Metadata-Version: 2.1
Name: kashgari-tf
Version: 0.5.3
Summary: Simple, Keras-powered multilingual NLP framework, allows you to build your models in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS) and text classification tasks. Includes BERT, GPT-2 and word2vec embedding.
Home-page: https://github.com/BrikerMan/Kashgari
Author: BrikerMan
Author-email: eliyar917@gmail.com
License: Apache License 2.0
Description: <h1 align="center">
            <a href='https://en.wikipedia.org/wiki/Mahmud_al-Kashgari'>Kashgari</a>
        </h1>
        
        <p align="center">
            <a href="https://github.com/BrikerMan/kashgari/blob/master/LICENSE">
                <img alt="GitHub" src="https://img.shields.io/github/license/BrikerMan/kashgari.svg?color=blue&style=popout">
            </a>
            <a href="https://travis-ci.com/BrikerMan/Kashgari">
                <img src="https://travis-ci.com/BrikerMan/Kashgari.svg?branch=master"/>
            </a>
            <a href='https://coveralls.io/github/BrikerMan/Kashgari?branch=master'>
                <img src='https://coveralls.io/repos/github/BrikerMan/Kashgari/badge.svg?branch=master' alt='Coverage Status'/>
            </a>
             <a href="https://pepy.tech/project/kashgari-tf">
                <img src="https://pepy.tech/badge/kashgari-tf"/>
            </a>
            <a href="https://pypi.org/project/kashgari-tf/">
                <img alt="PyPI" src="https://img.shields.io/pypi/v/kashgari-tf.svg">
            </a>
        </p>
        
        <h4 align="center">
            <a href="#overview">Overview</a> |
            <a href="#performance">Performance</a> |
            <a href="#quick-start">Quick start</a> |
            <a href="https://kashgari.bmio.net/">Documentation</a> |
            <a href="https://kashgari-zh.bmio.net/">中文文档</a> |
            <a href="https://kashgari.bmio.net/about/contributing/">Contributing</a>
        </h4>
        
        🎉🎉🎉 We are proud to announce that we entirely rewrote Kashgari with tf.keras, now Kashgari comes with easier to understand API and is faster! 🎉🎉🎉
        
        ## Overview
        
        Kashgari 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.
        
        - **Human-friendly**. Kashgari's code is straightforward, well documented and tested, which makes it very easy to understand and modify.
        - **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.
        - **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.
        - **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. 
        - **Production Ready**. Kashgari could export model with `SavedModel` format for tensorflow serving, you could directly deploy it on the cloud. 
        
        ## Our Goal
        
        - **Academic users** Easier experimentation to prove their hypothesis without coding from scratch.
        - **NLP beginners** Learn how to build an NLP project with production level code quality.
        - **NLP developers** Build a production level classification/labeling model within minutes.
        
        ## Performance
        
        | Task                     | Language | Dataset                   | Score          | Detail                                                                                                             |
        | ------------------------ | -------- | ------------------------- | -------------- | ------------------------------------------------------------------------------------------------------------------ |
        | 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) |
        
        ## Tutorials
        
        Here is a set of quick tutorials to get you started with the library:
        
        - [Tutorial 1: Text Classification](https://kashgari.bmio.net/tutorial/text-classification/)
        - [Tutorial 2: Text Labeling](https://kashgari.bmio.net/tutorial/text-labeling/)
        - [Tutorial 3: Language Embedding](https://kashgari.bmio.net/embeddings/)
        
        There are also articles and posts that illustrate how to use Kashgari:
        
        - [15 分钟搭建中文文本分类模型](https://eliyar.biz/nlp_chinese_text_classification_in_15mins/)
        - [基于 BERT 的中文命名实体识别（NER)](https://eliyar.biz/nlp_chinese_bert_ner/)
        - [BERT/ERNIE 文本分类和部署](https://eliyar.biz/nlp_train_and_deploy_bert_text_classification/)
        - [五分钟搭建一个基于BERT的NER模型](https://www.jianshu.com/p/1d6689851622)
        - [Multi-Class Text Classification with Kashgari in 15 minutes](https://medium.com/@BrikerMan/multi-class-text-classification-with-kashgari-in-15mins-c3e744ce971d)
        
        ## Quick start
        
        ### Requirements and Installation
        
        🎉🎉🎉 We renamed the tf.keras version as **kashgari-tf** 🎉🎉🎉
        
        The project is based on TensorFlow 1.14.0 and Python 3.6+, because it is 2019 and type hinting is cool.
        
