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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ceshine/t5-paraphrase-paws-msrp-opinosis | a54ecca4603c7ff7bc497ffc97d1dc7dd5f485d2 | 2021-09-22T08:16:39.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"transformers",
"paraphrasing",
"paraphrase",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | ceshine | null | ceshine/t5-paraphrase-paws-msrp-opinosis | 1,561 | null | transformers | 1,500 | ---
language: en
tags:
- t5
- paraphrasing
- paraphrase
license: apache-2.0
---
# T5-base Parapharasing model fine-tuned on PAWS, MSRP, and Opinosis
More details in [ceshine/finetuning-t5 Github repo](https://github.com/ceshine/finetuning-t5/tree/master/paraphrase) |
monsoon-nlp/bert-base-thai | 9b5ca3cc1b41c8ff91c57d34e50e77d29ec7d2c1 | 2022-02-15T19:21:29.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"th",
"arxiv:1609.08144",
"arxiv:1508.07909",
"transformers"
] | feature-extraction | false | monsoon-nlp | null | monsoon-nlp/bert-base-thai | 1,561 | 1 | transformers | 1,501 | ---
language: th
---
# BERT-th
Adapted from https://github.com/ThAIKeras/bert for HuggingFace/Transformers library
## Pre-tokenization
You must run the original ThaiTokenizer to have your tokenization match that of the original model.
If you skip this step, you will not do much better than
mBERT or random chance!
... |
csebuetnlp/banglat5 | c3a6a2bac3e318e065b3d2be88f91ae289b8c67d | 2022-05-24T11:15:26.000Z | [
"pytorch",
"t5",
"text2text-generation",
"bn",
"arxiv:2205.11081",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | csebuetnlp | null | csebuetnlp/banglat5 | 1,560 | 1 | transformers | 1,502 | ---
language:
- bn
licenses:
- cc-by-nc-sa-4.0
---
# BanglaT5
This repository contains the pretrained checkpoint of the model **BanglaT5**. This is a sequence to sequence transformer model pretrained with the ["Span Corruption"]() objective. Finetuned models using this checkpoint achieve state-of-the-art results on ... |
bespin-global/klue-sroberta-base-continue-learning-by-mnr | d5a9b36c4620a79996adce86facbed7261f93cf6 | 2022-04-04T09:19:55.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | bespin-global | null | bespin-global/klue-sroberta-base-continue-learning-by-mnr | 1,554 | null | sentence-transformers | 1,503 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# bespin-global/klue-sroberta-base-continue-learning-by-mnr
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector... |
VoVanPhuc/sup-SimCSE-VietNamese-phobert-base | ae1275825875314c5b772b93280fbf14dbed86c5 | 2021-05-28T05:42:03.000Z | [
"pytorch",
"roberta",
"arxiv:2104.08821",
"transformers"
] | null | false | VoVanPhuc | null | VoVanPhuc/sup-SimCSE-VietNamese-phobert-base | 1,549 | 2 | transformers | 1,504 |
#### Table of contents
1. [Introduction](#introduction)
2. [Pretrain model](#models)
3. [Using SimeCSE_Vietnamese with `sentences-transformers`](#sentences-transformers)
- [Installation](#install1)
- [Example usage](#usage1)
4. [Using SimeCSE_Vietnamese with `transformers`](#transformers)
- [Installation](#install2... |
sentence-transformers/nli-bert-base | d5604f34c50678d07bd65a2cad9b996dae053a76 | 2022-06-15T23:20:12.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/nli-bert-base | 1,544 | null | sentence-transformers | 1,505 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... |
Davlan/distilbert-base-multilingual-cased-ner-hrl | 6c3d663fb9d1b22e6f000595e9ce74597021a68a | 2022-06-27T10:49:50.000Z | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"ar",
"de",
"en",
"es",
"fr",
"it",
"lv",
"nl",
"pt",
"zh",
"multilingual",
"transformers",
"autotrain_compatible"
] | token-classification | false | Davlan | null | Davlan/distilbert-base-multilingual-cased-ner-hrl | 1,539 | 4 | transformers | 1,506 | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# distilbert-base-multilingual-cased-ner-hrl
## Model description
**distilbert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Span... |
prajjwal1/bert-tiny-mnli | 3488cd7cf0799da403ee9544ca7310c4dfcce634 | 2021-10-05T18:00:12.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1908.08962",
"arxiv:2110.01518",
"transformers"
] | text-classification | false | prajjwal1 | null | prajjwal1/bert-tiny-mnli | 1,539 | null | transformers | 1,507 | The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](... |
prithivida/informal_to_formal_styletransfer | 472cedcfc522615f77e64bedc54b4ef710fe71d3 | 2021-06-19T08:30:19.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | prithivida | null | prithivida/informal_to_formal_styletransfer | 1,539 | 6 | transformers | 1,508 | ## This model belongs to the Styleformer project
[Please refer to github page](https://github.com/PrithivirajDamodaran/Styleformer)
|
alvaroalon2/biobert_genetic_ner | ebb8c1e20ebbfcd98c6a4df8802c32fdbc2f9028 | 2021-07-07T12:36:25.000Z | [
"pytorch",
"bert",
"token-classification",
"English",
"dataset:JNLPBA",
"dataset:BC2GM",
"transformers",
"NER",
"Biomedical",
"Genetics",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | alvaroalon2 | null | alvaroalon2/biobert_genetic_ner | 1,534 | 2 | transformers | 1,509 | ---
language: "English"
license: apache-2.0
tags:
- token-classification
- NER
- Biomedical
- Genetics
datasets:
- JNLPBA
- BC2GM
---
BioBERT model fine-tuned in NER task with JNLPBA and BC2GM corpus for genetic class entities.
