YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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: โ†’ classify the genre of a book
  • rating: โ†’ predict the numeric rating
  • title: โ†’ 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


Training Details

  • Data source: Custom CSV with columns: title, description, genre, rating
  • Preprocessing: Merged title and description โ†’ 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

โš ๏ธ 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

Downloads last month
7
Safetensors
Model size
60.5M params
Tensor type
F32
ยท
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support