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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
delvan/DialoGPT-medium-DwightV1 | eba600f8bb554a42b871329fdc9569687afd8e16 | 2021-10-24T20:29:11.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | delvan | null | delvan/DialoGPT-medium-DwightV1 | 467 | null | transformers | 2,400 | ---
tags:
- conversational
---
#DialoGPT medium based model of Dwight Schrute, trained with 10 context lines of history for 20 epochs. |
dhtocks/Topic-Classification | 6ff7f547583d0c48b862f349f9ca11747731ad61 | 2022-01-12T03:14:00.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | dhtocks | null | dhtocks/Topic-Classification | 467 | null | transformers | 2,401 | Entry not found |
facebook/nllb-200-1.3B | 592daca35b1d4712f683a3401240ed61f0854685 | 2022-07-19T15:46:08.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"bn",
"bho",
"bjn",
"bo",
"bs",
"bug"... | text2text-generation | false | facebook | null | facebook/nllb-200-1.3B | 467 | 1 | transformers | 2,402 | ---
language:
- ace
- acm
- acq
- aeb
- af
- ajp
- ak
- als
- am
- apc
- ar
- ars
- ary
- arz
- as
- ast
- awa
- ayr
- azb
- azj
- ba
- bm
- ban
- be
- bem
- bn
- bho
- bjn
- bo
- bs
- bug
- bg
- ca
- ceb
- cs
- cjk
- ckb
- crh
- cy
- da
- de
- dik
- dyu
- dz
- el
- en
- eo
- et
- eu
- ee
- fo
- fj
- fi
- fon
- fr
- fu... |
IlyaGusev/xlm_roberta_large_headline_cause_full | 481b4dfb94058bbcd8d47330c45755fa69481533 | 2022-07-13T15:35:52.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"ru",
"en",
"dataset:IlyaGusev/headline_cause",
"arxiv:2108.12626",
"transformers",
"xlm-roberta-large",
"license:apache-2.0"
] | text-classification | false | IlyaGusev | null | IlyaGusev/xlm_roberta_large_headline_cause_full | 465 | null | transformers | 2,403 | ---
language:
- ru
- en
tags:
- xlm-roberta-large
datasets:
- IlyaGusev/headline_cause
license: apache-2.0
widget:
- text: "Песков опроверг свой перевод на удаленку</s>Дмитрий Песков перешел на удаленку"
---
# XLM-RoBERTa HeadlineCause Full
## Model description
This model was trained to predict the presence of caus... |
uer/chinese_roberta_L-2_H-768 | 1aa682dea961da5d795029b2a5d097099982662c | 2022-07-15T08:11:17.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"arxiv:1908.08962",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uer | null | uer/chinese_roberta_L-2_H-768 | 465 | null | transformers | 2,404 | ---
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... |
HamidRezaAttar/gpt2-product-description-generator | 207b5c894c24825678a2d7e11e5494f30ebe3cc4 | 2022-04-30T09:53:14.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"arxiv:1706.03762",
"transformers",
"license:apache-2.0"
] | text-generation | false | HamidRezaAttar | null | HamidRezaAttar/gpt2-product-description-generator | 464 | 6 | transformers | 2,405 | ---
language: en
tags:
- text-generation
license: apache-2.0
widget:
- text: "Maximize your bedroom space without sacrificing style with the storage bed."
- text: "Handcrafted of solid acacia in weathered gray, our round Jozy drop-leaf dining table is a space-saving."
- text: "Our plush and luxurious Emmett modular sof... |
cross-encoder/nli-deberta-base | c4dd278f8b91ff189eecea98e76a9b371ed1db37 | 2021-08-05T08:40:53.000Z | [
"pytorch",
"deberta",
"text-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"transformers",
"deberta-base-base",
"license:apache-2.0",
"zero-shot-classification"
] | zero-shot-classification | false | cross-encoder | null | cross-encoder/nli-deberta-base | 464 | 2 | transformers | 2,406 | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- deberta-base-base
datasets:
- multi_nli
- snli
metrics:
- accuracy
license: apache-2.0
---
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples... |
worsterman/DialoGPT-small-mulder | c08bdc69d797c730c41d003cf619df6bb4585b3c | 2021-06-20T22:50:26.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | worsterman | null | worsterman/DialoGPT-small-mulder | 463 | null | transformers | 2,407 | ---
tags:
- conversational
---
# DialoGPT Trained on the Speech of Fox Mulder from The X-Files |
Skoltech/russian-inappropriate-messages | 2f0eca13446320dafb6f9743c56d812fa6f19a11 | 2021-05-18T22:39:46.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"ru",
"transformers",
"toxic comments classification"
] | text-classification | false | Skoltech | null | Skoltech/russian-inappropriate-messages | 462 | 3 | transformers | 2,408 | ---
language:
- ru
tags:
- toxic comments classification
licenses:
- cc-by-nc-sa
---
## General concept of the model
#### Proposed usage
The **'inappropriateness'** substance we tried to collect in the dataset and detect with the model **is NOT a substitution of toxicity**, it is rather a derivative of toxicity. S... |
arbml/wav2vec2-large-xlsr-53-arabic-egyptian | d21ab2f8afebd15f8d3e9c95d2a77343c6f78d7b | 2021-07-05T18:12:38.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"???",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | arbml | null | arbml/wav2vec2-large-xlsr-53-arabic-egyptian | 462 | 2 | transformers | 2,409 | ---
language: ???
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Arabic Egyptian by Zaid
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: C... |
Pensador777critico/DialoGPT-small-RickandMorty | 1c42f5be6b7a0e42229317f9b42321a94b317d81 | 2021-08-31T09:17:11.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Pensador777critico | null | Pensador777critico/DialoGPT-small-RickandMorty | 461 | null | transformers | 2,410 | ---
tags:
- conversational
---
# Rick and Morty DialoGPT Model |
seyonec/PubChem10M_SMILES_BPE_396_250 | a8829bbdf9a6fbd64c91950389687934b3de8394 | 2021-05-20T21:01:53.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/PubChem10M_SMILES_BPE_396_250 | 461 | null | transformers | 2,411 | Entry not found |
flax-sentence-embeddings/all_datasets_v3_mpnet-base | d442c0e304ff575c261b9bbdf8b13fcbe6ee933c | 2021-08-18T11:16:43.000Z | [
"pytorch",
"mpnet",
"fill-mask",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0"
] | sentence-similarity | false | flax-sentence-embeddings | null | flax-sentence-embeddings/all_datasets_v3_mpnet-base | 460 | 4 | sentence-transformers | 2,412 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
---
# all-mpnet-base-v1
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... |
satyaalmasian/temporal_tagger_BERT_tokenclassifier | 46bdd518ce24100e9eca478d714e145b86a50380 | 2021-09-21T11:23:18.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | satyaalmasian | null | satyaalmasian/temporal_tagger_BERT_tokenclassifier | 460 | 2 | transformers | 2,413 | # BERT based temporal tagged
Token classifier for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.com/satya77/Transformer_Temporal_Tagger).
