Create README.md

README.md

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+---
+language:
+- ru
+
+tags:
+- toxic comments classification
+
+---
+
+## General concept of the model
+
+
+Sensitive topics are such topics that have a high chance of initiating a toxic conversation: homophobia, politics, racism, etc. This dataset uses 18 topics. 
+
+More details can be found [in this article ](https://www.aclweb.org/anthology/2021.bsnlp-1.4/) presented at the workshop for Balto-Slavic NLP at the EACL-2021 conference. 
+This paper presents the first version of this dataset. Here you can see the last version of the dataset which is significantly larger and also properly filtered.
+
+
+## Licensing Information
+
+[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
+
+[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
+
+[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
+[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png
+
+## Citation
+
+If you find this repository helpful, feel free to cite our publication:
+
+```
+@inproceedings{babakov-etal-2021-detecting,
+    title = "Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation",
+    author = "Babakov, Nikolay  and
+      Logacheva, Varvara  and
+      Kozlova, Olga  and
+      Semenov, Nikita  and
+      Panchenko, Alexander",
+    booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
+    month = apr,
+    year = "2021",
+    address = "Kiyv, Ukraine",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2021.bsnlp-1.4",
+    pages = "26--36",
+    abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.",
+}
+```