ConveRT for Response Selection

Transformer-based model for selecting response based on similarity - where I have made modifications based on codertimo's work
This repository is an unofficial implementation for PolyAI's ConveRT model
You can use BERT instead of scratch Transformer Encoder, where I have applied Korean DistilBERT

Process

1. Preprocess

• Place train.txt file and test.txt file under '/data'
• Sample files for both train.txt and test.txt can be found under the same directory
• The label column is a unique index for each response
• The tokenizer used here is for Korean so you will need to change if necessary
• Then, run preprocess.py

2. Train

• After preprocess, run train.py
• Model or train config such as number of encoder layer, learning rate and batch size should be defined in '/src/constant.py'
• If you want to use Korean DistilBERT, with 6 layers, give a flag -kobert True
• Default similarity metric is dot-product or you can change to cosine similarity with -loss_type cosine