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-sv-en | e4f28e50a1614873bbe8d16d3c48b52e37778ead | 2021-09-10T14:06:11.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"sv",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sv-en | 33,838 | 3 | transformers | 400 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-en
* source languages: sv
* target languages: en
* OPUS readme: [sv-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
aatmasidha/distilbert-base-uncased-finetuned-emotion | 2ca535b8d9150b688d221a3c5814c7451cfe7304 | 2022-07-25T18:31:49.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | aatmasidha | null | aatmasidha/distilbert-base-uncased-finetuned-emotion | 33,718 | null | transformers | 401 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, t... |
ml6team/bert-base-uncased-city-country-ner | e38e683af1174120b192661dbdcbc2358fe56964 | 2022-07-01T07:27:25.000Z | [
"pytorch",
"tf",
"bert",
"token-classification",
"en",
"dataset:Ultra Fine Entity Typing",
"transformers",
"address-NER",
"NER",
"bert-base-uncased",
"autotrain_compatible"
] | token-classification | false | ml6team | null | ml6team/bert-base-uncased-city-country-ner | 33,121 | 5 | transformers | 402 | ---
language:
- en
tags:
- token-classification
- address-NER
- NER
- bert-base-uncased
datasets:
- Ultra Fine Entity Typing
metrics:
- Precision
- Recall
- F1 Score
widget:
- text: "Hi, I am Kermit and I live in Berlin"
- text: "It is very difficult to find a house in Berlin, Germany."
- text: "ML6 is a very cool co... |
dccuchile/bert-base-spanish-wwm-cased | 56a7647b957a4230fc3f80dafbe80f2ba9b0de73 | 2022-05-31T15:01:30.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"es",
"arxiv:1904.09077",
"arxiv:1906.01502",
"arxiv:1812.10464",
"arxiv:1901.07291",
"arxiv:1904.02099",
"arxiv:1906.01569",
"arxiv:1908.11828",
"transformers",
"masked-lm",
"autotrain_compatible"
] | fill-mask | false | dccuchile | null | dccuchile/bert-base-spanish-wwm-cased | 32,758 | 11 | transformers | 403 | ---
language:
- es
tags:
- masked-lm
---
# BETO: Spanish BERT
BETO is a [BERT model](https://github.com/google-research/bert) trained on a [big Spanish corpus](https://github.com/josecannete/spanish-corpora). BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below yo... |
nlpconnect/vit-gpt2-image-captioning | 27b41be193be4c2dc238990bad1c8d874b272a83 | 2022-07-01T07:38:36.000Z | [
"pytorch",
"vision-encoder-decoder",
"transformers",
"image-to-text",
"image-captioning",
"license:apache-2.0"
] | image-to-text | false | nlpconnect | null | nlpconnect/vit-gpt2-image-captioning | 32,117 | 7 | transformers | 404 | ---
tags:
- image-to-text
- image-captioning
license: apache-2.0
---
# nlpconnect/vit-gpt2-image-captioning
This is an image captioning model training by @ydshieh in flax, this is pytorch version of https://huggingface.co/ydshieh/vit-gpt2-coco-en-ckpts model.
# Sample running code
```python
from transformers imp... |
ufal/robeczech-base | b154a976e0241a2cfb537600fcf936a8540caeb9 | 2022-04-24T11:33:18.000Z | [
"pytorch",
"tf",
"roberta",
"fill-mask",
"cs",
"arxiv:2105.11314",
"transformers",
"Czech",
"RoBERTa",
"ÚFAL",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | ufal | null | ufal/robeczech-base | 32,074 | 3 | transformers | 405 | ---
language: "cs"
tags:
- Czech
- RoBERTa
- ÚFAL
license: "cc-by-nc-sa-4.0"
---
# RobeCzech model
RobeCzech is a monolingual RoBERTa language representation model trained on Czech data.
RobeCzech model is released publicly at [LINDAT](https://hdl.handle.net/11234/1-3691) and [Hugging Face](https://huggingface.co/uf... |
facebook/mbart-large-50 | eab25f78110b11bfbf981249a6204e258f8a3312 | 2022-06-25T17:07:01.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"multilingual",
"ar",
"cs",
"de",
"en",
"es",
"et",
"fi",
"fr",
"gu",
"hi",
"it",
"ja",
"kk",
"ko",
"lt",
"lv",
"my",
"ne",
"nl",
"ro",
"ru",
"si",
"tr",
"vi",
"zh",
"af",
"az",
"bn",
"fa",
"he",
"hr",
... | text2text-generation | false | facebook | null | facebook/mbart-large-50 | 31,857 | 10 | transformers | 406 | ---
language:
- multilingual
- ar
- cs
- de
- en
- es
- et
- fi
- fr
- gu
- hi
- it
- ja
- kk
- ko
- lt
- lv
- my
- ne
- nl
- ro
- ru
- si
- tr
- vi
- zh
- af
- az
- bn
- fa
- he
- hr
- id
- ka
- km
- mk
- ml
- mn
- mr
- pl
- ps
- pt
- sv
- sw
- ta
- te
- th
- tl
- uk
- ur
- xh
- gl
- sl
license: mit
tags:
- mbart-50... |
google/pegasus-cnn_dailymail | 811b08dd23ebf40cbe121d5c49b268150604bb8f | 2021-03-27T08:09:17.000Z | [
"pytorch",
"rust",
"pegasus",
"text2text-generation",
"en",
"arxiv:1912.08777",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | google | null | google/pegasus-cnn_dailymail | 31,698 | 8 | transformers | 407 | ---
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: [@... |
princeton-nlp/sup-simcse-roberta-large | d34da58f734b9cc9e617cc37a2321badffdd0ecf | 2021-05-20T19:36:20.000Z | [
"pytorch",
"jax",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | princeton-nlp | null | princeton-nlp/sup-simcse-roberta-large | 31,034 | 1 | transformers | 408 | Entry not found |
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract | 3be15ab62caee5db1d45f923410798cdea920010 | 2021-09-22T20:10:45.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"en",
"arxiv:2007.15779",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract | 30,533 | 18 | transformers | 409 | ---
language: en
tags:
- exbert
license: mit
widget:
- text: "[MASK] is a tyrosine kinase inhibitor."
---
## PubMedBERT (abstracts only)
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on gener... |
julien-c/bert-xsmall-dummy | 9d3811da21adb66feb315118023f528ed10c6b18 | 2021-05-19T20:53:10.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | julien-c | null | julien-c/bert-xsmall-dummy | 30,444 | null | transformers | 410 | ## How to build a dummy model
```python
from transformers BertConfig, BertForMaskedLM, BertTokenizer, TFBertForMaskedLM
SMALL_MODEL_IDENTIFIER = "julien-c/bert-xsmall-dummy"
DIRNAME = "./bert-xsmall-dummy"
config = BertConfig(10, 20, 1, 1, 40)
model = BertForMaskedLM(config)
model.save_pretrained(DIRNAME)
tf_mode... |
prajjwal1/bert-medium | ce27ec2944bd32b66ed837edb9c77eb7301b8ecc | 2021-10-27T18:30:16.000Z | [
"pytorch",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"transformers",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"license:mit"
] | null | false | prajjwal1 | null | prajjwal1/bert-medium | 30,244 | 1 | transformers | 411 | ---
language:
- en
license:
- mit
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
---
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).
This is one of the smaller ... |
Helsinki-NLP/opus-mt-it-en | 23f2c7f29233a3e0accc900625d65ddf6a49b93e | 2021-09-10T13:52:52.000Z | [
"pytorch",
"marian",
"text2text-generation",
"it",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-it-en | 30,184 | 2 | transformers | 412 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-it-en
* source languages: it
* target languages: en
* OPUS readme: [it-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/it-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Davlan/xlm-roberta-base-wikiann-ner | 172d1f30d99d7de340494c6aedd6b6702f6c5021 | 2022-06-27T10:36:50.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"ar",
"as",
"bn",
"ca",
"en",
"es",
"eu",
"fr",
"gu",
"hi",
"id",
"ig",
"mr",
"pa",
"pt",
"sw",
"ur",
"vi",
"yo",
"zh",
"multilingual",
"dataset:wikiann",
"transformers",
"autotrain_compatible"
] | token-classification | false | Davlan | null | Davlan/xlm-roberta-base-wikiann-ner | 29,899 | 2 | transformers | 413 | Hugging Face's logo
---
language:
- ar
- as
- bn
- ca
- en
- es
- eu
- fr
- gu
- hi
- id
- ig
- mr
- pa
- pt
- sw
- ur
- vi
- yo
- zh
- multilingual
datasets:
- wikiann
---
# xlm-roberta-base-wikiann-ner
## Model description
**xlm-roberta-base-wikiann-ner** is the first **Named Entity ... |
dccuchile/bert-base-spanish-wwm-uncased | 767afcc9ffdf900341128e9e0bfe44d522461c51 | 2022-05-31T15:02:39.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"es",
"arxiv:1904.09077",
"arxiv:1906.01502",
"arxiv:1812.10464",
"arxiv:1901.07291",
"arxiv:1904.02099",
"arxiv:1906.01569",
"arxiv:1908.11828",
"transformers",
"masked-lm",
"autotrain_compatible"
] | fill-mask | false | dccuchile | null | dccuchile/bert-base-spanish-wwm-uncased | 29,767 | 11 | transformers | 414 | ---
language:
- es
tags:
- masked-lm
---
# BETO: Spanish BERT
BETO is a [BERT model](https://github.com/google-research/bert) trained on a [big Spanish corpus](https://github.com/josecannete/spanish-corpora). BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you... |
valhalla/distilbart-mnli-12-6 | f14383bf830237f338e7d597955f156590fddc2e | 2021-06-14T10:32:03.000Z | [
"pytorch",
"jax",
"bart",
"text-classification",
"dataset:mnli",
"transformers",
"distilbart",
"distilbart-mnli",
"zero-shot-classification"
] | zero-shot-classification | false | valhalla | null | valhalla/distilbart-mnli-12-6 | 29,717 | 3 | transformers | 415 | ---
datasets:
- mnli
tags:
- distilbart
- distilbart-mnli
pipeline_tag: zero-shot-classification
---
# DistilBart-MNLI
distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingfa... |
textattack/bert-base-uncased-imdb | c70b9f391af2067f7eff69a03940218bba9b8d39 | 2021-05-20T07:42:02.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/bert-base-uncased-imdb | 29,518 | 1 | transformers | 416 | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a cla... |
facebook/hubert-large-ls960-ft | ece5fabbf034c1073acae96d5401b25be96709d8 | 2022-05-24T10:43:42.000Z | [
"pytorch",
"tf",
"hubert",
"automatic-speech-recognition",
"en",
"dataset:libri-light",
"dataset:librispeech_asr",
"arxiv:2106.07447",
"transformers",
"speech",
"audio",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | facebook | null | facebook/hubert-large-ls960-ft | 29,509 | 18 | transformers | 417 | ---
language: en
datasets:
- libri-light
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: hubert-large-ls960-ft
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
n... |
ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli | ff7d96201d917e7fcc8b5b95f2631602a0777428 | 2020-10-17T02:05:17.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | false | ynie | null | ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli | 29,417 | 2 | transformers | 418 | Entry not found |
prithivida/parrot_adequacy_model | 87a35bc291d7455cfc86fc5f6a374c92de0156af | 2022-05-27T02:47:22.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | prithivida | null | prithivida/parrot_adequacy_model | 29,200 | 2 | transformers | 419 | ---
license: apache-2.0
---
Parrot
THIS IS AN ANCILLARY MODEL FOR PARROT PARAPHRASER
1. What is Parrot?
Parrot is a paraphrase-based utterance augmentation framework purpose-built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. Please refer to the GitHub page or The mo... |
wietsedv/bert-base-dutch-cased | 2d09de2a6f34a25cb194c39e6281c5fbef317032 | 2021-05-20T09:12:57.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | wietsedv | null | wietsedv/bert-base-dutch-cased | 29,112 | null | transformers | 420 | # BERTje: A Dutch BERT model
BERTje is a Dutch pre-trained BERT model developed at the University of Groningen.
