modelId stringlengths 4 112 | sha stringlengths 40 40 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringclasses 29
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values | __index_level_0__ int64 0 38.5k | readme stringlengths 0 186k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TransQuest/monotransquest-da-multilingual | cd947f301588992a749d22fc867e535bc9cb1703 | 2021-06-03T19:06:25.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"multilingual-multilingual",
"transformers",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0"
] | text-classification | false | TransQuest | null | TransQuest/monotransquest-da-multilingual | 3,818 | null | transformers | 1,000 | ---
language: multilingual-multilingual
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation... |
eugenesiow/bart-paraphrase | 561b9d9631d608b8c63c01ecb64b5f030cabdd73 | 2021-09-13T10:02:50.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:quora",
"dataset:paws",
"arxiv:1910.13461",
"transformers",
"paraphrase",
"seq2seq",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | eugenesiow | null | eugenesiow/bart-paraphrase | 3,805 | 3 | transformers | 1,001 | ---
language: en
license: apache-2.0
tags:
- transformers
- bart
- paraphrase
- seq2seq
datasets:
- quora
- paws
---
# BART Paraphrase Model (Large)
A large BART seq2seq (text2text generation) model fine-tuned on 3 paraphrase datasets.
## Model description
The BART model was proposed in [BART: Denoising Sequence-to-Se... |
camembert/camembert-large | df7dbf53dd70551faa6b4ec45deb4a566445c7cc | 2020-12-11T21:35:25.000Z | [
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"transformers"
] | null | false | camembert | null | camembert/camembert-large | 3,801 | 4 | transformers | 1,002 | ---
language: fr
---
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretrain... |
sentence-transformers/msmarco-MiniLM-L-6-v3 | 195276c0c8647b99dfe128bd8bc4ecd1a66d41f8 | 2022-06-15T21:52:00.000Z | [
"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/msmarco-MiniLM-L-6-v3 | 3,781 | 3 | sentence-transformers | 1,003 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-MiniLM-L-6-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense ... |
microsoft/wavlm-base-plus | 4c66d4806a428f2e922ccfa1a962776e232d487b | 2021-12-22T17:23:24.000Z | [
"pytorch",
"wavlm",
"feature-extraction",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.13900",
"transformers",
"speech"
] | feature-extraction | false | microsoft | null | microsoft/wavlm-base-plus | 3,775 | 2 | transformers | 1,004 | ---
language:
- en
datasets:
tags:
- speech
inference: false
---
# WavLM-Base-Plus
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
**Note**: This model... |
KoboldAI/GPT-J-6B-Adventure | e2c00dc99f986f2430f5d34c0214969cee786755 | 2021-12-24T19:32:09.000Z | [
"pytorch",
"gptj",
"text-generation",
"transformers"
] | text-generation | false | KoboldAI | null | KoboldAI/GPT-J-6B-Adventure | 3,772 | 2 | transformers | 1,005 | Entry not found |
flair/ner-dutch | 16f9e2a2e2c6b739c723b81a8d72a923f4e46b0a | 2021-03-02T22:03:57.000Z | [
"pytorch",
"nl",
"dataset:conll2003",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-dutch | 3,769 | null | flair | 1,006 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: nl
datasets:
- conll2003
widget:
- text: "George Washington ging naar Washington."
---
# Dutch NER in Flair (default model)
This is the standard 4-class NER model for Dutch that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score... |
PlanTL-GOB-ES/roberta-large-bne-sqac | 49f9afb2bf305084e1c8c61046369123a60bd0c5 | 2022-04-06T14:43:56.000Z | [
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:PlanTL-GOB-ES/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | PlanTL-GOB-ES | null | PlanTL-GOB-ES/roberta-large-bne-sqac | 3,764 | 2 | transformers | 1,007 | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "qa"
- "question answering"
datasets:
- "PlanTL-GOB-ES/SQAC"
metrics:
- "f1"
---
# Spanish RoBERTa-large trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
RoBERTa-large-bne is a transformer-... |
mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es | 99818221720ac345078458b0b0489d61b21fe137 | 2021-05-20T00:22:53.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"es",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es | 3,764 | 1 | transformers | 1,008 | ---
language: es
thumbnail: https://i.imgur.com/jgBdimh.png
---
# BETO (Spanish BERT) + Spanish SQuAD2.0
This model is provided by [BETO team](https://github.com/dccuchile/beto) and fine-tuned on [SQuAD-es-v2.0](https://github.com/ccasimiro88/TranslateAlignRetrieve) for **Q&A** downstream task.
## Details of the lan... |
Salesforce/codet5-base-multi-sum | 4c34d0047a64ff95973d49d2cc0e61ae37fc2cd0 | 2021-11-23T09:54:43.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:code_search_net",
"arxiv:2109.00859",
"arxiv:1909.09436",
"arxiv:1907.11692",
"arxiv:2002.08155",
"transformers",
"codet5",
"license:bsd-3",
"autotrain_compatible"
] | text2text-generation | false | Salesforce | null | Salesforce/codet5-base-multi-sum | 3,753 | 6 | transformers | 1,009 | ---
license: BSD-3
tags:
- codet5
datasets:
- code_search_net
inference: true
---
# CodeT5-base for Code Summarization
[CodeT5-base](https://huggingface.co/Salesforce/codet5-base) model fine-tuned on CodeSearchNet data in a multi-lingual training setting (
Ruby/JavaScript/Go/Python/Java/PHP) for code summarization. I... |
UBC-NLP/MARBERT | ef5bf8d54e104731fc045d5c76e72af8a23988cf | 2022-01-19T20:37:55.000Z | [
"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/MARBERT | 3,747 | 6 | transformers | 1,010 | ---
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="200" height="200" align="right"/>
**MARBERT** is one of three models de... |
unicamp-dl/translation-en-pt-t5 | 8418d7e9b1837687137af06624cb3596b45c9343 | 2021-10-11T03:47:21.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"pt",
"dataset:EMEA",
"dataset:ParaCrawl 99k",
"dataset:CAPES",
"dataset:Scielo",
"dataset:JRC-Acquis",
"dataset:Biomedical Domain Corpora",
"transformers",
"translation",
"autotrain_compatible"
] | translation | false | unicamp-dl | null | unicamp-dl/translation-en-pt-t5 | 3,743 | 5 | transformers | 1,011 | ---
language:
- en
- pt
datasets:
- EMEA
- ParaCrawl 99k
- CAPES
- Scielo
- JRC-Acquis
- Biomedical Domain Corpora
tags:
- translation
metrics:
- bleu
---
# Introduction
This repository brings an implementation of T5 for translation in EN-PT tasks using a modest hardware setup. We propose some changes... |
londogard/flair-swe-ner | f7ec252c72488deafa3cec6e27d9d1e18a3376ca | 2021-03-29T08:06:38.000Z | [
"pytorch",
"sv",
"dataset:SUC 3.0",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | londogard | null | londogard/flair-swe-ner | 3,742 | null | flair | 1,012 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: sv
datasets:
- SUC 3.0
widget:
- text: "Hampus bor i Skåne och har levererat denna model idag."
---
Published with ❤️ from [londogard](https://londogard.com).
## Swedish NER in Flair (SUC 3.0)
F1-Score: **85.6** (SUC 3.0)
Predicts 8 tags:
|*... |
flair/ner-english-ontonotes | 4e50d09d85d60fd36e2c78175d4e405b1e3caa8c | 2021-03-02T22:07:31.000Z | [
"pytorch",
"en",
"dataset:ontonotes",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-english-ontonotes | 3,728 | 1 | flair | 1,013 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- ontonotes
widget:
- text: "On September 1st George Washington won 1 dollar."
---
## English NER in Flair (Ontonotes default model)
This is the 18-class NER model for English that ships with [Flair](https://github.com/flairNLP/fl... |
flair/upos-multi | 236615d6d0770325a1870c2659899e098cf71953 | 2021-03-02T22:16:39.000Z | [
"pytorch",
"en",
"de",
"fr",
"it",
"nl",
"pl",
"es",
"sv",
"da",
"no",
"fi",
"cs",
"dataset:ontonotes",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/upos-multi | 3,707 | 3 | flair | 1,014 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language:
- en
- de
- fr
- it
- nl
- pl
- es
- sv
- da
- no
- fi
- cs
datasets:
- ontonotes
widget:
- text: "Ich liebe Berlin, as they say"
---
## Multilingual Universal Part-of-Speech Tagging in Flair (default model)
This is the default multilingua... |
dumitrescustefan/bert-base-romanian-cased-v1 | 9718c77b8a4f402f3d2a9202e9c918f7fdcdcceb | 2021-11-02T15:25:55.000Z | [
"pytorch",
"jax",
"bert",
"ro",
"transformers"
] | null | false | dumitrescustefan | null | dumitrescustefan/bert-base-romanian-cased-v1 | 3,685 | 4 | transformers | 1,015 | ---
language: ro
---
# bert-base-romanian-cased-v1
The BERT **base**, **cased** model for Romanian, trained on a 15GB corpus, version 
### How to use
```python
from transformers import AutoTokenizer, AutoModel
import torch
# load tokenizer and model
t... |
Norod78/hebrew-bad_wiki-gpt_neo-tiny | a71dae1355352449475f8cb3066e85533197603e | 2022-07-19T18:11:08.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"he",
"arxiv:1910.09700",
"arxiv:2105.09680",
"transformers",
"license:mit"
] | text-generation | false | Norod78 | null | Norod78/hebrew-bad_wiki-gpt_neo-tiny | 3,683 | null | transformers | 1,016 | ---
language: he
thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg
widget:
- text: "מתמטיקה:"
- text: "עליית המכונות"
- text: "ויקיפדיה העברית"
- text: "האירוויזיון הוא"
- text: "דוד בן-גוריון היה"
license: mit
---
# hebrew-bad_wiki-gpt_neo-tiny
## Table of Contents
- [Model Details](#model-deta... |
rabindralamsal/BERTsent | 9514b1314be823ab18e320b361247ffcd94e8d83 | 2022-07-01T03:51:37.000Z | [
"pytorch",
"tf",
"roberta",
"text-classification",
"arxiv:2206.10471",
"transformers"
] | text-classification | false | rabindralamsal | null | rabindralamsal/BERTsent | 3,681 | 2 | transformers | 1,017 | # Sentiment Analysis of English Tweets (including COVID-19-specific tweets) with BERTsent
**BERTsent**: A finetuned **BERT** based **sent**iment classifier for English language tweets.
BERTsent is trained with SemEval 2017 corpus (39k plus tweets) and is based on [bertweet-base](https://github.com/VinAIResearch/BERTw... |
Langboat/mengzi-bert-base | a685cb1101fb1ea116e8432b2e14042194e4738b | 2021-10-14T09:01:34.000Z | [
"pytorch",
"bert",
"fill-mask",
"zh",
"arxiv:2110.06696",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Langboat | null | Langboat/mengzi-bert-base | 3,670 | 15 | transformers | 1,018 | ---
language:
- zh
license: apache-2.0
widget:
- text: "生活的真谛是[MASK]。"
---
# Mengzi-BERT base model (Chinese)
Pretrained model on 300G Chinese corpus. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task.
[Mengzi: A lightweight yet Powerful Chine... |
valhalla/t5-base-qg-hl | 6b9bc6f65b1df793cd1d08674b149263b0b88515 | 2021-06-23T14:40:47.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:squad",
"arxiv:1910.10683",
"transformers",
"question-generation",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/t5-base-qg-hl | 3,665 | 1 | transformers | 1,019 | ---
datasets:
- squad
tags:
- question-generation
widget:
- text: "<hl> 42 <hl> is the answer to life, the universe and everything. </s>"
- text: "Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>"
- text: "Although <hl> practicality <hl> beats purity </s>"
license: mit
---
## T5 fo... |
bert-base-german-dbmdz-cased | 1338901726062fab13465d4b37f0f0c55b662a78 | 2022-07-18T20:03:25.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-base-german-dbmdz-cased | 3,662 | null | transformers | 1,020 | ---
language: de
license: mit
---
This model is the same as [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-cased) for details on the model. |
gagan3012/k2t-base | 1e12a3b7f8393611eba2c3db5f992cf154b9debf | 2021-09-22T08:27:23.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:WebNLG",
"dataset:Dart",
"transformers",
"keytotext",
"k2t-base",
"Keywords to Sentences",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | gagan3012 | null | gagan3012/k2t-base | 3,662 | null | transformers | 1,021 | ---
language: en
thumbnail: Keywords to Sentences
tags:
- keytotext
- k2t-base
- Keywords to Sentences
license: mit
datasets:
- WebNLG
- Dart
metrics:
- NLG
---
# keytotext
Idea is to build a model... |
facebook/wav2vec2-large-xlsr-53-spanish | 6efd2b0f2ca644652c1c9e24cdbb0c374126e1c9 | 2021-07-06T03:09:28.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:common_voice",
"transformers",
"speech",
"audio",
"license:apache-2.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-large-xlsr-53-spanish | 3,660 | 3 | transformers | 1,022 | ---
language: es
datasets:
- common_voice
tags:
- speech
- audio
- automatic-speech-recognition
license: apache-2.0
---
## Evaluation on Common Voice ES Test
```python
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import torch
i... |
nreimers/TinyBERT_L-4_H-312_v2 | d782507ee95c6565fe5924fcd6090999055e8db6 | 2021-05-28T11:02:32.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | nreimers | null | nreimers/TinyBERT_L-4_H-312_v2 | 3,657 | null | transformers | 1,023 | This is the [General_TinyBERT_v2(4layer-312dim)](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/TinyBERT) ported to Huggingface transformers. |
IDEA-CCNL/Erlangshen-MegatronBert-1.3B | cba3245ff39f55f33146868c6872c7600ea24d60 | 2022-05-10T10:15:32.000Z | [
"pytorch",
"megatron-bert",
"zh",
"transformers",
"bert",
"NLU",
"FewCLUE",
"license:apache-2.0"
] | null | false | IDEA-CCNL | null | IDEA-CCNL/Erlangshen-MegatronBert-1.3B | 3,627 | 2 | transformers | 1,024 | ---
language:
- zh
license: apache-2.0
tags:
- bert
- NLU
- FewCLUE
inference: true
---
# Erlangshen-MegatronBert-1.3B model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
Encoder structure-based Bidirection language model, focusing on solving various natural language unde... |
bvanaken/CORe-clinical-diagnosis-prediction | e469bc793a49547eb0cab1c5e129c914af340e19 | 2022-02-17T09:36:23.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"medical",
"clinical",
"diagnosis"
] | text-classification | false | bvanaken | null | bvanaken/CORe-clinical-diagnosis-prediction | 3,603 | 2 | transformers | 1,025 | ---
language: "en"
tags:
- bert
- medical
- clinical
- diagnosis
- text-classification
thumbnail: "https://core.app.datexis.com/static/paper.png"
widget:
- text: "Patient with hypertension presents to ICU."
