Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

This repository is the implementation of the paper Sentence-Bert a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity.

This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

install the dependencies for this project by running the following commands in your terminal:

 pip install -r requirements.txt

run the model by running the following command in your terminal:

python src/sbert.py --train_file="./input/wolof.csv" \
                        --max_length=150 \
                        --epochs=10 \
                        --learning_rate=3e-8 \
                        --epsilone=1e-9 \
                        --train_batch_size=3 \
                        --model_name="bert-base-cased"