Update README.md

README.md

@@ -10,7 +10,7 @@ language_creators:
 license: []
 multilinguality:
 - multilingual
-pretty_name: 'doc2doc information retrieval'
+pretty_name: 'Swiss Doc2doc Information Retrieval'
 size_categories:
 - 100K<n<1M
 source_datasets:
@@ -62,7 +62,7 @@ https://huggingface.co/spaces/huggingface/datasets-tagging
 
 ### Dataset Summary
 
-doc2doc is a multilingual, diachronic dataset of 130K Swiss Federal Supreme Court (FSCS) cases annotated with law citations and ruling citations, posing a challenging text classification task. As unique label we are using decision_id of cited rulings and uuid of cited law articles, which can be found in the SwissCourtRulingCorpus. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
+Swiss_doc2doc_ir is a multilingual, diachronic dataset of 131K Swiss Federal Supreme Court (FSCS) cases annotated with law citations and ruling citations, posing a challenging text classification task. As unique label we are using decision_id of cited rulings and uuid of cited law articles, which can be found in the SwissCourtRulingCorpus. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
 
 ### Supported Tasks and Leaderboards
 
@@ -70,7 +70,7 @@ doc2doc can be used as information retrieval task.
 
 ### Languages
 
-Switzerland has four official languages with three languages (German 85K, French 30k and Italian 10k) being represented. The decisions are written by the judges and clerks in the language of the proceedings.
+Switzerland has four official languages with three languages (German 86K, French 30k and Italian 10k) being represented. The decisions are written by the judges and clerks in the language of the proceedings.
 
 ## Dataset Structure
 
@@ -133,16 +133,16 @@ The dataset was split date-stratisfied
 
 | Language   | Subset     | Number of Documents (Training/Validation/Test) | 
 |------------|------------|--------------------------------------------|  
-| German     | **de**     |  /  /                          |
-| French     | **fr**     |  /  /                       |
-| Italian    | **it**     |  /  /                           |
+| German     | **de**     | 86'832 (59'170 / 19'002 / 8'660)               |
+| French     | **fr**     | 46'203 (30'513 / 10'816 / 4'874)               |
+| Italian    | **it**     | 8'306 (5'673 / 1'855 / 778)                          |
 
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-The dataset was curated by Stern et al. (2023).
+The dataset was created by Stern et al. (2023).
 
 ### Source Data
 
@@ -159,10 +159,11 @@ The original data are published from the Swiss Federal Supreme Court (https://ww
 #### Annotation process
 
 The decisions have been annotated with the citation ids using html tags and parsers.
+For more details on laws (rcds/swiss_legislation) and rulings (rcds/swiss_rulings).
 
 #### Who are the annotators?
 
-Ronja Stern annotated the citations.
+Stern annotated the citations.
 Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
 
 ### Personal and Sensitive Information