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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TencentGameMate/chinese-hubert-base | fce0375452b1dd6c080ac3248d423d4d037bc831 | 2022-06-24T01:52:57.000Z | [
"pytorch",
"hubert",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | TencentGameMate | null | TencentGameMate/chinese-hubert-base | 953 | 3 | transformers | 1,800 | ---
license: mit
---
Pretrained on 10k hours WenetSpeech L subset. More details in [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
This model does not have a tokenizer as it was pretrained on audio alone.
In order to use this model speech recognition, a tokenizer... |
Norod78/hebrew-gpt_neo-small | 12ac2ca1aac05eeaab5e3bb278fd4e31180b7545 | 2022-07-04T12:43:15.000Z | [
"pytorch",
"jax",
"onnx",
"gpt_neo",
"text-generation",
"he",
"transformers",
"license:mit"
] | text-generation | false | Norod78 | null | Norod78/hebrew-gpt_neo-small | 946 | null | transformers | 1,801 | ---
language: he
thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg
widget:
- text: "עוד בימי קדם"
- text: "קוראים לי דורון ואני מעוניין ל"
- text: "קוראים לי איציק ואני חושב ש"
- text: "החתול שלך מאוד חמוד ו"
license: mit
---
# hebrew-gpt_neo-small
Hebrew text generation model based on [Eleuther... |
Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition | a1e1c0fb14af7e4c4c2dfb2f35860da8b744f1b0 | 2022-07-24T10:47:37.000Z | [
"pytorch",
"wav2vec2",
"ru",
"dataset:Aniemore/resd",
"transformers",
"audio-classification",
"audio",
"emotion",
"emotion-recognition",
"emotion-classification",
"speech",
"license:mit",
"model-index"
] | audio-classification | false | Aniemore | null | Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition | 946 | 2 | transformers | 1,802 | ---
language: ru
tags:
- audio-classification
- audio
- emotion
- emotion-recognition
- emotion-classification
- speech
license: mit
datasets:
- Aniemore/resd
model-index:
- name: XLS-R Wav2Vec2 For Russian Speech Emotion Classification by Nikita Davidchuk
results:
- task:
name: Audio Emotion Recognition
... |
DataikuNLP/tiny-random-bert | 7fb1dc6c16498e2028cc74afcf64319302fab101 | 2021-11-19T16:27:38.000Z | [
"pytorch",
"tf",
"bert",
"transformers"
] | null | false | DataikuNLP | null | DataikuNLP/tiny-random-bert | 944 | null | transformers | 1,803 | Entry not found |
mrm8488/mbart-large-finetuned-opus-en-es-translation | a9bfab17eba27c9bf319056017d610141ce137f0 | 2021-01-26T12:24:37.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"en",
"es",
"dataset:opus100",
"transformers",
"translation",
"autotrain_compatible"
] | translation | false | mrm8488 | null | mrm8488/mbart-large-finetuned-opus-en-es-translation | 944 | 2 | transformers | 1,804 | ---
tags:
- translation
language:
- en
- es
datasets:
- opus100
---
### mbart-large-en-es
This is mbart-large-cc25, finetuned on opus100 for English to Spanish translation.
It scores BLEU **32.54** on test set.
|
sultan/BioM-ALBERT-xxlarge | c85649a7b3345b7de438e59c03f8f702e3aebc76 | 2021-10-12T21:23:31.000Z | [
"pytorch",
"albert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sultan | null | sultan/BioM-ALBERT-xxlarge | 944 | 1 | transformers | 1,805 | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer ... |
funnel-transformer/small-base | 373ecb760257d0059f1efdf58b3796d4d616ad0a | 2020-12-11T21:40:41.000Z | [
"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/small-base | 943 | null | transformers | 1,806 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
- gigaword
---
# Funnel Transformer small model (B4-4-4 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 in
[this... |
codeparrot/codeparrot | 065248a99f051da363b1c2cbf05da943c8b6211b | 2022-06-24T08:28:28.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"code",
"dataset:codeparrot/codeparrot-clean-train",
"transformers",
"generation",
"model-index"
] | text-generation | false | codeparrot | null | codeparrot/codeparrot | 943 | 24 | transformers | 1,807 | ---
language: code
tags:
- code
- gpt2
- generation
datasets:
- codeparrot/codeparrot-clean-train
widget:
- text: "from transformer import"
example_title: "Transformers"
- text: "def print_hello_world():\n\t"
example_title: "Hello World!"
- text: "def get_file_size(filepath):"
example_title: "File size"
- text: "... |
Helsinki-NLP/opus-mt-it-fr | e2b19cab7c3ec41f1f314afc78a4e9423605f553 | 2021-09-10T13:53:00.000Z | [
"pytorch",
"marian",
"text2text-generation",
"it",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-it-fr | 942 | null | transformers | 1,808 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-it-fr
* source languages: it
* target languages: fr
* OPUS readme: [it-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/it-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
UrukHan/t5-russian-spell | 9e1e7dda2468aac53b8e4aa1ae30dc8fd9e9c47c | 2022-04-04T18:55:49.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | UrukHan | null | UrukHan/t5-russian-spell | 942 | 1 | transformers | 1,809 | ---
tags:
- generated_from_trainer
model-index:
- name: t5-russian-spell
results: []
widget:
- text: "ывсем привет выныканалетоп армии и это двадцать пятый день спец операций на украине ет самый главной новости российские военные ракетами кинжалы калибр уничтожили крупную военную топливную базу украины ракетным ударо... |
Stancld/longt5-tglobal-large-16384-pubmed-3k_steps | 6949726515747477615ee1cecb31ff5d34ca3add | 2022-06-20T15:45:47.000Z | [
"pytorch",
"jax",
"longt5",
"text2text-generation",
"en",
"dataset:ccdv/pubmed-summarization",
"arxiv:2112.07916",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | Stancld | null | Stancld/longt5-tglobal-large-16384-pubmed-3k_steps | 941 | 7 | transformers | 1,810 | ---
language: en
datasets:
- ccdv/pubmed-summarization
license: apache-2.0
---
## Introduction
[Google's LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) introduced as an extension of a successful [T5 model](https://arxiv.org/pdf/1910.10683.pdf).
This is an uno... |
prithivida/passive_to_active_styletransfer | 81b7ef1c02f244ac9030d928f2c5a01a037c2430 | 2021-06-23T13:45:25.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | prithivida | null | prithivida/passive_to_active_styletransfer | 940 | 3 | transformers | 1,811 | ## This model belongs to the Styleformer project
[Please refer to github page](https://github.com/PrithivirajDamodaran/Styleformer)
|
Helsinki-NLP/opus-mt-cy-en | 775c85089bc7a55c8203bff544e9fa34cd4ba7ca | 2021-09-09T21:29:44.000Z | [
"pytorch",
"marian",
"text2text-generation",
"cy",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-cy-en | 938 | null | transformers | 1,812 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-cy-en
* source languages: cy
* target languages: en
* OPUS readme: [cy-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/cy-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large | 242150e6be5fb4f7cdb66dbc6bffac542ff828ca | 2021-06-20T19:02:48.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nreimers | null | nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large | 935 | null | transformers | 1,813 | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) |
MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary | e239423547914eda65121a26121768656f7a6400 | 2022-07-28T16:23:45.000Z | [
"pytorch",
"deberta-v2",
"text-classification",
"en",
"dataset:multi_nli",
"dataset:anli",
"dataset:fever",
"dataset:lingnli",
"arxiv:2104.07179",
"arxiv:2111.09543",
"transformers",
"zero-shot-classification",
"license:mit"
] | zero-shot-classification | false | MoritzLaurer | null | MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary | 934 | null | transformers | 1,814 | ---
language:
- en
license: mit
tags:
- text-classification
- zero-shot-classification
metrics:
- accuracy
datasets:
- multi_nli
- anli
- fever
- lingnli
pipeline_tag: zero-shot-classification
---
# DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary
## Model description
This model was trained on 782 357 hypothesis-premise... |
microsoft/swin-base-patch4-window12-384-in22k | c2c8cfc218cfcf3f43091c4476137ed4f2fed9a2 | 2022-05-16T18:01:06.000Z | [
"pytorch",
"tf",
"swin",
"image-classification",
"dataset:imagenet-21k",
"arxiv:2103.14030",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/swin-base-patch4-window12-384-in22k | 932 | null | transformers | 1,815 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: http... |
roberta-large-openai-detector | 5002d695ecf610d8bbfb1fa0d14f1575185b4915 | 2022-07-22T08:07:41.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1904.09751",
"arxiv:1910.09700",
"transformers",
"exbert",
"license:mit"
] | text-classification | false | null | null | roberta-large-openai-detector | 928 | 1 | transformers | 1,816 | ---
language: en
license: mit
tags:
- exbert
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa Large OpenAI Detector
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmen... |
vinai/vinai-translate-vi2en | 89ca166856afab4610d0fcc4bff495940b5200ad | 2022-07-06T07:19:15.000Z | [
"pytorch",
"tf",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | vinai | null | vinai/vinai-translate-vi2en | 928 | null | transformers | 1,817 | # A Vietnamese-English Neural Machine Translation System
Our pre-trained VinAI Translate models `vinai/vinai-translate-vi2en` and `vinai/vinai-translate-en2vi` are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental resul... |
thu-coai/LongLM-small | efadc09553dbe3bfc965dc82aeb9be3d1f224212 | 2021-11-24T05:12:01.000Z | [
"pytorch",
"t5",
"text2text-generation",
"zh",
"arxiv:2108.12960",
"transformers",
"lm-head",
"autotrain_compatible"
] | text2text-generation | false | thu-coai | null | thu-coai/LongLM-small | 925 | 1 | transformers | 1,818 | ---
language:
- zh
thumbnail: http://coai.cs.tsinghua.edu.cn/coai/img/logo.png?v=13923
tags:
- pytorch
- lm-head
- zh
datasets:
metrics:
widget:
- text: "小咕噜对靳司寒完全是个自来熟，小家伙爬进他怀里小手搂着他的脖子，奶声奶气的要求：“靳蜀黎,你给咕噜讲故事好不好？”讲故事？童话故事吗？“我不会。”小家伙明显不信。嘟着小嘴大眼汪汪的盯着他，“哼。”小家伙轻轻哼了一声,靳司寒默了半晌，<extra_id_1>"
- text: "美女亲自打招呼，这可是破天荒第一次，之前不管他献多少次... |
IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese | 7022713ba0b7754e6017d9acfa18d894fdaad847 | 2022-07-30T02:15:06.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"zh",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | IDEA-CCNL | null | IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese | 924 | 2 | transformers | 1,819 | ---
language: zh
tags:
- summarization
inference: False
---
IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese model (Chinese) has 523M million parameter, pretrained on 180G Chinese data with GSG task which is stochastically sample important sentences with sampled gap sentence ratios by 25%. The pretraining task just as sa... |
BSC-TeMU/roberta-base-biomedical-es | 6e457abe0082958dc8cb7762c9ec8ed8b8a7b2c0 | 2021-10-21T10:28:29.000Z | [
"pytorch",
"roberta",
"fill-mask",
"es",
"arxiv:2109.03570",
"arxiv:2109.07765",
"transformers",
"biomedical",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | BSC-TeMU | null | BSC-TeMU/roberta-base-biomedical-es | 923 | 3 | transformers | 1,820 | ---
language:
- es
tags:
- biomedical
- spanish
license: apache-2.0
metrics:
- ppl
widget:
- text: "El único antecedente personal a reseñar era la <mask> arterial."
- text: "Las radiologías óseas de cuerpo entero no detectan alteraciones <mask>, ni alteraciones vertebrales."
