Valentwin: Using Self-Supervised Contrastive Learning on Language Model for Schema Matching Datasets

ValenTwin is a schema matching framework that uses self-supervised contrastive learning to train the model, uses the model to generate embeddings of table columns, then uses different similarity measures to match the column embeddings. We provide two types of zip files for the datasets:1. `data.zip` contains the raw data files, the ground truth files, the sampled data (n=[100, 200, 300, 400, 500] used in the experiments, as well as the contrastive data used to train the model.2. `data-raw.zip` contains only the raw data files and the ground truth files. You can sample the data and generate the contrastive dataset yourself by following step 1 and 2 in the `How to Run` section. Download and unzip one of the zip files to the `data` folder.