ebiquity-v2-stemmed.py

import re

import datasets

_CITATION = """
@INPROCEEDINGS{
  8998477,
  author={O. M. {Singh} and A. {Padia} and A. {Joshi}},
  booktitle={2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC)},
  title={Named Entity Recognition for Nepali Language},
  year={2019},
  volume={},
  number={},
  pages={184-190},
  keywords={Named Entity Recognition;Nepali;Low-resource;BiLSTM;CNN;Grapheme},
  doi={10.1109/CIC48465.2019.00031},
  ISSN={null},
  month={Dec},}
"""

_DESCRIPTION = """
Ebiquity V2 (stemmed) dataset for Nepali NER task. The dataset is tagged with BIO scheme.
"""

_URL = "https://raw.githubusercontent.com/mani-rai/nepali-ner/master/data/ebiquity_v2/stemmed/total.bio"


class EbiquityV2StemmedConfig(datasets.BuilderConfig):
    """BuilderConfig for Ebiquity V2 Stemmed"""

    def __init__(self, **kwargs):
        """BuilderConfig for Ebiquity V2 Stemmed.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(EbiquityV2StemmedConfig, self).__init__(**kwargs)


class EbiquityV2Stemmed(datasets.GeneratorBasedBuilder):
    """Ebiquity V2 Stemmed"""

    BUILDER_CONFIGS = [
        EbiquityV2StemmedConfig(name="ebiquity-v2-stemmed", version=datasets.Version("1.0.0"), description="Ebiquity v2 stemmed 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-PER",
                                "I-PER",
                                "B-ORG",
                                "I-ORG",
                                "B-LOC",
                                "I-LOC",
                                "B-MISC",
                                "I-MISC",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://arxiv.org/abs/1908.05828",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file})
        ]

    def _generate_examples(self, 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 = re.split('\t+', line)
                    tokens.append(splits[0])
                    ner_tags.append(splits[1].rstrip())
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "ner_tags": ner_tags,
            }