modelId stringlengths 4 112 | sha stringlengths 40 40 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringclasses 29
values | private bool 1
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values | __index_level_0__ int64 0 38.5k | readme stringlengths 0 186k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Helsinki-NLP/opus-mt-en-roa | ac8c907c99b8939697a1793862d4c34159c408d7 | 2021-01-18T08:15:13.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"en",
"it",
"ca",
"rm",
"es",
"ro",
"gl",
"co",
"wa",
"pt",
"oc",
"an",
"id",
"fr",
"ht",
"roa",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-roa | 1,108 | null | transformers | 1,700 | ---
language:
- en
- it
- ca
- rm
- es
- ro
- gl
- co
- wa
- pt
- oc
- an
- id
- fr
- ht
- roa
tags:
- translation
license: apache-2.0
---
### eng-roa
* source group: English
* target group: Romance languages
* OPUS readme: [eng-roa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-roa/R... |
Skoltech/russian-sensitive-topics | a5deed3c020f78a0ddb404b86609e2cf5693c3f1 | 2021-05-18T22:41:20.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"ru",
"arxiv:2103.05345",
"transformers",
"toxic comments classification"
] | text-classification | false | Skoltech | null | Skoltech/russian-sensitive-topics | 1,106 | 3 | transformers | 1,701 | ---
language:
- ru
tags:
- toxic comments classification
licenses:
- cc-by-nc-sa
---
## General concept of the model
This model is trained on the dataset of sensitive topics of the Russian language. The concept of sensitive topics is described [in this article ](https://www.aclweb.org/anthology/2021.bsnlp-1.4/) pre... |
fnlp/elasticbert-base | 08b6aa4eb88ef6bb6dd6294edb8b8b11120f5b98 | 2021-10-28T10:54:47.000Z | [
"pytorch",
"elasticbert",
"fill-mask",
"arxiv:2110.07038",
"transformers",
"autotrain_compatible"
] | fill-mask | false | fnlp | null | fnlp/elasticbert-base | 1,104 | 3 | transformers | 1,702 | # ElasticBERT-BASE
## Model description
This is an implementation of the `base` version of ElasticBERT.
[**Towards Efficient NLP: A Standard Evaluation and A Strong Baseline**](https://arxiv.org/pdf/2110.07038.pdf)
Xiangyang Liu, Tianxiang Sun, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Hu... |
seyonec/SMILES_tokenized_PubChem_shard00_50k | cc844b1d17d99e51e36205e812def4f77c8e4ac4 | 2021-05-20T21:10:29.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/SMILES_tokenized_PubChem_shard00_50k | 1,102 | null | transformers | 1,703 | Entry not found |
Yanzhu/bertweetfr-base | 90de75c9b6f530bf1831ba22aee06f04f7c94703 | 2021-06-13T07:20:37.000Z | [
"pytorch",
"camembert",
"fill-mask",
"fr",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Yanzhu | null | Yanzhu/bertweetfr-base | 1,095 | 2 | transformers | 1,704 | ---
language: "fr"
---
Domain-adaptive pretraining of camembert-base using 15 GB of French Tweets |
allenai/ivila-block-layoutlm-finetuned-docbank | 1991156f842c9ae1a3eef19ec365a7af3f1ae064 | 2021-09-27T22:56:28.000Z | [
"pytorch",
"layoutlm",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | allenai | null | allenai/ivila-block-layoutlm-finetuned-docbank | 1,093 | null | transformers | 1,705 | Entry not found |
facebook/s2t-medium-librispeech-asr | 782ffebb9f762136f76e4b58afbb30b19a4da5a1 | 2022-02-07T15:04:00.000Z | [
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"arxiv:1904.08779",
"transformers",
"audio",
"license:mit"
] | automatic-speech-recognition | false | facebook | null | facebook/s2t-medium-librispeech-asr | 1,093 | 3 | transformers | 1,706 | ---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
pipeline_tag: automatic-speech-recognition
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingfac... |
google/canine-c | 1e8c8b3a4e860cb2a23a14c3fbba61ef3aed51f6 | 2021-08-13T08:24:13.000Z | [
"pytorch",
"canine",
"feature-extraction",
"multilingual",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2103.06874",
"transformers",
"license:apache-2.0"
] | feature-extraction | false | google | null | google/canine-c | 1,093 | 1 | transformers | 1,707 | ---
language: multilingual
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# CANINE-c (CANINE pre-trained with autoregressive character loss)
Pretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper [CANINE: Pre-training an Efficient Tokeniz... |
google/rembert | 65da5133da36e29dfca67d4f0dd9f7f9db21b563 | 2022-05-27T15:05:23.000Z | [
"pytorch",
"tf",
"rembert",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"bs",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"haw",
"hi",... | null | false | google | null | google/rembert | 1,093 | 6 | transformers | 1,708 | ---
language:
- multilingual
- af
- am
- ar
- az
- be
- bg
- bn
- bs
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- hi
- hmn
- hr
- ht
- hu
- hy
- id
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
-... |
malper/unikud | 73fdb7a158634826a73b66846d1b743fd700e990 | 2022-04-25T02:11:25.000Z | [
"pytorch",
"canine",
"he",
"transformers"
] | null | false | malper | null | malper/unikud | 1,093 | null | transformers | 1,709 | ---
language:
- he
---
Please see [this model's DagsHub repository](https://dagshub.com/morrisalp/unikud) for information on usage. |
TypicaAI/magbert-ner | 069ef5c4d8e7334fb89c2e54fe8f58d55b099ee7 | 2020-12-11T21:30:45.000Z | [
"pytorch",
"camembert",
"token-classification",
"fr",
"transformers",
"autotrain_compatible"
] | token-classification | false | TypicaAI | null | TypicaAI/magbert-ner | 1,091 | null | transformers | 1,710 | ---
language: fr
widget:
- text: "Je m'appelle Hicham et je vis a Fès"
---
# MagBERT-NER: a state-of-the-art NER model for Moroccan French language (Maghreb)
## Introduction
[MagBERT-NER] is a state-of-the-art NER model for Moroccan French language (Maghreb). The MagBERT-NER model was fine-tuned for NER Task based t... |
anonymous-german-nlp/german-gpt2 | 2c3dbb0a9dc4fd368fdb256d5093cd37c13d4936 | 2021-05-21T13:20:42.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"de",
"transformers",
"license:mit"
] | text-generation | false | anonymous-german-nlp | null | anonymous-german-nlp/german-gpt2 | 1,091 | null | transformers | 1,711 | ---
language: de
widget:
- text: "Heute ist sehr schönes Wetter in"
license: mit
---
# German GPT-2 model
**Note**: This model was de-anonymized and now lives at:
https://huggingface.co/dbmdz/german-gpt2
Please use the new model name instead! |
HooshvareLab/bert-base-parsbert-ner-uncased | 3d87e20bbca18f8d8d9d545cacd198aee69371fd | 2021-05-18T20:43:54.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"fa",
"arxiv:2005.12515",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | HooshvareLab | null | HooshvareLab/bert-base-parsbert-ner-uncased | 1,090 | null | transformers | 1,712 | ---
language: fa
license: apache-2.0
---
## ParsBERT: Transformer-based Model for Persian Language Understanding
ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base.
Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
Al... |
raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed | 60897ba4bdcfb7f6cf88d18f75bbd0f9399f5908 | 2021-11-05T07:33:08.000Z | [
"pytorch",
"roberta",
"token-classification",
"en",
"dataset:ncbi-disease",
"dataset:bc5cdr",
"transformers",
"ner",
"ncbi",
"disease",
"pubmed",
"bioinfomatics",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | raynardj | null | raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed | 1,090 | 4 | transformers | 1,713 | ---
language:
- en
tags:
- ner
- ncbi
- disease
- pubmed
- bioinfomatics
license: apache-2.0
datasets:
- ncbi-disease
- bc5cdr
widget:
- text: "Hepatocyte nuclear factor 4 alpha (HNF4α) is regulated by different promoters to generate two isoforms, one of which functions as a tumor suppressor. Here, the authors reveal t... |
dbmdz/convbert-base-turkish-mc4-uncased | 5d8c2e7856ba8f71c627eb8b00df6edd306b328a | 2021-09-23T10:41:21.000Z | [
"pytorch",
"tf",
"convbert",
"fill-mask",
"tr",
"dataset:allenai/c4",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/convbert-base-turkish-mc4-uncased | 1,088 | null | transformers | 1,714 | ---
language: tr
license: mit
datasets:
- allenai/c4
---
# 🇹🇷 Turkish ConvBERT 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/2378174... |
german-nlp-group/electra-base-german-uncased | 5a79890051f8df23591f06710012d399b7e17d9b | 2021-05-24T13:26:08.000Z | [
"pytorch",
"electra",
"pretraining",
"de",
"transformers",
"commoncrawl",
"uncased",
"umlaute",
"umlauts",
"german",
"deutsch",
"license:mit"
] | null | false | german-nlp-group | null | german-nlp-group/electra-base-german-uncased | 1,087 | 2 | transformers | 1,715 | ---
language: de
license: mit
thumbnail: "https://raw.githubusercontent.com/German-NLP-Group/german-transformer-training/master/model_cards/german-electra-logo.png"
tags:
- electra
- commoncrawl
- uncased
- umlaute
- umlauts
- german
- deutsch
---
# German Electra Uncased
<img width="300px" src="https:/... |
Tidum/DialoGPT-large-Michael | 15bcc27d9effca5ea4e67b33ebd387e5bd860718 | 2022-02-06T19:59:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Tidum | null | Tidum/DialoGPT-large-Michael | 1,086 | null | transformers | 1,716 | ---
tags:
- conversational
---
#Michael DialoGPT Model |
textattack/albert-base-v2-imdb | e377b81678ba240cd835375c5853bb590e10e75a | 2020-07-06T16:34:24.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/albert-base-v2-imdb | 1,086 | null | transformers | 1,717 | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classi... |
zhayunduo/roberta-base-stocktwits-finetuned | e4fd3e0fcc2af47df76ddc74d90840fe5a7ec299 | 2022-04-18T07:40:25.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | zhayunduo | null | zhayunduo/roberta-base-stocktwits-finetuned | 1,085 | 0 | transformers | 1,718 | ---
license: apache-2.0
---
## **Sentiment Inferencing model for stock related commments**
#### *A project by NUS ISS students Frank Cao, Gerong Zhang, Jiaqi Yao, Sikai Ni, Yunduo Zhang*
<br />
### Description
This model is fine tuned with roberta-base model on 3200000 comments from stocktwits, with the user labe... |
microsoft/swin-large-patch4-window12-384-in22k | df0f89cc75d470a35ff4bb5d0e53fbdbbe377bb3 | 2022-05-16T18:40:51.000Z | [
"pytorch",
"tf",
"swin",
"image-classification",
"dataset:imagenet-21k",
"arxiv:2103.14030",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/swin-large-patch4-window12-384-in22k | 1,084 | 1 | transformers | 1,719 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: http... |
Luyu/co-condenser-marco-retriever | 2149ab984d2aea9c39cf7e6bbc2041a5302866a2 | 2021-09-02T14:43:18.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | Luyu | null | Luyu/co-condenser-marco-retriever | 1,079 | 2 | transformers | 1,720 | Entry not found |
soheeyang/rdr-ctx_encoder-single-nq-base | 289088ddb79e14a0ca555548b106a5e594bf6ba2 | 2021-04-15T15:58:10.000Z | [
"pytorch",
"tf",
"dpr",
"arxiv:2010.10999",
"arxiv:2004.04906",
"transformers"
] | null | false | soheeyang | null | soheeyang/rdr-ctx_encoder-single-nq-base | 1,076 | null | transformers | 1,721 | # rdr-ctx_encoder-single-nq-base
Reader-Distilled Retriever (`RDR`)
Sohee Yang and Minjoon Seo, [Is Retriever Merely an Approximator of Reader?](https://arxiv.org/abs/2010.10999), arXiv 2020
The paper proposes to distill the reader into the retriever so that the retriever absorbs the strength of the reader while kee... |
sentence-transformers/gtr-t5-large | fd31cff184d356b3a9a5794706551fc5306071a2 | 2022-02-09T12:33:08.000Z | [
"pytorch",
"t5",
"en",
"arxiv:2112.07899",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/gtr-t5-large | 1,074 | 1 | sentence-transformers | 1,722 | ---
pipeline_tag: sentence-similarity
language: en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/gtr-t5-large
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 d... |
SkolkovoInstitute/russian_toxicity_classifier | 2b9a086ec05c2dc202fea11ed15f317b1676b18c | 2021-12-08T15:41:00.000Z | [
"pytorch",
"tf",
"bert",
"text-classification",
"ru",
"transformers",
"toxic comments classification"
] | text-classification | false | SkolkovoInstitute | null | SkolkovoInstitute/russian_toxicity_classifier | 1,070 | 6 | transformers | 1,723 | ---
language:
- ru
tags:
- toxic comments classification
licenses:
- cc-by-nc-sa
---
Bert-based classifier (finetuned from [Conversational Rubert](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)) trained on merge of Russian Language Toxic Comments [dataset](https://www.kaggle.com/blackmoon/russia... |
m3hrdadfi/distilbert-zwnj-wnli-mean-tokens | 6d0d94f899be52bc72f68f3f3b5800650cb0395b | 2021-06-28T18:05:51.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | m3hrdadfi | null | m3hrdadfi/distilbert-zwnj-wnli-mean-tokens | 1,069 | null | sentence-transformers | 1,724 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
widget:
source_sentence: "مردی در حال خوردن پاستا است."
sentences:
- 'مردی در حال خوردن خوراک است.'
- 'مردی در حال خوردن یک تکه نان است.'