This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: htt... |
facebook/esm-1b | 09a25a9cbce6d278b1da9146321500d0d9e07db4 | 2021-11-12T17:13:02.000Z | [
"pytorch",
"esm",
"fill-mask",
"arxiv:1907.11692",
"arxiv:1810.04805",
"arxiv:1603.05027",
"transformers",
"autotrain_compatible"
] | fill-mask | false | facebook | null | facebook/esm-1b | 1,533 | 7 | transformers | 1,510 | # **ESM-1b**
ESM-1b ([paper](https://www.pnas.org/content/118/15/e2016239118#:~:text=https%3A//doi.org/10.1073/pnas.2016239118), [repository](https://github.com/facebookresearch/esm)) is a transformer protein language model, trained on protein sequence data without label supervision. The model is pretrained on Uniref5... |
Helsinki-NLP/opus-mt-gl-en | 9a0170e5a81324078b87675a597a65cc6ff92487 | 2021-01-18T08:52:37.000Z | [
"pytorch",
"marian",
"text2text-generation",
"gl",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-gl-en | 1,532 | null | transformers | 1,511 | ---
language:
- gl
- en
tags:
- translation
license: apache-2.0
---
### glg-eng
* source group: Galician
* target group: English
* OPUS readme: [glg-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/glg-eng/README.md)
* model: transformer-align
* source language(s): glg
* target langua... |
vinai/vinai-translate-en2vi | 4eeefc237431f28d4e8048a262c88a80ce07a2ab | 2022-07-06T08:33:18.000Z | [
"pytorch",
"tf",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | vinai | null | vinai/vinai-translate-en2vi | 1,532 | null | transformers | 1,512 | # 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... |
sshleifer/distill-pegasus-xsum-16-4 | 2b576a5f863f49550a3bf3db25c8e72cc97dd23c | 2020-10-14T16:16:54.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | sshleifer | null | sshleifer/distill-pegasus-xsum-16-4 | 1,529 | 2 | transformers | 1,513 | ---
language: en
tags:
- summarization
---
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@... |
Rostlab/prot_t5_base_mt_uniref50 | 3fb12c6025327b105f6f602827a5f66259f334f9 | 2021-06-23T03:55:50.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | Rostlab | null | Rostlab/prot_t5_base_mt_uniref50 | 1,525 | null | transformers | 1,514 | ---
tags:
- summarization
widget:
- text: "predict protein ms : Met Gly Leu Pro Val Ser Trp Ala Pro Pro Ala Leu"
---
|
scottykwok/wav2vec2-large-xlsr-cantonese | bae7b4405c2d88961c7d11b1a6769658f6dce1f0 | 2022-07-19T15:22:01.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"zh",
"dataset:common_voice",
"transformers",
"license:cc-by-sa-4.0"
] | automatic-speech-recognition | false | scottykwok | null | scottykwok/wav2vec2-large-xlsr-cantonese | 1,522 | null | transformers | 1,515 | ---
language: zh
tags:
- automatic-speech-recognition
license: cc-by-sa-4.0
datasets:
- common_voice
metrics:
- cer
---
# Wav2vec2-large-xlsr-cantonese
This model was based on [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53), finetuned using Common Voice/zh-HK/6.1.0.
The training code ... |
facebook/xlm-roberta-xl | cd9a69a5ee20ea0a261196037b24c0eafff34358 | 2022-01-28T16:22:30.000Z | [
"pytorch",
"xlm-roberta-xl",
"fill-mask",
"multilingual",
"arxiv:2105.00572",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | facebook | null | facebook/xlm-roberta-xl | 1,520 | 2 | transformers | 1,516 | ---
language: multilingual
license: mit
---
# XLM-RoBERTa-XL (xlarge-sized model)
XLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by ... |
its5Q/rugpt3large_mailqa | c629c9f150e29c36acac4a8a2e9bab2963a50b45 | 2022-07-07T09:49:07.000Z | [
"pytorch",
"gpt2",
"text-generation",
"ru",
"transformers",
"PyTorch",
"Transformers"
] | text-generation | false | its5Q | null | its5Q/rugpt3large_mailqa | 1,519 | 2 | transformers | 1,517 | ---
language:
- ru
tags:
- PyTorch
- Transformers
---
# rugpt3large\_mailqa
Model was finetuned with sequence length 1024 for 516000 steps on a dataset of otvet.mail.ru questions and answers. The raw dataset can be found [here](https://www.kaggle.com/datasets/atleast6characterss/otvetmailru-full). Beware that the data... |
Helsinki-NLP/opus-mt-ceb-en | 2f433caadadc020231e840797eb513c287cf4894 | 2021-01-18T07:53:40.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ceb",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ceb-en | 1,517 | null | transformers | 1,518 | ---
language:
- ceb
- en
tags:
- translation
license: apache-2.0
---
### ceb-eng
* source group: Cebuano
* target group: English
* OPUS readme: [ceb-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ceb-eng/README.md)
* model: transformer-align
* source language(s): ceb
* target langua... |
elgeish/wav2vec2-large-lv60-timit-asr | 7db32147e521892ab8a63c7cd6008b060876181e | 2021-07-06T01:39:41.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:timit_asr",
"transformers",
"audio",
"speech",
"license:apache-2.0"
] | automatic-speech-recognition | false | elgeish | null | elgeish/wav2vec2-large-lv60-timit-asr | 1,510 | null | transformers | 1,519 | ---
language: en
datasets:
- timit_asr
tags:
- audio
- automatic-speech-recognition
- speech
license: apache-2.0
---
# Wav2Vec2-Large-LV60-TIMIT
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60)
on the [timit_asr dataset](https://huggingface.co/datasets/timit_asr).
When us... |
sentence-transformers/nli-roberta-base-v2 | 64c0737f24398dce1ec9ae04f363dc6b220dceaf | 2022-06-15T22:41:43.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/nli-roberta-base-v2 | 1,509 | null | sentence-transformers | 1,520 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/nli-roberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense ve... |
yoshitomo-matsubara/bert-base-uncased-mnli | 38c02ebe3cf589c8aa25dfb852aba7b904c29739 | 2021-05-29T21:43:56.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:mnli",
"dataset:ax",
"transformers",
"mnli",
"ax",
"glue",
"torchdistill",
"license:apache-2.0"
] | text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-base-uncased-mnli | 1,509 | null | transformers | 1,521 | ---
language: en
tags:
- bert
- mnli
- ax
- glue
- torchdistill
license: apache-2.0
datasets:
- mnli
- ax
metrics:
- accuracy
---
`bert-base-uncased` fine-tuned on MNLI dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/y... |
wptoux/albert-chinese-large-qa | 02a1762ffdc88ce77fad185f9c3098dba0f27ece | 2021-03-09T07:48:40.000Z | [
"pytorch",
"albert",
"question-answering",
"zh",
"dataset:webqa",
"dataset:dureader",
"transformers",
"Question Answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | wptoux | null | wptoux/albert-chinese-large-qa | 1,508 | 1 | transformers | 1,522 | ---
language:
- zh
tags:
- Question Answering
license: apache-2.0
datasets:
- webqa
- dureader
---
# albert-chinese-large-qa
Albert large QA model pretrained from baidu webqa and baidu dureader datasets.
## Data source
+ baidu webqa 1.0
+ baidu dureader
## Traing Method
We combined the two datasets together and crea... |
Lalita/marianmt-zh_cn-th | 3c440603f723b3ef2624c25057d62a62bab015e7 | 2021-06-29T11:25:02.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"torch==1.8.0",
"autotrain_compatible"
] | translation | false | Lalita | null | Lalita/marianmt-zh_cn-th | 1,505 | null | transformers | 1,523 | ---
tags:
- translation
- torch==1.8.0
widget:
- text: "Inference Unavailable"
---
### marianmt-zh_cn-th
* source languages: zh_cn
* target languages: th
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set scores: syllable: 15.95, word: 8.43
## Training
Training scripts ... |
ankur310794/bart-base-keyphrase-generation-kpTimes | 3d0d7234b09b8cc55f25f53c08e1a9210857d4fa | 2021-04-09T08:38:24.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ankur310794 | null | ankur310794/bart-base-keyphrase-generation-kpTimes | 1,504 | 0 | transformers | 1,524 | Entry not found |
Helsinki-NLP/opus-mt-de-fr | 6aa8c4011488513f5575b235ce75d6d795d90b35 | 2021-09-09T21:31:13.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"de",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-de-fr | 1,503 | null | transformers | 1,525 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-de-fr
* source languages: de
* target languages: fr
* OPUS readme: [de-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-en-hi | 108ec718a95d9cf96bdb27345a6012c60e141da1 | 2021-03-02T16:17:47.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"en",
"hi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-hi | 1,502 | 4 | transformers | 1,526 | ---
language:
- en
- hi
tags:
- translation
license: apache-2.0
---
### eng-hin
* source group: English
* target group: Hindi
* OPUS readme: [eng-hin](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hin/README.md)
* model: transformer-align
* source language(s): eng
* target language(... |
bionlp/bluebert_pubmed_mimic_uncased_L-24_H-1024_A-16 | e4a3d79282d3b1fc123b562de18b4c18b50a9176 | 2021-09-24T07:46:34.000Z | [
"pytorch",
"jax",
"en",
"dataset:PubMed",
"dataset:MIMIC-III",
"transformers",
"bert",
"bluebert",
"license:cc0-1.0"
] | null | false | bionlp | null | bionlp/bluebert_pubmed_mimic_uncased_L-24_H-1024_A-16 | 1,502 | null | transformers | 1,527 | ---
language:
- en
tags:
- bert
- bluebert
license: cc0-1.0
datasets:
- PubMed
- MIMIC-III
---
# BlueBert-Base, Uncased, PubMed and MIMIC-III
## Model description
A BERT model pre-trained on PubMed abstracts and clinical notes ([MIMIC-III](https://mimic.physionet.org/)).
## Intended uses & limitations
#### How t... |
ozcangundes/mt5-multitask-qa-qg-turkish | 05f063c45a3ad0cdcdac26eb823c7aac2d625aa6 | 2021-06-23T15:24:09.000Z | [
"pytorch",
"jax",
"mt5",
"text2text-generation",
"tr",
"dataset:TQUAD",
"transformers",
"question-answering",
"question-generation",
"multitask-model",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | ozcangundes | null | ozcangundes/mt5-multitask-qa-qg-turkish | 1,500 | 0 | transformers | 1,528 | ---
language: tr
datasets:
- TQUAD
tags:
- question-answering
- question-generation
- multitask-model
license: apache-2.0
---
# mT5-small based Turkish Multitask (Answer Extraction, Question Generation and Question Answering) System
[Google's Multilingual T5-small](https://github.com/google-research/multilingual-t5... |
slauw87/bart_summarisation | e6097186162a3d9d75ba0a1297640f985baadf52 | 2021-09-20T05:27:36.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:samsum",
"transformers",
"sagemaker",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | slauw87 | null | slauw87/bart_summarisation | 1,493 | 4 | transformers | 1,529 |
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
datasets:
- samsum
model-index:
- name: bart-large-cnn-samsum
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "SAMSum Corpus: A Human-annotated Dialogue Dat... |
Helsinki-NLP/opus-mt-sq-en | c4c55527072468e3f7401d6717aeb9824d1d7345 | 2021-09-10T14:04:20.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sq",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sq-en | 1,492 | 1 | transformers | 1,530 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sq-en
* source languages: sq
* target languages: en
* OPUS readme: [sq-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sq-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Helsinki-NLP/opus-mt-bg-en | 3a34359f5781368c7748219c2868ffd065f24df0 | 2021-09-09T21:27:33.000Z | [
"pytorch",
"marian",
"text2text-generation",
"bg",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-bg-en | 1,490 | 1 | transformers | 1,531 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-bg-en
* source languages: bg
* target languages: en
* OPUS readme: [bg-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bg-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
sentence-transformers/bert-large-nli-max-tokens | 1738a181e9e77a09752f92e6dbde15f4a5527d5c | 2022-06-15T23:14:28.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/bert-large-nli-max-tokens | 1,490 | null | sentence-transformers | 1,532 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... |
Jonesy/LisaOnIce | 67fc2267ff59323174edd742555326cadf9c1528 | 2022-04-27T12:41:47.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Jonesy | null | Jonesy/LisaOnIce | 1,490 | null | transformers | 1,533 | ---
tags:
- conversational
---
# DialoGPT-medium Model of Simpsons Episode s8e6 "Lisa On Ice"
|
cardiffnlp/twitter-roberta-base-mar2022 | 4dbf97378f905571e34b6399573db2f4d92f7aaf | 2022-04-18T10:53:59.000Z | [
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.03829",
"transformers",
"autotrain_compatible"
] | fill-mask | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-mar2022 | 1,486 | 2 | transformers | 1,534 | # Twitter March 2022 (RoBERTa-base, 128M)
This is a RoBERTa-base model trained on 128.06M tweets until the end of March 2022.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For... |
memray/bart_wikikp | 6e7a50d0535d0407b398facb4c381d3a9b1ca69d | 2022-03-05T22:07:16.000Z | [
"pytorch",
"bart",
"feature-extraction",
"transformers"
] | feature-extraction | false | memray | null | memray/bart_wikikp | 1,481 | 1 | transformers | 1,535 | Entry not found |
UBC-NLP/AraT5-base-title-generation | f26ed5960b5ccff858860ab346040dd1a05d032e | 2022-05-26T18:29:45.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"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 T... | text2text-generation | false | UBC-NLP | null | UBC-NLP/AraT5-base-title-generation | 1,476 | 2 | transformers | 1,536 | ---
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-title-generation
# AraT5: Text-t... |
nlp-waseda/roberta-large-japanese | 28df4e50db51cf0130977770ffd6ab18fc834e3e | 2022-06-10T23:33:42.000Z | [
"pytorch",
"roberta",
"fill-mask",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | nlp-waseda | null | nlp-waseda/roberta-large-japanese | 1,475 | 10 | transformers | 1,537 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
mask_token: "[MASK]"
widget:
- text: "早稲田 大学 で 自然 言語 処理 を [MASK] する 。"
---
# nlp-waseda/roberta-large-japanese
## Model description
This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.
