Merge branch 'main' of github.com:zjunlp/DeepKE into main
This commit is contained in:
tlk-dsg
commit
f96b4ca300
19
README.md
19
README.md
|
|
@ -33,16 +33,17 @@ DeepKE is a knowledge extraction toolkit supporting **low-resource** and **docum
|
|||
|
||||
# What's New
|
||||
|
||||
## Dec,2021
|
||||
* We added `dockerfile` to create enviroment automaticly.
|
||||
## 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.
|
||||
## Oct,2021
|
||||
* The code of deepke-v2.0 was released.
|
||||
## Before
|
||||
## Dec, 2021
|
||||
* We have added `dockerfile` to create the enviroment automatically.
|
||||
## Nov, 2021
|
||||
* 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
|
||||
* `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
|
||||
|
||||
|
|
|
|||
28
README_CN.md
28
README_CN.md
|
|
@ -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Loader，Model部分包含Module、Encoder和Forwarder，Core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]()
|
||||
|
||||
|
||||
<!--  -->
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue