readme: ---
  language: 
    - en
  license: mit
  
  tags:
    - text-classification
  
  inference: false
  
  widget:
  - text: "Why do we need an NFQA taxonomy?"
  ---
  
  # Non Factoid Question Category classification in English
  ## NFQA model
  
  Repository: [https://github.com/Lurunchik/NF-CATS](https://github.com/Lurunchik/NF-CATS)
  
  Model trained with NFQA dataset. Base model is [roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2), a RoBERTa-based model for the task of Question Answering, fine-tuned using the SQuAD2.0 dataset.
  
  Uses `NOT-A-QUESTION`, `FACTOID`, `DEBATE`, `EVIDENCE-BASED`, `INSTRUCTION`, `REASON`, `EXPERIENCE`, `COMPARISON` labels.
  
  ## How to use NFQA cat with HuggingFace
  
  ##### Load NFQA cat and its tokenizer:
  ```python
  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")
  ```
  
  ##### Make prediction using helper function:
  ```python
  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'
  ```
  
  ## Demo 
  You can test the model via [hugginface space](https://huggingface.co/spaces/Lurunchik/nf-cats).
  
  [![demo.png](demo.png)](https://huggingface.co/spaces/Lurunchik/nf-cats)
  
  
  ## Citation
  
  If you use `NFQA-cats` in your work, please cite [this paper](https://dl.acm.org/doi/10.1145/3477495.3531926)
  
  ```
  @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! 🤗