- text: "En el <mask> toraco-abdómino-pél... |
imxly/sentence_rtb3 | ac6aefbe53994837a744006c493e9980874179c7 | 2021-05-19T20:21:34.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | imxly | null | imxly/sentence_rtb3 | 923 | null | transformers | 1,821 | Entry not found |
UBC-NLP/AraT5-base | ed49be981b4df4040e83de16fd559e191b87429f | 2022-05-26T18:25:19.000Z | [
"pytorch",
"tf",
"t5",
"ar",
"transformers",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation"
] | null | false | UBC-NLP | null | UBC-NLP/AraT5-base | 922 | 4 | transformers | 1,822 | ---
language:
- ar
tags:
- Arabic T5
- MSA
- Twitter
- Arabic Dialect
- Arabic Machine Translation
- Arabic Text Summarization
- Arabic News Title and Question Generation
- Arabic Paraphrasing and Transliteration
- Arabic Code-Switched Translation
---
# AraT5-base
# AraT5: Text-to-Text Transformers... |
jonatasgrosman/wav2vec2-large-xlsr-53-portuguese | 6ec4ebf736ed4a6cb093a2b8665e16d55ba0fcc6 | 2022-07-27T23:38:25.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_6_0",
"transformers",
"audio",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_6_0",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"lice... | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-large-xlsr-53-portuguese | 922 | 7 | transformers | 1,823 | ---
language: pt
license: apache-2.0
datasets:
- common_voice
- mozilla-foundation/common_voice_6_0
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
- mozilla-foundation/common_voice_6_0
- pt
- robust-speech-event
- speech
- xlsr-fine-tuning-week
model-index:
- name: XLSR Wav2Vec2 ... |
tscholak/3vnuv1vf | 18d448ce4c0f85d8cf9c06ffae8e197d10515ec1 | 2022-01-10T21:49:25.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:spider",
"arxiv:2109.05093",
"transformers",
"text2sql",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | tscholak | null | tscholak/3vnuv1vf | 921 | 2 | transformers | 1,824 | ---
language:
- en
thumbnail: "https://repository-images.githubusercontent.com/401779782/c2f46be5-b74b-4620-ad64-57487be3b1ab"
tags:
- text2sql
widget:
- "How many singers do we have? | concert_singer | stadium : stadium_id, location, name, capacity, highest, lowest, average | singer : singer_id, name, country, song... |
StanfordAIMI/stanford-deidentifier-base | 41f1cf1c95cb9c25643f625b5aeae663d7e07663 | 2022-07-18T03:38:21.000Z | [
"pytorch",
"bert",
"en",
"dataset:radreports",
"transformers",
"token-classification",
"sequence-tagger-model",
"pubmedbert",
"uncased",
"radiology",
"biomedical",
"license:mit"
] | token-classification | false | StanfordAIMI | null | StanfordAIMI/stanford-deidentifier-base | 921 | 1 | transformers | 1,825 | ---
widget:
- text: "PROCEDURE: Chest xray. COMPARISON: last seen on 1/1/2020 and also record dated of March 1st, 2019. FINDINGS: patchy airspace opacities. IMPRESSION: The results of the chest xray of January 1 2020 are the most concerning ones. The patient was transmitted to another service of UH Medical Center under... |
cross-encoder/stsb-TinyBERT-L-4 | a0fde64e9dea230cae6957f45eaa3a4685620b01 | 2021-08-05T08:41:47.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/stsb-TinyBERT-L-4 | 920 | null | transformers | 1,826 | ---
license: apache-2.0
---
# 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-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset]... |
Helsinki-NLP/opus-mt-bn-en | 1b349f7c24ee5f832ca19138d23cf78de5869e80 | 2021-01-18T07:51:55.000Z | [
"pytorch",
"marian",
"text2text-generation",
"bn",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-bn-en | 919 | null | transformers | 1,827 | ---
language:
- bn
- en
tags:
- translation
license: apache-2.0
---
### ben-eng
* source group: Bengali
* target group: English
* OPUS readme: [ben-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ben-eng/README.md)
* model: transformer-align
* source language(s): ben
* target languag... |
google/t5-efficient-base | 6b14ca76d201b73fe751ea16df730c5c999ef736 | 2022-02-15T10:49:53.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2109.10686",
"transformers",
"deep-narrow",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-efficient-base | 917 | 2 | transformers | 1,828 | ---
language:
- en
datasets:
- c4
tags:
- deep-narrow
inference: false
license: apache-2.0
---
# T5-Efficient-BASE (Deep-Narrow version)
T5-Efficient-BASE is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https:/... |
M-CLIP/XLM-Roberta-Large-Vit-L-14 | ad70f9333ca5fc97d85b1491b939cf721cd2bad8 | 2022-06-02T23:25:42.000Z | [
"pytorch",
"tf",
"multilingual"
] | null | false | M-CLIP | null | M-CLIP/XLM-Roberta-Large-Vit-L-14 | 917 | null | null | 1,829 | ---
language: multilingual
---
## Multilingual-clip: XLM-Roberta-Large-Vit-L-14
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `ViT-L-14` can be retrieved via instructions found on OpenAI's ... |
RuRI/Talkmodel01 | bd6f30cb7839b3955919959f5d58ebca366563f8 | 2021-09-17T00:34:28.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | RuRI | null | RuRI/Talkmodel01 | 911 | null | transformers | 1,830 | Entry not found |
mrm8488/mobilebert-finetuned-pos | 54dad38c9125524220389490ce914b9c85d598da | 2021-03-12T08:08:35.000Z | [
"pytorch",
"rust",
"mobilebert",
"token-classification",
"en",
"transformers",
"pos",
"license:mit",
"autotrain_compatible"
] | token-classification | false | mrm8488 | null | mrm8488/mobilebert-finetuned-pos | 911 | 4 | transformers | 1,831 | ---
language: en
tags:
- mobilebert
- pos
license: mit
---
|
pszemraj/led-large-book-summary | 16ef34e1d8d5c43c1e5025463848f30531a12077 | 2022-07-21T09:03:04.000Z | [
"pytorch",
"led",
"text2text-generation",
"en",
"dataset:kmfoda/booksum",
"arxiv:2105.08209",
"transformers",
"summarization",
"summary",
"longformer",
"booksum",
"long-document",
"long-form",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | pszemraj | null | pszemraj/led-large-book-summary | 911 | 3 | transformers | 1,832 | ---
language:
- en
tags:
- summarization
- led
- summary
- longformer
- booksum
- long-document
- long-form
license: apache-2.0
datasets:
- kmfoda/booksum
metrics:
- rouge
widget:
- text: large earthquakes along a given fault segment do not occur at random intervals
because it takes time to accumulate the strain en... |
EleutherAI/enformer-official-rough | affe5713ae9017460706a44108289b13c5fee16c | 2022-06-12T20:46:42.000Z | [
"pytorch",
"enformer",
"transformers",
"license:cc-by-4.0"
] | null | false | EleutherAI | null | EleutherAI/enformer-official-rough | 910 | 4 | transformers | 1,833 | ---
license: cc-by-4.0
inference: false
---
# Enformer
Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.... |
microsoft/DialogRPT-human-vs-machine | 735475522c2e95409e38a6f7ce714ca72a6bb219 | 2021-05-23T09:16:47.000Z | [
"pytorch",
"gpt2",
"text-classification",
"arxiv:2009.06978",
"transformers"
] | text-classification | false | microsoft | null | microsoft/DialogRPT-human-vs-machine | 908 | null | transformers | 1,834 | # Demo
Please try this [➤➤➤ Colab Notebook Demo (click me!)](https://colab.research.google.com/drive/1cAtfkbhqsRsT59y3imjR1APw3MHDMkuV?usp=sharing)
| Context | Response | `human_vs_machine` score |
| :------ | :------- | :------------: |
| I love NLP! | I'm not sure if it's a good idea. | 0.000 |
| I love NLP!... |
facebook/levit-128S | dd3c14ead498eab264fe0ed2053dc30940393467 | 2022-06-01T13:20:18.000Z | [
"pytorch",
"levit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2104.01136",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/levit-128S | 906 | 1 | transformers | 1,835 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https... |
PlanTL-GOB-ES/bsc-bio-es | 623c437d1f056466142fd2e73b4e905dc2ef07ff | 2022-04-11T11:02:40.000Z | [
"pytorch",
"roberta",
"fill-mask",
"es",
"transformers",
"biomedical",
"clinical",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | PlanTL-GOB-ES | null | PlanTL-GOB-ES/bsc-bio-es | 905 | null | transformers | 1,836 | ---
language:
- es
tags:
- biomedical
- clinical
- spanish
license: apache-2.0
metrics:
- ppl
widget:
- text: "El único antecedente personal a reseñar era la <mask> arterial."
