--- library_name: transformers tags: [] --- # Model DistilBERT Book Genre Classifier A transformer-based model fine-tuned to classify Goodreads reviews into eight distinct genres. Key Features Model: DistilBERT (Transformer-based) Dataset: Goodreads book reviews Output: 8 genre categories Task: Multi-class text classification ### Model Description This model is a specialized text classification tool designed to bridge the gap between subjective reader feedback and structured metadata. By fine-tuning the DistilBERT architecture a lighter, faster version of the original BERT model—this classifier can interpret the nuance, tone, and vocabulary used in Goodreads reviews to predict a book's genre. Unlike traditional keyword matching, this model uses deep learning to understand the context of a review. It can distinguish between the "thrilling" elements of a mystery and the "breathless" excitement found in a romance. This makes it an ideal solution for organizing large digital libraries, enhancing recommendation engines, or helping authors understand how their work is being perceived by specific audiences. ## Supported Genres The model categorizes reviews into the following eight **Label** **Genre** 0 Children 1 Comics & Graphic 2 Fantasy & Paranormal 3 History & Biography 4 Mystery, Thriller & Crime 5 Poetry 6 Romance 7 Young Adult ## Training Configuration | Parameter | Value | | :--- | :--- | | **Base Model** | distilbert-base-cased | | **Epochs** | 3 | | **Train Batch Size** | 16 | | **Eval Batch Size** | 32 | | **Learning Rate** | 3e-5 | | **Warmup Steps** | 100 | | **Weight Decay** | 0.01 | | **Max Sequence Length** | 512 | | **Training Samples** | 6,400 | | **Test Samples** | 1,600 | | **Platform** | Kaggle (GPU T4 x2) | | **Experiment Tracking** | Weights & Biases | ## Training Results | Epoch | Training Loss | Validation Loss | Accuracy | F1 | |-------|--------------|-----------------|----------|----| | 1 | 2.543057 | 2.389233 | 0.583125 | 0.582607 | | 2 | 1.955018 | 2.249788 | 0.603750 | 0.605045 | | 3 | 1.433714 | 2.274478 | 0.608750 | 0.611895 | ## Final Evaluation Metrics | Metric | Score | |--------|-------| | Accuracy | 0.608750 | | F1 Score | 0.611895 | | Eval Loss | 2.274478 | ## Installation Install the required libraries: ```bash pip install transformers torch ``` ## Quick Inference Use the Hugging Face pipeline API: ```bash from huggingface_hub import login # Login to HuggingFace using token from Kaggle Secrets login(token=HF_TOKEN) print("Logged in to HuggingFace!") HF_USERNAME = "Rashmii30" REPO_NAME = "distilbert-goodreads-genres" HF_REPO = f"{HF_USERNAME}/{REPO_NAME}" # Push model weights to HuggingFace Hub print("Pushing model to HuggingFace Hub...") model.push_to_hub(HF_REPO) print("Model pushed successfully!") # Push tokenizer to same repository print("Pushing tokenizer to HuggingFace Hub...") tokenizer.push_to_hub(HF_REPO) print("Tokenizer pushed successfully!") # Log the HuggingFace URL to W&B run summary wandb.run.summary["huggingface_model"] = f"https://huggingface.co/{HF_REPO}" print(f"HuggingFace model URL logged to W&B!") print(f"Your model is live at: https://huggingface.co/{HF_REPO}") ``` ## Dataset The model was trained using the **UCSD Book Graph / Goodreads Reviews Dataset**, which contains large-scale Goodreads book reviews across multiple genres. 8 genres were selected 2,000 reviews sampled per genre Stratified train-test split: 800 training samples per genre 200 test samples per genre ## Project Highlights Fine-tuned transformer-based NLP classifier GPU training on Kaggle Experiment tracking using Weights & Biases Hugging Face model deployment Reproducible MLOps workflow ## Technologies Used Python PyTorch Hugging Face Transformers Dataset Weights & Biases Kaggle Notebooks ## Author **Name**: Rashmi Kumari **Roll Number**: G25AIT2083 ## Model Repository Hugging Face Model Repository
