Metadata-Version: 2.1
Name: zeugma
Version: 0.42
Summary: Unified framework for word embeddings (Word2Vec, GloVe, FastText, ...) compatible with scikit-learn Pipeline
Home-page: https://github.com/nkthiebaut
Author: Nicolas Thiebaut
Author-email: nkthiebaut@gmail.com
License: MIT
Download-URL: https://github.com/nkthiebaut/zeugma/archive/0.42.tar.gz
Description: .. -*- mode: rst -*-
        
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        ======
        Zeugma
        ======
        
        Unified framework for word embeddings (Word2Vec, GloVe, FastText, ...) use in machine learning pipelines, compatible with `scikit-learn Pipelines <http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html>`_.
        
        Installation
        ============
        
        Install package with ``pip install zeugma``.
        
        
        Examples
        ========
        
        Embedding transformers can be either be used with downloaded embeddings (they
        all come with a default embedding URL) or trained.
        
        Pretrained embeddings
        --------------------------------
        
        As an illustrative example the **cosine similarity** of the sentences *zeugma* and *figure of speech* is computed using the GloVeTransformer
        with **downloaded embeddings** (default URL is used here)::
        
            >>> from zeugma.embeddings import EmbeddingTransformer
            >>> glove = EmbeddingTransformer('glove')
            >>> embeddings = glove.transform(['zeugma', 'figure of speech'])
            >>> from sklearn.metrics.pairwise import cosine_similarity
            >>> cosine_similarity(embeddings)[0, 1]
            0.32840478
        
        Training embeddings
        -------------------
        
        To train your own Word2Vec embeddings use the `Gensim sklearn API <https://radimrehurek.com/gensim/sklearn_api/w2vmodel.html>`_.
        
        
        Fine-tuning embeddings
        ----------------------
        
        Embeddings fine tuning (training embeddings with preloaded values) will be implemented in the future.
        
Keywords: embeddings
Platform: UNKNOWN
Description-Content-Type: text/x-rst
