Metadata-Version: 2.1
Name: band
Version: 0.2.4
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
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
Requires-Dist: tensorflow
Requires-Dist: numpy (==1.16.4)
Requires-Dist: pandas (==0.23.4)
Requires-Dist: scikit-learn (>=0.21.1)
Requires-Dist: h5py
Requires-Dist: gensim (>=3.5.0)
Requires-Dist: seqeval (==0.0.10)
Requires-Dist: keras-self-attention

# 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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<br />

<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/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)
  - [安装步骤](#安装步骤)
- [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. xxxxx x.x.x
2. xxxxx x.x.x

###### **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
    ```

### 文件目录说明
```
filetree 
├── ARCHITECTURE.md
├── LICENSE.txt
├── README.md
├── /account/
├── /bbs/
├── /docs/
│  ├── /rules/
│  │  ├── backend.txt
│  │  └── frontend.txt
├── manage.py
├── /oa/
├── /static/
├── /templates/
├── useless.md
└── /util/
```


### 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MIT 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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