import from S3

dataset_infos.json

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+{"default": {"description": "We describe a dataset developed for Named Entity Recognition in German federal court decisions. \nIt consists of approx. 67,000 sentences with over 2 million tokens. \nThe resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: \nperson, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, \nordinance, European legal norm, regulation, contract, court decision, and legal literature. \nThe legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. \nThe dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, \nwas developed for training an NER service for German legal documents in the EU project Lynx.\n", "citation": "@inproceedings{leitner2019fine,\n  author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},\n  title = {{Fine-grained Named Entity Recognition in Legal Documents}},\n  booktitle = {Semantic Systems. The Power of AI and Knowledge\n                  Graphs. Proceedings of the 15th International Conference\n                  (SEMANTiCS 2019)},\n  year = 2019,\n  editor = {Maribel Acosta and Philippe Cudr\u00e9-Mauroux and Maria\n                  Maleshkova and Tassilo Pellegrini and Harald Sack and York\n                  Sure-Vetter},\n  keywords = {aip},\n  publisher = {Springer},\n  series = {Lecture Notes in Computer Science},\n  number = {11702},\n  address = {Karlsruhe, Germany},\n  month = 9,\n  note = {10/11 September 2019},\n  pages = {272--287},\n  pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}\n}\n", "homepage": "https://github.com/elenanereiss/Legal-Entity-Recognition", "license": "", "features": {"id": {"dtype": "int32", "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": ["O", "B-PER", "I-PER", "B-RR", "I-RR", "B-AN", "I-AN", "B-LD", "I-LD", "B-ST", "I-ST", "B-STR", "I-STR", "B-LDS", "I-LDS", "B-ORG", "I-ORG", "B-UN", "I-UN", "B-INN", "I-INN", "B-GRT", "I-GRT", "B-MRK", "I-MRK", "B-GS", "I-GS", "B-VO", "I-VO", "B-EUN", "I-EUN", "B-VS", "I-VS", "B-VT", "I-VT", "B-RS", "I-RS", "B-LIT", "I-LIT"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": {"input": "tokens", "output": "ner_tags"}, "builder_name": "ler", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 38531395, "num_examples": 66723, "dataset_name": "ler"}}, "download_checksums": {"https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler.conll": {"num_bytes": 19692859, "checksum": "b05bf29720519d3d4a871677189035390607140887e871e30e8abc68ed01581f"}}, "download_size": 19692859, "post_processing_size": null, "dataset_size": 38531395, "size_in_bytes": 58224254}}

dummy/1.0.0/dummy_data/ler.conll

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+Prozesskostenhilfe O
+- O
+Entschädigung O
+für O
+überlange O
+Verfahrensdauer O
+- O
+Revisionsverfahren O

ler.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fine-grained Named Entity Recognition in Legal Documents"""
+
+from __future__ import absolute_import, division, print_function
+
+import datasets
+
+_CITATION = """\
+@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}
+}
+"""
+
+_DESCRIPTION = """\
+We describe a dataset developed for Named Entity Recognition in German federal court decisions. 
+It consists of approx. 67,000 sentences with over 2 million tokens. 
+The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: 
+person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, 
+ordinance, European legal norm, regulation, contract, court decision, and legal literature. 
+The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. 
+The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, 
+was developed for training an NER service for German legal documents in the EU project Lynx.
+"""
+
+_URL = "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler.conll"
+
+
+class Ler(datasets.GeneratorBasedBuilder):
+    """
+    We describe a dataset developed for Named Entity Recognition in German federal court decisions.
+    It consists of approx. 67,000 sentences with over 2 million tokens.
+    The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes:
+    person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law,
+    ordinance, European legal norm, regulation, contract, court decision, and legal literature.
+    The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions.
+    The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format,
+    was developed for training an NER service for German legal documents in the EU project Lynx.
+    """
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.ClassLabel(
+                            names=[
+                                "O",
+                                "B-PER",
+                                "I-PER",
+                                "B-RR",
+                                "I-RR",
+                                "B-AN",
+                                "I-AN",
+                                "B-LD",
+                                "I-LD",
+                                "B-ST",
+                                "I-ST",
+                                "B-STR",
+                                "I-STR",
+                                "B-LDS",
+                                "I-LDS",
+                                "B-ORG",
+                                "I-ORG",
+                                "B-UN",
+                                "I-UN",
+                                "B-INN",
+                                "I-INN",
+                                "B-GRT",
+                                "I-GRT",
+                                "B-MRK",
+                                "I-MRK",
+                                "B-GS",
+                                "I-GS",
+                                "B-VO",
+                                "I-VO",
+                                "B-EUN",
+                                "I-EUN",
+                                "B-VS",
+                                "I-VS",
+                                "B-VT",
+                                "I-VT",
+                                "B-RS",
+                                "I-RS",
+                                "B-LIT",
+                                "I-LIT",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=datasets.info.SupervisedKeysData(input="tokens", output="ner_tags"),
+            # Homepage of the dataset for documentation
+            homepage="https://github.com/elenanereiss/Legal-Entity-Recognition",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        dl_file = dl_manager.download(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_file},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """ Yields examples. """
+        with open(filepath, "r", encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            ner_tags = []
+            for line in f:
+                if line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {"id": guid, "tokens": tokens, "ner_tags": ner_tags}
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    # conll2002 tokens are space separated
+                    splits = line.split(" ")
+                    tokens.append(splits[0])
+                    ner_tags.append(splits[1].rstrip())