feat: readme

README.md

@@ -1,3 +1,61 @@
----
-license: mit
----
+---
+language:
+- en
+license: mit
+library_name: pytorch
+task_categories:
+- other
+- tabular-data
+pipeline_tag: other
+domain:
+- recommendation
+- collaborative-filtering
+tags:
+- two-tower
+- movie-recommendation
+- pytorch
+- collaborative-filtering
+- neural-collaborative-filtering
+metrics:
+- rmse
+- mae
+- precision
+- recall
+datasets:
+- movielens
+---
+
+# Two-Tower Movie Recommendation Model
+
+## Model Description
+
+This is a two-tower neural collaborative filtering model for movie recommendations, trained on MovieLens data.
+
+### Architecture
+- **User Tower**: Embeds user IDs and learns user representations
+- **Movie Tower**: Embeds movie IDs + genre features and learns item representations  
+- **Prediction Layer**: Combines user and movie representations to predict ratings
+
+### Model Details
+- **Parameters**: 14.2M
+- **Embedding Dimension**: 64/128/256/512 (configurable)
+- **Hidden Layers**: [128, 64] / [256, 128, 64] / [512, 256, 128]
+- **Framework**: PyTorch
+- **Training**: Mixed precision, batch size 1024-8192
+
+## Intended Use
+
+### Direct Use
+```python
+from transformers import AutoModel
+import torch
+
+# Load model
+model = AutoModel.from_pretrained("matjs/movie_recommendation_tt_small")
+
+# Predict rating
+user_id = torch.tensor([123])
+movie_id = torch.tensor([456]) 
+genre_features = torch.tensor([[1, 0, 1, 0, 0]])  # One-hot genres
+
+rating = model(user_id, movie_id, genre_features)