## How ... |
Helsinki-NLP/opus-mt-en-mul | cd721e7a7abeea36f81bf7cea89a77f105b0ddc6 | 2021-01-18T08:13:09.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"ca",
"es",
"os",
"eo",
"ro",
"fy",
"cy",
"is",
"lb",
"su",
"an",
"sq",
"fr",
"ht",
"rm",
"cv",
"ig",
"am",
"eu",
"tr",
"ps",
"af",
"ny",
"ch",
"uk",
"sl",
"lt",
"tk",
"sg",
"ar",
"lg",
"bg",
"... | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-mul | 1,474 | 4 | transformers | 1,538 | ---
language:
- en
- ca
- es
- os
- eo
- ro
- fy
- cy
- is
- lb
- su
- an
- sq
- fr
- ht
- rm
- cv
- ig
- am
- eu
- tr
- ps
- af
- ny
- ch
- uk
- sl
- lt
- tk
- sg
- ar
- lg
- bg
- be
- ka
- gd
- ja
- si
- br
- mh
- km
- th
- ty
- rw
- te
- mk
- or
- wo
- kl
- mr
- ru
- yo
- hu
- fo
- zh
- ti
- co
- ee
- oc
- sn
- mt
... |
blanchefort/rubert-base-cased-sentiment-rusentiment | 997e7bb8e95be5bc71903ca235f76598230e9d90 | 2021-05-19T13:04:19.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"ru",
"dataset:RuSentiment",
"transformers",
"sentiment"
] | text-classification | false | blanchefort | null | blanchefort/rubert-base-cased-sentiment-rusentiment | 1,471 | null | transformers | 1,539 | ---
language:
- ru
tags:
- sentiment
- text-classification
datasets:
- RuSentiment
---
# RuBERT for Sentiment Analysis
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuSentiment](http://text-machine.cs.uml.edu/projects/ruse... |
cointegrated/rut5-base-multitask | aa908001447c0efd0a51a15edcac4c6dce247f81 | 2021-10-11T17:49:16.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"ru",
"en",
"transformers",
"russian",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | cointegrated | null | cointegrated/rut5-base-multitask | 1,467 | 8 | transformers | 1,540 |
---
language: ["ru", "en"]
tags:
- russian
license: mit
widget:
- text: "fill | Почему они не ___ на меня?"
---
This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) with only some Rusian and English embeddings left.
More details are given in a Russian post: https://habr.com/ru... |
jonatasgrosman/wav2vec2-xls-r-1b-russian | 4bce14e5905d144dc8bd9c289b8028351b831612 | 2022-07-27T23:39:43.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-xls-r-1b-russian | 1,460 | 2 | transformers | 1,541 | ---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- ru
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R Wav2Vec2 Russian by Jonatas Grosman
results:
- task:
name: Automatic Spee... |
m3hrdadfi/bert-fa-base-uncased-wikinli-mean-tokens | a3688fa119b8a43fea49d9636798de285a1c7c15 | 2021-05-28T06:00:37.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"fa",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | m3hrdadfi | null | m3hrdadfi/bert-fa-base-uncased-wikinli-mean-tokens | 1,460 | null | transformers | 1,542 | ---
language: fa
license: apache-2.0
---
# ParsBERT + Sentence Transformers
Please follow the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transformers) repo for the latest information about previous and current models.
```bibtex
@misc{SentenceTransformerWiki,
author = {Mehrdad Farahani},
title =... |
sdadas/polish-roberta-base-v2 | ee587adb0e7ab0b0e42f080589ddeb03b7f928ef | 2022-02-19T10:07:31.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"license:lgpl-3.0",
"autotrain_compatible"
] | fill-mask | false | sdadas | null | sdadas/polish-roberta-base-v2 | 1,459 | 1 | transformers | 1,543 | ---
license: lgpl-3.0
---
|
uer/t5-v1_1-base-chinese-cluecorpussmall | eb304532aed1ed8a29fac66b08b5e9cfcfb5b4ad | 2022-07-15T08:21:39.000Z | [
"pytorch",
"tf",
"mt5",
"text2text-generation",
"zh",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uer | null | uer/t5-v1_1-base-chinese-cluecorpussmall | 1,459 | 8 | transformers | 1,544 | ---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "作为电子extra0的平台，京东绝对是领先者。如今的刘强extra1已经是身价过extra2的老板。"
---
# Chinese T5 Version 1.1
## Model description
This is the set of Chinese T5 Version 1.1 models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https:/... |
ckiplab/albert-base-chinese | ed9a51e41fcf0cb4dec5aa3cbd7cdeb40b3e0099 | 2022-05-10T03:28:08.000Z | [
"pytorch",
"albert",
"fill-mask",
"zh",
"transformers",
"lm-head",
"license:gpl-3.0",
"autotrain_compatible"
] | fill-mask | false | ckiplab | null | ckiplab/albert-base-chinese | 1,457 | 2 | transformers | 1,545 | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- lm-head
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, pa... |
bergum/xtremedistil-l6-h384-go-emotion | 262275f2d541e5bf124f72ac1aab0999b35aff1d | 2022-07-14T10:00:08.000Z | [
"pytorch",
"bert",
"text-classification",
"dataset:go_emotions",
"transformers",
"license:apache-2.0",
"model-index"
] | text-classification | false | bergum | null | bergum/xtremedistil-l6-h384-go-emotion | 1,455 | 5 | transformers | 1,546 | ---
license: apache-2.0
datasets:
- go_emotions
metrics:
- accuracy
model-index:
- name: xtremedistil-emotion
results:
- task:
name: Multi Label Text Classification
type: multi_label_classification
dataset:
name: go_emotions
type: emotion
args: default
metrics:
- name: Acc... |
knkarthick/bart-large-xsum-samsum | 56ced735ebc4a7eb6105647f2cbd4a07dd131895 | 2022-07-20T08:29:15.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:samsum",
"transformers",
"seq2seq",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | knkarthick | null | knkarthick/bart-large-xsum-samsum | 1,455 | null | transformers | 1,547 | ---
language: en
tags:
- bart
- seq2seq
- summarization
license: apache-2.0
datasets:
- samsum
widget:
- text: "Hannah: Hey, do you have Betty's number?\nAmanda: Lemme check\nAmanda: Sorry,\
\ can't find it.\nAmanda: Ask Larry\nAmanda: He called her last time we were at\
\ the park together\nHannah: I don't kno... |
pszemraj/long-t5-tglobal-base-16384-book-summary | 4e991f6c4eb3b5f7d1e6c2531b878c084c79a9be | 2022-07-27T21:34:28.000Z | [
"pytorch",
"longt5",
"text2text-generation",
"dataset:kmfoda/booksum",
"arxiv:2112.07916",
"arxiv:2105.08209",
"transformers",
"summarization",
"summary",
"booksum",
"long-document",
"long-form",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | pszemraj | null | pszemraj/long-t5-tglobal-base-16384-book-summary | 1,452 | 3 | transformers | 1,548 | ---
tags:
- summarization
- summary
- 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 energy for the rupture. The rates
... |
jonatasgrosman/wav2vec2-xls-r-1b-spanish | f4711d7a6d8a69fab0c06c53a8588546771d6a16 | 2022-07-27T23:40:05.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-xls-r-1b-spanish | 1,443 | 4 | transformers | 1,549 | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R Wav2Vec2 Spanish by Jonatas Grosman
results:
- task:
name: Automatic Spee... |
mrm8488/bert2bert_shared-spanish-finetuned-summarization | db00caece1809f3850e24ae2ad43f530adcc836e | 2021-06-15T08:37:40.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"es",
"dataset:mlsum",
"transformers",
"summarization",
"news",
"autotrain_compatible"
] | summarization | false | mrm8488 | null | mrm8488/bert2bert_shared-spanish-finetuned-summarization | 1,439 | 5 | transformers | 1,550 | ---
tags:
- summarization
- news
language: es
datasets:
- mlsum
widget:
- text: 'Al filo de las 22.00 horas del jueves, la Asamblea de Madrid vive un momento sorprendente: Vox decide no apoyar una propuesta del PP en favor del blindaje fiscal de la Comunidad. Se ha roto la unidad de los tres partidos de derechas. Es un... |
colorfulscoop/sbert-base-ja | ecb8a98cd5176719ff7ab0d770a27420118732cf | 2021-08-08T06:47:42.000Z | [
"pytorch",
"bert",
"feature-extraction",
"ja",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"license:cc-by-sa-4.0"
] | sentence-similarity | false | colorfulscoop | null | colorfulscoop/sbert-base-ja | 1,436 | 6 | sentence-transformers | 1,551 | ---
language: ja
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
widget:
source_sentence: "走るのが趣味です"
sentences:
- 外をランニングするのが好きです
- 運動はそこそこです
- 走るのは嫌いです
license: cc-by-sa-4.0
---
# Sentence BERT base Japanese model
This reposit... |
google/bert_for_seq_generation_L-24_bbc_encoder | c817d1fd1be2ffa69431227a1fe320544943d4db | 2020-09-11T07:57:22.000Z | [
"pytorch",
"bert-generation",
"transformers"
] | null | false | google | null | google/bert_for_seq_generation_L-24_bbc_encoder | 1,432 | null | transformers | 1,552 | Entry not found |
publichealthsurveillance/PHS-BERT | 863b4b47baa31a5cc05e310028f3f90d9c096c8c | 2022-07-29T03:39:46.000Z | [
"pytorch",
"bert",
"fill-mask",
"arxiv:2204.04521",
"transformers",
"autotrain_compatible"
] | fill-mask | false | publichealthsurveillance | null | publichealthsurveillance/PHS-BERT | 1,431 | 2 | transformers | 1,553 | # PHS-BERT
We present and release [PHS-BERT](https://arxiv.org/abs/2204.04521), a transformer-based pretrained language model (PLM), to identify tasks related to public health surveillance (PHS) on social media. Compared with existing PLMs that are mainly evaluated on limited tasks, PHS-BERT achieved state-of-the-art ... |
Helsinki-NLP/opus-mt-eu-en | 60daa3812648c76b3522a038246f3851728ca2ae | 2021-09-09T21:46:20.000Z | [
"pytorch",
"marian",
"text2text-generation",
"eu",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-eu-en | 1,426 | 1 | transformers | 1,554 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-eu-en
* source languages: eu
* target languages: en
* OPUS readme: [eu-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/eu-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
nvidia/segformer-b5-finetuned-ade-640-640 | 92fa5463ede2d14a30ba25dfac3a7a52df049f4f | 2022-07-20T09:53:07.000Z | [
"pytorch",
"tf",
"segformer",
"dataset:scene_parse_150",
"arxiv:2105.15203",
"transformers",
"vision",
"image-segmentation",
"license:apache-2.0"
] | image-segmentation | false | nvidia | null | nvidia/segformer-b5-finetuned-ade-640-640 | 1,425 | 4 | transformers | 1,555 | ---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- scene_parse_150
widget:
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
example_title: House
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_... |
jason9693/soongsil-bert-base | 98850850c415707ffa4ee0edd3514f009e3486b5 | 2022-07-13T05:32:09.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"ko",
"transformers",
"autotrain_compatible"
] | fill-mask | false | jason9693 | null | jason9693/soongsil-bert-base | 1,420 | null | transformers | 1,556 | ---
language: ko
widget:
- 숭실대학교 글로벌<mask>학부
--- |
bigscience/test-bloomd-6b3 | c10cdb6042075edb4aeefe6bcaff3e3d421e12b4 | 2022-07-07T02:06:28.000Z | [
"pytorch",
"bloom",
"transformers"
] | null | false | bigscience | null | bigscience/test-bloomd-6b3 | 1,420 | null | transformers | 1,557 | Entry not found |
textattack/distilbert-base-uncased-CoLA | a4987947954e3b9717c81605155d423c7b9be0a5 | 2020-07-06T16:29:03.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/distilbert-base-uncased-CoLA | 1,410 | null | transformers | 1,558 | ## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... |
Helsinki-NLP/opus-mt-fr-de | 473168cb217c0d605c975d7e6b33be7f5956d247 | 2021-09-09T21:53:23.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"fr",
"de",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-fr-de | 1,409 | null | transformers | 1,559 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-fr-de
* source languages: fr
* target languages: de
* OPUS readme: [fr-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
svalabs/ger-roberta | 7e6096f74b75d7f41aa32c1970b8c3a67ddd5b2c | 2021-05-20T22:04:35.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | svalabs | null | svalabs/ger-roberta | 1,402 | 1 | transformers | 1,560 | Entry not found |
studio-ousia/luke-large-finetuned-tacred | ba3d02d7791d738d6bd480592ed814525124fbbc | 2022-03-23T12:31:16.000Z | [
"pytorch",
"luke",
"transformers"
] | null | false | studio-ousia | null | studio-ousia/luke-large-finetuned-tacred | 1,401 | 1 | transformers | 1,561 | Entry not found |
facebook/nllb-200-3.3B | 96f0d3f9eb2c3f5eb2c176ecd9393c803d0a28ff | 2022-07-19T15:46:35.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"bn",
"bho",
"bjn",
"bo",
"bs",
"bug"... | text2text-generation | false | facebook | null | facebook/nllb-200-3.3B | 1,393 | 8 | transformers | 1,562 | ---
language:
- ace
- acm
- acq
- aeb
- af
- ajp
- ak
- als
- am
- apc
- ar
- ars
- ary
- arz
- as
- ast
- awa
- ayr
- azb
- azj
- ba
- bm
- ban
- be
- bem
- bn
- bho
- bjn
- bo
- bs
- bug
- bg
- ca
- ceb
- cs
- cjk
- ckb
- crh
- cy
- da
- de
- dik
- dyu
- dz
- el
- en
- eo
- et
- eu
- ee
- fo
- fj
- fi
- fon
- fr
- fu... |
douwekiela/resnet-18-finetuned-dogfood | 52a41984b7b2d00d53962f5e52e3de3cb55ad600 | 2022-06-27T12:38:50.000Z | [
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"dataset:imagefolder",
"dataset:lewtun/dog_food",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | douwekiela | null | douwekiela/resnet-18-finetuned-dogfood | 1,391 | null | transformers | 1,563 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
- lewtun/dog_food
metrics:
- accuracy
model-index:
- name: resnet-18-finetuned-dogfood
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: lewtun/dog_food
type: lewtun/dog_foo... |
anton-l/wav2vec2-large-xlsr-53-russian | 85cb34787cc7499533a682925c82e72f0faff9eb | 2021-07-05T20:26:00.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | anton-l | null | anton-l/wav2vec2-large-xlsr-53-russian | 1,382 | 1 | transformers | 1,564 | ---
language: ru
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Russian XLSR Wav2Vec2 Large 53 by Anton Lozhkov
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
... |
nguyenvulebinh/envibert | f9b0bf1135a56b5d70625bb080bc55e5676bad87 | 2021-12-19T14:20:51.000Z | [
"pytorch",
"roberta",
"fill-mask",
"vi",
"transformers",
"exbert",
"license:cc-by-nc-4.0",
"autotrain_compatible"
] | fill-mask | false | nguyenvulebinh | null | nguyenvulebinh/envibert | 1,382 | null | transformers | 1,565 | ---
language: vi
tags:
- exbert
license: cc-by-nc-4.0
---
# RoBERTa for Vietnamese and English (envibert)
This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named ***envibert***. The model architecture is custom for production so it only contains 70M parameters.
