caseencoder's Introduction

CaseEncoder

Accepted by EMNLP2023

CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding

Before Start

Please unzip all .blk.zip files in data

A few parameters in config/Caseformer.config need to be specified:

train_data_path, valid_data_path in config/Caseformer.config is the path of the pre-training and validation dataset, which is not included in this repo due to the space limit. But we provide exactly the same checkpoint of CaseEncoder reported in our paper here.
test_kara_dataset is the test dataset you would like to use. We provide three choices: lecard, cail-lcr21, and cail-lcr22.

Model Training

Training from the start:

torchrun --standalone --nnodes=1 --nproc_per_node=YOUR_GPU_NUMBER train.py --config config/Caseformer.config --gpu YOUR_GPU_LIST 2>&1 | tee -a log/Caseformer.log

Training from a checkpoint:

torchrun --standalone --nnodes=1 --nproc_per_node=YOUR_GPU_NUMBER train.py --checkpoint YOUR_CHECKPOINT_PATH --config config/Caseformer.config --gpu YOUR_GPU_LIST 2>&1 | tee -a log/Caseformer.log

Model Evaluation

To validate the checkpoint of CaseEncoder

torchrun --standalone --nnodes=1 --nproc_per_node=1 test.py --checkpoint YOUR_CHECKPOINT_PATH --config config/Caseformer.config --gpu 0 --result YOUR_RESULT_STORAGE_PATH

where YOUR_CHECKPOINT_PATH is the path of CaseEncoder checkpoint you download.

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caseencoder's Issues

query for the the processed training data

Hello, I am currently reading your paper. Could you provide the processed training data that includes 'xf_article' and 'fg_article_vector'? This data is used in the formatter/CaseformerFormatter.py file because I want to replicate the process described in section 4.1 of your paper. Thank you very much.