Update README file.

README.md

@@ -14,7 +14,7 @@ size_categories:
 ## Summary
 
 This dataset contains user comments from an Austrian online newspaper. 
-The comments have been  annotated by 2 or more out of 8 annotators 
+The comments have been  annotated by 4 or more out of 11 annotators 
 as to how strong sexism/mysogyny is present in the comment.
 
 For each comment, the code of the annotator and the label assigned is given for all 
@@ -22,7 +22,10 @@ annotators which have annotated that comment.  Labels represent the severity of
 sexism/misogyny present in the comment from 0 (none), 
 1 (mild), 2 (present), 3 (strong) to 4 (severe). 
 
-The dataset currently contains 7995 comments. 
+The dataset contains 7984 comments. We provide the data using the same split as was used for 
+the [GermEval2024 GerMS-Detecht shared task](https://ofai.github.io/GermEval2024-GerMS/) with 
+a training set of 5998 comments and a test set of 1986 comments. No dev set is provided as the 
+choice of dev set may be best left to the machine learning researcher/engineer. 
 
 A unique propery of this corpus is that it contains only a small portion of sexist/misogynyst remarks
 which use strong language, curse-words or otherwise blatantly offending terms, a large number 
@@ -40,37 +43,26 @@ All comments are in a single JSONL file, one comment per line with the following
   * `round`: comments were annotated in rounds of 100, this gives the round identifier as a string containing a two-digit round number, e.g. "00" or "13"
   * `source`: the code which identifies how comments which are likely negative and positive examples where selected for the annotation round
   
-### Annotator codes - the following table shows the possible annotator codes and the number of comments annotated by each of them:
-
-| Annotator code | Annotations | 
-| -- | --: | 
-| A1m  |  1298 |
-| A2f  |  7995 |
-| A3m  |  1699 |
-| A4m  |  1898 |
-| A5f  |  2097 |
-| A7f  |  1698 |
-| A8f  |  2498 | 
-| A9f  |  3897 |
-  
-The suffix of the annotator code identifies the self-declared gender (f=female, m=male) of the annotator. 
+### Annotator codes - the following table shows the possible annotator codes and the number of comments annotated by each of them for the Train, Test and 
+combined data:
 
-### Comment source identifiers
+| Annotator code | Train | Test | All |
+| --   |  --:  |  --:  |  --: | 
+| A001 |   970 |  328  | 1298 |
+| A002 |  5998 | 1986  | 7984 |
+| A003 |  1242 |  456  | 1698 |
+| A004 |  1394 |  504  | 1898 |
+| A005 |  1552 |  542  | 2094 |
+| A007 |  1246 |  451  | 1697 |
+| A008 |  1849 |  649  | 2498 |
+| A009 |  2923 |  971  | 3894 |
+| A010 |  5998 | 1986  | 7984 |
+| A011 |   927 |  114  | 1041 |
+| A012 |  5998 | 1661  | 7659 |
 
-The following comment source identifiers are present in the `source` field of the given number of comments:
+Annotor IDs are anonymized and deliberately do not give demographic information. Among the 11 annotators there were
+4 male and 7 female annotators. There were 7 annotators who are content moderators and 4 annotators who are not.
 
-| Comment source | number of comments | 
-| --- | ---: | 
-| forum1-sexist            |   1400
-| meld02/meld02neg         |   1000
-| meld02/neg01             |    999
-| meld04/meld04neg         |    899
-| forum2                   |    800
-| forum1                   |    799
-| meld02CLpos/meld02CLneg  |    700
-| meld01/meld01neg         |    698
-| meld03/meld03neg         |    500
-| meld01/neg01             |    200
 
 
 ## Language
@@ -92,4 +84,16 @@ See the detailled [datasheet](./datasheet.md)
 
 ## Papers
 
-TBD
+Brigitte Krenn, Johann Petrak, Marina Kubina, and Christian Burger. 2024. 
+_Germs-at: A sex-ism/misogyny dataset of forum comments from an Austrian online newspaper._
+In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7728–7739.
+
+```
+@inproceedings{krenn2024,
+  title={{GERMS-AT}: A Sexism/Misogyny Dataset of Forum Comments from an {A}ustrian Online Newspaper},
+  author={Krenn, Brigitte and Petrak, Johann and Kubina, Marina and Burger, Christian},
+  booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
+  pages={7728--7739},
+  year={2024}
+}
+```