Awesome

SG-BERT

This repository contains the implementation of Self-Gudied Contrastive Learning for BERT Sentence Representations (ACL 2021). (Disclaimer: the code is a little bit cluttered as this is not a cleaned version.)

When using this code for the following work, please cite our paper with the BibTex below.

@inproceedings{kim-etal-2021-self,
title = "Self-Guided Contrastive Learning for {BERT} Sentence Representations",
author = "Kim, Taeuk  and
  Yoo, Kang Min  and
  Lee, Sang-goo",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.197",
doi = "10.18653/v1/2021.acl-long.197",
pages = "2528--2540",
abstract = "Although BERT and its variants have reshaped the NLP landscape, it still remains unclear how best to derive sentence embeddings from such pre-trained Transformers. In this work, we propose a contrastive learning method that utilizes self-guidance for improving the quality of BERT sentence representations. Our method fine-tunes BERT in a self-supervised fashion, does not rely on data augmentation, and enables the usual [CLS] token embeddings to function as sentence vectors. Moreover, we redesign the contrastive learning objective (NT-Xent) and apply it to sentence representation learning. We demonstrate with extensive experiments that our approach is more effective than competitive baselines on diverse sentence-related tasks. We also show it is efficient at inference and robust to domain shifts.",}

Pre-requisite Python Libraries

Please install the following libraries specified in the requirements.txt first before running our code.

certifi==2022.5.18.1
charset-normalizer==2.0.12
click==8.1.3
dataclasses==0.6
dill==0.3.5.1
filelock==3.7.1
future==0.18.2
idna==3.3
importlib-metadata==4.11.4
joblib==1.1.0
nltk==3.7
numpy==1.21.6
packaging==21.3
protobuf==3.20.0
pyparsing==3.0.9
regex==2022.6.2
requests==2.27.1
sacremoses==0.0.53
scikit-learn==1.0.2
scipy==1.7.3
sentence-transformers==0.3.9
sentencepiece==0.1.91
six==1.16.0
threadpoolctl==3.1.0
tokenizers==0.9.3
torch==1.7.0
tqdm==4.64.0
transformers==3.5.1
typing_extensions==4.2.0
urllib3==1.26.9
zipp==3.8.0

How to Run Code

python training.py