# 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  

---

## Installation

```bash
!pip install pandas torch sentence-transformers scikit-learn

```

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## Loading the Model

```python
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

```python
.
├── 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.