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        "description": "# word-embedder\n\n[![travis][travis-image]][travis-url]\n[![pypi][pypi-image]][pypi-url]\n\n[travis-image]: https://img.shields.io/travis/Yoctol/word-embedder.svg?style=flat\n[travis-url]: https://travis-ci.org/Yoctol/word-embedder\n[pypi-image]: https://img.shields.io/pypi/v/word-embedder.svg?style=flat\n[pypi-url]: https://pypi.python.org/pypi/word-embedder\n\nGet pretrained word embedding\n\n\n## Installation\n\n### Requirements\n* Linux\n* Python 3.6 and up\n\n`$ pip install word-embedder`\n\n## Usage\n\n### Lookup all existed embedders\n```python\n\nfrom word_embedder import lib\n\nlib.list_all_embedders()  # returns a list of embedder name\n\n```\n\n### Use an existed embedder\n\n1. load an embedder called OHOH\n```python\n\nfrom word_embedder import lib\n\nname = 'OHOH'  # embedder name\nembedder = lib[name]\n\n```\n\n2. extract a word vector\n\n- (1) given a word 'juice' (str)\n    ```python\n\n    word = 'juice'\n    embedder[word]  # returns the corresponding word vector\n\n    # Note: if 'juice' is not in the vocabulary, \n    # OOVError would be raised.\n\n    ```\n\n- (2) given an index 3 (int)\n    ```python\n\n    index = 3\n    embedder[index]  # returns the corresponding word vector\n\n    # Note: if the index is out of range of vocabulary size,\n    # OOVError would be raised.\n\n    ```\n\n\n",
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