# Model description
... |
pranaydeeps/Ancient-Greek-BERT | db7bf93218e0fdaf880320bd4968aab1efdbf9f6 | 2021-09-24T15:07:58.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | pranaydeeps | null | pranaydeeps/Ancient-Greek-BERT | 459 | 2 | transformers | 2,414 | # Ancient Greek BERT
<img src="https://ichef.bbci.co.uk/images/ic/832xn/p02m4gzb.jpg"/>
The first and only available Ancient Greek sub-word BERT model!
State-of-the-art post fine-tuning on Part-of-Speech Tagging and Morphological Analysis.
Pre-trained weights are made available for a standard 12 layer, 768d BERT-ba... |
BSC-TeMU/roberta-base-bne | 052845e3a3abcabb150e4724d2c85f0ab59dd67e | 2021-10-21T10:30:31.000Z | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | BSC-TeMU | null | BSC-TeMU/roberta-base-bne | 457 | 8 | transformers | 2,415 | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
datasets:
- "bne"
metrics:
- "ppl"
widget:
- text: "Este año las campanadas de La Sexta las presentará <mask>."
- text: "David Broncano es un presentador de La <mask>."
- text: "Gracias a los datos de la BNE se ha podido <... |
flax-community/gpt-2-spanish | 359ff61122561956ce53fc4caaf660b9d66a5248 | 2022-04-22T11:16:44.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"es",
"dataset:oscar",
"transformers"
] | text-generation | false | flax-community | null | flax-community/gpt-2-spanish | 457 | 1 | transformers | 2,416 | ---
language: es
tags:
- text-generation
datasets:
- oscar
widgets:
- text: "Érase un vez "
- text: "Frase: Esta película es muy agradable. Sentimiento: positivo
Frase: Odiaba esta película, apesta. Sentimiento: negativo
Frase: Esta película fue bastante mala. Sentimiento: "
---
# Spanish GPT-2
GPT-2 model trained fr... |
izumi-lab/bert-small-japanese-fin | fe4d803446cc47c33233b187b221585fc428d5c8 | 2022-03-19T09:38:08.000Z | [
"pytorch",
"bert",
"fill-mask",
"ja",
"dataset:wikipedia",
"dataset:securities reports",
"dataset:summaries of financial results",
"arxiv:2003.10555",
"transformers",
"finance",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | izumi-lab | null | izumi-lab/bert-small-japanese-fin | 456 | null | transformers | 2,417 | ---
language: ja
license: cc-by-sa-4.0
tags:
- finance
datasets:
- wikipedia
- securities reports
- summaries of financial results
widget:
- text: 流動[MASK]は、1億円となりました。
---
# BERT small Japanese finance
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese languag... |
nvidia/stt_en_conformer_ctc_large | bf01f044a18d14ccba90d7de497e6664fa68a669 | 2022-06-25T01:04:09.000Z | [
"nemo",
"en",
"dataset:librispeech_asr",
"dataset:fisher_corpus",
"dataset:Switchboard-1",
"dataset:WSJ-0",
"dataset:WSJ-1",
"dataset:National Singapore Corpus Part 1",
"dataset:National Singapore Corpus Part 6",
"dataset:vctk",
"dataset:VoxPopuli (EN)",
"dataset:Europarl-ASR (EN)",
"dataset... | automatic-speech-recognition | false | nvidia | null | nvidia/stt_en_conformer_ctc_large | 456 | 8 | nemo | 2,418 | ---
language:
- en
library_name: nemo
datasets:
- librispeech_asr
- fisher_corpus
- Switchboard-1
- WSJ-0
- WSJ-1
- National Singapore Corpus Part 1
- National Singapore Corpus Part 6
- vctk
- VoxPopuli (EN)
- Europarl-ASR (EN)
- Multilingual LibriSpeech (2000 hours)
- mozilla-foundation/common_voice_7_0
thumbnail: nul... |
GroNLP/gpt2-medium-italian-embeddings | 6b38344ac1d0e476546d610ed482c3f045b9844d | 2021-05-21T09:52:26.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"it",
"arxiv:2012.05628",
"transformers",
"adaption",
"recycled",
"gpt2-medium"
] | text-generation | false | GroNLP | null | GroNLP/gpt2-medium-italian-embeddings | 455 | null | transformers | 2,419 | ---
language: it
tags:
- adaption
- recycled
- gpt2-medium
pipeline_tag: text-generation
---
# GPT-2 recycled for Italian (medium, adapted lexical embeddings)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)... |
clue/albert_chinese_small | f2fc21cd3fd18fa7df1ee046c7963351af8f4753 | 2020-12-11T21:35:52.000Z | [
"pytorch",
"albert",
"zh",
"transformers"
] | null | false | clue | null | clue/albert_chinese_small | 455 | null | transformers | 2,420 | ---
language: zh
---
## albert_chinese_small
### Overview
**Language model:** albert-small
**Model size:** 18.5M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results o... |
cointegrated/rut5-base | d2e012d05083539e962a338f9a27769337ea4469 | 2021-06-23T12:03:57.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"ru",
"en",
"transformers",
"russian",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | cointegrated | null | cointegrated/rut5-base | 455 | null | transformers | 2,421 | ---
language: ["ru", "en"]
tags:
- russian
license: mit
---
This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Russian and some English embeddings left.
* The original model has 582M parameters, with 384M of them being input and output embeddings.
