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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
HaoHu/vit-base-patch16-224-in21k-classify-4scence | 71d615a3bd25998d3d8b4684b9feb08dbaf77949 | 2022-07-24T16:02:55.000Z | [
"pytorch",
"vit",
"image-classification",
"transformers",
"license:other"
] | image-classification | false | HaoHu | null | HaoHu/vit-base-patch16-224-in21k-classify-4scence | 70 | null | transformers | 5,400 | ---
license: other
---
train this model on the Contest
the original dataset is
链接: https://pan.baidu.com/s/1pr094NZ2QMj3nLy12gfa6g 密码: kb7a |
Akashpb13/xlsr_kurmanji_kurdish | cfa28c05cf423a45538613190880b3aa335e11ed | 2022-07-24T11:13:35.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"kmr",
"ku",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-in... | automatic-speech-recognition | false | Akashpb13 | null | Akashpb13/xlsr_kurmanji_kurdish | 69 | 2 | transformers | 5,401 | ---
language:
- kmr
- ku
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- kmr
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/xlsr_kurmanji_kurdish
result... |
Helsinki-NLP/opus-mt-gv-en | c02ace54b4c05a93551a57b72abffe5410883453 | 2021-09-09T21:59:55.000Z | [
"pytorch",
"marian",
"text2text-generation",
"gv",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-gv-en | 69 | null | transformers | 5,402 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-gv-en
* source languages: gv
* target languages: en
* OPUS readme: [gv-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/gv-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
M-FAC/bert-mini-finetuned-mrpc | df30494cb0306e73f26b01f2a262500e3399eb19 | 2021-12-13T08:15:19.000Z | [
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"transformers"
] | text-classification | false | M-FAC | null | M-FAC/bert-mini-finetuned-mrpc | 69 | null | transformers | 5,403 | # BERT-mini model finetuned with M-FAC
This model is finetuned on MRPC dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default ... |
airesearch/wangchanberta-base-wiki-syllable | c21b6b012537891233bb454df05fea4deac0bdae | 2021-09-11T09:38:36.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"th",
"arxiv:1907.11692",
"arxiv:2101.09635",
"transformers",
"autotrain_compatible"
] | fill-mask | false | airesearch | null | airesearch/wangchanberta-base-wiki-syllable | 69 | null | transformers | 5,404 | ---
language: th
---
# WangchanBERTa base model: `wangchanberta-base-wiki-syllable`
<br>
Pretrained RoBERTa BASE model on Thai Wikipedia corpus.
The script and documentation can be found at [this reposiryory](https://github.com/vistec-AI/thai2transformers).
<br>
## Model description
<br>
The architecture of the p... |
akhooli/xlm-r-large-arabic-toxic | c87447910b4639a701ac961cd188c96cb4f11263 | 2020-12-11T21:32:20.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"ar",
"en",
"transformers",
"license:mit"
] | text-classification | false | akhooli | null | akhooli/xlm-r-large-arabic-toxic | 69 | null | transformers | 5,405 | ---
language:
- ar
- en
license: mit
---
### xlm-r-large-arabic-toxic (toxic/hate speech classifier)
Toxic (hate speech) classification (Label_0: non-toxic, Label_1: toxic) of Arabic comments by fine-tuning XLM-Roberta-Large.
Zero shot classification of other languages (also works in mixed languages - ex. Arabic & ... |
huggingartists/platina | 82f4531528d68ed7b914a5611d070c99c9626360 | 2021-09-29T17:06:31.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"dataset:huggingartists/platina",
"transformers",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm"
] | text-generation | false | huggingartists | null | huggingartists/platina | 69 | null | transformers | 5,406 | ---
language: en
datasets:
- huggingartists/platina
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:9... |
jcblaise/electra-tagalog-base-cased-discriminator | 8a8f3ffd0ed8fbfb8b7cfaec8ed45e99e90adbb4 | 2021-11-12T03:23:38.000Z | [
"pytorch",
"electra",
"pretraining",
"tl",
"transformers",
"tagalog",
"filipino",
"license:gpl-3.0"
] | null | false | jcblaise | null | jcblaise/electra-tagalog-base-cased-discriminator | 69 | null | transformers | 5,407 | ---
language: tl
tags:
- electra
- tagalog
- filipino
license: gpl-3.0
inference: false
---
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) ... |
pparasurama/raceBERT | dc586588e7bd022cb74f03b511b6d7cc075702ad | 2021-12-07T01:21:45.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | pparasurama | null | pparasurama/raceBERT | 69 | 1 | transformers | 5,408 | Entry not found |
pucpr/clinicalnerpt-quantitative | c1e9dc6fb2d579485140c7655ce7a4badea1da87 | 2021-10-13T09:31:50.000Z | [
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"transformers",
"autotrain_compatible"
] | token-classification | false | pucpr | null | pucpr/clinicalnerpt-quantitative | 69 | 3 | transformers | 5,409 | ---
language: "pt"
widget:
- text: "Paciente faz uso de losartana 50mg, HCTZ 25mg DM ha 25 anos."
- text: "Paciente com Sepse pulmonar em D8 tazocin (paciente não recebeu por 2 dias Atb)."
datasets:
- SemClinBr
thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png"
--... |
sarahlintang/IndoBERT | b49b5ed020c51076acc24bb00f6b63f9161102ce | 2021-05-20T04:51:45.000Z | [
"pytorch",
"jax",
"bert",
"id",
"dataset:oscar",
"transformers"
] | null | false | sarahlintang | null | sarahlintang/IndoBERT | 69 | null | transformers | 5,410 | ---
language: id
datasets:
- oscar
---
# IndoBERT (Indonesian BERT Model)
## Model description
IndoBERT is a pre-trained language model based on BERT architecture for the Indonesian Language.
This model is base-uncased version which use bert-base config.
## Intended uses & limitations
#### How to use
```python
fr... |
vitouphy/wav2vec2-xls-r-300m-english | 8d878c78d96fd2841116b7b894b93985edc8c716 | 2022-05-24T11:08:07.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"librispeech_asr",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | vitouphy | null | vitouphy/wav2vec2-xls-r-300m-english | 69 | 1 | transformers | 5,411 | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
- generated_from_trainer
- hf-asr-leaderboard
- librispeech_asr
- robust-speech-event
datasets:
- librispeech_asr
model-index:
- name: XLS-R-300M - English
results:
- task:
name: Automatic Speech Recognition
type: automatic... |
IDEA-CCNL/Erlangshen-Longformer-330M | a50c01a4c08401689f14eda06f2e4da996db90a0 | 2022-07-17T09:22:17.000Z | [
"pytorch",
"longformer",
"zh",
"transformers",
"license:apache-2.0"
] | null | false | IDEA-CCNL | null | IDEA-CCNL/Erlangshen-Longformer-330M | 69 | null | transformers | 5,412 | ---
language:
- zh
license: apache-2.0
widget:
- text: "生活的真谛是[MASK]。"
---
# longformer model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
We modify the original position code of longformer to [rotational position coding](https://github.com/ZhuiyiTechnology/roformer), and u... |
dimbyTa/rock-challenge-DeiT-solo | 38067d789dd7a7cef2a1b7086a5461620719a8d9 | 2022-04-23T12:05:21.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | dimbyTa | null | dimbyTa/rock-challenge-DeiT-solo | 69 | null | transformers | 5,413 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rock-challenge-DeiT-solo
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8866301774978638
---
# rock-chal... |
iyzg/computer-stuff | ab492de29a4d712c6d250c629b435970d8a50fab | 2022-05-13T21:55:51.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | iyzg | null | iyzg/computer-stuff | 69 | null | transformers | 5,414 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: computer-stuff
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8839285969734192
---
# computer-stuff
Au... |
dinalzein/xlm-roberta-base-finetuned-language-identification | c369439fa6258c051d624f2da8903481a4830033 | 2022-05-25T09:52:27.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | dinalzein | null | dinalzein/xlm-roberta-base-finetuned-language-identification | 69 | null | transformers | 5,415 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-language-identification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, the... |
jamie613/mt5_fill_puntuation | a3041da41ee127fe2ef62a64fbadb7019c182e6e | 2022-07-11T19:00:55.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | jamie613 | null | jamie613/mt5_fill_puntuation | 69 | null | transformers | 5,416 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mt5_fill_puntuation
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. -->
# mt5_fill_puntua... |
waboucay/camembert-large-finetuned-xnli_fr_3_classes-finetuned-rua_wl_3_classes | 0c3176815b3cc89dc21a269931c9061f8971348b | 2022-06-20T09:34:17.000Z | [
"pytorch",
"camembert",
"text-classification",
"fr",
"transformers",
"nli"
] | text-classification | false | waboucay | null | waboucay/camembert-large-finetuned-xnli_fr_3_classes-finetuned-rua_wl_3_classes | 69 | null | transformers | 5,417 | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 72.4 | 72.2 |
| test ... |
AI4Sec/cyner-xlm-roberta-base | 1e400729cac0561170f8f441d35791768b661201 | 2022-02-22T16:23:17.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"license:mit",
"autotrain_compatible"
] | token-classification | false | AI4Sec | null | AI4Sec/cyner-xlm-roberta-base | 68 | null | transformers | 5,418 | ---
license: mit
---
|
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 | 4f806dbc260d6ce3d6aed0cbf875f668cc1b5480 | 2021-09-02T08:31:10.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | DataikuNLP | null | DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 | 68 | null | sentence-transformers | 5,419 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2
**This model is a copy of [this model repository](https://huggingface.co/sentence-transformers/paraphrase-multilingua... |
InfoCoV/Cro-CoV-cseBERT | 1dd9ad73fdaec21bae223e8ea328705691159763 | 2021-12-09T12:39:35.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | InfoCoV | null | InfoCoV/Cro-CoV-cseBERT | 68 | null | transformers | 5,420 | ## Usage:
```
from sentence_transformers import models
from sentence_transformers import SentenceTransformer
word_embedding_model = models.Transformer('Cro-CoV-cseBERT')
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
pooling_mode_mean_tokens=True,
... |
Jean-Baptiste/roberta-ticker | 13ee076648e9fb3dfcdcca2e475efae71cf2aebe | 2021-08-30T12:58:23.000Z | [
"pytorch",
"roberta",
"token-classification",
"en",
"transformers",
"autotrain_compatible"
] | token-classification | false | Jean-Baptiste | null | Jean-Baptiste/roberta-ticker | 68 | null | transformers | 5,421 | ---
language: en
widget:
- text: "I am going to buy 100 shares of cake tomorrow"
---
# roberta-ticker: model was fine-tuned from Roberta to detect financial tickers
## Introduction
This is a model specifically designed to identify tickers in text.
Model was trained on transformed dataset from following Kaggle datase... |
Langboat/mengzi-oscar-base | 71863d5bfb453b86f08da6e205c6202a1d5a7373 | 2021-10-14T02:17:53.000Z | [
"pytorch",
"bert",
"fill-mask",
"zh",
"arxiv:2110.06696",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Langboat | null | Langboat/mengzi-oscar-base | 68 | 4 | transformers | 5,422 | ---
language:
- zh
license: apache-2.0
---
# Mengzi-oscar-base (Chinese Multi-modal pre-training model)
Mengzi-oscar is trained based on the Multi-modal pre-training model [Oscar](https://github.com/microsoft/Oscar), and is initialized using [Mengzi-Bert-Base](https://github.com/Langboat/Mengzi). 3.7M pairs of imag... |
LeBenchmark/wav2vec2-FR-7K-base | 17c16a4c81e1305df482a54c344c5bf490be8ab2 | 2021-11-23T18:01:03.000Z | [
"pytorch",
"wav2vec2",
"feature-extraction",
"fr",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | LeBenchmark | null | LeBenchmark/wav2vec2-FR-7K-base | 68 | null | transformers | 5,423 | ---
language: "fr"
thumbnail:
tags:
- wav2vec2
license: "apache-2.0"
---
# LeBenchmark: wav2vec2 base model trained on 7K hours of French speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. For more information on... |
SajjadAyoubi/clip-fa-text | ad048ce7b7e62cebc7fdbe96f9e745f79cb6823d | 2021-12-22T19:02:56.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2103.00020",
"transformers"
] | feature-extraction | false | SajjadAyoubi | null | SajjadAyoubi/clip-fa-text | 68 | null | transformers | 5,424 | # CLIPfa: Connecting Farsi Text and Images
OpenAI released [`the paper Learning Transferable Visual Models From Natural Language Supervision`](https://arxiv.org/abs/2103.00020) in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching t... |
Wikidepia/indobert-lite-squad | 9d94c6279e1488ae8ac75514fa1856b41425e2d4 | 2021-03-31T13:26:55.000Z | [
"pytorch",
"albert",
"question-answering",
"id",
"transformers",
"autotrain_compatible"
] | question-answering | false | Wikidepia | null | Wikidepia/indobert-lite-squad | 68 | 1 | transformers | 5,425 | ---
language: id
widget:
- text: "Kapan Einstein melepas kewarganegaraan Jerman?"
