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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
openclimatefix/dgmr-latent-conditioning-stack | 81151f1561c1334263ee65c8eb3f38856c776f78 | 2022-06-20T08:24:16.000Z | [
"pytorch",
"transformers"
] | null | false | openclimatefix | null | openclimatefix/dgmr-latent-conditioning-stack | 95 | null | transformers | 4,700 | Entry not found |
othrif/wav2vec2-large-xlsr-egyptian | 4cfa2d83da399280eaba031bdcbec7b73613442e | 2021-03-29T02:46:30.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"arz",
"dataset:https://arabicspeech.org/",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | othrif | null | othrif/wav2vec2-large-xlsr-egyptian | 95 | null | transformers | 4,701 | ---
language: arz
datasets:
- https://arabicspeech.org/
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Egyptian Arabic by Othmane Rifki
results:
- task:
name: Speech Recognition
type: automatic-speech-r... |
tartuNLP/EstBERT_NER | fc6f195676c5ae365aac5d12d820dd9bb107a3e2 | 2022-05-06T06:29:01.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"et",
"arxiv:2011.04784",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | token-classification | false | tartuNLP | null | tartuNLP/EstBERT_NER | 95 | null | transformers | 4,702 | ---
language: et
license: cc-by-4.0
widget:
- text: "Eesti President on Alar Karis."
---
# EstBERT_NER
## Model description
EstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by [Tkachenko et al](https://www.aclweb.or... |
yoshitomo-matsubara/bert-base-uncased-stsb | 8fc0be283c7af38c97c6b7151d921a1f84f647b4 | 2021-05-29T21:58:50.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:stsb",
"transformers",
"stsb",
"glue",
"torchdistill",
"license:apache-2.0"
] | text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-base-uncased-stsb | 95 | null | transformers | 4,703 | ---
language: en
tags:
- bert
- stsb
- glue
- torchdistill
license: apache-2.0
datasets:
- stsb
metrics:
- pearson correlation
- spearman correlation
---
`bert-base-uncased` fine-tuned on STS-B dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.res... |
mt-empty/english-assyrian | 27fec608a66ff550100bfc7d56001a8a71db94d5 | 2022-03-14T11:01:52.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"as",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | mt-empty | null | mt-empty/english-assyrian | 95 | null | transformers | 4,704 | ---
language:
- en
- as
tags:
- translation
license: apache-2.0
metrics:
- sacrebleu
---
https://github.com/mt-empty/assyrian-translation-model
This is an English to Assyrian/Eastern Syriac machine translation model, it uses [English to Arabic](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) model as the base mode... |
Helsinki-NLP/opus-mt-tc-big-en-ar | e2140a8272b3ea1e147084a35117649263b4408d | 2022-06-01T13:02:37.000Z | [
"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-en-ar | 95 | null | transformers | 4,705 | ---
language:
- ar
- en
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-ar
results:
- task:
name: Translation eng-ara
type: translation
args: eng-ara
dataset:
name: flores101-devtest
type: flores_101
args: eng ara devtest
metrics... |
north/t5_xl_NCC_lm | b445c8c4dd5958f859de7f9a6587758a49b575db | 2022-06-01T19:41:43.000Z | [
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"no",
"nn",
"sv",
"dk",
"is",
"en",
"dataset:nbailab/NCC",
"dataset:mc4",
"dataset:wikipedia",
"arxiv:2104.09617",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | north | null | north/t5_xl_NCC_lm | 95 | null | transformers | 4,706 | ---
language:
- no
- nn
- sv
- dk
- is
- en
datasets:
- nbailab/NCC
- mc4
- wikipedia
widget:
- text: <extra_id_0> hver uke samles Regjeringens medlemmer til Statsråd på <extra_id_1>. Dette organet er øverste <extra_id_2> i Norge. For at møtet skal være <extra_id_3>, må over halvparten av regjeringens <extra_id_4> ... |
RohanJoshi28/twitter_sentiment_analysisv1 | cc7bd2e55a7f39dea523a6f50b75005ede4120ab | 2022-05-29T20:14:08.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | RohanJoshi28 | null | RohanJoshi28/twitter_sentiment_analysisv1 | 95 | null | transformers | 4,707 | Entry not found |
FigoMe/news-gpt-neo-1.3B-keywords-line-by-line-reverse | 4ef8e25b4d39ba58612e36e48c8becd9785e6fee | 2022-06-01T17:15:31.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | FigoMe | null | FigoMe/news-gpt-neo-1.3B-keywords-line-by-line-reverse | 95 | null | transformers | 4,708 | Entry not found |
Peltarion/dnabert-distilbert | eb65755814f5e0e934ecf74a573e7bac2b661ef3 | 2022-07-02T11:28:16.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"DNA",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | Peltarion | null | Peltarion/dnabert-distilbert | 95 | null | transformers | 4,709 | ---
tags:
- DNA
license: mit
---
## DistilDNA model
This is a distilled version of [DNABERT](https://github.com/jerryji1993/DNABERT) by using DistilBERT technique. It has a BERT architecture with 6 layers and 768 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and metho... |
ckiplab/bert-base-han-chinese | 274f25f098e41e7fe2d1a8f032cde0460c7dc8c8 | 2022-07-04T08:04:03.000Z | [
"pytorch",
"bert",
"fill-mask",
"zh",
"transformers",
"lm-head",
"license:gpl-3.0",
"autotrain_compatible"
] | fill-mask | false | ckiplab | null | ckiplab/bert-base-han-chinese | 95 | null | transformers | 4,710 | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- lm-head
- bert
- zh
license: gpl-3.0
---
# CKIP BERT Base Han Chinese
Pretrained model on Ancient Chinese language using a masked language modeling (MLM) objective.
## Homepage
* [ckiplab/han-transformers](ht... |
furrutiav/beto_coherence | c0f23f38c50776b194ada97ca3784077b6b0a402 | 2022-07-12T00:29:30.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers",
"license:gpl-2.0"
] | feature-extraction | false | furrutiav | null | furrutiav/beto_coherence | 95 | null | transformers | 4,711 | ---
license: gpl-2.0
---
|
neulab/gpt2-large-finetuned-wikitext103 | 8ad278e42033da88bd34b5e810390c88bef565c3 | 2022-07-14T15:38:45.000Z | [
"pytorch",
"gpt2",
"text-generation",
"arxiv:2201.12431",
"transformers"
] | text-generation | false | neulab | null | neulab/gpt2-large-finetuned-wikitext103 | 95 | null | transformers | 4,712 | This is a `gpt2-large` model, finetuned on the Wikitext-103 dataset.