* After shrin... |
dbmdz/bert-base-german-europeana-cased | b8ff4bdc5fbd85910c18ea206f9348f6103c68b3 | 2021-05-19T14:54:00.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"de",
"transformers",
"historic german",
"license:mit"
] | null | false | dbmdz | null | dbmdz/bert-base-german-europeana-cased | 455 | null | transformers | 2,422 | ---
language: de
license: mit
tags:
- "historic german"
---
# 🤗 + 📚 dbmdz BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources German Europeana BERT models 🎉
# German Europeana BERT
We use the open source [Europeana newspapers](http://www.europeana-news... |
facebook/s2t-wav2vec2-large-en-de | 0d0ec5b6a24227d815094d0ca0ced8ce662b85bc | 2021-11-14T20:12:38.000Z | [
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"en",
"de",
"dataset:covost2",
"dataset:librispeech_asr",
"arxiv:2104.06678",
"transformers",
"audio",
"speech-translation",
"speech2text2",
"license:mit"
] | automatic-speech-recognition | false | facebook | null | facebook/s2t-wav2vec2-large-en-de | 455 | 2 | transformers | 2,423 | ---
language:
- en
- de
datasets:
- covost2
- librispeech_asr
tags:
- audio
- speech-translation
- automatic-speech-recognition
- speech2text2
license: mit
pipeline_tag: automatic-speech-recognition
widget:
- example_title: Common Voice 1
src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.m... |
NikkiTiredAf/DialoGPT-small-billy2 | bcd8e95c78e1d262618738ad24b5425e43a65edb | 2022-06-20T05:46:46.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | NikkiTiredAf | null | NikkiTiredAf/DialoGPT-small-billy2 | 455 | null | transformers | 2,424 | ---
tags:
- conversational
---
# Billy DialoGPT Model |
9pinus/macbert-base-chinese-medical-collation | 6cddc419b86a546bfab115dd05a3782a43beb1e0 | 2022-02-25T10:26:38.000Z | [
"pytorch",
"bert",
"token-classification",
"zh",
"transformers",
"Token Classification",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | 9pinus | null | 9pinus/macbert-base-chinese-medical-collation | 454 | 3 | transformers | 2,425 | ---
license: apache-2.0
language: zh
tags:
- Token Classification
metrics:
- precision
- recall
- f1
- accuracy
---
## Model description
This model is a fine-tuned version of macbert for the purpose of spell checking in medical application scenarios. We fine-tuned macbert Chinese base version on a 300M ... |
NlpHUST/electra-base-vn | be0d6dd0ff6f233ca61c70190d25c0384423102b | 2021-12-19T09:19:08.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | NlpHUST | null | NlpHUST/electra-base-vn | 454 | 2 | transformers | 2,426 | # ELECTRA
## Introduction
**ELECTRA** is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to t... |
tunib/electra-ko-small | a3e3d24e0960f0a442be7e2593cb613bd7827e46 | 2021-09-17T08:59:08.000Z | [
"pytorch",
"electra",
"pretraining",
"arxiv:2003.10555",
"transformers"
] | null | false | tunib | null | tunib/electra-ko-small | 454 | 3 | transformers | 2,427 | # 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... |
Salesforce/codet5-large-ntp-py | fe53abf996a6de5b126642f5044e9a98e572cdbd | 2022-07-07T11:55:42.000Z | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2109.00859",
"arxiv:2207.01780",
"arxiv:1909.09436",
"transformers",
"license:bsd-3-clause",
"autotrain_compatible"
] | text2text-generation | false | Salesforce | null | Salesforce/codet5-large-ntp-py | 454 | 2 | transformers | 2,428 | ---
license: bsd-3-clause
---
# CodeT5 (large-size model pretrained with NTP objective on Python)
## Model description
CodeT5 is a family of encoder-decoder language models for code from the paper: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxi... |
csarron/mobilebert-uncased-squad-v1 | 14da15e8b5e1e35d188445b7724168e69d251e17 | 2020-12-11T21:36:24.000Z | [
"pytorch",
"mobilebert",
"question-answering",
"en",
"dataset:squad",
"arxiv:2004.02984",
"transformers",
"license:mit",
"autotrain_compatible"
] | question-answering | false | csarron | null | csarron/mobilebert-uncased-squad-v1 | 453 | null | transformers | 2,429 | ---
language: en
thumbnail:
license: mit
tags:
- question-answering
- mobilebert
datasets:
- squad
metrics:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía o... |
unitary/unbiased-toxic-roberta | 851fcea0b57478fb28c012e56d6b0dd6077523f3 | 2021-08-16T16:42:01.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"arxiv:1703.04009",
"arxiv:1905.12516",
"transformers"
] | text-classification | false | unitary | null | unitary/unbiased-toxic-roberta | 453 | 3 | transformers | 2,430 | <div align="center">
**⚠️ Disclaimer:**
The huggingface models currently give different results to the detoxify library (see issue [here](https://github.com/unitaryai/detoxify/issues/15)). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify
# 🙊 Detoxify
## To... |
flax-sentence-embeddings/st-codesearch-distilroberta-base | 65b0f39bfa41c59993f62b57447c942e371b7135 | 2021-07-05T11:40:15.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"dataset:code_search_net",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | flax-sentence-embeddings | null | flax-sentence-embeddings/st-codesearch-distilroberta-base | 452 | 6 | sentence-transformers | 2,431 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- code_search_net
---
# flax-sentence-embeddings/st-codesearch-distilroberta-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional... |
google/multiberts-seed_4 | 8e1f8267eedb194a9779d1010656fc19e30b4e69 | 2021-11-05T22:14:20.000Z | [
"pytorch",
"tf",
"bert",
"pretraining",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"transformers",
"multiberts",
"multiberts-seed_4",
"license:apache-2.0"
] | null | false | google | null | google/multiberts-seed_4 | 452 | null | transformers | 2,432 | ---
language: en
tags:
- multiberts
- multiberts-seed_4
license: apache-2.0
---
# MultiBERTs - Seed 4
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://gi... |
philschmid/bart-base-samsum | 807745bf0e30e6bb98dd5b4fe2c5f8a1d7185ea8 | 2022-06-24T11:24:25.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:samsum",
"transformers",
"sagemaker",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | philschmid | null | philschmid/bart-base-samsum | 452 | 1 | transformers | 2,433 |
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
datasets:
- samsum
widget:
- text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n\
Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:\
\ ok.\nJeff: and how can I get started? \... |
google/multiberts-seed_5 | 73b6810aed2fb957e82e653e54f49713a455d185 | 2021-11-05T22:15:58.000Z | [
"pytorch",
"tf",
"bert",
