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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
rasta/distilbert-base-uncased-finetuned-fashion | 9b6f70e275f1a0b7a4cf5569e1f53fe9a5cd1738 | 2022-05-09T08:10:22.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | rasta | null | rasta/distilbert-base-uncased-finetuned-fashion | 53 | 2 | transformers | 5,900 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-fashion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, t... |
anas-awadalla/splinter-large-few-shot-k-1024-finetuned-squad-seed-2 | 23f58c42ae6e81cc1f4a7560ae3c3e57dfb482a5 | 2022-05-14T23:31:40.000Z | [
"pytorch",
"splinter",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/splinter-large-few-shot-k-1024-finetuned-squad-seed-2 | 53 | null | transformers | 5,901 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: splinter-large-few-shot-k-1024-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete ... |
CEBaB/bert-base-uncased.CEBaB.absa.exclusive.seed_42 | b31e1ab0039a987ee80fda6f256ee1c88fe34223 | 2022-05-17T18:43:42.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | CEBaB | null | CEBaB/bert-base-uncased.CEBaB.absa.exclusive.seed_42 | 53 | null | transformers | 5,902 | Entry not found |
Cirilaron/DialoGPT-medium-jetstreamsam | 02fc2375c982ea3de186a4883b034cfa5b6d3c68 | 2022-06-09T12:37:07.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Cirilaron | null | Cirilaron/DialoGPT-medium-jetstreamsam | 53 | null | transformers | 5,903 | ---
tags:
- conversational
---
#Samuel Rodrigues from Metal Gear Rising DialoGPT Model |
Kittipong/wav2vec2-th-vocal-domain | 4f5fec019d8b0b9f5be8e0da0ff3c2acb59d6fb1 | 2022-06-12T12:34:43.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"license:cc-by-sa-4.0"
] | automatic-speech-recognition | false | Kittipong | null | Kittipong/wav2vec2-th-vocal-domain | 53 | null | transformers | 5,904 | ---
license: cc-by-sa-4.0
---
|
cwkeam/m-ctc-t-large-sequence-lid | 0391241ef74c94275a8d8cbfb1b7fc3f0ca66ea0 | 2022-06-29T04:31:03.000Z | [
"pytorch",
"mctct",
"text-classification",
"en",
"dataset:librispeech_asr",
"dataset:common_voice",
"arxiv:2111.00161",
"transformers",
"speech",
"license:apache-2.0"
] | text-classification | false | cwkeam | null | cwkeam/m-ctc-t-large-sequence-lid | 53 | null | transformers | 5,905 | ---
language: en
datasets:
- librispeech_asr
- common_voice
tags:
- speech
license: apache-2.0
---
# M-CTC-T
Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. I... |
shash2409/bert-finetuned-squad | 4ea4437bc266e648ab369ad7552dcae25d90fe47 | 2022-07-03T19:32:27.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | shash2409 | null | shash2409/bert-finetuned-squad | 53 | null | transformers | 5,906 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->... |
semy/finetuning-sentiment-model-sst | 83734031b99d78b425fa3adaa7c6779d7b958ac2 | 2022-07-01T12:47:28.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | semy | null | semy/finetuning-sentiment-model-sst | 53 | null | transformers | 5,907 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: finetuning-sentiment-model-sst
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. -->
# fine... |
zhifei/autotrain-chinese-title-summarization-1-1084539138 | 0bd24fbcde53d2e03c0fbeb8187ad822af0b1970 | 2022-07-04T08:49:18.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"unk",
"dataset:zhifei/autotrain-data-chinese-title-summarization-1",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | zhifei | null | zhifei/autotrain-chinese-title-summarization-1-1084539138 | 53 | null | transformers | 5,908 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- zhifei/autotrain-data-chinese-title-summarization-1
co2_eq_emissions: 0.004484038360707097
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1084539138
- CO2 Emissions (in grams): 0.004484038360707097
## V... |
okho0653/Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model | 91d77d8debe3f8769c755eeedc0f42858fdf297d | 2022-07-08T03:54:48.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | okho0653 | null | okho0653/Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model | 53 | null | transformers | 5,909 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model
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... |
Lvxue/finetuned-mbart-large-10epoch | 07b2e2e5e1629c407746ede1f21243f6dd9ae3f1 | 2022-07-11T03:11:38.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"en",
"ro",
"dataset:wmt16",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Lvxue | null | Lvxue/finetuned-mbart-large-10epoch | 53 | null | transformers | 5,910 | ---
language:
- en
- ro
tags:
- generated_from_trainer
datasets:
- wmt16
model-index:
- name: finetuned-mbart-large-10epoch
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 com... |
CogComp/roberta-temporal-predictor | aa4d28dcd3baacce849e269b4dbeeef35e52f8a2 | 2022-03-22T20:15:03.000Z | [
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.00436",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | CogComp | null | CogComp/roberta-temporal-predictor | 52 | null | transformers | 5,911 | ---
license: mit
widget:
- text: "The man turned on the faucet <mask> water flows out."
- text: "The woman received her pension <mask> she retired."
---
# roberta-temporal-predictor
A RoBERTa-base model that is fine-tuned on the [The New York Times Annotated Corpus](https://catalog.ldc.upenn.edu/LDC2008T19)
to ... |
Hate-speech-CNERG/dehatebert-mono-arabic | e592a5ee3b913ec33286ee90fb27c7f7f1a8b996 | 2021-09-25T13:54:53.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"ar",
"arxiv:2004.06465",
"transformers",
"license:apache-2.0"
] | text-classification | false | Hate-speech-CNERG | null | Hate-speech-CNERG/dehatebert-mono-arabic | 52 | null | transformers | 5,912 | ---
language: ar
license: apache-2.0
---
This model is used detecting **hatespeech** in **Arabic language**. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model.