It achieves a perplexity of **10.56** using a "sliding window" context, using the `run_clm.py` script at [https://github.com/neulab/knn-transformers](https://github.com/neulab/knn-transformers).
| Base LM: | `distilgpt2` | `gpt2` |
| :--- ... |
Amalq/schizophrenia-roberta-large | 5914d6c8121f0339694317d3d9f321d29e649d16 | 2022-07-26T21:38:32.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:SMHD",
"dataset:Schizophrenia Reddit",
"arxiv:1806.05258",
"transformers",
"Transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Amalq | null | Amalq/schizophrenia-roberta-large | 95 | null | transformers | 4,713 | ---
language: en
tags:
- Transformers
license: apache-2.0
datasets:
- SMHD
- Schizophrenia Reddit
---
# SchizophreniaRoberta model
is a model initialized with [roberta-large](https://huggingface.co/roberta-large) and trained with Schizophrenia Reddit, a subset of [Self-Reported Mental Health Diagnoses (SMHD) dataset]... |
Helsinki-NLP/opus-mt-cs-de | f5a1b1443dc5381df3a0a83d790b3c2eb16cf811 | 2021-09-09T21:29:18.000Z | [
"pytorch",
"marian",
"text2text-generation",
"cs",
"de",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-cs-de | 94 | null | transformers | 4,714 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-cs-de
* source languages: cs
* target languages: de
* OPUS readme: [cs-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/cs-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-ko-es | 6a5a499d1635016abfe1c289a26dd039b55cf5ae | 2020-08-21T14:42:47.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ko",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ko-es | 94 | null | transformers | 4,715 | ---
language:
- ko
- es
tags:
- translation
license: apache-2.0
---
### kor-spa
* source group: Korean
* target group: Spanish
* OPUS readme: [kor-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-spa/README.md)
* model: transformer-align
* source language(s): kor kor_Hang kor_Latn... |
Helsinki-NLP/opus-mt-nl-es | 2b106c525d9a4b17769f562fde0aac3997aad530 | 2021-09-10T13:59:11.000Z | [
"pytorch",
"marian",
"text2text-generation",
"nl",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-nl-es | 94 | null | transformers | 4,716 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nl-es
* source languages: nl
* target languages: es
* OPUS readme: [nl-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nl-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
funnel-transformer/intermediate-base | 40356a7e0969916d0b958333c61ba21f611bcab8 | 2020-12-11T21:40:21.000Z | [
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | funnel-transformer | null | funnel-transformer/intermediate-base | 94 | null | transformers | 4,717 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
- gigaword
---
# Funnel Transformer intermediate model (B6-6-6 without decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced i... |
huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad | 35d84905f0e8a5f6ee25104ed20fbed73c299103 | 2021-05-19T20:06:17.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | huggingface | null | huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad | 94 | 2 | transformers | 4,718 | Entry not found |
junnyu/roformer_chinese_sim_char_ft_small | c2f6b597902a58686723b5bee929f150e51fa011 | 2022-04-15T03:51:50.000Z | [
"pytorch",
"roformer",
"text-generation",
"zh",
"transformers",
"tf2.0"
] | text-generation | false | junnyu | null | junnyu/roformer_chinese_sim_char_ft_small | 94 | 2 | transformers | 4,719 | ---
language: zh
tags:
- roformer
- pytorch
- tf2.0
inference: False
---
# 安装
- pip install roformer==0.4.3
# 使用
```python
import torch
import numpy as np
from roformer import RoFormerForCausalLM, RoFormerConfig
from transformers import BertTokenizer
device = torch.device('cuda:0' if torch.cuda.is_available() else 'c... |
madlag/bert-large-uncased-wwm-squadv2-x2.63-f82.6-d16-hybrid-v1 | 1c1e994ef2a74026daeb86cb7a562bbf9475f645 | 2021-06-16T17:12:46.000Z | [
"pytorch",
"tf",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:mit",
"autotrain_compatible"
] | question-answering | false | madlag | null | madlag/bert-large-uncased-wwm-squadv2-x2.63-f82.6-d16-hybrid-v1 | 94 | null | transformers | 4,720 | ---
language: en
thumbnail:
license: mit
tags:
- question-answering
-
-
datasets:
- squad_v2
metrics:
- squad_v2
widget:
- text: "Where is the Eiffel Tower located?"
context: "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, whose... |
microsoft/trocr-large-stage1 | 263390badaa806a561702715213cae4a5f059267 | 2022-07-01T07:39:08.000Z | [
"pytorch",
"vision-encoder-decoder",
"arxiv:2109.10282",
"transformers",
"trocr",
"image-to-text"
] | image-to-text | false | microsoft | null | microsoft/trocr-large-stage1 | 94 | 2 | transformers | 4,721 | ---
tags:
- trocr
- image-to-text
---
# TrOCR (large-sized model, pre-trained only)
TrOCR pre-trained only model. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](http... |
mrm8488/t5-base-finetuned-qasc | 7c26f8e64578318f9e0c3223880a1cc68739ddc7 | 2020-12-11T21:55:50.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:qasc",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-finetuned-qasc | 94 | 1 | transformers | 4,722 | ---
language: en
datasets:
- qasc
---
# T5-base fine-tuned on QASC
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [QASC](https://allenai.org/data/qasc) for **QA** (via *sentence composition*) downstream task.
## Details of T5
The **T5** model was presented i... |
speechbrain/sepformer-whamr16k | 0b73df532deb3215baf372ca5a90512ba0c75c2a | 2021-11-30T00:53:21.000Z | [
"en",
"dataset:WHAMR!",
"arxiv:2010.13154",
"arxiv:2106.04624",
"speechbrain",
"audio-to-audio",
"audio-source-separation",
"Source Separation",
"Speech Separation",
"WHAM!",
"SepFormer",
"Transformer",
"pytorch",
"license:apache-2.0"
] | audio-to-audio | false | speechbrain | null | speechbrain/sepformer-whamr16k | 94 | 1 | speechbrain | 4,723 | ---
language: "en"
thumbnail:
tags:
- audio-to-audio
- audio-source-separation
- Source Separation
- Speech Separation
- WHAM!
- SepFormer
- Transformer
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- WHAMR!
metrics:
- SI-SNRi
- SDRi
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&rep... |
uclanlp/plbart-java-clone-detection | f05211c8f2ef087522c5ac571c69b2e377b39371 | 2021-11-09T17:18:43.000Z | [
"pytorch",
"plbart",
"text-classification",
"transformers"
] | text-classification | false | uclanlp | null | uclanlp/plbart-java-clone-detection | 94 | null | transformers | 4,724 | Entry not found |
yoshitomo-matsubara/bert-base-uncased-cola | 106940f94f17e702ae37d740922d679677667c3c | 2021-05-29T21:40:15.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:cola",
"transformers",
"cola",
"glue",
"torchdistill",
"license:apache-2.0"
] | text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-base-uncased-cola | 94 | null | transformers | 4,725 | ---
language: en
tags:
- bert
- cola
- glue
- torchdistill
license: apache-2.0
datasets:
- cola
metrics:
- matthew's correlation
---
`bert-base-uncased` fine-tuned on CoLA dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/githu... |
nickmuchi/vit-base-xray-pneumonia | 7e99827336046d85c0f85884405034df80b08ebd | 2022-03-09T05:43:35.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"dataset:chest xrays",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | nickmuchi | null | nickmuchi/vit-base-xray-pneumonia | 94 | null | transformers | 4,726 | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- chest xrays
widget:
- src: https://drive.google.com/uc?id=1yqnhD4Wjt4Y_NGLtijTGGaaw9GL497kQ
example_title: PNEUMONIA
- src: https://drive.google.com/uc?id=1xjcIEDb8kuSd4wF44gCEgsc0PfRvs53m
example_title: NORMAL
metrics:
- acc... |
UrukHan/wav2vec2-russian | c74e309d12ca9c2be2d69d51f3adb744603a00ff | 2022-04-18T10:33:51.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | UrukHan | null | UrukHan/wav2vec2-russian | 94 | 2 | transformers | 4,727 | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-russian
results: []
widget:
- src: https://cdn-media.huggingface.co/speech_samples/common_voice_ru_18849022.mp3
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proof... |
voidism/diffcse-bert-base-uncased-trans | 77046440b79536bb8d37842cf86034f69a8577bd | 2022-05-01T19:24:20.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2204.10298",
"arxiv:2104.08821",
"arxiv:2111.00899",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | voidism | null | voidism/diffcse-bert-base-uncased-trans | 94 | 1 | transformers | 4,728 | ---
license: apache-2.0
---
# DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
[](https://github.com/voidism/DiffCSE/)
[](https://colab.rese... |
rmihaylov/roberta-base-sentiment-bg | 47106fae3b98b8ae395c661ecd83e90eba51999f | 2022-04-19T15:58:12.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"transformers",
"torch",
"license:mit"
] | text-classification | false | rmihaylov | null | rmihaylov/roberta-base-sentiment-bg | 94 | null | transformers | 4,729 | ---
inference: false
language:
- bg
license: mit
datasets:
- oscar
- chitanka
- wikipedia
tags:
- torch
---
# ROBERTA BASE (cased) trained on private Bulgarian sentiment-analysis dataset
This is a Multilingual Roberta model.
This model is cased: it does make a difference between bulgarian and Bulgarian.
### How to ... |
qanastek/51-languages-classifier | 966ca1a15a30f218ad48561943f046d809d4ed26 | 2022-05-19T12:56:56.000Z | [
"pytorch",
"dataset:qanastek/MASSIVE",
"arxiv:1911.02116",
"Transformers",
"text-classification",
"multi-class-classification",
"license:cc-by-4.0"
] | text-classification | false | qanastek | null | qanastek/51-languages-classifier | 94 | 1 | null | 4,730 | ---
tags:
- Transformers
- text-classification
- multi-class-classification
languages:
- af-ZA
- am-ET
- ar-SA
- az-AZ
- bn-BD
- cy-GB
- da-DK
- de-DE
- el-GR
- en-US
- es-ES
- fa-IR
- fi-FI
- fr-FR
- he-IL
- hi-IN
- hu-HU
- hy-AM
- id-ID
- is-IS
- it-IT
- ja-JP
- jv-ID
- ka-GE
- km-KH
- kn-IN
- ko-KR
- lv-LV
- ml-IN
-... |
josh-oo/german-gpt2-easy-new-padding | 5d8da72b39d8dbe27496005c8d8aa1800cf51adc | 2022-06-29T09:45:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | josh-oo | null | josh-oo/german-gpt2-easy-new-padding | 94 | null | transformers | 4,731 | Entry not found |
Evelyn18/distilbert-base-uncased-becas-7 | 90be144d188753450444f9cdd08d4388ccd85428 | 2022-07-01T20:39:35.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Evelyn18 | null | Evelyn18/distilbert-base-uncased-becas-7 | 94 | null | transformers | 4,732 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-becas-7
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... |
pineappleSoup/DialoGPT-medium-707 | 80f8be57a6d8fd8a58d21a5db2f6fc463668ffe0 | 2022-07-28T10:24:17.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | pineappleSoup | null | pineappleSoup/DialoGPT-medium-707 | 94 | null | transformers | 4,733 | ---
tags:
- conversational
---
# 707 DialoGPT Model
Chatbot for the character 707 from Mystic Messenger. |
autoevaluate/distilbert-base-cased-distilled-squad | d62c5ac3e62b8d308d894fab57e5ec5b88a44040 | 2022-07-20T13:17:25.000Z | [
"pytorch",
"tf",
"rust",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | autoevaluate | null | autoevaluate/distilbert-base-cased-distilled-squad | 94 | null | transformers | 4,734 | ---
language: "en"
datasets:
- squad
metrics:
- squad
license: apache-2.0
---
# DistilBERT base cased distilled SQuAD
> Note: This model is a clone of [`distilbert-base-cased-distilled-squad`](https://huggingface.co/distilbert-base-cased-distilled-squad) for internal testing.
This model is a fine-tune checkpoint of ... |
BSC-TeMU/RoBERTalex | 2a1c89fd468a362368463b4126751fdd51c4d847 | 2021-10-26T10:10:38.000Z | [
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:legal_ES",
"dataset:temu_legal",
"arxiv:2110.12201",
"transformers",
"legal",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | BSC-TeMU | null | BSC-TeMU/RoBERTalex | 93 | 4 | transformers | 4,735 | ---
language:
- es
license: apache-2.0
tags:
- legal
- spanish
datasets:
- legal_ES
- temu_legal
metrics:
- ppl
widget:
- text: "La ley fue <mask> finalmente."
- text: "El Tribunal <mask> desestimó el recurso de amparo."
- text: "Hay base legal dentro del marco <mask> actual."
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN... |
Hate-speech-CNERG/dehatebert-mono-french | 7c0e8c45e9176581e57d4ae7e52327258116f969 | 2021-09-25T13:51:14.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"fr",
"arxiv:2004.06465",
"transformers",
"license:apache-2.0"
] | text-classification | false | Hate-speech-CNERG | null | Hate-speech-CNERG/dehatebert-mono-french | 93 | 2 | transformers | 4,736 | ---
language: fr
license: apache-2.0
---
This model is used detecting **hatespeech** in **French language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model.
