Merge branch 'main' of github.com:zjunlp/DeepKE into main
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README.md
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README.md
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@ -34,15 +34,16 @@ DeepKE is a knowledge extraction toolkit supporting **low-resource** and **docum
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# What's New
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# What's New
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## Dec, 2021
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## Dec, 2021
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* We added `dockerfile` to create enviroment automaticly.
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* We have added `dockerfile` to create the enviroment automatically.
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## Nov, 2021
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## Nov, 2021
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* The demo of deepke was released,which supports real-time extration without trainging and deploying.
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* The demo of DeepKE, supporting real-time extration without deploying and training, has been released.
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* The documentation of deepke was released,which contains the details of deepke,such as source codes and datasets.
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* The documentation of DeepKE, containing the details of DeepKE such as source codes and datasets, has been released.
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## Oct, 2021
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## Oct, 2021
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* The code of deepke-v2.0 was released.
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## Before
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* `pip install deepke`
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* `pip install deepke`
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* The code of deepke-v1.0 was released.
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* The codes of deepke-v2.0 have been released.
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## May, 2021
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* `pip install deepke`
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* The codes of deepke-v1.0 have been released.
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# Prediction
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# Prediction
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README_CN.md
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README_CN.md
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@ -24,14 +24,27 @@ DeepKE 是一个支持<b>低资源、长篇章</b>的知识抽取工具，可以
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<br>
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<br>
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# 新版特性
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## 2021年12月
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- 加入`dockerfile`以便自动创建环境
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## 2021年11月
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- 发布DeepKE demo页面，支持实时抽取，无需部署和训练模型
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- 发布DeepKE文档，包含DeepKE源码和数据集等详细信息
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## 2021年10月
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- `pip install deepke`
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- deepke-v2.0发布
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## 2021年5月
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- `pip install deepke`
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- deepke-v1.0发布
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<br>
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### 进行预测
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# 进行预测
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下面使用一个demo展示预测过程<br>
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下面使用一个demo展示预测过程<br>
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<img src="pics/demo.gif" width="636" height="494" align=center>
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<img src="pics/demo.gif" width="636" height="494" align=center>
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<br>
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<br>
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## 模型架构
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# 模型架构
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Deepke的架构图如下所示
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Deepke的架构图如下所示
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@ -39,11 +52,14 @@ Deepke的架构图如下所示
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<img src="pics/architectures.png">
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<img src="pics/architectures.png">
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</h3>
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</h3>
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DeepKE包括了三个模块，可以进行命名实体识别、关系抽取以及属性抽取任务，在各个模块下包括各自的子模块。其中关系抽取模块就有常规模块、文档级抽取模块以及低资源少样本模块。在每一个子模块中，包含实现分词、预处理等功能的一个工具集合，以及编码、训练和预测部分。
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- DeepKE为三个知识抽取功能（命名实体识别、关系抽取和属性抽取）设计了一个统一的框架
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- 可以在不同场景下实现不同功能。比如，可以在标准全监督、低资源少样本和文档级设定下进行关系抽取
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- 每一个应用场景由三个部分组成：Data部分包含Tokenizer、Preprocessor和Loader，Model部分包含Module、Encoder和Forwarder，Core部分包含Training、Evaluation和Prediction
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<br>
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<br>
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## 快速上手
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# 快速上手
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DeepKE支持pip安装使用，以常规全监督设定关系抽取为例，经过以下五个步骤就可以实现一个常规关系抽取模型
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DeepKE支持pip安装使用，以常规全监督设定关系抽取为例，经过以下五个步骤就可以实现一个常规关系抽取模型
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@ -317,14 +333,10 @@ python predict.py
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[关系抽取Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/few-shot/tutorial.ipynb)
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[关系抽取Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/few-shot/tutorial.ipynb)
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[关系抽取Colab]()
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- 篇章级：
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- 篇章级：
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[关系抽取Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/document/tutorial.ipynb)
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[关系抽取Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/document/tutorial.ipynb)
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[关系抽取Colab]()
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