"pretraining",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"transformers",
"multiberts",
"multiberts-seed_5",
"license:apache-2.0"
] | null | false | google | null | google/multiberts-seed_5 | 451 | null | transformers | 2,434 | ---
language: en
tags:
- multiberts
- multiberts-seed_5
license: apache-2.0
---
# MultiBERTs - Seed 5
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://gi... |
google/multiberts-seed_7 | ec918353afaee9c14e71395a2573d9b858e78638 | 2021-11-05T22:19:19.000Z | [
"pytorch",
"tf",
"bert",
"pretraining",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"transformers",
"multiberts",
"multiberts-seed_7",
"license:apache-2.0"
] | null | false | google | null | google/multiberts-seed_7 | 451 | null | transformers | 2,435 | ---
language: en
tags:
- multiberts
- multiberts-seed_7
license: apache-2.0
---
# MultiBERTs - Seed 7
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://gi... |
studio-ousia/luke-large-finetuned-open-entity | 35df92918b7e8ab3283a6bff5d51ea61bcb94650 | 2021-04-26T16:10:58.000Z | [
"pytorch",
"luke",
"transformers"
] | null | false | studio-ousia | null | studio-ousia/luke-large-finetuned-open-entity | 451 | null | transformers | 2,436 | Entry not found |
tae898/emoberta-base | 64377bdd2a1d7bc5ecdac9a4fbd219002663df1e | 2022-03-16T11:01:29.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:MELD",
"dataset:IEMOCAP",
"arxiv:2108.12009",
"transformers",
"emoberta",
"license:mit"
] | text-classification | false | tae898 | null | tae898/emoberta-base | 451 | 2 | transformers | 2,437 | ---
language: en
tags:
- emoberta
- roberta
license: mit
datasets:
- MELD
- IEMOCAP
---
Check https://github.com/tae898/erc for the details
[Watch a demo video!](https://youtu.be/qbr7fNd6J28)
# Emotion Recognition in Coversation (ERC)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/... |
google/multiberts-seed_6 | fe06acd33aecd0a25facb1b397a64cfcaf74c9ea | 2021-11-05T22:17:37.000Z | [
"pytorch",
"tf",
"bert",
"pretraining",
"en",
"arxiv:2106.16163",
"arxiv:1908.08962",
"transformers",
"multiberts",
"multiberts-seed_6",
"license:apache-2.0"
] | null | false | google | null | google/multiberts-seed_6 | 450 | null | transformers | 2,438 | ---
language: en
tags:
- multiberts
- multiberts-seed_6
license: apache-2.0
---
# MultiBERTs - Seed 6
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://gi... |
google/vit-large-patch32-384 | a2b30ad36d02e99f045cd2ecfc71e0ae16991efa | 2022-01-28T10:24:24.000Z | [
"pytorch",
"tf",
"jax",
"vit",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | google | null | google/vit-large-patch32-384 | 450 | 3 | transformers | 2,439 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-21k
---
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000... |
sentence-transformers/sentence-t5-xxl | 0a2f720e57c36306fbfca6025baba48828555764 | 2022-02-09T14:06:28.000Z | [
"pytorch",
"t5",
"en",
"arxiv:2108.08877",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/sentence-t5-xxl | 450 | null | sentence-transformers | 2,440 | ---
pipeline_tag: sentence-similarity
language: en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/sentence-t5-xxl
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 76... |
Naturealbe/DialoGPT-small-Technoblade | 03d45234348358b2d29f92bd26211ffac3418bd0 | 2022-07-03T18:37:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Naturealbe | null | Naturealbe/DialoGPT-small-Technoblade | 449 | null | transformers | 2,441 | ---
tags:
- conversational
---
# Technoblade DialoGPT Model |
steeldream/letov | 21e383798651ed1ad2a2f842ddb431817679bfdb | 2022-07-10T16:45:45.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"license:cc-by-4.0"
] | text-generation | false | steeldream | null | steeldream/letov | 449 | null | transformers | 2,442 | ---
license: cc-by-4.0
---
|
arijitx/wav2vec2-large-xlsr-bengali | a3dd1342030ba7478c832d6abdf7538f6e0224cb | 2021-09-23T13:07:14.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"Bengali",
"dataset:OpenSLR",
"transformers",
"bn",
"audio",
"speech",
"license:cc-by-sa-4.0",
"model-index"
] | automatic-speech-recognition | false | arijitx | null | arijitx/wav2vec2-large-xlsr-bengali | 448 | 1 | transformers | 2,443 | ---
language: Bengali
datasets:
- OpenSLR
metrics:
- wer
tags:
- bn
- audio
- automatic-speech-recognition
- speech
license: cc-by-sa-4.0
model-index:
- name: XLSR Wav2Vec2 Bengali by Arijit
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: OpenSLR
... |
nguyenvulebinh/vi-mrc-base | 72baf191692f5be375838c9c4de59d6667af6231 | 2022-03-13T20:54:14.000Z | [
"pytorch",
"roberta",
"question-answering",
"vi",
"vn",
"en",
"dataset:squad",
"transformers",
"license:cc-by-nc-4.0",
"autotrain_compatible"
] | question-answering | false | nguyenvulebinh | null | nguyenvulebinh/vi-mrc-base | 448 | 3 | transformers | 2,444 | ---
language:
- vi
- vn
- en
tags:
- question-answering
- pytorch
datasets:
- squad
license: cc-by-nc-4.0
pipeline_tag: question-answering
metrics:
- squad
widget:
- text: "Bình là chuyên gia về gì ?"
context: "Bình Nguyễn là một người đam mê với lĩnh vực xử lý ngôn ngữ tự nhiên . Anh nhận chứng chỉ Google Develope... |
pysentimiento/bertweet-hate-speech | 8913cd6a2515f3e033c3b097f68d3bfb41079c54 | 2021-12-05T15:13:53.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"arxiv:2106.09462",
"transformers",
"twitter",
"hate-speech"
] | text-classification | false | pysentimiento | null | pysentimiento/bertweet-hate-speech | 448 | 1 | transformers | 2,445 | ---
language:
- en
tags:
- twitter
- hate-speech
---
# Hate Speech detection in Spanish
## robertuito-hate-speech
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speec... |
Helsinki-NLP/opus-mt-ga-en | 4497dea247606effb578b6c45a4e40d670e84bc9 | 2021-01-18T08:50:12.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ga",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ga-en | 447 | null | transformers | 2,446 | ---
language:
- ga
- en
tags:
- translation
license: apache-2.0
---
### gle-eng
* source group: Irish
* target group: English
* OPUS readme: [gle-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gle-eng/README.md)
* model: transformer-align
* source language(s): gle
* target language(... |
dbddv01/gpt2-french-small | 66ffadbe0b789cc90040f7b3b801c3999270e272 | 2021-05-21T15:25:05.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"fr",
"transformers",
"french",
"model"
] | text-generation | false | dbddv01 | null | dbddv01/gpt2-french-small | 447 | 2 | transformers | 2,447 | ---
language: "fr"
tags:
- french
- gpt2
- model
---
A small french language model for french text generation (and possibly more NLP tasks...)
