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- W4288407534 abstract "Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks. We evaluate on a suite of tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains. We demonstrate statistically significant improvements over BERT and achieve new state-of-the-art results on several of these tasks. The code and pretrained models are available at https://github.com/allenai/scibert/." @default.
- W4288407534 created "2022-07-29" @default.
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- W4288407534 date "2019-03-26" @default.
- W4288407534 modified "2023-10-17" @default.
- W4288407534 title "SciBERT: A Pretrained Language Model for Scientific Text" @default.
- W4288407534 doi "https://doi.org/10.48550/arxiv.1903.10676" @default.
- W4288407534 hasPublicationYear "2019" @default.
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