modelId stringlengths 4 112 | sha stringlengths 40 40 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringclasses 29
values | private bool 1
class | author stringlengths 2 38 ⌀ | config null | id stringlengths 4 112 | downloads float64 0 36.8M ⌀ | likes float64 0 712 ⌀ | library_name stringclasses 17
values | __index_level_0__ int64 0 38.5k | readme stringlengths 0 186k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
google/mobilebert-uncased | 1f90a6c24c7879273a291d34a849033eba2dbc0f | 2021-04-19T13:32:58.000Z | [
"pytorch",
"tf",
"rust",
"mobilebert",
"pretraining",
"en",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/mobilebert-uncased | 115,430 | 4 | transformers | 200 | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
## MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attention... |
hf-internal-testing/tiny-random-electra | e5efa6ccdff6b9cd0c387c90ff4af92686bc0c12 | 2021-09-17T19:22:20.000Z | [
"pytorch",
"tf",
"electra",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-electra | 114,761 | null | transformers | 201 | Entry not found |
hf-internal-testing/tiny-random-albert | 213e37e1c19981fbcee4f5f9ebefee8db74c8db1 | 2021-09-17T19:23:59.000Z | [
"pytorch",
"tf",
"albert",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-albert | 114,473 | null | transformers | 202 | Entry not found |
sentence-transformers/all-mpnet-base-v1 | 5db7848555eaffcf26ac367f5dc9e0711acb2106 | 2021-08-31T07:35:23.000Z | [
"pytorch",
"mpnet",
"fill-mask",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/all-mpnet-base-v1 | 113,021 | 3 | sentence-transformers | 203 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
---
# all-mpnet-base-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used... |
philschmid/bart-large-cnn-samsum | 78e20b3792d507739ebb9e5a417bcc87606d3293 | 2022-07-04T13:10:54.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:samsum",
"transformers",
"sagemaker",
"summarization",
"model-index",
"autotrain_compatible"
] | summarization | false | philschmid | null | philschmid/bart-large-cnn-samsum | 112,471 | 16 | transformers | 204 |
---
language: en
tags:
- sagemaker
- bart
- summarization
datasets:
- samsum
widget:
- text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n\
Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:\
\ ok.\nJeff: and how can I get started? \nJeff: where can I f... |
cross-encoder/stsb-roberta-large | 9e35bf01ec28b309411c8903d0d4165567303eb4 | 2021-08-05T08:42:03.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | cross-encoder | null | cross-encoder/stsb-roberta-large | 111,228 | 3 | transformers | 205 | ---
license: apache-2.0
---
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset]... |
Helsinki-NLP/opus-mt-ru-en | a06887d8d700245d4b10a20b6d89c1ad778f33c2 | 2022-07-14T08:56:05.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"ru",
"en",
"transformers",
"translation",
"license:cc-by-4.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ru-en | 110,930 | 6 | transformers | 206 | ---
tags:
- translation
license: cc-by-4.0
---
### opus-mt-ru-en
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [How to Get Sta... |
sentence-transformers/paraphrase-distilroberta-base-v1 | de91d53e03a544451c0e18312391a3f279f7f4ef | 2022-06-15T20:03:06.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/paraphrase-distilroberta-base-v1 | 110,780 | null | sentence-transformers | 207 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-distilroberta-base-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensi... |
EleutherAI/gpt-neo-1.3B | 797174552ae47f449ab70b684cabcb6603e5e85e | 2021-12-31T13:48:33.000Z | [
"pytorch",
"jax",
"rust",
"gpt_neo",
"text-generation",
"en",
"dataset:the_pile",
"transformers",
"text generation",
"causal-lm",
"license:apache-2.0"
] | text-generation | false | EleutherAI | null | EleutherAI/gpt-neo-1.3B | 110,054 | 40 | transformers | 208 | ---
language:
- en
tags:
- text generation
- pytorch
- causal-lm
license: apache-2.0
datasets:
- the_pile
---
# GPT-Neo 1.3B
## Model Description
GPT-Neo 1.3B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 1.3B represents the num... |
hfl/chinese-electra-180g-small-discriminator | 19998e79c480fa7f3892b3c035e4362fe497efcf | 2021-03-03T01:04:26.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"zh",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0"
] | null | false | hfl | null | hfl/chinese-electra-180g-small-discriminator | 109,135 | 7 | transformers | 209 | ---
language:
- zh
license: "apache-2.0"
---
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compa... |
seyonec/ChemBERTa-zinc-base-v1 | 761d6a18cf99db371e0b43baf3e2d21b3e865a20 | 2021-05-20T20:55:33.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"chemistry",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/ChemBERTa-zinc-base-v1 | 109,134 | 1 | transformers | 210 | ---
tags:
- chemistry
---
# ChemBERTa: Training a BERT-like transformer model for masked language modelling of chemical SMILES strings.
Deep learning for chemistry and materials science remains a novel field with lots of potiential. However, the popularity of transfer learning based methods in areas such as NLP and ... |
vinai/bertweet-base | f9365bd897a63399564af0859aa981deb6deb0f3 | 2022-06-08T04:43:30.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vinai | null | vinai/bertweet-base | 108,322 | 11 | transformers | 211 | # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.... |
Sahajtomar/German_Zeroshot | d5b0a26665b8538bcb3faa1e63a634cca4c8ee1b | 2021-05-18T22:22:18.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"dataset:xnli",
"transformers",
"nli",
"xnli",
"de",
"zero-shot-classification"
] | zero-shot-classification | false | Sahajtomar | null | Sahajtomar/German_Zeroshot | 108,175 | 9 | transformers | 212 | ---
language: multilingual
tags:
- text-classification
- pytorch
- nli
- xnli
- de
datasets:
- xnli
pipeline_tag: zero-shot-classification
widget:
- text: "Letzte Woche gab es einen Selbstmord in einer nahe gelegenen kolonie"
candidate_labels: "Verbrechen,Tragödie,Stehlen"
hypothesis_template: "In deisem geht es u... |
bert-base-german-dbmdz-uncased | 78540bb3887dee683eb7d1dc01abe831fe2d1b6d | 2022-07-18T20:04:08.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-base-german-dbmdz-uncased | 106,927 | 2 | transformers | 213 | ---
language: de
license: mit
---
This model is the same as [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-uncased) for details on the model.