The model is trained with different learning rates... |
Helsinki-NLP/opus-mt-aed-es | a56c16908eafa534660838102b535b32f40581a3 | 2021-09-09T21:25:50.000Z | [
"pytorch",
"marian",
"text2text-generation",
"aed",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-aed-es | 52 | null | transformers | 5,913 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-aed-es
* source languages: aed
* target languages: es
* OPUS readme: [aed-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/aed-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* d... |
Helsinki-NLP/opus-mt-de-fi | bbd50eeefdc1e26d75f6a806495192b55878c04a | 2021-09-09T21:31:05.000Z | [
"pytorch",
"marian",
"text2text-generation",
"de",
"fi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-de-fi | 52 | null | transformers | 5,914 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-de-fi
* source languages: de
* target languages: fi
* OPUS readme: [de-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-fi-sv | 4f951b1b01773808d66e0868a3e53cf964f73362 | 2021-09-09T21:51:05.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"sv",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-sv | 52 | null | transformers | 5,915 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fi-sv
* source languages: fi
* target languages: sv
* OPUS readme: [fi-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-sv/README.md)
* dataset: opus+bt
* model: transformer-align
* pre-processing: normalization + SentencePiece
* do... |
RJ3vans/SignTagger | 177222c11b652437211b35052b8e1298a6dcc691 | 2021-08-13T09:00:50.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | RJ3vans | null | RJ3vans/SignTagger | 52 | null | transformers | 5,916 | This model is used to tag the tokens in an input sequence with information about the different signs of syntactic complexity that they contain. For more details, please see Chapters 2 and 3 of my thesis (http://rgcl.wlv.ac.uk/~richard/Evans2020_SentenceSimplificationForTextProcessing.pdf).
It was derived using code wr... |
SEBIS/code_trans_t5_base_code_documentation_generation_javascript_multitask_finetune | 7d1881514432cb3860195e0b8e466809cddbb1bd | 2021-06-23T04:31:36.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_base_code_documentation_generation_javascript_multitask_finetune | 52 | null | transformers | 5,917 | ---
tags:
- summarization
widget:
- text: "function isStandardBrowserEnv ( ) { if ( typeof navigator !== 'undefined' && ( navigator . product === 'ReactNative' || navigator . product === 'NativeScript' || navigator . product === 'NS' ) ) { return false ; } return ( typeof window !== 'undefined' && typeof document !== '... |
alireza7/ARMAN-MSR-persian-base-PN-summary | 3312c43fc7514afa6a40b5c558a7e662761f8810 | 2021-09-29T19:14:47.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | alireza7 | null | alireza7/ARMAN-MSR-persian-base-PN-summary | 52 | null | transformers | 5,918 | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). |
asafaya/albert-large-arabic | bb5cad09b4480a6403a52ec2d83386dc98471d1e | 2022-02-11T13:52:18.000Z | [
"pytorch",
"tf",
"albert",
"fill-mask",
"ar",
"dataset:oscar",
"dataset:wikipedia",
"transformers",
"masked-lm",
"autotrain_compatible"
] | fill-mask | false | asafaya | null | asafaya/albert-large-arabic | 52 | 1 | transformers | 5,919 | ---
language: ar
datasets:
- oscar
- wikipedia
tags:
- ar
- masked-lm
---
# Arabic-ALBERT Large
Arabic edition of ALBERT Large pretrained language model
_If you use any of these models in your work, please cite this work as:_
```
@software{ali_safaya_2020_4718724,
author = {Ali Safaya},
title = {A... |
dbmdz/electra-base-turkish-mc4-uncased-generator | 2352dd9268eef698305ac0dc1f22eb59e73f55d8 | 2021-09-23T10:43:54.000Z | [
"pytorch",
"tf",
"electra",
"fill-mask",
"tr",
"dataset:allenai/c4",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/electra-base-turkish-mc4-uncased-generator | 52 | null | transformers | 5,920 | ---
language: tr
license: mit
datasets:
- allenai/c4
---
# 🇹🇷 Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[![DOI](https://zenodo.org/badge/23781745... |
dennlinger/bert-wiki-paragraphs | c8d6e5285fe3ea801834ef1f385a5518a4c91281 | 2021-09-30T20:13:44.000Z | [
"pytorch",
"bert",
"text-classification",
"arxiv:2012.03619",
"arxiv:1803.09337",
"transformers"
] | text-classification | false | dennlinger | null | dennlinger/bert-wiki-paragraphs | 52 | null | transformers | 5,921 | # BERT-Wiki-Paragraphs
Authors: Satya Almasian\*, Dennis Aumiller\*, Lucienne-Sophie Marmé, Michael Gertz
Contact us at `<lastname>@informatik.uni-heidelberg.de`
Details for the training method can be found in our work [Structural Text Segmentation of Legal Documents](https://arxiv.org/abs/2012.03619).
The trainin... |
diarsabri/LaDPR-query-encoder | 600d1091763cd2418ba805d72f55d4bed1c6d6b4 | 2021-05-05T21:00:08.000Z | [
"pytorch",
"dpr",
"feature-extraction",
"transformers"
] | feature-extraction | false | diarsabri | null | diarsabri/LaDPR-query-encoder | 52 | null | transformers | 5,922 | Language Model 1
For Language agnostic Dense Passage Retrieval |
flax-community/indonesian-roberta-base | 6cedc13543d3e59e980c435d28a2346d9f2bad31 | 2021-07-10T08:19:46.000Z | [
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"id",
"dataset:oscar",
"arxiv:1907.11692",
"transformers",
"indonesian-roberta-base",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | flax-community | null | flax-community/indonesian-roberta-base | 52 | 5 | transformers | 5,923 | ---
language: id
tags:
- indonesian-roberta-base
license: mit
datasets:
- oscar
widget:
- text: "Budi telat ke sekolah karena ia <mask>."
---
## Indonesian RoBERTa Base
Indonesian RoBERTa Base is a masked language model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. It was trained on the [OSCAR... |
google/t5-3b-ssm | de842a05eabdc2688bd66a84b83227e933ed8e5e | 2020-12-07T19:49:00.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:wikipedia",
"arxiv:2002.08909",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-3b-ssm | 52 | 1 | transformers | 5,924 | ---
language: en
datasets:
- c4
- wikipedia
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**.