⚠️ **The new home of this model is the [GroNLP](https://huggingface.co/GroNLP) organization.**
BERTje now lives at: [`GroNLP/bert-base-dutch-cased`](https://huggingface.co/GroNLP/bert-base-dutch-cased)
Th... |
huawei-noah/TinyBERT_General_4L_312D | 34707a33cd59a94ecde241ac209bf35103691b43 | 2021-05-19T20:03:32.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1909.10351",
"transformers"
] | null | false | huawei-noah | null | huawei-noah/TinyBERT_General_4L_312D | 29,099 | 1 | transformers | 421 | TinyBERT: Distilling BERT for Natural Language Understanding
========
TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learni... |
Helsinki-NLP/opus-mt-en-it | 4c56f4ddc9fcfccec7799f5cef4d90f7c99dd658 | 2021-09-09T21:36:26.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"it",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-it | 29,011 | 3 | transformers | 422 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-it
* source languages: en
* target languages: it
* OPUS readme: [en-it](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-it/README.md)
* dataset: opus
* model: transformer
* pre-processing: normalization + SentencePiece
* download or... |
cross-encoder/ms-marco-electra-base | 69c22886dd57c67783a8f48af5b86a35657df8f6 | 2021-08-05T08:40:12.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/ms-marco-electra-base | 28,888 | null | transformers | 423 | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch).... |
aubmindlab/aragpt2-base | e5f5983ece6f9546e77f7096c1ed06d11a4100fe | 2022-04-07T11:39:04.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B Arabic Corpus",
"dataset:OSCAR Arabic Unshuffled",
"arxiv:2012.15520",
"transformers"
] | text-generation | false | aubmindlab | null | aubmindlab/aragpt2-base | 28,795 | 1 | transformers | 424 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
- OSCAR Arabic Unshuffled
widget:
- text: "يحكى أن مزارعا مخادعا قام ببيع بئر الماء الموجود في أرضه لجاره مقابل مبلغ كبير من المال"
- text: "القدس مدينة تاريخية، بناها الكنعانيون في"
- text: "كان يا ما كان في قديم الزمان"
---
# Ara... |
Helsinki-NLP/opus-mt-en-ru | b4544f727b37dc7b186fa5d5a99baa74cd2f4128 | 2021-09-09T21:38:48.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"en",
"ru",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-ru | 28,487 | 3 | transformers | 425 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-ru
* source languages: en
* target languages: ru
* OPUS readme: [en-ru](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ru/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
hf-internal-testing/tiny-bert-for-token-classification | f89ef50d84f2959688279d4b2c09faf823da2069 | 2021-12-16T11:04:05.000Z | [
"pytorch",
"tf",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | hf-internal-testing | null | hf-internal-testing/tiny-bert-for-token-classification | 28,369 | 1 | transformers | 426 | Small model used as a token-classification to enable fast tests on that pipeline.
|
samrawal/bert-base-uncased_clinical-ner | db93d0fda8da893ad16484174f11ebc1ecb00a49 | 2022-05-28T15:56:53.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | samrawal | null | samrawal/bert-base-uncased_clinical-ner | 28,078 | 6 | transformers | 427 | A Named Entity Recognition model for clinical entities (`problem`, `treatment`, `test`)
The model has been trained on the [i2b2 (now n2c2) dataset](https://n2c2.dbmi.hms.harvard.edu) for the 2010 - Relations task. Please visit the n2c2 site to request access to the dataset. |
valhalla/t5-small-qg-hl | 9fdee3255929ba5f0b9d45e76e1e0184f664a368 | 2021-06-23T14:43:48.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"dataset:squad",
"arxiv:1910.10683",
"transformers",
"question-generation",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/t5-small-qg-hl | 28,016 | null | transformers | 428 | ---
datasets:
- squad
tags:
- question-generation
widget:
- text: "<hl> 42 <hl> is the answer to life, the universe and everything. </s>"
- text: "Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>"
- text: "Simple is better than <hl> complex <hl>. </s>"
license: mit
---
## T5 for qu... |
ramsrigouthamg/t5_squad_v1 | 9555145a47b5794e372b2d0b5a5331cf12e1afb7 | 2021-06-23T13:48:31.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ramsrigouthamg | null | ramsrigouthamg/t5_squad_v1 | 27,514 | 2 | transformers | 429 | Entry not found |
Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two | 7b1a724a178c639a4b3446c0ff8f13d19be4f471 | 2022-06-24T09:45:07.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:hatexplain",
"arxiv:2012.10289",
"transformers",
"license:apache-2.0"
] | text-classification | false | Hate-speech-CNERG | null | Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two | 26,892 | 3 | transformers | 430 | ---
language: en
license: apache-2.0
datasets:
- hatexplain
---
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#ev... |
cardiffnlp/twitter-roberta-base | aafc670a946810bfad6966fcabeb5b37b6388930 | 2021-05-20T15:13:17.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2010.12421",
"transformers",
"autotrain_compatible"
] | fill-mask | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base | 26,697 | 8 | transformers | 431 | # Twitter-roBERTa-base
This is a roBERTa-base model trained on ~58M tweets, described and evaluated in the [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). To evaluate this and other LMs on Twitter-specific data, please refer to the [Tweeteval official repository](https://github.... |
google/t5-xl-lm-adapt | 6e644c517fc8a8a79a657e528be5c60777d87652 | 2021-11-01T13:59:47.000Z | [
"pytorch",
"tf",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"transformers",
"t5-lm-adapt",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-xl-lm-adapt | 26,550 | 1 | transformers | 432 | ---
language: en
datasets:
- c4
tags:
- t5-lm-adapt
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transform... |
facebook/wav2vec2-large-lv60 | 0cde644b64dac88d8416bec1c92a4099b850ba0b | 2021-12-28T12:45:09.000Z | [
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"transformers",
"speech",
"license:apache-2.0"
] | null | false | facebook | null | facebook/wav2vec2-large-lv60 | 26,358 | 3 | transformers | 433 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Large-LV60
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your ... |
MilaNLProc/feel-it-italian-sentiment | dbbe12ab3220c99e4c347f0f74ac22b10ff29406 | 2022-07-07T14:38:12.000Z | [
"pytorch",
"tf",
"camembert",
"text-classification",
"it",
"transformers",
"sentiment",
"Italian",
"license:mit"
] | text-classification | false | MilaNLProc | null | MilaNLProc/feel-it-italian-sentiment | 26,330 | 5 | transformers | 434 | ---
language: it
license: mit
tags:
- sentiment
- Italian
---
# FEEL-IT: Emotion and Sentiment Classification for the Italian Language
## FEEL-IT Python Package
You can find the package that uses this model for emotion and sentiment classification **[here](https://github.com/MilaNLProc/feel-it)** it is meant to be a... |
flaubert/flaubert_base_cased | 86dac38e2ee7dcefac08dfb2c5c901e8c1cf401e | 2021-05-19T16:54:23.000Z | [
"pytorch",
"flaubert",
"fill-mask",
"fr",
"dataset:flaubert",
"transformers",
"bert",
"language-model",
"flue",
"french",
"bert-base",
"flaubert-base",
"cased",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | flaubert | null | flaubert/flaubert_base_cased | 26,140 | 1 | transformers | 435 | ---
language: fr
license: mit
datasets:
- flaubert
metrics:
- flue
tags:
- bert
- language-model
- flaubert
- flue
- french
- bert-base
- flaubert-base
- cased
---
# FlauBERT: Unsupervised Language Model Pre-training for French
**FlauBERT** is a French BERT trained on a very large and heterogeneous French corpus. M... |
aubmindlab/aragpt2-medium | 9d991bf755953507ed7685213e76fc35482abf97 | 2022-04-07T11:37:07.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B Arabic Corpus",
"dataset:OSCAR Arabic Unshuffled",
"arxiv:2012.15520",
"transformers"
] | text-generation | false | aubmindlab | null | aubmindlab/aragpt2-medium | 26,026 | 2 | transformers | 436 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
- OSCAR Arabic Unshuffled
widget:
- text: "يحكى أن مزارعا مخادعا قام ببيع بئر الماء الموجود في أرضه لجاره مقابل مبلغ كبير من المال"
- text: "القدس مدينة تاريخية، بناها الكنعانيون في"
- text: "كان يا ما كان في قديم الزمان"
---
# Arabic GPT2
<im... |
asi/gpt-fr-cased-small | d4ddd1d506690415df78683829f5aba3878888a3 | 2021-06-30T13:47:26.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"fr",
"transformers",
"text-generation",
"license:apache-2.0"
] | text-generation | false | asi | null | asi/gpt-fr-cased-small | 25,932 | 2 | transformers | 437 | ---
language:
- fr
tags:
- tf
- pytorch
- gpt2
- text-generation
license: apache-2.0
thumbnail: https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png
---
<img src="https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png" width="200">
## Model description
**GPT-fr** 🇫🇷 is... |
cardiffnlp/twitter-xlm-roberta-base | 152d26b5e474a09a99599ed33a41b9cf9d85556d | 2021-04-28T16:24:53.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"multilingual",
"arxiv:2104.12250",
"transformers",
"autotrain_compatible"
] | fill-mask | false | cardiffnlp | null | cardiffnlp/twitter-xlm-roberta-base | 25,881 | 6 | transformers | 438 | ---
language: multilingual
widget:
- text: "🤗🤗🤗<mask>"
- text: "🔥The goal of life is <mask> . 🔥"
- text: "Il segreto della vita è l’<mask> . ❤️"
- text: "Hasta <mask> 👋!"
