Update README
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xxupiano
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@ -132,7 +132,6 @@ python predict.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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@ -141,7 +140,7 @@ python predict.py
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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`数据局进行训练
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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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@ -269,7 +268,6 @@ python predict.py
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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)
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@ -54,20 +54,51 @@ There is a demonstration of prediction.<br>
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## Quickstart
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Take the fully supervised attribute extraction for example.
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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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1. Download basic codes `git clone https://github.com/zjunlp/DeepKE.git `
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2. Create a virtual environment (recommend `anaconda`) `conda create -n deepke python=3.8`
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3. Enter the environment `conda activate deepke`
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4. Install dependent packages
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- If use deepke directly: `pip install deepke`
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- If modify source codes before usage:
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run `python setup.py install` firstly,
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after modification, run `python setup.py develop`
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5. Enter the corresponding directory `cd DeepKE/example/re/standard`
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6. Train `python run.py` (Parameters for training can be changed in the `conf` folder)
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7. Predict `python predict.py`(Parameters for prediction can be changed in the `conf` folder)
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### Requirements
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@ -102,18 +133,38 @@ Take the fully supervised attribute extraction for example.
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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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- The standard module is implemented by the pretrained model *BERT*.
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- Enter `DeepKE/example/ner/standard`.
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- The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.
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- **Train**: `python run.py`
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- **Predict**: `python predict.py`
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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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- This module is in the low-resouce scenario.
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- Enter `DeepKE/example/ner/few-shot`.
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- The directory where the model is loaded and saved and the configuration parameters can be cusomized in the `conf` folder.
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- **Train with *CoNLL-2003***: `python run.py`
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- **Train in the few-shot scenario**: `python run.py +train=few_shot`. Users can modify `load_path` in `conf/train/few_shot.yaml` with the use of existing loaded model.
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- **Predict**: 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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**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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@ -130,27 +181,54 @@ Take the fully supervised attribute extraction for example.
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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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- The standard module is implemented by common deep learning models, including CNN, RNN, Capsule, GCN, Transformer and the pretrained model.
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- Enter the `DeepKE/example/re/standard` folder.
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- The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.
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- **Train**: `python run.py`
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- **Predict**: `python predict.py`
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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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- This module is in the low-resouce scenario.
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- Enter `DeepKE/example/re/few-shot` .
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- **Train**: `python run.py`
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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.
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And the path saving logs generated in training can be customized by `log_dir`.
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- **Predict**: `python predict.py`
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- **[DOCUMENT](https://github.com/zjunlp/deepke/blob/test_new_deepke/example/re/document)**
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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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- Enter `DeepKE/example/re/document` .
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- **Train**: `python run.py`
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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.
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And the path saving logs generated in training can be customized by `log_dir`.
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- **Predict**: `python predict.py`
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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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@ -166,11 +244,56 @@ Take the fully supervised attribute extraction for example.
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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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- The standard module is implemented by common deep learning models, including CNN, RNN, Capsule, GCN, Transformer and the pretrained model.
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- Enter the `DeepKE/example/ae/standard` folder.
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- The dataset and parameters can be customized in the `data` folder and `conf` folder respectively.
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- **Train**: `python run.py`
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- **Predict**: `python predict.py`
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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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[REColab](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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