| # 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. | |