---
# CORe Model - Clinical Diagnosis Prediction
## Model description
The CORe (_Clinical Outcome Representat... |
mrm8488/t5-base-finetuned-e2m-intent | 84f655dbb0f40e64e12ad1a61c125a1225fc2917 | 2020-12-11T21:55:39.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:event2Mind",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-finetuned-e2m-intent | 3,603 | 3 | transformers | 1,026 | ---
language: en
datasets:
- event2Mind
---
# T5-base fine-tuned on event2Mind for **Intent Prediction** 🤔
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [event2Mind](https://huggingface.co/nlp/viewer/?dataset=event2Mind) dataset for **Intent Prediction**.
##... |
transformersbook/pegasus-samsum | f00170164d55821831b9396cc3da176af59f30ec | 2022-02-05T17:05:28.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"dataset:samsum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | transformersbook | null | transformersbook/pegasus-samsum | 3,574 | null | transformers | 1,027 | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum-test
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. -->
# pegasus-samsum-t... |
KETI-AIR/ke-t5-small | 3a2efa3a340d88de8aa93be0cad7884c34a64128 | 2021-06-23T03:13:34.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | KETI-AIR | null | KETI-AIR/ke-t5-small | 3,565 | null | transformers | 1,028 | Entry not found |
facebook/rag-sequence-base | 7c7ae51878178639f47b6d416bef67a35a5a41f9 | 2020-12-11T21:39:37.000Z | [
"pytorch",
"rag",
"arxiv:2005.11401",
"transformers",
"license:apache-2.0"
] | null | false | facebook | null | facebook/rag-sequence-base | 3,565 | null | transformers | 1,029 | ---
license: apache-2.0
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
---
## RAG
This is a non-finetuned version of the RAG-Sequence model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)
by Patrick Lewis, Ethan Perez, Aleksand... |
princeton-nlp/sup-simcse-bert-large-uncased | 6711247726a5d5f78c17babf57d76fa99f7b1fdf | 2021-05-20T02:56:23.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | princeton-nlp | null | princeton-nlp/sup-simcse-bert-large-uncased | 3,562 | null | transformers | 1,030 | Entry not found |
ainize/bart-base-cnn | b90bc9a7c93de6449a8c531ed5f957d84649b99a | 2021-06-21T09:52:44.000Z | [
"pytorch",
"bart",
"feature-extraction",
"en",
"dataset:cnn_dailymail",
"transformers",
"summarization",
"license:apache-2.0"
] | summarization | false | ainize | null | ainize/bart-base-cnn | 3,549 | null | transformers | 1,031 | ---
language: en
license: apache-2.0
datasets:
- cnn_dailymail
tags:
- summarization
- bart
---
# BART base model fine-tuned on CNN Dailymail
- This model is a [bart-base model](https://huggingface.co/facebook/bart-base) fine-tuned on the [CNN/Dailymail summarization dataset](https://huggingface.co/datasets/cnn_dailym... |
Sahajtomar/GBERTQnA | 23294bc03a38a1b8a51fb7bfd78c63f444c84b31 | 2021-05-18T22:19:34.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"de",
"dataset:mlqa",
"transformers",
"autotrain_compatible"
] | question-answering | false | Sahajtomar | null | Sahajtomar/GBERTQnA | 3,546 | 3 | transformers | 1,032 |
---
language: de
tags:
- pytorch
- tf
- bert
datasets:
- mlqa
metrics:
