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    "info": {
        "author": "BrikerMan",
        "author_email": "eliyar917@gmail.com",
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            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 3.6",
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        "description": "<h1 align=\"center\">\n    <a href='https://en.wikipedia.org/wiki/Mahmud_al-Kashgari'>Kashgari</a>\n</h1>\n\n<p align=\"center\">\n    <a href=\"https://github.com/BrikerMan/kashgari/blob/master/LICENSE\">\n        <img alt=\"GitHub\" src=\"https://img.shields.io/github/license/BrikerMan/kashgari.svg?color=blue&style=popout\">\n    </a>\n    <a href=\"https://travis-ci.com/BrikerMan/Kashgari\">\n        <img src=\"https://travis-ci.com/BrikerMan/Kashgari.svg?branch=master\"/>\n    </a>\n    <a href='https://coveralls.io/github/BrikerMan/Kashgari?branch=master'>\n        <img src='https://coveralls.io/repos/github/BrikerMan/Kashgari/badge.svg?branch=master' alt='Coverage Status'/>\n    </a>\n     <a href=\"https://pepy.tech/project/kashgari-tf\">\n        <img src=\"https://pepy.tech/badge/kashgari-tf\"/>\n    </a>\n    <a href=\"https://pypi.org/project/kashgari-tf/\">\n        <img alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/kashgari-tf.svg\">\n    </a>\n</p>\n\n<h4 align=\"center\">\n    <a href=\"#overview\">Overview</a> |\n    <a href=\"#performance\">Performance</a> |\n    <a href=\"#quick-start\">Quick start</a> |\n    <a href=\"https://kashgari.bmio.net/\">Documentation</a> |\n    <a href=\"https://kashgari-zh.bmio.net/\">\u4e2d\u6587\u6587\u6863</a> |\n    <a href=\"https://kashgari.bmio.net/about/contributing/\">Contributing</a>\n</h4>\n\n\ud83c\udf89\ud83c\udf89\ud83c\udf89 We are proud to announce that we entirely rewrote Kashgari with tf.keras, now Kashgari comes with easier to understand API and is faster! \ud83c\udf89\ud83c\udf89\ud83c\udf89\n\n## Overview\n\nKashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks.\n\n- **Human-friendly**. Kashgari's code is straightforward, well documented and tested, which makes it very easy to understand and modify.\n- **Powerful and simple**. Kashgari allows you to apply state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS) and classification.\n- **Built-in transfer learning**. Kashgari built-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model.\n- **Fully scalable**. Kashgari provides a simple, fast, and scalable environment for fast experimentation, train your models and experiment with new approaches using different embeddings and model structure. \n- **Production Ready**. Kashgari could export model with `SavedModel` format for tensorflow serving, you could directly deploy it on the cloud. \n\n## Our Goal\n\n- **Academic users** Easier experimentation to prove their hypothesis without coding from scratch.\n- **NLP beginners** Learn how to build an NLP project with production level code quality.\n- **NLP developers** Build a production level classification/labeling model within minutes.\n\n## Performance\n\n| Task                     | Language | Dataset                   | Score          | Detail                                                                                                             |\n| ------------------------ | -------- | ------------------------- | -------------- | ------------------------------------------------------------------------------------------------------------------ |\n| Named Entity Recognition | Chinese  | People's Daily Ner Corpus | **94.46** (F1) | [Text Labeling Performance Report](https://kashgari.bmio.net/tutorial/text-labeling/#performance-report) |\n\n## Tutorials\n\nHere is a set of quick tutorials to get you started with the library:\n\n- [Tutorial 1: Text Classification](https://kashgari.bmio.net/tutorial/text-classification/)\n- [Tutorial 2: Text Labeling](https://kashgari.bmio.net/tutorial/text-labeling/)\n- [Tutorial 3: Language Embedding](https://kashgari.bmio.net/embeddings/)\n\nThere are also articles and posts that illustrate how to use Kashgari:\n\n- [15 \u5206\u949f\u642d\u5efa\u4e2d\u6587\u6587\u672c\u5206\u7c7b\u6a21\u578b](https://eliyar.biz/nlp_chinese_text_classification_in_15mins/)\n- [\u57fa\u4e8e BERT \u7684\u4e2d\u6587\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\uff08NER)](https://eliyar.biz/nlp_chinese_bert_ner/)\n- [BERT/ERNIE \u6587\u672c\u5206\u7c7b\u548c\u90e8\u7f72](https://eliyar.biz/nlp_train_and_deploy_bert_text_classification/)\n- [\u4e94\u5206\u949f\u642d\u5efa\u4e00\u4e2a\u57fa\u4e8eBERT\u7684NER\u6a21\u578b](https://www.jianshu.com/p/1d6689851622)\n- [Multi-Class Text Classification with Kashgari in 15 minutes](https://medium.com/@BrikerMan/multi-class-text-classification-with-kashgari-in-15mins-c3e744ce971d)\n\n## Quick start\n\n### Requirements and Installation\n\n\ud83c\udf89\ud83c\udf89\ud83c\udf89 We renamed again for consistency and clarity. From now on, it is all `kashgari`. \ud83c\udf89\ud83c\udf89\ud83c\udf89\n\nThe project is based on Python 3.6+, because it is 2019 and type hinting is cool.