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NERDE.py

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+# 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.
+
+"""
+(pt) NERDE: NER na Defesa Econômica
+(en) NERDE: NER on Economic Defense
+"""
+
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+#_CITATION = """"""
+
+_DESCRIPTION = """
+(pt) NERDE é um dataset para NER a partir de documentos jurídicos da defesa econômica em português do Brasil, foi criado em colaboração com o Cade e o laboratório LATITUDE/UnB.
+(en) NERDE is a NER dataset from economic defense legal documents in Brazilian Portuguese, created in collaboration with Cade and the LATITUDE/UnB laboratory.
+"""
+
+_HOMEPAGE = "https://github.com/guipaiva/NERDE"
+
+_TRAINING_FILE = "train.conll"
+_DEV_FILE = "dev.conll"
+_TEST_FILE = "test.conll"
+
+
+class NerdeDataset(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="NERDE", version=VERSION, description="Economic Defense NER dataset"),
+    ]
+    
+     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=[
+                                "O",
+                                "B-ORG",
+                                "I-ORG",
+                                "B-PER",
+                                "I-PER",
+                                "B-TEMPO",
+                                "I-TEMPO",
+                                "B-LOC",
+                                "I-LOC",
+                                "B-LEG",
+                                "I-LEG",
+                                "B-DOCS",
+                                "I-DOCS",
+                                "B-VALOR",
+                                "I-VALOR"
+                            ]
+                        )
+                    ),
+                }
+            ), 
+            supervised_keys=None,
+            homepage=_HOMEPAGE
+        )
+
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": _TRAINING_FILE,
+            "dev": _DEV_FILE,
+            "test": _TEST_FILE,
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": downloaded_files["train"], "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": downloaded_files["dev"], "split": "validation"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": downloaded_files["test"], "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+
+        logger.info("⏳ Generating examples from = %s", filepath)
+
+        with open(filepath, encoding="utf-8") as f:
+
+            guid = 0
+            tokens = []
+            ner_tags = []
+
+            for line in f:
+                if line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    splits = line.split(" ")
+                    tokens.append(splits[0])
+                    ner_tags.append(splits[1].rstrip())
+
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }