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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- text-classification |
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inference: false |
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widget: |
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- text: "Why do we need an NFQA taxonomy?" |
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--- |
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# Non Factoid Question Category classification in English |
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## NFQA model |
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Repository: [https://github.com/Lurunchik/NF-CATS](https://github.com/Lurunchik/NF-CATS) |
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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. |
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Uses `NOT-A-QUESTION`, `FACTOID`, `DEBATE`, `EVIDENCE-BASED`, `INSTRUCTION`, `REASON`, `EXPERIENCE`, `COMPARISON` labels. |
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## How to use NFQA cat with HuggingFace |
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##### Load NFQA cat and its tokenizer: |
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```python |
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from transformers import AutoTokenizer |
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from nfqa_model import RobertaNFQAClassification |
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nfqa_model = RobertaNFQAClassification.from_pretrained("Lurunchik/nf-cats") |
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nfqa_tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2") |
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``` |
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##### Make prediction using helper function: |
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```python |
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def get_nfqa_category_prediction(text): |
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output = nfqa_model(**nfqa_tokenizer(text, return_tensors="pt")) |
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index = output.logits.argmax() |
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return nfqa_model.config.id2label[int(index)] |
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get_nfqa_category_prediction('how to assign category?') |
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# result |
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#'INSTRUCTION' |
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``` |
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## Demo |
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You can test the model via [hugginface space](https://huggingface.co/spaces/Lurunchik/nf-cats). |
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[](https://huggingface.co/spaces/Lurunchik/nf-cats) |
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## Citation |
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If you use `NFQA-cats` in your work, please cite [this paper](https://dl.acm.org/doi/10.1145/3477495.3531926) |
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``` |
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@misc{bolotova2022nfcats, |
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author = {Bolotova, Valeriia and Blinov, Vladislav and Scholer, Falk and Croft, W. Bruce and Sanderson, Mark}, |
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title = {A Non-Factoid Question-Answering Taxonomy}, |
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year = {2022}, |
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isbn = {9781450387323}, |
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publisher = {Association for Computing Machinery}, |
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address = {New York, NY, USA}, |
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url = {https://doi.org/10.1145/3477495.3531926}, |
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doi = {10.1145/3477495.3531926}, |
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booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval}, |
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pages = {1196–1207}, |
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numpages = {12}, |
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keywords = {question taxonomy, non-factoid question-answering, editorial study, dataset analysis}, |
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location = {Madrid, Spain}, |
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series = {SIGIR '22} |
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} |
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``` |
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Enjoy! 🤗 |