        ```bash
        pip install kashgari-tf
        # CPU
        pip install tensorflow==1.14.0
        # GPU
        pip install tensorflow-gpu==1.14.0
        ```
        
        ### Example Usage
        
        Let's run an NER labeling model with Bi_LSTM Model.
        
        ```python
        from kashgari.corpus import ChineseDailyNerCorpus
        from kashgari.tasks.labeling import BiLSTM_Model
        
        train_x, train_y = ChineseDailyNerCorpus.load_data('train')
        test_x, test_y = ChineseDailyNerCorpus.load_data('test')
        valid_x, valid_y = ChineseDailyNerCorpus.load_data('valid')
        
        model = BiLSTM_Model()
        model.fit(train_x, train_y, valid_x, valid_y, epochs=50)
        
        """
        _________________________________________________________________
        Layer (type)                 Output Shape              Param #
        =================================================================
        input (InputLayer)           (None, 97)                0
        _________________________________________________________________
        layer_embedding (Embedding)  (None, 97, 100)           320600
        _________________________________________________________________
        layer_blstm (Bidirectional)  (None, 97, 256)           235520
        _________________________________________________________________
        layer_dropout (Dropout)      (None, 97, 256)           0
        _________________________________________________________________
        layer_time_distributed (Time (None, 97, 8)             2056
        _________________________________________________________________
        activation_7 (Activation)    (None, 97, 8)             0
        =================================================================
        Total params: 558,176
        Trainable params: 558,176
        Non-trainable params: 0
        _________________________________________________________________
        Train on 20864 samples, validate on 2318 samples
        Epoch 1/50
        20864/20864 [==============================] - 9s 417us/sample - loss: 0.2508 - acc: 0.9333 - val_loss: 0.1240 - val_acc: 0.9607
        
        """
        ```
        
        ### Run with GPT-2 Embedding
        
        ```python
        from kashgari.embeddings import GPT2Embedding
        from kashgari.corpus import ChineseDailyNerCorpus
        from kashgari.tasks.labeling import BiGRU_Model
        
        train_x, train_y = ChineseDailyNerCorpus.load_data('train')
        valid_x, valid_y = ChineseDailyNerCorpus.load_data('valid')
        
        gpt2_embedding = GPT2Embedding('<path-to-gpt-model-folder>', sequence_length=30)
        model = BiGRU_Model(gpt2_embedding)
        model.fit(train_x, train_y, valid_x, valid_y, epochs=50)
        ```
        
        ### Run with Bert Embedding
        
        ```python
        from kashgari.embeddings import BERTEmbedding
        from kashgari.tasks.labeling import BiGRU_Model
        from kashgari.corpus import ChineseDailyNerCorpus
        
        bert_embedding = BERTEmbedding('<bert-model-folder>', sequence_length=30)
        model = BiGRU_Model(bert_embedding)
        
        train_x, train_y = ChineseDailyNerCorpus.load_data()
        model.fit(train_x, train_y)
        ```
        
        ## Contributing
        
        Thanks 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.
        
        ## Reference
        
        This library is inspired by and references following frameworks and papers.
        
        - [flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)](https://github.com/zalandoresearch/flair)
        - [anago - Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging](https://github.com/Hironsan/anago)
        - [Chinese-Word-Vectors](https://github.com/Embedding/Chinese-Word-Vectors)
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >3.6
Description-Content-Type: text/markdown