... |
jonatasgrosman/wav2vec2-xls-r-1b-portuguese | 006bc2f9c3fa2364fd7a0fbccc350e9786d45735 | 2022-07-27T23:39:54.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-xls-r-1b-portuguese | 1,380 | 2 | transformers | 1,566 | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- pt
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R Wav2Vec2 Portuguese by Jonatas Grosman
results:
- task:
name: Automatic S... |
aloxatel/bert-base-mnli | 8310aae4bcf78cf1e3ab4b66ac1cda7455447f0b | 2021-05-18T23:31:06.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | aloxatel | null | aloxatel/bert-base-mnli | 1,379 | null | transformers | 1,567 | Entry not found |
mrm8488/bert-multi-cased-finetuned-xquadv1 | 1751251942b8f911f2658475a19f2d8767138bf3 | 2021-05-20T00:29:15.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"multilingual",
"arxiv:1910.11856",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/bert-multi-cased-finetuned-xquadv1 | 1,378 | 1 | transformers | 1,568 | ---
language: multilingual
thumbnail:
---
# BERT (base-multilingual-cased) fine-tuned for multilingual Q&A
This model was created by [Google](https://github.com/google-research/bert/blob/master/multilingual.md) and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) like data for multilingual (`11 different lang... |
microsoft/swin-base-patch4-window12-384 | 0c86592b628ac7b09a19ab701c0a76f00b33ce25 | 2022-05-16T18:32:57.000Z | [
"pytorch",
"tf",
"swin",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/swin-base-patch4-window12-384 | 1,377 | 1 | transformers | 1,569 | ---
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... |
sentence-transformers/all-MiniLM-L6-v1 | a65f6476ba7ba5a7b3595f37a5331a2a08797fa5 | 2021-08-30T20:00:14.000Z | [
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"sentence-transformers",
"sentence-similarity",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/all-MiniLM-L6-v1 | 1,372 | 2 | sentence-transformers | 1,570 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
---
# all-MiniLM-L6-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used ... |
microsoft/deberta-base-mnli | a80a6eb013898011540b19bf1f64e21eb61e53d6 | 2021-12-09T13:36:31.000Z | [
"pytorch",
"rust",
"deberta",
"text-classification",
"en",
"arxiv:2006.03654",
"transformers",
"deberta-v1",
"deberta-mnli",
"license:mit"
] | text-classification | false | microsoft | null | microsoft/deberta-base-mnli | 1,368 | 1 | transformers | 1,571 | ---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) impro... |
IDEA-CCNL/Erlangshen-Roberta-110M-Similarity | d2a55ff1afd453d9170d8d2cba54d7b575535b32 | 2022-05-12T09:50:42.000Z | [
"pytorch",
"bert",
"text-classification",
"zh",
"transformers",
"NLU",
"NLI",
"license:apache-2.0"
] | text-classification | false | IDEA-CCNL | null | IDEA-CCNL/Erlangshen-Roberta-110M-Similarity | 1,365 | 1 | transformers | 1,572 | ---
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-Similarity, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
We collect 20 paraphrace datasets in the Chinese domain for f... |
Geotrend/bert-base-en-fr-de-cased | df74315d628f1084b9f22f04b11a9b27ca24e568 | 2021-05-18T19:18:39.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Geotrend | null | Geotrend/bert-base-en-fr-de-cased | 1,364 | null | transformers | 1,573 | ---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# bert-base-en-fr-de-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://hugg... |
dbmdz/electra-base-italian-xxl-cased-discriminator | 9dc80d590b251f8472138761144ba37a932b8936 | 2020-12-11T21:37:19.000Z | [
"pytorch",
"electra",
"pretraining",
"it",
"dataset:wikipedia",
"transformers",
"license:mit"
] | null | false | dbmdz | null | dbmdz/electra-base-italian-xxl-cased-discriminator | 1,364 | null | transformers | 1,574 | ---
language: it
license: mit
datasets:
- wikipedia
---
# 🤗 + 📚 dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models 🎉
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikiped... |
saibo/legal-roberta-base | e0d78f4e064ff27621d61fa2320c79addb528d81 | 2021-08-31T15:36:35.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"en",
"transformers",
"legal",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | saibo | null | saibo/legal-roberta-base | 1,363 | 2 | transformers | 1,575 | ---
language:
- en
tags:
- legal
license: apache-2.0
metrics:
- precision
- recall
---
# LEGAL-ROBERTA
We introduce LEGAL-ROBERTA, which is a domain-specific language representation model fine-tuned on large-scale legal corpora(4.6 GB).