- text: "Las radiologías óseas de cuerpo entero no detectan alteraciones <mask>, ni alteraciones vertebrales."
- text: "En el <mask> toraco-a... |
benjamin/gerpt2 | 76b77997c1a715c3cf61a8d086fb75baa3816ded | 2022-05-11T09:17:11.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"de",
"transformers",
"license:mit"
] | text-generation | false | benjamin | null | benjamin/gerpt2 | 904 | 2 | transformers | 1,837 | ---
language: de
widget:
- text: "In einer schockierenden Entdeckung fanden Wissenschaftler eine Herde Einhörner, die in einem abgelegenen, zuvor unerforschten Tal in den Anden lebten."
license: mit
---
# GerPT2
German large and small versions of GPT2:
- https://huggingface.co/benjamin/gerpt2
- https://huggingface... |
JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k | 98b76c842d1fae9868f74b331b298a92eee3c12e | 2021-09-23T15:48:58.000Z | [
"pytorch",
"dataset:Libri2Mix",
"dataset:sep_noisy",
"asteroid",
"audio",
"ConvTasNet",
"audio-to-audio",
"license:cc-by-sa-4.0"
] | audio-to-audio | false | JorisCos | null | JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k | 903 | null | asteroid | 1,838 | ---
tags:
- asteroid
- audio
- ConvTasNet
- audio-to-audio
datasets:
- Libri2Mix
- sep_noisy
license: cc-by-sa-4.0
---
## Asteroid model `JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k`
Description:
This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroi... |
facebook/dino-vitb8 | 745a5a92b1e313ab3c2e95a558df5566b5b8e253 | 2021-08-25T17:40:41.000Z | [
"pytorch",
"vit",
"feature-extraction",
"dataset:imagenet-1k",
"arxiv:2010.11929",
"arxiv:2104.14294",
"transformers",
"dino",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/dino-vitb8 | 903 | 2 | transformers | 1,839 | ---
license: apache-2.0
tags:
- dino
datasets:
- imagenet-1k
---
# Vision Transformer (base-sized model, patch size 8) trained using DINO
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/201... |
speechbrain/lang-id-voxlingua107-ecapa | 9835356c3e7d6525f9182813b4b229e9226d53fc | 2022-06-25T03:42:48.000Z | [
"multilingual",
"dataset:VoxLingua107",
"arxiv:2106.04624",
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"license:apache-2.0"
] | audio-classification | false | speechbrain | null | speechbrain/lang-id-voxlingua107-ecapa | 903 | 6 | speechbrain | 1,840 | ---
language: multilingual
thumbnail:
tags:
- audio-classification
- speechbrain
- embeddings
- Language
- Identification
- pytorch
- ECAPA-TDNN
- TDNN
- VoxLingua107
license: "apache-2.0"
datasets:
- VoxLingua107
metrics:
- Accuracy
widget:
- example_title: English Sample
src: https://cdn-media.huggingface.co/speech... |
navteca/bart-large-mnli | c39c03bcf29d1dab341409eee0b8cd3d7fa68b8a | 2021-08-06T13:59:01.000Z | [
"pytorch",
"jax",
"bart",
"text-classification",
"en",
"dataset:multi_nli",
"arxiv:1909.00161",
"transformers",
"zero-shot-classification",
"license:mit"
] | zero-shot-classification | false | navteca | null | navteca/bart-large-mnli | 902 | 2 | transformers | 1,841 | ---
datasets:
- multi_nli
language: en
license: mit
pipeline_tag: zero-shot-classification
tags:
- bart
- zero-shot-classification
---
# Bart large model for NLI-based Zero Shot Text Classification
This model uses [bart-large](https://huggingface.co/facebook/bart-large).
## Training Data
This model was trained on the... |
CAMeL-Lab/bert-base-arabic-camelbert-mix | 9be352797bdf28a9ae21e2ae582aaaca7abdb22d | 2021-09-14T14:34:32.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"Arabic",
"Dialect",
"Egyptian",
"Gulf",
"Levantine",
"Classical Arabic",
"MSA",
"Modern Standard Arabic",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | CAMeL-Lab | null | CAMeL-Lab/bert-base-arabic-camelbert-mix | 901 | 6 | transformers | 1,842 | ---
language:
- ar
license: apache-2.0
tags:
- Arabic
- Dialect
- Egyptian
- Gulf
- Levantine
- Classical Arabic
- MSA
- Modern Standard Arabic
widget:
- text: "الهدف من الحياة هو [MASK] ."
---
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection... |
castorini/ance-msmarco-doc-maxp | 95207533d035adaddeb195da8484cb6cfaa366f3 | 2021-05-20T15:17:50.000Z | [
"pytorch",
"roberta",
"arxiv:2007.00808",
"transformers"
] | null | false | castorini | null | castorini/ance-msmarco-doc-maxp | 901 | null | transformers | 1,843 | This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arx... |
M-CLIP/M-BERT-Base-69 | e5bf2855224ca5294be65b45344ddcd06219c41d | 2021-05-18T21:33:14.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | M-CLIP | null | M-CLIP/M-BERT-Base-69 | 900 | null | transformers | 1,844 | <br />
<p align="center">
<h1 align="center">M-BERT Base 69</h1>
<p align="center">
<a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/M-BERT%20Base%2069">Github Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you need to d... |
SkolkovoInstitute/rubert-base-corruption-detector | 27965caf27a4897bd0df76128dc8707ca7e212a7 | 2021-12-18T09:28:50.000Z | [
"pytorch",
"bert",
"text-classification",
"ru",
"transformers",
"fluency"
] | text-classification | false | SkolkovoInstitute | null | SkolkovoInstitute/rubert-base-corruption-detector | 900 | null | transformers | 1,845 | ---
language:
- ru
tags:
- fluency
---
This is a model for evaluation of naturalness of short Russian texts. It has been trained to distinguish human-written texts from their corrupted versions.