The model is trained with different learning rate... |
Helsinki-NLP/opus-mt-tr-ar | 9883a63af0aef0043dfce9a04a231ea9b6f3d722 | 2020-08-21T14:42:51.000Z | [
"pytorch",
"marian",
"text2text-generation",
"tr",
"ar",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-tr-ar | 93 | null | transformers | 4,737 | ---
language:
- tr
- ar
tags:
- translation
license: apache-2.0
---
### tur-ara
* source group: Turkish
* target group: Arabic
* OPUS readme: [tur-ara](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-ara/README.md)
* model: transformer
* source language(s): tur
* target language(s): a... |
QianWeiTech/GPT2-News | ebcc0d7d17deb00af1f256d05d1b61228819062a | 2021-05-21T11:02:49.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | QianWeiTech | null | QianWeiTech/GPT2-News | 93 | null | transformers | 4,738 | Entry not found |
allegro/plt5-large | b446ea4115bc01549ab832e01d138203cfe1324a | 2021-08-19T17:01:13.000Z | [
"pytorch",
"t5",
"text2text-generation",
"pl",
"dataset:ccnet",
"dataset:nkjp",
"dataset:wikipedia",
"dataset:open subtitles",
"dataset:free readings",
"transformers",
"T5",
"translation",
"summarization",
"question answering",
"reading comprehension",
"license:cc-by-4.0",
"autotrain... | translation | false | allegro | null | allegro/plt5-large | 93 | 2 | transformers | 4,739 | ---
language: pl
tags:
- T5
- translation
- summarization
- question answering
- reading comprehension
datasets:
- ccnet
- nkjp
- wikipedia
- open subtitles
- free readings
license: cc-by-4.0
---
# plT5 Large
**plT5** models are T5-based language models trained on Polish corpora. The models were optimized for the orig... |
edixo/road_good_damaged_condition | 70171ea85efe8c9105d793440b8aa62be857e8e6 | 2021-07-05T14:43:15.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | edixo | null | edixo/road_good_damaged_condition | 93 | null | transformers | 4,740 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: road_good_damaged_condition
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9583333134651184
---
# road_g... |
frgfm/rexnet1_0x | 9f2b2d7e23dcf64a13a4df8abe4b8ca2afa973cb | 2022-07-20T00:53:57.000Z | [
"pytorch",
"onnx",
"dataset:frgfm/imagenette",
"arxiv:2007.00992",
"transformers",
"image-classification",
"license:apache-2.0"
] | image-classification | false | frgfm | null | frgfm/rexnet1_0x | 93 | null | transformers | 4,741 | ---
license: apache-2.0
tags:
- image-classification
- pytorch
- onnx
datasets:
- frgfm/imagenette
---
# ReXNet-1.0x model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ReXNet architecture was introduced in [this paper](https://arxiv.org/pdf/2007.00992.pdf).
## Model description
The core i... |
huggingface-course/mt5-small-finetuned-amazon-en-es | 7e7155d1e44ced6b274adcd33223f698af16d185 | 2021-11-11T17:26:47.000Z | [
"pytorch",
"tf",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | huggingface-course | null | huggingface-course/mt5-small-finetuned-amazon-en-es | 93 | 1 | transformers | 4,742 | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, th... |
munggok/mt5-translate-en-id | 2b56f07d3f29fde43ff8cf1e09ca0976c4039965 | 2021-01-25T12:40:58.000Z | [
"pytorch",
"t5",
"text2text-generation",
"id",
"dataset:OPUS",
"dataset:CC-aligned",
"transformers",
"translation",
"license:mit",
"autotrain_compatible"
] | translation | false | munggok | null | munggok/mt5-translate-en-id | 93 | null | transformers | 4,743 | ---
tags:
- translation
language: "id"
license: "mit"
datasets:
- OPUS
- CC-aligned
widget:
- text: "I love you"
---
## MT5-Large-Translate-en-id
## Prefix use
Use prefix "translate:" before input to generate the translation
e.g
"translate: i love you"
## Training data
Opus (Open Subtittle and Wikimatrix)
CCaligne... |
ncduy/phobert-large-finetuned-vietnamese_students_feedback | b99e157e2beae2c3b8bcaa6175102b647ca320ba | 2022-01-06T05:55:30.000Z | [
"pytorch",
"roberta",
"text-classification",
"dataset:vietnamese_students_feedback",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | ncduy | null | ncduy/phobert-large-finetuned-vietnamese_students_feedback | 93 | null | transformers | 4,744 | ---
tags:
- generated_from_trainer
datasets:
- vietnamese_students_feedback
metrics:
- accuracy
model-index:
- name: phobert-large-finetuned-vietnamese_students_feedback
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: vietnamese_students_feedback
type:... |
nguyenvulebinh/vi-mrc-large | 732c3096bbc2b9c7360e46ffb93c4f89692dafdb | 2022-03-13T20:53:44.000Z | [
"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-large | 93 | null | transformers | 4,745 | ---
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... |
openclimatefix/dgmr-discriminator | d0d6e85d81d3524b52295668788bccfa47ac9327 | 2022-06-20T08:19:22.000Z | [
"pytorch",
"transformers"
] | null | false | openclimatefix | null | openclimatefix/dgmr-discriminator | 93 | null | transformers | 4,746 | Entry not found |
sentence-transformers/roberta-large-nli-mean-tokens | 122c0aac5edb08467a65bd04bce837ea1208efd1 | 2021-08-05T08:30:37.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/roberta-large-nli-mean-tokens | 93 | null | sentence-transformers | 4,747 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... |
chinhon/pegasus-newsroom-rewriter | 8f2eef17627f17af9100dd10a19e243722a88ecb | 2022-03-18T10:51:57.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | chinhon | null | chinhon/pegasus-newsroom-rewriter | 93 | 1 | transformers | 4,748 | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-newsroom-rewriter
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-news... |
Salesforce/codegen-6B-multi | ba33ebe5dd88700dfedd924a4417df39d7a75627 | 2022-06-28T17:44:08.000Z | [
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"transformers",
"license:bsd-3-clause"
] | text-generation | false | Salesforce | null | Salesforce/codegen-6B-multi | 93 | null | transformers | 4,749 | ---
license: bsd-3-clause
---
# CodeGen (CodeGen-Multi 6B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan W... |
patrickvonplaten/wav2vec2-base-timit-demo-google-colab | 38b281e77efc6a390bf8b4473d8f4d744ecd2f5c | 2022-05-10T12:33:52.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | patrickvonplaten | null | patrickvonplaten/wav2vec2-base-timit-demo-google-colab | 93 | null | transformers | 4,750 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
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. -->
... |
ai4bharat/IndicBART-XLSum | 0d527c39c318282e7c8cbbcd479b6bad9f46a599 | 2022-05-14T15:09:17.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"dataset:csebuetnlp/xlsum",
"arxiv:2109.02903",
"transformers",
"multilingual",
"nlp",
"indicnlp",
"autotrain_compatible"
] | text2text-generation | false | ai4bharat | null | ai4bharat/IndicBART-XLSum | 93 | null | transformers | 4,751 |
---
languages:
- bn
- gu
- hi
- mr
- pa
- ta
- te
datasets:
- csebuetnlp/xlsum
tags:
- multilingual
- nlp
- indicnlp
widget:
- टेसा जॉवल का कहना है कि मृतकों और लापता लोगों के परिजनों की मदद के लिए एक केंद्र स्थापित किया जा रहा है. उन्होंने इस हादसे के तीन के बाद भी मृतकों की सूची जारी करने में हो रही देरी के बारे ... |
ncfrey/ChemGPT-4.7M | 7438a282460b3038e17a27e25b85b1376e9a23e2 | 2022-06-15T15:17:11.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"chemistry"
] | text-generation | false | ncfrey | null | ncfrey/ChemGPT-4.7M | 93 | null | transformers | 4,752 | ---
tags:
- chemistry
---
# ChemGPT 4.7M
ChemGPT is based on the GPT-Neo model and was introduced in the paper [Neural Scaling of Deep Chemical Models](https://chemrxiv.org/engage/chemrxiv/article-details/627bddd544bdd532395fb4b5).
## Model description
ChemGPT is a transformers model for generative molecular modelin... |
LooksLikeIveLost/DialoGPT-medium-me | a01a8a16183cc43e250c676aab0540c54e4ab1fa | 2022-05-20T02:16:30.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | LooksLikeIveLost | null | LooksLikeIveLost/DialoGPT-medium-me | 93 | null | transformers | 4,753 | ---
tags:
- conversational
---
#Me Bot |
bigscience-catalogue-lm-data/sgpt-nli-bloom-1b3 | 24a4ede287f478b4217b79b434528ba35b43316a | 2022-07-10T15:23:17.000Z | [
"pytorch",
"bloom",
"feature-extraction",
"arxiv:2202.08904",
"sentence-transformers",
"sentence-similarity"
] | sentence-similarity | false | bigscience-catalogue-lm-data | null | bigscience-catalogue-lm-data/sgpt-nli-bloom-1b3 | 93 | 2 | sentence-transformers | 4,754 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# sgpt-nli-bloom-1b3
## Usage
For usage instructions, refer to: https://github.com/Muennighoff/sgpt#symmetric-semantic-search
The model was trained with the command
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3... |
bloom-testing/test-bloomd-350m-fix-master-ci | 4d0c88371a597294e0d01c76b754c2659acb3f6c | 2022-07-16T00:58:37.000Z | [
"pytorch",
"bloom",
"feature-extraction",
"transformers"
] | feature-extraction | false | bloom-testing | null | bloom-testing/test-bloomd-350m-fix-master-ci | 93 | null | transformers | 4,755 | Entry not found |
Cinnamon/electra-small-japanese-discriminator | 556f337383b3421fa3276a6787e88c5cc2e3a0cd | 2020-12-11T21:26:13.000Z | [
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
] | null | false | Cinnamon | null | Cinnamon/electra-small-japanese-discriminator | 92 | 1 | transformers | 4,756 | ---
language: ja
license: apache-2.0
---
## Japanese ELECTRA-small
We provide a Japanese **ELECTRA-Small** model, as described in [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
Our pretraining process employs subword units derived from the [J... |
PaulLerner/dpr_question_encoder_triviaqa_without_viquae | 0e4feb8b7a09fee824989b911f7f83cc8b5fa6b7 | 2022-02-18T13:55:05.000Z | [
"pytorch",
"dpr",
"feature-extraction",
"transformers"
] | feature-extraction | false | PaulLerner | null | PaulLerner/dpr_question_encoder_triviaqa_without_viquae | 92 | null | transformers | 4,757 | Entry not found |
assemblyai/distilbert-base-uncased-qqp | c6a84d6432d9eccfa1850c252322fb1e0e77fcb4 | 2021-06-14T22:13:49.000Z | [
"pytorch",
"distilbert",
"text-classification",
"arxiv:1910.01108",
"transformers"
] | text-classification | false | assemblyai | null | assemblyai/distilbert-base-uncased-qqp | 92 | null | transformers | 4,758 | # DistilBERT-Base-Uncased for Duplicate Question Detection
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) originally released in ["DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"](https://arxiv.org/abs/1910.01108) and traine... |
facebook/wav2vec2-large-it-voxpopuli | 06983d0205d75ad2b6ff6b31ef0cff420091ec85 | 2021-07-06T02:18:35.000Z | [
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"it",
"arxiv:2101.00390",
"transformers",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"license:cc-by-nc-4.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-large-it-voxpopuli | 92 | null | transformers | 4,759 | ---
language: it
tags:
- audio
- automatic-speech-recognition
- voxpopuli
license: cc-by-nc-4.0
---
# Wav2Vec2-Large-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained on the it unlabeled subset of [VoxPopuli corpus](https:/... |
huggingtweets/girlmeat5557 | e8bbda0e4e4ff17aa2acaaae5caa91d1d3f0a424 | 2021-05-22T05:31:49.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/girlmeat5557 | 92 | null | transformers | 4,760 | ---
language: en
thumbnail: https://www.huggingtweets.com/girlmeat5557/1617790352329/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/137359295938... |
huggingtweets/huxijin_gt | 5875c1a5dac528529f57cd66c6cb788b38bf69f9 | 2021-05-22T07:20:55.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/huxijin_gt | 92 | null | transformers | 4,761 | ---
language: en
thumbnail: https://www.huggingtweets.com/huxijin_gt/1603826688877/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/typography@0.2.x/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { col... |
ml6team/gpt-2-medium-conditional-quote-generator | d24ccfffa33b49ff47ed4474622231f26dc66f73 | 2021-05-23T09:38:59.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | ml6team | null | ml6team/gpt-2-medium-conditional-quote-generator | 92 | 6 | transformers | 4,762 | This model has been finetuned on the [`Quotes-500K`](https://github.com/ShivaliGoel/Quotes-500K) dataset to generate quotes based on given topics. To generate a quote, use the following input prompt:
`Given Topics: topic 1 | topic 2 | ... | topic n. Related Quote: ` |
mrm8488/gpt2-finetuned-recipes-cooking_v2 | c7b08a08939ef24841e4b3d756b3bc75a81faffa | 2021-05-23T10:25:08.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers"
] | text-generation | false | mrm8488 | null | mrm8488/gpt2-finetuned-recipes-cooking_v2 | 92 | null | transformers | 4,763 | ---
language: en
thumbnail:
widget:
- text: "HuggingFace Cake:"
---
|
navteca/quora-roberta-base | 0919bfda01351c5074b84550e96cbe4207234b60 | 2021-03-25T16:10:08.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"dataset:quora",
"transformers",
"license:mit"
] | text-classification | false | navteca | null | navteca/quora-roberta-base | 92 | null | transformers | 4,764 | ---
datasets:
- quora
language: en
license: mit
pipeline_tag: text-classification
tags:
- roberta
- text-classification
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-en... |
nielsr/coref-roberta-large | b228bea5218e3575342d440385dcd2d7bd809738 | 2021-01-21T10:07:15.000Z | [
"pytorch",
"en",
"dataset:wikipedia",
"dataset:quoref",
"dataset:docred",
"dataset:fever",
"dataset:gap",
"dataset:winograd_wsc",
"dataset:winogender",
"dataset:glue",
"arxiv:2004.06870",
"transformers",
"exbert",
"license:apache-2.0"
] | null | false | nielsr | null | nielsr/coref-roberta-large | 92 | null | transformers | 4,765 | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- wikipedia
- quoref
- docred
- fever
- gap
- winograd_wsc
- winogender
- glue
---
# CorefRoBERTa large model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in... |
persiannlp/wikibert-base-parsinlu-multiple-choice | b52a5055ac9977d7fef340cabc743ceddf54b574 | 2021-09-23T16:20:58.000Z | [
"pytorch",
"jax",
"bert",
"multiple-choice",
"fa",
"multilingual",
"dataset:parsinlu",
"transformers",
"wikibert",
"persian",
"farsi",
"license:cc-by-nc-sa-4.0",
"text-classification"
] | text-classification | false | persiannlp | null | persiannlp/wikibert-base-parsinlu-multiple-choice | 92 | null | transformers | 4,766 | ---
language:
- fa
- multilingual
thumbnail: https://upload.wikimedia.org/wikipedia/commons/a/a2/Farsi.svg
tags:
- multiple-choice
- wikibert
- persian
- farsi
pipeline_tag: text-classification
license: cc-by-nc-sa-4.0
datasets:
- parsinlu
metrics:
- accuracy
---
# Multiple-Choice Question Answering (مدل برای پاسخ به ... |
trituenhantaoio/bert-base-vietnamese-diacritics-uncased | c9451f959df56b17cfce1cc14ee80951577874f5 | 2021-05-20T08:05:47.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"transformers"
] | null | false | trituenhantaoio | null | trituenhantaoio/bert-base-vietnamese-diacritics-uncased | 92 | null | transformers | 4,767 | ## Usage
```python
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
model = BertForSequenceClassification.from_pretrained("trituenhantaoio/bert-base-vietnamese-diacritics-uncased")
tokenizer = BertTokenizer.from_pretrained("trituenhantaoio/bert-base-vietnamese-diacritics-unc... |
pysentimiento/robertuito-ner | e3c0367de51f0f91899a94c43a20de9a0913c7c0 | 2022-07-21T11:22:07.000Z | [
"pytorch",
"roberta",
"token-classification",
"es",
"arxiv:2106.09462",
"arxiv:2111.09453",
"transformers",
"twitter",
"sentiment-analysis",
"autotrain_compatible"
] | token-classification | false | pysentimiento | null | pysentimiento/robertuito-ner | 92 | null | transformers | 4,768 | ---
language:
- es
tags:
- twitter
- sentiment-analysis
---
# Named Entity Recognition model for Spanish/English
## robertuito-ner
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with the Spanish/English split of the [LinCE NER co... |
xlm-mlm-xnli15-1024 | c86c766c25685d110275169e45babb27636d89c2 | 2022-07-22T08:10:39.000Z | [
"pytorch",
"tf",
"xlm",
"fill-mask",
"multilingual",
"en",
"fr",
"es",
"de",
"el",
"bg",
"ru",
"tr",
"ar",
"vi",
"th",
"zh",
"hi",
"sw",
"ur",
"arxiv:1901.07291",
"arxiv:1910.09700",
"transformers",
"license:cc-by-nc-4.0",
"autotrain_compatible"
] | fill-mask | false | null | null | xlm-mlm-xnli15-1024 | 91 | null | transformers | 4,769 | ---
language:
- multilingual
- en
- fr
- es
- de
- el
- bg
- ru
- tr
- ar
- vi
- th
- zh
- hi
- sw
- ur
license: cc-by-nc-4.0
---
# xlm-mlm-xnli15-1024
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training De... |
Helsinki-NLP/opus-mt-cs-fr | 3040852ec5404c1da928602fa1ec636b6ddf9a2e | 2021-09-09T21:29:29.000Z | [
"pytorch",
"marian",
"text2text-generation",
"cs",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-cs-fr | 91 | null | transformers | 4,770 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-cs-fr
* source languages: cs
* target languages: fr
* OPUS readme: [cs-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/cs-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-en-et | f696ce2db3f802cf4dd723ea97b2af1eda90c7e9 | 2021-09-09T21:35:13.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"et",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-et | 91 | null | transformers | 4,771 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-et
* source languages: en
* target languages: et
* OPUS readme: [en-et](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-et/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-sl-ru | f537bd30d780082fffad4b80036fca19c87a67a8 | 2020-08-21T14:42:49.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sl",
"ru",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sl-ru | 91 | null | transformers | 4,772 | ---
language:
- sl
- ru
tags:
- translation
license: apache-2.0
---
### slv-rus
* source group: Slovenian
* target group: Russian
* OPUS readme: [slv-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/slv-rus/README.md)
* model: transformer-align
* source language(s): slv
* target langu... |
HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary | 5f8fd46cd438d48ce4ff9fb9a01024b857f6204c | 2021-05-18T20:56:29.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"fa",
"transformers",
"license:apache-2.0"
] | text-classification | false | HooshvareLab | null | HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary | 91 | 1 | transformers | 4,773 | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](... |
KoichiYasuoka/bert-base-japanese-char-extended | ec39844667602ffc6fc2fa1958ee683b667421f8 | 2022-06-20T22:21:54.000Z | [
"pytorch",
"bert",
"fill-mask",
"ja",
"transformers",
"japanese",
"masked-lm",
"wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | KoichiYasuoka | null | KoichiYasuoka/bert-base-japanese-char-extended | 91 | null | transformers | 4,774 | ---
language:
- "ja"
tags:
- "japanese"
- "masked-lm"
- "wikipedia"
license: "cc-by-sa-4.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
widget:
- text: "酸素ボンベを充[MASK]する。"
---
# bert-base-japanese-char-extended
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts, derived from [bert-ba... |
allenai/unifiedqa-v2-t5-base-1363200 | 48d92192cfceb184fc6593c1e60b9752a5877cc3 | 2022-02-22T00:26:46.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/unifiedqa-v2-t5-base-1363200 | 91 | 1 | transformers | 4,775 | # Further details: https://github.com/allenai/unifiedqa
|
cointegrated/rut5-small-chitchat | 6a8dd478cfecbb26a4637be2c101c131dd931fde | 2021-07-18T21:50:13.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ru",
"transformers",
"dialogue",
"russian",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | cointegrated | null | cointegrated/rut5-small-chitchat | 91 | 3 | transformers | 4,776 | ---
language: "ru"
tags:
- dialogue
- russian
license: mit
---
This is a version of the [cointegrated/rut5-small](https://huggingface.co/cointegrated/rut5-small) model fine-tuned on some Russian dialogue data. It is not very smart and creative, but it is small and fast, and can serve as a fallback response generator f... |
dtomas/roberta-base-bne-irony | 772b696b754dc0c279b8ae569b3604907034268c | 2021-12-22T13:55:36.000Z | [
"pytorch",
"roberta",
"text-classification",
"es",
"transformers",
"irony",
"sarcasm",
"spanish"
] | text-classification | false | dtomas | null | dtomas/roberta-base-bne-irony | 91 | null | transformers | 4,777 | ---
language:
- es
tags:
- irony
- sarcasm
- spanish
widget:
- text: "¡Cómo disfruto peleándome con los Transformers!"
