AventIQ-AI/all-MiniLM-L6-v2-movie-recommendation-system

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

pip install transformers torch

Loading the Model

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

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.