context: "Setelah menghabiskan waktu satu tahun di Praha, Einstein tinggal di Swiss antara tahun 1895 dan 1914, melepas kewarganegaraan Jermannya pada tahun 1896, dan lulus sarjana dari sekolah politeknik federal Swiss (kelak Eidgenössi... |
avichr/heBERT_NER | fc4a87775d8cb716b9803b73672ebcd53e933c4a | 2022-01-11T17:00:46.000Z | [
"pytorch",
"bert",
"token-classification",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible"
] | token-classification | false | avichr | null | avichr/heBERT_NER | 68 | null | transformers | 5,426 | # HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HeBERT is a Hebrew pretrained language model. It is based on [Google's BERT](https://arxiv.org/abs/1810.04805) architecture and... |
flax-community/gpt-neo-1.3B-code-clippy | d4807ec0fc2ee779705487898e1a4e9ff2c9eb28 | 2021-07-11T21:30:30.000Z | [
"pytorch",
"jax",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | flax-community | null | flax-community/gpt-neo-1.3B-code-clippy | 68 | 1 | transformers | 5,427 | Entry not found |
gealexandri/palobert-base-greek-uncased-v1 | 2a9e2202eef049421eeba46ecac7d2eb9a0b5d2c | 2021-08-18T07:25:30.000Z | [
"pytorch",
"tf",
"roberta",
"fill-mask",
"el",
"arxiv:1907.11692",
"transformers",
"autotrain_compatible"
] | fill-mask | false | gealexandri | null | gealexandri/palobert-base-greek-uncased-v1 | 68 | 1 | transformers | 5,428 | ---
language: el
---
# PaloBERT
## Model description
A Greek language model based on [RoBERTa](https://arxiv.org/abs/1907.11692)
## Training data
The training data is a corpus of 458,293 documents collected from Greek social media accounts. It also contains a GTP-2 tokenizer trained from scratch on the same corpus... |
huggingtweets/spacebananaza | 219f8d2f57689b645766789ad1fbf49d2347da5e | 2021-05-22T23:35:52.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/spacebananaza | 68 | null | transformers | 5,429 | ---
language: en
thumbnail: https://www.huggingtweets.com/spacebananaza/1617774737011/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/13700635945... |
m3hrdadfi/albert-fa-base-v2-clf-persiannews | a2220e4c30ad56524876e75d618146f8fc956d6a | 2020-12-26T08:36:46.000Z | [
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"transformers",
"license:apache-2.0"
] | text-classification | false | m3hrdadfi | null | m3hrdadfi/albert-fa-base-v2-clf-persiannews | 68 | 1 | transformers | 5,430 | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was tra... |
m3hrdadfi/bert2bert-fa-news-headline | 7e55193b2a18886abc903639e5c8c1b88e316cda | 2020-12-11T21:50:16.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"fa",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | m3hrdadfi | null | m3hrdadfi/bert2bert-fa-news-headline | 68 | null | transformers | 5,431 | ---
language: fa
license: apache-2.0
tags:
- summarization
---
A Bert2Bert model on VoA Persian Corpus (a medium-sized corpus of 7.9 million words, 2003-2008) generates headlines. The model achieved a 25.30 ROUGE-2 score.
For more detail, please follow the [News Headline Generation](https://github.com/m3hrdadfi/new... |
mbeck/roberta-base-squad2 | 3bd552f8aba1fce5a55168821c0822c1417d72df | 2021-05-20T17:46:33.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | mbeck | null | mbeck/roberta-base-squad2 | 68 | null | transformers | 5,432 | Entry not found |
prajjwal1/albert-base-v2-mnli | eabf43d13275c6db402b572349d7167393b9eab7 | 2021-10-05T17:55:48.000Z | [
"pytorch",
"albert",
"text-classification",
"arxiv:2110.01518",
"transformers"
] | text-classification | false | prajjwal1 | null | prajjwal1/albert-base-v2-mnli | 68 | null | transformers | 5,433 | If you use the model, please consider citing the paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
... |
projecte-aina/roberta-base-ca-cased-pos | e9882f8a9d677b24076f267b16faf9d726cacd31 | 2022-06-14T15:13:27.000Z | [
"pytorch",
"roberta",
"token-classification",
"ca",
"dataset:universal_dependencies",
"arxiv:1907.11692",
"transformers",
"catalan",
"part of speech tagging",
"pos",
"CaText",
"Catalan Textual Corpus",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | projecte-aina | null | projecte-aina/roberta-base-ca-cased-pos | 68 | null | transformers | 5,434 | ---
language:
- ca
license: apache-2.0
tags:
- "catalan"
- "part of speech tagging"
- "pos"
- "CaText"
- "Catalan Textual Corpus"
datasets:
- "universal_dependencies"
metrics:
- f1
inference:
parameters:
aggregation_strategy: "first"
model-index:
- name: roberta-base-ca-cased-pos
results:
- tas... |
pucpr/clinicalnerpt-laboratory | f07078e758e3ad375d2ea33065d24bd8d95f2b4d | 2021-10-13T09:32:17.000Z | [
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"transformers",
"autotrain_compatible"
] | token-classification | false | pucpr | null | pucpr/clinicalnerpt-laboratory | 68 | 3 | transformers | 5,435 | ---
language: "pt"
widget:
- text: "Exame de creatinina urinaria: 41, 8 mg/dL."
