Upload tlunified_ner.py with huggingface_hub

tlunified_ner.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+from datasets.download.download_manager import DownloadManager
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """
+@inproceedings{miranda-2023-developing,
+    title = {Developing a Named Entity Recognition Dataset for Tagalog},
+    author = "Miranda, Lester James Validad",
+    booktitle = "Proceedings of the First Workshop for Southeast Asian Language Processing (SEALP),"
+    month = nov,
+    year = 2023,
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["tgl"]
+_DATASETNAME = "tlunified_ner"
+_DESCRIPTION = """\
+This dataset contains the annotated TLUnified corpora from Cruz and Cheng
+(2021). It is a curated sample of around 7,000 documents for the named entity
+recognition (NER) task. The majority of the corpus are news reports in Tagalog,
+resembling the domain of the original ConLL 2003. There are three entity types:
+Person (PER), Organization (ORG), and Location (LOC).
+"""
+
+_HOMEPAGE = "https://huggingface.co/ljvmiranda921/tlunified-ner"
+_LICENSE = Licenses.GPL_3_0.value
+_URLS = {
+    "train": "https://huggingface.co/datasets/ljvmiranda921/tlunified-ner/resolve/main/corpus/iob/train.iob",
+    "dev": "https://huggingface.co/datasets/ljvmiranda921/tlunified-ner/resolve/main/corpus/iob/dev.iob",
+    "test": "https://huggingface.co/datasets/ljvmiranda921/tlunified-ner/resolve/main/corpus/iob/test.iob",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class TLUnifiedNERDataset(datasets.GeneratorBasedBuilder):
+    """Tagalog Named Entity Recognition dataset from https://huggingface.co/ljvmiranda921/tlunified-ner"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "seq_label"
+    LABEL_CLASSES = ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(datasets.features.ClassLabel(names=self.LABEL_CLASSES)),
+                }
+            )
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.seq_label_features(self.LABEL_CLASSES)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        data_files = {
+            "train": Path(dl_manager.download_and_extract(_URLS["train"])),
+            "dev": Path(dl_manager.download_and_extract(_URLS["dev"])),
+            "test": Path(dl_manager.download_and_extract(_URLS["test"])),
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_files["train"], "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_files["dev"], "split": "dev"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_files["test"], "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yield examples as (key, example) tuples"""
+        # The only difference between the source schema and the seacrowd seq_label schema is the dictionary keys.
+        # The implementation is the same.
+        label_key = "ner_tags" if self.config.schema == "source" else "labels"
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            ner_tags = []
+            for line in f:
+                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            label_key: ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    # TLUnified-NER iob are separated by \t
+                    token, ner_tag = line.split("\t")
+                    tokens.append(token)
+                    ner_tags.append(ner_tag.rstrip())
+            # Last example
+            if tokens:
+                yield guid, {
+                    "id": str(guid),
+                    "tokens": tokens,
+                    label_key: ner_tags,
+                }