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# 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
```
---
## 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})")
```
---
---
## 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`
---
## Quantization
Post-training quantization was applied using PyTorch’s `half()` precision (FP16) to reduce model size and inference time.
---
## 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
```
---
## Limitations
- The model is trained specifically for Movie Recommendation on Movies Dataset.
- FP16 quantization may result in slight numerical instability in edge cases.
---
## Contributing
Feel free to open issues or submit pull requests to improve the model or documentation.
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