Update parquet files

.gitattributes

@@ -14,3 +14,4 @@
 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
+default/ler-train.parquet filter=lfs diff=lfs merge=lfs -text

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}}

default/ler-train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:87c26bf3b30384c897060969f96a324101f1c65082c7b65b3dd2475b1891ffef
+size 7061354

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

@@ -1,166 +0,0 @@
-# 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())