A Generic Information Search... With a Mind of its Own!
GISMO is a NLP tool to rank and organize a corpus of documents according to a query.
Gismo stands for Generic Information Search... with a Mind of its Own.
- Free software: MIT License
- Github: https://github.com/balouf/gismo/
- Documentation: https://balouf.github.io/gismo/
Features
Gismo combines three main ideas:
- TF-IDTF: a symmetric version of the TF-IDF embedding.
- DIteration: a fast, push-based, variant of the PageRank algorithm.
- Fuzzy dendrogram: a variant of the Louvain clustering algorithm.
Quickstart
Install gismo:
$ pip install gismo
Use gismo in a Python project:
>>> from gismo.common import toy_source_dict
>>> from gismo import Corpus, Embedding, CountVectorizer, Gismo
>>> corpus = Corpus(toy_source_dict, to_text=lambda x: x['content'])
>>> embedding = Embedding(vectorizer=CountVectorizer(dtype=float))
>>> embedding.fit_transform(corpus)
>>> gismo = Gismo(corpus, embedding)
>>> gismo.rank("Mogwaï")
>>> gismo.get_features_by_rank()
['mogwaï', 'gizmo', 'chinese', 'in', 'demon', 'folklore', 'is']
To get the hang of a typical Gismo workflow, you can check the Toy Example notebook. For more advanced uses, look at the other tutorials or directly the reference section.
Credits
Thomas Bonald, Anne Bouillard, Marc-Olivier Buob, Dohy Hong for their helpful contribution.
This package was created with Cookiecutter and the francois-durand/package_helper project template.
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Release files for gismo 0.5.4
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