allenai/scispacy

Health v2 · maintenance only

Adoption not part of the score

1988 stars · 256 forks observed · 2026-08-28

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

scispaCy is a Python library providing full spaCy pipelines, models, and custom pipes for processing scientific, biomedical, and clinical text. It includes a custom tokenizer, POS tagger, syntactic parser, entity span detection, and NER models trained on corpora like CRAFT, JNLPBA, and BC5CDR.

Use cases

• extract named entities like diseases and chemicals from biomedical papers
• parse and POS-tag clinical notes with spaCy
• tokenize scientific text with domain-specific rules
• identify genes, proteins, and cell types in PubMed abstracts
• build a biomedical text mining pipeline in Python

When to choose

• you need spaCy-compatible NLP models tailored to biomedical or clinical text
• you want pretrained NER for entities like genes, diseases, chemicals, or cell lines
• you need a scientific tokenizer and parser that outperforms generic spaCy models on biomedical data

When to avoid

• your text is general-domain news or web content where standard spaCy models suffice
• you need state-of-the-art transformer-based biomedical NLP beyond what these models provide
• you are not working in the Python/spaCy ecosystem

Facets

library · maturity active

nlp parser machine-learning bioinformatics healthcare python spacy biomedical-text ner scientific-documents tokenization natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="allenai/scispacy")