**Introduction**
This french gpt2 model is based on openai GPT-2 small model.
It was trained on a <b>very small (190Mb) dataset </b> from french wikipedia using Transfer Le... |
dspoka/units-gen-d-u | 1206e1a403b6156d8584aa8db3cb97090c77bc8d | 2021-11-25T10:11:55.000Z | [
"pytorch",
"roberta",
"transformers"
] | null | false | dspoka | null | dspoka/units-gen-d-u | 447 | null | transformers | 2,448 | Entry not found |
facebook/wav2vec2-xls-r-2b-22-to-16 | 402bde655039c16f485f74897a63eeba8c3e02c6 | 2022-05-26T22:23:54.000Z | [
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"multilingual",
"fr",
"de",
"es",
"ca",
"it",
"ru",
"zh",
"pt",
"fa",
"et",
"mn",
"nl",
"tr",
"ar",
"sv",
"lv",
"sl",
"ta",
"ja",
"id",
"cy",
"en",
"dataset:common_voice",
"dataset:multilingual... | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-xls-r-2b-22-to-16 | 447 | 9 | transformers | 2,449 | ---
language:
- multilingual
- fr
- de
- es
- ca
- it
- ru
- zh
- pt
- fa
- et
- mn
- nl
- tr
- ar
- sv
- lv
- sl
- ta
- ja
- id
- cy
- en
datasets:
- common_voice
- multilingual_librispeech
- covost2
tags:
- speech
- xls_r
- automatic-speech-recognition
- xls_r_translation
pipeline_tag: automatic-speech-recognition
l... |
ian0delond/model-test | 548ed19c3fdb1e0ca76cc5e7489aa0ffb1d50321 | 2022-06-30T20:51:38.000Z | [
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | ian0delond | null | ian0delond/model-test | 447 | null | transformers | 2,450 | ---
tags:
- generated_from_trainer
model-index:
- name: model-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. -->
# model-test
This model is a fine-tuned versi... |
camembert/camembert-base-ccnet | 135373bcc71a3b85030c8880b683b5058943f93f | 2020-12-11T21:35:15.000Z | [
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"transformers"
] | null | false | camembert | null | camembert/camembert-base-ccnet | 446 | null | transformers | 2,451 | ---
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... |
nateraw/food | 8991abd49ea01ebf502aeda51d4f12a59c603e01 | 2022-05-17T17:44:24.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"dataset:food101",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | nateraw | null | nateraw/food | 445 | 1 | transformers | 2,452 | ---
license: apache-2.0
tags:
- generated_from_trainer
- image-classification
- pytorch
datasets:
- food101
metrics:
- accuracy
model-index:
- name: food101_outputs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food-101
type: food101
args: de... |
doc2query/all-with_prefix-t5-base-v1 | 453aa7323e8efa07ef1d48af9e2285391285f8a0 | 2021-10-19T12:52:47.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:sentence-transformers/reddit-title-body",
"dataset:sentence-transformers/embedding-training-data",
"arxiv:1904.08375",
"arxiv:2104.08663",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | doc2query | null | doc2query/all-with_prefix-t5-base-v1 | 444 | 1 | transformers | 2,453 | ---
language: en
datasets:
- sentence-transformers/reddit-title-body
- sentence-transformers/embedding-training-data
widget:
- text: "text2reddit: Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significa... |
gorkemgoknar/gpt2chatbotenglish | d18684a4d566c2e7f2acdd164415bd7533e8290d | 2021-11-22T11:13:11.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"conversational",
"license:cc-by-4.0"
] | conversational | false | gorkemgoknar | null | gorkemgoknar/gpt2chatbotenglish | 444 | 2 | transformers | 2,454 | ---
language:
- en
thumbnail:
tags:
- gpt2
- conversational
license: cc-by-4.0
widget:
- text: Hello there
context: 'Gandalf'
---
# GPT2 Persona Chatbot based on Movie Characters
Model used for https://www.metayazar.com/chatbot
GPT2 Small Trained on movie scripts (especially Sci-fi)
Usual HF api will not work see ... |
l-yohai/bigbird-roberta-base-mnli | 36bf0e91ade84c3510ffc96e3a0b6b0f2df5d746 | 2022-02-07T09:16:09.000Z | [
"pytorch",
"big_bird",
"text-classification",
"transformers"
] | text-classification | false | l-yohai | null | l-yohai/bigbird-roberta-base-mnli | 443 | null | transformers | 2,455 | Entry not found |
Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit | 1e82af08d6d871242572f6a18d643d0d1069782a | 2022-06-18T20:45:44.000Z | [
"pytorch",
"gpt_neo",
"feature-extraction",
"arxiv:2202.08904",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | Muennighoff | null | Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit | 442 | null | sentence-transformers | 2,456 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# SGPT-125M-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval fol... |
ValkyriaLenneth/longformer_zh | 87c8e565be16e9644f7c6ef818a554ce1ceeabbe | 2022-01-06T03:50:20.000Z | [
"pytorch",
"longformer",
"feature-extraction",
"transformers"
] | feature-extraction | false | ValkyriaLenneth | null | ValkyriaLenneth/longformer_zh | 442 | 1 | transformers | 2,457 | # 中文预训练Longformer模型 | Longformer_ZH with PyTorch
相比于Transformer的O(n^2)复杂度，Longformer提供了一种以线性复杂度处理最长4K字符级别文档序列的方法。Longformer Attention包括了标准的自注意力与全局注意力机制，方便模型更好地学习超长序列的信息。
Compared with O(n^2) complexity for Transformer model, Longformer provides an efficient method for processing long-document level sequence in Linea... |
obi/deid_roberta_i2b2 | 0d64fa18019b1ba34322288881ad9031f2e3fdcc | 2022-02-16T14:41:33.000Z | [
"pytorch",
"roberta",
"token-classification",
"english",
"dataset:I2B2",
"arxiv:1907.11692",
"transformers",
"deidentification",
"medical notes",
"ehr",
"phi",
"license:mit",
"autotrain_compatible"
] | token-classification | false | obi | null | obi/deid_roberta_i2b2 | 442 | null | transformers | 2,458 | ---
language:
- english
thumbnail: "https://www.onebraveidea.org/wp-content/uploads/2019/07/OBI-Logo-Website.png"
tags:
- deidentification
- medical notes
- ehr
- phi
datasets:
- I2B2
metrics:
- F1
- Recall
- Precision
widget:
- text: "Physician Discharge Summary Admit date: 10/12/1982 Discharge date:... |
apple/deeplabv3-mobilevit-small | 531026df27bacb5e63f6d12c74915c799845b12c | 2022-06-02T10:54:17.000Z | [
"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-small | 442 | 3 | transformers | 2,459 | ---
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 (small-sized model)
MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduc... |
antoniocappiello/bert-base-italian-uncased-squad-it | ec30dd86cff1906fa4ed479287c06d98b48e9c8d | 2021-12-15T10:01:14.000Z | [
"pytorch",
"question-answering",
"it",
"transformers",
"autotrain_compatible"
] | question-answering | false | antoniocappiello | null | antoniocappiello/bert-base-italian-uncased-squad-it | 441 | null | transformers | 2,460 | ---
language: it
widget:
- text: "Quando nacque D'Annunzio?"
context: "D'Annunzio nacque nel 1863"
---
# Italian Bert Base Uncased on Squad-it
## Model description
This model is the uncased base version of the italian BERT (which you may find at `dbmdz/bert-base-italian-uncased`) trained on the question answering ... |
tbs17/MathBERT-custom | db3e8ee98c43e1bef5ae02c7fb7623a840f321e3 | 2022-07-04T01:01:11.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | tbs17 | null | tbs17/MathBERT-custom | 441 | 4 | transformers | 2,461 | #### MathBERT model (custom vocab)
Pretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.
#### Model description
MathBERT is a transformers model pretrained on a large corpus of ... |
ml6team/distilbert-base-german-cased-toxic-comments | 92d1f1c641db3226d637ab09019a9df44fa007f6 | 2022-06-15T22:10:04.000Z | [
"pytorch",
"distilbert",
"text-classification",
"de",
"dataset:germeval21",
"arxiv:1701.08118",
"transformers",
"german",
"classification"
] | text-classification | false | ml6team | null | ml6team/distilbert-base-german-cased-toxic-comments | 440 | 4 | transformers | 2,462 | ---
language:
- de
tags:
- distilbert
- german
- classification
datasets:
- germeval21
widget:
- text: "Das ist ein guter Punkt, so hatte ich das noch nicht betrachtet."
example_title: "Agreement (non-toxic)"
- text: "Wow, was ein geiles Spiel. Glückwunsch."
example_title: "Football (non-toxic)"
- text: "Halt deine... |
textattack/bert-base-uncased-rotten_tomatoes | f0be8f91098b02ec780661c1e589eeb416519522 | 2021-05-20T07:47:13.000Z | [
"pytorch",
"jax",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | textattack | null | textattack/bert-base-uncased-rotten_tomatoes | 440 | null | transformers | 2,463 | ## bert-base-uncased fine-tuned with TextAttack on the rotten_tomatoes dataset
This `bert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 64, a le... |
seeksery/DialoGPT-calig2 | 43332f7c47e6ae847f4fcbd4f3cb03686be76590 | 2022-07-26T13:52:24.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | seeksery | null | seeksery/DialoGPT-calig2 | 439 | null | transformers | 2,464 | ---
tags:
- conversational
--- |
sentence-transformers/facebook-dpr-question_encoder-single-nq-base | 4af249243a8a724a781ebeb159d82a78ee32a33c | 2022-06-15T23:43:46.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/facebook-dpr-question_encoder-single-nq-base | 438 | null | sentence-transformers | 2,465 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/facebook-dpr-question_encoder-single-nq-base
This is a port of the [DPR Model](https://github.com/facebookresearch/DPR) to [sentence-transformer... |
uclanlp/visualbert-vcr | becbc174cbce1a977121cca39d84048051a36021 | 2021-05-31T11:12:33.000Z | [
"pytorch",
"visual_bert",
"transformers"
] | null | false | uclanlp | null | uclanlp/visualbert-vcr | 438 | null | transformers | 2,466 | Entry not found |
HHousen/distil-led-large-cnn-16384 | 7cc576aee63d56c763e2eb2badf726659862274e | 2021-02-02T00:58:07.000Z | [
"pytorch",
"led",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | HHousen | null | HHousen/distil-led-large-cnn-16384 | 437 | 2 | transformers | 2,467 | ---
language: en
datasets:
- cnn_dailymail
license: apache-2.0
---
## DistilLED Large CNN 16384
*distil-led-large-cnn-16384* was initialized from [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6), in a fashion similar to [allenai/led-large-16384](https://huggingface.co/allenai/l... |
allenai/unifiedqa-v2-t5-large-1363200 | a69158391f8fb9c6bbfdd4f28b699a19e73d7d28 | 2022-02-22T00:36:53.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/unifiedqa-v2-t5-large-1363200 | 437 | null | transformers | 2,468 | # Further details: https://github.com/allenai/unifiedqa
|
uw-madison/yoso-4096 | 51a64263ef39d9e633f821a13ec9fafe5711061f | 2022-01-12T13:36:04.000Z | [
"pytorch",
"yoso",
"fill-mask",
"arxiv:2111.09714",
"transformers",
"autotrain_compatible"
] | fill-mask | false | uw-madison | null | uw-madison/yoso-4096 | 437 | null | transformers | 2,469 | # YOSO
YOSO model for masked language modeling (MLM) for sequence length 4096.
## About YOSO
The YOSO model was proposed in [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fun... |
sentence-transformers/stsb-distilroberta-base-v2 | 0078748de585303ee3757abf215b748aa7be581e | 2022-06-15T22:26:42.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/stsb-distilroberta-base-v2 | 436 | null | sentence-transformers | 2,470 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/stsb-distilroberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional d... |
gilf/french-postag-model | d4206b8aa47cbf1b282450f454f67f15290c263b | 2021-05-19T17:22:22.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | gilf | null | gilf/french-postag-model | 435 | 1 | transformers | 2,471 | ## About
The *french-postag-model* is a part of speech tagging model for French that was trained on the *free-french-treebank* dataset available on
[github](https://github.com/nicolashernandez/free-french-treebank). The base tokenizer and model used for training is *'bert-base-multilingual-cased'*.
