204.03905.pdf)
```
@misc{BioBAR... |
Nakul24/YC_Bot | 8181a7985d79549e5f0e404505620f674ffd7355 | 2022-07-05T16:23:59.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Nakul24 | null | Nakul24/YC_Bot | 613 | 1 | transformers | 2,141 | ---
tags:
- conversational
---
|
sentence-transformers/bert-base-nli-cls-token | e843bdcd4b124d765c5a2d100118212610719537 | 2022-06-15T23:01:27.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/bert-base-nli-cls-token | 611 | null | sentence-transformers | 2,142 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
---
# bert-base-nli-cls-token
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedd... |
ckiplab/albert-base-chinese-pos | aa611efbdd4bfef7c33b0594d0cee70575fb5f69 | 2022-05-10T03:28:09.000Z | [
"pytorch",
"albert",
"token-classification",
"zh",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | token-classification | false | ckiplab | null | ckiplab/albert-base-chinese-pos | 609 | null | transformers | 2,143 | ---
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 Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word seg... |
symanto/xlm-roberta-base-snli-mnli-anli-xnli | f3bad0b1d5570ea4e218fd8a581fe1e5928cef9e | 2021-09-30T12:38:34.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"ar",
"bg",
"de",
"el",
"en",
"es",
"fr",
"ru",
"th",
"tr",
"ur",
"vn",
"zh",
"dataset:SNLI",
"dataset:MNLI",
"dataset:ANLI",
"dataset:XNLI",
"transformers",
"zero-shot-classification"
] | text-classification | false | symanto | null | symanto/xlm-roberta-base-snli-mnli-anli-xnli | 609 | 3 | transformers | 2,144 | ---
language:
- ar
- bg
- de
- el
- en
- es
- fr
- ru
- th
- tr
- ur
- vn
- zh
datasets:
- SNLI
- MNLI
- ANLI
- XNLI
tags:
- zero-shot-classification
---
A cross-attention NLI model trained for zero-shot and few-shot text classification.
The base model is [xlm-roberta-base](https://hugging... |
efederici/sentence-bert-base | 42925afbeb54e2939bc3e47ee57749cbf0fb722d | 2022-07-06T07:34:00.000Z | [
"pytorch",
"bert",
"feature-extraction",
"it",
"dataset:stsb_multi_mt",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | efederici | null | efederici/sentence-bert-base | 609 | 2 | sentence-transformers | 2,145 | ---
pipeline_tag: sentence-similarity
language:
- it
datasets:
- stsb_multi_mt
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-bert-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense v... |
microsoft/beit-large-patch16-224-pt22k-ft22k | 13c2a914288e08e09073f353d9a45e15aac0697e | 2022-01-28T10:19:02.000Z | [
"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-large-patch16-224-pt22k-ft22k | 608 | 2 | transformers | 2,146 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-21k
---
# BEiT (large-sized model, fine-tuned on ImageNet-22k)
BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fi... |
mpariente/ConvTasNet_Libri1Mix_enhsingle_8k | 139ef2d9e78a9fbff8ab1811a45559574ecf7392 | 2021-09-23T16:12:15.000Z | [
"pytorch",
"dataset:LibriMix",
"dataset:enh_single",
"asteroid",
"audio",
"ConvTasNet",
"license:cc-by-sa-4.0"
] | null | false | mpariente | null | mpariente/ConvTasNet_Libri1Mix_enhsingle_8k | 608 | 1 | asteroid | 2,147 | ---
tags:
- asteroid
- audio
- ConvTasNet
datasets:
- LibriMix
- enh_single
license: cc-by-sa-4.0
---
## Asteroid model
Imported from this Zenodo [model page](https://zenodo.org/record/3970768).
## Description:
This model was trained by Brij Mohan using the Librimix/ConvTasNet recipe in Asteroid.
It was trained on ... |
yair/HeadlineGeneration | 201ba637b12dd26f54a5d047e50c1ddd68a41f94 | 2021-05-05T07:26:40.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | yair | null | yair/HeadlineGeneration | 608 | 1 | transformers | 2,148 | hello
|
nateraw/bert-base-uncased-ag-news | 5d0540f678d6603278d1ca4fab838e6746f53a82 | 2021-09-22T09:28:21.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"dataset:ag_news",
"transformers",
"ag_news",
"license:mit"
] | text-classification | false | nateraw | null | nateraw/bert-base-uncased-ag-news | 607 | 1 | transformers | 2,149 | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- ag_news
- pytorch
license: mit
datasets:
- ag_news
metrics:
- accuracy
---
# bert-base-uncased-ag-news
## Model description
`bert-base-uncased` finetuned ... |
facebook/wav2vec2-conformer-rel-pos-large | c35728baaed9c06f3301cb14f6e961f543a6851e | 2022-06-15T08:11:48.000Z | [
"pytorch",
"wav2vec2-conformer",
"pretraining",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"transformers",
"speech",
"license:apache-2.0"
] | null | false | facebook | null | facebook/wav2vec2-conformer-rel-pos-large | 607 | 3 | transformers | 2,150 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Conformer-Large with Relative Position Embeddings
Wav2Vec2 Conformer with relative position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech i... |
chenxran/orion-instance-generator | bc34fff8dbf8925734b42f3caa3aae6d94776c54 | 2022-05-21T16:31:10.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | chenxran | null | chenxran/orion-instance-generator | 607 | null | transformers | 2,151 | Entry not found |
EMBEDDIA/litlat-bert | 0ff2fcc527af01aa224c997f8159b6ede2fdd034 | 2022-02-28T13:46:36.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"lt",
"lv",
"en",
"multilingual",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | EMBEDDIA | null | EMBEDDIA/litlat-bert | 606 | 2 | transformers | 2,152 | ---
language:
- lt
- lv
- en
- multilingual
license: cc-by-sa-4.0
---
# LitLat BERT
LitLat BERT is a trilingual model, using xlm-roberta-base architecture, trained on Lithuanian, Latvian, and English corpora. Focusing on three languages, the model performs better than [multilingual BERT](https://huggingface.co/bert-... |
NDugar/ZSD-microsoft-v2xxlmnli | f363b5890155e89a89e0d25b2db72bfc6c099813 | 2021-11-03T11:18:27.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"en",
"arxiv:2006.03654",
"transformers",
"deberta-v1",
"deberta-mnli",
"license:mit",
"zero-shot-classification"
] | zero-shot-classification | false | NDugar | null | NDugar/ZSD-microsoft-v2xxlmnli | 606 | 3 | transformers | 2,153 | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
pipeline_tag: zero-shot-classification
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERT... |
lgris/wav2vec2-large-xlsr-open-brazilian-portuguese | 861b31010394ed861460cffbca062643648e5793 | 2022-04-01T20:32:58.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:common_voice",
"dataset:mls",
"dataset:cetuc",
"dataset:lapsbm",
"dataset:voxforge",
"arxiv:2012.03411",
"transformers",
"audio",
"speech",
"portuguese-speech-corpus",
"PyTorch",
"hf-asr-leaderboard",
"license:apac... | automatic-speech-recognition | false | lgris | null | lgris/wav2vec2-large-xlsr-open-brazilian-portuguese | 606 | 2 | transformers | 2,154 | ---
language: pt
datasets:
- common_voice
- mls
- cetuc
- lapsbm
- voxforge
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: Lucas Gris XLSR Wav2Vec2 Large 53 Brazilian Portug... |
uer/gpt2-chinese-couplet | 62fc2e851dc4df9d3e9d89ddc3f7c1e5a29e3a21 | 2022-02-20T05:01:07.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"transformers"
] | text-generation | false | uer | null | uer/gpt2-chinese-couplet | 606 | 2 | transformers | 2,155 | ---
language: zh
widget:
- text: "[CLS]国 色 天 香 ， 姹 紫 嫣 红 ， 碧 水 青 云 欣 共 赏 -"
---
# Chinese Couplet GPT2 Model
## Model description
The model is used to generate Chinese couplets. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace fr... |
alon-albalak/xlm-roberta-large-xquad | 7b9dc808bf9ffd04e568f4be1836d805f48e40a2 | 2021-11-05T20:23:38.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"dataset:xquad",
"transformers",
"multilingual",
"autotrain_compatible"
] | question-answering | false | alon-albalak | null | alon-albalak/xlm-roberta-large-xquad | 605 | 1 | transformers | 2,156 | ---
tags:
- multilingual
datasets:
- xquad
---
# xlm-roberta-large for multilingual QA
# Overview
**Language Model**: xlm-roberta-large \
**Downstream task**: Extractive QA \
**Training data**: [XQuAD](https://github.com/deepmind/xquad) \
**Testing Data**: [XQuAD](https://github.com/deepmind/xquad)
# Hyperparameters... |
a1noack/bart-large-gigaword | 227fafcd7b922f8abcf0d5f0a8880d15ae5b302e | 2021-07-21T21:26:04.000Z | [
"pytorch",
"bart",
"dataset:gigaword",
"transformers",
"summarization",
"license:mit"
] | summarization | false | a1noack | null | a1noack/bart-large-gigaword | 602 | null | transformers | 2,157 | ---
tags:
- summarization
datasets:
- gigaword
license: mit
thumbnail: https://en.wikipedia.org/wiki/Bart_Simpson#/media/File:Bart_Simpson_200px.png
---
# BART for Gigaword
- This model was created by fine-tuning the `facebook/bart-large-cnn` weights (also on HuggingFace) for the Gigaword dataset. The model was fine-t... |
facebook/dino-vits8 | fc5da873905bdf18d2fbad89dafc45de22d8f4fa | 2021-08-25T17:37:35.000Z | [
"pytorch",
"vit",
"feature-extraction",
"dataset:imagenet-1k",
"arxiv:2010.11929",
"arxiv:2104.14294",
"transformers",
"dino",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/dino-vits8 | 602 | 2 | transformers | 2,158 | ---
license: apache-2.0
tags:
- dino
datasets:
- imagenet-1k
---
# Vision Transformer (small-sized model, patch size 8) trained using DINO
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/20... |
d4data/biomedical-ner-all | 7aa74de711ded74f1e4dd7af873d5ec4c5c608f9 | 2022-06-26T05:40:58.000Z | [
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"Token Classification",
"license:apache-2.0",
"co2_eq_emissions",
"autotrain_compatible"
] | token-classification | false | d4data | null | d4data/biomedical-ner-all | 602 | 1 | transformers | 2,159 | ---
license: apache-2.0
language:
- en
tags:
- Token Classification
co2_eq_emissions: 0.0279399890043426 Kg
widget:
- text: "CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations.
The symptoms occurred during rest, 2–3 times per week, lasted up to 30 minutes at a time and were assoc... |
jjzha/spanbert-base-cased | b7120cdda3f5dd79b61f49cfb86ae4ee45b8294c | 2022-07-26T08:14:45.000Z | [
"pytorch",
"bert",
"en",
"transformers",
"retrained",
"SpanBERT"
] | null | false | jjzha | null | jjzha/spanbert-base-cased | 601 | null | transformers | 2,160 | ---
language:
- en
tags:
- retrained
- SpanBERT
---
SpanBERT
This is the SpanBERT 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 Associat... |
Raychanan/bert-base-chinese-FineTuned-Binary-Best | cea5c20e347c9aa770860109672cb356f83f5c12 | 2021-05-18T21:56:08.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Raychanan | null | Raychanan/bert-base-chinese-FineTuned-Binary-Best | 600 | null | transformers | 2,161 | Entry not found |
ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition | 17cf17c4ec7c4d083a3ac50a3c98d44088434cee | 2021-09-21T20:59:32.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | audio-classification | false | ehcalabres | null | ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition | 599 | 11 | transformers | 2,162 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model_index:
name: wav2vec2-lg-xlsr-en-speech-emotion-recognition
---
# Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0
The model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatas... |
BlackSamorez/rudialogpt3_medium_based_on_gpt2_2ch | 59d120c6cb013de2c3909d22e57a2260e0e16df7 | 2022-06-05T14:29:01.000Z | [
"pytorch",
"gpt2",
"text-generation",
"ru",
"transformers",
"conversational"
] | conversational | false | BlackSamorez | null | BlackSamorez/rudialogpt3_medium_based_on_gpt2_2ch | 599 | 1 | transformers | 2,163 | ---
language:
- ru
thumbnail:
tags:
- conversational
---
DialoGPT on Russian language
Based on [Grossmend/rudialogpt3_medium_based_on_gpt2](https://huggingface.co/Grossmend/rudialogpt3_medium_based_on_gpt2)
Fine tuned on [2ch /b/ dialogues](https://huggingface.co/datasets/BlackSamorez/2ch_b_dialogues) data. To imp... |
deutsche-telekom/mt5-small-sum-de-en-v1 | 449e879e5554349c284d9a45bef28aaba4385f30 | 2021-09-23T13:48:30.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"de",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"dataset:wiki_lingua",
"dataset:mlsum",
"dataset:swiss_text_2019",
"transformers",
"summarization",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | summarization | false | deutsche-telekom | null | deutsche-telekom/mt5-small-sum-de-en-v1 | 598 | 3 | transformers | 2,164 | ---
language:
- de
- en
license: cc-by-nc-sa-4.0
tags:
- summarization
datasets:
- cnn_dailymail
- xsum
- wiki_lingua
- mlsum
- swiss_text_2019
---
# mT5-small-sum-de-en-v1
This is a bilingual summarization model for English and German. It is based on the multilingual T5 model [google/mt5-small](https://huggingfac... |
kredor/punctuate-all | d2b63af491274847502712c75a8943a456169125 | 2022-04-28T05:26:05.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | kredor | null | kredor/punctuate-all | 598 | null | transformers | 2,165 | This is based on [Oliver Guhr's work](https://huggingface.co/oliverguhr/fullstop-punctuation-multilang-large). The difference is that it is a finetuned xlm-roberta-base instead of an xlm-roberta-large and on twelve languages instead of four. The languages are: English, German, French, Spanish, Bulgarian, Italian, Polis... |
sentence-transformers/bert-large-nli-mean-tokens | 321b149057b64563bfc7bd2b2fb7d685ea9e4014 | 2022-06-15T22:16:03.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/bert-large-nli-mean-tokens | 597 | null | sentence-transformers | 2,166 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
---
**⚠️ 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... |
dandelin/vilt-b32-finetuned-coco | 2f3f7f3f62a3f4f429c26309256485bbd9b0e40a | 2022-01-23T09:45:24.000Z | [
"pytorch",
"vilt",
"arxiv:2102.03334",
"transformers",
"license:apache-2.0"
] | null | false | dandelin | null | dandelin/vilt-b32-finetuned-coco | 595 | null | transformers | 2,167 | ---
license: apache-2.0
---
# Vision-and-Language Transformer (ViLT), fine-tuned on COCO
Vision-and-Language Transformer (ViLT) model fine-tuned on [COCO](https://cocodataset.org/#home). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/... |
bvanaken/CORe-clinical-outcome-biobert-v1 | f57bd8bae74a778058b1d2d45ce4ea12bc806b7e | 2021-05-19T13:34:58.000Z | [
"pytorch",
"jax",
"bert",
"en",
"transformers",
"medical",
"clinical"
] | null | false | bvanaken | null | bvanaken/CORe-clinical-outcome-biobert-v1 | 594 | 3 | transformers | 2,168 | ---
language: "en"
tags:
- bert
- medical
- clinical
thumbnail: "https://core.app.datexis.com/static/paper.png"
---
# CORe Model - BioBERT + Clinical Outcome Pre-Training
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission N... |
daveni/twitter-xlm-roberta-emotion-es | ab57a1137b2eb1f6c90fc77b0a4c4ced7dbd4d60 | 2022-04-28T09:49:06.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"es",
"transformers",
"Emotion Analysis"
] | text-classification | false | daveni | null | daveni/twitter-xlm-roberta-emotion-es | 594 | 2 | transformers | 2,169 | ---
language:
- es
tags:
- Emotion Analysis
---
**Note**: This model & model card are based on the [finetuned XLM-T for Sentiment Analysis](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)
# twitter-XLM-roBERTa-base for Emotion Analysis
This is a XLM-roBERTa-base model trained on ~198M tweets ... |
felinecity/DioloGPT-small-KaeyaBot | 5a337d5e71543801846df24610e39d3234e0c4e0 | 2022-01-13T00:34:48.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | felinecity | null | felinecity/DioloGPT-small-KaeyaBot | 594 | null | transformers | 2,170 | ---
tags:
- conversational
---
# DioloGPT KaeyaBot model |
textattack/roberta-base-RTE | de14c050a0da06785eec4c151b1778cb1d111d0a | 2021-05-20T22:10:37.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/roberta-base-RTE | 594 | null | transformers | 2,171 | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classifi... |
KoboldAI/GPT-Neo-2.7B-Picard | 3a960ae005ab3af21278572dee45b5efdf0ffc27 | 2022-03-20T13:02:38.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"en",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-Neo-2.7B-Picard | 593 | 3 | transformers | 2,172 | ---
language: en
license: mit
---
# GPT-Neo 2.7B - Picard
## Model Description
GPT-Neo 2.7B-Picard is a finetune created using EleutherAI's GPT-Neo 2.7B model.
## Training data
The training data contains around 1800 ebooks, mostly in the sci-fi and fantasy genres.
### How to use
You can use this model directly with a p... |
chambliss/distilbert-for-food-extraction | d7d194fb9c2ce6ea36b80be0133d331f58532980 | 2020-10-14T21:58:56.000Z | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | chambliss | null | chambliss/distilbert-for-food-extraction | 593 | 1 | transformers | 2,173 | Entry not found |
mrm8488/bert-mini-5-finetuned-squadv2 | 7c240aba1471ad8f37798ded3aeff8fdd664d09d | 2021-05-20T00:25:56.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/bert-mini-5-finetuned-squadv2 | 593 | null | transformers | 2,174 | Entry not found |
amine/bert-base-5lang-cased | c2df409a7019fdead7fe7a21eda2338cb475e73f | 2021-05-18T23:35:02.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"fr",
"es",
"de",
"zh",
"dataset:wikipedia",
"transformers",
"multilingual",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | amine | null | amine/bert-base-5lang-cased | 592 | null | transformers | 2,175 | ---
language:
- en
- fr
- es
- de
- zh
tags:
- pytorch
- bert
- multilingual
- en
- fr
- es
- de
- zh
datasets: wikipedia
license: apache-2.0
inference: false
---
# bert-base-5lang-cased
This is a smaller version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handles o... |
cointegrated/rubert-tiny-sentiment-balanced | 0156ad2feebc300b208a5c120330a771f28a9af5 | 2021-08-29T11:34:44.000Z | [
"pytorch",
"bert",
"text-classification",
"ru",
"transformers",
"russian",
"classification",
"sentiment",
"multiclass"
] | text-classification | false | cointegrated | null | cointegrated/rubert-tiny-sentiment-balanced | 592 | null | transformers | 2,176 | ---
language: ["ru"]
tags:
- russian
- classification
- sentiment
- multiclass
widget:
- text: "Какая гадость эта ваша заливная рыба!"
---
This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of sentiment for short Russian texts.
The problem is fo... |
flair/ner-german-legal | ec83b13d1cc3f462c671dec3acef1aeb4e2a9ea3 | 2021-02-26T15:40:55.000Z | [
"pytorch",
"de",
"dataset:legal",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-german-legal | 591 | null | flair | 2,177 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: de
datasets:
- legal
widget:
- text: "Herr W. verstieß gegen § 36 Abs. 7 IfSG."
---
## NER for German Legal Text in Flair (default model)
This is the legal NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
F1-S... |
sagorsarker/codeswitch-spaeng-lid-lince | f0385677c8f48242fc5b9d4d25d24b960cd2da1c | 2021-06-11T04:12:00.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"es",
"en",
"dataset:lince",
"transformers",
"codeswitching",
"spanish-english",
"language-identification",
"license:mit",
"autotrain_compatible"
] | token-classification | false | sagorsarker | null | sagorsarker/codeswitch-spaeng-lid-lince | 591 | null | transformers | 2,178 | ---
language:
- es
- en
datasets:
- lince
license: mit
tags:
- codeswitching
- spanish-english
- language-identification
---
# codeswitch-spaeng-lid-lince
This is a pretrained model for **language identification** of `spanish-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)
This model is t... |
mmillet/distilrubert-tiny-2ndfinetune-epru | 3e985055f16fc805358a9065b6be4f116807720e | 2022-06-10T20:46:22.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | mmillet | null | mmillet/distilrubert-tiny-2ndfinetune-epru | 590 | null | transformers | 2,179 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilrubert-tiny-2ndfinetune-epru
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 re... |
dbmdz/electra-base-turkish-cased-discriminator | 9659a044cd77c80b7d5aea7864758543af141903 | 2020-12-11T21:37:26.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"tr",
"transformers",
"license:mit"
] | null | false | dbmdz | null | dbmdz/electra-base-turkish-cased-discriminator | 589 | null | transformers | 2,180 | ---
language: tr
license: mit
---
# 🤗 + 📚 dbmdz Turkish ELECTRA model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased ELECTRA base model for Turkish 🎉
# Turkish ELECTRA model
We release a base ELEC**TR**A model for Turkish, that was trained on the same d... |
razent/SciFive-large-Pubmed_PMC-MedNLI | 99fffdb01c4d30f6289090ae2d2ce4f5b7bc4970 | 2022-03-22T04:05:21.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:pubmed",
"dataset:pmc/open_access",
"arxiv:2106.03598",
"transformers",
"mednli",
"autotrain_compatible"
] | text2text-generation | false | razent | null | razent/SciFive-large-Pubmed_PMC-MedNLI | 589 | 1 | transformers | 2,181 | ---
language:
- en
tags:
- text2text-generation
- mednli
datasets:
- pubmed
- pmc/open_access
widget:
- text: "mednli: sentence1: In the ED, initial VS revealed T 98.9, HR 73, BP 121/90, RR 15, O2 sat 98% on RA. sentence2: The patient is hemodynamically stable"
---
# SciFive Pubmed+PMC Large on MedNLI
## Introdu... |
xlm-mlm-tlm-xnli15-1024 | 79f909ec8d0e5b3ed19940f85fccb2bb2d028f6c | 2022-07-22T08:10:34.000Z | [
"pytorch",
"tf",
"xlm",
"fill-mask",
"multilingual",
"en",
"fr",
"es",
"de",
"el",
"bg",
"ru",
"tr",
"ar",
"vi",
"th",
"zh",
"hi",
"sw",
"ur",
"arxiv:1901.07291",
"arxiv:1910.09700",
"transformers",
"license:cc-by-nc-4.0",
"autotrain_compatible"
] | fill-mask | false | null | null | xlm-mlm-tlm-xnli15-1024 | 587 | null | transformers | 2,182 | ---
language:
- multilingual
- en
- fr
- es
- de
- el
- bg
- ru
- tr
- ar
- vi
- th
- zh
- hi
- sw
- ur
license: cc-by-nc-4.0
---
# xlm-mlm-tlm-xnli15-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Trainin... |
pritamdeka/S-Biomed-Roberta-snli-multinli-stsb | 97bd79d282037621d2e75b13b3f6a11a1d38e55f | 2022-03-09T11:54:11.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | pritamdeka | null | pritamdeka/S-Biomed-Roberta-snli-multinli-stsb | 586 | null | sentence-transformers | 2,183 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# S-Biomed-Roberta-snli-multinli-stsb
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... |
mrm8488/bert-mini-finetuned-age_news-classification | 3ec65518ec27cd9851400bf3df347fda6ba68fc0 | 2021-05-20T00:26:16.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"dataset:ag_news",
"transformers",
"news",
"classification",
"mini"
] | text-classification | false | mrm8488 | null | mrm8488/bert-mini-finetuned-age_news-classification | 585 | 3 | transformers | 2,184 | ---
language: en
tags:
- news
- classification
- mini
datasets:
- ag_news
widget:
- text: "Israel withdraws from Gaza camp Israel withdraws from Khan Younis refugee camp in the Gaza Strip, after a four-day operation that left 11 dead."
---
# BERT-Mini fine-tuned on age_news dataset for news classification
Test set ac... |
NlpHUST/gpt-neo-vi-small | b63ce90cdb2e69a5398ccf63baf74285efeeacbb | 2021-04-23T07:21:34.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | NlpHUST | null | NlpHUST/gpt-neo-vi-small | 584 | null | transformers | 2,185 | ---
language:
- vi
tags:
- text generation
- pytorch
# GPT-Neo-small for vietnamese
First GPT for vietnamese
## Model Description
GPT-Neo-vi-small is a transformer model designed using EleutherAI's replication of the GPT-3 architecture.
## Training data
GPT-Neo-vi-smal was trained on the News datasets, a large scale ... |
markussagen/xlm-roberta-longformer-base-4096 | 98bc749a58deebb8811a07a57040a3219277b61f | 2022-03-30T09:24:39.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"multilingual",
"dataset:wikitext",
"transformers",
"longformer",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | markussagen | null | markussagen/xlm-roberta-longformer-base-4096 | 583 | 10 | transformers | 2,186 | ---
tags:
- longformer
language: multilingual
license: apache-2.0
datasets:
- wikitext
---
## XLM-R Longformer Model / XLM-Long
XLM-R Longformer (or XLM-Long for short) is a XLM-R model that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the ... |
tau/splinter-base | d6bc929405a27b7502bbab767f615c89b0e52373 | 2021-08-17T14:09:19.000Z | [
"pytorch",
"splinter",
"question-answering",
"en",
"transformers",
"SplinterModel",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | tau | null | tau/splinter-base | 582 | 1 | transformers | 2,187 | ---
language: en
tags:
- splinter
- SplinterModel
license: apache-2.0
---
# Splinter base model
Splinter-base is the pretrained model discussed in the paper [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) (at ACL 2021). Its original repository can be found [h... |
antoiloui/belgpt2 | af72b5d53d2be0e47fac2df7367d7205cd73e8dd | 2021-05-21T13:21:55.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fr",
"transformers",
"license:mit"
] | text-generation | false | antoiloui | null | antoiloui/belgpt2 | 581 | null | transformers | 2,188 | ---
language:
- fr
license:
- mit
widget:
- text: "Hier, Elon Musk a"
- text: "Pourquoi a-t-il"
- text: "Tout à coup, elle"
---
# Belgian GPT-2 🇧🇪
**A GPT-2 model pre-trained on a very large and heterogeneous French corpus (~60Gb).**
## Usage
You can use BelGPT-2 with [🤗 transformers](https://github.com/huggingf... |
marrrcin/PolBERTa-base-polish-cased-v1 | e930aab409927552cc43911b3011db3deced16bf | 2021-05-20T17:45:35.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | marrrcin | null | marrrcin/PolBERTa-base-polish-cased-v1 | 581 | null | transformers | 2,189 | Entry not found |
openai/imagegpt-small | 0e11e1401d0dfe6e45e4c0b2d92808b239cc9501 | 2022-06-30T06:46:51.000Z | [
"pytorch",
"imagegpt",
"dataset:imagenet-21k",
"transformers",
"vision",
"license:apache-2.0"
] | null | false | openai | null | openai/imagegpt-small | 581 | null | transformers | 2,190 | ---
license: apache-2.0
tags:
- vision
datasets:
- imagenet-21k
---
# ImageGPT (small-sized model)
ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper [Generative Pretraining from Pixels](https://cdn.openai.com/papers/Gener... |
patrickvonplaten/wav2vec2-base-100h-with-lm | 0612413f4d1532f2e50c039b2f014722ea59db4e | 2022-05-23T23:09:37.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | patrickvonplaten | null | patrickvonplaten/wav2vec2-base-100h-with-lm | 581 | 5 | transformers | 2,191 | Hello |
uer/gpt2-chinese-ancient | e3306e958d8d6b6b161276f83ac6214bcd693273 | 2022-07-15T08:26:24.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"transformers"
] | text-generation | false | uer | null | uer/gpt2-chinese-ancient | 581 | 1 | transformers | 2,192 | ---
language: zh
widget:
- text: "[CLS]当是时"
---
# Chinese Ancient GPT2 Model
## Model description
The model is used to generate ancient Chinese. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-chinese-ancient... |
albert-xlarge-v2 | 0ba5a4b12dff18dbb93712e5ab5ea252c09728d8 | 2021-01-13T15:34:57.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | albert-xlarge-v2 | 580 | 1 | transformers | 2,193 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT XLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-re... |
valhalla/bart-large-sst2 | 9f63197f937e01b890f892902e7afc809b9c0b06 | 2022-04-05T11:50:37.000Z | [
"pytorch",
"bart",
"text-classification",
"transformers"
] | text-classification | false | valhalla | null | valhalla/bart-large-sst2 | 580 | null | transformers | 2,194 | Entry not found |
obrizum/all-MiniLM-L6-v2 | 3825f80b81a4edec9da5f16a020659f4f827ae18 | 2022-05-09T06:48:12.000Z | [
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"sentence-transformers",
"sentence-similarity",
"license:apache-2.0"
] | feature-extraction | false | obrizum | null | obrizum/all-MiniLM-L6-v2 | 580 | null | sentence-transformers | 2,195 | ---
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
---
# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used f... |
salti/bert-base-multilingual-cased-finetuned-squad | 0368c75d052222a1188f1fd5c5b97f4064e58567 | 2021-05-19T01:26:36.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"multilingual",
"dataset:squad",
"dataset:arcd",
"dataset:xquad",
"transformers",
"autotrain_compatible"
] | question-answering | false | salti | null | salti/bert-base-multilingual-cased-finetuned-squad | 579 | 5 | transformers | 2,196 | ---
language:
- multilingual
datasets:
- squad
- arcd
- xquad
---
# Multilingual BERT fine-tuned on SQuADv1.1
[**WandB run link**](https://wandb.ai/salti/mBERT_QA/runs/wkqzhrp2)
**GPU**: Tesla P100-PCIE-16GB
## Training Arguments
```python
max_seq_length = 512
doc_stride = 25... |
prithivida/formal_to_informal_styletransfer | 45b495b2b2992d9e43bb96b69dd6c7fd86282db2 | 2021-06-21T08:08:37.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | prithivida | null | prithivida/formal_to_informal_styletransfer | 578 | 3 | transformers | 2,197 | ## This model belongs to the Styleformer project
[Please refer to github page](https://github.com/PrithivirajDamodaran/Styleformer)
|
UBC-NLP/MARBERTv2 | fe88db9db8ccdb0c4e1627495f405c44a5f89066 | 2022-03-30T21:52:31.000Z | [
"pytorch",
"tf",
"bert",
"fill-mask",
"ar",
"transformers",
"Arabic BERT",
"MSA",
"Twitter",
"Masked Langauge Model",
"autotrain_compatible"
] | fill-mask | false | UBC-NLP | null | UBC-NLP/MARBERTv2 | 577 | 4 | transformers | 2,198 | ---
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"/>
**MARBERTv2** is one of... |
blinoff/roberta-base-russian-v0 | ece3e93280de7fe6f2f95bbbfc1182a87e78e1c5 | 2021-05-20T14:29:09.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"ru",
"transformers",
"autotrain_compatible"
] | fill-mask | false | blinoff | null | blinoff/roberta-base-russian-v0 | 575 | 1 | transformers | 2,199 | ---
language: ru
widget:
- text: "Мозг — это машина <mask>, которая пытается снизить ошибку в прогнозе."
---
# RoBERTa-like language model trained on part of part of TAIGA corpus
## Training Details
- about 60k steps
![]()
## Example pipeline
```python
from transformers import pipeline
from transformers import Ro... |
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