scispacy 0.0.0.post0

A full SpaCy pipeline and models for scientific/biomedical documents.

SciSpaCy

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.

Usage

Using SciSpaCy as is

To use SciSpaCy as is, follow these steps:

1. Clone this repository
2. From within this repository, run ./scripts/create_model_package.sh ./scispacy/models/combined_rule_tokenizer_and_segmenter
3. Run python setup.py sdist
4. Run pip install --user dist/scispacy-1.0.0.tar.gz
5. Run pip install --user dist/en_scispacy_core_web_sm-1.0.0.tar.gz

Once you have completed the above steps, you can load SciSpaCy as you would any other spaCy model. For example:

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

To make full use of this package, you will also need to preprocess the text that you will be running through spaCy. This means passing the raw text through custom_tokenizer.remove_new_lines() before passing it through spaCy.

Modifying SciSpaCy

Changing the tokenizer or segmenter

To change the tokenizer or segmenter, all you need to do is change the tokenization or segmentation function, rebuild the model folder, and then follow the above steps for using SciSpaCy as is. In detail:

1. Change the tokenizer (combined_rule_tokenizer() in scispacy/custom_tokenizer.py) and/or segmenter(combined_rule_sentence_segmenter() in scispacy/custom_sentence_segmenter.py)
2. Rebuild the model folder by running save_model(create_combined_rule_model, /path/to/model/folder) in scispacy/util.py
3. Edit the newly create meta.json as you see fit
4. Go through the steps above for using SciSpaCy as is

Adding a new pipe or further customization

Adding a new pipe requires that you

1. Create your new pipe
2. Save your model following the pattern described above for changing the tokenizer or segmenter (steps 2 and 3)
3. Add your new pipe to Language.factories in proto_model/__init__.py
4. Follow the steps for using SciSpaCy as is