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german-ler.py

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+# 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