An official source code for paper MulCS: Towards a unified Deep Representation for Multilingual Code Search.
The proposed MulCS is implemented with python 3.7.11 on a NVIDIA Tesla V100 GPU.
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torch==1.5.0
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tqdm==4.62.2
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numpy==1.19.5
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scikit_learn==0.24.2
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Step1: data preparation
data link
https://drive.google.com/file/d/1_-BcLEerRFA8Ms7d9xUmWr8ai-EC4ZPp/view?usp=sharing
checkpoint link
https://drive.google.com/file/d/1aacga6uakq_PNVFiwA49maovb9lYaSA9/view?usp=sharing
Download the data and checkpoint folders, unzip them, and put them directly in the home directory.
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Step2: train
python train.py --data_path ./data/ --model IREmbeder -
Step3: test
python test.py --data_path ./data/ --model IREmbeder --reload_from 100
Parameter setting
- data_path: the path of dataset
- model: the name of the model.
- reload_from: checkpoint for testing
- lr: learning rate(1e-3)