The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4) and subsequently a... |
gsarti/it5-small | 5b4b3e313cbc2b00a135a55daa3fe826ac077b25 | 2022-03-09T11:56:34.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"it",
"dataset:gsarti/clean_mc4_it",
"arxiv:2203.03759",
"transformers",
"seq2seq",
"lm-head",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | gsarti | null | gsarti/it5-small | 52 | 1 | transformers | 5,925 | ---
language:
- it
datasets:
- gsarti/clean_mc4_it
tags:
- seq2seq
- lm-head
license: apache-2.0
inference: false
thumbnail: https://gsarti.com/publication/it5/featured.png
---
# Italian T5 Small 🇮🇹
The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-s... |
huggingtweets/tilda_tweets | 2d85aa279ff77324cb7172a82e7eae68f0ffe15b | 2021-05-23T02:19:01.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/tilda_tweets | 52 | null | transformers | 5,926 | ---
language: en
thumbnail: https://www.huggingtweets.com/tilda_tweets/1614119818814/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/124709567988... |
nbroad/deberta-v3-xsmall-squad2 | 4b1d92d2daed14c72a00446afe3e436122b96d4f | 2022-07-22T14:03:41.000Z | [
"pytorch",
"tensorboard",
"deberta-v2",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | nbroad | null | nbroad/deberta-v3-xsmall-squad2 | 52 | null | transformers | 5,927 | ---
license: cc-by-4.0
widget:
- context: DeBERTa improves the BERT and RoBERTa models using disentangled attention
and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa
on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further
improved the efficiency of DeB... |
nlp4good/psych-search | 894dbb27a8ab4f284b9659ceb6578c6f431d35dc | 2021-09-22T09:29:47.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"en",
"dataset:PubMed",
"transformers",
"mental-health",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | nlp4good | null | nlp4good/psych-search | 52 | null | transformers | 5,928 | ---
language:
- en
tags:
- mental-health
license: apache-2.0
datasets:
- PubMed
---
# Psych-Search
Psych-Search is a work in progress to bring cutting edge NLP to mental health practitioners. The model detailed here serves as a foundation for traditional classification models as well as NLU models for a Psych-Search ap... |
shtoshni/spanbert_coreference_large | b93b0b352fd0153550f18878505b4ad284b97e10 | 2021-03-28T14:23:36.000Z | [
"pytorch",
"transformers"
] | null | false | shtoshni | null | shtoshni/spanbert_coreference_large | 52 | null | transformers | 5,929 | Entry not found |
uf-aice-lab/math-roberta | e535977f65f11632a830a8af74e9cad598c25944 | 2022-02-11T20:21:02.000Z | [
"pytorch",
"roberta",
"text-generation",
"en",
"transformers",
"nlp",
"math learning",
"education",
"license:mit"
] | text-generation | false | uf-aice-lab | null | uf-aice-lab/math-roberta | 52 | null | transformers | 5,930 | ---
language:
- en
tags:
- nlp
- math learning
- education
license: mit
---
# Math-RoBerta for NLP tasks in math learning environments
This model is fine-tuned RoBERTa-large trained with 8 Nvidia RTX 1080Ti GPUs using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (https://www.mathnati... |
wpnbos/xlm-roberta-base-conll2002-dutch | 4bb41e4849d873d8fcb49f249342492eaf1f0c31 | 2022-04-20T19:28:55.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"nl",
"dataset:conll2002",
"arxiv:1911.02116",
"transformers",
"Named Entity Recognition",
"autotrain_compatible"
] | token-classification | false | wpnbos | null | wpnbos/xlm-roberta-base-conll2002-dutch | 52 | null | transformers | 5,931 | ---
language:
- nl
tags:
- Named Entity Recognition
- xlm-roberta
datasets:
- conll2002
metrics:
- f1: 90.57
---
# XLM-RoBERTa base ConLL-2002 Dutch
XLM-Roberta base model finetuned on ConLL-2002 Dutch train set, which is a Named Entity Recognition dataset containing the following classes: PER, LOC, ORG and MISC.... |
IIC/dpr-spanish-question_encoder-squades-base | 87da269c24ef47fa7dc2bb19ebedb408d9d7aeb1 | 2022-04-02T15:08:08.000Z | [
"pytorch",
"bert",
"fill-mask",
"es",
"dataset:squad_es",
"arxiv:2004.04906",
"transformers",
"sentence similarity",
"passage retrieval",
"model-index",
"autotrain_compatible"
] | fill-mask | false | IIC | null | IIC/dpr-spanish-question_encoder-squades-base | 52 | 3 | transformers | 5,932 | ---
language:
- es
tags:
- sentence similarity # Example: audio
- passage retrieval # Example: automatic-speech-recognition
datasets:
- squad_es
metrics:
- eval_loss: 0.08608942725107592
- eval_accuracy: 0.9925325215819639
- eval_f1: 0.8805402320715237
- average_rank: 0.27430093209054596
model-index:
- name: dpr-sp... |
IDEA-CCNL/Bigan-Transformer-XL-denoise-1.1B | 0484d5e9d159d112a543c1990231762f8a700d2d | 2022-04-13T07:25:42.000Z | [
"pytorch",
"zh",
"transformers",
"license:apache-2.0"
] | null | false | IDEA-CCNL | null | IDEA-CCNL/Bigan-Transformer-XL-denoise-1.1B | 52 | null | transformers | 5,933 | ---
language:
- zh
license: apache-2.0
---
# Abstract
This is a Chinese transformer-xl model trained on [Wudao dataset](https://resource.wudaoai.cn/home?ind&name=WuDaoCorpora%202.0&id=1394901288847716352)
and finetuned on a denoise dataset constructed by our team. The denoise task is to reconstruct a fluent and c... |
Helsinki-NLP/opus-mt-tc-big-en-ro | 5d0c15b53f631dc74430fe8153c8ed8d02cc7290 | 2022-06-01T13:01:57.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"ro",
"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-ro | 52 | null | transformers | 5,934 | ---
language:
- en
- ro
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-ro
results:
- task:
name: Translation eng-ron
type: translation
args: eng-ron
dataset:
name: flores101-devtest
type: flores_101
args: eng ron devtest
metrics... |
allenai/aspire-contextualsentence-multim-compsci | 60ee0b096626723196fa620f3b10f1ad11ed1214 | 2022-04-24T20:05:57.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:2111.08366",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | allenai | null | allenai/aspire-contextualsentence-multim-compsci | 52 | null | transformers | 5,935 | ---
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.org/abs/2111.... |
mismayil/kogito-rc-bert | 91f506c45ea47507608565da4690526a41ff38c2 | 2022-04-28T20:25:32.000Z | [
"pytorch",
"transformers",
"license:mit"
] | null | false | mismayil | null | mismayil/kogito-rc-bert | 52 | null | transformers | 5,936 | ---
license: mit
---
|
north/t5_small_NCC | 8d6f518677ac227731ebf64a180274f3071479d7 | 2022-06-01T19:40:24.000Z | [
"pytorch",
"tf",
"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_small_NCC | 52 | null | transformers | 5,937 | ---
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> ... |
ENM/sciBERT-case-finetuned-breastcancer | 8302412461c9bd71a9ed7b3762e2a208cb74f66b | 2022-06-06T23:26:49.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-generation",
"transformers",
"generated_from_trainer",
"model-index"