- f1
- em
---
### QA Model trained on MLQA dataset for german langauge.
MODEL used for fine tuning is GBERT Large by deepset.ai
## MLQA DEV (german)
EM: 63.82
F1: 77.20
## XQUAD TEST (german)
EM: 65.96
F1: 80.85
## Model inferencing:
```py... |
google/long-t5-tglobal-large | 31b0467e03f47bac014085e1d4fa0ec37dd43c21 | 2022-06-22T09:04:33.000Z | [
"pytorch",
"jax",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/long-t5-tglobal-large | 3,542 | 3 | transformers | 1,033 | ---
license: apache-2.0
language: en
---
# LongT5 (transient-global attention, large-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released i... |
NonzeroCornet34/DialoGPT-small-philbot | 7d9c87dd713e1116f368f94cae92eec4416599ec | 2022-04-12T21:29:09.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | NonzeroCornet34 | null | NonzeroCornet34/DialoGPT-small-philbot | 3,541 | null | transformers | 1,034 | ---
tags:
- conversational
---
# Philip DialoGPT Model |
google/mt5-xl | 28f55016820aa79b09609598744f950493129012 | 2022-05-27T15:06:44.000Z | [
"pytorch",
"tf",
"jax",
"mt5",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
... | text2text-generation | false | google | null | google/mt5-xl | 3,530 | 2 | transformers | 1,035 | ---
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
-... |
AK270802/DialoGPT-small-harrypotter | 5e5434fd66c852ebf69cc07279d85f55a645768e | 2022-01-16T11:19:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | AK270802 | null | AK270802/DialoGPT-small-harrypotter | 3,524 | null | transformers | 1,036 | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model |
yoshitomo-matsubara/bert-large-uncased-sst2 | 4b108fe9e563ba9dc910985e350f3b48799a1c03 | 2021-05-29T21:34:13.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:sst2",
"transformers",
"sst2",
"glue",
"torchdistill",
"license:apache-2.0"
] | text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-large-uncased-sst2 | 3,516 | null | transformers | 1,037 | ---
language: en
tags:
- bert
- sst2
- glue
- torchdistill
license: apache-2.0
datasets:
- sst2
metrics:
- accuracy
---
`bert-large-uncased` fine-tuned on SST-2 dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo... |
nbroad/ESG-BERT | bb721809897061818c997a223cc9ab4789cc8b05 | 2021-12-16T21:42:26.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"license:apache-2.0"
] | text-classification | false | nbroad | null | nbroad/ESG-BERT | 3,511 | 6 | transformers | 1,038 | ---
language:
- en
tags:
- text-classification
- bert
- pytorch
license: apache-2.0
widget:
- text: "In fiscal year 2019, we reduced our comprehensive carbon footprint for the fourth consecutive year—down 35 percent compared to 2015, when Apple’s carbon emissions peaked, even as net revenue increased by 11 percent over... |
jonatasgrosman/wav2vec2-large-xlsr-53-spanish | 1e07f6b2a88e191565a1fee030fffc8cae4fec2b | 2022-07-27T23:38:03.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_6_0",
"transformers",
"audio",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_6_0",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"lice... | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-large-xlsr-53-spanish | 3,505 | 12 | transformers | 1,039 | ---
language: es
license: apache-2.0
datasets:
- common_voice
- mozilla-foundation/common_voice_6_0
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- es
- hf-asr-leaderboard
- mozilla-foundation/common_voice_6_0
- robust-speech-event
- speech
- xlsr-fine-tuning-week
model-index:
- name: XLSR Wav2Vec2 ... |
sberbank-ai/ruclip-vit-base-patch32-384 | 1f7f08e5437de5dd5beba7a448983b7e4135891b | 2022-01-10T00:21:50.000Z | [
"pytorch",
"transformers"
] | null | false | sberbank-ai | null | sberbank-ai/ruclip-vit-base-patch32-384 | 3,503 | 1 | transformers | 1,040 | # ruclip-vit-base-patch32-384
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processi... |
Helsinki-NLP/opus-mt-th-en | 90080f69e69c567e2b145fc8723c1e53f4f760e6 | 2020-08-21T14:42:50.000Z | [
"pytorch",
"marian",
"text2text-generation",
"th",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-th-en | 3,484 | null | transformers | 1,041 | ---
language:
- th
- en
tags:
- translation
license: apache-2.0
---
### tha-eng
* source group: Thai
* target group: English
* OPUS readme: [tha-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tha-eng/README.md)
* model: transformer-align
* source language(s): tha
* target language(s... |
satvikag/chatbot | 5736b2051dfc768c950ddd700d11e9e92ffa6d0e | 2021-06-04T20:08:11.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"license:mit"
] | conversational | false | satvikag | null | satvikag/chatbot | 3,479 | 6 | transformers | 1,042 | ---
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You).... |
akreal/tiny-random-bert | 843b6aea20ebae1c96598b6187b1bc26c105652a | 2021-08-18T14:42:20.000Z | [
"pytorch",
"tf",
"bert",
"transformers"
] | null | false | akreal | null | akreal/tiny-random-bert | 3,477 | null | transformers | 1,043 | This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-bert
Changes: use old format for `pytorch_model.bin`.
|
hivemind/gpt-j-6B-8bit | 636b67ff80cb47e083bdfd8074c45857f72cac65 | 2022-02-10T23:15:54.000Z | [
"pytorch",
"gptj",
"text-generation",
"arxiv:2106.09685",
"arxiv:2110.02861",
"transformers"
] | text-generation | false | hivemind | null | hivemind/gpt-j-6B-8bit | 3,463 | 63 | transformers | 1,044 | ### Quantized EleutherAI/gpt-j-6b with 8-bit weights
This is a version of EleutherAI's GPT-J with 6 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti)**.
Here's how to run it: [![colab](https://camo.githubusercontent.com/84f0493939... |
Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static | 8e70cc9712549ac63d3226143b81e3fadd631b01 | 2022-06-10T02:43:06.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"transformers",
"text-classfication",
"int8",
"Intel® Neural Compressor",
"PostTrainingStatic",
"license:apache-2.0"
] | text-classification | false | Intel | null | Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static | 3,456 | null | transformers | 1,045 | ---
language: en
license: apache-2.0
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- PostTrainingStatic
datasets:
- sst2
metrics:
- accuracy
---
# INT8 DistilBERT base uncased finetuned SST-2
### Post-training static quantization
This is an INT8 PyTorch model quantized with [Intel® Neural Compresso... |
jonatasgrosman/wav2vec2-large-xlsr-53-russian | 228399bda8b8608cad580f4d71c0461358af07f9 | 2022-07-27T23:36:55.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_6_0",
"transformers",
"audio",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_6_0",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"lice... | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-large-xlsr-53-russian | 3,444 | 9 | transformers | 1,046 | ---
language: ru
license: apache-2.0
datasets:
- common_voice
- mozilla-foundation/common_voice_6_0
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
- mozilla-foundation/common_voice_6_0
- robust-speech-event
- ru
- speech
- xlsr-fine-tuning-week
model-index:
- name: XLSR Wav2Vec2 ... |
Yale-LILY/brio-cnndm-uncased | b3f3618fca366b8d70b460f8c32d65dab8e9322e | 2022-03-31T02:44:44.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Yale-LILY | null | Yale-LILY/brio-cnndm-uncased | 3,442 | 3 | transformers | 1,047 | Entry not found |
twmkn9/distilroberta-base-squad2 | e5db80d50055024330ab14b27a9e7841734becd9 | 2021-05-20T22:45:57.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | twmkn9 | null | twmkn9/distilroberta-base-squad2 | 3,427 | null | transformers | 1,048 | This model is [Distilroberta base](https://huggingface.co/distilroberta-base) trained on SQuAD v2 as:
```
export SQUAD_DIR=../../squad2
python3 run_squad.py
--model_type robberta
--model_name_or_path distilroberta-base
--do_train
--do_eval
--overwrite_cache
--do_lower_case
--version_... |
hackathon-pln-es/jurisbert-finetuning-ner | ec54eac180ee0e89c26b15bf205c51f3125e7de3 | 2022-04-02T13:08:31.000Z | [
"pytorch",
"roberta",
"token-classification",
"dataset:scjnugacj/scjn_dataset_ner",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | hackathon-pln-es | null | hackathon-pln-es/jurisbert-finetuning-ner | 3,420 | 6 | transformers | 1,049 | ---
languages:
- es
licenses:
- cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- scjnugacj/scjn_dataset_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: jurisbert-finetuning-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: s... |
hf-internal-testing/tiny-random-unispeech-sat | 4a779774d9473a62b9436b87cf9ac885b97e6f16 | 2022-01-26T13:47:21.000Z | [
"pytorch",
"unispeech-sat",
"audio-classification",
"transformers"
] | audio-classification | false | hf-internal-testing | null | hf-internal-testing/tiny-random-unispeech-sat | 3,399 | null | transformers | 1,050 | Entry not found |
staka/fugumt-en-ja | bf625f6aa260d78f000ab096ab7782ed4acb1770 | 2022-05-29T08:27:41.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"ja",
"transformers",
"translation",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | translation | false | staka | null | staka/fugumt-en-ja | 3,398 | 2 | transformers | 1,051 | ---
license: cc-by-sa-4.0
language:
- en
- ja
tags:
- translation
---
# FuguMT
This is a translation model using Marian-NMT.
For more details, please see [my repository](https://github.com/s-taka/fugumt).
* source language: en
* target language: ja
### How to use
This model uses transformers and sentencepiece... |
facebook/muppet-roberta-large | 87df24857474bf92dc6789bf1e5a8d73bc7510cb | 2021-06-28T21:44:41.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2101.11038",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | facebook | null | facebook/muppet-roberta-large | 3,387 | 3 | transformers | 1,052 | ---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
# Muppet: Massive Multi-task Representations with Pre-Finetuning
# RoBERTa large model
This is a Massive Multi-task Pre-finetuned version of Roberta large. It was introduced in
[this paper](https://arxiv.org/abs/2101.110... |
uer/chinese_roberta_L-4_H-512 | 7cfbfa6bc21973661117c736747841c7e51ca79f | 2022-07-15T08:12:26.000Z | [
"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-512 | 3,383 | 2 | transformers | 1,053 | ---
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... |
Team-PIXEL/pixel-base | 303131a01e1c8cdcc158d455df9ab75afe9795f4 | 2022-07-15T00:24:50.000Z | [
"pytorch",
"pixel",
"en",
"dataset:wikipedia",
"dataset:bookcorpusopen",
"arxiv:2207.06991",
"arxiv:2111.06377",
"transformers",
"pretraining",
"license:apache-2.0"
] | null | false | Team-PIXEL | null | Team-PIXEL/pixel-base | 3,381 | 17 | transformers | 1,054 | ---
license: apache-2.0
tags:
- pretraining
- pixel
datasets:
- wikipedia
- bookcorpusopen
language:
- en
---
# PIXEL (Pixel-based Encoder of Language)
PIXEL is a language model trained to reconstruct masked image patches that contain rendered text. PIXEL was pretrained on the *English* Wikipedia and Bookcorpus (in t... |
google/bert_uncased_L-6_H-512_A-8 | dd53ec6ca9d05e0a91b309c4e137f31988888071 | 2021-05-19T17:34:01.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-6_H-512_A-8 | 3,377 | null | transformers | 1,055 | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... |
NHStudios/DialoGPT-small-jake | cff628bcf40f6184934668cc5afe9a86021101cd | 2022-05-04T15:48:06.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | NHStudios | null | NHStudios/DialoGPT-small-jake | 3,373 | null | transformers | 1,056 | ---
tags:
- conversational
---
# Jake Peralta DialoGPT Model |
flair/ner-spanish-large | 9d4671d2f345c1258f37a29ce2321067f2ed296e | 2021-05-08T15:36:59.000Z | [
"pytorch",
"es",
"dataset:conll2003",
"arxiv:2011.06993",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | flair | null | flair/ner-spanish-large | 3,364 | 3 | flair | 1,057 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: es
datasets:
- conll2003
widget:
- text: "George Washington fue a Washington"
---
## Spanish NER in Flair (large model)
This is the large 4-class NER model for Spanish that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **9... |
GroNLP/hateBERT | f56d507e4b6a64413aff29e541e1b2178ee79d67 | 2021-08-09T16:09:32.000Z | [
"pytorch",
"bert",
"fill-mask",
"en",
"transformers",
"HateBERT",
"text classification",
"abusive language",
"hate speech",
"offensive language",
"autotrain_compatible"
] | fill-mask | false | GroNLP | null | GroNLP/hateBERT | 3,352 | 6 | transformers | 1,058 | ---
language: en
tags:
- HateBERT
- text classification
- abusive language
- hate speech
- offensive language
---
#
[Tommaso Caselli](https://www.semanticscholar.org/author/Tommaso-Caselli/1864635) •
[Valerio Basile](https://www.semanticscholar.org/author/Valerio-Basile/3101511) •
[Jelena Mitrovic](https://www.semant... |
asahi417/tner-xlm-roberta-base-ontonotes5 | 122d8bf1ed931dd9571b2c3f38317e04ba648a3b | 2021-02-13T00:07:17.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | asahi417 | null | asahi417/tner-xlm-roberta-base-ontonotes5 | 3,348 | 3 | transformers | 1,059 | # 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-ontonotes5")
model = A... |
shtoshni/longformer_coreference_joint | c0336f7402fa98e4293f2995df587fb19519e5b0 | 2021-11-12T15:52:54.000Z | [
"pytorch",
"longformer",
"feature-extraction",
"arxiv:2109.09667",
"transformers"
] | feature-extraction | false | shtoshni | null | shtoshni/longformer_coreference_joint | 3,343 | null | transformers | 1,060 | Longformer-large model finetuned for the coreference resolution task. The model is fine-tuned over a mixture of OntoNotes, LitBank, and PreCo. The model is released as part of [this paper](https://arxiv.org/pdf/2109.09667.pdf). Note that the document encoder is to be used with the rest of the model parameters to perfor... |
RarePizzaDog/Apes_Bot | e90a334ea9410ef53f029263e51c441d67313c19 | 2022-04-09T19:21:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | RarePizzaDog | null | RarePizzaDog/Apes_Bot | 3,339 | null | transformers | 1,061 | ---
tags:
- conversational
---
# 9APES DialoGPT Model |
csebuetnlp/mT5_m2m_crossSum | cbbbc2408fa8fa65e75bc4e2acce6ea4a7395008 | 2022-04-22T15:12:26.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"am",
"ar",
"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
"ha",
"hi",
"ig",
"id",
"ja",
"rn",
"ko",
"ky",
"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",
"si",
"so",
"es",
"sw",
"ta",
"te... | summarization | false | csebuetnlp | null | csebuetnlp/mT5_m2m_crossSum | 3,337 | 1 | transformers | 1,062 | ---
tags:
- summarization
- mT5
language:
- am
- ar
- az
- bn
- my
- zh
- en
- fr
- gu
- ha
- hi
- ig
- id
- ja
- rn
- ko
- ky
- mr
- ne
- om
- ps
- fa
- pcm
- pt
- pa
- ru
- gd
- sr
- si
- so
- es
- sw
- ta
- te
- th
- ti
- tr
- uk
- ur
- uz
- vi
- cy
- yo
licenses:
- cc-by-nc-sa-4.0
widget:
- text: "Videos that say a... |
allegro/herbert-klej-cased-v1 | 6953ff83476f8e7a4afb4131cb629c0cffde6c9e | 2021-05-28T16:18:22.000Z | [
"pytorch",
"jax",
"roberta",
"pl",
"arxiv:2005.00630",
"transformers"
] | null | false | allegro | null | allegro/herbert-klej-cased-v1 | 3,321 | 1 | transformers | 1,063 | ---
language: pl
---
# HerBERT
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish Corpora
using only MLM objective with dynamic masking of whole words. For more details, please refer to:
