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README.md
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</a>
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</p>
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<p align="center">
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<b>简体中文 | <a href="https://github.com/zjunlp/DeepKE/blob/main/README_ENGLISH.md">English</a></b>
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<b><a href="https://github.com/zjunlp/DeepKE/blob/main/README_CN.md">简体中文</a> | English</b>
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</p>
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<h1 align="center">
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<p>基于深度学习的开源中文知识图谱抽取框架</p>
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</h1>
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DeepKE 是一个支持<b>低资源、长篇章</b>的知识抽取工具，可以基于<b>PyTorch</b>实现<b>命名实体识别</b>、<b>关系抽取</b>和<b>属性抽取</b>功能。
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<br>
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<h2 align="center">
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<p>A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population</p>
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</h2>
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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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DeepKE is a knowledge extraction toolkit supporting **low-resource** and **document-level** scenarios. It provides three functions based on **PyTorch**, including **Named Entity Recognition**, **Relation Extraciton** and **Attribute Extraction**.
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<br>
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## Prediction
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There is a demonstration of prediction.<br>
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<img src="pics/demo.gif" width="636" height="494" align=center>
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<br>
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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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## Model Framework
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<h3 align="center">
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<img src="pics/architectures.png">
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</h3>
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<p align="center">
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Figure 1: The framework of DeepKE
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</p>
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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 contains three modules for **named entity recognition**, **relation extraction** and **attribute extraction**, the three tasks respectively.
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- Each module has its own submodules. For example, there are **standard**, **document-level** and **few-shot** submodules in the attribute extraction modular.
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- Each submodule compose of three parts: a **collection of tools**, which can function as tokenizer, dataloader, preprocessor and the like, a **encoder** and a part for **training and prediction**
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<br>
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## 快速上手
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## Quickstart
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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* is supported `pip install deepke`. Take the fully supervised attribute extraction for example.
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**Step 1** 下载代码 ```git clone https://github.com/zjunlp/DeepKE.git```（别忘记star和fork哈！！！）
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**Step1** Download basic codes `git clone https://github.com/zjunlp/DeepKE.git ` (Please star✨ and fork :memo:)
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**Step 2** 使用anaconda创建虚拟环境，进入虚拟环境(提供Dockerfile源码可自行创建镜像，位于docker文件夹中)
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**Step2** Create a virtual environment using`Anaconda` and enter it.
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```
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We also provide dockerfile source code, you can create your own image, which is located in the docker folder.
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```bash
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conda create -n deepke python=3.8
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conda activate deepke
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```
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1） 基于pip安装，直接使用
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```
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pip install deepke
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```
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1. Install *DeepKE* with `pip`
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2） 基于源码安装
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```bash
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pip install deepke
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```
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```
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python setup.py install
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2. Install *DeepKE* with source codes
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python setup.py develop
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```
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```bash
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python setup.py install
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python setup.py develop
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```
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**Step 3** 进入任务文件夹，以常规关系抽取为例
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**Step3** Enter the task directory
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```
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```bash
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cd DeepKE/example/re/standard
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```
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**Step 4** 模型训练，训练用到的参数可在conf文件夹内修改
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**Step4** Training (Parameters for training can be changed in the `conf` folder)
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```
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```bash
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python run.py
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```
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**Step 5** 模型预测。预测用到的参数可在conf文件夹内修改
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**Step5** Prediction (Parameters for prediction can be changed in the `conf` folder)
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```
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```bash
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python predict.py
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```
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### 环境依赖
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### Requirements
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> python == 3.8
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@ -105,11 +117,13 @@ python predict.py
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- opt-einsum==3.3.0
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- ujson
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### 具体功能介绍
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### Introduction of Three Functions
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#### 1. 命名实体识别NER
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#### 1. Named Entity Recognition
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- 命名实体识别是从非结构化的文本中识别出实体和其类型。数据为txt文件，样式范例为：
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- Named entity recognition seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, organizations, etc.
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- The data is stored in `.txt` files. Some instances as following:
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| Sentence | Person | Location | Organization |
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| :----------------------------------------------------------: | :------------------------: | :------------: | :----------------------------: |
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| 《红楼梦》是中央电视台和中国电视剧制作中心根据中国古典文学名著《红楼梦》摄制于1987年的一部古装连续剧，由王扶林导演，周汝昌、王蒙、周岭等多位红学家参与制作。 | 王扶林，周汝昌，王蒙，周岭 | 中国 | 中央电视台，中国电视剧制作中心 |
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| 秦始皇兵马俑位于陕西省西安市，1961年被国务院公布为第一批全国重点文物保护单位，是世界八大奇迹之一。 | 秦始皇 | 陕西省，西安市 | 国务院 |
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- 具体流程请进入详细的README中
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- **[常规全监督STANDARD](https://github.com/zjunlp/deepke/blob/main/example/ner/standard)**
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**Step1**: 进入`DeepKE/example/ner/standard`，数据集和参数配置可以分别在`data`和`conf`文件夹中修改；<br>
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**Step2**: 模型训练
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```
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python run.py
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```
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**Step3**: 模型预测
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```
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python predict.py
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```
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- **[少样本FEW-SHOT](https://github.com/zjunlp/DeepKE/tree/main/example/ner/few-shot)**
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**Step1**: 进入`DeepKE/example/ner/few-shot`，模型加载和保存位置以及参数配置可以在`conf`文件夹中修改；<br>
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**Step2**：模型训练，默认使用`CoNLL-2003`数据集进行训练
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```
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python run.py +train=few_shot
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```
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若要加载模型，修改`few_shot.yaml`中的`load_path`；<br>
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**Step3**：在`config.yaml`中追加`- predict`，`predict.yaml`中修改`load_path`为模型路径以及`write_path`为预测结果的保存路径，完成修改后使用
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- Read the detailed process in specific README
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- **[STANDARD (Fully Supervised)](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/ner/standard)**
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**Step1** Enter `DeepKE/example/ner/standard`. The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.<br>
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**Step2** Training
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```bash
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python run.py
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```
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**Step3** Prediction
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```bash
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python predict.py
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```
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#### 2. 关系抽取RE
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- **[FEW-SHOT](https://github.com/zjunlp/DeepKE/tree/test_new_deepke/example/ner/few-shot)**
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- 关系抽取是从非结构化的文本中抽取出实体之间的关系，以下为几个样式范例，数据为csv文件：
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**Step1** Enter `DeepKE/example/ner/few-shot`. The directory where the model is loaded and saved and the configuration parameters can be cusomized in the `conf` folder.<br>
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**Step2** Training with default `CoNLL-2003` dataset.
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```bash
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python run.py +train=few_shot
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```
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Users can modify `load_path` in `conf/train/few_shot.yaml` with the use of existing loaded model.<br>
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**Step3** Add `- predict` to `conf/config.yaml`, modify `loda_path` as the model path and `write_path` as the path where the predicted results are saved in `conf/predict.yaml`, and then run `python predict.py`
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```bash
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python predict.py
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```
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#### 2. Relation Extraction
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- Relationship extraction is the task of extracting semantic relations between entities from a unstructured text.
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- The data is stored in `.csv` files. Some instances as following:
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| Sentence | Relation | Head | Head_offset | Tail | Tail_offset |
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| :----------------------------------------------------: | :------: | :--------: | :---------: | :--------: | :---------: |
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| 《九玄珠》是在纵横中文网连载的一部小说，作者是龙马。 | 连载网站 | 九玄珠 | 1 | 纵横中文网 | 7 |
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| 提起杭州的美景，西湖总是第一个映入脑海的词语。 | 所在城市 | 西湖 | 8 | 杭州 | 2 |
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- 具体流程请进入详细的README中，RE包括了以下三个子功能
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- **[常规全监督STANDARD](https://github.com/zjunlp/deepke/blob/main/example/re/standard)**
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- Read the detailed process in specific README
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**Step1**：进入`DeepKE/example/re/standard`，数据集和参数配置可以分别进入`data`和`conf`文件夹中修改；<br>
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**Step2**：模型训练
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- **[STANDARD (Fully Supervised)](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/standard)**
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```
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**Step1** Enter the `DeepKE/example/re/standard` folder. The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.<br>
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**Step2** Training
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```bash
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python run.py
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```
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**Step3**：模型预测
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```
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python predict.py
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```
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- **[少样本FEW-SHOT](https://github.com/zjunlp/deepke/blob/main/example/re/few-shot)**
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**Step1**：进入`DeepKE/example/re/few-shot`，数据集和参数配置可以分别进入`data`和`conf`文件夹中修改；<br>
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**Step2**：模型训练，如需从上次训练的模型开始训练：设置`conf/train.yaml`中的`train_from_saved_model`为上次保存模型的路径，每次训练的日志默认保存在根目录，可用`log_dir`来配置；<br>
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```
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python run.py
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```
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**Step3**：模型预测
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**Step3** Prediction
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```
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```bash
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python predict.py
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```
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- **[文档级DOCUMENT](https://github.com/zjunlp/deepke/blob/main/example/re/document)** <br>
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```train_distant.json```由于文件太大，请自行从Google Drive上下载到data/目录下；<br>
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**Step1**：进入`DeepKE/example/re/document`，数据集和参数配置可以分别进入`data`和`conf`文件夹中修改；<br>
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**Step2**：模型训练，如需从上次训练的模型开始训练：设置`conf/train.yaml`中的`train_from_saved_model`为上次保存模型的路径，每次训练的日志默认保存在根目录，可用`log_dir`来配置；
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```
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- **[FEW-SHOT](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/few-shot)**
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**Step1** Enter `DeepKE/example/re/few-shot`. The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.<br>
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**Step 2** Training. Start with the model trained last time: modify `train_from_saved_model` in `conf/train.yaml`as the path where the model trained last time was saved. And the path saving logs generated in training can be customized by `log_dir`. <br>
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```bash
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python run.py
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```
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**Step3**：模型预测
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```
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**Step3** Prediction
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```bash
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python predict.py
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```
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#### 3. 属性抽取AE
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- **[DOCUMENT](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/document)**<br>
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- 数据为csv文件，样式范例为：
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Download the model `train_distant.json` from [*Google Drive*](https://drive.google.com/drive/folders/1c5-0YwnoJx8NS6CV2f-NoTHR__BdkNqw) to `data/`.
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**Step1** Enter `DeepKE/example/re/document`. The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.<br>
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**Step2** Training. Start with the model trained last time: modify `train_from_saved_model` in `conf/train.yaml`as the path where the model trained last time was saved. And the path saving logs generated in training can be customized by `log_dir`.
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```bash
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python run.py
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```
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**Step3** Prediction
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```bash
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python predict.py
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```
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#### 3. Attribute Extraction
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- Attribute extraction is to extract attributes for entities in a unstructed text.
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- The data is stored in `.csv` files. Some instances as following:
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| Sentence | Att | Ent | Ent_offset | Val | Val_offset |
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| :----------------------------------------------------------: | :------: | :------: | :--------: | :-----------: | :--------: |
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@ -220,73 +242,73 @@ python predict.py
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| 杨缨，字绵公，号钓溪，松溪县人，祖籍将乐，是北宋理学家杨时的七世孙 | 朝代 | 杨缨 | 0 | 北宋 | 22 |
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| 2014年10月1日许鞍华执导的电影《黄金时代》上映 | 上映时间 | 黄金时代 | 19 | 2014年10月1日 | 0 |
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- 具体流程请进入详细的README中
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- **[常规全监督STANDARD](https://github.com/zjunlp/deepke/blob/main/example/ae/standard)**
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**Step1**：进入`DeepKE/example/re/standard`，数据集和参数配置可以分别进入`data`和`conf`文件夹中修改；<br>
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**Step2**：模型训练
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- Read the detailed process in specific README
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- **[STANDARD (Fully Supervised)](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/ae/standard)**
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```
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**Step1** Enter the `DeepKE/example/ae/standard` folder. The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.<br>
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**Step2** Training
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```bash
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python run.py
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```
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**Step3**：模型预测
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```
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**Step3** Prediction
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```bash
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python predict.py
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```
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### Notebook教程
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本工具提供了若干Notebook和Google Colab教程，用户可针对性调试学习。
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- 常规设定：
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[命名实体识别Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/standard/tutorial.ipynb)
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[命名实体识别Colab](https://colab.research.google.com/drive/1rFiIcDNgpC002q9BbtY_wkeBUvbqVxpg?usp=sharing)
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[关系抽取Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/standard/tutorial.ipynb)
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[关系抽取Colab](https://colab.research.google.com/drive/1o6rKIxBqrGZNnA2IMXqiSsY2GWANAZLl?usp=sharing)
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[属性抽取Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ae/standard/tutorial.ipynb)
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[属性抽取Colab](https://colab.research.google.com/drive/1pgPouEtHMR7L9Z-QfG1sPYkJfrtRt8ML?usp=sharing)
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- 低资源：
|
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[命名实体识别Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/few-shot/tutorial.ipynb)
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[命名实体识别Colab](https://colab.research.google.com/drive/1Xz0sNpYQNbkjhebCG5djrwM8Mj2Crj7F?usp=sharing)
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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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||||
|
||||
[关系抽取Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/document/tutorial.ipynb)
|
||||
|
||||
[关系抽取Colab]()
|
||||
|
||||
|
||||
<!--  -->
|
||||
## Notebook Tutorial
|
||||
|
||||
## 备注（常见问题）
|
||||
This toolkit provides many `Jupyter Notebook` and `Google Colab` tutorials. Users can study *DeepKE* with them.
|
||||
|
||||
1. 使用 Anaconda 时，建议添加国内镜像，下载速度更快。如[镜像](https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/)。
|
||||
2. 使用 pip 时，建议使用国内镜像，下载速度更快，如阿里云镜像。
|
||||
3. 安装后提示 `ModuleNotFoundError: No module named 'past'`，输入命令 `pip install future` 即可解决。
|
||||
4. 使用语言预训练模型时，在线安装下载模型比较慢，更建议提前下载好，存放到 pretrained 文件夹内。具体存放文件要求见文件夹内的 `README.md`。
|
||||
5. DeepKE老版本位于[deepke-v1.0](https://github.com/zjunlp/DeepKE/tree/deepke-v1.0)分支，用户可切换分支使用老版本，老版本的能力已全部迁移到标准设定关系抽取([example/re/standard](https://github.com/zjunlp/DeepKE/blob/main/example/re/standard/README.md))中。
|
||||
- Standard Setting<br>
|
||||
|
||||
[NER Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/standard/tutorial.ipynb)
|
||||
|
||||
[NER Colab](https://colab.research.google.com/drive/1KpJFAT1nZfGDfnuNMZn02_okIU08j46d?usp=sharing)
|
||||
|
||||
[RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/standard/tutorial.ipynb)
|
||||
|
||||
[RE Colab](https://colab.research.google.com/drive/1o6rKIxBqrGZNnA2IMXqiSsY2GWANAZLl?usp=sharing)
|
||||
|
||||
[AE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ae/standard/tutorial.ipynb)
|
||||
|
||||
[AE Colab](https://colab.research.google.com/drive/1pgPouEtHMR7L9Z-QfG1sPYkJfrtRt8ML)
|
||||
|
||||
- Low-resource<br>
|
||||
|
||||
[NER Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/few-shot/tutorial.ipynb)
|
||||
|
||||
[NER Colab](https://colab.research.google.com/drive/1Xz0sNpYQNbkjhebCG5djrwM8Mj2Crj7F?usp=sharing)
|
||||
|
||||
[RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/few-shot/tutorial.ipynb)
|
||||
|
||||
[RE Colab]()
|
||||
|
||||
- Document-level<br>
|
||||
|
||||
[RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/document/tutorial.ipynb)
|
||||
|
||||
[RE Colab]()
|
||||
|
||||
<br>
|
||||
|
||||
## 项目成员
|
||||
## Tips
|
||||
|
||||
浙江大学：张宁豫、陶联宽、余海洋、陈想、徐欣、田玺、李磊、黎洲波、邓淑敏、姚云志、叶宏彬、谢辛、郑国轴、陈华钧
|
||||
1. Using nearest mirror, like [THU](https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/) in China, will speed up the installation of *Anaconda*.
|
||||
2. Using nearest mirror, like [aliyun](http://mirrors.aliyun.com/pypi/simple/) in China, will speed up `pip install XXX`.
|
||||
3. When encountering `ModuleNotFoundError: No module named 'past'`，run `pip install future` .
|
||||
4. It's slow to install the pretrained language models online. Recommend download pretrained models before use and save them in the `pretrained` folder. Read `README.md` in every task directory to check the specific requirement for saving pretrained models.
|
||||
5. The old version of *DeepKE* is in the [deepke-v1.0](https://github.com/zjunlp/DeepKE/tree/deepke-v1.0) branch. Users can change the branch to use the old version. The old version has been totally transfered to the standard relation extraction ([example/re/standard](https://github.com/zjunlp/DeepKE/blob/main/example/re/standard/README.md)).
