Movie-Recommendation-Using-Sentence-Transormer

Sentence Transformer Quantized Model for Movie Recommendation on Movie-Lens-Dataset

This repository hosts a quantized version of the Sentence Transformer model, fine-tuned for Movie Recommendation using the Movie Lens dataset. The model has been optimized using FP16 quantization for efficient deployment without significant accuracy loss.

Model Details

• Model Architecture: Sentence Transformer
• Task: Movie Recommendation
• Dataset: Movie Lens Dataset
• Quantization: Float16
• Fine-tuning Framework: Hugging Face Transformers

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Installation

!pip install pandas torch sentence-transformers scikit-learn

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Loading the Model

from sentence_transformers import SentenceTransformer, InputExample, losses, util
import torch

# Load  model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2', device=device)

# pass the movie name
recommend_by_movie_name("Toy Story")


# Recommend Movies
def recommend_by_movie_name(movie_name, top_k=5):
    titles = movie_subset["title"].tolist()
    matches = get_close_matches(movie_name, titles, n=1, cutoff=0.6)
    
    if not matches:
        print(f"❌ Movie '{movie_name}' not found in dataset.")
        return
    
    matched_title = matches[0]
    movie_index = movie_subset[movie_subset["title"] == matched_title].index[0]
    
    query_embedding = movie_embeddings[movie_index]
    scores = util.pytorch_cos_sim(query_embedding, movie_embeddings)[0]
    top_results = torch.topk(scores, k=top_k + 1)

    print(f"\n🎬 Recommendations for: {matched_title}")
    for score, idx_tensor in zip(top_results[0][1:], top_results[1][1:]):  # skip itself
        idx = idx_tensor.item()  # ✅ Convert tensor to int
        title = movie_subset.iloc[idx]["title"]
        print(f"  {title} (Score: {score:.4f})")

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Fine-Tuning Details

Dataset

The dataset is sourced from Hugging Face’s Movie-Lens dataset. It contains 20,000 movies and their genres.

Training

• Epochs: 2
• warmup_steps: 100
• show_progress_bar: True
• Evaluation strategy: epoch

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Quantization

Post-training quantization was applied using PyTorch’s half() precision (FP16) to reduce model size and inference time.

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Repository Structure

.
├── quantized-model/               # Contains the quantized model files
│   ├── config.json
│   ├── model.safetensors
│   ├── tokenizer_config.json
│   ├── modules.json
│   └── special_tokens_map.json
│   ├── sentence_bert_config.jason
│   └── tokenizer.json
│   ├── config_sentence_transformers.jason
│   └── vocab.txt

├── README.md                      # Model documentation

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Limitations

• The model is trained specifically for Movie Recommendation on Movies Dataset.
• FP16 quantization may result in slight numerical instability in edge cases.

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Contributing

Feel free to open issues or submit pull requests to improve the model or documentation.