Update README.md

README.md

@@ -85,7 +85,7 @@ language:
 - it
 pretty_name: VIRC
 size_categories:
-- 1K<n<10K
+- n<1K
 ---
 
 # Vulnerable Identities Recognition Corpus (VIRC) for Hate Speech Analysis
@@ -99,15 +99,22 @@ VIRC is designed to support the study of hate speech in headlines from two langu
 - *Derogatory Mentions*: Marks phrases that are derogatory towards vulnerable groups.
 - *Dangerous Speech*: Highlights parts of the text perceived as potentially inciting hate or perpetuating harmful stereotypes.
 
+## Data
+  - `Spanish`: The Spanish datasets are split into two sets, *agreement* and *disagreement*. *Agreement* set contains the data annotated by the two original annotators, while the *disagreement* set contains the news where no agreement was reached and a third annotator was needed.
+  - `Italian`: The Italian data consists of only one set annotated by two annotators.
 
 ## Annotation
-The `VIRC_Guidelines.pdf` contains the annotation guidelines provided to annotators. This can be seen sintetized in the paper.
-
-## Data
-  - `Spanish:` The Spanish datasets are split into three sets, *1*, *2* and *disagreement*. Sets *1* and *2* contain data annotated by the two original annotators, while the *disagreement* set contains the news where no agreement was reached and a third annotator was needed.
-  - `Italian:` The Italian data are divided into two sets (*1* and *2*), annotated by two annotators.
-  - `Disaggregated:` The files containing all the annotations are named according to the following schema *annotations_{language}_{set}.json*.
-  - `Gold-standard:` The gold-standard dataset generated through the process explained in the paper correspond to the *ita_gold.csv* and *spa_gold.csv* files.
+The `VIRC_Guidelines.pdf` contains the annotation guidelines provided to annotators. This can be seen sintetized in the paper. The dataset is provided with several split according to the following schema:
+ - `all`: 
+ - `all_tags`:
+ - `gold_tags`:
+ - `spa_all_tags`:
+ - `spa_gold_tags`:
+ - `ita_all_tags`:
+ - `ita_gold_tags`: 
+ - `all_comments`:
+ - `spa_all_comments`:
+ - `ita_all_comments`:
 
 ## Acknowledgements
 This work is supported by the Predoctoral Grant (PIPF-2022/COM-25947) of the Consejería de Educación, Ciencia y Universidades de la Comunidad de Madrid, Spain. Arianna Longo's work has been supported by aequa-tech.