Merge branch 'main' of https://huggingface.co/Skoltech/russian-inappropriate-messages into main

README.md

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+---
+language:
+- ru
+
+tags:
+- toxic comments classification
+
+licenses:
+- cc-by-nc-sa
+---
+
+## General concept of the model
+
+This model is trained on the dataset of inappropriate messages of the Russian language. The concept of inappropriateness is described [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. Please note that this article describes the first version of the dataset, while the model is trained on the extended version of the dataset open-sourced on our [GitHub](https://github.com/skoltech-nlp/inappropriate-sensitive-topics/blob/main/Version2/appropriateness/Appropriateness.csv) or on [kaggle](https://www.kaggle.com/nigula/russianinappropriatemessages). The properties of the dataset is the same as the one described in the article, the only difference is the size.
+
+The model was trained, validated and tested only on the samples with 100% confidence, which allowed to get the following metrics on test set: 
+
+|              | precision | recall | f1-score | support |
+|--------------|----------|--------|----------|---------|
+| 0            | 0.92     | 0.93   | 0.93     | 7839    |
+| 1            | 0.80     | 0.76   | 0.78     | 2726    |
+| accuracy     |          |        | 0.89     | 10565   |
+| macro avg    | 0.86     | 0.85   | 0.85     | 10565   |
+| weighted avg | 0.89     | 0.89   | 0.89     | 10565   |
+
+## 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.",
+}
+```