Update README.md

added dataset card information

README.md

@@ -67,11 +67,22 @@ German Legal Sentences (GLS) is an automatically generated training dataset for
 
 ### Supported Tasks and Leaderboards
 
-[More Information Needed]
+The main associated task is *Semantic Similarity Ranking*. We propose to use the *Mean Reciprocal Rank* (MRR) cut at the tenth position as well as MAP and Recall on Rankings of size 200. As baselines we provide the follows: 
+
+| Method                            | MRR@10   | MAP@200    | Recall@200  |
+|-----------------------------------|---------:|-----------:|------------:|
+| BM25 - default `(k1=1.2; b=0.75)` |     25.7 |       17.6 |        42.9 |
+| BM25 - tuned `(k1=0.47; b=0.97)`  |     26.2 |       18.1 |        43.3 |
+| [CoRT](https://arxiv.org/abs/2010.10252)             |     31.2 |       21.4 |        56.2 |
+| [CoRT + BM25](https://arxiv.org/abs/2010.10252)      |     32.1 |       22.1 |        67.1 |
+
+In addition, we want to support a *Citation Recommendation* task in the future.
+
+If you wish to contribute evaluation measures or give any suggestion or critique, please write an [e-mail](mailto:marco.wrzalik@hs-rm.de).
 
 ### Languages
 
-The text in this dataset is in english
+This dataset contains texts from the specific domain of German court decisions.
 
 ## Dataset Structure
 
@@ -126,7 +137,7 @@ We use [SoMaJo](https://github.com/tsproisl/SoMaJo) to perform sentence tokenizi
 
 #### Who are the source language producers?
 
-[More Information Needed]
+The source language originates in the context of German court proceedings.
 
 ### Annotations
 
@@ -140,13 +151,13 @@ The annotations are machine-generated.
 
 ### Personal and Sensitive Information
 
-[More Information Needed]
+The source documents are already public and anonymized.
 
 ## Considerations for Using the Data
 
 ### Social Impact of Dataset
 
-[More Information Needed]
+With this dataset, we strive towards better accessibility of court decisions to the general public by accelerating research on semantic search technologies. We hope that emerging search technologies will enable the layperson to find relevant information without knowing the specific terms used by lawyers.
 
 ### Discussion of Biases
 
@@ -168,7 +179,7 @@ The annotations are machine-generated.
 
 ### Citation Information
 
-[More Information Needed]
+Coming soon!
 
 ### Contributions