spacy-udpipe 0.0.1

Use fast UDPipe models directly in spaCy

spaCy + UDPipe

This package wraps the fast and efficient UDPipe language-agnostic NLP pipeline (via its Python bindings), so you can use UDPipe pretrained models as a spaCy pipeline for 50+ languages out-of-the-box. Inspired by spacy-stanfordnlp, this package offers slightly less accurate models that are in turn much faster (see benchmarks for UDPipe and StanfordNLP).

Installation

Use the package manager pip to install spacy-udpipe.

pip install git+https://github.com/asajatovic/spacy-udpipe

After installation, use spacy_udpipe.download(lang) to download the pretrained model for the desired language.

Usage

The loaded UDPipeLanguage class returns a spaCy Language object, i.e., the nlp object you can use to process text and create a Doc object.

import spacy_udpipe

text = "Wikipedia is a free online encyclopedia, created and edited by volunteers around the world."
nlp = spacy_udpipe.load("en")

doc = nlp(text)
for token in doc:
    print(token.text, token.lemma_, token.pos_, token.dep_)

As all attributes are computed once and set in the custom Tokenizer, the nlp.pipeline is empty.

Authors and acknowledgment

Created by Antonio Šajatović during an internship at Text Analysis and Knowledge Engineering Lab (TakeLab).

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

To start the tests, just run pytest in the root source directory.

License

MIT © TakeLab

Project status

Maintained by Text Analysis and Knowledge Engineering Lab (TakeLab).

Notes

• All available pretrained models are licensed under CC BY-NC-SA 4.0.

• All annotations match with Spacy's, except for token.tag_, which map from CoNLL XPOS tag (language-specific part-of-speech tag), defined for each language separately by the corresponding Universal Dependencies treebank.

• Full list of supported languages and models is available in languages.json.

• This package exposes a spacy_languages entry point in its setup.py so full suport for serialization is enabled:

  nlp = spacy_udpipe.load("en")
  nlp.to_disk("./udpipe-spacy-model")
  

  To properly load a saved model, you must pass the udpipe_model argument when loading it:

  udpipe_model = spacy_udpipe.UDPipeModel("en")
  nlp = spacy.load("./udpipe-spacy-model", udpipe_model=udpipe_model)