Create README.md

README.md

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+# Book Recommendation System with Bert
+
+## 📌 Overview
+
+This repository hosts the quantized version of the bert-base-cased model fine-tuned for movie reccommendation tasks. The model has been trained on the wykonos/movies dataset from HF中国镜像站. The model is quantized to Float16 (FP16) to optimize inference speed and efficiency while maintaining high performance.
+
+## 🏗 Model Details
+
+- **Model Architecture:** bert-base-cased
+- **Task:** Book Recommendation System  
+- **Dataset:** HF中国镜像站's `wykonos/movies`  
+- **Quantization:** Float16 (FP16) for optimized inference  
+- **Fine-tuning Framework:** HF中国镜像站 Transformers  
+
+## 🚀 Usage
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Loading the Model
+
+```python
+from transformers import BertTokenizerFast, BertForSequenceClassification
+import torch
+```
+
+### Question Answer Example
+
+```python
+model_name = "AventIQ-AI/bert-movie-recommendation-system"
+model = BertForSequenceClassification.from_pretrained(model_name)
+tokenizer = BertTokenizerFast.from_pretrained(model_name)
+
+genre_to_label = {
+    "Action": 0, "Adventure": 1, "Animation": 2, "Comedy": 3, "Crime": 4,
+    "Documentary": 5, "Drama": 6, "Family": 7, "Fantasy": 8, "History": 9,
+    "Horror": 10, "Music": 11, "Mystery": 12, "Romance": 13, "Science Fiction": 14,
+    "TV Movie": 15, "Thriller": 16, "War": 17, "Western": 18
+}
+
+def recommend_movies(genre, top_n=10):
+    """Return a list of movies for a given genre."""
+    if genre not in genre_to_label:
+        return "Unknown Genre"
+    # Filter dataset for movies in the requested genre
+    genre_movies = df[df["genres"].str.contains(genre, case=False, na=False)]["title"].tolist()
+ 
+    # Return top N movies (or all if fewer exist)
+    return genre_movies[:top_n]
+
+genres_to_test = ["Horror", "Comedy", "Drama"]
+for genre in genres_to_test:
+    recommended_movies = recommend_movies(genre)
+    print(f"Genre: {genre} -> Recommended Movies: {recommended_movies}")
+```
+
+## ⚡ 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.
+
+## Evaluation Metrics: NDCG
+
+NDCG → If close to 1, the ranking matches expected relevance. Our model's NDCG score is 0.84
+
+## 🔧 Fine-Tuning Details
+
+### Dataset
+The **wykonos/movies** dataset was used for training and evaluation. The dataset consists of **texts**.
+
+### Training Configuration
+- **Number of epochs**: 5
+- **Batch size**: 8
+- **Evaluation strategy**: epochs
+
+
+## 📂 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.