Natural language processing (NLP) utils: word embeddings (Word2Vec, GloVe, FastText, ...) and preprocessing transformers, compatible with scikit-learn Pipelines.
📝 Natural language processing (NLP) utils: word embeddings (Word2Vec, GloVe, FastText, …) and preprocessing transformers, compatible with scikit-learn Pipelines. 🛠
Install package with pip install zeugma.
Embedding transformers can be either be used with downloaded embeddings (they all come with a default embedding URL) or trained.
As an illustrative example the cosine similarity of the sentences what is zeugma and a figure of speech is computed using the GloVe pretrained embeddings.:
>>> from zeugma.embeddings import EmbeddingTransformer >>> glove = EmbeddingTransformer('glove') >>> embeddings = glove.transform(['what is zeugma', 'a figure of speech']) >>> from sklearn.metrics.pairwise import cosine_similarity >>> cosine_similarity(embeddings)[0, 1] 0.8721696
To train your own Word2Vec embeddings use the Gensim sklearn API.
Embeddings fine tuning (training embeddings with preloaded values) will be implemented in the future.
Usage examples are present in the examples folder.
Additional examples using Zeugma can be found in some posts of my blog.
Feel free to fork this repo and submit a Pull Request.
The development workflow for this repo is the following:
- create a virtual environment: python -m venv venv && source venv/bin/activate
- install required packages: pip install -r requirements.txt
- install the pre-commit hooks: pre-commit install
- run the test suite with: pytest from the root folder
Distribution via PyPI
To upload a new version to PyPI, simply:
- tag your new version on git: git tag -a x.x -m "my tag message"
- update the download_url field in the setup.py file
- commit, push the code and the tag (git push origin x.x), and make a PR
- once the updated code is present in master run python setup.py sdist bdist_wheel from the root of the package to distribute it.
To build the documentation locally simply run make html from the docs folder.
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