## Supported Tag... |
aubmindlab/bert-large-arabertv02 | 067c6e0c564e4e4faa2ac0bcfec3f7b0e8d9143f | 2022-04-07T08:26:52.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B Arabic Corpus",
"dataset:OSCAR Arabic Unshuffled",
"arxiv:2003.00104",
"transformers",
"autotrain_compatible"
] | fill-mask | false | aubmindlab | null | aubmindlab/bert-large-arabertv02 | 434 | 1 | transformers | 2,472 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
- OSCAR Arabic Unshuffled
widget:
- text: " عاصمة لبنان هي [MASK] ."
---
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" w... |
Augustvember/wokka2 | 171fd15c9a2dff4c496e723ecd736fbf35d36e08 | 2021-08-08T10:59:54.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Augustvember | null | Augustvember/wokka2 | 433 | null | transformers | 2,473 | ---
tags:
- conversational
---
|
allenai/macaw-11b | efbdfd3889acdfd1cdb36aebafbc202a28b845d3 | 2021-09-21T15:59:00.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/macaw-11b | 432 | 3 | transformers | 2,474 | ---
language: en
widget:
- text: $answer$ ; $mcoptions$ ; $question$ = What is the color of a cloudy sky?
license: apache-2.0
---
# macaw-11b
## Model description
Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of
general question answering,
showing robu... |
google/vit-large-patch16-224 | 9e2727f4250d3973839eecfa5c4b42e41b709a50 | 2022-06-23T07:50:15.000Z | [
"pytorch",
"tf",
"jax",
"vit",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | google | null | google/vit-large-patch16-224 | 432 | 1 | transformers | 2,475 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet-1k
- imagenet-21k
---
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,... |
DaNLP/da-bert-emotion-binary | c5f9be72ce0e3daa1b90372ca1a6ba929df4a67a | 2021-09-23T13:37:13.000Z | [
"pytorch",
"tf",
"bert",
"text-classification",
"da",
"dataset:social media",
"transformers",
"emotion",
"license:cc-by-sa-4.0"
] | text-classification | false | DaNLP | null | DaNLP/da-bert-emotion-binary | 431 | 1 | transformers | 2,476 | ---
language:
- da
tags:
- bert
- pytorch
- emotion
license: cc-by-sa-4.0
datasets:
- social media
metrics:
- f1
widget:
- text: Der er et træ i haven.
---
# Danish BERT for emotion detection
The BERT Emotion model detects whether a Danish text is emotional or not.
It is based on the pretrained [Danish BERT](https:/... |
charsiu/en_w2v2_fc_10ms | e9bf8dd314313fc57f6e4d0b5425bde4bbeac80f | 2021-10-03T02:09:48.000Z | [
"pytorch",
"wav2vec2",
"transformers"
] | null | false | charsiu | null | charsiu/en_w2v2_fc_10ms | 431 | null | transformers | 2,477 | Entry not found |
google/bert_uncased_L-4_H-768_A-12 | 0e3749de42f4e094609bc52dc7b0554a3a6bcc52 | 2021-05-19T17:31:28.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-4_H-768_A-12 | 431 | null | transformers | 2,478 | ---
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... |
jhgan/ko-sbert-multitask | 869e0dbcd4b13d9765bf1d7ea05d05baf2e6ec0c | 2021-12-27T12:35:56.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | jhgan | null | jhgan/ko-sbert-multitask | 431 | null | sentence-transformers | 2,479 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# ko-sbert-multitask
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like c... |
google/roberta2roberta_L-24_cnn_daily_mail | 103d110ba258873ce4d7c06b2c72e555bfce9aaf | 2020-12-11T21:43:09.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"arxiv:1907.12461",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | google | null | google/roberta2roberta_L-24_cnn_daily_mail | 430 | 1 | transformers | 2,480 | ---
language: en
license: apache-2.0
datasets:
- cnn_dailymail
tags:
- summarization
---
# Roberta2Roberta_L-24_cnn_daily_mail EncoderDecoder model
The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](http... |
michiyasunaga/LinkBERT-base | 5b245d3b75fd6b739b2b699fdba02bd3abf49d53 | 2022-03-31T00:38:32.000Z | [
"pytorch",
"bert",
"feature-extraction",
"en",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:2203.15827",
"transformers",
"exbert",
"linkbert",
"fill-mask",
"question-answering",
"text-classification",
"token-classification",
"license:apache-2.0"
] | text-classification | false | michiyasunaga | null | michiyasunaga/LinkBERT-base | 430 | 2 | transformers | 2,481 | ---
license: apache-2.0
language: en
datasets:
- wikipedia
- bookcorpus
tags:
- bert
- exbert
- linkbert
- feature-extraction
- fill-mask
- question-answering
- text-classification
- token-classification
---
## LinkBERT-base
LinkBERT-base model pretrained on English Wikipedia articles along with hy... |
kakife3586/BadEka | 79a7b5746fa900db408c67864e6605775e9d4855 | 2022-07-30T04:07:32.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | kakife3586 | null | kakife3586/BadEka | 430 | 1 | transformers | 2,482 | Entry not found |
Elron/bleurt-base-512 | 4f4abeeba7c29ded45fc90b8a66eb49c8569f587 | 2021-10-04T13:23:33.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Elron | null | Elron/bleurt-base-512 | 428 | 1 | transformers | 2,483 | \n## BLEURT
Pytorch version of the original BLEURT models from ACL paper ["BLEURT: Learning Robust Metrics for Text Generation"](https://aclanthology.org/2020.acl-main.704/) by
Thibault Sellam, Dipanjan Das and Ankur P. Parikh of Google Research.