- text: "Parcial de urina com 150mg/dL de priteinas, ph de 5,0 e 1034 leucocitos."
datasets:
- SemClinBr
thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png"
---
<img src="https://raw.g... |
shahrukhx01/paraphrase-mpnet-base-v2-fuzzy-matcher | 7ef60acd1a14c67e772a7176147ff07fb907f680 | 2021-07-12T19:56:01.000Z | [
"pytorch",
"mpnet",
"feature-extraction",
"transformers",
"fuzzy-matching",
"fuzzy-search",
"entity-resolution",
"record-linking",
"structured-data-search"
] | feature-extraction | false | shahrukhx01 | null | shahrukhx01/paraphrase-mpnet-base-v2-fuzzy-matcher | 68 | null | transformers | 5,436 | ---
tags:
- fuzzy-matching
- fuzzy-search
- entity-resolution
- record-linking
- structured-data-search
---
A Siamese BERT architecture trained at character levels tokens for embedding based Fuzzy matching.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://ww... |
shibing624/code-autocomplete-gpt2-base | 3304a8144629d3241c9921fae39cf1d73aadf977 | 2022-02-15T07:21:54.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"code",
"autocomplete",
"license:apache-2.0"
] | text-generation | false | shibing624 | null | shibing624/code-autocomplete-gpt2-base | 68 | 1 | transformers | 5,437 | ---
language:
- en
tags:
- code
- autocomplete
- pytorch
- en
license: "apache-2.0"
---
# GPT2 for Code AutoComplete Model
code-autocomplete, a code completion plugin for Python.
**code-autocomplete** can automatically complete the code of lines and blocks with GPT2.
## Usage
Open source repo：[co... |
stanford-crfm/celebrimbor-gpt2-medium-x81 | 812043eceb4bef1b02ca07a90564cdd54d2a2d1f | 2022-06-20T11:22:39.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | stanford-crfm | null | stanford-crfm/celebrimbor-gpt2-medium-x81 | 68 | null | transformers | 5,438 | Entry not found |
uhhlt/am-roberta | 71c9111a6c3537a6709e9459b54b9ef73a185561 | 2022-01-29T12:41:40.000Z | [
"pytorch",
"roberta",
"fill-mask",
"am",
"dataset:Amharic corpus from LT group, UHH",
"transformers",
"Amharic",
"Semetic language",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | uhhlt | null | uhhlt/am-roberta | 68 | 1 | transformers | 5,439 | ---
language:
- am
thumbnail: "https://raw.githubusercontent.com/uhh-lt/amharicmodels/master/logo.png?token=AAIB2MYMI6TSIK7CHWYGHKTBQ3FQS"
tags:
- Amharic
- Semetic language
license: "mit"
datasets:
- Amharic corpus from LT group, UHH
widget:
- text: "አበበ <mask> በላ ።"
- text: "የአገሪቱ አጠቃላይ የስንዴ አቅርቦት ሶስት አራተኛው የሚመረተ... |
ziedsb19/tunbert_zied | a8f4668e9b2cbf83827b2e6a74e4c791c51006ab | 2021-09-15T14:04:41.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | ziedsb19 | null | ziedsb19/tunbert_zied | 68 | 2 | transformers | 5,440 |
## 🧐 About <a name = "about"></a>
tunbert_zied is language model for the tunisian dialect based on a similar architecture to the RoBERTa model created BY zied sbabti.
The model was trained for over 600 000 phrases written in the tunisian dialect.
## 🏁 Getting Started <a name = "getting_started"></a>
... |
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_editorials_TEST_essays_05_03_2022-06_26_52 | 47120759c6241041d834b7882ea8e257ecea0a8d | 2022-03-05T05:29:22.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ali2066 | null | ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_editorials_TEST_essays_05_03_2022-06_26_52 | 68 | null | transformers | 5,441 | Entry not found |
praf-choub/bart-CaPE-cnn | 0757e02672a22c8c99855157abcb73d8d75816e6 | 2022-06-14T04:50:58.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"arxiv:2110.07166",
"transformers",
"summarization",
"license:bsd-3-clause",
"autotrain_compatible"
] | summarization | false | praf-choub | null | praf-choub/bart-CaPE-cnn | 68 | null | transformers | 5,442 | ---
language: en
tags:
- summarization
license: bsd-3-clause
datasets:
- cnn_dailymail
---
Citation
```
@misc{https://doi.org/10.48550/arxiv.2110.07166,
doi = {10.48550/ARXIV.2110.07166},
url = {https://arxiv.org/abs/2110.07166},
author = {Choubey, Prafulla Kumar and Fabbri, Alexander R. and Vig, Jesse and Wu, C... |
pszemraj/opt-350m-email-generation | 280daa0817ddb5dd42693037279a09a81c66a526 | 2022-06-25T22:30:34.000Z | [
"pytorch",
"opt",
"text-generation",
"dataset:aeslc",
"transformers",
"generated_from_trainer",
"custom-license",
"no-commercial",
"email",
"auto-complete",
"license:other"
] | text-generation | false | pszemraj | null | pszemraj/opt-350m-email-generation | 68 | 1 | transformers | 5,443 | ---
license: other
tags:
- generated_from_trainer
- opt
- custom-license
- no-commercial
- email
- auto-complete
datasets:
- aeslc
widget:
- text: "Hey <NAME>,\n\nThank you for signing up for my weekly newsletter. Before we get started, you'll have to confirm your email address."
example_title: "newsletter"
- text:... |
totoro4007/cryptoroberta-base-finetuned | 07c94393727e60bf60d85b8d377aefa3e2e83463 | 2022-05-21T08:38:59.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | false | totoro4007 | null | totoro4007/cryptoroberta-base-finetuned | 68 | null | transformers | 5,444 | Entry not found |
mehnaazasad/swin-tiny-patch4-window7-224-finetuned-eurosat | aa48e1ee5aa7f1285538c11b30f00bc8b2174dcd | 2022-05-25T16:11:42.000Z | [
"pytorch",
"tensorboard",
"swin",
"image-classification",
"dataset:image_folder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | mehnaazasad | null | mehnaazasad/swin-tiny-patch4-window7-224-finetuned-eurosat | 68 | null | transformers | 5,445 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
... |
apple/deeplabv3-mobilevit-x-small | aacb7714f951e619793828143558cad3defed0db | 2022-06-02T10:53:23.000Z | [
"pytorch",
"coreml",
"mobilevit",
"dataset:pascal-voc",
"arxiv:2110.02178",
"arxiv:1706.05587",
"transformers",
"vision",
"image-segmentation",
"license:other"
] | image-segmentation | false | apple | null | apple/deeplabv3-mobilevit-x-small | 68 | 1 | transformers | 5,446 | ---
license: other
tags:
- vision
- image-segmentation
datasets:
- pascal-voc
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-2.jpg
example_title: Cat
---
# MobileViT + DeepLabV3 (extra small-sized model)
MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was in... |
tinkoff-ai/response-quality-classifier-large | e64393251b97d9e1dec23e2e9e8b6eb29b3625eb | 2022-06-01T06:34:44.000Z | [
"pytorch",
"roberta",
"text-classification",
"ru",
"transformers",
"conversational",
"license:mit"
] | text-classification | false | tinkoff-ai | null | tinkoff-ai/response-quality-classifier-large | 68 | null | transformers | 5,447 | ---
license: mit
widget:
- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]супер, вот только проснулся, у тебя как?"
