add Readme

README.md

@@ -1,3 +1,46 @@
 ---
-license: mit
+language: 
+  - en
+
+tags:
+  - text-classification
+
 ---
+# 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.
+
+## License
+
+`nf-cats` is an open-source library for non-commercial use and scientific research purposes only.
+
+
+## Citation
+
+If you use `NDQA-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! 🤗