] | text-generation | false | ENM | null | ENM/sciBERT-case-finetuned-breastcancer | 52 | null | transformers | 5,938 | ---
tags:
- generated_from_trainer
model-index:
- name: sciBERT-case-finetuned-breastcancer
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. -->
# sciBERT-case-finetu... |
azaninello/GPT2-icc | a3656f5725b73310b9cee801b5cd28a4d6687b32 | 2022-06-27T12:48:19.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | azaninello | null | azaninello/GPT2-icc | 52 | null | transformers | 5,939 | Entry not found |
ddegenaro/reu_midsummer_test | 567fe9ee20a6bee00b46a4180b571acf29db96b0 | 2022-07-07T22:25:48.000Z | [
"pytorch",
"bert",
"transformers",
"license:mit"
] | null | false | ddegenaro | null | ddegenaro/reu_midsummer_test | 52 | null | transformers | 5,940 | ---
license: mit
---
This is a test of my methodology. |
pstroe/roberta-base-latin-cased2 | 61489ed06482c9ebe28eec49577c391bd326f0ed | 2022-07-29T17:07:03.000Z | [
"pytorch",
"roberta",
"fill-mask",
"arxiv:2009.10053",
"transformers",
"autotrain_compatible"
] | fill-mask | false | pstroe | null | pstroe/roberta-base-latin-cased2 | 52 | null | transformers | 5,941 | ## RoBERTa Latin model, version 2 --> model card not finished yet
This is a Latin RoBERTa-based LM model, version 2.
The intention of the Transformer-based LM is twofold: on the one hand, it will be used for the evaluation of HTR results; on the other, it should be used as a decoder for the TrOCR architecture.
The t... |
naver-clova-ix/donut-base-finetuned-rvlcdip | 1d40bcc9c7314654e955c708c56513b9dd1f1f0e | 2022-07-19T13:57:17.000Z | [
"pytorch",
"donut",
"transformers",
"license:mit"
] | null | false | naver-clova-ix | null | naver-clova-ix/donut-base-finetuned-rvlcdip | 52 | null | transformers | 5,942 | ---
license: mit
---
|
adamnik/electra-entailment-detection | a853ffe98acd43d13c43407898af25c1402431e5 | 2022-07-20T01:37:58.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers",
"license:mit"
] | text-classification | false | adamnik | null | adamnik/electra-entailment-detection | 52 | null | transformers | 5,943 | ---
license: mit
---
|
crumb/gpt-joke | efb7d77d9f3d7899311919ea70d32e0021a64e29 | 2022-07-26T03:38:02.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | crumb | null | crumb/gpt-joke | 52 | null | transformers | 5,944 | Entry not found |
obl1t/DialoGPT-medium-Jotaro | 0145859b0309ea95d8cf9a58764d149c59b20b6b | 2022-07-27T00:36:02.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | obl1t | null | obl1t/DialoGPT-medium-Jotaro | 52 | null | transformers | 5,945 | ---
tags:
- conversational
---
#Jotaro DialoGPT Model |
valurank/xsum_headline_generator | 735a8376630a660fb388031249a48d00f8956897 | 2022-07-27T11:19:28.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | valurank | null | valurank/xsum_headline_generator | 52 | 1 | transformers | 5,946 | ---
tags:
- generated_from_trainer
model-index:
- name: final_xsum_headline_generator
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. -->
# final_xsum_headline_gener... |
Abirate/gpt_3_finetuned_multi_x_science | 82ac4e2d59cb09b91bc63c0f3e2f4b242533a3b8 | 2022-01-15T06:16:57.000Z | [
"pytorch"
] | null | false | Abirate | null | Abirate/gpt_3_finetuned_multi_x_science | 51 | null | null | 5,947 | ---
- Text Generation
- PyTorch
- Transformers
- gpt_neo
- text generation
---
## Petrained Model Description: Open Source Version of GPT-3
Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text.
It is the third-generation language prediction... |
DTAI-KULeuven/robbertje-1-gb-non-shuffled | bf7851ebc117a44908a9e4499f03d7b671d888c9 | 2022-06-29T12:44:41.000Z | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_c... | fill-mask | false | DTAI-KULeuven | null | DTAI-KULeuven/robbertje-1-gb-non-shuffled | 51 | null | transformers | 5,948 | ---
language: "nl"
thumbnail: "https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png"
tags:
- Dutch
- Flemish
- RoBERTa
- RobBERT
- RobBERTje
license: mit
datasets:
- oscar
- oscar (NL)
- dbrd
- lassy-ud
- europarl-mono
- conll2002
widget:
- text: "Hallo, ik ben RobBERTje, een gedistilleer... |
GKLMIP/roberta-hindi-romanized | cc3e71e4199aae4f1dd10236ee7bc1aa428a9e4b | 2021-10-13T13:46:13.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | GKLMIP | null | GKLMIP/roberta-hindi-romanized | 51 | null | transformers | 5,949 | If you use our model, please consider citing our paper:
```
@InProceedings{,
author="Huang, Xixuan
and Lin, Nankai
and Li, Kexin
and Wang, Lianxi
and Gan SuiFu",
title="HinPLMs: Pre-trained Language Models for Hindi",
booktitle="The International Conference on Asian Language Processing",
year="2021",
publisher="IEEE Xp... |
Helsinki-NLP/opus-mt-en-fiu | 7b3d4f15ad924bee8e4b2964160751e61ccdc7c7 | 2021-01-18T08:07:39.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"se",
"fi",
"hu",
"et",
"fiu",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-fiu | 51 | null | transformers | 5,950 | ---
language:
- en
- se
- fi
- hu
- et
- fiu
tags:
- translation
license: apache-2.0
---
### eng-fiu
* source group: English
* target group: Finno-Ugrian languages
* OPUS readme: [eng-fiu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-fiu/README.md)
* model: transformer
* source lan... |
Helsinki-NLP/opus-mt-fi-ZH | b9d39ad47c1d2f01b38a64916bbcb867eb1d3e53 | 2021-09-09T21:46:27.000Z | [
"pytorch",
"marian",
"text2text-generation",
"fi",
"zh",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fi-ZH | 51 | null | transformers | 5,951 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fi-ZH
* source languages: fi
* target languages: cmn,cn,yue,ze_zh,zh_cn,zh_CN,zh_HK,zh_tw,zh_TW,zh_yue,zhs,zht,zh
* OPUS readme: [fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/... |
Helsinki-NLP/opus-mt-ru-sv | 05e8dfc573d362eb318386dbc2966b55aad490cc | 2020-08-21T14:42:49.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ru",
"sv",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ru-sv | 51 | null | transformers | 5,952 | ---
language:
- ru
- sv
tags:
- translation
license: apache-2.0
---
### rus-swe
* source group: Russian
* target group: Swedish
* OPUS readme: [rus-swe](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-swe/README.md)
* model: transformer-align
* source language(s): rus
* target languag... |
aware-ai/roberta-large-squad-classification | e09bb6e3d8447674b66912bff7f9cf1b8093a21b | 2021-05-20T12:35:01.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"dataset:squad_v2",
"transformers"
] | text-classification | false | aware-ai | null | aware-ai/roberta-large-squad-classification | 51 | null | transformers | 5,953 | ---
datasets:
- squad_v2
---
# Roberta-LARGE finetuned on SQuADv2
This is roberta-large model finetuned on SQuADv2 dataset for question answering answerability classification
## Model details
This model is simply an Sequenceclassification model with two inputs (context and question) in a list.