The code for model conversion was originated from [this notebook](http... |
crystina-z/xdpr-tied-msmarco | 29e93fd7922297fed5187bb57c43c09f86b2560e | 2022-05-16T23:02:08.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | crystina-z | null | crystina-z/xdpr-tied-msmarco | 427 | null | transformers | 2,484 | Entry not found |
codegram/calbert-tiny-uncased | f5e29a94ba5944f2b57d211bbc8cdea6d774b236 | 2020-12-11T21:36:14.000Z | [
"pytorch",
"albert",
"ca",
"transformers",
"masked-lm",
"catalan",
"exbert",
"license:mit"
] | null | false | codegram | null | codegram/calbert-tiny-uncased | 426 | null | transformers | 2,485 | ---
language: "ca"
tags:
- masked-lm
- catalan
- exbert
license: mit
---
# Calbert: a Catalan Language Model
## Introduction
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its `tiny-uncased` version (the one you're looking at)... |
digit82/kolang-t5-base | 73e6db9cc164510c94c08195987ffd693897af4a | 2021-05-20T01:04:25.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | digit82 | null | digit82/kolang-t5-base | 426 | null | transformers | 2,486 | Entry not found |
ktrapeznikov/gpt2-medium-topic-news-v2 | 3a2d72aa8fc6b56e56238cadec20f0c7fa4c74dd | 2021-05-23T06:14:58.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | ktrapeznikov | null | ktrapeznikov/gpt2-medium-topic-news-v2 | 426 | 1 | transformers | 2,487 | ---
language:
- en
thumbnail:
widget:
- text: "topic climate source washington post title "
---
# GPT2-medium-topic-news
## Model description
GPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title
## Intended uses & limitations
#### How to use
To generate a news article text cond... |
allenai/wmt16-en-de-dist-12-1 | 0969ea66b79ae8a2516de3143f7e699ed77d5e3d | 2020-12-11T21:33:20.000Z | [
"pytorch",
"fsmt",
"text2text-generation",
"en",
"de",
"dataset:wmt16",
"arxiv:2006.10369",
"transformers",
"translation",
"wmt16",
"allenai",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | allenai | null | allenai/wmt16-en-de-dist-12-1 | 425 | null | transformers | 2,488 |
---
language:
- en
- de
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de.
For more details, please, see [Deep En... |
healx/biomedical-slot-filling-reader-large | 52f5d59225e06057329246cf3e0d074b2b2ed9c2 | 2021-11-16T09:15:15.000Z | [
"pytorch",
"bert",
"question-answering",
"arxiv:2109.08564",
"transformers",
"autotrain_compatible"
] | question-answering | false | healx | null | healx/biomedical-slot-filling-reader-large | 425 | null | transformers | 2,489 | Reader model for Biomedical slot filling see https://arxiv.org/abs/2109.08564 for details. The model is initialized with [biobert-large](https://huggingface.co/dmis-lab/biobert-large-cased-v1.1). |
Radicalkiddo/DialoGPT-small-Radical | d75bae5d849c3e04d075cfa1ef0edb0a1ecf57d7 | 2022-02-18T23:21:39.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Radicalkiddo | null | Radicalkiddo/DialoGPT-small-Radical | 424 | null | transformers | 2,490 | ---
tags:
- conversational
---
# radical DialoGPT Model |
monologg/koelectra-small-finetuned-nsmc | 6eddccf6902f2d806fbdf99d6cd52d81dcc4d49a | 2020-08-18T18:43:17.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | monologg | null | monologg/koelectra-small-finetuned-nsmc | 424 | null | transformers | 2,491 | Entry not found |
ml6team/keyphrase-extraction-distilbert-inspec | d7a71e220849b5f578f83a847d3b2ec1f5101a69 | 2022-06-16T14:20:55.000Z | [
"pytorch",
"distilbert",
"token-classification",
"en",
"dataset:midas/inspec",
"transformers",
"keyphrase-extraction",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | ml6team | null | ml6team/keyphrase-extraction-distilbert-inspec | 424 | null | transformers | 2,492 | ---
language: en
license: mit
tags:
- keyphrase-extraction
datasets:
- midas/inspec
metrics:
- seqeval
widget:
- text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document.
Thanks to these keyphrases humans can understand the content of a text very quickly a... |
yechen/bert-base-chinese | 2d43912ed8aa6d7bd7bc132645fa0580f7106ce7 | 2021-05-01T04:00:07.000Z | [
"pytorch",
"tf",
"fill-mask",
"zh",
"transformers",
"autotrain_compatible"
] | fill-mask | false | yechen | null | yechen/bert-base-chinese | 423 | null | transformers | 2,493 | ---
language: zh
---
|
cambridgeltl/mirror-bert-base-uncased-word | c48c66ea6cbaa1c97590fee2c0cefef6e38f1fe9 | 2021-09-29T23:04:29.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.08027",
"transformers"
] | feature-extraction | false | cambridgeltl | null | cambridgeltl/mirror-bert-base-uncased-word | 422 | 1 | transformers | 2,494 | ---
language: en
tags:
- word-embeddings
- word-similarity
### mirror-bert-base-uncased-word
An unsupervised word encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf). Trained with a set of unlabelled words, using [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the base model... |
cardiffnlp/bertweet-base-sentiment | 087bddb2a452eb364661dee2872adece27d9697c | 2021-05-20T14:50:57.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/bertweet-base-sentiment | 421 | null | transformers | 2,495 | |
Helsinki-NLP/opus-mt-tc-big-ar-en | 4bcf62f599694f87ef20d85b358575d108cd785b | 2022-06-01T12:59:27.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ar",
"en",
"transformers",
"translation",
"opus-mt-tc",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tc-big-ar-en | 421 | null | transformers | 2,496 | ---
language:
- ar
- en
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-ar-en
results:
- task:
name: Translation ara-eng
type: translation
args: ara-eng
dataset:
name: flores101-devtest
type: flores_101
args: ara eng devtest
metrics... |
uclanlp/plbart-csnet | eb9f06eb49af981a7bde3f367b60e0e82f1faaf6 | 2021-11-23T18:12:21.000Z | [
"pytorch",
"plbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uclanlp | null | uclanlp/plbart-csnet | 420 | null | transformers | 2,497 | Entry not found |
ionite/DialoGPT-medium-McKayAI-v2 | cbd031405701f164e06a626d63b90f2c569e7412 | 2021-11-20T04:49:17.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ionite | null | ionite/DialoGPT-medium-McKayAI-v2 | 419 | null | transformers | 2,498 | ---
tags:
- conversational
---
# McKayAI DialoGPT Model |
pucpr/clinicalnerpt-disease | 936144a59fed6848f290f24a625b77d258026edb | 2021-10-13T09:33:02.000Z | [
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"transformers",
"autotrain_compatible"
] | token-classification | false | pucpr | null | pucpr/clinicalnerpt-disease | 419 | 5 | transformers | 2,499 | ---
language: "pt"
widget:
- text: "DEVIDO AO FATO DE TER DPOC E APRESENTADO DISFUNÇÃO RESPIRATÓRIA AGUDA COM INFILTRADO PULMONAR EM BASE DIREITA"
- text: "Paciente com Sepse pulmonar em D8 tazocin (paciente não recebeu por 2 dias Atb)."
datasets:
- SemClinBr
thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR... |
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