314 lines
12 KiB
Markdown
314 lines
12 KiB
Markdown
<p align="center">
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<a href="https://github.com/zjunlp/deepke"> <img src="pics/logo.png" width="400"/></a>
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<p>
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<p align="center">
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<a href="https://deepke.openkg.cn">
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<img alt="Documentation" src="https://img.shields.io/badge/DeepKE-website-green">
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</a>
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<a href="https://pypi.org/project/deepke/#files">
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<img alt="PyPI" src="https://img.shields.io/pypi/v/deepke">
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</a>
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<a href="https://github.com/zjunlp/DeepKE/blob/master/LICENSE">
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<img alt="GitHub" src="https://img.shields.io/github/license/zjunlp/deepke">
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</a>
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</p>
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<p align="center">
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<b><a href="https://github.com/zjunlp/DeepKE/blob/main/README.md">简体中文</a> | English</b>
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</p>
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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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DeepKE is a knowledge extraction toolkit supporting **low-resource** and **document-level** scenarios. It provides three functions based **PyTorch**, including **Named Entity Recognition**, **Relation Extraciton** and **Attribute Extraction**.
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<br>
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## Online Demo
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[demo](https://deepke.openkg.cn)
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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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## 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 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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## Quickstart
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*DeepKE* is supported `pip install deepke`. Take the fully supervised attribute extraction for example.
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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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**Step2** Create a virtual environment using`Anaconda` and enter it
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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. Install *DeepKE* with `pip`
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```bash
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pip install deepke
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```
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2. Install *DeepKE* with source codes
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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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**Step3** Enter the task directory
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```bash
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cd DeepKE/example/re/standard
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```
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**Step4** Training (Parameters for training can be changed in the `conf` folder)
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```bash
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python run.py
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```
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**Step5** Prediction (Parameters for prediction can be changed in the `conf` folder)
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```bash
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python predict.py
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```
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### Requirements
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> python == 3.8
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- torch == 1.5
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- hydra-core == 1.0.6
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- tensorboard == 2.4.1
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- matplotlib == 3.4.1
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- transformers == 3.4.0
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- jieba == 0.42.1
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- scikit-learn == 0.24.1
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- pytorch-transformers == 1.2.0
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- seqeval == 1.2.2
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- tqdm == 4.60.0
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- opt-einsum==3.3.0
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- ujson
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### Introduction of Three Functions
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#### 1. Named Entity Recognition
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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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| 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 |
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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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- 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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- **[FEW-SHOT](https://github.com/zjunlp/DeepKE/tree/test_new_deepke/example/ner/few-shot)**
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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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| 《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 |
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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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- Read the detailed process in specific README
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- **[STANDARD (Fully Supervised)](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/standard)**
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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** Prediction
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```bash
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python predict.py
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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** Prediction
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```bash
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python predict.py
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```
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- **[DOCUMENT](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/document)**<br>
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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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| 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 |
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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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- 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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**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** Prediction
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```bash
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python predict.py
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```
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## Notebook Tutorial
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This toolkit provides many `Jupyter Notebook` and `Google Colab` tutorials. Users can study *DeepKE* with them.
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- Standard Setting<br>
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[NER Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/standard/tutorial.ipynb)
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[NER Colab](https://colab.research.google.com/drive/1KpJFAT1nZfGDfnuNMZn02_okIU08j46d?usp=sharing)
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[RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/standard/tutorial.ipynb)
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[RE Colab](https://colab.research.google.com/drive/1o6rKIxBqrGZNnA2IMXqiSsY2GWANAZLl?usp=sharing)
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[AE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ae/standard/tutorial.ipynb)
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[AE Colab](https://colab.research.google.com/drive/1pgPouEtHMR7L9Z-QfG1sPYkJfrtRt8ML)
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- Low-resource<br>
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[NER Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/few-shot/tutorial.ipynb)
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[NER Colab](https://colab.research.google.com/drive/1Xz0sNpYQNbkjhebCG5djrwM8Mj2Crj7F?usp=sharing)
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[RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/few-shot/tutorial.ipynb)
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[RE Colab]()
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- Document-level<br>
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[RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/document/tutorial.ipynb)
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[RE Colab]()
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<br>
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## Tips
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1. Using nearest mirror, like [THU](https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/) in China, will speed up the installation of *Anaconda*.
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2. Using nearest mirror, like [aliyun](http://mirrors.aliyun.com/pypi/simple/) in China, will speed up `pip install XXX`.
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3. When encountering `ModuleNotFoundError: No module named 'past'`，run `pip install future` .
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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.
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<br>
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## Developers
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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
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Alibaba DAMO: Chuanqi Tan, Fei Huang
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