README.md

metadata

language: en
license: mit
tags:
  - text2text-generation
  - multitask
  - genre-classification
  - rating-prediction
  - title-generation
  - t5
metrics:
  - accuracy
  - rmse
  - bleu
base_model: google/t5-small
pipeline_tag: text2text-generation
library_name: transformers

T5 Multitask Model for Book Genre, Rating, and Title Tasks

This model was trained on a custom dataset of book descriptions and titles. It supports:

• genre: → classify the genre of a book
• rating: → predict the numeric rating
• title: → generate a book title

---

Usage

from transformers import T5Tokenizer, T5ForConditionalGeneration

model = T5ForConditionalGeneration.from_pretrained("AbrarFahim75/t5-multitask-book")
tokenizer = T5Tokenizer.from_pretrained("AbrarFahim75/t5-multitask-book")

input_text = "genre: A dark and stormy night in an abandoned castle."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

---

Model Details

• Base model: google/t5-small
• Language: English
• Model type: T5 fine-tuned on multi-task dataset (genre, rating, title)
• License: MIT
• Author: AbrarFahim75
• Repository: t5-multitask-book

---

Training Details

• Data source: Custom CSV with columns: title, description, genre, rating
• Preprocessing: Merged title and description → formatted prompts like:
  • "genre: <desc>"
  • "rating: <desc>"
  • "title: <desc>"
• Epochs: 3
• Optimizer: AdamW
• Batch size: 8
• Loss: Cross-entropy

---

Evaluation

Task	Metric	Value (sample, dev split)
Genre Classification	Accuracy	~0.78 (sample set)
Rating Prediction	RMSE	~0.42
Title Generation	BLEU	~15.3

⚠️ These are informal evaluations using validation slices from the dataset.

---

Intended Use

Direct Use:

• Classifying book genres from text
• Predicting numeric ratings from descriptions
• Auto-generating book titles

Out-of-Scope Use:

• Non-book-related input
• Use in high-stakes recommendation without human review

---

Limitations and Biases

• Trained on a limited dataset of books (genre/bias unknown)
• May underperform on texts outside typical fiction/non-fiction boundaries
• Language is English only

---

Citation

If you use this model, please cite:

@misc{fahim2025t5bookmultitask,
  title={T5 Multitask for Book Tasks},
  author={Md Abrar Fahim},
  year={2025},
  url={https://huggingface.co/AbrarFahim75/t5-multitask-book}
}

---

Contact

For questions, please reach out at huggingface.co/AbrarFahim75