ea6321b97f | ||
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.. | ||
conf | ||
README.md | ||
README_CN.md | ||
predict.py | ||
requirements.txt | ||
run.py |
README.md
Easy Start
English | 简体中文
Requirements
python == 3.8
- torch == 1.5.0
- transformers == 3.4.0
- opt-einsum == 3.3.0
- ujson
- deepke
Download Code
git clone https://github.com/zjunlp/DeepKE.git
cd DeepKE/example/re/document
Install with Pip
- Create and enter the python virtual environment.
- Install dependencies:
pip install -r requirements.txt
.
Train and Predict
-
Dataset
-
Download the dataset to this directory.
wget 120.27.214.45/Data/re/document/data.tar.gz tar -xzvf data.tar.gz
-
The dataset DocRED is stored in
data
:-
dev.json
:Validation set -
rel_info.json
:Relation set -
rel2id.json
:Relation labels - ID -
test.json
:Test set -
train_annotated.json
:Training set annotated manually -
train_distant.json
: Training set generated by distant supervision
-
-
-
Training
-
Parameters, model paths and configuration for training are in the
conf
folder and users can modify them before training. -
Training on DocRED
python run.py
-
The trained model is stored in the current directory by default.
-
Start to train from last-trained model
modify
train_from_saved_model
in.yaml
as the path of the last-trained model -
Logs for training are stored in the current directory by default and the path can be configured by modifying
log_dir
in.yaml
-
-
Prediction
python predict.py
- After prediction, generated
result.json
is stored in the current directory
- After prediction, generated