- 'دختری بچه ای را حمل ... |
uer/bart-chinese-6-960-cluecorpussmall | b8eb755e2597cdf448078b70248d2d5cde9cd17b | 2021-10-08T14:47:18.000Z | [
"pytorch",
"bart",
"text2text-generation",
"Chinese",
"dataset:CLUECorpusSmall",
"arxiv:1909.05658",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | uer | null | uer/bart-chinese-6-960-cluecorpussmall | 1,069 | 1 | transformers | 1,725 | ---
language: Chinese
datasets: CLUECorpusSmall
widget:
- text: "作为电子[MASK]的平台，京东绝对是领先者。如今的刘强[MASK]已经是身价过[MASK]的老板。"
---
# Chinese BART
## Model description
This model is pre-trained by [UER-py](https://arxiv.org/abs/1909.05658).
## How to use
You can use this model directly with a pipeline for text2text genera... |
flyswot/flyswot | efc837358a25ea97d69e67a6c253531391a32c65 | 2022-06-15T17:32:16.000Z | [
"pytorch",
"convnext",
"image-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | image-classification | false | flyswot | null | flyswot/flyswot | 1,064 | null | transformers | 1,726 | ---
tags:
- generated_from_trainer
model-index:
- name: flyswot
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. -->
# flyswot
This model is a fine-tuned version of ... |
flaubert/flaubert_large_cased | a5fdc16154e92c75d7adde577e183793ad19d040 | 2021-05-19T16:55:50.000Z | [
"pytorch",
"flaubert",
"fill-mask",
"fr",
"dataset:flaubert",
"transformers",
"bert",
"language-model",
"flue",
"french",
"bert-large",
"flaubert-large",
"cased",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | flaubert | null | flaubert/flaubert_large_cased | 1,063 | null | transformers | 1,727 | ---
language: fr
license: mit
datasets:
- flaubert
metrics:
- flue
tags:
- bert
- language-model
- flaubert
- flue
- french
- bert-large
- flaubert-large
- cased
---
# FlauBERT: Unsupervised Language Model Pre-training for French
**FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus.... |
sentence-transformers/gtr-t5-xl | 0b2448c8b50fa688f209d70b083cf3ad934e0e37 | 2022-02-09T12:29:08.000Z | [
"pytorch",
"t5",
"en",
"arxiv:2112.07899",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/gtr-t5-xl | 1,062 | null | sentence-transformers | 1,728 | ---
pipeline_tag: sentence-similarity
language: en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/gtr-t5-xl
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dime... |
textattack/roberta-base-STS-B | 3bea43e748145fbd2bcefba0004e360785c76564 | 2021-05-20T22:12:47.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/roberta-base-STS-B | 1,061 | null | transformers | 1,729 | ## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a regressio... |
Huffon/sentence-klue-roberta-base | a5aca746f7931205aa44992e81fdeb7faf7c443c | 2021-06-20T17:32:17.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"ko",
"dataset:klue",
"arxiv:1908.10084",
"sentence-transformers"
] | feature-extraction | false | Huffon | null | Huffon/sentence-klue-roberta-base | 1,060 | 4 | sentence-transformers | 1,730 | ---
language: ko
tags:
- roberta
- sentence-transformers
datasets:
- klue
---
# KLUE RoBERTa base model for Sentence Embeddings
This is the `sentence-klue-roberta-base` model. The sentence-transformers repository allows to train and use Transformer models for generating sentence and text embeddings.
The model is des... |
adalbertojunior/distilbert-portuguese-cased | 0c2eff56791a23ae3451ed7bd0e3350e50a9b44b | 2022-02-04T02:30:57.000Z | [
"pytorch",
"bert",
"feature-extraction",
"pt",
"transformers"
] | feature-extraction | false | adalbertojunior | null | adalbertojunior/distilbert-portuguese-cased | 1,059 | 4 | transformers | 1,731 | ---
language:
- pt
---
This model was distilled from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased)
## Usage
```python
from transformers import AutoTokenizer # Or BertTokenizer
from transformers import AutoModelForPreTraining # Or BertForPreTraining for loading pretraining heads
from ... |
mdraw/german-news-sentiment-bert | 7b4abebe1c3fcfbc62dc0435e480807a80c18210 | 2021-05-19T23:11:49.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | mdraw | null | mdraw/german-news-sentiment-bert | 1,059 | null | transformers | 1,732 | # German sentiment BERT finetuned on news data
Sentiment analysis model based on https://huggingface.co/oliverguhr/german-sentiment-bert, with additional training on German news texts about migration.
This model is part of the project https://github.com/text-analytics-20/news-sentiment-development, which explores sen... |
FPTAI/vibert-base-cased | 728a91287e4517d9312066a6aa048fadf4e41e91 | 2021-05-19T11:15:49.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
] | null | false | FPTAI | null | FPTAI/vibert-base-cased | 1,058 | 1 | transformers | 1,733 | Entry not found |
flyswot/convnext-tiny-224_flyswot | c6d4b2138e10efeafef8f5305ce16270ca583618 | 2022-04-05T16:08:35.000Z | [
"pytorch",
"convnext",
"image-classification",
"dataset:image_folder",
"transformers",
"generated_from_trainer",
"model-index"
] | image-classification | false | flyswot | null | flyswot/convnext-tiny-224_flyswot | 1,057 | null | transformers | 1,734 | ---
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- f1
model-index:
- name: convnext-tiny-224_flyswot
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: F1
... |
superb/wav2vec2-base-superb-ks | 372e0486cd83e6f0c05c20a27262e9ca09450d24 | 2021-11-04T16:03:39.000Z | [
"pytorch",
"wav2vec2",
"audio-classification",
"en",
"dataset:superb",
"arxiv:2105.01051",
"transformers",
"speech",
"audio",
"license:apache-2.0"
] | audio-classification | false | superb | null | superb/wav2vec2-base-superb-ks | 1,055 | 7 | transformers | 1,735 | ---
language: en
datasets:
- superb
tags:
- speech
- audio
- wav2vec2
- audio-classification
widget:
- example_title: Speech Commands "down"
src: https://cdn-media.huggingface.co/speech_samples/keyword_spotting_down.wav
- example_title: Speech Commands "go"
src: https://cdn-media.huggingface.co/speech_samples/keywo... |
aypan17/roberta-base-imdb | b2f9bf35af2965658efdf2d6a116f4cf7dbc2827 | 2022-02-24T07:33:44.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers",
"license:mit"
] | text-classification | false | aypan17 | null | aypan17/roberta-base-imdb | 1,055 | null | transformers | 1,736 | ---
license: mit
---
TrainingArgs:
lr=2e-5,
train-batch-size=16,
eval-batch-size=16,
num-train-epochs=5,
weight-decay=0.01,
|
facebook/incoder-6B | 89aa16923e2ad52a292a87c38d019128b970161f | 2022-07-16T18:33:46.000Z | [
"pytorch",
"xglm",
"text-generation",
"arxiv:2204.05999",
"transformers",
"code",
"python",
"javascript",
"license:cc-by-nc-4.0"
] | text-generation | false | facebook | null | facebook/incoder-6B | 1,052 | 13 | transformers | 1,737 | ---
license: "cc-by-nc-4.0"
tags:
- code
- python
- javascript
---
# InCoder 6B
A 6B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.
The model was trained on public open-source repositories w... |
stanford-crfm/alias-gpt2-small-x21 | e954ab0a77651c595f108c42b1c0da12df14d0d6 | 2022-06-20T09:54:01.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | stanford-crfm | null | stanford-crfm/alias-gpt2-small-x21 | 1,051 | 1 | transformers | 1,738 | Entry not found |
nlpaueb/sec-bert-base | d511591f5e74052afdab08f1f14c4ff2a1e55749 | 2022-04-28T14:46:31.000Z | [
"pytorch",
"tf",
"bert",
"pretraining",
"en",
"arxiv:2203.06482",
"transformers",
"finance",
"financial",
"license:cc-by-sa-4.0",
"fill-mask"
] | fill-mask | false | nlpaueb | null | nlpaueb/sec-bert-base | 1,051 | 9 | transformers | 1,739 | ---
language: en
pipeline_tag: fill-mask
license: cc-by-sa-4.0
thumbnail: https://i.ibb.co/0yz81K9/sec-bert-logo.png
tags:
- finance
- financial
widget:
- text: "Total net sales [MASK] 2% or $5.4 billion during 2019 compared to 2018."
- text: "Total net sales decreased 2% or $5.4 [MASK] during 2019 compared t... |
Helsinki-NLP/opus-mt-no-da | 8b7d67f3ab9c3a048ab2ea4cde7daa7ea3eb5792 | 2020-08-21T14:42:48.000Z | [
"pytorch",
"marian",
"text2text-generation",
"no",
"da",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-no-da | 1,047 | 1 | transformers | 1,740 | ---
language:
- no
- da
tags:
- translation
license: apache-2.0
---
### nor-dan
* source group: Norwegian
* target group: Danish
* OPUS readme: [nor-dan](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-dan/README.md)
* model: transformer-align
* source language(s): nno nob
* target la... |
fnlp/cpt-base | 6e62b6b19f0c18590fed77d6553f1fdbe2e8535a | 2021-10-29T07:10:40.000Z | [
"pytorch",
"bart",
"feature-extraction",
"zh",
"arxiv:2109.05729",
"transformers",
"fill-mask",
"text2text-generation",
"text-classification",
"Summarization",
"Chinese",
"CPT",
"BART",
"BERT",
"seq2seq"
] | text-classification | false | fnlp | null | fnlp/cpt-base | 1,047 | 5 | transformers | 1,741 | ---
tags:
- fill-mask
- text2text-generation
- fill-mask
- text-classification
- Summarization
- Chinese
- CPT
- BART
- BERT
- seq2seq
language: zh
---
# Chinese CPT-Base
## Model description
This is an implementation of CPT-Base. To use CPT, please import the file `modeling_cpt.py` (**Download** [Here](https://gi... |
VietAI/vit5-large-vietnews-summarization | 7b72ccd3a6b38595db1ced95beb8836ec57ca52e | 2022-07-12T18:03:54.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"vi",
"dataset:cc100",
"transformers",
"summarization",
"license:mit",
"autotrain_compatible"
] | summarization | false | VietAI | null | VietAI/vit5-large-vietnews-summarization | 1,047 | 1 | transformers | 1,742 | ---
language: vi
datasets:
- cc100
tags:
- summarization
license: mit
widget:
- text: "vietnews: VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân tạo đẳng cấp quốc tế tại Việt Nam."
---
# ViT5-large Finetuned on `vie... |
sshleifer/distill-pegasus-xsum-16-8 | 41797aa90d88956d720033cf3030e219b2dfef40 | 2020-10-08T03:05:56.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | sshleifer | null | sshleifer/distill-pegasus-xsum-16-8 | 1,043 | 1 | transformers | 1,743 | ---
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: [@... |
quincyqiang/nezha-cn-base | 59bec85826eb29229edb5a2d10f971884677095c | 2022-04-24T08:00:22.000Z | [
"pytorch",
"transformers"
] | null | false | quincyqiang | null | quincyqiang/nezha-cn-base | 1,038 | null | transformers | 1,744 | ## NeZha-Pytorch
pytorch版NEZHA，适配transformers
### 安装
> pip install git+https://github.com/yanqiangmiffy/Nezha-Pytorch.git
### 权重下载地址
https://github.com/lonePatient/NeZha_Chinese_PyTorch
### torch使用样例
```
import torch
from transformers import BertTokenizer
from nezha import NeZhaModel, NeZhaConfig
text = "今天[MASK]... |
nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large | a61886e95cedc4cd2440f71cf9a55320ee1d8e06 | 2021-06-20T19:02:23.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nreimers | null | nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large | 1,037 | 2 | transformers | 1,745 | # MiniLMv2
This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm) |
WinKawaks/vit-tiny-patch16-224 | fd78e4f96a9936843a178feae1ed30453b59b44d | 2022-01-30T18:04:38.000Z | [
"pytorch",
"vit",
"image-classification",
"dataset:imagenet",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | WinKawaks | null | WinKawaks/vit-tiny-patch16-224 | 1,036 | 2 | transformers | 1,746 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet
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
- s... |
ThomasNLG/t5-qg_squad1-en | f9ae97448212aaee033ed43561e9253929ae71c9 | 2021-07-09T07:45:35.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:squad",
"transformers",
"qg",
"question",
"generation",
"SQuAD",
"metric",
"nlg",
"t5-small",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | ThomasNLG | null | ThomasNLG/t5-qg_squad1-en | 1,035 | 1 | transformers | 1,747 | ---
language: en
tags:
- qg
- question
- generation
- SQuAD
- metric
- nlg
- t5-small
license: mit
datasets:
- squad
model-index:
- name: t5-qg_squad1-en
results:
- task:
name: Question Generation
type: Text2Text-Generation
widget:
- text: "sv1 </s> Louis 14 </s> Louis 14 was a French King."
---
# t... |
google/pegasus-newsroom | 0c90cf856d45526f6e8efe7b5ec9fcb64c9a3fe6 | 2020-10-22T16:33:31.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | google | null | google/pegasus-newsroom | 1,035 | 2 | transformers | 1,748 | ---
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: [@... |
patrickvonplaten/bert2bert-cnn_dailymail-fp16 | 51b5d5cac0fa0ed09ed505df5800579996a2fe12 | 2020-12-12T11:22:49.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | patrickvonplaten | null | patrickvonplaten/bert2bert-cnn_dailymail-fp16 | 1,035 | null | transformers | 1,749 | # Bert2Bert Summarization with 🤗 EncoderDecoder Framework
This model is a Bert2Bert model fine-tuned on summarization.