|
||||
|
||||
达摩院：谭传奇、陈漠沙、黄非
|
||||
<br>
|
||||
|
||||
## Developers
|
||||
|
||||
Zhejiang University: Ningyu Zhang, Liankuan Tao, Haiyang Yu, Xiang Chen, Xin Xu, Xi Tian, Lei Li, Zhoubo Li, Shumin Deng, Yunzhi Yao, Hongbin Ye, Xin Xie, Guozhou Zheng, Huajun Chen
|
||||
|
||||
DAMO Academy: Chuanqi Tan, Fei Huang
|
||||
|
|
|
|||
|
|
@ -0,0 +1,292 @@
|
|||
<p align="center">
|
||||
<a href="https://github.com/zjunlp/deepke"> <img src="pics/logo.png" width="400"/></a>
|
||||
<p>
|
||||
<p align="center">
|
||||
<a href="https://deepke.openkg.cn">
|
||||
<img alt="Documentation" src="https://img.shields.io/badge/DeepKE-website-green">
|
||||
</a>
|
||||
<a href="https://pypi.org/project/deepke/#files">
|
||||
<img alt="PyPI" src="https://img.shields.io/pypi/v/deepke">
|
||||
</a>
|
||||
<a href="https://github.com/zjunlp/DeepKE/blob/master/LICENSE">
|
||||
<img alt="GitHub" src="https://img.shields.io/github/license/zjunlp/deepke">
|
||||
</a>
|
||||
</p>
|
||||
<p align="center">
|
||||
<b>简体中文 | <a href="https://github.com/zjunlp/DeepKE/blob/main/README.md">English</a></b>
|
||||
</p>
|
||||
|
||||
<h1 align="center">
|
||||
<p>基于深度学习的开源中文知识图谱抽取框架</p>
|
||||
</h1>
|
||||
|
||||
DeepKE 是一个支持<b>低资源、长篇章</b>的知识抽取工具，可以基于<b>PyTorch</b>实现<b>命名实体识别</b>、<b>关系抽取</b>和<b>属性抽取</b>功能。
|
||||
|
||||
<br>
|
||||
|
||||
|
||||
### 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
|
||||
|
||||
Deepke的架构图如下所示
|
||||
|
||||
<h3 align="center">
|
||||
<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。
|
||||
|
||||
<br>
|
||||
|
||||
## 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
|
||||
|
||||
**Step 1** 下载代码 ```git clone https://github.com/zjunlp/DeepKE.git```（别忘记star和fork哈！！！）
|
||||
|
||||
**Step 2** 使用anaconda创建虚拟环境，进入虚拟环境(提供Dockerfile源码可自行创建镜像，位于docker文件夹中)
|
||||
|
||||
```
|
||||
conda create -n deepke python=3.8
|
||||
|
||||
conda activate deepke
|
||||
```
|
||||
1） 基于pip安装，直接使用
|
||||
|
||||
```
|
||||
pip install deepke
|
||||
```
|
||||
|
||||
2） 基于源码安装
|
||||
|
||||
```
|
||||
python setup.py install
|
||||
|
||||
python setup.py develop
|
||||
```
|
||||
|
||||
**Step 3** 进入任务文件夹，以常规关系抽取为例
|
||||
|
||||
```
|
||||
cd DeepKE/example/re/standard
|
||||
```
|
||||
|
||||
**Step 4** 模型训练，训练用到的参数可在conf文件夹内修改
|
||||
|
||||
```
|
||||
python run.py
|
||||
```
|
||||
|
||||
**Step 5** 模型预测。预测用到的参数可在conf文件夹内修改
|
||||
|
||||
```
|
||||
python predict.py
|
||||
```
|
||||
|
||||
### 环境依赖
|
||||
|
||||
> python == 3.8
|
||||
|
||||
- torch == 1.5
|
||||
- hydra-core == 1.0.6
|
||||
- tensorboard == 2.4.1
|
||||
- matplotlib == 3.4.1
|
||||
- transformers == 3.4.0
|
||||
- jieba == 0.42.1
|
||||
- scikit-learn == 0.24.1
|
||||
- pytorch-transformers == 1.2.0
|
||||
- seqeval == 1.2.2
|
||||
- tqdm == 4.60.0
|
||||
- opt-einsum==3.3.0
|
||||
- ujson
|
||||
|
||||
### 具体功能介绍
|
||||
|
||||
#### 1. 命名实体识别NER
|
||||
|
||||
- 命名实体识别是从非结构化的文本中识别出实体和其类型。数据为txt文件，样式范例为：
|
||||
|
||||
| Sentence | Person | Location | Organization |
|
||||
| :----------------------------------------------------------: | :------------------------: | :------------: | :----------------------------: |
|
||||
| 本报北京9月4日讯记者杨涌报道：部分省区人民日报宣传发行工作座谈会9月3日在4日在京举行。 | 杨涌 | 北京 | 人民日报 |
|
||||
| 《红楼梦》是中央电视台和中国电视剧制作中心根据中国古典文学名著《红楼梦》摄制于1987年的一部古装连续剧，由王扶林导演，周汝昌、王蒙、周岭等多位红学家参与制作。 | 王扶林，周汝昌，王蒙，周岭 | 中国 | 中央电视台，中国电视剧制作中心 |
|
||||
| 秦始皇兵马俑位于陕西省西安市，1961年被国务院公布为第一批全国重点文物保护单位，是世界八大奇迹之一。 | 秦始皇 | 陕西省，西安市 | 国务院 |
|
||||
|
||||
- 具体流程请进入详细的README中
|
||||
- **[常规全监督STANDARD](https://github.com/zjunlp/deepke/blob/main/example/ner/standard)**
|
||||
|
||||
**Step1**: 进入`DeepKE/example/ner/standard`，数据集和参数配置可以分别在`data`和`conf`文件夹中修改；<br>
|
||||
|
||||
**Step2**: 模型训练
|
||||
|
||||
```
|
||||
python run.py
|
||||
```
|
