HuggingFace's Transformers: State-of-the-art Natural Language Processing
Transformers от HuggingFace: современные методы обработки естественного языка
2019-10-09
SCID: 54.1/wp99kuje
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HuggingFace Transformers libraryTransformer architecturesTransformersnatural language processingpretrained models
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Abstract (AI)
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}.
Key Findings
1
The library backs its architectures with a curated collection of pretrained models made available to the community.
2
The project aims to democratize advances in model architecture and pretraining by making them accessible via the GitHub repository https://github.com/huggingface/transformers.
3
Transformers is an open-source library that provides carefully engineered state-of-the-art Transformer architectures under a unified API.
4
Transformers is designed to be extensible for researchers, simple for practitioners, and fast and robust for industrial deployments.
Research Object
HuggingFace Transformers open-source library
Research Subject
Provision and engineering of state-of-the-art Transformer model architectures and curated pretrained models under a unified, extensible API for research, practitioner use, and industrial deployment
Publication Details
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2019-10-09
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References available in scid.ai4
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