Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- de
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- named-entity-recognition
+---
+
+# Dataset Card Creation Guide
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/elenanereiss/Legal-Entity-Recognition
+- **Repository:** None
+- **Paper:** https://link.springer.com/chapter/10.1007/978-3-030-33220-4_20
+- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
+- **Point of Contact:** Georg Rehm (georg.rehm@dfki.de)
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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german_legal_entity_recognition.py

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+import os
+
+import datasets
+
+
+_DESCRIPTION = """\
+"""
+_HOMEPAGE_URL = "https://github.com/elenanereiss/Legal-Entity-Recognition"
+_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}}
+"""
+_DATA_URL = "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/dataset_courts.zip"
+_VERSION = "1.0.0"
+_COURTS = ["bag", "bfh", "bgh", "bpatg", "bsg", "bverfg", "bverwg"]
+_COURTS_FILEPATHS = {court: f"{court}.conll" for court in _COURTS}
+_ALL = "all"
+
+
+class GermanLegalEntityRecognitionConfig(datasets.BuilderConfig):
+    def __init__(self, *args, courts=None, **kwargs):
+        super().__init__(*args, version=datasets.Version(_VERSION, ""), **kwargs)
+        self.courts = courts
+
+    @property
+    def filepaths(self):
+        return [_COURTS_FILEPATHS[court] for court in self.courts]
+
+
+class GermanLegalEntityRecognition(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        GermanLegalEntityRecognitionConfig(name=court, courts=[court], description=f"Court. {court}.")
+        for court in _COURTS
+    ] + [GermanLegalEntityRecognitionConfig(name=_ALL, courts=_COURTS, description=f"All courts included.")]
+    BUILDER_CONFIG_CLASS = GermanLegalEntityRecognitionConfig
+    DEFAULT_CONFIG_NAME = _ALL
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            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",
+                            ]
+                        )
+                    ),
+                },
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download_and_extract(_DATA_URL)
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"datapath": path})]
+
+    def _generate_examples(self, datapath):
+        sentence_counter = 0
+        for filepath in self.config.filepaths:
+            filepath = os.path.join(datapath, filepath)
+            with open(filepath, encoding="utf-8") as f:
+                current_words = []
+                current_labels = []
+                for row in f:
+                    row = row.rstrip()
+                    row_split = row.split()
+                    if len(row_split) == 2:
+                        token, label = row_split
+                        current_words.append(token)
+                        current_labels.append(label)
+                    else:
+                        if not current_words:
+                            continue
+                        assert len(current_words) == len(current_labels), "word len doesnt match label length"
+                        sentence = (
+                            sentence_counter,
+                            {
+                                "id": str(sentence_counter),
+                                "tokens": current_words,
+                                "ner_tags": current_labels,
+                            },
+                        )
+                        sentence_counter += 1
+                        current_words = []
+                        current_labels = []
+                        yield sentence
+
+                # if something remains:
+                if current_words:
+                    sentence = (
+                        sentence_counter,
+                        {
+                            "id": str(sentence_counter),
+                            "tokens": current_words,
+                            "ner_tags": current_labels,
+                        },
+                    )
+                    yield sentence