The result is either [... |
abhinavkulkarni/bigbird-roberta-base-finetuned-squad | f20ddf6920760090f34e803e9ca4570bd4f1ecdc | 2022-02-07T06:32:01.000Z | [
"pytorch",
"tensorboard",
"big_bird",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | abhinavkulkarni | null | abhinavkulkarni/bigbird-roberta-base-finetuned-squad | 51 | null | transformers | 5,954 | Entry not found |
anjulRajendraSharma/wav2vec2-indian-english | 30cce397b8be2d27250f9c0fe8c5748b48a732a6 | 2022-06-10T06:14:49.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | anjulRajendraSharma | null | anjulRajendraSharma/wav2vec2-indian-english | 51 | null | transformers | 5,955 | Entry not found |
huggingtweets/borisdayma | bef6d3d54322e05b3de16b332a4c2a9def4da13b | 2022-06-27T21:46:28.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/borisdayma | 51 | null | transformers | 5,956 | ---
language: en
thumbnail: http://www.huggingtweets.com/borisdayma/1656366383066/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; widt... |
hyunwoo3235/kogpt-neo-125M | ba315830d07baaf383d63314b321968c62cc3543 | 2021-08-06T14:45:23.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | hyunwoo3235 | null | hyunwoo3235/kogpt-neo-125M | 51 | null | transformers | 5,957 | Entry not found |
johnpaulbin/meme-titles | 10f1e9387207ef5e84053bdc642f030f9c51ef1f | 2021-12-08T02:57:43.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | johnpaulbin | null | johnpaulbin/meme-titles | 51 | null | transformers | 5,958 | Trained on ~400 youtube titles of meme compilations on youtube.
WARNING: may produce offensive content. |
lg/openinstruct_1k1 | ac84c5debc9980ba0d823728740f2062d48ceca6 | 2021-05-20T23:37:33.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | lg | null | lg/openinstruct_1k1 | 51 | null | transformers | 5,959 | # This model is probably not what you're looking for. |
macedonizer/al-roberta-base | a48686ad136910e750bff614cf6c47926412c6cb | 2021-09-22T08:58:28.000Z | [
"pytorch",
"roberta",
"fill-mask",
"al",
"dataset:wiki-sh",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | macedonizer | null | macedonizer/al-roberta-base | 51 | 1 | transformers | 5,960 | ---
language:
- al
thumbnail: https://huggingface.co/macedonizer/al-roberta-base/lets-talk-about-nlp-al.jpg
tags:
- masked-lm
license: apache-2.0
datasets:
- wiki-sh
---
# AL-RoBERTa base model
Pretrained model on Albanian language using a masked language modeling (MLM) objective. It was introduced in this paper and f... |
maxpe/twitter-roberta-base_semeval18_emodetection | e08cc473008ed93553379d5ffce259ea050e35d6 | 2021-10-27T15:19:07.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | maxpe | null | maxpe/twitter-roberta-base_semeval18_emodetection | 51 | null | transformers | 5,961 | # Twitter-roBERTa-base_SemEval18_Emodetection
This is a Twitter-roBERTa-base model trained on ~7000 tweets in English annotated for 11 emotion categories in [SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification](https://competitions.codalab.org/competitions/17751).
Run the classifier on the test ... |
ontocord/fastspeech2-en | 7d09d28eb5efb46833d2c8c66d731faf608abcde | 2021-04-08T06:57:54.000Z | [
"pytorch",
"fastspeech2",
"en",
"dataset:LJSpeech",
"dataset:LibriTTS",
"arxiv:2006.04558",
"transformers",
"audio",
"TTS",
"license:apache-2.0"
] | null | false | ontocord | null | ontocord/fastspeech2-en | 51 | null | transformers | 5,962 | ---
language: en
datasets:
- LJSpeech
- LibriTTS
tags:
- audio
- TTS
license: apache-2.0
---
# ontocord/fastspeech2-en
Modified version of the text-to-speech system [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech] (https://arxiv.org/abs/2006.04558v1).
## Installation
```
git clone https://github.com/onto... |
r3dhummingbird/DialoGPT-small-neku | d377a5862c58a8d88abdf04b616e19c14dfff469 | 2021-06-08T00:50:01.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"license:mit"
] | conversational | false | r3dhummingbird | null | r3dhummingbird/DialoGPT-small-neku | 51 | null | transformers | 5,963 | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) trained on a game character, Neku Sakuraba from [The ... |
sgugger/finetuned-bert-mrpc | b8f2adf0fcc33362a8df61165e531a2e1bcce9d2 | 2021-07-14T20:43:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | false | sgugger | null | sgugger/finetuned-bert-mrpc | 51 | null | transformers | 5,964 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model_index:
- name: finetuned-bert-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metric:
name: F1
... |
spencerh/rightpartisan | 9c7e7548435839b11c2479f209c313aedd6eb0e4 | 2021-04-23T19:26:52.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | spencerh | null | spencerh/rightpartisan | 51 | null | transformers | 5,965 | # Text classifier using DistilBERT to determine Partisanship
## This is one of the single-class partisan detecting models. (see leftpartisan/leftcenterpartisan/rightcenterpartisan/centerpartisan)
label_0 refers to "other" while label_1 refers to "right" (right as in right-leaning).
