Upload legalglue.py

legalglue.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.
+"""LegalGLUE: A Benchmark Dataset for Legal NLP models."""
+
+import csv
+import json
+import textwrap
+
+import datasets
+
+
+_DESCRIPTION = """\
+Legal General Language Understanding Evaluation (LegalGLUE) benchmark is
+a collection of datasets for evaluating model performance across a diverse set of legal NLP tasks
+"""
+
+GERMAN_LER = [
+    "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"]
+
+
+class LegalGlueConfig(datasets.BuilderConfig):
+    """BuilderConfig for LegalGLUE."""
+
+    def __init__(
+        self,
+        label_classes,  #the list of classes of the labels
+        multi_label,    #boolean, if the task is multi-label
+        homepage,       #homepage of the original dataset
+        citation,       #citation for the dataset
+        data_url,
+        data_files,
+        **kwargs,
+    ):
+        super(LegalGlueConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs)
+        self.label_classes = label_classes
+        self.multi_label = multi_label
+        self.homepage = homepage
+        self.citation = citation
+        self.data_url = data_url
+        self.data_files = data_files
+
+
+
+class LexGLUE(datasets.GeneratorBasedBuilder):
+    """LegalGLUE: A Benchmark Dataset for Legal Language Understanding"""
+
+    BUILDER_CONFIGS = [
+        LexGlueConfig(
+            name="german_ler",
+            description=textwrap.dedent(
+                """\
+            description"""
+            ),
+            label_classes=GERMAN_LER,
+            multi_label=False,
+            data_url="https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/dataset_courts.zip",
+            data_files=["bag.conll", "bfh.conll", "bgh.conll", "bpatg.conll", "bsg.conll", "bverfg.conll", "bverwg.conll"],
+            homepage="https://github.com/elenanereiss/Legal-Entity-Recognition",
+            citation=textwrap.dedent("""\
+                @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}}
+            """)
+        )
+    ]
+
+    def _info(self):
+        if self.config.name == "german_ler":
+            features = {
+                "id": datasets.Value("string"),
+                "tokens": datasets.Sequence(datasets.Value("string")),
+                "ner_tags": datasets.Sequence(
+                    datasets.features.ClassLabel(
+                        names=self.config.label_classes
+                    )
+                )
+            }
+
+        return datasets.DatasetInfo(
+            description=self.config.description,
+            features=datasets.Features(features),
+            homepage=self.config.homepage,
+            citation=self.config.citation,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive = dl_manager.download(self.config.data_url)
+        if self.config_name == "german_ler":
+            return datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": self.config.data_files,
+                    "split": "train",
+                    "files": dl_manager.iter_archive(archive),
+                },
+            )
+        else:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": self.config.data_files,
+                        "split": "train",
+                        "files": dl_manager.iter_archive(archive),
+                        },
+                    ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": self.config.data_files,
+                        "split": "test",
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": self.config.data_files,
+                        "split": "validation",
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, filepath, split, files):
+        if self.config_name == "german_ler":
+            texts, labels = [], []
+            for path, file in files:
+                if path in filepath:
+                    raw_text = file.read_text(encoding="utf-8").strip()
+                    raw_sentences = re.split(r'\n\t?\n', raw_text)
+
+                    for sentence in raw_sentences:
+                        tokens = []
+                        tags = []
+                        for line in sentence.split('\n'):
+                            token, tag = line.split()
+                            tokens.append(token)
+                            tags.append(tag)
+                        texts.append(tokens)
+                        labels.append(tags)
+            for i in enumerate(texts):
+                tokens = text[i]
+                ner_tags = labels[i]
+                yield i, {
+                        "id": str(i),
+                        "tokens": tokens,
+                        "ner_tags": ner_tags,
+                        }