Delete german_ler.py

german_ler.py

@@ -1,185 +0,0 @@
-# coding=utf-8
-# Copyright 2020 HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-import datasets
-
-
-_DESCRIPTION = """\
-A dataset of Legal Documents from German federal court decisions for Named Entity Recognition. The dataset is human-annotated with 19 fine-grained entity classes. The dataset consists of approx. 67,000 sentences and contains 54,000 annotated entities.
-"""
-
-_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}
-}
-"""
-_URL = {
-    "train": "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler_train.conll",
-    "dev": "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler_dev.conll",  
-    "test": "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler_test.conll",
-}
-_VERSION = "1.0.0"
-
-
-class German_LER(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version(_VERSION)
-
-    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):
-        """Returns SplitGenerators."""
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        data_dir = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"datapath": data_dir["train"], "split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"datapath": data_dir["test"], "split": "test"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"datapath": data_dir["dev"], "split": "dev"},
-            ),
-        ]
-    
-                            
-    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