\n\n| Backend          | pypi version                           | desc            |\n| ---------------- | -------------------------------------- | --------------- |\n| TensorFlow 2.x   | `pip install 'kashgari>=2.0.0'`        | coming soon     |\n| TensorFlow 1.14+ | `pip install 'kashgari>=1.0.0,<2.0.0'` | current version |\n| Keras            | `pip install 'kashgari<1.0.0'`         | legacy version  |\n\n[Find more info about the name changing.](https://github.com/BrikerMan/Kashgari/releases/tag/v1.0.0)\n\n### Example Usage\n\nLet's run an NER labeling model with Bi_LSTM Model.\n\n```python\nfrom kashgari.corpus import ChineseDailyNerCorpus\nfrom kashgari.tasks.labeling import BiLSTM_Model\n\ntrain_x, train_y = ChineseDailyNerCorpus.load_data('train')\ntest_x, test_y = ChineseDailyNerCorpus.load_data('test')\nvalid_x, valid_y = ChineseDailyNerCorpus.load_data('valid')\n\nmodel = BiLSTM_Model()\nmodel.fit(train_x, train_y, valid_x, valid_y, epochs=50)\n\n\"\"\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #\n=================================================================\ninput (InputLayer)           (None, 97)                0\n_________________________________________________________________\nlayer_embedding (Embedding)  (None, 97, 100)           320600\n_________________________________________________________________\nlayer_blstm (Bidirectional)  (None, 97, 256)           235520\n_________________________________________________________________\nlayer_dropout (Dropout)      (None, 97, 256)           0\n_________________________________________________________________\nlayer_time_distributed (Time (None, 97, 8)             2056\n_________________________________________________________________\nactivation_7 (Activation)    (None, 97, 8)             0\n=================================================================\nTotal params: 558,176\nTrainable params: 558,176\nNon-trainable params: 0\n_________________________________________________________________\nTrain on 20864 samples, validate on 2318 samples\nEpoch 1/50\n20864/20864 [==============================] - 9s 417us/sample - loss: 0.2508 - acc: 0.9333 - val_loss: 0.1240 - val_acc: 0.9607\n\n\"\"\"\n```\n\n### Run with GPT-2 Embedding\n\n```python\nfrom kashgari.embeddings import GPT2Embedding\nfrom kashgari.corpus import ChineseDailyNerCorpus\nfrom kashgari.tasks.labeling import BiGRU_Model\n\ntrain_x, train_y = ChineseDailyNerCorpus.load_data('train')\nvalid_x, valid_y = ChineseDailyNerCorpus.load_data('valid')\n\ngpt2_embedding = GPT2Embedding('<path-to-gpt-model-folder>', sequence_length=30)\nmodel = BiGRU_Model(gpt2_embedding)\nmodel.fit(train_x, train_y, valid_x, valid_y, epochs=50)\n```\n\n### Run with Bert Embedding\n\n```python\nfrom kashgari.embeddings import BERTEmbedding\nfrom kashgari.tasks.labeling import BiGRU_Model\nfrom kashgari.corpus import ChineseDailyNerCorpus\n\nbert_embedding = BERTEmbedding('<bert-model-folder>', sequence_length=30)\nmodel = BiGRU_Model(bert_embedding)\n\ntrain_x, train_y = ChineseDailyNerCorpus.load_data()\nmodel.fit(train_x, train_y)\n```\n\n## Sponsors\n\nSupport this project by becoming a sponsor. Your issues and feature request will be prioritized.[[Become a sponsor](https://www.patreon.com/join/brikerman?)]\n\n## Contributing\n\nThanks for your interest in contributing! There are many ways to get involved; start with the [contributor guidelines](https://kashgari.bmio.net/about/contributing/) and then check these open issues for specific tasks.\n\nFeel free to join the WeChat group if you want to more involved in Kashgari's development.\n\n![](http://s3.bmio.net/kashgari-qr-code.jpeg)\n\n## Reference\n\nThis library is inspired by and references following frameworks and papers.\n\n- [flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)](https://github.com/zalandoresearch/flair)\n- [anago - Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging](https://github.com/Hironsan/anago)\n- [Chinese-Word-Vectors](https://github.com/Embedding/Chinese-Word-Vectors)\n\n\n",
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        "name": "kashgari",
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