example_title: "Dialog example 1"
- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESPONSE_TOKEN]норм"
example_title: "Dialog example 2"
- text: "[CLS]привет[SEP]привет![SEP]как дела?[RESP... |
facebook/genre-linking-aidayago2 | d93982cbdbd1559229535a5810d92c4d7b1a0883 | 2022-06-14T14:10:29.000Z | [
"pytorch",
"tf",
"jax",
"bart",
"text2text-generation",
"en",
"arxiv:2010.00904",
"arxiv:1910.13461",
"arxiv:1911.03814",
"transformers",
"retrieval",
"entity-retrieval",
"named-entity-disambiguation",
"entity-disambiguation",
"named-entity-linking",
"entity-linking",
"autotrain_comp... | text2text-generation | false | facebook | null | facebook/genre-linking-aidayago2 | 68 | null | transformers | 5,448 | ---
language:
- en
tags:
- retrieval
- entity-retrieval
- named-entity-disambiguation
- entity-disambiguation
- named-entity-linking
- entity-linking
- text2text-generation
---
# GENRE
The GENRE (Generative ENtity REtrieval) system as presented in [Autoregressive Entity Retrieval](https://arxiv.org/abs/2010.00904... |
yogeshchandrasekharuni/entity-extraction-v0 | fbd911f4e6c8ef1b28ef694dd5c49b7c239c3d75 | 2022-06-08T05:15:30.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | yogeshchandrasekharuni | null | yogeshchandrasekharuni/entity-extraction-v0 | 68 | null | transformers | 5,449 | Entry not found |
DancingIguana/music-generation | 1d40a863406913c97cdb15d877b15b4273276158 | 2022-06-13T16:48:57.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | DancingIguana | null | DancingIguana/music-generation | 68 | 1 | transformers | 5,450 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: music-generation
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. -->
# music-generation
... |
zluvolyote/CUBERT | 17f4fec984e4d169445d2985c2c09feb60b5f3e5 | 2022-07-12T15:09:51.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | fill-mask | false | zluvolyote | null | zluvolyote/CUBERT | 68 | null | transformers | 5,451 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: CUBERT
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. -->
# CUBERT
This model is a fine-tuned ... |
eslamxm/MBART-finetuned-Spanish | 0cef59aaf30a2567c20d6d20567717860d2c4e0d | 2022-06-18T11:37:56.000Z | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"dataset:wiki_lingua",
"transformers",
"summarization",
"Mbart",
"seq2seq",
"es",
"abstractive summarization",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | summarization | false | eslamxm | null | eslamxm/MBART-finetuned-Spanish | 68 | null | transformers | 5,452 | ---
tags:
- summarization
- Mbart
- seq2seq
- es
- abstractive summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: MBART-finetuned-Spanish
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably ... |
abhishek/deberta-v3-base-autotrain | d7c27f41f9cc47e73195973ddf02b5462dbb173c | 2022-06-30T13:13:18.000Z | [
"pytorch",
"deberta-v2",
"en",
"arxiv:2006.03654",
"arxiv:2111.09543",
"transformers",
"deberta",
"deberta-v3",
"fill-mask",
"license:mit"
] | fill-mask | false | abhishek | null | abhishek/deberta-v3-base-autotrain | 68 | null | transformers | 5,453 | ---
language: en
tags:
- deberta
- deberta-v3
- fill-mask
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT... |
Farshid/distilbert-base-uncased_allagree3 | 3874321c2a69ffdbc33c65f914f7af302232f961 | 2022-07-04T21:04:03.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:financial_phrasebank",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Farshid | null | Farshid/distilbert-base-uncased_allagree3 | 68 | null | transformers | 5,454 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased_allagree3
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial... |
vasugoel/K-12BERT | d980e0c88dfa477efb4b0325933f651601e36a6a | 2022-07-14T07:54:54.000Z | [
"pytorch",
"bert",
"fill-mask",
"en",
"dataset:vasugoel/K-12Corpus",
"arxiv:2205.12335",
"transformers",
"education",
"K-12",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | vasugoel | null | vasugoel/K-12BERT | 68 | 2 | transformers | 5,455 | ---
language: en
tags:
- education
- K-12
license: apache-2.0
datasets:
- vasugoel/K-12Corpus
---
## K-12BERT model
K-12BERT is a model trained by performing continued pretraining on the K-12Corpus. Since, performance of BERT like models on domain adaptive tasks have shown great progress, we noticed the lack of such ... |
jordyvl/biobert-base-cased-v1.2_ncbi_disease-CRFsubset-first-ner | d0c22b4327f748dd456eb3d12d6e244033a9f0be | 2022-07-15T11:45:09.000Z | [
"pytorch",
"tensorboard",
"bert",
"transformers"
] | null | false | jordyvl | null | jordyvl/biobert-base-cased-v1.2_ncbi_disease-CRFsubset-first-ner | 68 | null | transformers | 5,456 | Entry not found |
lisaterumi/postagger-bio-portuguese | 8c944d9922ba36daa986286010d328971c370d7d | 2022-07-15T14:13:28.000Z | [
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:MacMorpho",
"transformers",
"autotrain_compatible"
] | token-classification | false | lisaterumi | null | lisaterumi/postagger-bio-portuguese | 68 | 1 | transformers | 5,457 | ---
language: "pt"
widget:
- text: "O paciente recebeu no hospital e falou com a médica"
datasets:
- MacMorpho
---
# Postagger Bio Portuguese
## Citation
```
coming soon
```
|
derwahnsinn/gpt2-mediummedlavallc | 3f1760e57909c9f47b7f4a705fd08eedcaff1db2 | 2022-07-30T02:59:35.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-generation | false | derwahnsinn | null | derwahnsinn/gpt2-mediummedlavallc | 68 | null | transformers | 5,458 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-mediummedlavallc