This was trained with 40,000 arti... |
ml6team/keyphrase-extraction-distilbert-kptimes | b2bdd8383b424ad54276cf26e31cc856d64f46c9 | 2022-06-16T14:20:34.000Z | [
"pytorch",
"distilbert",
"token-classification",
"en",
"dataset:midas/kptimes",
"arxiv:1911.12559",
"transformers",
"keyphrase-extraction",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | ml6team | null | ml6team/keyphrase-extraction-distilbert-kptimes | 51 | null | transformers | 5,966 | ---
language: en
license: mit
tags:
- keyphrase-extraction
datasets:
- midas/kptimes
metrics:
- seqeval
widget:
- text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document.
Thanks to these keyphrases humans can understand the content of a text very quickly ... |
Awais/Audio_Source_Separation | 043c6dcde8480460f4cf6db0b30405b6831f91b3 | 2022-04-03T11:03:43.000Z | [
"pytorch",
"dataset:Libri2Mix",
"dataset:sep_clean",
"asteroid",
"audio",
"ConvTasNet",
"audio-to-audio",
"license:cc-by-sa-4.0"
] | audio-to-audio | false | Awais | null | Awais/Audio_Source_Separation | 51 | null | asteroid | 5,967 | ---
tags:
- asteroid
- audio
- ConvTasNet
- audio-to-audio
datasets:
- Libri2Mix
- sep_clean
license: cc-by-sa-4.0
---
## Asteroid model `Awais/Audio_Source_Separation`
Imported from [Zenodo](https://zenodo.org/record/3873572#.X9M69cLjJH4)
Description:
This model was trained by Joris Cosentino using the librimix reci... |
Toshifumi/bert-base-multilingual-cased-finetuned-emotion | 59b25fd61666730e719e8830207b77c178fc4f5a | 2022-04-14T08:27:21.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Toshifumi | null | Toshifumi/bert-base-multilingual-cased-finetuned-emotion | 51 | null | transformers | 5,968 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: bert-base-multilingual-cased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: de... |
Helsinki-NLP/opus-mt-tc-big-cat_oci_spa-en | a023bee2de806635db5963d1e0fa250044e97a35 | 2022-06-01T12:59:11.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ca",
"en",
"es",
"oc",
"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-cat_oci_spa-en | 51 | null | transformers | 5,969 | ---
language:
- ca
- en
- es
- oc
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-cat_oci_spa-en
results:
- task:
name: Translation cat-eng
type: translation
args: cat-eng
dataset:
name: flores101-devtest
type: flores_101
args: cat eng ... |
ai4bharat/MultiIndicSentenceSummarization | d1f87d17cc7a2f1ac5b6246d706d56d8af6aba34 | 2022-04-30T10:26:02.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicSentenceSummarization",
"arxiv:2203.05437",
"transformers",
"sentence-summarization",
"multilingual",
"nlp",
"indicnlp",
"license:mit",
"a... | text2text-generation | false | ai4bharat | null | ai4bharat/MultiIndicSentenceSummarization | 51 | null | transformers | 5,970 | ---
tags:
- sentence-summarization
- multilingual
- nlp
- indicnlp
datasets:
- ai4bharat/IndicSentenceSummarization
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- mit
widget:
- जम्मू एवं कश्मीर के अनंतनाग जिले में शनिवार को सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादियों को मार गिराया गया। </... |
mismayil/kogito-rc-distilbert | 8e52330f42d27c1be33960a976bd041ad1f905c5 | 2022-04-28T15:39:21.000Z | [
"pytorch",
"transformers",
"license:mit"
] | null | false | mismayil | null | mismayil/kogito-rc-distilbert | 51 | null | transformers | 5,971 | ---
license: mit
---
|
jenspt/bert_regression | f4414f944a12bb5d84fca52312cdec485b4baaa1 | 2022-05-04T08:12:54.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | jenspt | null | jenspt/bert_regression | 51 | null | transformers | 5,972 | Entry not found |
RJ3vans/SSCCVspanTagger | 0658684da6c0b4873733d75571b8fe2ca1766058 | 2022-07-14T11:08:28.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | RJ3vans | null | RJ3vans/SSCCVspanTagger | 51 | null | transformers | 5,973 | Entry not found |
chanind/frame-semantic-transformer-small | 6ad6032e26af582346a8af6d2d4b43854610ee22 | 2022-05-23T19:08:46.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | chanind | null | chanind/frame-semantic-transformer-small | 51 | null | transformers | 5,974 | ---
license: apache-2.0
---
Fine-tuned T5 small model for use as a frame semantic parser in the [Frame Semantic Transformer](https://github.com/chanind/frame-semantic-transformer) project. This model is trained on data from [FrameNet](https://framenet2.icsi.berkeley.edu/).
### Usage
This is meant to be used a part o... |
RUCAIBox/mtl-question-generation | 63cdb9af203520d0688ebab5fac7dd1b3d201f7d | 2022-06-27T02:27:24.000Z | [
"pytorch",
"mvp",
"en",
"arxiv:2206.12131",
"transformers",
"text-generation",
"text2text-generation",
"license:apache-2.0"
] | text2text-generation | false | RUCAIBox | null | RUCAIBox/mtl-question-generation | 51 | null | transformers | 5,975 | ---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
pipeline_tag: text2text-generation
widget:
- text: "Generate the question based on the answer: boxing [X_SEP] A bolo punch is a punch used in martial arts . A hook is a punch in boxing ."
example_title: "Example1"
- text: "Generate ... |
sschellhammer/SciTweets_SciBert | d2998a11f3574c88e0da8eb39c761932f84cc43b | 2022-06-09T14:03:30.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:cc-by-4.0"
] | text-classification | false | sschellhammer | null | sschellhammer/SciTweets_SciBert | 51 | null | transformers | 5,976 | ---
license: cc-by-4.0
widget:
- text: "Study: Shifts in electricity generation spur net job growth, but coal jobs decline - via @DukeU https://www.eurekalert.org/news-releases/637217"
example_title: "All categories"
- text: "Shifts in electricity generation spur net job growth, but coal jobs decline"
example_title... |
nvidia/tts_hifigan | 3ba1fed954276287015654bf4c78060ffc9a4772 | 2022-06-29T21:31:29.000Z | [
"nemo",
"en",
"dataset:ljspeech",
"arxiv:2010.05646",
"text-to-speech",
"speech",
"audio",
"Vocoder",
"GAN",
"pytorch",
"NeMo",
"Riva",
"license:cc-by-4.0"
] | text-to-speech | false | nvidia | null | nvidia/tts_hifigan | 51 | 1 | nemo | 5,977 | ---
language:
- en
library_name: nemo
datasets:
- ljspeech
thumbnail: null
tags:
- text-to-speech
- speech
- audio
- Vocoder
- GAN
- pytorch
- NeMo
- Riva
license: cc-by-4.0
---
# NVIDIA Hifigan Vocoder (en-US)
<style>
img {
display: inline;
}
</style>
| [![Model architecture](https://img.shields.io/badge/Model_Arch-H... |
ClassCat/roberta-base-latin-v2 | 7e8f8efb4b82341f9509b60aef77824ae34a8c5f | 2022-07-14T00:20:13.000Z | [
"pytorch",
"roberta",
"fill-mask",
"la",
"dataset:cc100",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | ClassCat | null | ClassCat/roberta-base-latin-v2 | 51 | 1 | transformers | 5,978 | ---
language: la
license: cc-by-sa-4.0
datasets:
- cc100
widget:
- text: quod est tibi <mask> ?"
- text: vita brevis, ars <mask>.
- text: errare <mask> est.
- text: usus est magister <mask>.
---
## RoBERTa Latin base model Version 2 (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model... |
SushantGautam/CodeGeneration | 3738e8e94a944caacc3cf2d3ff8fb3e08909fb8a | 2022-07-07T03:13:37.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | fill-mask | false | SushantGautam | null | SushantGautam/CodeGeneration | 51 | null | transformers | 5,979 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: CodeGeneration
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. -->
# CodeGen... |
knkarthick/TOPIC-DIALOGSUM-VALIDATION-XSUM | f884177017ffda84a3b600a1f59f6266db02a78a | 2022-07-08T05:59:41.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | knkarthick | null | knkarthick/TOPIC-DIALOGSUM-VALIDATION-XSUM | 51 | null | transformers | 5,980 | Entry not found |
dsivakumar/text2sql | a9abd8fd33c01721b13b174ead4d0d4b33a57314 | 2022-07-13T07:27:17.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:wikisql",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | dsivakumar | null | dsivakumar/text2sql | 51 | null | transformers | 5,981 | ---
language:
- en
datasets:
- wikisql
widget:
- text: "English to SQL: Show me the average age of of wines in Italy by provinces"
- text: "English to SQL: What is the current series where the new series began in June 2011?"
---
#import transformers
```
from transformers import (
T5ForConditionalGeneration,
... |
bloom-testing/test-bloomd-350m-CI | c1078f05edfc27ae119a3eb8969056101d0f6c16 | 2022-07-15T22:51:44.000Z | [
"pytorch",
"bloom",
"feature-extraction",
"transformers"
] | feature-extraction | false | bloom-testing | null | bloom-testing/test-bloomd-350m-CI | 51 | null | transformers | 5,982 | Entry not found |
bloom-testing/test-bloomd-350m-facelift | a2076c0d301ede655c186b4d005b034b4bd01c78 | 2022-07-15T23:05:47.000Z | [
"pytorch",
"bloom",
"feature-extraction",
"transformers"
] | feature-extraction | false | bloom-testing | null | bloom-testing/test-bloomd-350m-facelift | 51 | null | transformers | 5,983 | Entry not found |
0x7194633/keyt5-large | 6aca9fe5edca51e69d13734271c0c60793c16831 | 2022-01-11T03:52:33.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ru",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | 0x7194633 | null | 0x7194633/keyt5-large | 50 | null | transformers | 5,984 | ---
language:
- ru
license: mit
inference:
parameters:
top_p: 1.0
widget:
- text: "В России может появиться новый штамм коронавируса «омикрон», что может привести к подъему заболеваемости в январе, заявил доцент кафедры инфекционных болезней РУДН Сергей Вознесенский. Он отметил, что вариант «дельта» вызывал больш... |
CouchCat/ma_ner_v7_distil | 9dd0c9b1f1a7fe22d313fe5a0d308c0fa0039e23 | 2021-02-28T20:54:46.000Z | [
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
] | token-classification | false | CouchCat | null | CouchCat/ma_ner_v7_distil | 50 | null | transformers | 5,985 | ---
language: en
license: mit
tags:
- ner
widget:
- text: "These shoes I recently bought from Tommy Hilfiger fit quite well. The shirt, however, has got a hole"
---
### Description
A Named Entity Recognition model trained on a customer feedback data using DistilBert.
Possible labels are in BIO-notation. Performan... |
Geotrend/bert-base-nl-cased | 51f86af423d9f9e72b9a81155875adcba9b571ba | 2021-05-18T20:02:19.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"nl",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Geotrend | null | Geotrend/bert-base-nl-cased | 50 | null | transformers | 5,986 | ---
language: nl
datasets: wikipedia
license: apache-2.0
---
# bert-base-nl-cased
We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://huggingface.co/disti... |
Helsinki-NLP/opus-mt-sem-en | 27d79ccca4adc1a2dd178024fa9edf5bc660e005 | 2020-08-21T14:42:49.000Z | [
"pytorch",
"marian",
"text2text-generation",
"mt",
"ar",
"he",
"ti",
"am",
"sem",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sem-en | 50 | null | transformers | 5,987 | ---
language:
- mt
- ar
- he
- ti
- am
- sem
- en
tags:
- translation
license: apache-2.0
---
### sem-eng
* source group: Semitic languages
* target group: English
* OPUS readme: [sem-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/sem-eng/README.md)
* model: transformer
* source lan... |
HueyNemud/das22-10-camembert_pretrained | a54f5177528f2e319b97b1f3960d0a00fd9e3ef3 | 2022-05-19T12:05:12.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | HueyNemud | null | HueyNemud/das22-10-camembert_pretrained | 50 | null | transformers | 5,988 | ---
tags:
- generated_from_trainer
model-index:
- name: CamemBERT pretrained on french trade directories from the XIXth century
results: []
---
# CamemBERT pretrained on french trade directories from the XIXth century
This mdoel is part of the material of the paper
> Abadie, N., Carlinet, E., Chazalon, J., Duménieu... |
RJ3vans/13.05.2022.SSCCVspanTagger | 095f0d0797a201b9e90b4c95d30d2b09770e6608 | 2021-10-28T09:50:19.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | RJ3vans | null | RJ3vans/13.05.2022.SSCCVspanTagger | 50 | null | transformers | 5,989 | Try the test sentences:
<i>My name is Sarah and I live in London[, which] is the largest city in the UK.</i>
<i>John thought that that was a strange idea.</i>
<i>It was on Tuesdays when Peter took Tess for a walk.</i>
<i>John was so large that he had to crouch to fit through the front door.</i>
The model should ta... |
apoorvumang/kgt5-wikikg90mv2 | 01c5197af858f32f62522665d2e040d325ea42ce | 2022-03-22T17:02:33.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | apoorvumang | null | apoorvumang/kgt5-wikikg90mv2 | 50 | null | transformers | 5,990 | ---
license: mit
widget:
- text: "Apoorv Umang Saxena| family name"
example_title: "Family name prediction"
- text: "Apoorv Saxena| country"
example_title: "Country prediction"
- text: "World War 2| followed by"
example_title: "followed by"
---
This is a t5-small model trained from scratch on WikiKG90Mv... |
asafaya/hubert-large-arabic-ft | 76875c200def77031c77363973258f1b49925cb3 | 2022-03-26T15:25:10.000Z | [
"hubert",
"feature-extraction",
"ar",
"dataset:commonvoice",
"arxiv:2106.07447",
"speechbrain",
"CTC",
"Attention",
"pytorch",
"Transformer",
"hf-asr-leaderboard",
"license:cc-by-nc-4.0",
"automatic-speech-recognition",
"model-index"
] | automatic-speech-recognition | false | asafaya | null | asafaya/hubert-large-arabic-ft | 50 | 1 | speechbrain | 5,991 | ---
language: "ar"
pipeline_tag: automatic-speech-recognition
tags:
- CTC
- Attention
- pytorch
- speechbrain
- Transformer
- hf-asr-leaderboard
license: "cc-by-nc-4.0"
datasets:
- commonvoice
metrics:
- wer
- cer
model-index:
- name: asafaya/hubert-large-arabic-ft
results:
- task:
name: Automatic Speech Rec... |
blizrys/distilbert-base-uncased-finetuned-mnli | 1722a09d8351d49906bf2fceaaee4eac2b7c0f0c | 2021-09-11T19:31:42.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | blizrys | null | blizrys/distilbert-base-uncased-finetuned-mnli | 50 | null | transformers | 5,992 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mnli
metrics:
- ... |
csatapathy/interview-ratings-bert | 6e138bfae1be2a716a8b5fa732714478ecaf3469 | 2021-05-19T14:33:34.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | csatapathy | null | csatapathy/interview-ratings-bert | 50 | null | transformers | 5,993 | Entry not found |
flax-community/gpt2-small-indonesian | a635ebaa0dc3bfe76071a74e6e1581428378533e | 2021-09-02T12:26:52.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"id",
"transformers"
] | text-generation | false | flax-community | null | flax-community/gpt2-small-indonesian | 50 | 2 | transformers | 5,994 | ---
language: id
widget:
- text: "Sewindu sudah kita tak berjumpa, rinduku padamu sudah tak terkira."
---
# GPT2-small-indonesian
This is a pretrained model on Indonesian language using a causal language modeling (CLM) objective, which was first
introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-l... |
huggingartists/ariana-grande | 9b31d93bb4ea82e4f0fdb1b553bb04ce58ec4624 | 2021-09-19T02:10:10.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"dataset:huggingartists/ariana-grande",
"transformers",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm"
] | text-generation | false | huggingartists | null | huggingartists/ariana-grande | 50 | null | transformers | 5,995 | ---
language: en
datasets:
- huggingartists/ariana-grande
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; he... |
huggingtweets/emailoctopus | 1b0f6f50bf9a4d272fe30663749d81519cf1b5ee | 2021-05-22T03:00:30.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/emailoctopus | 50 | null | transformers | 5,996 | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
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) {
.pros... |
iamalpharius/GPT-Small-BenderBot | 6092a5fcd20be607f69bd65a4a9b00fcd85063e0 | 2021-10-14T12:47:24.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | iamalpharius | null | iamalpharius/GPT-Small-BenderBot | 50 | null | transformers | 5,997 | ---
tags:
- conversational
---
# Bender DialoGPT model |
julien-c/EsperBERTo-small | 2439f60ef33a0d46d85da5001d52aeda5b00ce9f | 2021-05-20T17:29:32.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"eo",
"transformers",
"autotrain_compatible"
] | fill-mask | false | julien-c | null | julien-c/EsperBERTo-small | 50 | 2 | transformers | 5,998 | ---
language: eo
thumbnail: https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png
widget:
- text: "Jen la komenco de bela <mask>."
- text: "Uno du <mask>"
- text: "Jen finiĝas bela <mask>."
---
# EsperBERTo: RoBERTa-like Language model trained on Esperanto
**Companion model to blog post https... |
julien-c/distilbert-sagemaker-1609802168 | 574fad7897a3379b995bfe9b0a8791dd1a857e58 | 2022-07-18T20:05:27.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"sagemaker"
] | text-classification | false | julien-c | null | julien-c/distilbert-sagemaker-1609802168 | 50 | null | transformers | 5,999 |
---
tags:
- sagemaker
datasets:
- imdb
---
## distilbert-sagemaker-1609802168
Trained from SageMaker HuggingFace extension.
Fine-tuned from [distilbert-base-uncased](/distilbert-base-uncased) on [imdb](/datasets/imdb) 🔥
#### Eval
| key | value |
| --- | ----- |
| eval_loss | 0.19187863171100616 |
| eval_accurac... |
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