---
# Twitter-XLM-Roberta-base
This is a XLM-Roberta-base model trained on ~198M multilingual tweets, described and evaluated in the [referen... |
Rostlab/prot_albert | 2384f5574ea4b85218ae7d5e21d17957105e672e | 2020-08-20T14:54:00.000Z | [
"pytorch",
"transformers"
] | null | false | Rostlab | null | Rostlab/prot_albert | 25,539 | 1 | transformers | 439 | Entry not found |
prithivida/parrot_fluency_model | e5224ff5b4109cd949ce25b0a6dff8d8cbdec7be | 2022-06-24T09:54:04.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | prithivida | null | prithivida/parrot_fluency_model | 25,319 | null | transformers | 440 | ---
license: apache-2.0
---
Parrot
THIS IS AN ANCILLARY MODEL FOR PARROT PARAPHRASER
1. What is Parrot?
Parrot is a paraphrase-based utterance augmentation framework purpose-built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. Please refer to the GitHub page or The mo... |
Helsinki-NLP/opus-mt-pl-en | 361ac28538863fafa2090bf91c36d02b9c596d5b | 2021-09-10T14:01:16.000Z | [
"pytorch",
"marian",
"text2text-generation",
"pl",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-pl-en | 25,315 | 2 | transformers | 441 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-pl-en
* source languages: pl
* target languages: en
* OPUS readme: [pl-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pl-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
TalTechNLP/voxlingua107-epaca-tdnn | f35eaf95daf2040cc68ececfd45bbb5e47c44b1c | 2021-11-04T13:37:27.000Z | [
"multilingual",
"dataset:VoxLingua107",
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"license:apache-2.0"
] | audio-classification | false | TalTechNLP | null | TalTechNLP/voxlingua107-epaca-tdnn | 25,127 | 14 | speechbrain | 442 | ---
language: multilingual
thumbnail:
tags:
- audio-classification
- speechbrain
- embeddings
- Language
- Identification
- pytorch
- ECAPA-TDNN
- TDNN
- VoxLingua107
license: "apache-2.0"
datasets:
- VoxLingua107
metrics:
- Accuracy
widget:
- example_title: English Sample
src: https://cdn-media.huggingface.co/speech... |
microsoft/xtremedistil-l6-h256-uncased | 8d58f0e6e83c1ab87f88d8c556ec537a111e2ee0 | 2021-08-05T17:49:53.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"en",
"arxiv:2106.04563",
"transformers",
"text-classification",
"license:mit"
] | text-classification | false | microsoft | null | microsoft/xtremedistil-l6-h256-uncased | 24,605 | 10 | transformers | 443 | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# XtremeDistilTransformers for Distilling Massive Neural Networks
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small uni... |
microsoft/resnet-50 | f5104f67a0a8892c17fa776add3e55999dc67893 | 2022-07-01T17:33:32.000Z | [
"pytorch",
"tf",
"resnet",
"image-classification",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/resnet-50 | 24,388 | 4 | transformers | 444 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
---
# ResNet-50 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team ... |
dbmdz/bert-base-turkish-cased | bd38a3ecfe5d183400a573b1903e940d8d34902b | 2021-05-19T15:14:46.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"tr",
"transformers",
"license:mit"
] | null | false | dbmdz | null | dbmdz/bert-base-turkish-cased | 24,344 | 14 | transformers | 445 | ---
language: tr
license: mit
---
# 🤗 + 📚 dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased model for Turkish 🎉
# 🇹🇷 BERTurk
BERTurk is a community-driven cased BERT model for Turkish.
