Nf Cats

🚀 Non Factoid Question Category classification in English

This project focuses on the classification of non - factoid question categories in English, using a model trained on the NFQA dataset to address text - classification tasks.

🚀 Quick Start

The project provides a model for non - factoid question category classification. The model is trained with the NFQA dataset, and the base model is [roberta - base - squad2](https://huggingface.co/deepset/roberta - base - squad2), a RoBERTa - based model for the Question Answering task, fine - tuned using the SQuAD2.0 dataset.

Repository: [https://github.com/Lurunchik/NF - CATS](https://github.com/Lurunchik/NF - CATS)

✨ Features

• The model uses labels such as NOT - A - QUESTION, FACTOID, DEBATE, EVIDENCE - BASED, INSTRUCTION, REASON, EXPERIENCE, COMPARISON.

📦 Installation

The installation mainly involves loading the model and its tokenizer.

from transformers import AutoTokenizer
from nfqa_model import RobertaNFQAClassification 

nfqa_model = RobertaNFQAClassification.from_pretrained("Lurunchik/nf - cats")
nfqa_tokenizer = AutoTokenizer.from_pretrained("deepset/roberta - base - squad2")

💻 Usage Examples

Basic Usage

# Load NFQA cat and its tokenizer
from transformers import AutoTokenizer
from nfqa_model import RobertaNFQAClassification 

nfqa_model = RobertaNFQAClassification.from_pretrained("Lurunchik/nf - cats")
nfqa_tokenizer = AutoTokenizer.from_pretrained("deepset/roberta - base - squad2")

Advanced Usage

# Make prediction using helper function
def get_nfqa_category_prediction(text):
    output = nfqa_model(**nfqa_tokenizer(text, return_tensors="pt"))
    index = output.logits.argmax()
    return nfqa_model.config.id2label[int(index)]

get_nfqa_category_prediction('how to assign category?')
# result
#'INSTRUCTION'

📚 Documentation

You can test the model via [hugginface space](https://huggingface.co/spaces/Lurunchik/nf - cats).

[](https://huggingface.co/spaces/Lurunchik/nf - cats)

📄 License

This project is licensed under the MIT license.

📄 Citation

If you use NFQA - cats in your work, please cite this paper

@misc{bolotova2022nfcats,
        author = {Bolotova, Valeriia and Blinov, Vladislav and Scholer, Falk and Croft, W. Bruce and Sanderson, Mark},
        title = {A Non - Factoid Question - Answering Taxonomy},
        year = {2022},
        isbn = {9781450387323},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        url = {https://doi.org/10.1145/3477495.3531926},
        doi = {10.1145/3477495.3531926},
        booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
        pages = {1196–1207},
        numpages = {12},
        keywords = {question taxonomy, non - factoid question - answering, editorial study, dataset analysis},
        location = {Madrid, Spain},
        series = {SIGIR '22}
}

Enjoy! 🤗