Instructions to use AbrarFahim75/t5-book-multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbrarFahim75/t5-book-multitask with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AbrarFahim75/t5-book-multitask")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AbrarFahim75/t5-book-multitask") model = AutoModelForSeq2SeqLM.from_pretrained("AbrarFahim75/t5-book-multitask") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use AbrarFahim75/t5-book-multitask with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AbrarFahim75/t5-book-multitask" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AbrarFahim75/t5-book-multitask", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AbrarFahim75/t5-book-multitask
- SGLang
How to use AbrarFahim75/t5-book-multitask with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "AbrarFahim75/t5-book-multitask" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AbrarFahim75/t5-book-multitask", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "AbrarFahim75/t5-book-multitask" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AbrarFahim75/t5-book-multitask", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AbrarFahim75/t5-book-multitask with Docker Model Runner:
docker model run hf.co/AbrarFahim75/t5-book-multitask
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 bookrating:โ predict the numeric ratingtitle: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
- 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 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 |
โ ๏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
- 4