Bert2Bert is a `EncoderDecoderModel`, meaning that both the encoder and the decoder are `bert-base-uncased`
BERT models. Leveraging the [EncoderDecoderFramework](https://huggingface.co/transformers... |
HooshvareLab/distilbert-fa-zwnj-base | e8b934b8c81b17c5e4a1a90325f5f25ced94e8d6 | 2021-03-16T16:30:29.000Z | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"fa",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | HooshvareLab | null | HooshvareLab/distilbert-fa-zwnj-base | 1,034 | null | transformers | 1,750 | ---
language: fa
license: apache-2.0
---
# DistilBERT
This model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.
## Questions?
Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/i... |
monologg/kocharelectra-small-discriminator | 7168f693b1744d07562d82bc25c4055831cd0a92 | 2020-05-27T17:37:41.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | monologg | null | monologg/kocharelectra-small-discriminator | 1,032 | null | transformers | 1,751 | Entry not found |
peterchou/nezha-chinese-base | 6f1362e07445fb84ac8fd18ef5599ed0c5aaab32 | 2021-05-20T02:32:33.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | peterchou | null | peterchou/nezha-chinese-base | 1,032 | 0 | transformers | 1,752 | Entry not found |
amazon/bort | 8f39f629b2b8eb3750d5bb98849c2424d4473403 | 2021-05-18T23:32:35.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"arxiv:2010.10499",
"transformers",
"autotrain_compatible"
] | fill-mask | false | amazon | null | amazon/bort | 1,029 | 4 | transformers | 1,753 | ⚠️ **Disclaimer** ⚠️
This model is community-contributed, and not supported by Amazon, Inc.
## BORT
[Amazon's BORT](https://www.amazon.science/blog/a-version-of-the-bert-language-model-thats-20-times-as-fast)
BORT is a highly compressed version of [bert-large](https://huggingface.co/bert-large-uncased) that is up ... |
doc2query/msmarco-t5-base-v1 | e673dca0dff1f19fda73ac62420eedf0219e692b | 2022-01-10T10:22:10.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:sentence-transformers/embedding-training-data",
"arxiv:1904.08375",
"arxiv:2104.08663",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | doc2query | null | doc2query/msmarco-t5-base-v1 | 1,026 | null | transformers | 1,754 | ---
language: en
datasets:
- sentence-transformers/embedding-training-data
widget:
- text: "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-orient... |
monologg/koelectra-small-v3-generator | c1a21223b2a1da968c64af074c26fa7e7edd928c | 2020-12-26T16:24:47.000Z | [
"pytorch",
"electra",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | monologg | null | monologg/koelectra-small-v3-generator | 1,021 | null | transformers | 1,755 | Entry not found |
cardiffnlp/tweet-topic-21-multi | 10858933bc2939c0a70050ccf23f044fec8148ce | 2022-06-09T10:36:05.000Z | [
"pytorch",
"tf",
"roberta",
"text-classification",
"arxiv:2202.03829",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/tweet-topic-21-multi | 1,020 | 2 | transformers | 1,756 | # tweet-topic-21-multi
This is a roBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for multi-label topic classification on a corpus of 11,267 tweets.
The original roBERTa-base model can be found [h... |
chenxran/orion-hypothesis-generator | d139e97003906b8e5443ad510364bd6e7fa03fc3 | 2022-05-22T05:15:33.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | chenxran | null | chenxran/orion-hypothesis-generator | 1,019 | null | transformers | 1,757 | Entry not found |
allegro/plt5-base | 5443c295a9dd170ce8e8b6eda22bb10ff23163cf | 2021-08-19T17:00:55.000Z | [
"pytorch",
"t5",
"text2text-generation",
"pl",
"dataset:ccnet",
"dataset:nkjp",
"dataset:wikipedia",
"dataset:open subtitles",
"dataset:free readings",
"transformers",
"T5",
"translation",
"summarization",
"question answering",
"reading comprehension",
"license:cc-by-4.0",
"autotrain... | translation | false | allegro | null | allegro/plt5-base | 1,017 | 4 | transformers | 1,758 | ---
language: pl
tags:
- T5
- translation
- summarization
- question answering
- reading comprehension
datasets:
- ccnet
- nkjp
- wikipedia
- open subtitles
- free readings
license: cc-by-4.0
---
# plT5 Base
**plT5** models are T5-based language models trained on Polish corpora. The models were optimized for the origi... |
mrm8488/t5-small-finetuned-emotion | cd1013ff513e564316b16919c5680be2885e4294 | 2020-12-11T21:56:24.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:emotion",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-small-finetuned-emotion | 1,014 | null | transformers | 1,759 | ---
language: en
datasets:
- emotion
---
# T5-small fine-tuned for Emotion Recognition 😂😢😡😃😯
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [small](https://huggingface.co/t5-small) fine-tuned on [emotion recognition](https://github.com/dair-ai/emotion_dataset) dataset ... |
voidful/dpr-question_encoder-bert-base-multilingual | 27a13bea0225a405e32531c1137d26ed2e4407d2 | 2021-02-21T09:00:19.000Z | [
"pytorch",
"dpr",
"feature-extraction",
"multilingual",
"dataset:NQ",
"dataset:Trivia",
"dataset:SQuAD",
"dataset:MLQA",
"dataset:DRCD",
"arxiv:2004.04906",
"transformers"
] | feature-extraction | false | voidful | null | voidful/dpr-question_encoder-bert-base-multilingual | 1,014 | 3 | transformers | 1,760 | ---
language: multilingual
datasets:
- NQ
- Trivia
- SQuAD
- MLQA
- DRCD
---
# dpr-ctx_encoder-bert-base-multilingual
## Description
Multilingual DPR Model base on bert-base-multilingual-cased.
[DPR model](https://arxiv.org/abs/2004.04906)
[DPR repo](https://github.com/facebookresearch/DPR)
## Data
1. [NQ](https:/... |
knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI | 846c141c494bc06f846d5609c14c21712c3a074d | 2022-06-27T15:27:56.000Z | [
"pytorch",
"tf",
"bart",
"text2text-generation",
"en",
"dataset:cnndaily/newyorkdaily/xsum/samsum/dialogsum/AMI Meeting Corpus",
"transformers",
"seq2seq",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | knkarthick | null | knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI | 1,012 | 1 | transformers | 1,761 | ---
language: en
tags:
- bart
- seq2seq
- summarization
license: apache-2.0
datasets:
- cnndaily/newyorkdaily/xsum/samsum/dialogsum/AMI Meeting Corpus
metrics:
- rouge
widget:
- text: |-
Hi, I'm David and I'm supposed to be an industrial designer. Um, I just got the project announcement about what the project is. D... |
allenai/tk-instruct-base-def-pos | 196e8998944bded8e53c6fe3a757a905a3d5382f | 2022-05-27T06:30:11.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:natural instructions v2.0",
"arxiv:1910.10683",
"arxiv:2204.07705",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/tk-instruct-base-def-pos | 1,011 | null | transformers | 1,762 | ---
language: en
license: apache-2.0
datasets:
- natural instructions v2.0
---
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built... |
idb-ita/gilberto-uncased-from-camembert | c0320d9b1d9f0e603391f24bb751f6cca9c89968 | 2020-04-24T16:01:20.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | idb-ita | null | idb-ita/gilberto-uncased-from-camembert | 1,008 | 1 | transformers | 1,763 | Entry not found |
microsoft/trocr-large-printed | e0ab580ecb4d45111dac1555f91a266cd53171de | 2022-07-01T07:39:34.000Z | [
"pytorch",
"vision-encoder-decoder",
"arxiv:2109.10282",
"transformers",
"trocr",
"image-to-text"
] | image-to-text | false | microsoft | null | microsoft/trocr-large-printed | 1,007 | 1 | transformers | 1,764 | ---
tags:
- trocr
- image-to-text
---
# TrOCR (large-sized model, fine-tuned on SROIE)
TrOCR model fine-tuned on the [SROIE dataset](https://rrc.cvc.uab.es/?ch=13). It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li ... |
pierrerappolt-okta/app | 915b7f89b6d4644139b9502e399f358071112123 | 2022-02-03T19:38:08.000Z | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | pierrerappolt-okta | null | pierrerappolt-okta/app | 1,006 | null | transformers | 1,765 | ---
inference:
parameters:
aggregation_strategy: first
---
. |
deepset/gelectra-large | 726e2e6ad4b1ff8a2ee172ac945d0faef62e5680 | 2022-07-26T12:38:01.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"dataset:oscar",
"arxiv:2010.10906",
"transformers",
"license:mit"
] | null | false | deepset | null | deepset/gelectra-large | 1,005 | 7 | transformers | 1,766 | ---
language: de
license: mit
datasets:
- wikipedia
- OPUS
- OpenLegalData
- oscar
---
# German ELECTRA large
Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-case... |
bigscience/bloom-760m | ae3a5ffeab7d36b7c1e2e362adbeeb4f824f4c30 | 2022-07-25T07:34:54.000Z | [
"pytorch",
"jax",
"bloom",
"feature-extraction",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
... | text-generation | false | bigscience | null | bigscience/bloom-760m | 1,004 | 3 | transformers | 1,767 | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-gener... |
aubmindlab/bert-large-arabertv2 | 9c9e35e196b88fbc4a3d738420f75d2ad854e8e6 | 2022-04-06T15:27:41.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B Arabic Corpus",
"dataset:OSCAR Arabic Unshuffled",
"arxiv:2003.00104",
"transformers",
"autotrain_compatible"
] | fill-mask | false | aubmindlab | null | aubmindlab/bert-large-arabertv2 | 1,001 | 2 | transformers | 1,768 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
- OSCAR Arabic Unshuffled
widget:
- text: " عاصم +ة لبنان هي [MASK] ."
---
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png"... |
jcblaise/bert-tagalog-base-uncased | eedcb4c434d90dea60092740e47903aede5284c3 | 2021-11-12T03:21:26.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"tl",
"transformers",
"tagalog",
"filipino",
"license:gpl-3.0",
"autotrain_compatible"
] | fill-mask | false | jcblaise | null | jcblaise/bert-tagalog-base-uncased | 1,001 | null | transformers | 1,769 | ---
language: tl
tags:
- bert
- tagalog
- filipino
license: gpl-3.0
inference: false
---
**Deprecation Notice**
This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available.
Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagal... |
mrm8488/bert2bert_shared-german-finetuned-summarization | f7aa176d43cd0c7d90255f98d8774e3de251d168 | 2021-05-27T12:13:27.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"de",
"dataset:mlsum",
"transformers",
"summarization",
"news",
"autotrain_compatible"
] | summarization | false | mrm8488 | null | mrm8488/bert2bert_shared-german-finetuned-summarization | 1,001 | 2 | transformers | 1,770 | ---
tags:
- summarization
- news
language: de
datasets:
- mlsum
widget:
- text: 'Wie geht man nach schrecklichen Ereignissen ambesten auf die Ängste und Sorgen von Kindern ein?Therapeuten haben eine klare Botschaft. Die Weltist voller Gefahren, Verbrechen und Schrecken -Krieg, Terrorismus, Umweltzerstörung und eben auc... |
microsoft/swin-large-patch4-window7-224 | d433db83a1c10a34c365fc4928186c8fb8c642dd | 2022-05-16T19:58:33.000Z | [
"pytorch",
"tf",
"swin",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/swin-large-patch4-window7-224 | 999 | null | transformers | 1,771 | ---
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... |
cointegrated/rut5-base-paraphraser | 89213d06450b722514e23ba55ae7c16a2203a3b8 | 2022-02-08T13:06:08.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ru",
"dataset:cointegrated/ru-paraphrase-NMT-Leipzig",
"transformers",
"russian",
"paraphrasing",
"paraphraser",
"paraphrase",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | cointegrated | null | cointegrated/rut5-base-paraphraser | 997 | 6 | transformers | 1,772 | ---
language: ["ru"]
tags:
- russian
- paraphrasing
- paraphraser
- paraphrase
license: mit
widget:
- text: "Каждый охотник желает знать, где сидит фазан."
datasets:
- cointegrated/ru-paraphrase-NMT-Leipzig
---
This is a paraphraser for Russian sentences described [in this Habr post](https://habr.com/ru/post/564916/).... |
henryk/bert-base-multilingual-cased-finetuned-dutch-squad2 | a0f963636c546b1bfa83717bb1329697c1ffcbe0 | 2021-05-19T19:02:45.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"nl",
"transformers",
"autotrain_compatible"
] | question-answering | false | henryk | null | henryk/bert-base-multilingual-cased-finetuned-dutch-squad2 | 997 | 4 | transformers | 1,773 | ---
language: nl
---
# Multilingual + Dutch SQuAD2.0
This model is the multilingual model provided by the Google research team with a fine-tuned dutch Q&A downstream task.
## Details of the language model
Language model ([**bert-base-multilingual-cased**](https://github.com/google-research/bert/blob/maste... |
skimai/spanberta-base-cased | 7f56c58981ddc9ce18e0abcf85ae2b4e54248063 | 2021-05-20T21:52:23.000Z | [
"pytorch",
"jax",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | skimai | null | skimai/spanberta-base-cased | 997 | null | transformers | 1,774 | Entry not found |
cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 | d5246c2d77849f8a3886b463b949c52b5cb7d075 | 2022-06-01T08:33:59.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ar",
"zh",
"nl",
"fr",
"de",
"hi",
"in",
"it",
"ja",
"pt",
"ru",
"es",
"vi",
"multilingual",
"dataset:unicamp-dl/mmarco",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 | 997 | 5 | transformers | 1,775 | ---
license: apache-2.0
language:
- en
- ar
- zh
- nl
- fr
- de
- hi
- in
- it
- ja
- pt
- ru
- es
- vi
- multilingual
datasets:
- unicamp-dl/mmarco
---
# Cross-Encoder for multilingual MS Marco
This model was trained on the [MMARCO](https://hf.co/unicamp-dl/mmarco) dataset. It is a machine translated version of MS MA... |
DaNLP/da-electra-hatespeech-detection | 4b6008f10efd2908de4b4e9592579415b9cdf808 | 2022-02-16T15:00:31.000Z | [
"pytorch",
"electra",
"text-classification",
"da",
"dataset:social media",
"transformers",
"hatespeech",
"license:cc-by-4.0"
] | text-classification | false | DaNLP | null | DaNLP/da-electra-hatespeech-detection | 996 | null | transformers | 1,776 | ---
language:
- da
tags:
- electra
- pytorch
- hatespeech
license: cc-by-4.0
datasets:
- social media
metrics:
- f1
widget:
- text: "Senile gamle idiot"
---
# Danish ELECTRA for hate speech (offensive language) detection
The ELECTRA Offensive model detects whether a Danish text is offensive or not.
