Implementation of Law-Match

This is the official implementation of the paper "Law Article-Enhanced Legal Case Matching: a Causal Learning Approach" based on PyTorch.

Overview

Law-Match(Sentence-Bert)

Law-Match(Bert-PLI)

Law-Match(Lawformer)

Law-Match(IOT-Match)

Parameters are set as default in the code.

Reproduction

Check the following instructions for reproducing experiments.

Dataset

The Dataset details is shown in dataset file

Quick Start

1. Download data

2. Train and evaluate our model:

python models/IV4sentence_bert_v5.py  
python Bert_PLI_models/IV4Bert_PLI.py 
python IV4lawformer_models/IV4lawformer_v5.py
python IOT-Match/IV4IOT-Match.py

3. Check training and evaluation process

Requirements

torch>=1.9.1+cu111
transformers>=4.18.0
numpy>=1.20.1
jieba>=0.42.1
six>=1.15.0
rouge>=1.0.1
tqdm>=4.59.0
scikit-learn>=0.24.1
pandas>=1.2.4
matplotlib>=3.3.4
termcolor>=1.1.0
networkx>=2.5
requests>=2.25.1
filelock>=3.0.12
gensim>=3.8.3
scipy>=1.6.2
seaborn>=0.11.1
boto3>=1.26.18
botocore>=1.29.18
pip>=22.0.3
packaging>=20.9
Pillow>=8.2.0
ipython>=7.22.0
regex>=2021.4.4
tokenizers>=0.12.1
PyYAML>=5.4.1
sacremoses>=0.0.53
psutil>=5.8.0
h5py>=2.10.0
msgpack>=1.0.2

Environments

We conducted the experiments based on the following environments:

• CUDA Version: 11.1
• torch version: 1.9.0
• OS: CentOS Linux release 7.4.1708 (Core)
• GPU: The NVIDIA 3090 GPU
• CPU: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz