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        "author": "Jay Yip",
        "author_email": "junpang.yip@gmail.com",
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        "classifiers": [
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            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "\n# bert-multitask-as-service\n\nA serving service for [bert-multitask-learning](https://github.com/JayYip/bert-multitask-learning)\n\n## Install\n\n```bash\npip install bert-multitask-server\npip install bert-multitask-client\n```\n\n## Getting Started\n\n1. Train and export model.\n\n    A typical trained checkpoint dir looks like below.\n\n    ```text\n    bert_serving_ckpt/\n    \u251c\u2500\u2500 *_label_encoder.pkl\n    \u251c\u2500\u2500 bert_config.json\n    \u251c\u2500\u2500 export_model\n    \u251c\u2500\u2500 params.json\n    \u2514\u2500\u2500 vocab.txt\n    ```\n\n2. Start server using CLI\n\n    ```bash\n    bert-multitask-serving-start -model_dir ~/CWS_NER_POS_ckpt/ -num_worker=4 -problem \"CWS|NER|POS\"\n    ```\n\n3. Use Client to Get Prediction\n\n    ```python\n    from bert_serving.client import BertClient\n    bc = BertClient()\n    bc.encode(['\u6211\u7231\u5317\u4eac\u5929\u5b89\u95e8'])\n    ```\n\n# Bert\u591a\u4efb\u52a1\u5b66\u4e60\u670d\u52a1\n\n\u4e00\u4e2a\u90e8\u7f72[Bert\u591a\u4efb\u52a1\u5b66\u4e60](https://github.com/JayYip/bert-multitask-learning)\u7684\u670d\u52a1\n\n## \u5b89\u88c5\n\n```bash\npip install bert-multitask-server\npip install bert-multitask-client\n```\n\n## \u5f00\u59cb\u4f7f\u7528\n\n1. \u8bad\u7ec3\u6a21\u578b\u5e76\u5bfc\u51fa\u6a21\u578b.\n\n    \u5bfc\u51fa\u540e\u7684\u6a21\u578b\u76ee\u5f55\u5e94\u8be5\u6709\u4ee5\u4e0b\u6587\u4ef6\n\n    ```text\n    bert_serving_ckpt/\n    \u251c\u2500\u2500 *_label_encoder.pkl\n    \u251c\u2500\u2500 bert_config.json\n    \u251c\u2500\u2500 export_model\n    \u251c\u2500\u2500 params.json\n    \u2514\u2500\u2500 vocab.txt\n    ```\n\n2. \u7528CLI\u542f\u52a8\u670d\u52a1\n\n    ```bash\n    bert-multitask-serving-start -model_dir models/ -num_worker=4 -problem \"fake_problem\"\n    ```\n\n3. \u7528\u5ba2\u6237\u7aef\u83b7\u53d6\u9884\u6d4b\u7ed3\u679c\n\n    ```python\n    from bert_serving.client import BertClient\n    bc = BertClient()\n    bc.encode(['\u6211\u7231\u5317\u4eac\u5929\u5b89\u95e8'])\n    ```\n\n\n",
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