|
smanjil/German-MedBERT | b4e8a3e260ca938390616816402ab23d98775b07 | 2022-06-13T16:52:46.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"German",
"autotrain_compatible"
] | fill-mask | false | smanjil | null | smanjil/German-MedBERT | 106,645 | 3 | transformers | 214 | ---
language: de
tags:
- exbert
- German
---
<a href="https://huggingface.co/exbert/?model=smanjil/German-MedBERT">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
# German Medical BERT
This is a fine-tuned model on the Medical domain for the German language and based on German B... |
facebook/contriever | 2bd46a25019aeea091fd42d1f0fd4801675cf699 | 2022-01-19T17:23:28.000Z | [
"pytorch",
"bert",
"arxiv:2112.09118",
"transformers"
] | null | false | facebook | null | facebook/contriever | 104,025 | 1 | transformers | 215 | This model has been trained without supervision following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever.
## Usage (HuggingFace Tr... |
T-Systems-onsite/cross-en-de-roberta-sentence-transformer | 17bdd111a862ec99279be149fc9efa4f9122bcc1 | 2022-06-16T18:13:54.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"de",
"en",
"dataset:STSbenchmark",
"arxiv:1908.10084",
"transformers",
"sentence_embedding",
"search",
"roberta",
"xlm-r-distilroberta-base-paraphrase-v1",
"paraphrase",
"license:mit"
] | feature-extraction | false | T-Systems-onsite | null | T-Systems-onsite/cross-en-de-roberta-sentence-transformer | 103,768 | 4 | transformers | 216 | ---
language:
- de
- en
license: mit
tags:
- sentence_embedding
- search
- pytorch
- xlm-roberta
- roberta
- xlm-r-distilroberta-base-paraphrase-v1
- paraphrase
datasets:
- STSbenchmark
metrics:
- Spearman’s rank correlation
- cosine similarity
---
# Cross English & German RoBERTa for Sentence Embeddings
This model... |
typeform/distilbert-base-uncased-mnli | 996dacf8ea284d96ea21f88a345fd7d597de1f1f | 2022-06-24T15:43:48.000Z | [
"pytorch",
"tf",
"distilbert",
"text-classification",
"en",
"dataset:multi_nli",
"arxiv:1910.09700",
"arxiv:2105.09680",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | false | typeform | null | typeform/distilbert-base-uncased-mnli | 102,610 | 11 | transformers | 217 | ---
language: en
pipeline_tag: zero-shot-classification
tags:
- distilbert
datasets:
- multi_nli
metrics:
- accuracy
---
# DistilBERT base model (uncased)
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitat... |
cardiffnlp/twitter-roberta-base-offensive | afebc0c0c4c58177a8e6ab683c25beffeb351135 | 2021-05-20T15:05:00.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"transformers"
] | text-classification | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-offensive | 101,518 | 4 | transformers | 218 | # Twitter-roBERTa-base for Offensive Language Identification
This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval of... |
openai-gpt | b3ab1942f7090e287d001cec22331dfc2764acf0 | 2022-07-22T07:57:33.000Z | [
"pytorch",
"tf",
"rust",
"openai-gpt",
"text-generation",
"en",
"arxiv:1705.11168",
"arxiv:1803.02324",
"arxiv:1910.09700",
"transformers",
"license:mit"
] | text-generation | false | null | null | openai-gpt | 100,975 | 7 | transformers | 219 | ---
language: en
license: mit
---
# OpenAI GPT
## 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](#evaluation)
- [Enviro... |
DeepPavlov/distilrubert-base-cased-conversational | 9b4f8c20bdc51934ef2ef586ef9afee85549cccb | 2022-05-06T11:58:43.000Z | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | false | DeepPavlov | null | DeepPavlov/distilrubert-base-cased-conversational | 100,957 | 1 | transformers | 220 | ---
language:
- ru
---
# distilrubert-base-cased-conversational
Conversational DistilRuBERT \(Russian, cased, 6‑layer, 768‑hidden, 12‑heads, 135.4M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational R... |
Helsinki-NLP/opus-mt-en-fr | a8fbc1c711cb6263e8a20c5229b210cc05c57ff0 | 2021-09-09T21:35:24.000Z | [
"pytorch",
"jax",
"marian",
"text2text-generation",
"en",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-fr | 100,160 | 3 | transformers | 221 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-fr
* source languages: en
* target languages: fr
* OPUS readme: [en-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
hf-internal-testing/tiny-random-bart | f2efe525625e508121ef8e13b7c37e6324073378 | 2021-11-18T11:36:51.000Z | [
"pytorch",
"tf",
"bart",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-bart | 100,122 | null | transformers | 222 | Entry not found |
microsoft/mpnet-base | 5b7474c98ab5f1801502f9d2348485acf4cbbe71 | 2020-12-03T15:59:01.000Z | [
"pytorch",
"tf",
"mpnet",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/mpnet-base | 96,566 | 8 | transformers | 223 | Entry not found |
ramsrigouthamg/t5-large-paraphraser-diverse-high-quality | 443d721ecccee1cb38cce6f50cfd6c15e44e6ea0 | 2021-09-21T05:21:49.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ramsrigouthamg | null | ramsrigouthamg/t5-large-paraphraser-diverse-high-quality | 96,120 | 11 | transformers | 224 | Blog post with more details as well as easy to use Google Colab link: https://towardsdatascience.com/high-quality-sentence-paraphraser-using-transformers-in-nlp-c33f4482856f
!pip install transformers==4.10.2
!pip install sentencepiece==0.1.96
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model =... |
dbmdz/distilbert-base-turkish-cased | 2fffa20b389e66113ec7182349efdec00fce1ff5 | 2021-01-24T01:01:22.000Z | [
"pytorch",
"tf",
"distilbert",
"tr",
"arxiv:1910.01108",
"transformers",
"license:mit"
] | null | false | dbmdz | null | dbmdz/distilbert-base-turkish-cased | 95,636 | 5 | transformers | 225 | ---
language: tr
license: mit
---
# 🤗 + 📚 dbmdz Distilled Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a (cased) distilled model for Turkish 🎉
# 🇹🇷 DistilBERTurk
DistilBERTurk is a community-driven cased distilled BERT model for Turkish.
... |
Vamsi/T5_Paraphrase_Paws | 3bbf07dc42d5ddc9ca77c5589ce7239b0b731832 | 2021-06-23T11:39:51.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"en",
"transformers",
"paraphrase-generation",
"text-generation",
"Conditional Generation",
"autotrain_compatible"
] | text-generation | false | Vamsi | null | Vamsi/T5_Paraphrase_Paws | 95,004 | 10 | transformers | 226 | ---
language: "en"
tags:
- paraphrase-generation
- text-generation
- Conditional Generation
inference: false
---
# Paraphrase-Generation
## Model description
T5 Model for generating paraphrases of english sentences. Trained on the [Google PAWS](https://github.com/google-research-datasets/paws) dataset.
## How t... |
unc-nlp/lxmert-base-uncased | 628572c96242d1496147beec1c13a1bb7869605d | 2021-03-10T02:39:25.000Z | [
"pytorch",
"tf",
"lxmert",
"feature-extraction",
"transformers"
] | feature-extraction | false | unc-nlp | null | unc-nlp/lxmert-base-uncased | 94,868 | null | transformers | 227 | Entry not found |
ThatSkyFox/DialoGPT-small-joshua | b42987a9651745cfa1112354b16a1c454492045f | 2021-10-24T17:12:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ThatSkyFox | null | ThatSkyFox/DialoGPT-small-joshua | 94,281 | null | transformers | 228 | ---
tags:
- conversational
---
#This is a chatbot trained on the transcript of the game "The World Ends with You" |
RUCAIBox/mvp | c1d9aeb879f3079101f716f1f6e7109fdd18b4e9 | 2022-06-27T02:27:44.000Z | [
"pytorch",
"mvp",
"en",
"arxiv:2206.12131",
"transformers",
"text-generation",
"text2text-generation",
"summarization",
"conversational",
"license:apache-2.0"
] | text2text-generation | false | RUCAIBox | null | RUCAIBox/mvp | 93,960 | 1 | transformers | 229 | ---
license: apache-2.0
language:
- en
tags:
- text-generation
- text2text-generation
- summarization
- conversational
pipeline_tag: text2text-generation
widget:
- text: "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons."
example_title: "Summarization"
- t... |
Davlan/bert-base-multilingual-cased-ner-hrl | 6f69c39cadcdba0ab1401fb1f164964e7557e471 | 2022-06-25T17:01:26.000Z | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"de",
"en",
"es",
"fr",
"it",
"lv",
"nl",
"pt",
"zh",
"multilingual",
"transformers",
"autotrain_compatible"
] | token-classification | false | Davlan | null | Davlan/bert-base-multilingual-cased-ner-hrl | 93,549 | 9 | transformers | 230 | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# bert-base-multilingual-cased-ner-hrl
## Model description
**bert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French,... |
google/mt5-base | d86816880b5acc27e697e52bc237e816dc828b17 | 2022-05-27T15:05:12.000Z | [
"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-base | 93,236 | 29 | transformers | 231 | ---
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
-... |
microsoft/MiniLM-L12-H384-uncased | 44acabbec0ef496f6dbc93adadea57f376b7c0ec | 2021-05-19T23:29:48.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"arxiv:2002.10957",
"arxiv:1810.04805",
"transformers",
"text-classification",
"license:mit"
] | text-classification | false | microsoft | null | microsoft/MiniLM-L12-H384-uncased | 92,474 | 14 | transformers | 232 | ---
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation
MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression o... |
sentence-transformers/distiluse-base-multilingual-cased | 47709c6fb2c53d30c871b05b8fb2693a5428d96a | 2022-06-21T14:55:22.000Z | [
"pytorch",
"tf",
"rust",
"distilbert",
"feature-extraction",
"multilingual",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/distiluse-base-multilingual-cased | 91,678 | 2 | sentence-transformers | 233 | ---
pipeline_tag: sentence-similarity
language: multilingual
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/distiluse-base-multilingual-cased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & par... |
joeddav/bart-large-mnli-yahoo-answers | d836606b3cf20652cf30283d6884ae26a11e5392 | 2021-06-14T10:44:33.000Z | [
"pytorch",
"jax",
"bart",
"text-classification",
"en",
"dataset:yahoo-answers",
"arxiv:1909.00161",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | false | joeddav | null | joeddav/bart-large-mnli-yahoo-answers | 91,462 | 3 | transformers | 234 | ---
language: en
tags:
- text-classification
- pytorch
datasets:
- yahoo-answers
pipeline_tag: zero-shot-classification
---
# bart-lage-mnli-yahoo-answers
## Model Description
This model takes [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) and fine-tunes it on Yahoo Answers topic classif... |
squeezebert/squeezebert-uncased | 7978b0c163f11850ec35d5cd541828159313ac41 | 2020-12-11T22:02:17.000Z | [
"pytorch",
"squeezebert",
"arxiv:2006.11316",
"arxiv:1904.00962",
"transformers"
] | null | false | squeezebert | null | squeezebert/squeezebert-uncased | 90,492 | 0 | transformers | 235 | language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in [this paper](https://arx... |
philschmid/distilbart-cnn-12-6-samsum | a823ff6685caf2db51911e2ad903aa55e5defb29 | 2022-07-26T20:01:15.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:samsum",
"transformers",
"sagemaker",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | philschmid | null | philschmid/distilbart-cnn-12-6-samsum | 89,618 | 4 | transformers | 236 |
---
language: en
tags:
- sagemaker
- bart
- summarization
license: apache-2.0
datasets:
- samsum
widget:
- text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n\
Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:\
\ ok.\nJeff: and how can I get started? \... |
distilbert-base-uncased-distilled-squad | 3ab4b58da34f3a23bbdd2626fc58ee6c91f9890b | 2022-07-22T08:03:29.000Z | [
"pytorch",
"tf",
"tflite",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | null | null | distilbert-base-uncased-distilled-squad | 89,003 | 10 | transformers | 237 | ---
language: en
datasets:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also know... |
facebook/dpr-reader-single-nq-base | 5114ec3299284784702848f7d4e598c59df1a35c | 2020-11-25T16:59:53.000Z | [
"pytorch",
"tf",
"dpr",
"transformers"
] | null | false | facebook | null | facebook/dpr-reader-single-nq-base | 88,755 | 1 | transformers | 238 | Entry not found |
