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        "author": "Denis Newman-Griffis",
        "author_email": "denis.newman.griffis@gmail.com",
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        "description": "# pyemblib\n\nA module for reading, writing, and using trained word embeddings.\n\n## Installation\n\nInstall with pip!\n\n```bash\npip install pyemblib\n```\n\n## Usage\n\nThis package currently supports word embeddings trained by the following packages:\n\n- [word2vec](https://code.google.com/archive/p/word2vec/)\n- [GloVe](https://nlp.stanford.edu/projects/glove/)\n\n### Reading\n\nBoth text-format and binary embedding files are supported.\n\nThe example below shows reading each format of embedding:\n```python\n## import text embeddings\ntext_embs = pyemblib.read('/tmp/text_embeddings.txt', mode=pyemblib.Mode.Text)\n## import binary embeddings\nbin_embs = pyemblib.read('/tmp/bin_embeddings.bin', mode=pyemblib.Mode.Binary)\n```\n\nEmbeddings are read as a `pyemblib.Embeddings` object, which inherits from Python's dictionary class; keys are words, and values are the embedding arrays.\n\nTo get the word vector for \"python\", just use dictionary access:\n```python\nvec = embs['python']\nprint(vec)\n# [ 0.001 -0.237 ... ]\n```\n\n### Writing\n\nThe same text and binary modes can be used for writing out embedding files as for reading.\n\n```python\nembs = { 'a' : np.array([0.3 0.1 -0.2]), 'b' : np.array([-0.9, -0.2, -0.2]) }\n## write as text\npyemblib.write(embs, '/tmp/text_embeddings.txt', mode=pyemblib.Mode.Text)\n## write as binary\npyemblib.write(embs, '/tmp/bin_embeddings.bin', mode=pyemblib.Mode.Binary)\n```\n\n## Feedback\nPlease report any issues you encounter to the [Github Issues page](https://github.com/drgriffis/pyemblib/issues)!\n\n\n",
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