Corruption sources: random replacement, deletion, addition, shuffling, and re-inflection of words and characters, ran... |
monologg/koelectra-base-v2-finetuned-korquad | 6cf15019cbd304a9cef33a1c94e66850814a66f9 | 2020-06-03T03:32:20.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | monologg | null | monologg/koelectra-base-v2-finetuned-korquad | 900 | null | transformers | 1,846 | Entry not found |
juliamendelsohn/framing_issue_generic | 6b35ca7630b0b6fb208e600ed4f3c236f8abe042 | 2021-05-20T17:27:30.000Z | [
"pytorch",
"roberta",
"transformers"
] | null | false | juliamendelsohn | null | juliamendelsohn/framing_issue_generic | 898 | null | transformers | 1,847 | Entry not found |
facebook/detr-resnet-101-panoptic | 4297151a5e287d2ed673392d2c0a1c6d46142d5c | 2022-06-27T08:34:50.000Z | [
"pytorch",
"detr",
"image-segmentation",
"dataset:coco",
"arxiv:2005.12872",
"transformers",
"vision",
"license:apache-2.0"
] | image-segmentation | false | facebook | null | facebook/detr-resnet-101-panoptic | 897 | 2 | transformers | 1,848 | ---
license: apache-2.0
tags:
- image-segmentation
- vision
datasets:
- coco
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/dog-cat.jpg
example_title: Dog & Cat
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/construction-site.jpg
example_title: Constructio... |
allenai/PRIMERA-multixscience | 69ef13b16f5edc76323f57af922c9b4c47bb7d5c | 2022-07-25T18:17:07.000Z | [
"pytorch",
"led",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/PRIMERA-multixscience | 896 | 1 | transformers | 1,849 | ---
license: apache-2.0
---
HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).
The original code can be found [here](https://github.com/allenai/PRIMER). You can find the script and notebook to train/evaluate the model in the original github rep... |
tner/roberta-large-tweetner-2020 | 768bf9f64587af66884cfb5053999594f341baa9 | 2022-07-08T11:45:18.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/roberta-large-tweetner-2020 | 896 | null | transformers | 1,850 | Entry not found |
ThomasNLG/t5-qa_squad2neg-en | 41de3e39d518801383740526946e70880d096cd8 | 2021-07-09T07:44:39.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:squad_v2",
"arxiv:2103.12693",
"transformers",
"qa",
"question",
"answering",
"SQuAD",
"metric",
"nlg",
"t5-small",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | ThomasNLG | null | ThomasNLG/t5-qa_squad2neg-en | 895 | null | transformers | 1,851 | ---
language: en
tags:
- qa
- question
- answering
- SQuAD
- metric
- nlg
- t5-small
license: mit
datasets:
- squad_v2
model-index:
- name: t5-qa_squad2neg-en
results:
- task:
name: Question Answering
type: extractive-qa
widget:
- text: "Who was Louis 14? </s> Louis 14 was a French King."
---
# t5-... |
OFA-Sys/OFA-base | 01ecca4855f318a69ed4821957ee23d499d28cc3 | 2022-07-25T11:52:55.000Z | [
"pytorch",
"ofa",
"transformers",
"license:apache-2.0"
] | null | false | OFA-Sys | null | OFA-Sys/OFA-base | 893 | 2 | transformers | 1,852 | ---
license: apache-2.0
---
# OFA-base
This is the **base** version of OFA pretrained model. OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image generation, visual grounding, image captioning, image classification, text generation, etc.) ... |
asahi417/relbert-roberta-large | 07c0062eb062303e48c0fe2544148af5fd6c76a4 | 2021-07-05T13:39:36.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | asahi417 | null | asahi417/relbert-roberta-large | 892 | null | transformers | 1,853 | # RelBERT
RoBERTa finetuned on the contrastive loss for lexical relation. Please take a look [the official repository](https://github.com/asahi417/relbert).
|
barissayil/bert-sentiment-analysis-sst | 969d390abf6b567c74ce1af74505b449734b4285 | 2021-06-11T09:47:14.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | barissayil | null | barissayil/bert-sentiment-analysis-sst | 891 | null | transformers | 1,854 | Entry not found |
MaryaAI/opus-mt-en-ar-finetuned-Math-13-10-en-to-ar | 133d8b58a906746884d78764aeb064bad1871dae | 2021-10-17T08:27:27.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:syssr_en_ar",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | MaryaAI | null | MaryaAI/opus-mt-en-ar-finetuned-Math-13-10-en-to-ar | 890 | null | transformers | 1,855 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- syssr_en_ar
model-index:
- name: opus-mt-en-ar-finetuned-Math-13-10-en-to-ar
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, ... |
textattack/distilbert-base-uncased-imdb | 5b0f46c2fc4b86bf21f0ec0409bed77ee142b332 | 2020-07-06T16:34:50.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/distilbert-base-uncased-imdb | 889 | null | transformers | 1,856 | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was... |
tanmoyio/wav2vec2-large-xlsr-bengali | 7447c623dca066b74d0299d0132dfca0674b6c8a | 2021-09-23T16:39:27.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"Bengali",
"dataset:OpenSLR",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:cc-by-sa-4.0",
"model-index"
] | automatic-speech-recognition | false | tanmoyio | null | tanmoyio/wav2vec2-large-xlsr-bengali | 888 | 2 | transformers | 1,857 | ---
language: Bengali
datasets:
- OpenSLR
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: cc-by-sa-4.0
model-index:
- name: XLSR Wav2Vec2 Bengali by Tanmoy Sarkar
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
datase... |
Helsinki-NLP/opus-mt-tc-big-en-es | 26b349a9177b11b92b2b56b47344ebe73e515817 | 2022-06-01T12:59:20.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"es",
"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-es | 887 | null | transformers | 1,858 | ---
language:
- en
- es
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-es
results:
- task:
name: Translation eng-spa
type: translation
args: eng-spa
dataset:
name: flores101-devtest
type: flores_101
args: eng spa devtest
metrics... |
shibing624/mengzi-t5-base-chinese-correction | 091e91da1215be5f40ae8d2273a7fe0b93b5354f | 2022-06-17T08:23:49.000Z | [
"pytorch",
"t5",
"text2text-generation",
"zh",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | shibing624 | null | shibing624/mengzi-t5-base-chinese-correction | 887 | 2 | transformers | 1,859 | ---
language:
- zh
tags:
- t5
- pytorch
- zh
license: "apache-2.0"
---
# T5 for Chinese Spelling Correction Model
中文拼写纠错模型
`shibing624/mengzi-t5-base-chinese-correction` evaluate SIGHAN2015 test data：
- Sentence Level: precision:0.8321, recall:0.6390, f1:0.7229
训练使用的数据集为下方提供的“SIGHAN+Wang271K中文纠错数据集”，在SIGHAN2015的测试... |
moha/arabert_c19 | eab96c316448fe535686332e35be64949f6ab7d7 | 2021-05-19T23:35:40.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2105.03143",
"arxiv:2004.04315",
"transformers",
"autotrain_compatible"
] | fill-mask | false | moha | null | moha/arabert_c19 | 885 | null | transformers | 1,860 | ---
language: ar
widget:
- text: "لكي نتجنب فيروس [MASK]"
---
# arabert_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
**ARABERT COVID-19** [Arxiv URL](https://arxiv.org/pdf/2105.03143.pdf) is a pretrained (fine-tuned) version of the AraBERT v2 model (https://huggingface.co/aubm... |
PlanTL-GOB-ES/roberta-base-biomedical-es | d672dee3226a354e0f9e5c11369fad6b6cb1f522 | 2022-04-08T14:10:27.000Z | [
"pytorch",
"roberta",
"fill-mask",
"es",
"arxiv:2109.03570",
"arxiv:2109.07765",
"transformers",
"biomedical",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | PlanTL-GOB-ES | null | PlanTL-GOB-ES/roberta-base-biomedical-es | 884 | 1 | transformers | 1,861 | ---
language:
- es
tags:
- biomedical
- spanish
license: apache-2.0
metrics:
- ppl
widget:
- text: "El único antecedente personal a reseñar era la <mask> arterial."
- text: "Las radiologías óseas de cuerpo entero no detectan alteraciones <mask>, ni alteraciones vertebrales."
- text: "En el <mask> toraco-abdómino-pél... |
twigs/bart-text2text-simplifier | f2071131fa949dda7dbc24734c13443efaa51da3 | 2022-07-18T21:21:06.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | twigs | null | twigs/bart-text2text-simplifier | 884 | null | transformers | 1,862 | Entry not found |
johngiorgi/declutr-sci-base | 34a174c9f34455c1f2705060742d46785ee2de02 | 2022-03-11T14:47:33.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"arxiv:2006.03659",
"transformers",
"autotrain_compatible"
] | fill-mask | false | johngiorgi | null | johngiorgi/declutr-sci-base | 882 | 3 | transformers | 1,863 | # DeCLUTR-sci-base
## Model description
This is the [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) model, with extended pretraining on over 2 million scientific papers from [S2ORC](https://github.com/allenai/s2orc/) using the self-supervised training strategy presented in ... |
Artem1/grammar_error_correcter_v1 | 376d3055b766ba13f6fc92152d6b5a25dd2f11e7 | 2022-07-14T18:54:45.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Artem1 | null | Artem1/grammar_error_correcter_v1 | 882 | null | transformers | 1,864 | Entry not found |
hfl/chinese-electra-base-discriminator | 44c5a47c42df39b11e9841ed602b2d49cdddd1af | 2021-03-03T01:40:07.000Z | [
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0"
] | null | false | hfl | null | hfl/chinese-electra-base-discriminator | 879 | 2 | transformers | 1,865 | ---
language:
- zh
license: "apache-2.0"
---
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size ... |
monologg/koelectra-base-v3-hate-speech | 8938df1530df593b9fce6926d1ff963ad07d23a3 | 2020-12-31T12:56:18.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | monologg | null | monologg/koelectra-base-v3-hate-speech | 876 | 2 | transformers | 1,866 | Entry not found |
fnlp/cpt-large | d3df73c7677993da4e871d9fcb1239469e52127c | 2022-07-18T08:01:01.000Z | [
"pytorch",
"bart",
"feature-extraction",
"zh",
"arxiv:2109.05729",
"transformers",
"fill-mask",
"text2text-generation",
"text-classification",
"Summarization",
"Chinese",
"CPT",
"BART",
"BERT",
"seq2seq"
] | text-classification | false | fnlp | null | fnlp/cpt-large | 875 | 7 | transformers | 1,867 | ---
tags:
- fill-mask
- text2text-generation
- fill-mask
- text-classification
- Summarization
- Chinese
- CPT
- BART
- BERT
- seq2seq
language: zh
---
# Chinese CPT-Large
## Model description
This is an implementation of CPT-Large. To use CPT, please import the file `modeling_cpt.py` (**Download** [Here](https://g... |
leo123/BERT-Preguntas-Respuestas-Posgrados | b4e6cabe7b6b1abd9374b01283b24592c749a068 | 2022-07-14T23:20:25.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | leo123 | null | leo123/BERT-Preguntas-Respuestas-Posgrados | 875 | null | transformers | 1,868 | ---
license: apache-2.0
---
|
anas-awadalla/bert-tiny-finetuned-squad | a40bd3413dd077c303653a88626b66b73de0ee04 | 2022-05-21T08:11:40.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/bert-tiny-finetuned-squad | 874 | null | transformers | 1,869 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-tiny-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
... |
shibing624/code-autocomplete-distilgpt2-python | 4a0986fce0baf2b583080207b8539d3fa62002d7 | 2022-02-15T07:18:50.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"code",
"autocomplete",
"license:apache-2.0"
] | text-generation | false | shibing624 | null | shibing624/code-autocomplete-distilgpt2-python | 874 | 7 | transformers | 1,870 | ---
language:
- en
tags:
- code
- autocomplete
- pytorch
- en
license: "apache-2.0"
---
# GPT2 for Code AutoComplete Model
code-autocomplete, a code completion plugin for Python.
**code-autocomplete** can automatically complete the code of lines and blocks with GPT2.
## Usage
Open source repo：[co... |
squeezebert/squeezebert-mnli-headless | d90683f6cb548ca6019ce3366d03f8652d836b7e | 2020-12-11T22:02:10.000Z | [
"pytorch",
"squeezebert",
"arxiv:2006.11316",
"arxiv:1904.00962",
"transformers"
] | null | false | squeezebert | null | squeezebert/squeezebert-mnli-headless | 874 | null | transformers | 1,871 | language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, `squeezebert-mnli-headless`, has been pretrained for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective and finetuned on the [Multi-Genre Natural Language ... |
akhooli/gpt2-small-arabic-poetry | 7c384f4f774a83aae4c66251050528c4b33b36d3 | 2021-08-07T08:06:39.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"ar",
"dataset:Arabic poetry from several eras",
"transformers"
] | text-generation | false | akhooli | null | akhooli/gpt2-small-arabic-poetry | 873 | 3 | transformers | 1,872 | ---
language: "ar"
tags:
- text-generation
datasets:
- Arabic poetry from several eras
---
# GPT2-Small-Arabic-Poetry
## Model description
Fine-tuned model of Arabic poetry dataset based on gpt2-small-arabic.