||||
|
||||
**Step3**: 模型预测
|
||||
```
|
||||
python predict.py
|
||||
```
|
||||
|
||||
- **[少样本FEW-SHOT](https://github.com/zjunlp/DeepKE/tree/main/example/ner/few-shot)**
|
||||
|
||||
**Step1**: 进入`DeepKE/example/ner/few-shot`，模型加载和保存位置以及参数配置可以在`conf`文件夹中修改；<br>
|
||||
|
||||
**Step2**：模型训练，默认使用`CoNLL-2003`数据集进行训练
|
||||
|
||||
```
|
||||
python run.py +train=few_shot
|
||||
```
|
||||
|
||||
若要加载模型，修改`few_shot.yaml`中的`load_path`；<br>
|
||||
|
||||
**Step3**：在`config.yaml`中追加`- predict`，`predict.yaml`中修改`load_path`为模型路径以及`write_path`为预测结果的保存路径，完成修改后使用
|
||||
|
||||
```
|
||||
python predict.py
|
||||
```
|
||||
|
||||
#### 2. 关系抽取RE
|
||||
|
||||
- 关系抽取是从非结构化的文本中抽取出实体之间的关系，以下为几个样式范例，数据为csv文件：
|
||||
|
||||
| Sentence | Relation | Head | Head_offset | Tail | Tail_offset |
|
||||
| :----------------------------------------------------: | :------: | :--------: | :---------: | :--------: | :---------: |
|
||||
| 《岳父也是爹》是王军执导的电视剧，由马恩然、范明主演。 | 导演 | 岳父也是爹 | 1 | 王军 | 8 |
|
||||
| 《九玄珠》是在纵横中文网连载的一部小说，作者是龙马。 | 连载网站 | 九玄珠 | 1 | 纵横中文网 | 7 |
|
||||
| 提起杭州的美景，西湖总是第一个映入脑海的词语。 | 所在城市 | 西湖 | 8 | 杭州 | 2 |
|
||||
|
||||
- 具体流程请进入详细的README中，RE包括了以下三个子功能
|
||||
- **[常规全监督STANDARD](https://github.com/zjunlp/deepke/blob/main/example/re/standard)**
|
||||
|
||||
**Step1**：进入`DeepKE/example/re/standard`，数据集和参数配置可以分别进入`data`和`conf`文件夹中修改；<br>
|
||||
|
||||
**Step2**：模型训练
|
||||
|
||||
```
|
||||
python run.py
|
||||
```
|
||||
|
||||
**Step3**：模型预测
|
||||
|
||||
```
|
||||
python predict.py
|
||||
```
|
||||
|
||||
- **[少样本FEW-SHOT](https://github.com/zjunlp/deepke/blob/main/example/re/few-shot)**
|
||||
|
||||
**Step1**：进入`DeepKE/example/re/few-shot`，数据集和参数配置可以分别进入`data`和`conf`文件夹中修改；<br>
|
||||
|
||||
**Step2**：模型训练，如需从上次训练的模型开始训练：设置`conf/train.yaml`中的`train_from_saved_model`为上次保存模型的路径，每次训练的日志默认保存在根目录，可用`log_dir`来配置；<br>
|
||||
|
||||
```
|
||||
python run.py
|
||||
```
|
||||
|
||||
**Step3**：模型预测
|
||||
|
||||
```
|
||||
python predict.py
|
||||
```
|
||||
|
||||
- **[文档级DOCUMENT](https://github.com/zjunlp/deepke/blob/main/example/re/document)** <br>
|
||||
```train_distant.json```由于文件太大，请自行从Google Drive上下载到data/目录下；<br>
|
||||
|
||||
**Step1**：进入`DeepKE/example/re/document`，数据集和参数配置可以分别进入`data`和`conf`文件夹中修改；<br>
|
||||
|
||||
**Step2**：模型训练，如需从上次训练的模型开始训练：设置`conf/train.yaml`中的`train_from_saved_model`为上次保存模型的路径，每次训练的日志默认保存在根目录，可用`log_dir`来配置；
|
||||
|
||||
```
|
||||
python run.py
|
||||
```
|
||||
**Step3**：模型预测
|
||||
|
||||
```
|
||||
python predict.py
|
||||
```
|
||||
|
||||
#### 3. 属性抽取AE
|
||||
|
||||
- 数据为csv文件，样式范例为：
|
||||
|
||||
| Sentence | Att | Ent | Ent_offset | Val | Val_offset |
|
||||
| :----------------------------------------------------------: | :------: | :------: | :--------: | :-----------: | :--------: |
|
||||
| 张冬梅，女，汉族，1968年2月生，河南淇县人 | 民族 | 张冬梅 | 0 | 汉族 | 6 |
|
||||
| 杨缨，字绵公，号钓溪，松溪县人，祖籍将乐，是北宋理学家杨时的七世孙 | 朝代 | 杨缨 | 0 | 北宋 | 22 |
|
||||
| 2014年10月1日许鞍华执导的电影《黄金时代》上映 | 上映时间 | 黄金时代 | 19 | 2014年10月1日 | 0 |
|
||||
|
||||
- 具体流程请进入详细的README中
|
||||
- **[常规全监督STANDARD](https://github.com/zjunlp/deepke/blob/main/example/ae/standard)**
|
||||
|
||||
**Step1**：进入`DeepKE/example/re/standard`，数据集和参数配置可以分别进入`data`和`conf`文件夹中修改；<br>
|
||||
|
||||
**Step2**：模型训练
|
||||
|
||||
```
|
||||
python run.py
|
||||
```
|
||||
|
||||
**Step3**：模型预测
|
||||
|
||||
```
|
||||
python predict.py
|
||||
```
|
||||
|
||||
### Notebook教程
|
||||
|
||||
本工具提供了若干Notebook和Google Colab教程，用户可针对性调试学习。
|
||||
|
||||
- 常规设定：
|
||||
|
||||
[命名实体识别Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/standard/tutorial.ipynb)
|
||||
|
||||
[命名实体识别Colab](https://colab.research.google.com/drive/1rFiIcDNgpC002q9BbtY_wkeBUvbqVxpg?usp=sharing)
|
||||
|
||||
