This repository contains custom pipes and models related to using spaCy for scientific documents. In particular, there is a custom tokenizer that adds tokenization rules on top of spaCy's rule-based tokenizer, and a custom sentence segmenter that adds sentence segmentation rules on top of spaCy's statistical sentence segmenter.

Installing scispacy requires two steps: installing the library and intalling the models. To install the library, run:

pip install scispacy

to install a model, run:

pip install <model url>

Note: We strongly recommend that you use an isolated Python environment (such as virtualenv or conda) to install scispacy. Additionally, scispacy uses modern features of Python and is such only available for Python 3.5 or greater.

Once you have completed the above steps and downloaded one of the models below, you can load SciSpaCy as you would any other spaCy model. For example:

import spacy
nlp = spacy.load("en_scispacy_core_web_sm")