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| import datasets
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| from pathlib import Path
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| import logging
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| _CITATION = """\
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| @mastersthesis{meraner2019grasping,
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| title={Grasping the Nettle: Neural Entity Recognition for Scientific and Vernacular Plant Names},
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| author={Meraner, Isabel},
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| year={2019},
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| school={Institute of Computational Linguistics, University of Zurich},
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| note={Available at: https://github.com/IsabelMeraner/BotanicalNER}
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| }
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| """
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| _DESCRIPTION = """\
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| BotanicalNER is a Named Entity Recognition dataset for scientific and vernacular plant names in German and English.
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| The dataset was created for a master thesis project at the University of Zurich focusing on identifying and
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| disambiguating plant names across multiple text genres to extract and preserve (ethno-)botanical knowledge.
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| """
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| _HOMEPAGE = "https://github.com/IsabelMeraner/BotanicalNER"
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| _LICENSE = "GPL-3.0"
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| _URL = "https://github.com/IsabelMeraner/BotanicalNER/archive/refs/heads/master.zip"
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| _NER_TAGS = ["O", "B-Scientific", "I-Scientific", "B-Vernacular", "I-Vernacular"]
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| _FILE_PATHS = {
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| "de": {
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| "train": [
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| "RESOURCES/corpora/training corpora/de/plantblog_corpus_de.tok.pos.iob.txt",
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| "RESOURCES/corpora/training corpora/de/wiki_abstractcorpus_de.tok.pos.iob.txt",
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| "RESOURCES/corpora/training corpora/de/TextBerg_subcorpus_de.tok.pos.iob.txt",
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| "RESOURCES/corpora/training corpora/de/botlit_corpus_de.tok.pos.iob.txt",
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| ],
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| "test": ["RESOURCES/corpora/gold_standard/de/combined.test.fold1GOLD_de.txt"],
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| "fungi": ["RESOURCES/corpora/gold_standard/de/test_fungi_de.tok.pos.iobGOLD.txt"],
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| },
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| "en": {
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| "train": [
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| "RESOURCES/corpora/training corpora/en/plantblog_corpus_en.tok.pos.iob.txt",
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| "RESOURCES/corpora/training corpora/en/wiki_abstractcorpus_en.tok.pos.iob.txt",
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| "RESOURCES/corpora/training corpora/en/TextBerg_subcorpus_en.tok.pos.iob.txt",
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| "RESOURCES/corpora/training corpora/en/botlit_corpus_en.tok.pos.iob.txt",
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| ],
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| "test": ["RESOURCES/corpora/gold_standard/en/combined.test.fold1GOLD_en.txt"],
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| "fungi": ["RESOURCES/corpora/gold_standard/en/test_fungi_en.tok.pos.iobGOLD.txt"],
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| },
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| }
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| class BotanicalNERConfig(datasets.BuilderConfig):
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| """BuilderConfig for BotanicalNER"""
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| def __init__(self, language="de", **kwargs):
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| super(BotanicalNERConfig, self).__init__(**kwargs)
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| self.language = language
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|
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| class BotanicalNER(datasets.GeneratorBasedBuilder):
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| """BotanicalNER dataset for plant name NER in German and English"""
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| VERSION = datasets.Version("1.0.0")
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| BUILDER_CONFIGS = [
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| BotanicalNERConfig(name="de", language="de", version=VERSION, description="German BotanicalNER dataset"),
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| BotanicalNERConfig(name="en", language="en", version=VERSION, description="English BotanicalNER dataset"),
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| ]
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| DEFAULT_CONFIG_NAME = "de"
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| def _info(self):
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| features = datasets.Features({
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| "id": datasets.Value("string"),
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| "tokens": datasets.Sequence(datasets.Value("string")),
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| "pos_tags": datasets.Sequence(datasets.Value("string")),
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| "ner_tags": datasets.Sequence(datasets.ClassLabel(names=_NER_TAGS)),
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| })
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| return datasets.DatasetInfo(
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| description=_DESCRIPTION,
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| features=features,
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| supervised_keys=None,
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| homepage=_HOMEPAGE,
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| license=_LICENSE,
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| citation=_CITATION,
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| )
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| def _split_generators(self, dl_manager):
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| """Returns SplitGenerators."""
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| data_dir = dl_manager.download_and_extract(_URL)
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| base_path = Path(data_dir) / "BotanicalNER-master"
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| language = self.config.language
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|
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| return [
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| datasets.SplitGenerator(
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| name=datasets.Split.TRAIN,
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| gen_kwargs={"filepaths": [base_path / f for f in _FILE_PATHS[language]["train"]]},
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| ),
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| datasets.SplitGenerator(
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| name=datasets.Split.TEST,
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| gen_kwargs={"filepaths": [base_path / f for f in _FILE_PATHS[language]["test"]]},
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| ),
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| datasets.SplitGenerator(
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| name="fungi",
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| gen_kwargs={"filepaths": [base_path / f for f in _FILE_PATHS[language]["fungi"]]},
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| ),
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| ]
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| def _generate_examples(self, filepaths):
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| """Yields examples from the dataset files."""
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| guid = 0
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| for filepath in filepaths:
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| logging.info(f"Generating examples from {filepath}")
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| with open(filepath, encoding="utf-8") as f:
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| tokens = []
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| pos_tags = []
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| ner_tags = []
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| for line in f:
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| line = line.strip()
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| if not line or line.startswith("-DOCSTART-"):
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| if tokens:
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| yield guid, {
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| "id": str(guid),
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| "tokens": tokens,
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| "pos_tags": pos_tags,
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| "ner_tags": ner_tags,
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| }
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| guid += 1
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| tokens = []
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| pos_tags = []
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| ner_tags = []
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| else:
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| parts = line.split("\t")
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| if len(parts) == 3:
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| tokens.append(parts[0])
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| pos_tags.append(parts[1])
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| ner_tags.append(parts[2])
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| else:
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| logging.warning(f"Skipping malformed line in {filepath}: '{line}'")
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|
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| if tokens:
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| yield guid, {
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| "id": str(guid),
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| "tokens": tokens,
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| "pos_tags": pos_tags,
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| "ner_tags": ner_tags,
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| }
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| guid += 1 |
