Upload botanical_ner.py

botanical_ner.py

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+# botanical_ner.py
+# Author : Amir Safari
+# @date: 2025-08-30
+import datasets
+from pathlib import Path
+import logging
+
+_CITATION = """\
+@mastersthesis{meraner2019grasping,
+    title={Grasping the Nettle: Neural Entity Recognition for Scientific and Vernacular Plant Names},
+    author={Meraner, Isabel},
+    year={2019},
+    school={Institute of Computational Linguistics, University of Zurich},
+    note={Available at: https://github.com/IsabelMeraner/BotanicalNER}
+}
+"""
+
+_DESCRIPTION = """\
+BotanicalNER is a Named Entity Recognition dataset for scientific and vernacular plant names in German and English.
+The dataset was created for a master thesis project at the University of Zurich focusing on identifying and
+disambiguating plant names across multiple text genres to extract and preserve (ethno-)botanical knowledge.
+"""
+
+_HOMEPAGE = "https://github.com/IsabelMeraner/BotanicalNER"
+_LICENSE = "GPL-3.0"
+_URL = "https://github.com/IsabelMeraner/BotanicalNER/archive/refs/heads/master.zip"
+
+_NER_TAGS = ["O", "B-Scientific", "I-Scientific", "B-Vernacular", "I-Vernacular"]
+
+
+_FILE_PATHS = {
+    "de": {
+        "train": [
+            "RESOURCES/corpora/training corpora/de/plantblog_corpus_de.tok.pos.iob.txt",
+            "RESOURCES/corpora/training corpora/de/wiki_abstractcorpus_de.tok.pos.iob.txt",
+            "RESOURCES/corpora/training corpora/de/TextBerg_subcorpus_de.tok.pos.iob.txt",
+            "RESOURCES/corpora/training corpora/de/botlit_corpus_de.tok.pos.iob.txt",
+        ],
+        "test": ["RESOURCES/corpora/gold_standard/de/combined.test.fold1GOLD_de.txt"],
+        "fungi": ["RESOURCES/corpora/gold_standard/de/test_fungi_de.tok.pos.iobGOLD.txt"],
+    },
+    "en": {
+        "train": [
+            "RESOURCES/corpora/training corpora/en/plantblog_corpus_en.tok.pos.iob.txt",
+            "RESOURCES/corpora/training corpora/en/wiki_abstractcorpus_en.tok.pos.iob.txt",
+            "RESOURCES/corpora/training corpora/en/TextBerg_subcorpus_en.tok.pos.iob.txt",
+            "RESOURCES/corpora/training corpora/en/botlit_corpus_en.tok.pos.iob.txt",
+        ],
+        "test": ["RESOURCES/corpora/gold_standard/en/combined.test.fold1GOLD_en.txt"],
+        "fungi": ["RESOURCES/corpora/gold_standard/en/test_fungi_en.tok.pos.iobGOLD.txt"],
+    },
+}
+
+class BotanicalNERConfig(datasets.BuilderConfig):
+    """BuilderConfig for BotanicalNER"""
+    def __init__(self, language="de", **kwargs):
+        super(BotanicalNERConfig, self).__init__(**kwargs)
+        self.language = language
+
+class BotanicalNER(datasets.GeneratorBasedBuilder):
+    """BotanicalNER dataset for plant name NER in German and English"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        BotanicalNERConfig(name="de", language="de", version=VERSION, description="German BotanicalNER dataset"),
+        BotanicalNERConfig(name="en", language="en", version=VERSION, description="English BotanicalNER dataset"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "de"
+
+    def _info(self):
+        features = datasets.Features({
+            "id": datasets.Value("string"),
+            "tokens": datasets.Sequence(datasets.Value("string")),
+            "pos_tags": datasets.Sequence(datasets.Value("string")), # Kept as string as the tag set is very large
+            "ner_tags": datasets.Sequence(datasets.ClassLabel(names=_NER_TAGS)),
+        })
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # Download and extract the single zip file
+        data_dir = dl_manager.download_and_extract(_URL)
+        base_path = Path(data_dir) / "BotanicalNER-master"
+        language = self.config.language
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepaths": [base_path / f for f in _FILE_PATHS[language]["train"]]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepaths": [base_path / f for f in _FILE_PATHS[language]["test"]]},
+            ),
+            datasets.SplitGenerator(
+                name="fungi",
+                gen_kwargs={"filepaths": [base_path / f for f in _FILE_PATHS[language]["fungi"]]},
+            ),
+        ]
+
+    def _generate_examples(self, filepaths):
+        """Yields examples from the dataset files."""
+        guid = 0
+        for filepath in filepaths:
+            logging.info(f"Generating examples from {filepath}")
+            with open(filepath, encoding="utf-8") as f:
+                tokens = []
+                pos_tags = []
+                ner_tags = []
+                for line in f:
+                    line = line.strip()
+                    if not line or line.startswith("-DOCSTART-"):
+                        if tokens:
+                            yield guid, {
+                                "id": str(guid),
+                                "tokens": tokens,
+                                "pos_tags": pos_tags,
+                                "ner_tags": ner_tags,
+                            }
+                            guid += 1
+                            tokens = []
+                            pos_tags = []
+                            ner_tags = []
+                    else:
+                        parts = line.split("\t")
+                        # The files consistently have 3 columns: token, pos, ner
+                        if len(parts) == 3:
+                            tokens.append(parts[0])
+                            pos_tags.append(parts[1])
+                            ner_tags.append(parts[2])
+                        else:
+                            logging.warning(f"Skipping malformed line in {filepath}: '{line}'")
+
+                # Yield the last sentence if the file does not end with a newline
+                if tokens:
+                    yield guid, {
+                        "id": str(guid),
+                        "tokens": tokens,
+                        "pos_tags": pos_tags,
+                        "ner_tags": ner_tags,
+                    }
+                    guid += 1