## Intended uses & limitations
#### How to use
An example is provided in this [colab notebook](https://co... |
nboost/pt-bert-large-msmarco | 654bca99ecec6faf688b274cb9c99333e9251c3f | 2021-05-20T01:25:29.000Z | [
"pytorch",
"jax",
"onnx",
"bert",
"transformers"
] | null | false | nboost | null | nboost/pt-bert-large-msmarco | 873 | 1 | transformers | 1,873 | Entry not found |
MaryaAI/opus-mt-ar-en-finetuned-ar-to-en | bc61a529dce1ad153ed501e41997e97d44b24262 | 2021-09-07T07:26:24.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:opus_wikipedia",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | MaryaAI | null | MaryaAI/opus-mt-ar-en-finetuned-ar-to-en | 872 | null | transformers | 1,874 | ---
tags:
- generated_from_trainer
datasets:
- opus_wikipedia
model-index:
- name: opus-mt-ar-en-finetuned-ar-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_wikipedia
type: opus_wikipedia
args: ar-en
---
<!-- This... |
microsoft/tapex-base | 968109c940c8b270a3eaec1532d596ba6c923b6a | 2022-05-17T08:25:49.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"arxiv:2107.07653",
"transformers",
"tapex",
"table-question-answering",
"license:mit",
"autotrain_compatible"
] | table-question-answering | false | microsoft | null | microsoft/tapex-base | 871 | 4 | transformers | 1,875 | ---
language: en
tags:
- tapex
- table-question-answering
license: mit
---
# TAPEX (base-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The ori... |
google/realm-cc-news-pretrained-scorer | a009929c7c945e823f1e0c4ee0ea3c737606a6de | 2022-01-06T06:23:03.000Z | [
"pytorch",
"realm",
"en",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/realm-cc-news-pretrained-scorer | 868 | null | transformers | 1,876 | ---
language: en
license: apache-2.0
---
# realm-cc-news-pretrained-scorer
## Model description
The REALM checkpoint pretrained with CC-News as target corpus and Wikipedia as knowledge corpus, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here]... |
stefan-it/bort | 3afaf7981024b80cfa229b2e271323f8c0aa1c6b | 2021-05-20T07:14:56.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | stefan-it | null | stefan-it/bort | 863 | null | transformers | 1,877 | Entry not found |
lgrobol/flaubert-minuscule | b2761368313c6c178ae6d3ac10332632e9af8170 | 2021-08-17T13:19:07.000Z | [
"pytorch",
"flaubert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | lgrobol | null | lgrobol/flaubert-minuscule | 861 | null | transformers | 1,878 | FlauBERT-minuscule
==================
A ridiculously small model for testing purposes. |
microsoft/resnet-101 | c2bf50f68263a35f102eb5c84ca91fc7352ceff3 | 2022-07-01T17:33:19.000Z | [
"pytorch",
"tf",
"resnet",
"image-classification",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/resnet-101 | 860 | 2 | transformers | 1,879 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
---
# ResNet-101 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team... |
SIC98/GPT2-python-code-generator | 525aec7829d6b9606e01f979d58cf4125fb906e5 | 2021-05-21T11:13:58.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | SIC98 | null | SIC98/GPT2-python-code-generator | 859 | 3 | transformers | 1,880 | Github
- https://github.com/SIC98/GPT2-python-code-generator |
Helsinki-NLP/opus-mt-is-en | 2334bf160857f518815cd97a0b7a3c5e81b7fa2e | 2021-09-09T22:12:09.000Z | [
"pytorch",
"marian",
"text2text-generation",
"is",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-is-en | 858 | 1 | transformers | 1,881 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-is-en
* source languages: is
* target languages: en
* OPUS readme: [is-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/is-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Lalita/marianmt-th-zh_cn | bab48cedd044cf1c5b9064943bf52a78728c9721 | 2021-06-29T14:06:47.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"torch==1.8.0",
"autotrain_compatible"
] | translation | false | Lalita | null | Lalita/marianmt-th-zh_cn | 858 | null | transformers | 1,882 | ---
tags:
- translation
- torch==1.8.0
widget:
- text: "Inference Unavailable"
---
### marianmt-th-zh_cn
* source languages: th
* target languages: zh_cn
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set scores: 15.53
## Training
Training scripts from [LalitaDeelert/NLP... |
savasy/bert-base-turkish-squad | 0309abfbf39abc803db200667d25a003affd5112 | 2021-05-20T04:56:01.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"tr",
"transformers",
"autotrain_compatible"
] | question-answering | false | savasy | null | savasy/bert-base-turkish-squad | 857 | 5 | transformers | 1,883 | ---
language: tr
---
# Turkish SQuAD Model : Question Answering
I fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD
* BERT-base: https://huggingface.co/dbmdz/bert-base-turkish-uncased
* TQuAD dataset: https://github.com/TQuad/turkish-nlp-qa-dataset
# Training Code
... |
sshleifer/distilbart-xsum-12-3 | 1d2bfbc16dcdd28720f9f1d37be764e5cc5c78c8 | 2021-06-14T07:57:16.000Z | [
"pytorch",
"jax",
"bart",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | sshleifer | null | sshleifer/distilbart-xsum-12-3 | 856 | 1 | transformers | 1,884 | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... |
bhadresh-savani/distilbert-base-uncased-sentiment-sst2 | b91676624bfec8bb96d31c4d0f1b13a491ebe65c | 2022-06-15T11:48:33.000Z | [
"pytorch",
"tf",
"jax",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"transformers",
"license:apache-2.0"
] | text-classification | false | bhadresh-savani | null | bhadresh-savani/distilbert-base-uncased-sentiment-sst2 | 854 | null | transformers | 1,885 | ---
language: en
license: apache-2.0
datasets:
- sst2
---
# distilbert-base-uncased-sentiment-sst2
This model will be able to identify positivity or negativity present in the sentence
## Dataset:
The Stanford Sentiment Treebank from GLUE
## Results:
```
***** eval metrics *****
epoch = 3.0
... |
tanlq/vit-base-patch16-224-in21k-finetuned-cifar10 | b180bcaf51fdf309391ae08a72494bf9fbf7d64a | 2022-04-04T08:20:16.000Z | [
"pytorch",
"vit",
"image-classification",
"dataset:cifar10",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | tanlq | null | tanlq/vit-base-patch16-224-in21k-finetuned-cifar10 | 854 | null | transformers | 1,886 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-cifar10
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar10
type: cifar10
args: plain_t... |
gorkemgoknar/gpt2-small-turkish | cecfcbf3dfbb3c9df280386790c0ac45d21ad9d9 | 2021-09-22T08:29:21.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"tr",
"dataset:wikipedia-turkish",
"transformers",
"turkish",
"license:apache-2.0"
] | text-generation | false | gorkemgoknar | null | gorkemgoknar/gpt2-small-turkish | 852 | 1 | transformers | 1,887 | ---
language:
- tr
thumbnail:
tags:
- gpt2
- turkish
license: apache-2.0
datasets:
- wikipedia-turkish
metrics:
- perplexity
- accuracy
widget:
- text: Bu yazıyı bir bilgisayar yazdı. Yazarken
context: ''
- text: İnternete kolay erişim sayesinde dünya daha da küçüldü. Bunun sonucunda
context: ''
---
# Turkish GP... |
uclanlp/visualbert-nlvr2 | 2cb80570d2326bbb1f3a954f967a1cd5bed949b6 | 2021-05-31T11:09:59.000Z | [
"pytorch",
"visual_bert",
"transformers"
] | null | false | uclanlp | null | uclanlp/visualbert-nlvr2 | 852 | null | transformers | 1,888 | Entry not found |
KBLab/sentence-bert-swedish-cased | e5e754ac75b8dddc1c15f52e11ef7d326792fd1e | 2022-07-28T14:18:47.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2004.09813",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | KBLab | null | KBLab/sentence-bert-swedish-cased | 851 | 3 | sentence-transformers | 1,889 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
widget:
- source_sentence: "Mannen åt mat."
sentences:
- "Han förtärde en närande och nyttig måltid."
- "Det var ett sunkigt hak med ganska gott käk."
- "Han inmundigade middagen t... |
YituTech/conv-bert-small | 9a11330184f20d78feb3fd45edd1e8dad23205e8 | 2021-02-24T11:26:46.000Z | [
"pytorch",
"tf",
"convbert",
"feature-extraction",
"transformers"
] | feature-extraction | false | YituTech | null | YituTech/conv-bert-small | 851 | 1 | transformers | 1,890 | Entry not found |
microsoft/markuplm-base | b907337efa18696ad8a213005d5db0946d5d2081 | 2022-01-11T12:32:38.000Z | [
"pytorch",
"markuplm",
"arxiv:2110.08518",
"transformers"
] | null | false | microsoft | null | microsoft/markuplm-base | 851 | 2 | transformers | 1,891 | # MarkupLM
**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extra... |
tuner007/pegasus_summarizer | a8980c11072794c107d4e8b7990c6a49f3da6a50 | 2022-07-28T06:38:07.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"transformers",
"seq2seq",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | tuner007 | null | tuner007/pegasus_summarizer | 848 | 7 | transformers | 1,892 | ---
language: en
license: apache-2.0
tags:
- pegasus
- seq2seq
- summarization
model-index:
- name: tuner007/pegasus_summarizer
results:
- task:
type: summarization
name: Summarization
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: train
metrics:
... |
vblagoje/retribert-base-uncased | 9241266f3afbc5b07435cfa8070871fc77ee3818 | 2021-11-11T07:23:38.000Z | [
"pytorch",
"retribert",
"feature-extraction",
"transformers"
] | feature-extraction | false | vblagoje | null | vblagoje/retribert-base-uncased | 847 | null | transformers | 1,893 | Entry not found |
ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli | 027e2b37d8b0c27965ee58d9da95cf994f1ee0f4 | 2020-10-17T01:54:45.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
] | text-classification | false | ynie | null | ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli | 847 | null | transformers | 1,894 | Entry not found |
henryk/bert-base-multilingual-cased-finetuned-polish-squad2 | f4d5b523b23dbe9c3bc4741755832ef1451fe5ce | 2021-05-19T19:05:33.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"pl",
"transformers",
"autotrain_compatible"
] | question-answering | false | henryk | null | henryk/bert-base-multilingual-cased-finetuned-polish-squad2 | 846 | 1 | transformers | 1,895 | ---
language: pl
---
# Multilingual + Polish SQuAD2.0
This model is the multilingual model provided by the Google research team with a fine-tuned polish Q&A downstream task.
## Details of the language model
Language model ([**bert-base-multilingual-cased**](https://github.com/google-research/bert/blob/master/multil... |
LIAMF-USP/roberta-large-finetuned-race | 671db4772791326255cbf6c4f33eff5d06db4e43 | 2021-05-20T12:08:36.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"multiple-choice",
"english",
"dataset:race",
"transformers",
"license:mit"
] | multiple-choice | false | LIAMF-USP | null | LIAMF-USP/roberta-large-finetuned-race | 845 | 3 | transformers | 1,896 | ---
language: "english"
license: "mit"
datasets:
- race
metrics:
- accuracy
---
# Roberta Large Fine Tuned on RACE
## Model description
This model is a fine-tuned model of Roberta-large applied on RACE
#### How to use
```python
import datasets
from transformers import RobertaTokenizer
from transformers import Ro... |
microsoft/xprophetnet-large-wiki100-cased | 1acad1643ddd54a44df6a1b797ada8373685d90e | 2020-12-11T21:51:18.000Z | [
"pytorch",
"xlm-prophetnet",
"text2text-generation",
"multilingual",
"arxiv:2001.04063",
"arxiv:2004.01401",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | microsoft | null | microsoft/xprophetnet-large-wiki100-cased | 845 | null | transformers | 1,897 | ---
language: multilingual
---
## xprophetnet-large-wiki100-cased
Cross-lingual version [ProphetNet](https://arxiv.org/abs/2001.04063), pretrained on [wiki100 xGLUE dataset](https://arxiv.org/abs/2004.01401).
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervise... |
kuzgunlar/electra-turkish-qa | 586ab1bc12af16bf396360db8a90cdccf514e4ec | 2020-07-31T09:15:54.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | kuzgunlar | null | kuzgunlar/electra-turkish-qa | 843 | 1 | transformers | 1,898 | Entry not found |
IDEA-CCNL/Erlangshen-Roberta-110M-NLI | ea0a42559acf675d8931336951e893fc5d466268 | 2022-05-12T09:48:51.000Z | [
"pytorch",
"bert",
"text-classification",
"zh",
"transformers",
"NLU",
"NLI",
"license:apache-2.0"
] | text-classification | false | IDEA-CCNL | null | IDEA-CCNL/Erlangshen-Roberta-110M-NLI | 843 | null | transformers | 1,899 | ---
language:
- zh
license: apache-2.0
tags:
- bert
- NLU
- NLI
inference: true
widget:
- text: "今天心情不好[SEP]今天很开心"
---
# Erlangshen-Roberta-110M-NLI, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
We collect 4 NLI（Natural Language Inference） datasets in the Chinese ... |
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