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A full spaCy pipeline and models for scientific/biomedical documents.
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Latest commit 810fe8e May 10, 2019

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, a POS tagger and syntactic parser trained on biomedical data and an entity span detection model. Separately, there are also NER models for more specific tasks.


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

pip install scispacy

to install a model (see our full selection of available models below), run a command like the following:

pip install

Note: We strongly recommend that you use an isolated Python environment (such as virtualenv or conda) to install scispacy. Take a look below in the "Setting up a virtual environment" section if you need some help with this. Additionally, scispacy uses modern features of Python and as such is only available for Python 3.6 or greater.

Setting up a virtual environment

Conda can be used set up a virtual environment with the version of Python required for scispaCy. If you already have a Python 3.6 or 3.7 environment you want to use, you can skip to the 'installing via pip' section.

  1. Download and install Conda.

  2. Create a Conda environment called "scispacy" with Python 3.6:

    conda create -n scispacy python=3.6
  3. Activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use scispaCy.

    source activate scispacy

Now you can install scispacy and one of the models using the steps above.

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

import spacy
nlp = spacy.load("en_core_sci_sm")
doc = nlp("Alterations in the hypocretin receptor 2 and preprohypocretin genes produce narcolepsy in some animals.")

Note on upgrading

If you are upgrading scispacy, you will need to download the models again, to get the model versions compatible with the version of scispacy that you have. The link to the model that you download should contain the version number of scispacy that you have.

Available Models

To install a model, click on the link below to download the model, and then run

pip install </path/to/download>

Alternatively, you can install directly from the URL by right-clicking on the link, selecting "Copy Link Address" and running

pip install CMD-V(to paste the copied URL)
Model Description Install URL
en_core_sci_sm A full spaCy pipeline for biomedical data. Download
en_core_sci_md A full spaCy pipeline for biomedical data with a larger vocabulary and word vectors. Download
en_ner_craft_md A spaCy NER model trained on the CRAFT corpus. Download
en_ner_jnlpba_md A spaCy NER model trained on the JNLPBA corpus. Download
en_ner_bc5cdr_md A spaCy NER model trained on the BC5CDR corpus. Download
en_ner_bionlp13cg_md A spaCy NER model trained on the BIONLP13CG corpus. Download


If you use ScispaCy in your research, please cite ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing.

  title={ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing},
  author={Mark Neumann and Daniel King and Iz Beltagy and Waleed Ammar},

ScispaCy is an open-source project developed by the Allen Institute for Artificial Intelligence (AI2). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.

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