Davlan/xlm-roberta-large-ner-hrl | 690312971788985228c98683d4b816fbf026b346 | 2022-06-27T10:49:56.000Z | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"ar",
"de",
"en",
"es",
"fr",
"it",
"lv",
"nl",
"pt",
"zh",
"multilingual",
"transformers",
"autotrain_compatible"
] | token-classification | false | Davlan | null | Davlan/xlm-roberta-large-ner-hrl | 88,660 | 3 | transformers | 239 | Hugging Face's logo
---
language:
- ar
- de
- en
- es
- fr
- it
- lv
- nl
- pt
- zh
- multilingual
---
# xlm-roberta-large-ner-hrl
## Model description
**xlm-roberta-large-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dut... |
patrickvonplaten/t5-tiny-random | a3735d6adf1f23b7c32e6622fd6da7bc46d7f123 | 2021-11-03T17:13:16.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | patrickvonplaten | null | patrickvonplaten/t5-tiny-random | 88,297 | 1 | transformers | 240 | |
hf-internal-testing/tiny-random-mbart | 63c7077c54936948aeb5a675e10489c945957824 | 2021-12-28T12:24:41.000Z | [
"pytorch",
"tf",
"mbart",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/tiny-random-mbart | 88,207 | null | transformers | 241 | Entry not found |
EleutherAI/gpt-neo-2.7B | 51568a6e0ae813a3f2a9da558ab7beac5e3acc24 | 2021-12-31T13:46:21.000Z | [
"pytorch",
"jax",
"rust",
"gpt_neo",
"text-generation",
"en",
"dataset:The Pile",
"transformers",
"text generation",
"causal-lm",
"license:apache-2.0"
] | text-generation | false | EleutherAI | null | EleutherAI/gpt-neo-2.7B | 87,789 | 86 | transformers | 242 | ---
language:
- en
tags:
- text generation
- pytorch
- causal-lm
license: apache-2.0
datasets:
- The Pile
---
# GPT-Neo 2.7B
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the num... |
allenai/scibert_scivocab_cased | 9be298ced05121c9e6e2b2cb9f508b47b8eae650 | 2021-05-19T11:40:28.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
] | null | false | allenai | null | allenai/scibert_scivocab_cased | 86,580 | 4 | transformers | 243 | # SciBERT
This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text.
The training corpus was papers taken from [Semantic Scholar](https://www.semanticscholar.org). Corpus size is 1.... |
mfeb/albert-xxlarge-v2-squad2 | e734d3529f402760605a24af13e02f6f092e96a0 | 2020-04-24T16:10:08.000Z | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | mfeb | null | mfeb/albert-xxlarge-v2-squad2 | 86,458 | 2 | transformers | 244 | Entry not found |
cmarkea/distilcamembert-base-sentiment | b7804e295dc3cf2aa8ce8cff83f22e0bdd249558 | 2022-05-24T15:56:45.000Z | [
"pytorch",
"tf",
"camembert",
"text-classification",
"fr",
"dataset:amazon_reviews_multi",
"dataset:allocine",
"transformers",
"license:mit"
] | text-classification | false | cmarkea | null | cmarkea/distilcamembert-base-sentiment | 86,119 | 7 | transformers | 245 | ---
language: fr
license: mit
datasets:
- amazon_reviews_multi
- allocine
widget:
- text: "Je pensais lire un livre nul, mais finalement je l'ai trouvé super !"
- text: "Cette banque est très bien, mais elle n'offre pas les services de paiements sans contact."
- text: "Cette banque est très bien et elle offre en plus l... |
dmis-lab/biobert-v1.1 | 551ca18efd7f052c8dfa0b01c94c2a8e68bc5488 | 2021-05-19T16:03:17.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | dmis-lab | null | dmis-lab/biobert-v1.1 | 85,159 | 6 | transformers | 246 | Entry not found |
DeepPavlov/rubert-base-cased-sentence | 78b5122d6365337dd4114281b0d08cd1edbb3bc8 | 2021-05-18T18:18:43.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1508.05326",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
] | feature-extraction | false | DeepPavlov | null | DeepPavlov/rubert-base-cased-sentence | 84,680 | 2 | transformers | 247 | ---
language:
- ru
---
# rubert-base-cased-sentence
Sentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) is a representation‑based sentence encoder for Russian. It is initialized with RuBERT and fine‑tuned on SNLI\[1\] google-translated to russian and on russian part of XNLI dev set\[2\... |
sshleifer/tiny-gpt2 | 5f91d94bd9cd7190a9f3216ff93cd1dd95f2c7be | 2021-05-23T12:55:11.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | sshleifer | null | sshleifer/tiny-gpt2 | 84,146 | 4 | transformers | 248 | Entry not found |
neuralmind/bert-large-portuguese-cased | aa302f6ea73b759f7df9cad58bd272127b67ec28 | 2021-05-20T01:31:09.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"pt",
"dataset:brWaC",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | neuralmind | null | neuralmind/bert-large-portuguese-cased | 83,959 | 13 | transformers | 249 | ---
language: pt
license: mit
tags:
- bert
- pytorch
datasets:
- brWaC
---
# BERTimbau Large (aka "bert-large-portuguese-cased")
## Introduction
BERTimbau Large is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performa... |
google/bert_uncased_L-12_H-768_A-12 | cc478efa3482fefe275eb2733363db9713d499ef | 2021-05-19T17:27:43.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-12_H-768_A-12 | 83,694 | 2 | transformers | 250 | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... |
Helsinki-NLP/opus-mt-ja-en | 6282eb0555cd0253dc9fac00c5fafb2825ad04b4 | 2021-09-10T13:53:12.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ja",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ja-en | 83,179 | 6 | transformers | 251 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ja-en
* source languages: ja
* target languages: en
* OPUS readme: [ja-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ja-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
sonoisa/sentence-bert-base-ja-mean-tokens | c5db458007569cea1374d4b5766193832c3fc285 | 2021-12-14T11:43:43.000Z | [
"pytorch",
"fill-mask",
"ja",
"sentence-transformers",
"sentence-bert",
"feature-extraction",
"sentence-similarity",
"license:cc-by-sa-4.0"
] | feature-extraction | false | sonoisa | null | sonoisa/sentence-bert-base-ja-mean-tokens | 82,730 | 3 | sentence-transformers | 252 | ---
language: ja
license: cc-by-sa-4.0
tags:
- sentence-transformers
- sentence-bert
- feature-extraction
- sentence-similarity
---
This is a Japanese sentence-BERT model.
日本語用Sentence-BERTモデル（バージョン1）です。
※: 精度が1.5ポイントほど向上した[バージョン2モデル](https://huggingface.co/sonoisa/sentence-bert-base-ja-mean-tokens-v2)もあります。
# 解説
... |
pyannote/embedding | 09a3ed256d0fddbf5616fd9fb5db917fcf002708 | 2022-03-23T09:24:30.000Z | [
"pytorch",
"tensorboard",
"dataset:voxceleb",
"pyannote-audio",
"pyannote",
"pyannote-audio-model",
"audio",
"voice",
"speech",
"speaker",
"speaker-recognition",
"speaker-verification",
"speaker-identification",
"speaker-embedding",
"license:mit"
] | null | false | pyannote | null | pyannote/embedding | 82,716 | 4 | pyannote-audio | 253 | ---
tags:
- pyannote
- pyannote-audio
- pyannote-audio-model
- audio
- voice
- speech
- speaker
- speaker-recognition
- speaker-verification
- speaker-identification
- speaker-embedding
datasets:
- voxceleb
license: mit
inference: false
---
# 🎹 Speaker embedding
Relies on pyannote.audio 2.0 currently in development... |
sentence-transformers/all-distilroberta-v1 | 57dd5d5be528ba968ef928103d92f95afc487e68 | 2022-07-11T21:04:19.000Z | [
"pytorch",
"rust",
"roberta",
"fill-mask",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:MS Marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:code_search_net",
"dataset:search_qa",
"dataset:eli5",
"dataset:snli",
"dataset:multi_nli"... | sentence-similarity | false | sentence-transformers | null | sentence-transformers/all-distilroberta-v1 | 81,479 | 4 | sentence-transformers | 254 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- MS Marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- nat... |
sentence-transformers/distilbert-base-nli-stsb-mean-tokens | 888433dbfd0f07dc22d9e038d9acb20e5ca7e0d5 | 2022-06-15T20:07:20.000Z | [
"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-mean-tokens | 81,248 | 4 | sentence-transformers | 255 | ---
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... |
vinai/phobert-base | 667b55927a1571811539f27c0f374429a1c75759 | 2022-06-08T04:44:26.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2003.00744",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vinai | null | vinai/phobert-base | 81,225 | 6 | transformers | 256 | # <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first publ... |
Helsinki-NLP/opus-mt-en-es | c8b63c83f30e46417ce423a585f9b9e20e1b877d | 2021-07-13T16:24:56.000Z | [
"pytorch",
"jax",
"marian",
"text2text-generation",
"en",
"es",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-es | 80,022 | 7 | transformers | 257 | ---
language:
- en
- es
tags:
- translation
license: apache-2.0
---
### eng-spa
* source group: English
* target group: Spanish
* OPUS readme: [eng-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-spa/README.md)
* model: transformer
* source language(s): eng
* target language(s): ... |
nreimers/BERT-Tiny_L-2_H-128_A-2 | 9cb03776b08d300ae73aa6ba4860a760c606f62d | 2021-05-28T11:05:21.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | nreimers | null | nreimers/BERT-Tiny_L-2_H-128_A-2 | 79,688 | null | transformers | 258 | This is the BERT-Medium model from Google: https://github.com/google-research/bert#bert. A BERT model with 2 layers, 128 hidden unit size, and 2 attention heads. |
cointegrated/rubert-tiny | f191937ca7511ae76b44b06a460f18ab1699c54b | 2022-01-28T11:42:37.000Z | [
"pytorch",
"bert",
"pretraining",
"ru",
"en",
"transformers",
"russian",
"fill-mask",
"embeddings",
"masked-lm",
"tiny",
"feature-extraction",
"sentence-similarity",
"license:mit"
] | feature-extraction | false | cointegrated | null | cointegrated/rubert-tiny | 79,161 | 9 | transformers | 259 | ---
language: ["ru", "en"]
tags:
- russian
- fill-mask
- pretraining
- embeddings
- masked-lm
- tiny
- feature-extraction
- sentence-similarity
license: mit
widget:
- text: "Миниатюрная модель для [MASK] разных задач."
