Transformers: State-of-the-Art Natural Language Processing

被引：0

作者：

Wolf, Thomas ^{[1
]}

Debut, Lysandre ^{[1
]}

Sanh, Victor ^{[1
]}

Chaumond, Julien ^{[1
]}

Delangue, Clement ^{[1
]}

Moi, Anthony ^{[1
]}

Cistac, Picnic ^{[1
]}

Rault, Tim ^{[1
]}

Louf, Remi ^{[1
]}

Funtowicz, Morgan ^{[1
]}

Davison, Joe ^{[1
]}

Shleifer, Sam ^{[1
]}

von Platen, Patrick ^{[1
]}

Ma, Clara ^{[1
]}

Jernite, Yacine ^{[1
]}

Plu, Julien ^{[1
]}

Xu, Canwen ^{[1
]}

Le Scao, Teven ^{[1
]}

Gugger, Sylvain ^{[1
]}

Drame, Mariama ^{[1
]}

Lhoest, Quentin ^{[1
]}

Rush, Alexander M. ^{[1
]}

机构：

[1] Hugging Face, Brooklyn, NY 11201 USA

来源：

PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING: SYSTEM DEMONSTRATIONS | 2020年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments.

引用

页码：38 / 45

页数：8

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