[关系抽取Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/standard/tutorial.ipynb)
|
||||
|
||||
[关系抽取Colab](https://colab.research.google.com/drive/1o6rKIxBqrGZNnA2IMXqiSsY2GWANAZLl?usp=sharing)
|
||||
|
||||
[属性抽取Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ae/standard/tutorial.ipynb)
|
||||
|
||||
[属性抽取Colab](https://colab.research.google.com/drive/1pgPouEtHMR7L9Z-QfG1sPYkJfrtRt8ML?usp=sharing)
|
||||
|
||||
- 低资源：
|
||||
|
||||
[命名实体识别Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/few-shot/tutorial.ipynb)
|
||||
|
||||
[命名实体识别Colab](https://colab.research.google.com/drive/1Xz0sNpYQNbkjhebCG5djrwM8Mj2Crj7F?usp=sharing)
|
||||
|
||||
[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]()
|
||||
|
||||
|
||||
<!--  -->
|
||||
|
||||
## 备注（常见问题）
|
||||
|
||||
1. 使用 Anaconda 时，建议添加国内镜像，下载速度更快。如[镜像](https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/)。
|
||||
2. 使用 pip 时，建议使用国内镜像，下载速度更快，如阿里云镜像。
|
||||
3. 安装后提示 `ModuleNotFoundError: No module named 'past'`，输入命令 `pip install future` 即可解决。
|
||||
4. 使用语言预训练模型时，在线安装下载模型比较慢，更建议提前下载好，存放到 pretrained 文件夹内。具体存放文件要求见文件夹内的 `README.md`。
|
||||
5. DeepKE老版本位于[deepke-v1.0](https://github.com/zjunlp/DeepKE/tree/deepke-v1.0)分支，用户可切换分支使用老版本，老版本的能力已全部迁移到标准设定关系抽取([example/re/standard](https://github.com/zjunlp/DeepKE/blob/main/example/re/standard/README.md))中。
|
||||
|
||||
<br>
|
||||
|
||||
## 项目成员
|
||||
|
||||
浙江大学：张宁豫、陶联宽、余海洋、陈想、徐欣、田玺、李磊、黎洲波、邓淑敏、姚云志、叶宏彬、谢辛、郑国轴、陈华钧
|
||||
|
||||
达摩院：谭传奇、陈漠沙、黄非
|
||||
|
||||
|
|
@ -1,310 +0,0 @@
|
|||
<p align="center">
|
||||
<a href="https://github.com/zjunlp/deepke"> <img src="pics/logo.png" width="400"/></a>
|
||||
<p>
|
||||
<p align="center">
|
||||
<a href="https://deepke.openkg.cn">
|
||||
<img alt="Documentation" src="https://img.shields.io/badge/DeepKE-website-green">
|
||||
</a>
|
||||
<a href="https://pypi.org/project/deepke/#files">
|
||||
<img alt="PyPI" src="https://img.shields.io/pypi/v/deepke">
|
||||
</a>
|
||||
<a href="https://github.com/zjunlp/DeepKE/blob/master/LICENSE">
|
||||
<img alt="GitHub" src="https://img.shields.io/github/license/zjunlp/deepke">
|
||||
</a>
|
||||
</p>
|
||||
<p align="center">
|
||||
<b><a href="https://github.com/zjunlp/DeepKE/blob/main/README.md">简体中文</a> | English</b>
|
||||
</p>
|
||||
|
||||
<br>
|
||||
|
||||
<h2 align="center">
|
||||
<p>A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population</p>
|
||||
</h2>
|
||||
|
||||
DeepKE is a knowledge extraction toolkit supporting **low-resource** and **document-level** scenarios. It provides three functions based on **PyTorch**, including **Named Entity Recognition**, **Relation Extraciton** and **Attribute Extraction**.
|
||||
|
||||
<br>
|
||||
|
||||
## Prediction
|
||||
|
||||
There is a demonstration of prediction.<br>
|
||||
<img src="pics/demo.gif" width="636" height="494" align=center>
|
||||
|
||||
<br>
|
||||
|
||||
## Model Framework
|
||||
|
||||
<h3 align="center">
|
||||
<img src="pics/architectures.png">
|
||||
</h3>
|
||||
<p align="center">
|
||||
Figure 1: The framework of DeepKE
|
||||
</p>
|
||||
|
||||
- DeepKE contains three modules for **named entity recognition**, **relation extraction** and **attribute extraction**, the three tasks respectively.
|
||||
- Each module has its own submodules. For example, there are **standard**, **document-level** and **few-shot** submodules in the attribute extraction modular.
|
||||
- Each submodule compose of three parts: a **collection of tools**, which can function as tokenizer, dataloader, preprocessor and the like, a **encoder** and a part for **training and prediction**
|
||||
|
||||
<br>
|
||||
|
||||
## Quickstart
|
||||
|
||||
*DeepKE* is supported `pip install deepke`. Take the fully supervised attribute extraction for example.
|
||||
|
||||
**Step1** Download basic codes `git clone https://github.com/zjunlp/DeepKE.git ` (Please star✨ and fork :memo:)
|
||||
|
||||
**Step2** Create a virtual environment using`Anaconda` and enter it
|
||||
|
||||
```bash
|
||||
conda create -n deepke python=3.8
|
||||
|
||||
conda activate deepke
|
||||
```
|
||||
|
||||
1. Install *DeepKE* with `pip`
|
||||
|
||||
```bash
|
||||
pip install deepke
|
||||
```
|
||||
|
||||
2. Install *DeepKE* with source codes
|
||||
|
||||
```bash
|
||||
python setup.py install
|
||||
|
||||
python setup.py develop
|
||||
```
|
||||
|
||||
**Step3** Enter the task directory
|
||||
|
||||
```bash
|
||||
cd DeepKE/example/re/standard
|
||||
```
|
||||
|
||||
**Step4** Training (Parameters for training can be changed in the `conf` folder)
|
||||
|
||||
```bash
|
||||
python run.py
|
||||
```
|
||||
|
||||
**Step5** Prediction (Parameters for prediction can be changed in the `conf` folder)
|
||||
|
||||
```bash
|
||||
python predict.py
|
||||
```
|
||||
|
||||
|
||||
|
||||
### Requirements
|
||||
|
||||
> python == 3.8
|
||||
|
||||
- torch == 1.5
|
||||
- hydra-core == 1.0.6
|
||||
- tensorboard == 2.4.1
|
||||
- matplotlib == 3.4.1
|
||||
- transformers == 3.4.0
|
||||
- jieba == 0.42.1
|
||||
- scikit-learn == 0.24.1
|
||||
- pytorch-transformers == 1.2.0
|
||||
- seqeval == 1.2.2
|
||||
- tqdm == 4.60.0
|
||||
- opt-einsum==3.3.0
|
||||
- ujson
|
||||
|
||||
### Introduction of Three Functions
|
||||
|
||||
#### 1. Named Entity Recognition
|
||||
|
||||
- Named entity recognition seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, organizations, etc.
|
||||
|
||||
- The data is stored in `.txt` files. Some instances as following:
|
||||
|
||||
| Sentence | Person | Location | Organization |
|
||||
| :----------------------------------------------------------: | :------------------------: | :------------: | :----------------------------: |
|
||||
| 本报北京9月4日讯记者杨涌报道：部分省区人民日报宣传发行工作座谈会9月3日在4日在京举行。 | 杨涌 | 北京 | 人民日报 |
|
||||
| 《红楼梦》是中央电视台和中国电视剧制作中心根据中国古典文学名著《红楼梦》摄制于1987年的一部古装连续剧，由王扶林导演，周汝昌、王蒙、周岭等多位红学家参与制作。 | 王扶林，周汝昌，王蒙，周岭 | 中国 | 中央电视台，中国电视剧制作中心 |
|
||||
| 秦始皇兵马俑位于陕西省西安市，1961年被国务院公布为第一批全国重点文物保护单位，是世界八大奇迹之一。 | 秦始皇 | 陕西省，西安市 | 国务院 |
|
||||
|
||||
- Read the detailed process in specific README
|
||||
- **[STANDARD (Fully Supervised)](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/ner/standard)**
|
||||
|
||||
**Step1** Enter `DeepKE/example/ner/standard`. The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.<br>
|
||||
|
||||
**Step2** Training
|
||||
|
||||
```bash
|
||||
python run.py
|
||||
```
|
||||
|
||||
**Step3** Prediction
|
||||
|
||||
```bash
|
||||
python predict.py
|
||||
```
|
||||
|
||||
- **[FEW-SHOT](https://github.com/zjunlp/DeepKE/tree/test_new_deepke/example/ner/few-shot)**
|
||||
|
||||
**Step1** Enter `DeepKE/example/ner/few-shot`. The directory where the model is loaded and saved and the configuration parameters can be cusomized in the `conf` folder.<br>
|
||||
|
||||
**Step2** Training with default `CoNLL-2003` dataset.
|
||||
|
||||
```bash
|
||||
python run.py +train=few_shot
|
||||
```
|
||||
|
||||
Users can modify `load_path` in `conf/train/few_shot.yaml` with the use of existing loaded model.<br>
|
||||
|
||||
**Step3** Add `- predict` to `conf/config.yaml`, modify `loda_path` as the model path and `write_path` as the path where the predicted results are saved in `conf/predict.yaml`, and then run `python predict.py`
|
||||
|
||||
```bash
|
||||
python predict.py
|
||||
```
|
||||
|
||||
#### 2. Relation Extraction
|
||||
|
||||
- Relationship extraction is the task of extracting semantic relations between entities from a unstructured text.
|
||||
|
||||
- The data is stored in `.csv` files. Some instances as following:
|
||||
|
||||
| Sentence | Relation | Head | Head_offset | Tail | Tail_offset |
|
||||
| :----------------------------------------------------: | :------: | :--------: | :---------: | :--------: | :---------: |
|
||||
| 《岳父也是爹》是王军执导的电视剧，由马恩然、范明主演。 | 导演 | 岳父也是爹 | 1 | 王军 | 8 |
|
||||
| 《九玄珠》是在纵横中文网连载的一部小说，作者是龙马。 | 连载网站 | 九玄珠 | 1 | 纵横中文网 | 7 |
|
||||
| 提起杭州的美景，西湖总是第一个映入脑海的词语。 | 所在城市 | 西湖 | 8 | 杭州 | 2 |
|
||||
|
||||
- Read the detailed process in specific README
|
||||
|
||||
- **[STANDARD (Fully Supervised)](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/standard)**
|
||||
|
||||
**Step1** Enter the `DeepKE/example/re/standard` folder. The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.<br>
|
||||
|
||||
**Step2** Training
|
||||
|
||||
```bash
|
||||
python run.py
|
||||
```
|
||||
|
||||
**Step3** Prediction
|
||||
|
||||
```bash
|
||||
python predict.py
|
||||
```
|
||||
|
||||
- **[FEW-SHOT](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/few-shot)**
|
||||
|
||||
**Step1** Enter `DeepKE/example/re/few-shot`. The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.<br>
|
||||
|
||||
**Step 2** Training. Start with the model trained last time: modify `train_from_saved_model` in `conf/train.yaml`as the path where the model trained last time was saved. And the path saving logs generated in training can be customized by `log_dir`. <br>
|
||||
|
||||
```bash
|
||||
python run.py
|
||||
```
|
||||
|
||||
**Step3** Prediction
|
||||
|
||||
```bash
|
||||
python predict.py
|
||||
```
|
||||
|
||||
- **[DOCUMENT](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/document)**<br>
|
||||
|
||||
Download the model `train_distant.json` from [*Google Drive*](https://drive.google.com/drive/folders/1c5-0YwnoJx8NS6CV2f-NoTHR__BdkNqw) to `data/`.
|
||||
|
||||
**Step1** Enter `DeepKE/example/re/document`. The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.<br>
|
||||
|
||||
**Step2** Training. Start with the model trained last time: modify `train_from_saved_model` in `conf/train.yaml`as the path where the model trained last time was saved. And the path saving logs generated in training can be customized by `log_dir`.
|
||||
|
||||
```bash
|
||||
python run.py
|
||||
```
|
||||
|
||||
**Step3** Prediction
|
||||
|
||||
```bash
|
||||
python predict.py
|
||||
```
|
||||
|
||||
#### 3. Attribute Extraction
|
||||
|
||||
- Attribute extraction is to extract attributes for entities in a unstructed text.
|
||||
|
||||
- The data is stored in `.csv` files. Some instances as following:
|
||||
|
||||
| Sentence | Att | Ent | Ent_offset | Val | Val_offset |
|
||||
| :----------------------------------------------------------: | :------: | :------: | :--------: | :-----------: | :--------: |
|
||||
| 张冬梅，女，汉族，1968年2月生，河南淇县人 | 民族 | 张冬梅 | 0 | 汉族 | 6 |
|
||||
| 杨缨，字绵公，号钓溪，松溪县人，祖籍将乐，是北宋理学家杨时的七世孙 | 朝代 | 杨缨 | 0 | 北宋 | 22 |
|
||||
| 2014年10月1日许鞍华执导的电影《黄金时代》上映 | 上映时间 | 黄金时代 | 19 | 2014年10月1日 | 0 |
|
||||
|
||||
- Read the detailed process in specific README
|
||||
- **[STANDARD (Fully Supervised)](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/ae/standard)**
|
||||
|
||||
**Step1** Enter the `DeepKE/example/ae/standard` folder. The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.<br>
|
||||
|
||||
**Step2** Training
|
||||
|
||||
```bash
|
||||
python run.py
|
||||
```
|
||||
|
||||
**Step3** Prediction
|
||||
|
||||
```bash
|
||||
python predict.py
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Notebook Tutorial
|
||||
|
||||
This toolkit provides many `Jupyter Notebook` and `Google Colab` tutorials. Users can study *DeepKE* with them.
|
||||
|
||||
- Standard Setting<br>
|
||||
|
||||
[NER Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/standard/tutorial.ipynb)
|
||||
|
||||
[NER Colab](https://colab.research.google.com/drive/1KpJFAT1nZfGDfnuNMZn02_okIU08j46d?usp=sharing)
|
||||
|
||||
[RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/standard/tutorial.ipynb)
|
||||
|
||||
[RE Colab](https://colab.research.google.com/drive/1o6rKIxBqrGZNnA2IMXqiSsY2GWANAZLl?usp=sharing)
|
||||
|
||||
[AE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ae/standard/tutorial.ipynb)
|
||||
|
||||
[AE Colab](https://colab.research.google.com/drive/1pgPouEtHMR7L9Z-QfG1sPYkJfrtRt8ML)
|
||||
|
||||
- Low-resource<br>
|
||||
|
||||
[NER Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/few-shot/tutorial.ipynb)
|
||||
|
||||
[NER Colab](https://colab.research.google.com/drive/1Xz0sNpYQNbkjhebCG5djrwM8Mj2Crj7F?usp=sharing)
|
||||
|
||||
[RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/few-shot/tutorial.ipynb)
|
||||
|
||||
[RE Colab]()
|
||||
|
||||
- Document-level<br>
|
||||
|
||||
[RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/document/tutorial.ipynb)
|
||||
|
||||
[RE Colab]()
|
||||
|
||||
<br>
|
||||
|
||||
## Tips
|
||||
|
||||
1. Using nearest mirror, like [THU](https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/) in China, will speed up the installation of *Anaconda*.
|
||||
2. Using nearest mirror, like [aliyun](http://mirrors.aliyun.com/pypi/simple/) in China, will speed up `pip install XXX`.
|
||||
3. When encountering `ModuleNotFoundError: No module named 'past'`，run `pip install future` .
|
||||
4. It's slow to install the pretrained language models online. Recommend download pretrained models before use and save them in the `pretrained` folder. Read `README.md` in every task directory to check the specific requirement for saving pretrained models.
|
||||
5. The old version of *DeepKE* is in the [deepke-v1.0](https://github.com/zjunlp/DeepKE/tree/deepke-v1.0) branch. Users can change the branch to use the old version. The old version has been totally transfered to the standard relation extraction ([example/re/standard](https://github.com/zjunlp/DeepKE/blob/main/example/re/standard/README.md)).
|
||||
|
||||
<br>
|
||||
|
||||
## Developers
|
||||
|
||||
Zhejiang University: Ningyu Zhang, Liankuan Tao, Haiyang Yu, Xiang Chen, Xin Xu, Xi Tian, Lei Li, Zhoubo Li, Shumin Deng, Yunzhi Yao, Hongbin Ye, Xin Xie, Guozhou Zheng, Huajun Chen
|
||||
|
||||
DAMO Academy: Chuanqi Tan, Fei Huang
|
||||
Loading…
Reference in New Issue