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README.md

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+# mlx7-two-tower-retrieval
+
+This repository contains models trained using the Two-Tower (Dual Encoder) architecture for document retrieval.
+
+## Model Description
+
+The Two-Tower model is a dual encoder neural network architecture designed for semantic search and document retrieval. It consists of two separate "towers" - one for encoding queries and one for encoding documents - that map text to dense vector representations in a shared embedding space.
+
+## Usage
+
+```python
+from twotower import load_model_from_hub
+from twotower.encoders import TwoTower
+from twotower.tokenisers import CharTokeniser
+
+# Load the model
+model, tokenizer, config = load_model_from_hub(
+    repo_id="mlx7-two-tower-retrieval",
+    model_class=TwoTower,
+    tokenizer_class=CharTokeniser
+)
+
+# Use for document embedding
+doc_ids = tokenizer.encode("This is a document")
+doc_embedding = model.encode_document(doc_ids)
+
+# Use for query embedding
+query_ids = tokenizer.encode("This is a query")
+query_embedding = model.encode_query(query_ids)
+```
+
+## Training
+
+This model was trained on the MS MARCO dataset using the Two-Tower architecture with contrastive learning.
+
+## Repository Information
+
+This model is part of the [Two-Tower Retrieval Model](https://github.com/yourusername/two-towers) project.