This model is based on distilbert-base-cased and was fine-tuned on the UCSD Goodreads book reviews dataset. It classifies a given book review text into one of 8 genres.

Model: distilbert-base-cased
Task: Multi-class text classification (8 genres)
Language: English
License: MIT

Supported Genres

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 Details

Parameter	Value
Base model	distilbert-base-cased
Epochs	3
Batch size (train)	16
Batch size (eval)	32
Learning rate	3e-5
Warmup steps	100
Weight decay	0.01
Max sequence length	512
Train samples	6,400
Test samples	1,600
Platform	Kaggle (GPU T4 x2)
Tracking	Weights & Biases

Results

Metric	Score
Accuracy	0.5831
F1 Score (weighted)	0.5810
Eval Loss	2.2847

Per-Epoch Results

Epoch	Training Loss	Validation Loss	Accuracy	F1
1	2.5710	2.5337	0.5525	0.5454
2	2.1273	2.2859	0.5981	0.5983
3	1.6126	2.2923	0.6094	0.6089

How to Use

python

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="sureshbabugandla/ML_OPS_ASSIGNMENT2"
)

result = classifier("This book was a thrilling mystery with unexpected twists.")
print(result)

Or load the model and tokenizer separately:

python

from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification

tokenizer = DistilBertTokenizerFast.from_pretrained("sureshbabugandla/ML_OPS_ASSIGNMENT2")
model = DistilBertForSequenceClassification.from_pretrained("sureshbabugandla/ML_OPS_ASSIGNMENT2")

Dataset

The model was trained on the UCSD Book Graph dataset, which contains book reviews from Goodreads across multiple genres. 2,000 reviews were sampled from each of the 8 genres, split into 800 train and 200 test samples per genre.

Developed By

Name: Suresh Babu Gandla
Roll Number: G25AIT2119

sureshbabugandla/ML_OPS_ASSIGNMENT2

DistilBERT Book Genre Classifier

Model Description