
# 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! 🤗
{"language": ["en"], "license": "mit", "tags": ["text-classification"], "inference": false, "widget": [{"text": "Why do we need an NFQA taxonomy?"}]}
transformers pytorch roberta text-classification custom_code en license:mit has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #custom_code #en #license-mit #has_space #region-us 


# Non Factoid Question Category classification in English
## NFQA model

Repository: URL

Model trained with NFQA dataset. Base model is 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:


##### Make prediction using helper function:


## Demo 
You can test the model via hugginface space.

![URL](URL


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


Enjoy! 
# Non Factoid Question Category classification in English ## NFQA model

Repository: URL

Model trained with NFQA dataset. Base model is 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: ##### Make prediction using helper function: ## Demo 
You can test the model via hugginface space.

![URL](URL


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


Enjoy!
TAGS
#transformers #pytorch #roberta #text-classification #custom_code #en #license-mit #has_space #region-us 
 # Non Factoid Question Category classification in English ## NFQA model

Repository: URL

Model trained with NFQA dataset. Base model is 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: ##### Make prediction using helper function: ## Demo 
You can test the model via hugginface space.

![URL](URL


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


Enjoy!
passage: TAGS
#transformers #pytorch #roberta #text-classification #custom_code #en #license-mit #has_space #region-us 
# Non Factoid Question Category classification in English## NFQA model

Repository: URL

Model trained with NFQA dataset. Base model is 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:##### Make prediction using helper function:## Demo 
You can test the model via hugginface space.

![URL](URL


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


Enjoy!