Papers
arxiv:2106.03598

SciFive: a text-to-text transformer model for biomedical literature

Published on May 28, 2021
Authors:
,
,
,
,
,

Abstract

In this report, we introduce SciFive, a domain-specific T5 model that has been pre-trained on large biomedical corpora. Our model outperforms the current SOTA methods (i.e. BERT, BioBERT, Base T5) on tasks in named entity relation, relation extraction, natural language inference, and question-answering. We show that text-generation methods have significant potential in a broad array of biomedical NLP tasks, particularly those requiring longer, more complex outputs. Our results support the exploration of more difficult text generation tasks and the development of new methods in this area

Community

Sign up or log in to comment

Models citing this paper 7

Browse 7 models citing this paper

Datasets citing this paper 0

No dataset linking this paper

Cite arxiv.org/abs/2106.03598 in a dataset README.md to link it from this page.

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/2106.03598 in a Space README.md to link it from this page.

Collections including this paper 0

No Collection including this paper

Add this paper to a collection to link it from this page.