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        "description": "# Embeddings\n\n\nThis package is designed to provide easy-to-use python class and cli\ninterfaces to:\n\n- clean corpuses in an efficient way in terms of computation time\n\n- generate word2vec embeddings (based on gensim) and directly write them to a format that is compatible with [Tensorflow Projector](http://projector.tensorflow.org/)\n\nThus, with two classes, or two commands, anyone should be able clean a corpus and generate embeddings that can be uploaded and visualized with Tensorflow Projector.\n\n## Getting started\n\n### Requirements\n\nThis packages requires ```gensim```, ```nltk```, and ```docopt``` to run. If\npip doesn't install this dependencies automatically, you can install it by\nrunning :\n```bash\npip install nltk docopt gensim\n```\n\n### Installation\n\nTo install this package, simply run :\n```bash\npip install embeddingsprep\n```\n\nFurther versions might include conda builds, but it's currently not the case.\n\n\n## Main features\n\n### Preprocessing\n\nFor Word2Vec, we want a soft yet important preprocessing. We want to denoise the text while keeping as much variety and information as possible. A detailed version of what is done during the preprocessing is available [here](./preprocessing/index.html)\n\n\n#### Usage example :\n\nCreating and saving a loadable configuration:\n```python\nfrom embeddingsprep.preprocessing.preprocessor import PreprocessorConfig, Preprocessor\nconfig = PreprocessorConfig('/tmp/logdir')\nconfig.set_config(writing_dir='/tmp/outputs')\nconfig.save_config()\n```\n\n```python\nprep = Preprocessor('/tmp/logdir')  # Loads the config object in /tmp/logdir if it exists\nprep.fit('~/mydata/')  # Fits the unigram & bigrams occurences\nprep.filter()  # Filters with all the config parameters\nprep.transform('~/mydata')  # Transforms the texts with the filtered vocab. \n```\n\n\n### Word2Vec\n\nFor the Word2Vec, we just wrote a simple wrapper that takes the\npreprocessed files as an input, trains a Word2Vec model with gensim and writes the vocab, embeddings .tsv files that can be visualized with tensorflow projector (http://projector.tensorflow.org/)\n\n#### Usage example:\n\n\n```python\nfrom embeddingsprep.models.word2vec import Word2Vec\nmodel = Word2Vec(emb_size=300, window=5, epochs=3)\nmodel.train('./my-preprocessed-data/')\nmodel.save('./my-output-dir')\n```\n\n## Contributing\n\nAny github issue, contribution or suggestion is welcomed! You can open issues on the [github repository](https://github.com/sally14/embeddings).\n\n\n",
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