Add model card

model_card.md

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+---
+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