Update evaluation results and add cite information.

README.md

@@ -20,6 +20,29 @@ Process of annotation included initial annotation of 100-200 comments followed b
 Examples that made it to the dataset are ones that had unanimous agreement or were resolved through discussion.
 
 
+### Citing
+
+To cite this dataset use the following bibtex.
+
+```
+@inproceedings{eskelinen-etal-2023-toxicity,
+    title = "Toxicity Detection in {F}innish Using Machine Translation",
+    author = "Eskelinen, Anni  and
+      Silvala, Laura  and
+      Ginter, Filip  and
+      Pyysalo, Sampo  and
+      Laippala, Veronika",
+    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
+    month = may,
+    year = "2023",
+    address = "T{\'o}rshavn, Faroe Islands",
+    publisher = "University of Tartu Library",
+    url = "https://aclanthology.org/2023.nodalida-1.68",
+    pages = "685--697",
+    abstract = "Due to the popularity of social media platforms and the sheer amount of user-generated content online, the automatic detection of toxic language has become crucial in the creation of a friendly and safe digital space. Previous work has been mostly focusing on English leaving many lower-resource languages behind. In this paper, we present novel resources for toxicity detection in Finnish by introducing two new datasets, a machine translated toxicity dataset for Finnish based on the widely used English Jigsaw dataset and a smaller test set of Suomi24 discussion forum comments originally written in Finnish and manually annotated following the definitions of the labels that were used to annotate the Jigsaw dataset. We show that machine translating the training data to Finnish provides better toxicity detection results than using the original English training data and zero-shot cross-lingual transfer with XLM-R, even with our newly annotated dataset from Suomi24.",
+}
+```
+
 ## Label definitions taken from Perspective API
 
 THREAT: Describes an intention to inflict pain, injury, or violence against an individual or group.
@@ -93,17 +116,17 @@ target does not need to be present nor does the target matter
 
 ## Evaluation results
 
-Evaluation results from using a fine-tuned `TurkuNLP/bert-base-finnish-cased-toxicity`. (This specific model currently not in Huggingface, but a large variant is)
+Evaluation results from using `TurkuNLP/bert-large-finnish-cased-toxicity`.
 
 | Label | Precision | Recall | F1 |
 |------ | ------------------- | ---------------------------- | ---------------------- |
-| identity attack | 54,8 | 67,2  | 60,4  |
-| insult | 48,6  | 61, 2  | 54,1  |
-| severe toxicity | 11,9  | 81 | 20,7 |
-| threat | 16,3 | 82,5 | 27,2 |
-| toxicity | 57,6 | 64,9 | 61,1 |
-| obscene | 57,7  | 68,9  | 62,8 | 
-| OVERALL | 41,4  | 67,2  | 51,2  |
+| identity attack | 73,2 | 32  | 44,6  |
+| insult | 59,4  | 646,8  | 52,4  |
+| severe toxicity | 12  | 28,6 | 16,9 |
+| threat | 32,4 | 28,6 | 30,4 |
+| toxicity | 60,4 | 79,2 | 68,5 |
+| obscene | 64,5  | 82,4 | 72,3 | 
+| OVERALL | 57,4  | 58,9  | 51,1  |
 | OVERALL weighted by original sample counts | 55,5  | 65,5  | 60,1  |
 
 ## Licensing Information