YuvarajK-g25ait2054/distilbert-goodreads-genres-V2

DistilBERT Goodreads Genre Classification

Model Details

Developed by: Yuvaraj K Model type: DistilBERT for Sequence Classification Language: English Finetuned from: distilbert-base-cased Training platform: Kaggle GPU Experiment tracking: Weights & Biases

Model Description

This model is a fine-tuned DistilBERT transformer model for multi-class text classification on Goodreads book review text.

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

• —Kaggle GPU training
• —secure API token handling with Kaggle Secrets
• —experiment tracking using Weights & Biases
• —evaluation artifact logging
• —model versioning and deployment using Hugging Face Hub
• —reproducibility with GitHub

DistilBERT was selected because it is computationally efficient, lightweight, and well-suited for free GPU environments while maintaining competitive NLP performance.

Training Data

Dataset: Goodreads book review classification dataset

Classification task: Multi-class genre classification

Number of classes: 8

Example classes:

• —Fiction
• —Fantasy
• —Mystery
• —Romance
• —Thriller
• —History
• —Science Fiction
• —Biography

Training Configuration

Base model: distilbert-base-cased

Hyperparameters:

• —Epochs: 3
• —Training batch size: 8
• —Evaluation batch size: 16
• —Weight decay: 0.01
• —Warmup steps: 100
• —Evaluation strategy: epoch
• —Save strategy: epoch
• —Optimizer: AdamW

Training platform:

• —Kaggle Notebook
• —GPU enabled

Evaluation Results

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "YuvarajK-g25ait2054/distilbert-goodreads-genres-V2"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "This mystery novel was suspenseful and brilliantly written."

inputs = tokenizer(
    text,
    return_tensors="pt",
    truncation=True,
    padding=True
)

outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=1)

print(prediction)

Intended Use

This model is intended for:

• —educational NLP experimentation
• —text classification demonstrations
• —academic MLOps assignments
• —transformer fine-tuning examples

Limitations

• —trained on assignment dataset only
• —performance may vary on unseen real-world text
• —not validated for production deployment
• —classification quality depends on dataset label quality

Resources

Weights & Biases Dashboard: https://wandb.ai/g25ait2054-iit-jodhpur/mlops-assignment2

Hugging Face Model: https://huggingface.co/YuvarajK-g25ait2054/distilbert-goodreads-genres-V2

Kaggle Notebook: https://www.kaggle.com/code/kyuvarajg25ait2054/g25ait2054-yuvaraj

GitHub Repository: https://github.com/YuvarajK-g25ait2054/Mlops_assignment2