## Demo
'This \<mask\> Agreement is between General Motors and John Murra... |
ethanyt/guwenbert-base | eff0d4a5196d7bf7b8be746c5c6437e89d8b9061 | 2021-06-02T03:27:16.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"zh",
"transformers",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | ethanyt | null | ethanyt/guwenbert-base | 1,362 | 1 | transformers | 1,576 | ---
language:
- "zh"
thumbnail: "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "pytorch"
license: "apache-2.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
widget:
- text... |
textattack/roberta-base-QNLI | 68887d836a1dc4aab8a053e1502d5bff2677ed14 | 2021-05-20T22:09:33.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/roberta-base-QNLI | 1,357 | null | transformers | 1,577 | Entry not found |
jhu-clsp/roberta-large-eng-ara-128k | 8557e84530e0833f9f9c647d277e4ff5881d135e | 2021-09-14T19:37:39.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"ar",
"en",
"dataset:arabic_billion_words",
"dataset:cc100",
"dataset:gigaword",
"dataset:oscar",
"dataset:wikipedia",
"transformers",
"bert",
"roberta",
"exbert",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | jhu-clsp | null | jhu-clsp/roberta-large-eng-ara-128k | 1,356 | 4 | transformers | 1,578 | ---
language:
- ar
- en
tags:
- bert
- roberta
- exbert
license: mit
datasets:
- arabic_billion_words
- cc100
- gigaword
- oscar
- wikipedia
---
# An English-Arabic Bilingual Encoder
```
from transformers import AutoModelForMaskedLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/roberta-large-eng-... |
rinna/japanese-gpt2-xsmall | e2dac72065c0da14d687ade9931549711e1f35fd | 2021-08-23T03:20:38.000Z | [
"pytorch",
"tf",
"gpt2",
"text-generation",
"ja",
"dataset:cc100",
"dataset:wikipedia",
"transformers",
"japanese",
"lm",
"nlp",
"license:mit"
] | text-generation | false | rinna | null | rinna/japanese-gpt2-xsmall | 1,355 | 5 | transformers | 1,579 | ---
language: ja
thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png
tags:
- ja
- japanese
- gpt2
- text-generation
- lm
- nlp
license: mit
datasets:
- cc100
- wikipedia
widget:
- text: "生命、宇宙、そして万物についての究極の疑問の答えは"
---
# japanese-gpt2-xsmall
This repository provides an... |
lighteternal/wav2vec2-large-xlsr-53-greek | c5c82840b689b827a2029deefe82670c7c5809a0 | 2022-03-26T10:12:37.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"el",
"dataset:common_voice",
"transformers",
"audio",
"hf-asr-leaderboard",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | lighteternal | null | lighteternal/wav2vec2-large-xlsr-53-greek | 1,354 | 1 | transformers | 1,580 | ---
language: el
datasets:
- common_voice
tags:
- audio
- hf-asr-leaderboard
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Greek by Lighteternal
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
data... |
KoboldAI/fairseq-dense-6.7B-Shinen | 5a6d1baba58d6cdd98fbd472b3501749d6e8ec5a | 2022-04-13T08:19:31.000Z | [
"pytorch",
"xglm",
"text-generation",
"en",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/fairseq-dense-6.7B-Shinen | 1,354 | null | transformers | 1,581 | ---
language: en
license: mit
---
# Fairseq-dense 6.7B - Shinen
## Model Description
Fairseq-dense 6.7B-Shinen is a finetune created using Fairseq's MoE dense model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
**Warning: THIS model is NOT suitable for use by minors. The model will o... |
aubmindlab/bert-base-arabert | 4b7ceb4967371d5e0b559b275e006f54d671c48e | 2021-05-19T11:49:06.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B Arabic Corpus",
"arxiv:2003.00104",
"transformers",
"autotrain_compatible"
] | fill-mask | false | aubmindlab | null | aubmindlab/bert-base-arabert | 1,352 | 7 | transformers | 1,582 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
widget:
- text: " عاصم +ة لبنان هي [MASK] ."
---
# !!! A newer version of this model is available !!! [AraBERTv2](https://huggingface.co/aubmindlab/bert-base-arabertv2)
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<im... |
megagonlabs/t5-base-japanese-web | 7a7211aacbdc06c47060793c6e032d22db2661af | 2021-09-06T10:32:21.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ja",
"dataset:mc4",
"dataset:wiki40b",
"arxiv:1910.10683",
"transformers",
"seq2seq",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | megagonlabs | null | megagonlabs/t5-base-japanese-web | 1,352 | 6 | transformers | 1,583 | ---
language: ja
tags:
- t5
- text2text-generation
- seq2seq
license: apache-2.0
datasets:
- mc4
- wiki40b
---
# t5-base-japanese-web (with Byte-fallback, 32K)
## Description
[megagonlabs/t5-base-japanese-web](https://huggingface.co/megagonlabs/t5-base-japanese-web) is a T5 (Text-to-Text Transfer Transformer) model ... |
google/bert2bert_L-24_wmt_de_en | 3b460d3f76f9a4cb0d8c2946a63a28fbe5f66a83 | 2020-12-11T21:41:14.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"en",
"de",
"dataset:wmt14",
"arxiv:1907.12461",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | google | null | google/bert2bert_L-24_wmt_de_en | 1,349 | 2 | transformers | 1,584 | ---
language:
- en
- de
license: apache-2.0
datasets:
- wmt14
tags:
- translation
---
# bert2bert_L-24_wmt_de_en EncoderDecoder model
The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev... |
mrm8488/t5-small-finetuned-quora-for-paraphrasing | bd3a2ea4f1d31fc3270e0118b1deb02a85902f0c | 2020-12-11T21:56:30.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:quora",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-small-finetuned-quora-for-paraphrasing | 1,348 | 5 | transformers | 1,585 | ---
language: en
datasets:
- quora
---
# T5-base fine-tuned on Quora question pair dataset for Question Paraphrasing ❓↔️❓
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [Quodra question pair](https://huggingface.co/nlp/viewer/?dataset=quora) dataset for **Quest... |
Helsinki-NLP/opus-mt-grk-en | ab6cfc132676a64ff077371a8140b2bcb30bb389 | 2021-01-18T08:53:09.000Z | [
"pytorch",
"marian",
"text2text-generation",
"el",
"grk",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-grk-en | 1,344 | null | transformers | 1,586 | ---
language:
- el
- grk
- en
tags:
- translation
license: apache-2.0
---
### grk-eng
* source group: Greek languages
* target group: English
* OPUS readme: [grk-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/grk-eng/README.md)
* model: transformer
* source language(s): ell grc_Grek... |
sentence-transformers/nli-roberta-large | b10cddcd7069bcd76ad00ac3142005892d4e83bd | 2021-08-05T08:28:34.000Z | [
"pytorch",
"jax",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/nli-roberta-large | 1,343 | null | sentence-transformers | 1,587 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net... |
M-CLIP/XLM-Roberta-Large-Vit-B-16Plus | e0035edeb83948e336724b7db6bd2c70c9750cf0 | 2022-07-20T17:28:54.000Z | [
"pytorch",
"tf",
"multilingual"
] | null | false | M-CLIP | null | M-CLIP/XLM-Roberta-Large-Vit-B-16Plus | 1,340 | 1 | null | 1,588 | ---
language: multilingual
---
## Multilingual-clip: XLM-Roberta-Large-Vit-B-16Plus
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-B-16Plus` can be retrieved via instructions found on `... |
princeton-nlp/unsup-simcse-bert-large-uncased | 5365919fdaeeab4b41ce3b963992a5648366c268 | 2021-05-20T02:59:52.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | princeton-nlp | null | princeton-nlp/unsup-simcse-bert-large-uncased | 1,339 | null | transformers | 1,589 | Entry not found |
alibaba-pai/pai-dkplm-medical-base-zh | e9e3272132ce4b7a13f1dd92a93a8b610c3e0b75 | 2022-05-17T02:25:18.000Z | [
"pytorch",
"bert",
"fill-mask",
"zh",
"arxiv:2205.00258",
"arxiv:2112.01047",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | alibaba-pai | null | alibaba-pai/pai-dkplm-medical-base-zh | 1,339 | 2 | transformers | 1,590 | ---
language: zh
pipeline_tag: fill-mask
widget:
- text: "感冒需要吃[MASK]"
- text: "人类的[MASK]温是37度"
tags:
- bert
license: apache-2.0
---
## Chinese DKPLM (Decomposable Knowledge-enhanced Pre-trained Language Model) for the medical domain
For Chinese natural language processing in specific domains, we provide **Chinese DKPL... |
deepmind/vision-perceiver-conv | 795b5eea5867940bd8fa46105029874afce6f037 | 2021-12-11T13:12:42.000Z | [
"pytorch",
"perceiver",
"image-classification",
"dataset:imagenet",
"arxiv:2107.14795",
"transformers",
"license:apache-2.0"
] | image-classification | false | deepmind | null | deepmind/vision-perceiver-conv | 1,338 | 3 | transformers | 1,591 | ---
license: apache-2.0
tags:
datasets:
- imagenet
---
# Perceiver IO for vision (convolutional processing)
Perceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https:/... |
cffl/bert-base-styleclassification-subjective-neutral | 1339b8de703cb52c729475a89427078052af8595 | 2022-07-12T11:57:42.000Z | [
"pytorch",
"bert",
"text-classification",
"arxiv:1911.09709",
"arxiv:1703.01365",
"transformers",
"license:apache-2.0"
] | text-classification | false | cffl | null | cffl/bert-base-styleclassification-subjective-neutral | 1,332 | 1 | transformers | 1,592 | ---
license: apache-2.0
---
# bert-base-styleclassification-subjective-neutral
## Model description
This [bert-base-uncased](https://huggingface.co/bert-base-uncased) model has been fine-tuned on the [Wiki Neutrality Corpus (WNC)](https://arxiv.org/pdf/1911.09709.pdf) - a parallel corpus of 180,000 biased and neutra... |
murali1996/bert-base-cased-spell-correction | d2a5bbccc41a0f4ff5e7c16e1c3b8d96ba8883b2 | 2021-05-20T01:04:57.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | murali1996 | null | murali1996/bert-base-cased-spell-correction | 1,326 | 4 | transformers | 1,593 | `bert-base-cased` trained for spelling correction. See [neuspell](https://github.com/neuspell/neuspell) repository for more details about training and evaluating the model. |
nvidia/segformer-b5-finetuned-cityscapes-1024-1024 | ff4c15ea9518e6aea09252e4ca719f049f11dc09 | 2022-07-20T09:53:14.000Z | [
"pytorch",
"tf",
"segformer",
"dataset:cityscapes",
"arxiv:2105.15203",
"transformers",
"vision",
"image-segmentation",
"license:apache-2.0"
] | image-segmentation | false | nvidia | null | nvidia/segformer-b5-finetuned-cityscapes-1024-1024 | 1,323 | 2 | transformers | 1,594 | ---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- cityscapes
widget:
- src: https://www.researchgate.net/profile/Anurag-Arnab/publication/315881952/figure/fig5/AS:667673876779033@1536197265755/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.jpg
example_ti... |
ethanyt/guwen-punc | 3e456b0b271984421c3012a56099420819d95eff | 2021-06-17T06:56:46.000Z | [
"pytorch",
"roberta",
"token-classification",
"zh",
"transformers",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"punctuation marker",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | ethanyt | null | ethanyt/guwen-punc | 1,314 | 3 | transformers | 1,595 | ---
language:
- "zh"
thumbnail: "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png"
tags:
- "chinese"
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "bert"
- "pytorch"
- "punctuation marker"
license: "apache-2.0"
pipeline_tag: "token-classification"
w... |
facebook/hubert-large-ll60k | ff022d095678a2995f3c49bab18a96a9e553f782 | 2021-11-05T12:42:57.000Z | [
"pytorch",
"tf",
"hubert",
"feature-extraction",
"en",
"dataset:libri-light",
"arxiv:2106.07447",
"transformers",
"speech",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/hubert-large-ll60k | 1,314 | 4 | transformers | 1,596 | ---
language: en
datasets:
- libri-light
tags:
- speech
license: apache-2.0
---
# Hubert-Large
[Facebook's Hubert](https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression)
The large model pretrained on 16kHz sampled speech audio. When using the m... |
KES/T5-KES | e43052db0de09ec41e86ff586a0ba4f1f9defd62 | 2022-07-02T02:41:16.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:jfleg",
"arxiv:1702.04066",
"transformers",
"sentence correction",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | KES | null | KES/T5-KES | 1,312 | 1 | transformers | 1,597 | ---
language: en
tags:
- sentence correction
- text2text-generation
license: cc-by-nc-sa-4.0
datasets:
- jfleg
---
# Model
This model utilises T5-base pre-trained model. It was fine tuned using a modified version of the [JFLEG](https://arxiv.org/abs/1702.04066) dataset and [Happy Transformer framework](https:/... |
airesearch/wav2vec2-large-xlsr-53-th | 3155938c549b23eee16b1d4b55dcb161b7fe4bcf | 2022-03-23T18:24:45.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"th",
"dataset:common_voice",
"transformers",
"audio",
"hf-asr-leaderboard",
"robust-speech-event",
"speech",
"xlsr-fine-tuning",
"license:cc-by-sa-4.0",
"model-index"
] | automatic-speech-recognition | false | airesearch | null | airesearch/wav2vec2-large-xlsr-53-th | 1,312 | 2 | transformers | 1,598 | ---
language: th
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
- robust-speech-event
- speech
- xlsr-fine-tuning
license: cc-by-sa-4.0
model-index:
- name: XLS-R-53 - Thai
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
... |
Intel/dpt-large-ade | c9a80469a44109742a2b44a820fe34eb897efb3c | 2022-04-14T08:29:24.000Z | [
"pytorch",
"dpt",
"dataset:scene_parse_150",
"arxiv:2103.13413",
"transformers",
"vision",
"image-segmentation",
"license:apache-2.0"
] | image-segmentation | false | Intel | null | Intel/dpt-large-ade | 1,311 | null | transformers | 1,599 | ---
license: apache-2.0
tags:
- vision
- image-segmentation
datasets:
- scene_parse_150
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: htt... |
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