Create README.md

README.md

@@ -0,0 +1,84 @@
+# Movie Recommendation System with Sentence Transformers (all-MiniLM-L6-v2)
+
+## 📌 Overview
+
+This repository hosts the quantized version of the all-MiniLM-L6-v2 model fine-tuned for movie reccommendation tasks. The model has been trained on the movielens_ratings dataset from Hugging Face. The model is quantized to Float16 (FP16) to optimize inference speed and efficiency while maintaining high performance.
+
+## 🏗 Model Details
+
+- **Model Architecture:** all-MiniLM-L6-v2
+- **Task:** Movie Recommendation System  
+- **Dataset:** Hugging Face's `movielens_ratings`  
+- **Quantization:** Float16 (FP16) for optimized inference  
+- **Fine-tuning Framework:** Hugging Face Transformers  
+
+## 🚀 Usage
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Loading the Model
+
+```python
+from sentence_transformers import SentenceTransformer, models
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_name = "AventIQ-AI/all-MiniLM-L6-v2-movie-recommendation-system"
+model = SentenceTransformer(model_name).to(device)
+```
+
+### Question Answer Example
+
+```python
+def generate_movies(genre, top_k=5):
+    genre_embedding = model.encode([genre], convert_to_tensor=True)
+    movie_embeddings = model.encode(df['title'].tolist(), convert_to_tensor=True)
+
+    scores = torch.nn.functional.cosine_similarity(genre_embedding, movie_embeddings)
+    top_results = torch.argsort(scores, descending=True)
+
+    # Get unique movies while preserving order
+    recommended_movies = []
+    seen = set()
+    for idx in top_results.tolist():
+        movie = df.iloc[idx]['title']
+        if movie not in seen:
+            recommended_movies.append(movie)
+            seen.add(movie)
+        if len(recommended_movies) == top_k:
+            break
+    return recommended_movies
+
+print("🎬 Recommended Movies for 'Action':", generate_movies("Action"))
+print("🎬 Recommended Movies for 'Comedy':", generate_movies("Comedy"))
+print("🎬 Recommended Movies for 'Sci-Fi':", generate_movies("Sci-Fi"))
+```
+
+## ⚡ Quantization Details
+
+Post-training quantization was applied using PyTorch's built-in quantization framework. The model was quantized to Float16 (FP16) to reduce model size and improve inference efficiency while balancing accuracy.
+
+## 📂 Repository Structure
+
+```
+.
+├── model/               # Contains the quantized model files
+├── tokenizer_config/    # Tokenizer configuration and vocabulary files
+├── model.safetensors/   # Quantized Model
+├── README.md            # Model documentation
+```
+
+## ⚠️ Limitations
+
+- The model may struggle for out of scope tasks.
+- Quantization may lead to slight degradation in accuracy compared to full-precision models.
+- Performance may vary across different writing styles and sentence structures.
+
+## 🤝 Contributing
+
+Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.