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. -->
# gpt2-mediummedlavall... |
CogComp/bart-faithful-summary-detector | 9ee8dc596655cde178f27a956659c4402bf6355f | 2021-06-13T17:18:36.000Z | [
"pytorch",
"jax",
"bart",
"text-classification",
"en",
"dataset:xsum",
"transformers",
"xsum",
"license:cc-by-sa-4.0"
] | text-classification | false | CogComp | null | CogComp/bart-faithful-summary-detector | 67 | null | transformers | 5,459 | ---
language:
- en
thumbnail: https://cogcomp.seas.upenn.edu/images/logo.png
tags:
- text-classification
- bart
- xsum
license: cc-by-sa-4.0
datasets:
- xsum
widget:
- text: "<s> Ban Ki-moon was elected for a second term in 2007. </s></s> Ban Ki-Moon was re-elected for a second term by the UN General Assembly, unoppos... |
Recognai/zeroshot_selectra_small | 5ab73975f5066eab7d53aafe6dd8a4aa82e8d165 | 2022-03-27T09:33:26.000Z | [
"pytorch",
"electra",
"text-classification",
"es",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"nli",
"license:apache-2.0"
] | zero-shot-classification | false | Recognai | null | Recognai/zeroshot_selectra_small | 67 | 3 | transformers | 5,460 | ---
language: es
tags:
- zero-shot-classification
- nli
- pytorch
datasets:
- xnli
pipeline_tag: zero-shot-classification
license: apache-2.0
widget:
- text: "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo"
candidate_labels: "cultura, sociedad, economia, salud, deportes"
---
# Ze... |
Rexhaif/rubert-base-srl-seqlabeling | a0924ffc21c3fb6c2de68db82c561642e710dbe1 | 2022-04-12T13:50:45.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | Rexhaif | null | Rexhaif/rubert-base-srl-seqlabeling | 67 | null | transformers | 5,461 | ---
tags:
- generated_from_trainer
model-index:
- name: rubert-base-srl-seqlabeling
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. -->
# rubert-base-srl-seqlabeling... |
SEBIS/code_trans_t5_large_commit_generation_multitask_finetune | 491f62f380090fc7bfe6717c5b9a8bc785bb7a57 | 2021-06-23T08:28:55.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_large_commit_generation_multitask_finetune | 67 | null | transformers | 5,462 | ---
tags:
- summarization
widget:
- text: "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ"
---
# CodeTrans model for git commit message generation
Pretrained model on git commit using the t5 large model architecture. It was fir... |
aware-ai/longformer-QA | 15ade5c427f009974f43f94b9b914c359a1a1cbb | 2020-08-07T09:40:36.000Z | [
"pytorch",
"tf",
"longformer",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | aware-ai | null | aware-ai/longformer-QA | 67 | null | transformers | 5,463 | Entry not found |
abhishek/autonlp-hindi-asr | b0a43e5f43208d20174596fb33aeddce821920f4 | 2021-07-05T18:39:26.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"autonlp",
"audio"
] | automatic-speech-recognition | false | abhishek | null | abhishek/autonlp-hindi-asr | 67 | 1 | transformers | 5,464 | ---
tags:
- autonlp
- automatic-speech-recognition
- audio
language: {language}
---
# Model Trained Using AutoNLP
- Problem type: Speech Recognition
|
andi611/distilbert-base-uncased-qa-boolq | 168ef0953f2bf4083670a8519a14db558a6f043c | 2021-08-02T09:45:17.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:boolq",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | false | andi611 | null | andi611/distilbert-base-uncased-qa-boolq | 67 | null | transformers | 5,465 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- boolq
metrics:
- accuracy
model_index:
- name: distilbert-base-uncased-boolq
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: boolq
type: boolq
args: default
metri... |
bvanaken/CORe-clinical-mortality-prediction | 1af1c4eb615bb3628b936e62decb4519b5775ae2 | 2021-11-30T13:28:29.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"medical",
"clinical",
"mortality"
] | text-classification | false | bvanaken | null | bvanaken/CORe-clinical-mortality-prediction | 67 | 1 | transformers | 5,466 | ---
language: "en"
tags:
- bert
- medical
- clinical
- mortality
thumbnail: "https://core.app.datexis.com/static/paper.png"
---
# CORe Model - Clinical Mortality Risk Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admi... |
fagner/envoy | e80e352bfcde73267e69de0f58d6e599f60b3453 | 2022-04-12T22:14:20.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | fagner | null | fagner/envoy | 67 | null | transformers | 5,467 | Entry not found |
jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_50_Epochs | df4099c55f10d9182af5815ee749b6bff8dabe12 | 2022-02-12T21:16:09.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | jfarray | null | jfarray/Model_paraphrase-multilingual-MiniLM-L12-v2_50_Epochs | 67 | null | sentence-transformers | 5,468 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
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 for tasks like cluster... |
laxya007/gpt2_tech | 8dac423df5b74acce9d264bc30f9d2ed24cab726 | 2021-05-23T08:18:57.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | laxya007 | null | laxya007/gpt2_tech | 67 | null | transformers | 5,469 | Entry not found |
mideind/IceBERT | 98f00d95344960c729e827d9779215a2deff9924 | 2022-03-17T13:50:07.000Z | [
"pytorch",
"roberta",
"fill-mask",
"is",
"arxiv:2201.05601",
"transformers",
"icelandic",
"masked-lm",
"license:agpl-3.0",
"autotrain_compatible"
] | fill-mask | false | mideind | null | mideind/IceBERT | 67 | null | transformers | 5,470 | ---
language: is
widget:
- text: Má bjóða þér <mask> í kvöld?
- text: Forseti <mask> er ágæt.
- text: Súpan var <mask> á bragðið.
tags:
- roberta
- icelandic
- masked-lm
- pytorch
license: agpl-3.0
---
# IceBERT
This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have ... |
mrm8488/t5-small-finetuned-boolq | 9bb945b5918dcb17ca906454f802ad8a0b6ae2eb | 2020-08-13T18:47:32.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-small-finetuned-boolq | 67 | null | transformers | 5,471 | Entry not found |
nyu-mll/roberta-med-small-1M-1 | a609533e0ddc94a74091c1a236c44f03f9f94772 | 2021-05-20T19:06:25.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nyu-mll | null | nyu-mll/roberta-med-small-1M-1 | 67 | null | transformers | 5,472 | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a repr... |
pritamdeka/S-PubMedBert-MS-MARCO | 0f9763f8d7e4d13f5f798d8cfc32e4afb8c7c8dd | 2022-01-28T13:48:52.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | pritamdeka | null | pritamdeka/S-PubMedBert-MS-MARCO | 67 | null | sentence-transformers | 5,473 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# pritamdeka/S-PubMedBert-MS-MARCO
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 fo... |
pvcastro/bert-portuguese-cased-rel-cp | 3e2c38244420ea447bc14f9647fcd69096a623c7 | 2021-06-18T15:12:46.000Z | [
"pytorch",
"transformers"
] | null | false | pvcastro | null | pvcastro/bert-portuguese-cased-rel-cp | 67 | null | transformers | 5,474 | Entry not found |
tareknaous/bert2bert-empathetic-response-msa | 239bd1e14e18d697958594a3e5eaa4c49f4ff6e1 | 2021-12-23T15:33:55.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tareknaous | null | tareknaous/bert2bert-empathetic-response-msa | 67 | 1 | transformers | 5,475 | Entry not found |
thu-coai/CDial-GPT2_LCCC-base | 4f06737e550db53a388329a43f634948b88c6de5 | 2020-12-23T07:10:27.000Z | [
"pytorch",
"transformers"
] | null | false | thu-coai | null | thu-coai/CDial-GPT2_LCCC-base | 67 | null | transformers | 5,476 | # CDial-GPT2_LCCC-base
https://github.com/thu-coai/CDial-GPT |
uer/roberta-small-word-chinese-cluecorpussmall | 6152f3fda1a704cf714a6af5f0be920811469613 | 2022-02-19T15:58:08.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uer | null | uer/roberta-small-word-chinese-cluecorpussmall | 67 | 1 | transformers | 5,477 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "最近一趟去北京的[MASK]几点发车"
---
# Chinese word-based RoBERTa Miniatures
## Model description
This is the set of 5 Chinese word-based RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/... |
vesteinn/ScandiBERT | 90f4917ed06d894298489df7a92ee46cfa81fc07 | 2022-03-13T22:15:57.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"is",
"da",
"sv",
"no",
"fo",
"transformers",
"roberta",
"icelandic",
"norwegian",
"faroese",
"danish",
"swedish",
"masked-lm",
"license:agpl-3.0",
"autotrain_compatible"
] | fill-mask | false | vesteinn | null | vesteinn/ScandiBERT | 67 | 2 | transformers | 5,478 | ---
language:
- is
- da
- sv
- no
- fo
widget:
- text: Fina lilla<mask>, jag vill inte bliva stur.
- text: Nu ved jeg, at du frygter<mask> og end ikke vil nægte mig din eneste søn..
- text: Það er vorhret á<mask>, napur vindur sem hvín.
- text: Ja, Gud signi<mask>, mítt land.
- text: Alle dyrene i<mask> må være venner.... |
Shanny/FinBERT | db0d6114e5739c69aa8b655d6412b05240b7b951 | 2022-03-12T11:12:29.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Shanny | null | Shanny/FinBERT | 67 | null | transformers | 5,479 | Entry not found |
navteca/ms-marco-MiniLM-L-12-v2 | 67d0e3a68308414b7d14df5dda93b40197609175 | 2022-03-14T15:56:35.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"sentence-transformers",
"license:mit"
] | text-classification | false | navteca | null | navteca/ms-marco-MiniLM-L-12-v2 | 67 | null | sentence-transformers | 5,480 | ---
language: en
license: mit
pipeline_tag: text-classification
tags:
- sentence-transformers
---
# Cross-Encoder for MS Marco
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. ... |
Anudev08/model_3 | 39aebeb3b7f4a09c6948269f543129f9623b4b2b | 2022-03-16T16:21:00.000Z | [
"pytorch",
"gpt2",
"text-classification",
"transformers"
] | text-classification | false | Anudev08 | null | Anudev08/model_3 | 67 | null | transformers | 5,481 | Entry not found |
James-kc-min/AGT_Roberta2 | a9326cd110c1e224bd92044bb72b4620908b027e | 2022-04-20T12:51:42.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | James-kc-min | null | James-kc-min/AGT_Roberta2 | 67 | null | transformers | 5,482 | Hugging Face's logo
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generated_from_traine... |
allenai/multicite-multilabel-scibert | 7696929cf8ba26c21cc1da0daeb76010575dff80 | 2022-05-10T17:45:24.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"scibert",
"license:mit"
] | text-classification | false | allenai | null | allenai/multicite-multilabel-scibert | 67 | 1 | transformers | 5,483 | ---
language: en
tags:
- scibert
license: mit
---
# MultiCite: Multi-label Citation Intent Classification with SciBERT (NAACL 2022)
This model has been trained on the data available here: https://github.com/allenai/multicite |
oliverguhr/spelling-correction-german-base | cb2310e6676c8dc01d681e13b0e7f23d48c3ad2d | 2022-06-13T12:08:25.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | oliverguhr | null | oliverguhr/spelling-correction-german-base | 67 | null | transformers | 5,484 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-base-spelling-de
results: []
widget:
- text: "das idst ein neuZr test"
example_title: "1"
---
This is an experimental model that should fix your typos and punctuation.
If you like to run your own experiments or train for a different l... |
witiko/mathberta | 3414676ab15cf98f39088c936d0cbd1b28b251b0 | 2022-06-29T17:22:05.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:arxmliv",
"dataset:math-stackexchange",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | witiko | null | witiko/mathberta | 67 | 0 | transformers | 5,485 | ---
language: en
license: mit
datasets:
- arxmliv
- math-stackexchange
---
# MathBERTa model
Pretrained model on English language and LaTeX using a masked language modeling
(MLM) objective. It was developed for [the ARQMath-3 shared task evaluation][1]
at CLEF 2022 and first released in [this repository][2]. This mod... |
NAACL2022/spider-trivia-question-encoder | 5540cd4b726dc5d6ae34fc232b56c085534b5632 | 2022-07-09T19:14:40.000Z | [
"pytorch",
"dpr",
"feature-extraction",
"arxiv:2112.07708",
"transformers"
] | feature-extraction | false | NAACL2022 | null | NAACL2022/spider-trivia-question-encoder | 67 | 4 | transformers | 5,486 | # Spider-TriviaQA: Question Encoder
This is the question encoder of the model fine-tuned on TriviaQA (and initialized from Spider) discussed in our paper [Learning to Retrieve Passages without Supervision](https://arxiv.org/abs/2112.07708).
