ms-marco-dual-encoder

MS MARCO Dual Encoder Model

This repository contains a Dual Encoder (Two-Tower) model trained on the Microsoft MS MARCO dataset for information retrieval tasks.

Model Details

• Architecture: Two-Tower (Dual Encoder)
• Embedding Dimension: 128
• Training Strategy: Triplet loss with margin 0.2
• Vocabulary Size: 50,001
• Dataset Size: 5,000
• Parameters:
  • Query Tower: 16,512
  • Document Tower: 16,512
  • Total: 33,024
• Training Device: cuda

Usage

import torch
from model import QryTower, DocTower

# Load the models
embedding_dim = 128
qry_model = QryTower(embedding_dim)
doc_model = DocTower(embedding_dim)

qry_model.load_state_dict(torch.load("qry_tower.pth"))
doc_model.load_state_dict(torch.load("doc_tower.pth"))

# Get embeddings for query and document
query_embedding = qry_model(preprocessed_query)
document_embedding = doc_model(preprocessed_document)

# Calculate similarity
similarity = torch.cosine_similarity(query_embedding, document_embedding)

Training

This model was trained for 5 epochs with a batch size of 32 and learning rate of 0.001.

License

MIT