remove empty trailing sentences

dataset_infos.json

@@ -1 +1 @@
-{"german_ler": {"description": "description", "citation": "@inproceedings{leitner2019fine,\nauthor = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},\ntitle = {{Fine-grained Named Entity Recognition in Legal Documents}},\nbooktitle = {Semantic Systems. The Power of AI and Knowledge\n  Graphs. Proceedings of the 15th International Conference\n  (SEMANTiCS 2019)},\nyear = 2019,\neditor = {Maribel Acosta and Philippe Cudr\u00e9-Mauroux and Maria\n  Maleshkova and Tassilo Pellegrini and Harald Sack and York\n  Sure-Vetter},\nkeywords = {aip},\npublisher = {Springer},\nseries = {Lecture Notes in Computer Science},\nnumber = {11702},\naddress = {Karlsruhe, Germany},\nmonth = 9,\nnote = {10/11 September 2019},\npages = {272--287},\npdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}}\n", "homepage": "https://github.com/elenanereiss/Legal-Entity-Recognition", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"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"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "legal_glue", "config_name": "german_ler", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 38854047, "num_examples": 66730, "dataset_name": "legal_glue"}}, "download_checksums": {"https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/dataset_courts.zip": {"num_bytes": 4392913, "checksum": "f0427df5fb8bfdefe5228bc0fa0e75e9cfa782d1a78e32582cce096473c88567"}}, "download_size": 4392913, "post_processing_size": null, "dataset_size": 38854047, "size_in_bytes": 43246960}}
+{"german_ler": {"description": "description", "citation": "@inproceedings{leitner2019fine,\nauthor = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},\ntitle = {{Fine-grained Named Entity Recognition in Legal Documents}},\nbooktitle = {Semantic Systems. The Power of AI and Knowledge\n  Graphs. Proceedings of the 15th International Conference\n  (SEMANTiCS 2019)},\nyear = 2019,\neditor = {Maribel Acosta and Philippe Cudr\u00e9-Mauroux and Maria\n  Maleshkova and Tassilo Pellegrini and Harald Sack and York\n  Sure-Vetter},\nkeywords = {aip},\npublisher = {Springer},\nseries = {Lecture Notes in Computer Science},\nnumber = {11702},\naddress = {Karlsruhe, Germany},\nmonth = 9,\nnote = {10/11 September 2019},\npages = {272--287},\npdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}}\n", "homepage": "https://github.com/elenanereiss/Legal-Entity-Recognition", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"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"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "legal_glue", "config_name": "german_ler", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 38853928, "num_examples": 66723, "dataset_name": "legal_glue"}}, "download_checksums": {"https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/dataset_courts.zip": {"num_bytes": 4392913, "checksum": "f0427df5fb8bfdefe5228bc0fa0e75e9cfa782d1a78e32582cce096473c88567"}}, "download_size": 4392913, "post_processing_size": null, "dataset_size": 38853928, "size_in_bytes": 43246841}}

legalglue.py

@@ -68,6 +68,21 @@ GERMAN_LER = [
     "I-VT",
     "O"]
 
+LENER_BR=[
+            "O",
+            "B-ORGANIZACAO",
+            "I-ORGANIZACAO",
+            "B-PESSOA",
+            "I-PESSOA",
+            "B-TEMPO",
+            "I-TEMPO",
+            "B-LOCAL",
+            "I-LOCAL",
+            "B-LEGISLACAO",
+            "I-LEGISLACAO",
+            "B-JURISPRUDENCIA",
+            "I-JURISPRUDENCIA",
+        ]
 
 class LegalGlueConfig(datasets.BuilderConfig):
     """BuilderConfig for LegalGLUE."""
@@ -128,7 +143,45 @@ class LegalGLUE(datasets.GeneratorBasedBuilder):
                 pages = {272--287},
                 pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}}
             """)
-        )
+        ),
+        # LegalGlueConfig(
+        #     name="lener_br",
+        #     description=textwrap.dedent(
+        #     """\
+        #     LeNER-Br is a Portuguese language dataset for named entity recognition
+        #     applied to legal documents. LeNER-Br consists entirely of manually annotated
+        #     legislation and legal cases texts and contains tags for persons, locations,
+        #     time entities, organizations, legislation and legal cases.
+        #     To compose the dataset, 66 legal documents from several Brazilian Courts were
+        #     collected. Courts of superior and state levels were considered, such as Supremo
+        #     Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas
+        #     Gerais and Tribunal de Contas da União. In addition, four legislation documents
+        #     were collected, such as "Lei Maria da Penha", giving a total of 70 documents
+        #     """
+        #     ),
+        #     label_classes=LENER_BR,
+        #     multi_label=False,
+        #     data_url="https://github.com/peluz/lener-br/raw/master/leNER-Br/",
+        #     data_files=["train/train.conll", "dev/dev.conll", "test/test.conll"],
+        #     homepage="https://cic.unb.br/~teodecampos/LeNER-Br/",
+        #     citation=textwrap.dedent("""\
+        #         @inproceedings{luz_etal_propor2018,
+        #         author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and
+        #         Renato R. R. {de Oliveira} and Matheus Stauffer and
+        #         Samuel Couto and Paulo Bermejo},
+        #         title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},
+        #         booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})},
+        #         publisher = {Springer},
+        #         series = {Lecture Notes on Computer Science ({LNCS})},
+        #         pages = {313--323},
+        #         year = {2018},
+        #         month = {September 24-26},
+        #         address = {Canela, RS, Brazil},
+        #         doi = {10.1007/978-3-319-99722-3_32},
+        #         url = {https://cic.unb.br/~teodecampos/LeNER-Br/},
+        #         }
+        #     """)
+        # )
     ]
 
     def _info(self):
@@ -163,6 +216,9 @@ class LegalGLUE(datasets.GeneratorBasedBuilder):
                     "files": [os.path.join(archive,file) for file in self.config.data_files]#dl_manager.iter_archive(archive),
                 },
             )]
+        #elif self.config_name == "lener_br":
+
+
         # else:
         #     return [
         #         datasets.SplitGenerator(
@@ -213,8 +269,9 @@ class LegalGLUE(datasets.GeneratorBasedBuilder):
                             token, tag = line.split()
                             tokens.append(token)
                             tags.append(tag.rstrip())
-                    texts.append(tokens)
-                    labels.append(tags)
+                    if tokens:
+                        texts.append(tokens)
+                        labels.append(tags)
 
             for i,token in enumerate(texts):
                 tokens = texts[i]