Some datasets used for pretraining and eval... |
deepset/gelectra-base-germanquad | 7cd7dcc35ff9e03550826b25c90b97b33db691a1 | 2022-07-26T14:47:06.000Z | [
"pytorch",
"tf",
"electra",
"question-answering",
"de",
"dataset:deepset/germanquad",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/gelectra-base-germanquad | 24,243 | 10 | transformers | 446 | ---
language: de
datasets:
- deepset/germanquad
license: mit
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
---
... |
aubmindlab/aragpt2-large | d5b30b822726c9ac29ed75fc4bb6c423f3621dd8 | 2022-04-07T11:34:39.000Z | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B Arabic Corpus",
"dataset:OSCAR Arabic Unshuffled",
"arxiv:2012.15520",
"transformers"
] | text-generation | false | aubmindlab | null | aubmindlab/aragpt2-large | 24,085 | null | transformers | 447 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
- OSCAR Arabic Unshuffled
inference: false
widget:
- text: "يحكى أن مزارعا مخادعا قام ببيع بئر الماء الموجود في أرضه لجاره مقابل مبلغ كبير من المال"
- text: "القدس مدينة تاريخية، بناها الكنعانيون في"
- text: "كان يا ما كان في قديم الز... |
MilaNLProc/feel-it-italian-emotion | b3be7db7ff41872383ac0a01c01c8a027d2893e3 | 2022-07-07T14:37:56.000Z | [
"pytorch",
"tf",
"camembert",
"text-classification",
"it",
"transformers",
"sentiment",
"emotion",
"Italian",
"license:mit"
] | text-classification | false | MilaNLProc | null | MilaNLProc/feel-it-italian-emotion | 23,937 | 6 | transformers | 448 | ---
language: it
license: mit
tags:
- sentiment
- emotion
- Italian
---
# FEEL-IT: Emotion and Sentiment Classification for the Italian Language
## FEEL-IT Python Package
You can find the package that uses this model for emotion and sentiment classification **[here](https://github.com/MilaNLProc/feel-it)** it... |
sberbank-ai/rugpt3small_based_on_gpt2 | f2f7c585b05a16726efe8974586e10b4d5939082 | 2021-09-21T19:30:41.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"ru",
"transformers",
"PyTorch",
"Transformers"
] | text-generation | false | sberbank-ai | null | sberbank-ai/rugpt3small_based_on_gpt2 | 23,788 | 5 | transformers | 449 | ---
language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: "https://github.com/sberbank-ai/ru-gpts"
---
# rugpt3small\_based\_on\_gpt2
Model was trained with sequence length 1024 using transformers by [SberDevices](https://sberdevices.ru/) team on 80B tokens around 3 epoch. After that model was finetuned on 2048 con... |
ckiplab/bert-base-chinese-ws | 60c22ced1c0ec221242906e8f9fbdf90fb560b77 | 2022-05-10T03:28:12.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"zh",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | token-classification | false | ckiplab | null | ckiplab/bert-base-chinese-ws | 23,748 | 2 | transformers | 450 | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- bert
- zh
license: gpl-3.0
---
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segment... |
Seznam/small-e-czech | 0e933d0b8d8dde2b3f5cc76444e221c0f407536c | 2022-05-11T10:46:25.000Z | [
"pytorch",
"tf",
"electra",
"cs",
"arxiv:2003.10555",
"arxiv:2112.01810",
"transformers",
"license:cc-by-4.0"
] | null | false | Seznam | null | Seznam/small-e-czech | 23,736 | 3 | transformers | 451 | ---
language: cs
license: cc-by-4.0
---
# Small-E-Czech
Small-E-Czech is an [Electra](https://arxiv.org/abs/2003.10555)-small model pretrained on a Czech web corpus created at [Seznam.cz](https://www.seznam.cz/) and introduced in an [IAAI 2022 paper](https://arxiv.org/abs/2112.01810). Like other pretrained models, it... |
felflare/bert-restore-punctuation | 954108a105ef1f89f08b71c25d6e33bb89cde724 | 2021-05-24T03:04:47.000Z | [
"pytorch",
"bert",
"token-classification",
"en",
"dataset:yelp_polarity",
"transformers",
"punctuation",
"license:mit",
"autotrain_compatible"
] | token-classification | false | felflare | null | felflare/bert-restore-punctuation | 23,698 | 24 | transformers | 452 | ---
language:
- en
tags:
- punctuation
license: mit
datasets:
- yelp_polarity
metrics:
- f1
---
# ✨ bert-restore-punctuation
[]()
This a bert-base-uncased model finetuned for punctuation restoration on [Yelp Reviews](https://www.tensorflow.org/datase... |
aubmindlab/aragpt2-mega | d7de129b4b6caaf3bec3512e839ffd14cb47163b | 2022-04-07T11:43:23.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B Arabic Corpus",
"dataset:OSCAR Arabic Unshuffled",
"arxiv:2012.15520",
"transformers"
] | text-generation | false | aubmindlab | null | aubmindlab/aragpt2-mega | 23,506 | null | transformers | 453 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
- OSCAR Arabic Unshuffled
inference: false
widget:
- text: "يحكى أن مزارعا مخادعا قام ببيع بئر الماء الموجود في أرضه لجاره مقابل مبلغ كبير من المال"
- text: "القدس مدينة تاريخية، بناها الكنعانيون في"
- text: "كان يا ما كان في قديم الز... |
sentence-transformers/nq-distilbert-base-v1 | a3dd10344d84c37c1d4a8be5d5021317900a2d19 | 2022-06-15T21:49:34.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/nq-distilbert-base-v1 | 23,490 | null | sentence-transformers | 454 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/nq-distilbert-base-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense ... |
sentence-transformers/clip-ViT-B-32-multilingual-v1 | 200b64f20b3cef15ade0d31b1392519a46024087 | 2022-06-15T20:17:26.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"multilingual",
"arxiv:2004.09813",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/clip-ViT-B-32-multilingual-v1 | 23,467 | 8 | sentence-transformers | 455 | ---
pipeline_tag: sentence-similarity
language: multilingual
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
---
# sentence-transformers/clip-ViT-B-32-multilingual-v1
This is a multi-lingual version of the OpenAI CLIP-ViT-B32 model. You can map text (in 50+ ... |
skt/kogpt2-base-v2 | d0c0df48bf2b2c9350dd855021a5b216f560c0c7 | 2021-09-23T16:29:28.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"ko",
"transformers",
"license:cc-by-nc-sa-4.0"
] | text-generation | false | skt | null | skt/kogpt2-base-v2 | 23,176 | 8 | transformers | 456 | ---
language: ko
tags:
- gpt2
license: cc-by-nc-sa-4.0
---
For more details: https://github.com/SKT-AI/KoGPT2
|
facebook/rag-token-nq | af32fa164f774a532dfb63c94b2e898e80434643 | 2021-03-12T10:55:22.000Z | [
"pytorch",
"tf",
"rag",
"en",
"dataset:wiki_dpr",
"arxiv:2005.11401",
"transformers",
"license:apache-2.0"
] | null | false | facebook | null | facebook/rag-token-nq | 23,138 | 6 | transformers | 457 | ---
language: en
license: apache-2.0
datasets:
- wiki_dpr
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