example_title: "Ironic"
- text: "Madrid es la capital de España"
example_title: "Non ironic"
---
# RoBERTa base finetuned for Spanish irony detection
## Model description
Model to pe... |
flax-community/wav2vec2-spanish | bd4d4e898c994eecd8df48e21a6c3abd316a26d6 | 2021-07-19T05:02:39.000Z | [
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"es",
"dataset:common_voice",
"arxiv:2006.11477",
"transformers",
"audio",
"automatic-speech-recognition"
] | automatic-speech-recognition | false | flax-community | null | flax-community/wav2vec2-spanish | 91 | null | transformers | 4,778 | ---
language: es
tags:
- audio
- automatic-speech-recognition
datasets:
- common_voice
---
# Wav2Vec2 Spanish
Wav2Vec2 model pre-trained using the Spanish portion of the Common Voice dataset. The model is trained with Flax and using TPUs sponsored by Google since this is part of the [Flax/Jax Community Week](https://... |
llange/xlm-roberta-large-spanish-clinical | 3ef89eb7322ba99a1125efb69eea06c94def53e4 | 2021-12-17T10:27:39.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2112.08754",
"transformers",
"autotrain_compatible"
] | fill-mask | false | llange | null | llange/xlm-roberta-large-spanish-clinical | 91 | null | transformers | 4,779 | # CLIN-X-ES: a pre-trained language model for the Spanish clinical domain
Details on the model, the pre-training corpus and the downstream task performance are given in the paper: "CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain" by Lukas Lange, Heike... |
philschmid/tiny-distilbert-classification | 2ec87b1f823ed23236b016ad3f7c767222021877 | 2021-09-02T07:43:52.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | philschmid | null | philschmid/tiny-distilbert-classification | 91 | null | transformers | 4,780 | # Test model
> ## This model is used to run tests for the Hugging Face DLCs |
reshinthadith/BashGPTNeo | 2088f79bd44b8a2c2e77cf98d08d91798eb3d05e | 2021-09-01T15:22:29.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"English",
"Bash",
"dataset:nlc2cmd",
"transformers",
"code-representation-learning",
"program-synthesis"
] | text-generation | false | reshinthadith | null | reshinthadith/BashGPTNeo | 91 | null | transformers | 4,781 | ---
language:
- English
- Bash
thumbnail: "Neural Program Synthesis for Bash"
tags:
- code-representation-learning
- program-synthesis
datasets:
- nlc2cmd
metrics:
- metric1
- metric2
---
# BashGPT-Neo
## What is it ?
BashGPT-Neo is a [Neural Program Synthesis](https://www.microsoft.com/en-us/research/project/ne... |
rexoscare/string_instrument_detector | 5054f6cf64bdeabdd60001bd4c87d35842cde21e | 2021-07-03T17:54:43.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | rexoscare | null | rexoscare/string_instrument_detector | 91 | null | transformers | 4,782 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: string_instrument_detector
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7395833134651184
---
# string_... |
chitanda/merit-roberta-large-v1 | ff498af4c27005fbfdecba7cabefc9e64eb3e5a8 | 2022-02-26T12:26:41.000Z | [
"pytorch",
"roberta",
"transformers",
"license:mit"
] | null | false | chitanda | null | chitanda/merit-roberta-large-v1 | 91 | null | transformers | 4,783 | ---
license: mit
---
|
KES/T5-TTParser | 88a9c1f519008c4ca705a9ad861e79ae66c7bb07 | 2022-06-04T20:11:13.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:Custom dataset",
"dataset:Creolised JFLEG",
"arxiv:1702.04066",
"transformers",
"Trinidad and Tobago English Parser",
"Caribe",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | KES | null | KES/T5-TTParser | 91 | 1 | transformers | 4,784 | ---
language: en
tags:
- Trinidad and Tobago English Parser
- text2text-generation
- Caribe
license: cc-by-nc-sa-4.0
datasets:
- Custom dataset
- Creolised JFLEG
---
# Trinidad English Creole Parser
This model was trained as a parser to Trinidad English Creole.
---
# Model
This model utilises T5-base pre-tr... |
IDEA-CCNL/Taiyi-vit-87M-D | 967326d6b96e1b1cb65f7e1e4377a367b309699a | 2022-05-12T02:52:56.000Z | [
"pytorch",
"vit",
"image-classification",
"transformers",
"license:apache-2.0"
] | image-classification | false | IDEA-CCNL | null | IDEA-CCNL/Taiyi-vit-87M-D | 91 | null | transformers | 4,785 | ---
license: apache-2.0
---
# Taiyi-vit-87M-D (base-sized model)
Based on pre-trained clip-vit-base **(patch 16, resolution 224x224)**, we introduce multimodal information. For multimodal pre-training tasks, we design several special training objectives in our paper. Our code and details of pre-training tasks will be... |
ckiplab/bert-tiny-chinese-ws | f4a11d4b00b06502c260d8134a375a4000b09d7b | 2022-05-10T03:28:12.000Z | [
"pytorch",
"bert",
"token-classification",
"zh",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | token-classification | false | ckiplab | null | ckiplab/bert-tiny-chinese-ws | 91 | null | transformers | 4,786 | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- bert
- zh
license: gpl-3.0
---
# CKIP BERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segment... |
bongsoo/sentencebert_v1.0 | 25a2079e0331317c8bc9059c2427ef234a66d851 | 2022-07-28T03:04:28.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers",
"en",
"ko"
] | sentence-similarity | false | bongsoo | null | bongsoo/sentencebert_v1.0 | 91 | 2 | sentence-transformers | 4,787 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- en
- ko
---
# sentencebert_v1.0
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 tas... |
ismail-lucifer011/autotrain-name_all-904029577 | 7a0ea3a6b0a2785686b27a4154e26236e7548d5f | 2022-05-24T15:43:22.000Z | [
"pytorch",
"distilbert",
"token-classification",
"en",
"dataset:ismail-lucifer011/autotrain-data-name_all",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | token-classification | false | ismail-lucifer011 | null | ismail-lucifer011/autotrain-name_all-904029577 | 91 | null | transformers | 4,788 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ismail-lucifer011/autotrain-data-name_all
co2_eq_emissions: 0.8375653425894861
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 904029577
- CO2 Emissions (in grams): 0.8375653425894861
## Validation Me... |
alibaba-pai/pai-dkplm-financial-base-zh | 1ce4f42ff8d5073d61886c0e3c6df501e694c815 | 2022-06-10T06:49:32.000Z | [
"pytorch",
"bert",
"pretraining",
"zh",
"arxiv:2205.00258",
"arxiv:2112.01047",
"transformers",
"license:apache-2.0",
"fill-mask"
] | fill-mask | false | alibaba-pai | null | alibaba-pai/pai-dkplm-financial-base-zh | 91 | 1 | transformers | 4,789 | ---
language: zh
pipeline_tag: fill-mask
widget:
- text: "根据新闻报道，三大[MASK]数午后集体涨超1％。"
- text: "用各种途径支持中小[MASK]企业融资。"
tags:
- bert
license: apache-2.0
---
## Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the financial domain
For Chinese natural language processing in specific domains, we ... |
docketanalyzer/distilroberta-base-ddcl | 09d99f735e5a3bdde0d74411a6c6c95ff9788f57 | 2021-05-20T16:12:23.000Z | [
"pytorch",
"roberta",
"transformers"
] | null | false | docketanalyzer | null | docketanalyzer/distilroberta-base-ddcl | 90 | null | transformers | 4,790 | Entry not found |
facebook/wav2vec2-large-fr-voxpopuli | 3a2f030a11d4d1cabf1a62e3c3c55239c6b59b96 | 2021-07-06T02:11:48.000Z | [
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"fr",
"arxiv:2101.00390",
"transformers",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"license:cc-by-nc-4.0"
] | automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-large-fr-voxpopuli | 90 | null | transformers | 4,791 | ---
language: fr
tags:
- audio
- automatic-speech-recognition
- voxpopuli
license: cc-by-nc-4.0
---
# Wav2Vec2-Large-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained on the fr unlabeled subset of [VoxPopuli corpus](https:/... |
federicopascual/finetuned-sentiment-analysis-model | 7190a2735c09fd9b62e9da30466cb7e382ff2645 | 2021-12-28T15:57:16.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | federicopascual | null | federicopascual/finetuned-sentiment-analysis-model | 90 | null | transformers | 4,792 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- precision
- recall
model-index:
- name: finetuned-sentiment-analysis-model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_t... |
google/tapas-tiny-finetuned-wtq | 7bc868b0c8c0ff220769a2e78b6306870fb80d8b | 2021-11-29T10:45:11.000Z | [
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wtq",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1508.00305",
"transformers",
"license:apache-2.0"
] | table-question-answering | false | google | null | google/tapas-tiny-finetuned-wtq | 90 | null | transformers | 4,793 | ---
language: en
tags:
- tapas
- table-question-answering
license: apache-2.0
datasets:
- wtq
---
# TAPAS tiny model fine-tuned on WikiTable Questions (WTQ)
This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_tiny_reset` checkpoint of the [original G... |
julien-c/flair-ner | 9b28741e755f4f34f588d50e812ae590a3b6e511 | 2020-11-26T22:01:14.000Z | [
"pytorch",
"en",
"dataset:conll2003",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | julien-c | null | julien-c/flair-ner | 90 | null | flair | 4,794 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- conll2003
inference: false
---
## Flair NER model `en-ner-conll03-v0.4.pt`
Imported from https://nlp.informatik.hu-berlin.de/resources/models/ner/
### Demo: How to use in Flair
```python
from flair.data import Sentence
from fl... |
liam168/chat-DialoGPT-small-zh | cf12fe8e8d5a7f4ca6c26ba47c249751597c34f8 | 2021-08-04T09:01:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"zh",
"transformers",
"license:apache-2.0"
] | text-generation | false | liam168 | null | liam168/chat-DialoGPT-small-zh | 90 | 1 | transformers | 4,795 | ---
language: zh
widget:
- text: "你们宿舍都是这么厉害的人吗"
license: apache-2.0
---
# liam168/chat-DialoGPT-small-zh
## Model description
用中文聊天数据训练的模型；
### How to use
Now we are ready to try out how the model works as a chatting partner!
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
m... |
orzhan/rugpt3-simplify-large | 96bbd89cc7bb5eb4646216fa869ae7f49a7f3432 | 2021-05-31T14:31:36.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | orzhan | null | orzhan/rugpt3-simplify-large | 90 | null | transformers | 4,796 | Text simplification model for Russian. Fine-tuned ruGPT3-large
https://github.com/orzhan/rusimscore
---
language: ru
|
allenai/aspire-sentence-embedder | 0379fd17fb957625b414a392646cb2406a070424 | 2022-03-09T00:03:57.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2111.08366",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | allenai | null | allenai/aspire-sentence-embedder | 90 | null | transformers | 4,797 | ---
license: apache-2.0
---
## Overview
Model included in a paper for modeling fine grained similarity between documents:
**Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
**Authors**: Sheshera Mysore, Arman Cohan, Tom Hope
**Paper**: https://arxiv.o... |
Helsinki-NLP/opus-mt-tc-big-zle-en | 2481ba3f65e32ebd1131528c2bc76ed6fe330c43 | 2022-06-01T13:09:45.000Z | [
"pytorch",
"marian",
"text2text-generation",
"be",
"en",
"ru",
"uk",
"zle",
"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-zle-en | 90 | null | transformers | 4,798 | ---
language:
- be
- en
- ru
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-en
results:
- task:
name: Translation rus-eng
type: translation
args: rus-eng
dataset:
name: flores101-devtest
type: flores_101
args: rus eng de... |
cambridgeltl/simctg_rocstories | 61b674aa71e858af99ff2fbd77f91d4a3c807cbb | 2022-06-25T19:33:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"arxiv:2202.06417",
"transformers"
] | text-generation | false | cambridgeltl | null | cambridgeltl/simctg_rocstories | 90 | null | transformers | 4,799 | This model provides a GPT-2 language model trained with SimCTG on the ROCStories benchmark [(Mostafazadeh et al., 2016)](https://aclanthology.org/N16-1098.pdf) based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply Sim... |
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