added lener_br dataset

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": 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}}
+{"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}, "lener_br": {"description": "LeNER-Br is a Portuguese language dataset for named entity recognition\napplied to legal documents. LeNER-Br consists entirely of manually annotated\nlegislation and legal cases texts and contains tags for persons, locations,\ntime entities, organizations, legislation and legal cases.\nTo compose the dataset, 66 legal documents from several Brazilian Courts were\ncollected. Courts of superior and state levels were considered, such as Supremo\nTribunal Federal, Superior Tribunal de Justi\u00e7a, Tribunal de Justi\u00e7a de Minas\nGerais and Tribunal de Contas da Uni\u00e3o. In addition, four legislation documents\nwere collected, such as \"Lei Maria da Penha\", giving a total of 70 documents\n", "citation": "@inproceedings{luz_etal_propor2018,\nauthor = {Pedro H. {Luz de Araujo} and Te'{o}filo E. {de Campos} and\nRenato R. R. {de Oliveira} and Matheus Stauffer and\nSamuel Couto and Paulo Bermejo},\ntitle = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},\nbooktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})},\npublisher = {Springer},\nseries = {Lecture Notes on Computer Science ({LNCS})},\npages = {313--323},\nyear = {2018},\nmonth = {September 24-26},\naddress = {Canela, RS, Brazil},\ndoi = {10.1007/978-3-319-99722-3_32},\nurl = {https://cic.unb.br/~teodecampos/LeNER-Br/},\n}\n", "homepage": "https://cic.unb.br/~teodecampos/LeNER-Br/", "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": 13, "names": ["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"], "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": "lener_br", "version": {"version_str": "1.1.0", "description": "", "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7153474101, "num_examples": 7828, "dataset_name": "legal_glue"}, "test": {"name": "test", "num_bytes": 195782089, "num_examples": 1177, "dataset_name": "legal_glue"}, "validation": {"name": "validation", "num_bytes": 280965196, "num_examples": 1390, "dataset_name": "legal_glue"}}, "download_checksums": {"https://github.com/peluz/lener-br/raw/master/leNER-Br/train/train.conll": {"num_bytes": 2142199, "checksum": "6fdf9066333c84565f9e3d28ee8f0f519336bece69b63f8d78b8de0fe96dcd47"}, "https://github.com/peluz/lener-br/raw/master/leNER-Br/test/test.conll": {"num_bytes": 438441, "checksum": "f90cd26a31afc2d1f132c4473d40c26d2283a98b374025fa5b5985b723dce825"}, "https://github.com/peluz/lener-br/raw/master/leNER-Br/dev/dev.conll": {"num_bytes": 402497, "checksum": "7e350feb828198031e57c21d6aadbf8dac92b19a684e45d7081c6cb491e2063b"}}, "download_size": 2983137, "post_processing_size": null, "dataset_size": 7630221386, "size_in_bytes": 7633204523}}

legalglue.py

@@ -144,44 +144,44 @@ class LegalGLUE(datasets.GeneratorBasedBuilder):
                 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/},
-        #         }
-        #     """)
-        # )
+        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):
@@ -195,7 +195,16 @@ class LegalGLUE(datasets.GeneratorBasedBuilder):
                     )
                 )
             }
-
+        elif self.config.name == "lener_br":
+            features = {
+                "id": datasets.Value("string"),
+                "tokens": datasets.Sequence(datasets.Value("string")),
+                "ner_tags": datasets.Sequence(
+                    datasets.features.ClassLabel(
+                        names=self.config.label_classes
+                    )
+                )
+            }
         return datasets.DatasetInfo(
             description=self.config.description,
             features=datasets.Features(features),
@@ -216,8 +225,42 @@ 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":
-
+        elif self.config.name == "lener_br":
+            urls_to_download = {
+            "train": self.config.data_url + self.config.data_files[0],
+            "dev": self.config.data_url + self.config.data_files[2],
+            "test": self.config.data_url + self.config.data_files[1],
+            }
+            archive = dl_manager.download_and_extract(urls_to_download)
+            return[
+                    datasets.SplitGenerator(
+                        name=datasets.Split.TRAIN,
+                        # These kwargs will be passed to _generate_examples
+                        gen_kwargs={
+                            "filepath": self.config.data_files,
+                            "split": "train",
+                            "files": archive["train"],
+                            },
+                        ),
+                    datasets.SplitGenerator(
+                        name=datasets.Split.TEST,
+                        # These kwargs will be passed to _generate_examples
+                        gen_kwargs={
+                            "filepath": self.config.data_files,
+                            "split": "test",
+                            "files": archive["test"],
+                        },
+                    ),
+                    datasets.SplitGenerator(
+                        name=datasets.Split.VALIDATION,
+                        # These kwargs will be passed to _generate_examples
+                        gen_kwargs={
+                            "filepath": self.config.data_files,
+                            "split": "validation",
+                            "files":  archive["dev"],
+                        },
+                    ),
+                ]
 
         # else:
         #     return [
@@ -281,3 +324,29 @@ class LegalGLUE(datasets.GeneratorBasedBuilder):
                         "tokens": tokens,
                         "ner_tags": ner_tags,
                         }
+        elif self.config.name == "lener_br":
+            with open(files, encoding="utf-8") as f:
+                id = 0
+                tokens = []
+                tags = []
+                for line in f:
+                    if line == "" or line == "\n":
+                        if tokens:
+                            yield id, {
+                                "id": str(id),
+                                "tokens": tokens,
+                                "ner_tags": tags,
+                                }
+                            id += 1
+                            tokens = []
+                            ner_tags = []
+                    else:
+                        token, tag = line.split()
+                        tokens.append(token)
+                        tags.append(tag.rstrip())
+
+                yield id, {
+                    "id": str(id),
+                    "tokens": tokens,
+                    "ner_tags": ner_tags,
+                    }