Evaluate Sentence-Transformer Models for Semantic Search Engine

This repository is dedicated to evaluating Sentence-Transformer models optimized for use within a semantic search engine. Our goal is to fine-tune and assess various Sentence-Transformer models to enhance the relevancy and accuracy of search results in semantic search with Amazon OpenSearch.

Overview

Semantic search engines use understanding beyond keyword matching to interpret the intent and contextual meaning of search queries. By leveraging Sentence-Transformer models, we aim to significantly improve the search experience, offering users more accurate, context-aware results.

Model Fine-Tuning Script

Prerequisites

Before running the script, ensure you have the following:

• Python 3.x installed
• Access to a command-line interface (CLI)
• Necessary permissions to run bash scripts and Python scripts on your system
• The required Python environment and dependencies installed

Setup

1. Clone the Repository: Ensure that you have the repository cloned to your local machine.

2. Environment Setup: Ensure that your Python environment is set up correctly and that all required packages are installed.

   pip install -r finetune/requirement.txt

3. Data Preparation: Prepare your training data and note its file path. The data file should be in .parquet format for the given Python script.

Running the Script

To run the script, you'll need to pass three arguments: the path to the training data, the directory where the model should be saved, and the number of training epochs.

1. Run the Bash Script: Execute the bash script with the required arguments. Here is the format for running the script:

   bash finetune/finetune.sh [path_to_training_data] [model_save_directory] [num_train_epochs]

   Replace [path_to_training_data] with the full path to your training data, [model_save_directory] with the path where you want the trained model to be saved, and [num_train_epochs] with the number of epochs for training.

   Example:

   bash finetune/finetune.sh  /path/to/training/data /path/to/save/directory 5

Notes

• Ensure that the paths provided are accessible and the user has the necessary read/write permissions.
• Adjust the number of epochs according to your model requirements and hardware capabilities.