{
    "info": {
        "author": "Gary Lai",
        "author_email": "gary@gary-lai.com",
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            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: MacOS",
            "Operating System :: POSIX",
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            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": "# Bert Embeddings\n\n[![Build Status](https://travis-ci.org/imgarylai/bert-embedding.svg?branch=master)](https://travis-ci.org/imgarylai/bert-embedding) [![codecov](https://codecov.io/gh/imgarylai/bert-embedding/branch/master/graph/badge.svg)](https://codecov.io/gh/imgarylai/bert-embedding) [![PyPI version](https://badge.fury.io/py/bert-embedding.svg)](https://pypi.org/project/bert-embedding/) [![Documentation Status](https://readthedocs.org/projects/bert-embedding/badge/?version=latest)](https://bert-embedding.readthedocs.io/en/latest/?badge=latest) \n\n\n[BERT](https://arxiv.org/abs/1810.04805), published by [Google](https://github.com/google-research/bert), is new way to obtain pre-trained language model word representation. Many NLP tasks are benefit from BERT to get the SOTA.\n\nThe goal of this project is to obtain the token embedding from BERT's pre-trained model. In this way, instead of building and do fine-tuning for an end-to-end NLP model, you can build your model by just utilizing or token embedding.\n\nThis project is implemented with [@MXNet](https://github.com/apache/incubator-mxnet). Special thanks to [@gluon-nlp](https://github.com/dmlc/gluon-nlp) team.\n\n## Install\n\n```\npip install bert-embedding\n# If you want to run on GPU machine, please install `mxnet-cu92`.\npip install mxnet-cu92\n```\n\n## Usage\n\n```python\nfrom bert_embedding import BertEmbedding\n\nbert_abstract = \"\"\"We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.\n Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers.\n As a result, the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. \nBERT is conceptually simple and empirically powerful. \nIt obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE benchmark to 80.4% (7.6% absolute improvement), MultiNLI accuracy to 86.7 (5.6% absolute improvement) and the SQuAD v1.1 question answering Test F1 to 93.2 (1.5% absolute improvement), outperforming human performance by 2.0%.\"\"\"\nsentences = bert_abstract.split('\\n')\nbert_embedding = BertEmbedding()\nresult = bert_embedding(sentences)\n```\nIf you want to use GPU, please import mxnet and set context\n\n```python\nimport mxnet as mx\nfrom bert_embedding import BertEmbedding\n\n...\n\nctx = mx.gpu(0)\nbert = BertEmbedding(ctx=ctx)\n```\n\nThis result is a list of a tuple containing (tokens, tokens embedding)\n\nFor example:\n\n```python\nfirst_sentence = result[0]\n\nfirst_sentence[0]\n# ['we', 'introduce', 'a', 'new', 'language', 'representation', 'model', 'called', 'bert', ',', 'which', 'stands', 'for', 'bidirectional', 'encoder', 'representations', 'from', 'transformers']\nlen(first_sentence[0])\n# 18\n\n\nlen(first_sentence[1])\n# 18\nfirst_token_in_first_sentence = first_sentence[1]\nfirst_token_in_first_sentence[1]\n# array([ 0.4805648 ,  0.18369392, -0.28554988, ..., -0.01961522,\n#        1.0207764 , -0.67167974], dtype=float32)\nfirst_token_in_first_sentence[1].shape\n# (768,)\n```\n\n## OOV\n\nThere are three ways to handle oov, avg (default), sum, and last. This can be specified in encoding. \n\n```python\n...\nbert_embedding = BertEmbedding()\nbert_embedding(sentences, 'sum')\n...\n```\n\n## Available pre-trained BERT models\n\n| |book_corpus_wiki_en_uncased|book_corpus_wiki_en_cased|wiki_multilingual|wiki_multilingual_cased|wiki_cn|\n|---|---|---|---|---|---|\n|bert_12_768_12|\u2713|\u2713|\u2713|\u2713|\u2713|\n|bert_24_1024_16|x|\u2713|x|x|x|\n\nExample of using the large pre-trained BERT model from Google \n\n```python\nfrom bert_embedding import BertEmbedding\n\nbert_embedding = BertEmbedding(model='bert_24_1024_16', dataset_name='book_corpus_wiki_en_cased')\n```\n\nSource: [gluonnlp](http://gluon-nlp.mxnet.io/model_zoo/bert/index.html) \n\n",
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