## Usage
We used weight sharing for the query encoder and passage encoder, s... |
sirreal/gpt-neo-125M-MC | 6036fadb648e9998f25a6bf4de7edaf4102a16d7 | 2022-07-12T00:59:31.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | sirreal | null | sirreal/gpt-neo-125M-MC | 67 | null | transformers | 5,487 | Entry not found |
HeyLucasLeao/gpt-neo-small-portuguese | 6c55098b12753c7a0848203d33195f6fa07cd092 | 2021-06-19T20:51:57.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | HeyLucasLeao | null | HeyLucasLeao/gpt-neo-small-portuguese | 66 | 1 | transformers | 5,488 | ## GPT-Neo Small Portuguese
#### Model Description
This is a finetuned version from GPT-Neo 125M by EletheurAI to Portuguese language.
#### Training data
It was trained from 227,382 selected texts from a PTWiki Dump. You can found all the data from here: https://archive.org/details/ptwiki-dump-20210520
#### Trainin... |
dbmdz/german-gpt2-faust | d5aa8328284f7305e3b1a3d680cee886a12d2b2f | 2021-05-21T15:26:08.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"de",
"transformers",
"license:mit"
] | text-generation | false | dbmdz | null | dbmdz/german-gpt2-faust | 66 | 1 | transformers | 5,489 | ---
language: de
widget:
- text: "Schon um die Liebe"
license: mit
---
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on various texts for German.
The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" ... |
google/t5-11b-ssm-tqa | 55330fe7015e311b675fa8a90a04cf39d1c4fc88 | 2020-12-07T08:38:28.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"dataset:trivia_qa",
"arxiv:2002.08909",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-11b-ssm-tqa | 66 | 3 | transformers | 5,490 | ---
language: en
datasets:
- c4
- wikipedia
- trivia_qa
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subse... |
junnyu/bert_chinese_mc_base | ef8daad8e9da8105c084f4e57b4f935b0504edd5 | 2021-05-20T05:28:56.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | junnyu | null | junnyu/bert_chinese_mc_base | 66 | 2 | transformers | 5,491 | https://github.com/alibaba-research/ChineseBLUE |
mahaamami/distilgpt2-finetuned-wikitext2 | 38627f52bdce02d2df63b3b0dfa27752ace8b29d | 2022-01-05T00:03:59.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | mahaamami | null | mahaamami/distilgpt2-finetuned-wikitext2 | 66 | null | transformers | 5,492 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
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. -->
# dist... |
microsoft/deberta-xxlarge-v2 | 21131a5441d2eb7936b758ca02a67baaa7a34f85 | 2021-02-11T02:05:17.000Z | [
"pytorch",
"deberta-v2",
"en",
"transformers",
"deberta",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-xxlarge-v2 | 66 | null | transformers | 5,493 | ---
language: en
tags: deberta
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
## This model is DEPRECATED, please use [DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)
|
monologg/koelectra-small-discriminator | fc3894f2a606ae0742b993ef36af00947bb3601e | 2020-12-26T16:23:23.000Z | [
"pytorch",
"electra",
"pretraining",
"ko",
"transformers"
] | null | false | monologg | null | monologg/koelectra-small-discriminator | 66 | null | transformers | 5,494 | ---
language: ko
---
# KoELECTRA (Small Discriminator)
Pretrained ELECTRA Language Model for Korean (`koelectra-small-discriminator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and tokenizer
```python
>>> from transfor... |
nguyenvulebinh/spoken-norm | cc1e681532fc4445d2ce47e9ed41a0be98d8204b | 2022-02-11T17:21:36.000Z | [
"pytorch",
"transformers"
] | null | false | nguyenvulebinh | null | nguyenvulebinh/spoken-norm | 66 | 4 | transformers | 5,495 | # Transformation spoken text to written text
This model is used for formatting raw asr text output from spoken text to written text (Eg. date, number, id, ...). It also supports formatting "out of vocab" by using external vocabulary.
Some of examples:
```text
input : tám giờ chín phút ngày mười tám tháng năm năm ha... |
textattack/distilbert-base-uncased-RTE | 9afcea1db32e40187490918bccdb9fc9cd3d4563 | 2020-07-06T16:31:28.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/distilbert-base-uncased-RTE | 66 | null | transformers | 5,496 | ## TextAttack Model Card
This `distilbert-base-uncased` 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... |
Beri/legal-qa | 67b37f86c53224e8818bff88611bd82806922d36 | 2022-03-02T05:12:04.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | Beri | null | Beri/legal-qa | 66 | null | transformers | 5,497 | Entry not found |
tsantos/PathologyBERT | 338fe40394931f12f34a7ac14260a666aa77f0c8 | 2022-07-21T01:13:31.000Z | [
"pytorch",
"bert",
"fill-mask",
"en",
"arxiv:1901.08746",
"arxiv:1903.10676",
"arxiv:1906.05474",
"arxiv:2205.06885",
"transformers",
"autotrain_compatible"
] | fill-mask | false | tsantos | null | tsantos/PathologyBERT | 66 | null | transformers | 5,498 | ---
language: "en"
tags:
- fill-mask
---
# PathologyBERT - Masked Language Model with Breast Pathology Specimens.
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. Recently, several studies have explored the utility and efficacy of co... |
microsoft/dit-large | 95e907dd11353009449ca44007cfdeee05224a62 | 2022-03-08T10:40:24.000Z | [
"pytorch",
"beit",
"arxiv:2203.02378",
"transformers",
"dit"
] | null | false | microsoft | null | microsoft/dit-large | 66 | 4 | transformers | 5,499 | ---
tags:
- dit
inference: false
---
# Document Image Transformer (large-sized model)
Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper [DiT: Self-supervised Pre-training for Document Image Transform... |
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