Merge branch 'main' of github.com:zjunlp/DeepKE into main

This commit is contained in:
tlk-dsg 2021-12-20 15:16:38 +08:00
commit f96b4ca300
2 changed files with 30 additions and 17 deletions

View File

@ -34,15 +34,16 @@ DeepKE is a knowledge extraction toolkit supporting **low-resource** and **docum
# What's New
## Dec, 2021
* We added `dockerfile` to create enviroment automaticly.
* We have added `dockerfile` to create the enviroment automatically.
## Nov, 2021
* The demo of deepke was released,which supports real-time extration without trainging and deploying.
* The documentation of deepke was released,which contains the details of deepke,such as source codes and datasets.
* The demo of DeepKE, supporting real-time extration without deploying and training, has been released.
* The documentation of DeepKE, containing the details of DeepKE such as source codes and datasets, has been released.
## Oct, 2021
* The code of deepke-v2.0 was released.
## Before
* `pip install deepke`
* The code of deepke-v1.0 was released.
* The codes of deepke-v2.0 have been released.
## May, 2021
* `pip install deepke`
* The codes of deepke-v1.0 have been released.
# Prediction

View File

@ -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
<br>
# 新版特性
## 2021年12月
- 加入`dockerfile`以便自动创建环境
## 2021年11月
- 发布DeepKE demo页面支持实时抽取无需部署和训练模型
- 发布DeepKE文档包含DeepKE源码和数据集等详细信息
## 2021年10月
- `pip install deepke`
- deepke-v2.0发布
## 2021年5月
- `pip install deepke`
- deepke-v1.0发布
<br>
### 进行预测
# 进行预测
下面使用一个demo展示预测过程<br>
<img src="pics/demo.gif" width="636" height="494" align=center>
<br>
## 模型架构
# 模型架构
Deepke的架构图如下所示
@ -39,11 +52,14 @@ Deepke的架构图如下所示
<img src="pics/architectures.png">
</h3>
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。
- 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
- 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
- 每一个应用场景由三个部分组成Data部分包含Tokenizer、Preprocessor和LoaderModel部分包含Module、Encoder和ForwarderCore部分包含Training、Evaluation和Prediction
<br>
## 快速上手
# 快速上手
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
@ -317,14 +333,10 @@ python predict.py
[关系抽取Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/few-shot/tutorial.ipynb)
[关系抽取Colab]()
- 篇章级:
[关系抽取Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/document/tutorial.ipynb)
[关系抽取Colab]()
<!-- ![image](https://user-images.githubusercontent.com/31753427/140022588-c3b38495-89b1-4f3c-8298-bcc1086f78bf.png) -->