metadata

language:
  - en
tags:
  - two-tower
  - dual-encoder
  - semantic-search
  - document-retrieval
  - information-retrieval
license: mit
datasets:
  - ms_marco

mlx7-two-tower-retrieval

This is a Two-Tower (Dual Encoder) model for document retrieval.

Model Description

The Two-Tower model maps queries and documents to dense vector representations in the same semantic space, allowing for efficient similarity-based retrieval.

Architecture

Tokenizer: Character-level tokenization
Embedding: Lookup embeddings with 64-dimensional vectors
Encoder: Mean pooling with 128-dimensional hidden layer

Intended Use

This model is designed for semantic search applications where traditional keyword matching is insufficient. It can be used to:

Encode documents and queries into dense vector representations
Retrieve relevant documents for a given query using vector similarity
Build semantic search engines

Limitations

Limited context window (maximum sequence length of 64 tokens)
English-language focused
No contextual understanding beyond simple semantic similarity

Training

Dataset: MS MARCO passage retrieval dataset
Training Method: Contrastive learning with triplet loss
Hardware: NVIDIA GPU