Contents

- SimCSE (Simple Contrastive Learning of Sentence Embeddings) Sample Code

• Generating Unsupervised & Supervised SimCSE Data
• SimCSE Train

Generating Unsupervised & Supervised SimCSE Data

• SimCSE Data Generation Scripts
  • This repository contains three scripts for generating SimCSE training datasets.
  • Each script creates both unsupervised samples and supervised samples (anchor, positive, negative) from an input CSV file.
  • They differ in how they construct anchor–positive–negative pairs, how the data is split, and how negatives are sampled.

1. Baseline

• Unsupervised learning data: converting all columns in a row to a text
• Mapping method of supervised learning data:
  • For each anchor, create anchor–positive pairs (number of positive columns pairs) using each positive column.
  • Attach one random negative to each pair.

python gen_data/gen_simcse_data.py --data_path {csv data path} --encoding {encoding} --desc_col {anchor column} --category_col {hard negative column} --positive_cols {positive column1, ...} --output_unsupervised {unsupervised train data save path} --output_supervised {supervised train data save path}

2. Domainwise Version

• Same as baseline, but with domain-based data separation. (Each domain gets its own dataset file.)

python gen_data/simcse_data_gen_domainwise.py --data_path {csv data path} --encoding {encoding} --desc_col {anchor column} --category_col {hard negative column} --positive_cols {positive column1, ...} ----domain_col {domain column} --output_dir {unsupervised, supervised train data save folder path}

3. Multi-Negative with Positive Fusion Version

• Unsupervised learning data: converting all columns in a row to a text
• Mapping method of supervised learning data:
  • All positive values are fused into a single string
    • E.g. "pos column1: xxx, pos column2: yyy, ..."
• For each anchor, attach multiple hard negatives (default: 5).
• One row per anchor

python gen_data/simcse_data_gen_fusion_multineg.py --data_path {csv data path} --encoding {encoding} --desc_col {anchor column} --category_col {hard negative column} --positive_cols {positive column1, ...} ----domain_col {domain column} --output_dir {unsupervised, supervised train data save folder path} --num_negatives {num of negatives}