Metadata-Version: 2.1
Name: band
Version: 0.2.5
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/sunyancn/band
Author: sunyancn
Author-email: sunyanhust@163.com
License: MIT License
Description: # BAND：BERT Application aNd Deployment
        
        A simple and efficient BERT model training and deployment framework，一个简单高效的 BERT 模型训练和部署框架
        
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        <p align="center">
          <a href="https://github.com/SunYanCN/BAND">
            <img src="figures/logo.png" alt="Logo" width="100" height="100">
          </a>
        
          <h3 align="center">BAND</h3>
          <p align="center">
            BAND：BERT Application aNd Deployment
            <br />
            <a href="https://sunyancn.github.io/BAND/"><strong>探索本项目的文档 »</strong></a>
            <br />
            <br />
            <a href="https://github.com/SunYanCN/BAND/tree/master/examples">查看Demo</a>
            ·
            <a href="https://github.com/SunYanCN/BERT-chinese-text-classification-and-deployment/issues/new?assignees=&labels=&template=bug_report.md&title=">报告Bug</a>
            ·
            <a href="https://github.com/SunYanCN/BERT-chinese-text-classification-and-deployment/issues/new?assignees=&labels=&template=feature_request.md&title=">提出新特性</a>
                ·
            <a href="https://github.com/SunYanCN/BERT-chinese-text-classification-and-deployment/issues/new?assignees=&labels=&template=custom.md&title=">问题交流</a>
          </p>
        
        </p>
         
        ## 目录
        
        - [上手指南](#上手指南)
          - [开发前的配置要求](#开发前的配置要求)
          - [安装方法](#安装方法)
        - [文件目录说明](#文件目录说明)
        - [开发的架构](#开发的架构)
        - [部署](#部署)
        - [使用到的框架](#使用到的框架)
        - [贡献者](#贡献者)
          - [如何参与开源项目](#如何参与开源项目)
        - [版本控制](#版本控制)
        - [作者](#作者)
        - [鸣谢](#鸣谢)
        
        ### 上手指南
        
        ###### **开发前的配置要求**
        
        1. Linux (Centos,Ubuntu.....)
        2. Python>=3.6
        3. Tensorflow>=1.13.1
        
        ###### **安装方法**
        安装band有两种方式：
        - Install from PyPi
            ```sh
            pip install band
            ```
        - Install From Git
            ```sh
            pip install git+https://www.github.com/sunyancn/band.git
            ```
        ###### 文本分类Demo
        1. 训练模型
            ```python
            import band
            from band.corpus import SMP2018ECDTCorpus
            from band.tasks.classification import BiLSTM_Model
            from band.callbacks import EvalCallBack
            from band import utils
            
            # Dataset
            dataset = SMP2018ECDTCorpus()
            
            model = BiLSTM_Model()
            eval_callback = EvalCallBack(kash_model=model,
                                         valid_x=dataset.valid_x,
                                         valid_y=dataset.valid_y,
                                         step=5)
            model.fit(dataset.train_x,
                      dataset.train_y,
                      dataset.valid_x,
                      dataset.valid_y,
                      batch_size=32,
                      callbacks=[eval_callback])
            
            model.evaluate(dataset.test_x, dataset.test_y)
            
            # Save model to `saved_classification_model` dir
            model.save('saved_classification_model')
            
            # Load model
            loaded_model = band.utils.load_model('saved_classification_model')
            
            # Use model to predict
            loaded_model.predict(dataset.test_x[:10])
            
            # Save model
            utils.convert_to_saved_model(model,
                                         model_path='saved_model/bilstm',
                                         version='1')
            ```
        
        2. 部署模型
            ```bash
            simple_tensorflow_serving --model_base_path="saved_model/bilstm"
            ```
        
        3. 启动WebAPP,参考[代码](https://github.com/SunYanCN/BAND/tree/master/webapp)
            ```
            python app.py
            ```
           
        ### 开发的架构
        
        <div align=center><img src="https://s2.ax1x.com/2019/11/20/Mf2YAU.md.png" width="500"/></div>
        
        ### 部署
        
        暂无
        
        ### 使用到的框架
        
        - [TensorFlow](https://getbootstrap.com)
        - [simple-tensorflow-serving](https://stfs.readthedocs.io/en/latest/index.html)
        
        ### 作者
        您可以通过以下方式联系我：
        - **Email**: sunyanhust@163.com
        - **NLP技术QQ交流群**：859886087
        
        > 您也可以在贡献者名单中参看所有参与该项目的开发者。
        
        
        ### 贡献者
        
        请阅读**CONTRIBUTING.md** 查阅为该项目做出贡献的开发者。
        
        #### 如何参与开源项目
        
        贡献使开源社区成为一个学习、激励和创造的绝佳场所。你所作的任何贡献都是**非常感谢**的。
        
        
        1. Fork the Project
        2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
        3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
        4. Push to the Branch (`git push origin feature/AmazingFeature`)
        5. Open a Pull Request
        
        ### 版权说明
        
        该项目签署了Apache授权许可，详情请参阅 [LICENSE](https://github.com/SunYanCN/BERT-chinese-text-classification-and-deployment/blob/master/LICENSE)
        
        ### 版本控制
        
        该项目使用Git进行版本管理。您可以在repository参看当前可用版本。
        
        ### 鸣谢
        - [Kashgari](https://github.com/BrikerMan/Kashgari)
        - [bert4keras](https://github.com/bojone/bert4keras)
        - [Free Logo Design](https://www.freelogodesign.org/)
        - [Headliner](https://github.com/as-ideas/headliner)
        
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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
