Niketh0503/distilbert-goodreads-genres

This model is a fine-tuned version of distilbert-base-cased for Goodreads book review genre classification. The model was trained using the Hugging Face Transformers library on the UCSD Goodreads dataset as part of an MLOps assignment.

The objective of the project was to build a complete MLOps workflow including:

fine-tuning a Hugging Face model experiment tracking using Weights & Biases (W&B) training on Kaggle GPU deployment to Hugging Face Hub Model Details Base Model: distilbert-base-cased Task: Text Classification Number of Labels: 8 Framework: Hugging Face Transformers Training Platform: Kaggle GPU Experiment Tracking: Weights & Biases (W&B) Labels The model predicts the following genres:

children comics_graphic fantasy_paranormal history_biography mystery_thriller_crime poetry romance young_adult Training Details Dataset UCSD Goodreads Reviews Dataset

Training Hyperparameters Epochs: 5 Batch Size: 16 Learning Rate: 3e-5 Max Sequence Length: 512 Evaluation Metrics Replace these values with your actual results:

Metric Score Accuracy 0.59125 F1 Score 0.59107 Eval Loss 1.15685 Usage from transformers import pipeline

classifier = pipeline( "text-classification", model="YOUR_USERNAME/distilbert-goodreads-genres" )

result = classifier("This book was amazing and full of adventure.") print(result)

Project Links GitHub Repository: https://github.com/niketh532002/MLOPS-ASSIGN2 Kaggle Notebook: https://www.kaggle.com/code/nikethvarma/notebook12a2641f7a Weights & Biases Dashboard: https://wandb.ai/nikethv6-iitj/mlops-assignment2/runs/jek60kbf?nw=nwusernikethv6 Hugging Face Model: https://huggingface.co/Niketh0503/distilbert-goodreads-genres