---
This is a very small distilled version of the [bert-base-multilingual-cased](https://huggingface.... |
hfl/chinese-bert-wwm | ab0aa81da273504efc8540aa4d0bbaa3016a1bb5 | 2021-05-19T19:07:49.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | hfl | null | hfl/chinese-bert-wwm | 78,869 | 18 | transformers | 260 | ---
language:
- zh
license: "apache-2.0"
---
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cu... |
facebook/wav2vec2-base | 0b5b8e868dd84f03fd87d01f9c4ff0f080fecfe8 | 2021-12-28T12:44:31.000Z | [
"pytorch",
"wav2vec2",
"pretraining",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"transformers",
"speech",
"license:apache-2.0"
] | null | false | facebook | null | facebook/wav2vec2-base | 78,091 | 14 | transformers | 261 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Base
[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 speech... |
microsoft/deberta-v3-small | 23bfba973812a80178eb6c2c600e85cc461ffc2c | 2022-01-13T17:59:25.000Z | [
"pytorch",
"tf",
"deberta-v2",
"en",
"arxiv:2006.03654",
"arxiv:2111.09543",
"transformers",
"deberta",
"deberta-v3",
"license:mit"
] | null | false | microsoft | null | microsoft/deberta-v3-small | 78,067 | 14 | transformers | 262 | ---
language: en
tags:
- deberta
- deberta-v3
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa m... |
clue/albert_chinese_tiny | 654acaf73c361ad56e4f4b1e2bb0023cbb1872b2 | 2020-12-11T21:35:55.000Z | [
"pytorch",
"albert",
"zh",
"transformers"
] | null | false | clue | null | clue/albert_chinese_tiny | 77,354 | 5 | transformers | 263 | ---
language: zh
---
## albert_chinese_tiny
### Overview
**Language model:** albert-tiny
**Model size:** 16M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on do... |
facebook/mbart-large-en-ro | 2534e987b9ed03c416bbbaefa1a39e3441439bdd | 2021-03-10T03:46:29.000Z | [
"pytorch",
"tf",
"mbart",
"en",
"ro",
"transformers",
"translation",
"license:mit"
] | translation | false | facebook | null | facebook/mbart-large-en-ro | 76,917 | null | transformers | 264 | ---
tags:
- translation
language:
- en
- ro
license: mit
---
### mbart-large-en-ro
This is mbart-large-cc25, finetuned on wmt_en_ro.
It scores BLEU 28.1 without post processing and BLEU 38 with postprocessing. Instructions in `romanian_postprocessing.md`
Original Code: https://github.com/pytorch/fairseq/tree/master... |
Helsinki-NLP/opus-mt-ar-en | 9c5efffe6f69dcb65d7156f40dfa27b54be34258 | 2021-09-09T21:26:15.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"ar",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ar-en | 76,095 | 5 | transformers | 265 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ar-en
* source languages: ar
* target languages: en
* OPUS readme: [ar-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ar-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
bert-large-uncased-whole-word-masking | 90d3333009848fae4860a5338419d17f70be940c | 2021-05-18T16:37:36.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | null | null | bert-large-uncased-whole-word-masking | 74,654 | 3 | transformers | 266 | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (uncased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository]... |
google/reformer-crime-and-punishment | 0e6c3decb8211d49bf881013425dc8b0448b3f5a | 2021-02-01T17:53:38.000Z | [
"pytorch",
"rust",
"reformer",
"text-generation",
"transformers"
] | text-generation | false | google | null | google/reformer-crime-and-punishment | 74,293 | null | transformers | 267 | ## Reformer Model trained on "Crime and Punishment"
Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English.
Crime and Punishment training data was taken from `gs://trax-ml/reformer/crime-and-punishment-2554.txt` and contains
roughly 0.5M tokens.
The ReformerLM model was trai... |
vennify/t5-base-grammar-correction | 9e4a09d21dca1072a69302df9261289d03c3ed78 | 2022-01-14T16:35:23.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:jfleg",
"arxiv:1702.04066",
"transformers",
"grammar",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] | text2text-generation | false | vennify | null | vennify/t5-base-grammar-correction | 74,162 | 16 | transformers | 268 | ---
language: en
tags:
- grammar
- text2text-generation
license: cc-by-nc-sa-4.0
datasets:
- jfleg
---
# T5 Grammar Correction
This model generates a revised version of inputted text with the goal of containing fewer grammatical errors.
It was trained with [Happy Transformer](https://github.com/EricFillion/happy-tr... |
deepset/roberta-large-squad2 | faa13e991d03fba7f6eb6a75356c4c0806e2588a | 2022-07-25T10:34:40.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/roberta-large-squad2 | 74,021 | 11 | transformers | 269 | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
--- |
Helsinki-NLP/opus-mt-ROMANCE-en | dd27a5df7623594b19ab50244084e2beddc2181c | 2021-09-09T21:25:31.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"roa",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ROMANCE-en | 73,229 | null | transformers | 270 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ROMANCE-en
* source languages: fr,fr_BE,fr_CA,fr_FR,wa,frp,oc,ca,rm,lld,fur,lij,lmo,es,es_AR,es_CL,es_CO,es_CR,es_DO,es_EC,es_ES,es_GT,es_HN,es_MX,es_NI,es_PA,es_PE,es_PR,es_SV,es_UY,es_VE,pt,pt_br,pt_BR,pt_PT,gl,lad,an,mwl,it,it_IT,co,nap,scn,vec,sc,ro,la
*... |
monologg/koelectra-small-v2-distilled-korquad-384 | 70c28f5b9e6b2bd05bb609f6be1f9f8ff918cd6f | 2020-06-04T17:39:49.000Z | [
"pytorch",
"tflite",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | monologg | null | monologg/koelectra-small-v2-distilled-korquad-384 | 72,726 | null | transformers | 271 | Entry not found |
Helsinki-NLP/opus-mt-en-zh | 93db7712e4698309ac17a80605adbf54dea5c8ee | 2021-09-09T21:40:41.000Z | [
"pytorch",
"tf",
"jax",
"rust",
"marian",
"text2text-generation",
"en",
"zh",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-zh | 71,580 | 19 | transformers | 272 | ---
