allenai/scibert

Health v2 · maintenance only

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1712 stars · 232 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SciBERT is a BERT-based pretrained language model trained on a large multi-domain corpus of scientific publications, released by the Allen Institute for AI. It provides pretrained weights and code that improve performance on downstream scientific NLP tasks such as sequence tagging, sentence classification, and dependency parsing.

Use cases

• extract named entities like chemicals and diseases from biomedical papers
• classify citation intent in scientific literature
• classify sentences in scientific abstracts by their role
• extract relations between scientific entities
• fine-tune a language model on scientific text with limited labeled data
• parse dependencies in scientific sentences

When to choose

• you need strong baselines or embeddings for scientific or biomedical NLP tasks
• you have limited labeled scientific data and want transfer learning from a domain-specific pretrained model
• you want a well-cited, Apache-2.0 licensed BERT variant for academic text

When to avoid

• you need a general-purpose language model for non-scientific text
• you need the latest large language models rather than BERT-era encoders
• you require actively maintained tooling, as the repo has seen few recent updates

Facets

library · maturity maintenance

nlp machine-learning parser machine-learning artificial-intelligence python bert scientific-text pretrained-model transformers named-entity-recognition sentence-classification natural-language-processing

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