---
## RAG
This is the RAG-Token Model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)
by Patrick Lewis, Ethan Perez, Alek... |
facebook/nllb-200-distilled-600M | 368f64e5d5437e922548864bc115edcaa97aed60 | 2022-07-19T15:43:23.000Z | [
"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-distilled-600M | 23,102 | 23 | transformers | 458 | ---
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... |
xlm-mlm-en-2048 | 509c94bad4a3a166f8d0206b92c44278721d6d34 | 2022-07-22T08:10:18.000Z | [
"pytorch",
"tf",
"xlm",
"fill-mask",
"en",
"arxiv:1901.07291",
"arxiv:1911.02116",
"arxiv:1910.09700",
"transformers",
"exbert",
"license:cc-by-nc-4.0",
"autotrain_compatible"
] | fill-mask | false | null | null | xlm-mlm-en-2048 | 23,003 | null | transformers | 459 | ---
language: en
tags:
- exbert
license: cc-by-nc-4.0
---
# xlm-mlm-en-2048
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7... |
microsoft/infoxlm-base | c67f260d5635cdeef35864fd2ce369d24eca1b34 | 2021-08-04T11:42:14.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2007.07834",
"transformers",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/infoxlm-base | 22,770 | 2 | transformers | 460 | # InfoXLM
**InfoXLM** (NAACL 2021, [paper](https://arxiv.org/pdf/2007.07834.pdf), [repo](https://github.com/microsoft/unilm/tree/master/infoxlm), [model](https://huggingface.co/microsoft/infoxlm-base)) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
**MD5**
```
b9d214025837... |
Geotrend/bert-base-ru-cased | 2810f3d4fa13f6a045fcda6a6e91bfb085e60396 | 2021-05-18T20:09:38.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ru",
"dataset:wikipedia",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Geotrend | null | Geotrend/bert-base-ru-cased | 22,626 | null | transformers | 461 | ---
language: ru
datasets: wikipedia
license: apache-2.0
---
# bert-base-ru-cased
We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://huggingface.co/disti... |
google/tapas-base-finetuned-wtq | e3dde1905dea877b0df1a5c057533e48327dee77 | 2022-07-14T10:12:59.000Z | [
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikitablequestions",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1508.00305",
"transformers",
"license:apache-2.0"
] | table-question-answering | false | google | null | google/tapas-base-finetuned-wtq | 22,322 | 22 | transformers | 462 | ---
language: en
tags:
- tapas
license: apache-2.0
datasets:
- wikitablequestions
---
# TAPAS base model fine-tuned on WikiTable Questions (WTQ)
This model has 2 versions which can be used. The default version corresponds to the `tapas_wtq_wikisql_sqa_inter_masklm_base_reset` checkpoint of the [original Github reposi... |
google/electra-base-generator | 1c65e3f5f4597679b87620707df5774c08c6606d | 2021-04-30T07:42:51.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"electra",
"fill-mask",
"en",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | google | null | google/electra-base-generator | 22,206 | 1 | transformers | 463 | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks usi... |
jonatasgrosman/wav2vec2-large-xlsr-53-english | 95977151cc235bfdef3ccd9fc5474c1bee9081e3 | 2022-07-27T23:37:25.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_6_0",
"transformers",
"audio",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_6_0",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"lice... | automatic-speech-recognition | false | jonatasgrosman | null | jonatasgrosman/wav2vec2-large-xlsr-53-english | 22,169 | 15 | transformers | 464 | ---
language: en
datasets:
- common_voice
- mozilla-foundation/common_voice_6_0
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- en
- hf-asr-leaderboard
- mozilla-foundation/common_voice_6_0
- robust-speech-event
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 ... |
cointegrated/rubert-tiny-toxicity | 635187bd9d0a97028c1be4dbc603efa41e108838 | 2022-01-31T21:56:30.000Z | [
"pytorch",
"bert",
"text-classification",
"ru",
"arxiv:2103.05345",
"transformers",
"russian",
"classification",
"toxicity",
"multilabel"
] | text-classification | false | cointegrated | null | cointegrated/rubert-tiny-toxicity | 21,675 | 5 | transformers | 465 | ---
language: ["ru"]
tags:
- russian
- classification
- toxicity
- multilabel
widget:
- text: "Иди ты нафиг!"
---
This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of toxicity and inappropriateness for short informal Russian texts, such as commen... |
aubmindlab/bert-base-arabertv02 | b214583e9b05a7bbc024d58daeb54a1b2a2997a0 | 2022-04-06T15:24:47.000Z | [
"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-base-arabertv02 | 21,626 | 6 | transformers | 466 | ---
language: ar
datasets:
- wikipedia
- OSIAN
- 1.5B Arabic Corpus
- OSCAR Arabic Unshuffled
widget:
- text: " عاصمة 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" w... |
armheb/DNA_bert_6 | a79a8fd96ad172f964a4dbef3f4d7545a5034baa | 2021-10-10T22:58:53.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | armheb | null | armheb/DNA_bert_6 | 21,612 | 2 | transformers | 467 | Entry not found |
google/t5-v1_1-large | 314bc112b191ec17b625ba81438dc73d6c23659d | 2021-06-23T01:59:26.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | google | null | google/t5-v1_1-large | 21,409 | 7 | transformers | 468 | ---
language: en
datasets:
- c4
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1
## Version 1.1
[T5 Version 1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/master/released_checkpoints.md#t511) includes the f... |
Helsinki-NLP/opus-mt-id-en | cbdb70ef26d3c5a6585e6a810da1003bd50bb6b3 | 2021-09-09T22:11:11.000Z | [
"pytorch",
"marian",
"text2text-generation",
"id",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-id-en | 21,326 | null | transformers | 469 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-id-en
* source languages: id
* target languages: en
* OPUS readme: [id-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/id-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
facebook/bart-large-xsum | e5a049949143586befac0f09a9716bde79b55e77 | 2021-06-14T07:39:59.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"bart",
"text2text-generation",
"en",
"arxiv:1910.13461",
"transformers",
"summarization",
"license:mit",
"autotrain_compatible"
] | summarization | false | facebook | null | facebook/bart-large-xsum | 21,298 | 4 | transformers | 470 | ---
tags:
- summarization
language:
- en
license: mit
---
### Bart model finetuned on xsum
docs: https://huggingface.co/transformers/model_doc/bart.html
finetuning: examples/seq2seq/ (as of Aug 20, 2020)
Metrics: ROUGE > 22 on xsum.
variants: search for distilbart
paper: https://arxiv.org/abs/1910.13461 |
bert-base-cased-finetuned-mrpc | f53cb9cb49541a34be140979efe098073a179b0f | 2021-05-18T16:08:38.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-base-cased-finetuned-mrpc | 21,220 | null | transformers | 471 | Entry not found |
facebook/blenderbot-3B | c468b2376f5f49d20624f31383023f2bbd360c8d | 2021-09-21T19:45:52.000Z | [
"pytorch",
"blenderbot",
"text2text-generation",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"transformers",
"convAI",
"conversational",
"facebook",
"license:apache-2.0",
"autotrain_compatible"
] | conversational | false | facebook | null | facebook/blenderbot-3B | 21,189 | 20 | transformers | 472 | ---
language:
- en
thumbnail:
tags:
- convAI
- conversational
- facebook
license: apache-2.0
datasets:
- blended_skill_talk
metrics:
- perplexity
---
## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes... |
twmkn9/bert-base-uncased-squad2 | d6f9bc70be3777da3bb065e6ce289e0261ece205 | 2021-05-20T08:21:23.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | twmkn9 | null | twmkn9/bert-base-uncased-squad2 | 20,916 | 1 | transformers | 473 | This model is [BERT base uncased](https://huggingface.co/bert-base-uncased) trained on SQuAD v2 as:
```
export SQUAD_DIR=../../squad2
python3 run_squad.py
--model_type bert
--model_name_or_path bert-base-uncased
--do_train
--do_eval
--overwrite_cache
--do_lower_case
--version_2_with_... |
studio-ousia/luke-base | 7438924defd9f3c2018d63c16073bf4bcb6a70aa | 2022-04-13T08:59:59.000Z | [
"pytorch",
"luke",
"fill-mask",
"en",
"arxiv:1906.08237",
"arxiv:1903.07785",
"arxiv:2002.01808",
"transformers",
"named entity recognition",
"entity typing",
"relation classification",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | studio-ousia | null | studio-ousia/luke-base | 20,850 | 8 | transformers | 474 | ---
language: en
thumbnail: https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png
tags:
- luke
- named entity recognition
- entity typing
- relation classification
- question answering
license: apache-2.0
---
## LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attenti... |
KoboldAI/fairseq-dense-13B-Shinen | c6db29f4afb5ffbdc9e2251ec91914be6fcb4339 | 2022-04-07T09:10:04.000Z | [
"pytorch",
"xglm",
"text-generation",
"en",
"transformers",
"license:mit"
] | text-generation | false | KoboldAI | null | KoboldAI/fairseq-dense-13B-Shinen | 20,734 | 1 | transformers | 475 | ---
language: en
license: mit
---
# Fairseq-dense 13B - Shinen
## Model Description
Fairseq-dense 13B-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 out... |
lewtun/roberta-base-bne-finetuned-amazon_reviews_multi | 48a974e668586c5bf9da83eca25806b4245ee86f | 2021-08-23T17:13:32.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:amazon_reviews_multi",
"transformers",
"generated_from_trainer",
"license:cc-by-4.0"
] | text-classification | false | lewtun | null | lewtun/roberta-base-bne-finetuned-amazon_reviews_multi | 20,705 | null | transformers | 476 | ---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model_index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: a... |
sonoisa/t5-base-japanese | da38f0ca07a6ad01de67e4e70dcb959e7d5063db | 2022-07-20T00:21:36.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"ja",
"dataset:wikipedia",
"dataset:oscar",
"dataset:cc100",
"transformers",
"text2text-generation",
"seq2seq",
"license:cc-by-sa-4.0"
] | feature-extraction | false | sonoisa | null | sonoisa/t5-base-japanese | 20,233 | 5 | transformers | 477 | ---
language: ja
tags:
- t5
- text2text-generation
- seq2seq
license: cc-by-sa-4.0
datasets:
- wikipedia
- oscar
- cc100
---
# 日本語T5事前学習済みモデル
This is a T5 (Text-to-Text Transfer Transformer) model pretrained on Japanese corpus.
次の日本語コーパス（約100GB）を用いて事前学習を行ったT5 (Text-to-Text Transfer Transformer) モデルです。
* [Wikipedi... |
hustvl/yolos-tiny | 3686e65df0c914833fc8cbeca745a33b374c499b | 2022-06-27T08:37:24.000Z | [
"pytorch",
"yolos",
"object-detection",
"dataset:coco",
"arxiv:2106.00666",
"transformers",
"vision",
"license:apache-2.0"
] | object-detection | false | hustvl | null | hustvl/yolos-tiny | 20,103 | 4 | transformers | 478 | ---
license: apache-2.0
tags:
- object-detection
- vision
datasets:
- coco
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
example_title: Savanna
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
example_title: Football Match
- s... |
oliverguhr/fullstop-dutch-sonar-punctuation-prediction | e680df1f96f17bb3001789b2ba2c78b007c5e3df | 2022-05-02T13:15:40.000Z | [
"pytorch",
"roberta",
"token-classification",
"nl",
"dataset:sonar",
"transformers",
"punctuation prediction",
"punctuation",
"license:mit",
"autotrain_compatible"
] | token-classification | false | oliverguhr | null | oliverguhr/fullstop-dutch-sonar-punctuation-prediction | 20,050 | null | transformers | 479 | ---
language:
- nl
tags:
- punctuation prediction
- punctuation
datasets: sonar
license: mit
widget:
- text: "hervatting van de zitting ik verklaar de zitting van het europees parlement die op vrijdag 17 december werd onderbroken te zijn hervat"
example_title: "Euro Parl Sample"
metrics:
- f1
---
## Model
Trained o... |
julien-c/dummy-unknown | 60b8d3fe22aebb024b573f1cca224db3126d10f3 | 2021-05-20T17:31:14.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"transformers",
"ci",
"autotrain_compatible"
] | fill-mask | false | julien-c | null | julien-c/dummy-unknown | 20,046 | null | transformers | 480 | ---
tags:
- ci
---
## Dummy model used for unit testing and CI
```python
import json
import os
from transformers import RobertaConfig, RobertaForMaskedLM, TFRobertaForMaskedLM
DIRNAME = "./dummy-unknown"
config = RobertaConfig(10, 20, 1, 1, 40)
model = RobertaForMaskedLM(config)
model.save_pretrained(DIRNAME)
t... |
TurkuNLP/bert-base-finnish-cased-v1 | 9800b205abb21a898401af85073e2849699f999b | 2022-06-10T08:43:15.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"fi",
"arxiv:1912.07076",
"arxiv:1908.04212",
"transformers",
"autotrain_compatible"
] | fill-mask | false | TurkuNLP | null | TurkuNLP/bert-base-finnish-cased-v1 | 20,018 | null | transformers | 481 | ---
language: fi
---
## Quickstart
**Release 1.0** (November 25, 2019)
We generally recommend the use of the cased model.
Paper presenting Finnish BERT: [arXiv:1912.07076](https://arxiv.org/abs/1912.07076)
## What's this?
A version of Google's [BERT](https://github.com/google-research/bert) deep transfer learning... |
google/mt5-large | bdd096d7cf0fc531444a0db2e0a9a209d0a5f8c0 | 2022-05-27T15:06:35.000Z | [
"pytorch",
"tf",
"jax",
"mt5",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
... | text2text-generation | false | google | null | google/mt5-large | 19,838 | 9 | transformers | 482 | ---
language:
- multilingual
- af
- am
- ar
- az
- be
- bg
- bn
- 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
- ht
- hu
- hy
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
- ... |
sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens | e614409446a9b8cc7eb7ac1087e11af9e99ab895 | 2022-06-15T20:39:21.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens | 19,765 | null | sentence-transformers | 483 | ---
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... |
Filosofas/DialoGPT-medium-PALPATINE | 321b76cbcf40d9c9efa7776ba1eb80be7946211a | 2022-02-08T11:50:03.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Filosofas | null | Filosofas/DialoGPT-medium-PALPATINE | 19,739 | 1 | transformers | 484 | ---
tags:
- conversational
---
# updated PALPATINE DialoGPT Model |
openai/clip-vit-large-patch14-336 | a2ab452d41e630fda015a7ad3e9751aa74081239 | 2022-04-22T14:58:59.000Z | [
"pytorch",
"clip",
"feature-extraction",
"transformers"
] | feature-extraction | false | openai | null | openai/clip-vit-large-patch14-336 | 19,736 | 5 | transformers | 485 | Entry not found |
IDEA-CCNL/Erlangshen-Roberta-330M-Similarity | 8ed6c66504212201bd8f542a2467741baef8a133 | 2022-05-12T09:49:57.000Z | [
"pytorch",
"bert",
"text-classification",
"zh",
"transformers",
"NLU",
"NLI",
"license:apache-2.0"
] | text-classification | false | IDEA-CCNL | null | IDEA-CCNL/Erlangshen-Roberta-330M-Similarity | 19,718 | null | transformers | 486 | ---
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-330M-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... |
onlplab/alephbert-base | 1745fb3ff5137e41e9eb4d6246e0758f63b93e46 | 2022-06-26T09:32:47.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"he",
"dataset:oscar",
"dataset:wikipedia",
"dataset:twitter",
"arxiv:1810.04805",
"transformers",
"language model",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | onlplab | null | onlplab/alephbert-base | 19,715 | 4 | transformers | 487 | ---
language:
- he
tags:
- language model
license: apache-2.0
datasets:
- oscar
- wikipedia
- twitter
---
# AlephBERT
## Hebrew Language Model
State-of-the-art language model for Hebrew.
Based on Google's BERT architecture [(Devlin et al. 2018)](https://arxiv.org/abs/1810.04805).
#### How to use
```python
from t... |
sentence-transformers/distilbert-base-nli-stsb-quora-ranking | f39736041df2a9460ef1525cc9052c3fa39bebc2 | 2022-06-15T22:01:40.000Z | [
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/distilbert-base-nli-stsb-quora-ranking | 19,695 | null | sentence-transformers | 488 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/distilbert-base-nli-stsb-quora-ranking
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 d... |
Rakib/roberta-base-on-cuad | bc6033499692e08cef629b94b5dad636df956b24 | 2021-07-03T18:10:33.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | Rakib | null | Rakib/roberta-base-on-cuad | 19,670 | null | transformers | 489 | Entry not found |
facebook/dino-vitb16 | f010b0593df30cda23c40c5fb62811f21e53f5ec | 2021-08-25T17:39:50.000Z | [
"pytorch",
"vit",
"feature-extraction",
"dataset:imagenet-1k",
"arxiv:2010.11929",
"arxiv:2104.14294",
"transformers",
"dino",
"license:apache-2.0"
] | feature-extraction | false | facebook | null | facebook/dino-vitb16 | 19,521 | 2 | transformers | 490 | ---
license: apache-2.0
tags:
- dino
datasets:
- imagenet-1k
---
# Vision Transformer (base-sized model, patch size 16) trained using DINO
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/20... |
SZTAKI-HLT/hubert-base-cc | f9b2da95ca4080247005d54b81a41bf98c65acb5 | 2021-05-19T11:29:35.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"hu",
"dataset:common_crawl",
"dataset:wikipedia",
"transformers",
"license:apache-2.0"
] | null | false | SZTAKI-HLT | null | SZTAKI-HLT/hubert-base-cc | 19,266 | 4 | transformers | 491 | ---
language: hu
license: apache-2.0
datasets:
- common_crawl
- wikipedia
---
# huBERT base model (cased)
## Model description
Cased BERT model for Hungarian, trained on the (filtered, deduplicated) Hungarian subset of the Common Crawl and a snapshot of the Hungarian Wikipedia.
## Intended uses & limitations
The m... |
google/electra-large-generator | bbb1b8938d38e9f5dbfaaecc869320388b4fefe2 | 2021-04-30T07:44:18.000Z | [
"pytorch",
"tf",
"jax",
"electra",
"fill-mask",
"en",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | google | null | google/electra-large-generator | 19,120 | 2 | transformers | 492 | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks usi... |
microsoft/swin-tiny-patch4-window7-224 | 83d40fb5b9320b349382208d9e7fe998484e99df | 2022-05-16T18:24:43.000Z | [
"pytorch",
"tf",
"swin",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2103.14030",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/swin-tiny-patch4-window7-224 | 19,068 | 6 | transformers | 493 | ---
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/msmarco-MiniLM-L6-cos-v5 | 16d295d3338a2f01448ba841fd181cc2ce7b63f4 | 2022-06-15T22:00:09.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-MiniLM-L6-cos-v5 | 18,806 | 3 | sentence-transformers | 494 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# msmarco-MiniLM-L6-cos-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **sema... |
charsiu/zh_w2v2_tiny_fc_10ms | dde9cc1a048e2cb16d0d741deeae5ea9bd634ffe | 2021-12-16T15:19:18.000Z | [
"pytorch",
"wav2vec2",
"transformers"
] | null | false | charsiu | null | charsiu/zh_w2v2_tiny_fc_10ms | 18,749 | 1 | transformers | 495 | Entry not found |
google/electra-large-discriminator | 96c9a247e8ef7e818408efedfbd5fd2a26aa13ae | 2021-04-30T07:38:14.000Z | [
"pytorch",
"tf",
"jax",
"electra",
"pretraining",
"en",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/electra-large-discriminator | 18,720 | 3 | transformers | 496 | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks usi... |
elastic/distilbert-base-uncased-finetuned-conll03-english | 0e98652673725eab6929978aeb28d8dffc614818 | 2022-06-24T09:30:50.000Z | [
"pytorch",
"distilbert",
"token-classification",
"en",
"dataset:conll2003",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | elastic | null | elastic/distilbert-base-uncased-finetuned-conll03-english | 18,717 | 7 | transformers | 497 | ---
language: en
license: apache-2.0
datasets:
- conll2003
model-index:
- name: elastic/distilbert-base-uncased-finetuned-conll03-english
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: v... |
vumichien/wav2vec2-large-xlsr-japanese-hiragana | 4110cbb24231daf76321af85b829a1baa686d289 | 2021-06-18T11:22:28.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ja",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | vumichien | null | vumichien/wav2vec2-large-xlsr-japanese-hiragana | 18,616 | 3 | transformers | 498 | ---
language: ja
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Japanese Hiragana by Chien Vu
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dat... |
textattack/bert-base-uncased-CoLA | 5fed03dd6bc5f0b40e86cb04cd1a16eb404ba391 | 2021-05-20T07:31:05.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | false | textattack | null | textattack/bert-base-uncased-CoLA | 18,432 | null | transformers | 499 | Entry not found |
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