language:
- en
- zh
tags:
- translation
license: apache-2.0
---
### eng-zho
* source group: English
* target group: Chinese
* OPUS readme: [eng-zho](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-zho/README.md)
* model: transformer
* source language(s): eng
* target language(s): cjy... |
monologg/koelectra-small-v3-discriminator | 7488f8db0f208beff4a1f3f9bb3ed04650a89ed7 | 2020-12-26T16:24:33.000Z | [
"pytorch",
"electra",
"pretraining",
"transformers"
] | null | false | monologg | null | monologg/koelectra-small-v3-discriminator | 70,824 | null | transformers | 273 | Entry not found |
deepset/xlm-roberta-base-squad2 | 8962e174ec8ad665bbf11edc2130487d2f7ea22a | 2022-07-25T07:17:34.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"dataset:squad_v2",
"transformers",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/xlm-roberta-base-squad2 | 70,545 | 10 | transformers | 274 | ---
datasets:
- squad_v2
license: cc-by-4.0
model-index:
- name: deepset/xlm-roberta-base-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- name: Exact Match
... |
roberta-large-mnli | 0dcbcf20673c006ac2d1e324954491b96f0c0015 | 2022-07-22T08:02:16.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"en",
"dataset:multi_nli",
"dataset:wikipedia",
"dataset:bookcorpus",
"arxiv:1907.11692",
"arxiv:1806.02847",
"arxiv:1804.07461",
"arxiv:1704.05426",
"arxiv:1508.05326",
"arxiv:1809.05053",
"arxiv:1910.09700",
"transformers",
... | text-classification | false | null | null | roberta-large-mnli | 69,490 | 25 | transformers | 275 | ---
language:
- en
license: mit
tags:
- autogenerated-modelcard
datasets:
- multi_nli
- wikipedia
- bookcorpus
---
# roberta-large-mnli
## 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](#ri... |
google/t5-v1_1-xxl | 6e80045f023a868fdb58e0b697d1ace5fe4880be | 2020-11-19T19:55:45.000Z | [
"pytorch",
"tf",
"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-xxl | 69,217 | 2 | transformers | 276 | ---
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... |
hfl/chinese-roberta-wwm-ext-large | a25cc9e05974bd9687e528edd516f2cfdb3f5db9 | 2022-03-01T09:15:16.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | hfl | null | hfl/chinese-roberta-wwm-ext-large | 68,277 | 18 | transformers | 277 | ---
language:
- zh
tags:
- bert
license: "apache-2.0"
---
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word ... |
prajjwal1/bert-small | 0ec5f86f27c1a77d704439db5e01c307ea11b9d4 | 2021-10-27T18:31:52.000Z | [
"pytorch",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"transformers",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"license:mit"
] | null | false | prajjwal1 | null | prajjwal1/bert-small | 67,809 | 5 | transformers | 278 | ---
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 ... |
sshleifer/tiny-marian-en-de | 7a6b5b34785930445aeb20a9a34543b72de6e267 | 2020-06-25T02:27:15.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/tiny-marian-en-de | 67,303 | null | transformers | 279 | Entry not found |
microsoft/codebert-base-mlm | 5c927614b8750b556dcf569cf8a211fbe20f688a | 2021-05-20T17:47:48.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2002.08155",
"transformers",
"autotrain_compatible"
] | fill-mask | false | microsoft | null | microsoft/codebert-base-mlm | 66,627 | 8 | transformers | 280 | ## CodeBERT-base-mlm
Pretrained weights for [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://arxiv.org/abs/2002.08155).
### Training Data
The model is trained on the code corpus of [CodeSearchNet](https://github.com/github/CodeSearchNet)
### Training Objective
This model is initialized wi... |
nyust-eb210/braslab-bert-drcd-384 | abb9294fe9c0605d2f498a3228bfc6a30e8d2fbb | 2021-05-31T14:47:20.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"zh-tw",
"dataset:DRCD",
"transformers",
"autotrain_compatible"
] | question-answering | false | nyust-eb210 | null | nyust-eb210/braslab-bert-drcd-384 | 65,856 | null | transformers | 281 | ---
language: zh-tw
datasets: DRCD
tasks: Question Answering
---
# BERT DRCD 384
This model is a fine-tune checkpoint of [bert-base-chinese](https://huggingface.co/bert-base-chinese), fine-tuned on DRCD dataset.
This model reaches a F1 score of 86.
This model reaches a EM score of 83.
Training Arguments:
- length:... |
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext | eaa409b6b7c9380a5f2ba7a59aa97712ff30f386 | 2021-09-22T20:09:56.000Z | [
"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-fulltext | 65,440 | 32 | transformers | 282 | ---
language: en
tags:
- exbert
license: mit
widget:
- text: "[MASK] is a tumor suppressor gene."
---
## PubMedBERT (abstracts + full text)
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 ge... |
sshleifer/tiny-distilroberta-base | d305c58110158c865cb6746c62d4511d4148a934 | 2021-10-22T16:10:44.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sshleifer | null | sshleifer/tiny-distilroberta-base | 64,865 | 2 | transformers | 283 | Entry not found |
microsoft/DialoGPT-small | f9c829d0285e7addb0667aeb6e33956916ec6cd0 | 2021-05-23T09:14:00.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"arxiv:1911.00536",
"transformers",
"conversational",
"license:mit"
] | conversational | false | microsoft | null | microsoft/DialoGPT-small | 64,443 | 8 | transformers | 284 | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation... |
nlpaueb/bert-base-uncased-contracts | f918d2e0cf491ba2c5fcf9f82d5e9603b8c5f3ea | 2022-04-28T14:43:56.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"en",
"transformers",
"legal",
"license:cc-by-sa-4.0",
"fill-mask"
] | fill-mask | false | nlpaueb | null | nlpaueb/bert-base-uncased-contracts | 64,347 | 5 | transformers | 285 | ---
language: en
pipeline_tag: fill-mask
license: cc-by-sa-4.0
thumbnail: https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png
tags:
- legal
widget:
- text: "This [MASK] Agreement is between General Motors and John Murray."
