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

Multiple style transfer via variational autoencoder

Repository for Multimodal AutoML Benchmark

A library for efficient similarity search and clustering of dense vectors.

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

A boosting-based Multiple Instance Learning (MIL) package that includes MIL-Boost and MCIL-Boost

MAg: a simple learning-based patient-level aggregation method for detecting microsatellite instability from whole-slide images

Identify the emotion of multiple speakers in an Audio Segment

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

Paddle-Skeleton-Based-Action-Recognition - DecoupleGCN-DropGraph, ASGCN, AGCN, STGCN

(CVPR 2022 Oral) Official implementation for "Surface Representation for Point Clouds"

Deep Sea Treasure Environment for Multi-Objective Optimization Research

A Genetic Programming platform for Python with TensorFlow for wicked-fast CPU and GPU support.

A Multi-modal Perception Tracker (MPT) for speaker tracking using both audio and visual modalities

Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition (AGRA, ACM 2020, Oral)

The official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness.

Pytorch implementation of BRECQ, ICLR 2021

Speed-Test - You can check your intenet speed using this tool

City-seeds - A random generator of cultural characteristics intended to spark ideas and help draw threads

StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color Image (ICCV 2021)