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Learning to perturb for contrastive learning of unsupervised sentence representations

Overview

We propose L2P-CSR, which adopts a learnable perturbation strategy for improving contrastive learning of sentence representations.

Train L2P-CSR

Evaluation

Our evaluation code for sentence embeddings is following the released code of SimCSE, it is based on a modified version of SentEval. It evaluates sentence embeddings on semantic textual similarity (STS) tasks and downstream transfer tasks. For STS tasks, our evaluation takes the "all" setting, and report Spearman's correlation.

Before evaluation, please download the evaluation datasets from the official website.

Training

Environment

To faithfully reproduce our results, please use the correct 1.8.1 pytorch version corresponding to your platforms/CUDA versions.

pip install torch==1.8.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html

Then run the following script to install the remaining dependencies,

pip install -r requirements.txt

Data

We utilize the released data from SimCSE that samples 1 million sentences from English Wikipedia. You can run data/download_wiki.sh to download it.

Training scripts

We provide the training scripts for BERT/RoBERTa-base/large. You can run it to automatically finish the training on BERT/RoBERTa-base/large backbone models, and then evaluate it.

bash run_unsup_example.sh

For BERT/RoBERTa-base models, we provide a single-GPU (or CPU) example, and for BERT/RoBERTa-large models we give a multiple-GPU example. We explain some important arguments in following:

  • --model_name_or_path: Pre-trained checkpoints to start with. We support BERT-based models (bert-base-uncased, bert-large-uncased) and RoBERTa-based models (RoBERTa-base, RoBERTa-large).

For results in the paper, we use 8 * Nvidia 3090 GPUs with CUDA 11. Using different types of devices or different versions of CUDA/other softwares may lead to slightly different performance.

Citation

Please cite our paper if you use L2P-CSR in your work:

@article{zhou2023learning,
  title={Learning to perturb for contrastive learning of unsupervised sentence representations},
  author={Zhou, Kun and Zhou, Yuanhang and Zhao, Wayne Xin and Wen, Ji-Rong},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  year={2023},
  publisher={IEEE}
}