Datasets:

elenanereiss
/

german-ler

Tasks:

Token Classification

Sub-tasks:

named-entity-recognition

Languages:

Multilinguality:

Size Categories:

Language Creators:

Annotations Creators:

expert-generated

Source Datasets:

ArXiv:

Tags:

ner, named entity recognition, legal ner, legal texts, label classification

DOI:

License:

Dataset card Viewer Files Files and versions Community

elenanereiss commited on Oct 25, 2022

Commit

25ee654

•

1 Parent(s): 4dfbe0d

Update german-ler.py

Files changed (1) hide show

german-ler.py +9 -8

german-ler.py CHANGED Viewed

@@ -174,16 +174,17 @@ class German_LER(datasets.GeneratorBasedBuilder):
                     current_labels.append(label)
                     # generate coarse-grained tags
-                    if label == 'O': continue
                     else:
                         bio, fine_tag = label.split("-")
-                        if fine_tag  in ['PER', 'RR', 'AN']: label = bio + '-PER'
-                        elif fine_tag  in ['LD', 'ST', 'STR', 'LDS']: label = bio + '-LOC'
-                        elif fine_tag  in ['ORG', 'UN', 'INN', 'GRT', 'MRK']: label = bio + '-ORG'
-                        elif fine_tag  in ['GS', 'VO', 'EUN']: label = bio + '-NRM'
-                        elif fine_tag  in ['VS', 'VT']: label = bio + '-REG'
-                        else: continue
-                    current_coarse_labels.append(label)
                 else:
                     if not current_words:

                     current_labels.append(label)
                     # generate coarse-grained tags
+                    new_label = ""
+                    if label == 'O': new_label = label
                     else:
                         bio, fine_tag = label.split("-")
+                        if fine_tag  in ['PER', 'RR', 'AN']: new_label = bio + '-PER'
+                        elif fine_tag  in ['LD', 'ST', 'STR', 'LDS']: new_label = bio + '-LOC'
+                        elif fine_tag  in ['ORG', 'UN', 'INN', 'GRT', 'MRK']: new_label = bio + '-ORG'
+                        elif fine_tag  in ['GS', 'VO', 'EUN']: new_label = bio + '-NRM'
+                        elif fine_tag  in ['VS', 'VT']: new_label = bio + '-REG'
+                        else: new_label = label
+                    current_coarse_labels.append(new_label)
                 else:
                     if not current_words: