Update README.md

README.md

@@ -12,7 +12,7 @@ multilinguality:
 paperswithcode_id: dataset-of-legal-documents
 pretty_name: German Named Entity Recognition in Legal Documents
 size_categories:
-- 2 Mio
+- 2M
 source_datasets:
 - original
 task_categories:
@@ -69,35 +69,73 @@ For more details see [https://arxiv.org/pdf/2003.13016v1.pdf](https://arxiv.org/
 German
 
 ## Dataset Structure
-
 ### Data Instances
-
+```
+{
+ 'id': '1',
+ 'tokens': ['Eine', 'solchermaßen', 'verzögerte', 'oder', 'bewusst', 'eingesetzte', 'Verkettung', 'sachgrundloser', 'Befristungen', 'schließt', '§', '14', 'Abs.', '2', 'Satz', '2', 'TzBfG', 'aus', '.'],
+ 'ner_tags': [38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3, 22, 22, 22, 22, 22, 22, 38, 38]
+}
+```
 ### Data Fields
 
-The data fields are the same among all splits.
-
-- `id`: a `string` feature.
-- `tokens`: a `list` of `string` features.
-- `ner_tags`: a `list` of classification labels.
+```
+{
+ 'id': Value(dtype='string', id=None),
+ 'tokens': Sequence(feature=Value(dtype='string', id=None), 
+                    length=-1, id=None),
+ 'ner_tags': Sequence(feature=ClassLabel(num_classes=39, 
+                                         names=['B-AN', 
+                                                'B-EUN', 
+                                                'B-GRT', 
+                                                'B-GS', 
+                                                'B-INN', 
+                                                'B-LD', 
+                                                'B-LDS', 
+                                                'B-LIT', 
+                                                'B-MRK', 
+                                                'B-ORG', 
+                                                'B-PER', 
+                                                'B-RR', 
+                                                'B-RS', 
+                                                'B-ST', 
+                                                'B-STR', 
+                                                'B-UN', 
+                                                'B-VO', 
+                                                'B-VS', 
+                                                'B-VT', 
+                                                'I-AN', 
+                                                'I-EUN', 
+                                                'I-GRT', 
+                                                'I-GS', 
+                                                'I-INN', 
+                                                'I-LD', 
+                                                'I-LDS', 
+                                                'I-LIT', 
+                                                'I-MRK', 
+                                                'I-ORG', 
+                                                'I-PER', 
+                                                'I-RR', 
+                                                'I-RS', 
+                                                'I-ST', 
+                                                'I-STR', 
+                                                'I-UN', 
+                                                'I-VO', 
+                                                'I-VS', 
+                                                'I-VT', 
+                                                'O'], 
+                                         id=None), 
+                      length=-1, 
+                      id=None)
+}
+```
 
 ### Data Splits
 
-```
-DatasetDict({
-    train: Dataset({
-        features: ['id', 'tokens', 'ner_tags'],
-        num_rows: 53384
-    })
-    test: Dataset({
-        features: ['id', 'tokens', 'ner_tags'],
-        num_rows: 6673
-    })
-    validation: Dataset({
-        features: ['id', 'tokens', 'ner_tags'],
-        num_rows: 6666
-    })
-})
-```
+|                         | train | validation | test |
+|-------------------------|------:|-----------:|-----:|
+| Input Sentences         | 53384 |    6666    | 6673 |
+
 
 <!-- 
 
@@ -182,26 +220,4 @@ For more details see [https://github.com/elenanereiss/Legal-Entity-Recognition/b
 }
 ```
 
-```
-@inproceedings{leitner2019fine,
-  author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},
-  title = {{Fine-grained Named Entity Recognition in Legal Documents}},
-  booktitle = {Semantic Systems. The Power of AI and Knowledge
-                  Graphs. Proceedings of the 15th International Conference
-                  (SEMANTiCS 2019)},
-  year = 2019,
-  editor = {Maribel Acosta and Philippe Cudré-Mauroux and Maria
-                  Maleshkova and Tassilo Pellegrini and Harald Sack and York
-                  Sure-Vetter},
-  keywords = {aip},
-  publisher = {Springer},
-  series = {Lecture Notes in Computer Science},
-  number = {11702},
-  address = {Karlsruhe, Germany},
-  month = 9,
-  note = {10/11 September 2019},
-  pages = {272--287},
-  pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}}
-```
-
 ### Contributions