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HuggingFace's Transformers: State-of-the-art Natural Language Processing

About

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. \textit{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. \textit{Transformers} is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at \url{https://github.com/huggingface/transformers}.

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R\'emi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, Alexander M. Rush• 2019

Related benchmarks

TaskDatasetResultRank
Mathematical ReasoningGSM8K--
246
Question Answering2Wiki
EM54.11
241
Multi-hop Question Answering2Wiki
Exact Match50.75
215
Instruction FollowingAlpaca--
173
Question AnsweringMuSiQue
F1 Score40.9
80
RetrievalHotpotQA
R@590.15
68
Question AnsweringQA--
47
Retrieval2Wiki
Recall@577.78
42
Code GenerationHumanEval
TPS (Tokens/s)1.15
31
Text SummarizationCNN/DM
TPS1.27
13
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Other info

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