This model is a fine-tuned DistilBERT model for multi-class text classification. It predicts the genre of a book based on the textual content of Goodreads reviews. The supported genres include:

poetry
children
comics & graphic
fantasy & paranormal
history & biography
mystery, thriller & crime
romance
young adult

The model was fine-tuned using the Hugging Face transformers library on a subset of the UCSD Goodreads Book Graph dataset.

Developed by: Nishant Thapa
Shared by: Nishant Thapa
Model type: Transformer-based sequence classification (DistilBERT)
Language(s): English
License: Apache-2.0
Finetuned from model: distilbert-base-cased

Model Sources

Repository: https://github.com/nthapa000/ass3_hf_goodreads
Base Model: https://huggingface.co/distilbert-base-cased
Dataset: https://mengtingwan.github.io/data/goodreads.html

Uses

Direct Use

The model can be used to classify a single Goodreads review or short text into one of the supported book genres.

Example:

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "nthapa/distilbert-goodreads-genres"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "A beautiful coming-of-age story with magic and dragons."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
    outputs = model(**inputs)
pred = outputs.logits.argmax(dim=-1).item()
print(pred)