diff --git a/README.md b/README.md
index 060c5a1..8644b35 100644
--- a/README.md
+++ b/README.md
@@ -13,82 +13,94 @@
- 简体中文 | English
+ 简体中文 | English
-
-
基于深度学习的开源中文知识图谱抽取框架
-
-
-DeepKE 是一个支持低资源、长篇章的知识抽取工具，可以基于PyTorch实现命名实体识别、关系抽取和属性抽取功能。
-
+
+
A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
+
-### 进行预测
-下面使用一个demo展示预测过程
+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**.
+
+
+
+## Prediction
+
+There is a demonstration of prediction.
-## 模型架构
-
-Deepke的架构图如下所示
+## Model Framework
+
+ Figure 1: The framework of DeepKE
+
-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 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**
-## 快速上手
+## Quickstart
-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
+*DeepKE* is supported `pip install deepke`. Take the fully supervised attribute extraction for example.
-**Step 1** 下载代码 ```git clone https://github.com/zjunlp/DeepKE.git```（别忘记star和fork哈！！！）
+**Step1** Download basic codes `git clone https://github.com/zjunlp/DeepKE.git ` (Please star✨ and fork :memo:)
-**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)
+**Step2** Create a virtual environment using`Anaconda` and enter it.
-```
+ We also provide dockerfile source code, you can create your own image, which is located in the docker folder.
+
+
+
+```bash
conda create -n deepke python=3.8
conda activate deepke
```
-1） 基于pip安装，直接使用
-```
-pip install deepke
-```
+1. Install *DeepKE* with `pip`
-2） 基于源码安装
+ ```bash
+ pip install deepke
+ ```
-```
-python setup.py install
+2. Install *DeepKE* with source codes
-python setup.py develop
-```
+ ```bash
+ 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
+**Step3** Enter the task directory
-```
+```bash
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
+**Step4** Training (Parameters for training can be changed in the `conf` folder)
-```
+```bash
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
+**Step5** Prediction (Parameters for prediction can be changed in the `conf` folder)
-```
+```bash
python predict.py
```
-### 环境依赖
+
+
+### Requirements
> python == 3.8
@@ -105,11 +117,13 @@ python predict.py
- opt-einsum==3.3.0
- ujson
-### 具体功能介绍
+### Introduction of Three Functions
-#### 1. 命名实体识别NER
+#### 1. Named Entity Recognition
-- 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：
+- 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 |
| :----------------------------------------------------------: | :------------------------: | :------------: | :----------------------------: |
@@ -117,43 +131,46 @@ python predict.py
| 《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；
-
- **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；
-
- **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`；
-
- **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
-
+- 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.
+
+ **Step2** Training
+
+ ```bash
+ python run.py
```
+
+ **Step3** Prediction
+
+ ```bash
python predict.py
```
-#### 2. 关系抽取RE
+ - **[FEW-SHOT](https://github.com/zjunlp/DeepKE/tree/test_new_deepke/example/ner/few-shot)**
-- 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：
+ **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.
+
+ **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.
+
+ **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 |
| :----------------------------------------------------: | :------: | :--------: | :---------: | :--------: | :---------: |
@@ -161,58 +178,63 @@ python predict.py
| 《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)**
+- Read the detailed process in specific README
- **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；
-
- **Step2**：模型训练
+ - **[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.
+
+ **Step2** Training
+
+ ```bash
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；
-
- **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
+ **Step3** Prediction
- ```
+ ```bash
python predict.py
```
- - **[文档级DOCUMENT](https://github.com/zjunlp/deepke/blob/main/example/re/document)**
- ```train_distant.json```由于文件太大，请自行从Google Drive上下载到data/目录下；
-
- **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；
-
- **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；
-
- ```
+ - **[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.
+
+ **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`.
+
+ ```bash
python run.py
```
- **Step3**：模型预测
-
- ```
+
+ **Step3** Prediction
+
+ ```bash
python predict.py
```
-#### 3. 属性抽取AE
+ - **[DOCUMENT](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/document)**
-- 数据为csv文件，样式范例为：
+ 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.
+
+ **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 |
| :----------------------------------------------------------: | :------: | :------: | :--------: | :-----------: | :--------: |
@@ -220,73 +242,73 @@ python predict.py
| 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；
-
- **Step2**：模型训练
+- 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.
+
+ **Step2** Training
+
+ ```bash
python run.py
```
-
- **Step3**：模型预测
- ```
+ **Step3** Prediction
+
+ ```bash
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]()
-
+## 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
+
+ [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
+
+ [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
+
+ [RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/document/tutorial.ipynb)
+
+ [RE Colab]()
-## 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
+
+## 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
diff --git a/README_CN.md b/README_CN.md
new file mode 100644
index 0000000..bbfcede
--- /dev/null
+++ b/README_CN.md
@@ -0,0 +1,292 @@
+
+ 
+
+
+
+ 
+
+
+ 
+
+
+ 
+
+
+
+ 简体中文 | English
+
+
+
+
基于深度学习的开源中文知识图谱抽取框架
+
+
+DeepKE 是一个支持低资源、长篇章的知识抽取工具，可以基于PyTorch实现命名实体识别、关系抽取和属性抽取功能。
+
+
+
+
+### 进行预测
+下面使用一个demo展示预测过程
+
+
+
+
+## 模型架构
+
+Deepke的架构图如下所示
+
+
+
+
+
+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。
+
+
+
+## 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；
+
+ **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；
+
+ **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`；
+
+ **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；
+
+ **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；
+
+ **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
+ ```
+
+ - **[文档级DOCUMENT](https://github.com/zjunlp/deepke/blob/main/example/re/document)**
+ ```train_distant.json```由于文件太大，请自行从Google Drive上下载到data/目录下；
+
+ **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；
+
+ **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；
+
+ **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。
+
+
+
+## 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
+
diff --git a/README_ENGLISH.md b/README_ENGLISH.md
deleted file mode 100644
index 3eee2ee..0000000
--- a/README_ENGLISH.md
+++ /dev/null
@@ -1,310 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- 简体中文 | English
-
-
-
-
-
-
A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
-
-
-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**.
-
-
-
-## Prediction
-
-There is a demonstration of prediction.
-
-
-
-
-## Model Framework
-
-
-
-
-
- Figure 1: The framework of DeepKE
-
-
-- 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**
-
-
-
-## 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.
-
- **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.
-
- **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.
-
- **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.
-
- **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.
-
- **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`.
-
- ```bash
- python run.py
- ```
-
- **Step3** Prediction
-
- ```bash
- python predict.py
- ```
-
- - **[DOCUMENT](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/document)**
-
- 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.
-
- **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.
-
- **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
-
- [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
-
- [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
-
- [RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/document/tutorial.ipynb)
-
- [RE Colab]()
-
-
-
-## 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)).
-
-
-
-## 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
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-DAMO Academy: Chuanqi Tan, Fei Huang