[KLEJ: Comprehensive Benchmark for Polish Language Understanding](http... |
huggingface-course/bert-finetuned-squad | cdce6f8f43121716ec99d2d2a28ff06ddbefa2e0 | 2021-11-11T17:49:56.000Z | [
"pytorch",
"tf",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | huggingface-course | null | huggingface-course/bert-finetuned-squad | 3,311 | 2 | transformers | 1,064 | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: test-bert-finetuned-squad
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. -->
# test-bert-f... |
cambridgeltl/BioRedditBERT-uncased | 53c71817b807682020273a0fa13aca033dfca292 | 2021-05-19T13:43:40.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"en",
"arxiv:2010.03295",
"transformers",
"BioNLP",
"social_media"
] | feature-extraction | false | cambridgeltl | null | cambridgeltl/BioRedditBERT-uncased | 3,299 | 2 | transformers | 1,065 | ---
language:
- en
tags:
- BioNLP
- social_media
---
# BioRedditBERT
## Model description
BioRedditBERT is a BERT model initialised from BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) and further pre-trained on health-related Reddit posts. Please view our paper [COMETA: A Corpus for Medical Entity Linking in... |
philschmid/BERT-Banking77 | e08d5e191921b9e0713327dc7e29293ecb286043 | 2022-06-24T14:31:58.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:banking77",
"transformers",
"autotrain",
"model-index",
"co2_eq_emissions"
] | text-classification | false | philschmid | null | philschmid/BERT-Banking77 | 3,290 | 1 | transformers | 1,066 | ---
tags: autotrain
language: en
widget:
- text: I am still waiting on my card?
datasets:
- banking77
model-index:
- name: BERT-Banking77
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: BANKING77
type: banking77
metrics:
- name: Accuracy
... |
deepset/gelectra-base-germanquad-distilled | 6a8efb1646c90f306b490a11a14e74cc617264e2 | 2021-12-07T14:49:28.000Z | [
"pytorch",
"electra",
"question-answering",
"de",
"dataset:deepset/germanquad",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/gelectra-base-germanquad-distilled | 3,288 | 1 | transformers | 1,067 | ---
language: de
datasets:
- deepset/germanquad
license: mit
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
---
... |
mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis | 0392a911472f7fa3db4ebacee570be79b16187f2 | 2021-09-16T18:43:08.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:financial_phrasebank",
"transformers",
"generated_from_trainer",
"financial",
"stocks",
"sentiment",
"license:apache-2.0",
"model-index"
] | text-classification | false | mrm8488 | null | mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis | 3,288 | 19 | transformers | 1,068 | ---
license: apache-2.0
tags:
- generated_from_trainer
- financial
- stocks
- sentiment
widget:
- text: "Operating profit totaled EUR 9.4 mn , down from EUR 11.7 mn in 2004 ."
datasets:
- financial_phrasebank
metrics:
- accuracy
model-index:
- name: distilRoberta-financial-sentiment
results:
- task:
name: Tex... |
NonzeroCornet34/DialoGPT-small-hansolo | 2ee056889d261a42265d5aee73fb4f220d693b40 | 2022-04-12T02:43:43.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | NonzeroCornet34 | null | NonzeroCornet34/DialoGPT-small-hansolo | 3,285 | null | transformers | 1,069 | ---
tags:
- conversational
---
# Han Solo DialoGPT Model |
hatmimoha/arabic-ner | ebf5b11a9673ff9cd4acf735c55dd45956b1858d | 2022-04-14T12:08:17.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"ar",
"transformers",
"autotrain_compatible"
] | token-classification | false | hatmimoha | null | hatmimoha/arabic-ner | 3,283 | 2 | transformers | 1,070 | ---
language: ar
---
# Arabic Named Entity Recognition Model
Pretrained BERT-based ([arabic-bert-base](https://huggingface.co/asafaya/bert-base-arabic)) Named Entity Recognition model for Arabic.
The pre-trained model can recognize the following entities:
1. **PERSON**
- و هذا ما نفاه المعاون السياسي للرئيس ***نبيه... |
tscholak/cxmefzzi | 2899ad9eafd58585ef3cb8634367c404c2d266e9 | 2022-01-10T21:49:50.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:spider",
"arxiv:2109.05093",
"transformers",
"text2sql",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | tscholak | null | tscholak/cxmefzzi | 3,282 | 2 | transformers | 1,071 | ---
language:
- en
thumbnail: "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab"
tags:
- text2sql
widget:
- "How many singers do we have? | concert_singer | stadium : stadium_id, location, name, capacity, highest, lowest, average | singer : singer_id, name, country, song... |
microsoft/resnet-18 | 2f536bd335677c6b111b3d103af458ef57a6145e | 2022-07-01T17:33:48.000Z | [
"pytorch",
"tf",
"resnet",
"image-classification",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/resnet-18 | 3,267 | null | transformers | 1,072 | ---
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: htt... |
darthrussel/DialoGPT-small-homerbot-halfdata | dcc0b9cb579623477dc7e5ccfb53d2e0aa2b2f7c | 2022-03-30T19:39:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | darthrussel | null | darthrussel/DialoGPT-small-homerbot-halfdata | 3,263 | null | transformers | 1,073 | ---
tags:
- conversational
---
# Homer DialoGPT Model half data |
Garsic/DialoGPT-medium-jill | 9b24790edb159d65d1fc3aea3533f8d2eb4abad0 | 2022-04-16T20:50:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Garsic | null | Garsic/DialoGPT-medium-jill | 3,254 | null | transformers | 1,074 | ---
tags:
- conversational
---
# dialog model
|
dbmdz/bert-base-turkish-128k-uncased | f5287aecee60f0c597c11c34341cb92d31c0e71b | 2021-05-19T15:13:16.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"tr",
"transformers",
"license:mit"
] | null | false | dbmdz | null | dbmdz/bert-base-turkish-128k-uncased | 3,245 | 4 | transformers | 1,075 | ---
language: tr
license: mit
---
# 🤗 + 📚 dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources an uncased model for Turkish 🎉
# 🇹🇷 BERTurk
BERTurk is a community-driven uncased BERT model for Turkish.
Some datasets used for pretraining and... |
uer/roberta-base-finetuned-dianping-chinese | 9498566e5da5b6cdc52f8eea002be9c24aae959a | 2022-02-20T07:57:32.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"zh",
"arxiv:1909.05658",
"arxiv:1708.02657",
"transformers"
] | text-classification | false | uer | null | uer/roberta-base-finetuned-dianping-chinese | 3,237 | 7 | transformers | 1,076 | ---
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... |
joeddav/distilbert-base-uncased-go-emotions-student | 8f145be763be749ae21d1209758c855d5ddf1b9c | 2021-02-19T22:15:52.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"en",
"dataset:go_emotions",
"transformers",
"tensorflow",
"license:mit"
] | text-classification | false | joeddav | null | joeddav/distilbert-base-uncased-go-emotions-student | 3,230 | 11 | transformers | 1,077 | ---
language: en
tags:
- text-classification
- pytorch
- tensorflow
datasets:
- go_emotions
license: mit
widget:
- text: "I feel lucky to be here."
---
# distilbert-base-uncased-go-emotions-student
## Model Description
This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dat... |
facebook/mgenre-wiki | dbb6f7bc18c4f477073231b125254182f1290155 | 2022-06-14T14:23:17.000Z | [
"pytorch",
"tf",
"jax",
"mbart",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bm",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
... | text2text-generation | false | facebook | null | facebook/mgenre-wiki | 3,230 | 5 | transformers | 1,078 | ---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bm
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gn
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- is
- it
- ja
- jv
- ka
- kg
- kk
- km
- kn
- ko
- ku
- ky
- la
- lg
- ln
- lo
- lt
... |
awvik360/DialoGPT-medium-plemons-04262022 | bb7f8797fa4d04ecb63c59f643b02c26f0598fc0 | 2022-04-27T01:46:29.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | awvik360 | null | awvik360/DialoGPT-medium-plemons-04262022 | 3,223 | null | transformers | 1,079 | ---
tags:
- conversational
---
# My Awesome Model |
sgugger/funnel-random-tiny | c6a1a5e19530e187b6cecd5457d69788645ef668 | 2021-04-08T19:31:32.000Z | [
"pytorch",
"tf",
"funnel",
"feature-extraction",
"transformers"
] | feature-extraction | false | sgugger | null | sgugger/funnel-random-tiny | 3,218 | null | transformers | 1,080 | Entry not found |
dbmdz/bert-base-italian-xxl-uncased | 08cf646465c0cab40fe7b68bf98ae9f7247d1804 | 2021-05-19T15:03:37.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"it",
"dataset:wikipedia",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/bert-base-italian-xxl-uncased | 3,206 | 4 | transformers | 1,081 | ---
language: it
license: mit
datasets:
- wikipedia
---
# 🤗 + 📚 dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models 🎉
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikiped... |
facebook/dpr-question_encoder-multiset-base | 1b547dba8676a9b96d143a6fffabe21b50553928 | 2020-11-25T16:59:33.000Z | [
"pytorch",
"tf",
"dpr",
"feature-extraction",
"transformers"
] | feature-extraction | false | facebook | null | facebook/dpr-question_encoder-multiset-base | 3,206 | null | transformers | 1,082 | Entry not found |
tunib/electra-ko-en-base | 8004b116b7cac4b0ade59d1da0e58641da725788 | 2021-09-28T07:50:21.000Z | [
"pytorch",
"electra",
"pretraining",
"arxiv:2003.10555",
"transformers"
] | null | false | tunib | null | tunib/electra-ko-en-base | 3,201 | 6 | transformers | 1,083 | # TUNiB-Electra
We release several new versions of the [ELECTRA](https://arxiv.org/abs/2003.10555) model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based... |
alvaroalon2/biobert_diseases_ner | ce0fd86ac9e145d1a6ca3455219843e0a855471f | 2021-07-07T12:35:55.000Z | [
"pytorch",
"bert",
"token-classification",
"English",
"dataset:BC5CDR-diseases",
"dataset:ncbi_disease",
"transformers",
"NER",
"Biomedical",
"Diseases",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | alvaroalon2 | null | alvaroalon2/biobert_diseases_ner | 3,179 | 6 | transformers | 1,084 | ---
language: "English"
license: apache-2.0
tags:
- token-classification
- NER
- Biomedical
- Diseases
datasets:
- BC5CDR-diseases
- ncbi_disease
---
BioBERT model fine-tuned in NER task with BC5CDR-diseases and NCBI-diseases corpus
This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at... |
chocoduck/Joey_bot | 1b7fdb7d87203116427a38b24890ded0df104f26 | 2022-03-20T11:31:56.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | chocoduck | null | chocoduck/Joey_bot | 3,173 | null | transformers | 1,085 | ---
tags:
- conversational
---
# My Awesome Model
|
snrspeaks/KeyPhraseTransformer | 4a31635920d6d0fcaf8d13eb9069cb898e3c1523 | 2022-03-25T13:05:44.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | snrspeaks | null | snrspeaks/KeyPhraseTransformer | 3,172 | 1 | transformers | 1,086 | ---
license: mit
---
|
lvwerra/gpt2-imdb | f1bfd819c6bee6c18fa5f95bfe88d9198839a435 | 2021-05-23T08:38:34.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | lvwerra | null | lvwerra/gpt2-imdb | 3,167 | 1 | transformers | 1,087 | # GPT2-IMDB
## What is it?
A GPT2 (`gpt2`) language model fine-tuned on the [IMDB dataset](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews).
## Training setting
The GPT2 language model was fine-tuned for 1 epoch on the IMDB dataset. All comments were joined into a single text file separated ... |
sentence-transformers/msmarco-roberta-base-v3 | 80e7e11abacef57acc1225f6b3517b74c42b27f2 | 2022-06-15T22:06:07.000Z | [
"pytorch",
"tf",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-roberta-base-v3 | 3,150 | null | sentence-transformers | 1,088 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-roberta-base-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dens... |
makiharukawa/DialoGPT-small-oples | 1f97f0d6f62bab9ca4604ffae2f8bc1ae17c2dc3 | 2022-04-22T14:22:15.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | makiharukawa | null | makiharukawa/DialoGPT-small-oples | 3,143 | null | transformers | 1,089 | ---
tags:
- conversational
---
# personal dialoGPT model |
sshleifer/distill-pegasus-cnn-16-4 | 2055eea8e1a19ac362d3f975ffbc6d9e57e3029c | 2020-10-08T03:05:37.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | sshleifer | null | sshleifer/distill-pegasus-cnn-16-4 | 3,141 | 1 | transformers | 1,090 | ---
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: [@... |
wissamantoun/araelectra-base-artydiqa | 5de9f5b88e471c6e95e1100e7aec89dfc783a4b9 | 2021-04-05T11:58:31.000Z | [
"pytorch",
"electra",
"question-answering",
"ar",
"dataset:tydiqa",
"arxiv:2012.15516",
"transformers",
"autotrain_compatible"
] | question-answering | false | wissamantoun | null | wissamantoun/araelectra-base-artydiqa | 3,131 | 2 | transformers | 1,091 | ---
language: ar
datasets:
- tydiqa
widget:
- text: "ما هو نظام الحكم في لبنان؟"
context: "لبنان أو (رسميا: الجمهورية اللبنانية)، هي دولة عربية واقعة في الشرق الأوسط في غرب القارة الآسيوية. تحدها سوريا من الشمال و الشرق، و فلسطين المحتلة - إسرائيل من الجنوب، وتطل من جهة الغرب على البحر الأبيض المتوسط. هو بلد ديمقراطي... |
gerulata/slovakbert | 0557b0aa92a9e5abb6d9ec977ce70bc90662083b | 2021-10-01T07:53:31.000Z | [
"pytorch",
"tf",
"roberta",
"fill-mask",
"sk",
"dataset:wikipedia",
"dataset:opensubtitles",
"dataset:oscar",
"dataset:gerulatawebcrawl",
"dataset:gerulatamonitoring",
"dataset:blbec.online",
"arxiv:2109.15254",
"transformers",
"SlovakBERT",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | gerulata | null | gerulata/slovakbert | 3,127 | 3 | transformers | 1,092 | ---
language: sk
tags:
- SlovakBERT
license: mit
datasets:
- wikipedia
- opensubtitles
- oscar
- gerulatawebcrawl
- gerulatamonitoring
- blbec.online
---
# SlovakBERT (base-sized model)
SlovakBERT pretrained model on Slovak language using a masked language modeling (MLM) objective. This model is case-se... |
xlm-mlm-ende-1024 | 9b403ad70d01ef2a24624f1d733b7274f92cbcda | 2022-07-22T08:08:01.000Z | [
"pytorch",
"tf",
"xlm",
"fill-mask",
"multilingual",
"en",
"de",
"arxiv:1901.07291",
"arxiv:1910.09700",
"transformers",
"license:cc-by-nc-4.0",
"autotrain_compatible"
] | fill-mask | false | null | null | xlm-mlm-ende-1024 | 3,125 | null | transformers | 1,093 | ---
language:
- multilingual
- en
- de
license: cc-by-nc-4.0
---
# xlm-mlm-ende-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental... |
deepset/bert-base-uncased-squad2 | 932875db3f21b4365cbac7504be7252e4e1d96b8 | 2022-07-26T08:36:29.000Z | [
"pytorch",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/bert-base-uncased-squad2 | 3,121 | 2 | transformers | 1,094 | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
model-index:
- name: deepset/bert-base-uncased-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- name:... |
TsinghuaAI/CPM-Generate | 0e4bcd995f9a9e70ba7c31f67df60e73a922676f | 2021-07-29T19:03:51.000Z | [
"pytorch",
"tf",
"gpt2",
"text-generation",
"zh",
"dataset:100GB Chinese corpus",
"arxiv:2012.00413",
"transformers",
"cpm",
"license:mit"
] | text-generation | false | TsinghuaAI | null | TsinghuaAI/CPM-Generate | 3,115 | 7 | transformers | 1,095 | ---
language:
- zh
tags:
- cpm
license: mit
datasets:
- 100GB Chinese corpus
---
# CPM-Generate
## Model description
CPM (Chinese Pre-trained Language Model) is a Transformer-based autoregressive language model, with 2.6 billion parameters and 100GB Chinese training data. To the best of our knowledge, CPM is the lar... |
PlanTL-GOB-ES/roberta-base-biomedical-clinical-es | 617bf244e3106b6d50abfc600d62b858d798867d | 2022-04-08T14:10:05.000Z | [
"pytorch",
"roberta",
"fill-mask",
"es",
"arxiv:2109.03570",
"arxiv:2109.07765",
"transformers",
"biomedical",
"clinical",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | PlanTL-GOB-ES | null | PlanTL-GOB-ES/roberta-base-biomedical-clinical-es | 3,113 | 5 | transformers | 1,096 | ---
language:
- es
tags:
- biomedical
- clinical
- spanish
license: apache-2.0
metrics:
- ppl
widget:
- text: "El único antecedente personal a reseñar era la <mask> arterial."
- text: "Las radiologías óseas de cuerpo entero no detectan alteraciones <mask>, ni alteraciones vertebrales."
- text: "En el <mask> toraco-a... |
microsoft/deberta-xlarge | b1f7182c4065333dc7cf4247570892cf1d8b7029 | 2022-01-13T18:33:03.000Z | [
"pytorch",
"tf",
"deberta",
"en",
"arxiv:2006.03654",
"transformers",
"deberta-v1",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-xlarge | 3,106 | 1 | transformers | 1,097 | ---
language: en
tags: deberta-v1
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. Wi... |
facebook/flava-full | 57949b6a84a80fd01c3dd62a09450d8670f1c418 | 2022-05-25T07:53:39.000Z | [
"pytorch",
"flava",
"pretraining",
"arxiv:2112.04482",
"arxiv:2108.10904",
"transformers",
"license:bsd-3-clause"
] | null | false | facebook | null | facebook/flava-full | 3,105 | 5 | transformers | 1,098 | ---
license: bsd-3-clause
---
## Model Card: FLAVA
## Model Details
FLAVA model was developed by the researchers at FAIR to understand if a single model can work across different modalities with a unified architecture. The model was pretrained solely using publicly available multimodal datasets containing 70M image-t... |
studio-ousia/luke-large | 0729d044dfe301d9ecabc222d60633f92ac450eb | 2022-04-13T09:06:10.000Z | [
"pytorch",
"luke",
"fill-mask",
"en",
"arxiv:1906.08237",
"arxiv:1903.07785",
"arxiv:2002.01808",
"transformers",
"named entity recognition",
"entity typing",
"relation classification",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | studio-ousia | null | studio-ousia/luke-large | 3,101 | 1 | transformers | 1,099 | ---
language: en
thumbnail: https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png
tags:
- luke
- named entity recognition
- entity typing
- relation classification
- question answering
license: apache-2.0
---
## LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attenti... |
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