---
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