VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning

Last update: Oct 24, 2022

Overview

VarCLR: Variable Representation Pre-training via Contrastive Learning

New: Paper accepted by ICSE 2022. Preprint at arXiv!

This repository contains code and pre-trained models for VarCLR, a contrastive learning based approach for learning semantic representations of variable names that effectively captures variable similarity, with state-of-the-art results on [email protected].

VarCLR: Variable Representation Pre-training via Contrastive Learning

Step 0: Install

pip install -e .

Step 1: Load a Pre-trained VarCLR Model

from varclr.models import Encoder
model = Encoder.from_pretrained("varclr-codebert")

Step 2: VarCLR Variable Embeddings

Get embedding of one variable

emb = model.encode("squareslab")
print(emb.shape)
# torch.Size([1, 768])

Get embeddings of list of variables (supports batching)

emb = model.encode(["squareslab", "strudel"])
print(emb.shape)
# torch.Size([2, 768])

Step 2: Get VarCLR Similarity Scores

Get similarity scores of N variable pairs

print(model.score("squareslab", "strudel"))
# [0.42812108993530273]
print(model.score(["squareslab", "average", "max", "max"], ["strudel", "mean", "min", "maximum"]))
# [0.42812108993530273, 0.8849745988845825, 0.8035818338394165, 0.889922022819519]

Get pairwise (N * M) similarity scores from two lists of variables

variable_list = ["squareslab", "strudel", "neulab"]
print(model.cross_score("squareslab", variable_list))
# [[1.0000007152557373, 0.4281214475631714, 0.7207341194152832]]
print(model.cross_score(variable_list, variable_list))
# [[1.0000007152557373, 0.4281214475631714, 0.7207341194152832],
#  [0.4281214475631714, 1.0000004768371582, 0.549992561340332],
#  [0.7207341194152832, 0.549992561340332, 1.000000238418579]]

Step 3: Reproduce IdBench Benchmark Results

Load the IdBench benchmark

from varclr.benchmarks import Benchmark

# Similarity on IdBench-Medium
b1 = Benchmark.build("idbench", variant="medium", metric="similarity")
# Relatedness on IdBench-Large
b2 = Benchmark.build("idbench", variant="large", metric="relatedness")

Compute VarCLR scores and evaluate

id1_list, id2_list = b1.get_inputs()
predicted = model.score(id1_list, id2_list)
print(b1.evaluate(predicted))
# {'spearmanr': 0.5248567181503295, 'pearsonr': 0.5249843473193132}

print(b2.evaluate(model.score(*b2.get_inputs())))
# {'spearmanr': 0.8012168379981921, 'pearsonr': 0.8021791703187449}

Let's compare with the original CodeBERT

codebert = Encoder.from_pretrained("codebert")
print(b1.evaluate(codebert.score(*b1.get_inputs())))
# {'spearmanr': 0.2056582946575104, 'pearsonr': 0.1995058696927054}
print(b2.evaluate(codebert.score(*b2.get_inputs())))
# {'spearmanr': 0.3909218857993804, 'pearsonr': 0.3378219622284688}

Results on IdBench benchmarks

Similarity

Method	Small	Medium	Large
FT-SG	0.30	0.29	0.28
LV	0.32	0.30	0.30
FT-cbow	0.35	0.38	0.38
VarCLR-Avg	0.47	0.45	0.44
VarCLR-LSTM	0.50	0.49	0.49
VarCLR-CodeBERT	0.53	0.53	0.51

Combined-IdBench	0.48	0.59	0.57
Combined-VarCLR	0.66	0.65	0.62

Relatedness

Method	Small	Medium	Large
LV	0.48	0.47	0.48
FT-SG	0.70	0.71	0.68
FT-cbow	0.72	0.74	0.73
VarCLR-Avg	0.67	0.66	0.66
VarCLR-LSTM	0.71	0.70	0.69
VarCLR-CodeBERT	0.79	0.79	0.80

Combined-IdBench	0.71	0.78	0.79
Combined-VarCLR	0.79	0.81	0.85

Pre-train your own VarCLR models

Coming soon.

Cite

If you find VarCLR useful in your research, please cite our [email protected]:

@misc{chen2021varclr,
      title={VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning},
      author={Qibin Chen and Jeremy Lacomis and Edward J. Schwartz and Graham Neubig and Bogdan Vasilescu and Claire Le Goues},
      year={2021},
      eprint={2112.02650},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}

VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning

Related tags

Overview

VarCLR: Variable Representation Pre-training via Contrastive Learning

Step 0: Install

Step 1: Load a Pre-trained VarCLR Model

Step 2: VarCLR Variable Embeddings

Get embedding of one variable

Get embeddings of list of variables (supports batching)

Step 2: Get VarCLR Similarity Scores

Get similarity scores of N variable pairs

Get pairwise (N * M) similarity scores from two lists of variables

Step 3: Reproduce IdBench Benchmark Results

Load the IdBench benchmark

Compute VarCLR scores and evaluate

Let's compare with the original CodeBERT

Results on IdBench benchmarks

Similarity

Relatedness

Pre-train your own VarCLR models

Cite

Owner

squaresLab

A python implementation of Yolov5 to detect fire or smoke in the wild in Jetson Xavier nx and Jetson nano

Drone Task1 - Drone Task1 With Python

This repository contains the source code for the paper First Order Motion Model for Image Animation

This repo contains research materials released by members of the Google Brain team in Tokyo.

Convert BART models to ONNX with quantization. 3X reduction in size, and upto 3X boost in inference speed

PED: DETR for Crowd Pedestrian Detection

University of Rochester 2021 Summer REU focusing on music sentiment transfer using CycleGAN

PyTorch implementation for the visual prior component (i.e. perception module) of the Visually Grounded Physics Learner [Li et al., 2020].

implement of SwiftNet:Real-time Video Object Segmentation

CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework

Pytorch implementation AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

Warning: This project does not have any current developer. See bellow.

The source code of the paper "SHGNN: Structure-Aware Heterogeneous Graph Neural Network"

Autonomous Ground Vehicle Navigation and Control Simulation Examples in Python

AWS documentation corpus for zero-shot open-book question answering.

Styleformer - Official Pytorch Implementation

Classify bird species based on their songs using SIamese Networks and 1D dilated convolutions.

Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition

Official Repo of my work for SREC Nandyal Machine Learning Bootcamp

Hierarchical Few-Shot Generative Models