It is based on th... |
indobenchmark/indobert-lite-base-p1 | 5b3f705b18a164b7917e4a94e8ed2cdbdbb8b639 | 2020-12-11T21:45:50.000Z | [
"pytorch",
"tf",
"albert",
"feature-extraction",
"id",
"dataset:Indo4B",
"arxiv:2009.05387",
"transformers",
"indobert",
"indobenchmark",
"indonlu",
"license:mit"
] | feature-extraction | false | indobenchmark | null | indobenchmark/indobert-lite-base-p1 | 995 | null | transformers | 1,777 | ---
language: id
tags:
- indobert
- indobenchmark
- indonlu
license: mit
inference: false
datasets:
- Indo4B
---
# IndoBERT-Lite Base Model (phase1 - uncased)
[IndoBERT](https://arxiv.org/abs/2009.05387) is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using... |
DeepPavlov/xlm-roberta-large-en-ru | da9f180b0d73f4a653f3aaebea87fd586746021d | 2021-11-15T08:46:05.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"en",
"ru",
"transformers"
] | feature-extraction | false | DeepPavlov | null | DeepPavlov/xlm-roberta-large-en-ru | 994 | null | transformers | 1,778 | ---
language:
- en
- ru
---
# XLM-RoBERTa-Large-En-Ru
## Model description
This model is a version XLM-RoBERTa with embeddings and vocabulary reduced to most frequent tokens in English and Russian.
|
Helsinki-NLP/opus-mt-es-fr | 4a8c0b48f85ccacc4557e2189a2a551418e4a68a | 2021-09-09T21:42:27.000Z | [
"pytorch",
"marian",
"text2text-generation",
"es",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-es-fr | 993 | 1 | transformers | 1,779 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-es-fr
* source languages: es
* target languages: fr
* OPUS readme: [es-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
egoitz/roberta-timex-semeval | 39f3a7ee360f745985631ea542fe511c89f2299b | 2021-05-20T16:15:19.000Z | [
"pytorch",
"jax",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | egoitz | null | egoitz/roberta-timex-semeval | 993 | null | transformers | 1,780 | Entry not found |
thunlp/Lawformer | d2452823634a0c5aff74b894c8b86f5ed346b964 | 2022-07-12T06:23:13.000Z | [
"pytorch",
"longformer",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | thunlp | null | thunlp/Lawformer | 992 | 2 | transformers | 1,781 | ## Lawformer
### Introduction
This repository provides the source code and checkpoints of the paper "Lawformer: A Pre-trained Language Model forChinese Legal Long Documents". You can download the checkpoint from the [huggingface model hub](https://huggingface.co/xcjthu/Lawformer) or from [here](https://data.thunlp.org... |
lanwuwei/GigaBERT-v4-Arabic-and-English | 94bcdd4d00243515c930d8d9a8c78b7ffe02e2b0 | 2021-05-19T21:19:13.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | lanwuwei | null | lanwuwei/GigaBERT-v4-Arabic-and-English | 989 | 1 | transformers | 1,782 | ## GigaBERT-v4
GigaBERT-v4 is a continued pre-training of [GigaBERT-v3](https://huggingface.co/lanwuwei/GigaBERT-v3-Arabic-and-English) on code-switched data, showing improved zero-shot transfer performance from English to Arabic on information extraction (IE) tasks. More details can be found in the following paper:
... |
bolbolzaban/gpt2-persian | 1c965e289795e1b24301cd3f4ee48e73519ac8ee | 2021-05-21T14:23:14.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fa",
"transformers",
"farsi",
"persian",
"license:apache-2.0"
] | text-generation | false | bolbolzaban | null | bolbolzaban/gpt2-persian | 986 | 4 | transformers | 1,783 | ---
language: fa
license: apache-2.0
tags:
- farsi
- persian
---
# GPT2-Persian
bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:
1. The context size is reduced from 1024 to 256 sub words in order to make the training affor... |
KBLab/bert-base-swedish-lowermix-reallysimple-ner | b9efcfa506f155e698fbaba5719bc06045bcfc90 | 2022-03-02T17:43:25.000Z | [
"pytorch",
"bert",
"token-classification",
"sv",
"dataset:KBLab/sucx3_ner",
"transformers",
"sequence-tagger-model",
"autotrain_compatible"
] | token-classification | false | KBLab | null | KBLab/bert-base-swedish-lowermix-reallysimple-ner | 984 | null | transformers | 1,784 | ---
model:
- KB/bert-base-swedish-cased
tags:
- token-classification
- sequence-tagger-model
- bert
language: sv
datasets:
- KBLab/sucx3_ner
widget:
- text: "Emil bor i Lönneberga"
---
# KB-BERT for NER
## Mixed cased and uncased data
This model is based on [KB-BERT](https://huggingface.co/KB/bert-base-swedish-cased... |
NbAiLab/nb-bert-large | 27e8180855f0de03688958c88a2e5702bfbf0bfd | 2021-09-23T15:53:00.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"no",
"transformers",
"norwegian",
"license:cc-by-4.0",
"fill-mask"
] | fill-mask | false | NbAiLab | null | NbAiLab/nb-bert-large | 980 | 2 | transformers | 1,785 | ---
language: no
license: cc-by-4.0
tags:
- norwegian
- bert
thumbnail: nblogo_3.png
pipeline_tag: fill-mask
widget:
- text: På biblioteket kan du låne en [MASK].