---
# LEGAL-BERT: The Muppets straight out of Law School
<img align="left" src="h... |
viktor-enzell/wav2vec2-large-voxrex-swedish-4gram | 3cafa40b3433cf9d1b3b2b65c37c48541f57d1a8 | 2022-06-02T13:40:29.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"sv",
"dataset:common_voice",
"dataset:NST Swedish ASR Database",
"dataset:P4",
"dataset:The Swedish Culturomics Gigaword Corpus",
"transformers",
"audio",
"speech",
"hf-asr-leaderboard",
"license:cc0-1.0",
"model-index"
] | automatic-speech-recognition | false | viktor-enzell | null | viktor-enzell/wav2vec2-large-voxrex-swedish-4gram | 62,723 | 3 | transformers | 286 | ---
language: sv
datasets:
- common_voice
- NST Swedish ASR Database
- P4
- The Swedish Culturomics Gigaword Corpus
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- hf-asr-leaderboard
- sv
license: cc0-1.0
model-index:
- name: Wav2vec 2.0 large VoxRex Swedish (C) with 4-gram
results:
- task:
... |
cmarkea/distilcamembert-base-ner | 21167ca4a0fd71a615e579dc4898c4079e86b014 | 2022-05-24T15:55:53.000Z | [
"pytorch",
"tf",
"camembert",
"token-classification",
"fr",
"dataset:Jean-Baptiste/wikiner_fr",
"transformers",
"license:mit",
"autotrain_compatible"
] | token-classification | false | cmarkea | null | cmarkea/distilcamembert-base-ner | 62,690 | 10 | transformers | 287 | ---
language: fr
license: mit
datasets:
- Jean-Baptiste/wikiner_fr
widget:
- text: "Boulanger, habitant à Boulanger et travaillant dans le magasin Boulanger situé dans la ville de Boulanger. Boulanger a écrit le livre éponyme Boulanger édité par la maison d'édition Boulanger."
- text: "Quentin Jerome Tarantino naît le ... |
dbmdz/bert-base-german-uncased | 26dd62c10449c89b8029c4440855983dfc5a5e83 | 2021-05-19T14:57:28.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/bert-base-german-uncased | 62,613 | 1 | transformers | 288 | ---
language: de
license: mit
---
# 🤗 + 📚 dbmdz German BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources another German BERT models 🎉
# German BERT
## Stats
In addition to the recently released [German BERT](https://deepset.ai/german-bert)
model by [d... |
facebook/opt-350m | 10517ad5b51c8c6e02db7824d8494721d4874488 | 2022-06-16T15:25:49.000Z | [
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"transformers",
"license:other"
] | text-generation | false | facebook | null | facebook/opt-350m | 62,324 | 10 | transformers | 289 | ---
language: en
inference: false
tags:
- text-generation
license: other
commercial: false
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github... |
sentence-transformers/msmarco-bert-base-dot-v5 | 668e63a378bc93d76c430af68338e550dc78df09 | 2022-06-15T20:34:19.000Z | [
"pytorch",
"tf",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/msmarco-bert-base-dot-v5 | 62,053 | 1 | sentence-transformers | 290 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# msmarco-bert-base-dot-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **sema... |
nghuyong/ernie-2.0-en | c18a9f28b99a65011e3a6c61e2109f03833a447b | 2021-05-20T01:42:24.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"en",
"arxiv:1907.12412",
"transformers"
] | null | false | nghuyong | null | nghuyong/ernie-2.0-en | 61,990 | 5 | transformers | 291 | ---
language: en
---
# ERNIE-2.0
## Introduction
ERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019,
which builds and learns incrementally pre-training tasks through constant multi-task learning.
Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including... |
sentence-transformers/stsb-roberta-base-v2 | b5e9e8dbc4a7d931c766a9113d1a04963a480c06 | 2022-06-15T20:05:42.000Z | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/stsb-roberta-base-v2 | 61,846 | null | sentence-transformers | 292 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/stsb-roberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense v... |
facebook/m2m100_1.2B | 90301acb1353eb6623e48973520b486612a57439 | 2022-05-26T22:26:41.000Z | [
"pytorch",
"rust",
"m2m_100",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"ast",
"az",
"ba",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"ff",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"g... | text2text-generation | false | facebook | null | facebook/m2m100_1.2B | 61,684 | 10 | transformers | 293 | ---
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ... |
microsoft/mdeberta-v3-base | 7d66e84a399b78accc72e0f61cd6d50f02ee1c2c | 2022-01-13T19:41:26.000Z | [
"pytorch",
"tf",
"deberta-v2",
"multilingual",
"arxiv:2006.03654",
"arxiv:2111.09543",
"transformers",
"deberta",
"deberta-v3",
"mdeberta",
"license:mit"
] | null | false | microsoft | null | microsoft/mdeberta-v3-base | 61,660 | 33 | transformers | 294 | ---
language: multilingual
tags:
- deberta
- deberta-v3
- mdeberta
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves... |
google/electra-small-generator | c04c64e3cca372b13615e71e51bc261f93905212 | 2021-04-29T15:23:28.000Z | [
"pytorch",
"tf",
"jax",
"electra",
"fill-mask",
"en",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | google | null | google/electra-small-generator | 61,641 | 2 | transformers | 295 | ---
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... |
valhalla/m2m100_tiny_random | 337a4a691b7e14ad1668f5f4e481eaea6ce59ba1 | 2021-03-05T09:03:18.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | valhalla | null | valhalla/m2m100_tiny_random | 61,625 | null | transformers | 296 | Entry not found |
Helsinki-NLP/opus-mt-nl-en | 642ab6dc2d08ca9b5706ff44191dc443eae738e1 | 2021-09-10T13:59:07.000Z | [
"pytorch",
"rust",
"marian",
"text2text-generation",
"nl",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-nl-en | 61,618 | 4 | transformers | 297 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nl-en
* source languages: nl
* target languages: en
* OPUS readme: [nl-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nl-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* downl... |
Rostlab/prot_bert_bfd | 6c5c8a55a52ff08a664dfd584aa1773f125a0487 | 2020-12-11T21:30:10.000Z | [
"pytorch",
"tf",
"fill-mask",
"protein",
"dataset:BFD",
"transformers",
"protein language model",
"autotrain_compatible"
] | fill-mask | false | Rostlab | null | Rostlab/prot_bert_bfd | 60,576 | 3 | transformers | 298 | ---
language: protein
tags:
- protein language model
datasets:
- BFD
---
# ProtBert-BFD model
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
[this repository](https://github.com/... |
facebook/opt-1.3b | aa6ac1e23bb9a499be2b7400079cd2a7b8a1309a | 2022-06-22T09:53:16.000Z | [
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"transformers",
"license:other"
] | text-generation | false | facebook | null | facebook/opt-1.3b | 60,368 | 9 | transformers | 299 | ---
language: en
inference: false
tags:
- text-generation
- opt
license: other
commercial: false
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://g... |
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