---
- **Release 1.0beta** (April 29, 2021)
# NB-BERT-large (beta)
## Description
NB-BERT-large is a general BERT-large model built on the large digital... |
speechbrain/vad-crdnn-libriparty | 5570a3fb5188f324fc087cc69786bed5cb10401e | 2022-06-26T23:17:47.000Z | [
"en",
"dataset:Urbansound8k",
"arxiv:2106.04624",
"speechbrain",
"VAD",
"SAD",
"Voice Activity Detection",
"Speech Activity Detection",
"Speaker Diarization",
"pytorch",
"CRDNN",
"LibriSpeech",
"LibryParty"
] | null | false | speechbrain | null | speechbrain/vad-crdnn-libriparty | 978 | 4 | speechbrain | 1,786 | ---
language: "en"
thumbnail:
tags:
- speechbrain
- VAD
- SAD
- Voice Activity Detection
- Speech Activity Detection
- Speaker Diarization
- pytorch
- CRDNN
- LibriSpeech
- LibryParty
datasets:
- Urbansound8k
metrics:
- Accuracy
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&typ... |
deepparag/DumBot | 61ec36094ce1c5e6fecd4e1830bc7399db991830 | 2022-01-21T15:40:27.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"license:mit"
] | conversational | false | deepparag | null | deepparag/DumBot | 977 | 2 | transformers | 1,787 | ---
thumbnail: https://cdn.discordapp.com/app-icons/870239976690970625/c02cae78ae105f07969cfd8f8ea3d0a0.png
tags:
- conversational
license: mit
---
# THIS AI IS OUTDATED. See [Aeona](https://huggingface.co/deepparag/Aeona)
An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-... |
facebook/deit-base-distilled-patch16-384 | d5642c165024ea0619ad72ab3e26d867eabdcdab | 2022-07-13T11:40:20.000Z | [
"pytorch",
"tf",
"deit",
"image-classification",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | facebook | null | facebook/deit-base-distilled-patch16-384 | 977 | null | transformers | 1,788 | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
---
# Distilled Data-efficient Image Transformer (base-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained at resolution 224x224 and fine-tuned at resolution 384x384 on ImageNet-1k (1 million images, 1,000 ... |
nguyenvulebinh/wav2vec2-base-vietnamese-250h | 69e9000591623e5a4fc2f502407860bcdc0de0b2 | 2021-11-04T15:35:49.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"vi",
"dataset:vlsp",
"dataset:vivos",
"transformers",
"audio",
"license:cc-by-nc-4.0",
"model-index"
] | automatic-speech-recognition | false | nguyenvulebinh | null | nguyenvulebinh/wav2vec2-base-vietnamese-250h | 974 | 8 | transformers | 1,789 | ---
language: vi
datasets:
- vlsp
- vivos
tags:
- audio
- automatic-speech-recognition
license: cc-by-nc-4.0
widget:
- example_title: VLSP ASR 2020 test T1
src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_0001-00010.wav
- example_title: VLSP ASR 2020 test T1
src: https... |
arunavsk1/my-awesome-pubmed-bert | bb06e2d1d8ababdc63214b8e699837ac31672a2c | 2022-06-06T01:51:45.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | arunavsk1 | null | arunavsk1/my-awesome-pubmed-bert | 971 | null | transformers | 1,790 | Entry not found |
Salesforce/codegen-16B-mono | a21420473d19b3ebfadbaefcc51cf1856f5f2c8f | 2022-06-28T17:48:18.000Z | [
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"transformers",
"license:bsd-3-clause"
] | text-generation | false | Salesforce | null | Salesforce/codegen-16B-mono | 965 | 14 | transformers | 1,791 | ---
license: bsd-3-clause
---
# CodeGen (CodeGen-Mono 16B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan W... |
Luyu/condenser | 7d0fa9eabec851f64882e728e1f92c59b8878f67 | 2021-08-13T13:38:57.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | Luyu | null | Luyu/condenser | 962 | null | transformers | 1,792 | Entry not found |
sismetanin/rubert-toxic-pikabu-2ch | 1e5d55aeca25ab0a91725abc08821694de7dd5ea | 2021-05-20T06:16:03.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"ru",
"transformers",
"toxic comments classification"
] | text-classification | false | sismetanin | null | sismetanin/rubert-toxic-pikabu-2ch | 961 | 4 | transformers | 1,793 | ---
language:
- ru
tags:
- toxic comments classification
---
## RuBERT-Toxic
RuBERT-Toxic is a [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) model fine-tuned on [Kaggle Russian Language Toxic Comments Dataset](https://www.kaggle.com/blackmoon/russian-language-toxic-comments). You can find a detailed ... |
scjnugacj/jurisbert | 721bd35f7ba879bf25d48d07ac2c3b710dd808fb | 2022-07-13T22:23:07.000Z | [
"pytorch",
"roberta",
"fill-mask",
"es",
"transformers",
"license:other",
"autotrain_compatible"
] | fill-mask | false | scjnugacj | null | scjnugacj/jurisbert | 960 | 6 | transformers | 1,794 | ---
language: es
license: other
widget:
- text: "Procedencia de la extinción de dominio considerando que los bienes utilizados para cometer el <mask>, se realizó sin el conocimiento del propietario de los bienes."
- text: "En lo que respecta a la regulación dentro del derecho civil, la adopción homoparental consiste en... |
openclimatefix/nowcasting_cnn_v4 | ddc9e15dbd57e2a55e84b0fa50d2349a6cef8f5f | 2022-07-29T13:17:48.000Z | [
"pytorch",
"transformers",
"nowcasting",
"forecasting",
"timeseries",
"remote-sensing",
"license:mit"
] | null | false | openclimatefix | null | openclimatefix/nowcasting_cnn_v4 | 960 | null | transformers | 1,795 | ---
license: mit
tags:
- nowcasting
- forecasting
- timeseries
- remote-sensing
---
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes i... |
PlanTL-GOB-ES/roberta-base-bne-sqac | 5c5f5de339fb28fbc7d44d417a00fc22e3df3dfd | 2022-04-06T14:43:44.000Z | [
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:PlanTL-GOB-ES/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | PlanTL-GOB-ES | null | PlanTL-GOB-ES/roberta-base-bne-sqac | 959 | 1 | transformers | 1,796 | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "qa"
- "question answering"
datasets:
- "PlanTL-GOB-ES/SQAC"
metrics:
- "f1"
---
# Spanish RoBERTa-base trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
RoBERTa-base-bne is a transformer-ba... |
GanjinZero/biobart-large | 74039cd67ada5928ea75fe24abd77656c7661276 | 2022-04-25T02:17:27.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"arxiv:2204.03905",
"transformers",
"biobart",
"biomedical",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | GanjinZero | null | GanjinZero/biobart-large | 958 | 1 | transformers | 1,797 | ---
language:
- en
license: apache-2.0
tags:
- bart
- biobart
- biomedical
inference: true
widget:
- text: "Influenza is a <mask> disease."
- type: "text-generation"
---
Paper: [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://arxiv.org/pdf/2204.03905.pdf)
```
@misc{BioBAR... |
cross-encoder/qnli-distilroberta-base | c7102de981e15ca7ef131517b94ff770d9e3c166 | 2021-08-05T08:41:18.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"arxiv:1804.07461",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/qnli-distilroberta-base | 956 | null | transformers | 1,798 | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
Given a question and paragraph, can the question be a... |
Helsinki-NLP/opus-mt-mk-en | 48a6ca1d5f81a873f28dd38eb6f8b1027f23ba2c | 2021-09-10T13:58:00.000Z | [
"pytorch",
"marian",
"text2text-generation",
"mk",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-mk-en | 955 | 1 | transformers | 1,799 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-mk-en
* source languages: mk
* target languages: en
* OPUS readme: [mk-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/mk-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
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