masakhaner2.py

# coding=utf-8
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""MasakhaNER: Named Entity Recognition for African Languages"""

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@article{Adelani2022MasakhaNER2A,
  title={MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition},
  author={David Ifeoluwa Adelani and Graham Neubig and Sebastian Ruder and Shruti Rijhwani and Michael Beukman and Chester Palen-Michel and Constantine Lignos and Jesujoba Oluwadara Alabi and Shamsuddeen Hassan Muhammad and Peter Nabende and Cheikh M. Bamba Dione and Andiswa Bukula and Rooweither Mabuya and Bonaventure F. P. Dossou and Blessing K. Sibanda and Happy Buzaaba and Jonathan Mukiibi and Godson Kalipe and Derguene Mbaye and Amelia Taylor and Fatoumata Kabore and Chris C. Emezue and Anuoluwapo Aremu and Perez Ogayo and Catherine W. Gitau and Edwin Munkoh-Buabeng and Victoire Memdjokam Koagne and Allahsera Auguste Tapo and Tebogo Macucwa and Vukosi Marivate and Elvis Mboning and Tajuddeen R. Gwadabe and Tosin P. Adewumi and Orevaoghene Ahia and Joyce Nakatumba-Nabende and Neo L. Mokono and Ignatius M Ezeani and Chiamaka Ijeoma Chukwuneke and Mofetoluwa Adeyemi and Gilles Hacheme and Idris Abdulmumin and Odunayo Ogundepo and Oreen Yousuf and Tatiana Moteu Ngoli and Dietrich Klakow},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.12391}
}
"""

_DESCRIPTION = """\
MasakhaNER 2.0 is the largest publicly available high-quality dataset for named entity recognition (NER) in 20 African languages.

Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.

Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
MasakhaNER is a named entity dataset consisting of PER, ORG, LOC, and DATE entities annotated by Masakhane for 20 African languages:
- Bambara (bam)
- Ghomala (bbj)
- Ewe (ewe)
- Fon (fon)
- Hausa (hau)
- Igbo (ibo)
- Kinyarwanda (kin)
- Luganda (lug)
- Dholuo (luo) 
- Mossi (mos)
- Chichewa (nya)
- Nigerian Pidgin
- chShona (sna)
- Kiswahili (swą)
- Setswana (tsn)
- Twi (twi)
- Wolof (wol)
- isiXhosa (xho)
- Yorùbá (yor)
- isiZulu (zul)

The train/validation/test sets are available for all the ten languages.

For more details see https://arxiv.org/abs/2103.11811
"""

_URL = "https://github.com/masakhane-io/masakhane-ner/raw/main/MasakhaNER2.0/data/"
_TRAINING_FILE = "train.txt"
_DEV_FILE = "dev.txt"
_TEST_FILE = "test.txt"


class Masakhaner2Config(datasets.BuilderConfig):
    """BuilderConfig for Masakhaner2"""

    def __init__(self, **kwargs):
        """BuilderConfig for Masakhaner2.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(Masakhaner2Config, self).__init__(**kwargs)


class Masakhaner2(datasets.GeneratorBasedBuilder):
    """Masakhaner2 dataset."""

    BUILDER_CONFIGS = [
        Masakhaner2Config(name="bam", version=datasets.Version("1.0.0"), description="Masakhaner2 Bambara dataset"),
        Masakhaner2Config(name="bbj", version=datasets.Version("1.0.0"), description="Masakhaner2 Ghomala dataset"),
        Masakhaner2Config(name="ewe", version=datasets.Version("1.0.0"), description="Masakhaner2 Ewe dataset"),
        Masakhaner2Config(name="fon", version=datasets.Version("1.0.0"), description="Masakhaner2 Fon dataset"),
        Masakhaner2Config(name="hau", version=datasets.Version("1.0.0"), description="Masakhaner2 Hausa dataset"),
        Masakhaner2Config(name="ibo", version=datasets.Version("1.0.0"), description="Masakhaner2 Igbo dataset"),
        Masakhaner2Config(name="kin", version=datasets.Version("1.0.0"), description="Masakhaner2 Kinyarwanda dataset"),
        Masakhaner2Config(name="lug", version=datasets.Version("1.0.0"), description="Masakhaner2 Luganda dataset"),
        Masakhaner2Config(name="luo", version=datasets.Version("1.0.0"), description="Masakhaner2 Luo dataset"),
        Masakhaner2Config(name="mos", version=datasets.Version("1.0.0"), description="Masakhaner2 Mossi dataset"),
        Masakhaner2Config(name="nya", version=datasets.Version("1.0.0"), description="Masakhaner2 Chichewa` dataset"),
        Masakhaner2Config(
            name="pcm", version=datasets.Version("1.0.0"), description="Masakhaner Nigerian-Pidgin dataset"
        ),
        Masakhaner2Config(name="sna", version=datasets.Version("1.0.0"), description="Masakhaner2 Shona dataset"),
        Masakhaner2Config(name="swa", version=datasets.Version("1.0.0"), description="Masakhaner2 Swahili dataset"),
        Masakhaner2Config(name="tsn", version=datasets.Version("1.0.0"), description="Masakhaner2 Setswana dataset"),
        Masakhaner2Config(name="twi", version=datasets.Version("1.0.0"), description="Masakhaner2 Twi dataset"),
        Masakhaner2Config(name="wol", version=datasets.Version("1.0.0"), description="Masakhaner2 Wolof dataset"),
        Masakhaner2Config(name="xho", version=datasets.Version("1.0.0"), description="Masakhaner2 Xhosa dataset"),
        Masakhaner2Config(name="yor", version=datasets.Version("1.0.0"), description="Masakhaner2 Yoruba dataset"),
        Masakhaner2Config(name="zul", version=datasets.Version("1.0.0"), description="Masakhaner2 Zulu 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-DATE",
                                "I-DATE",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://arxiv.org/abs/2210.12391",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{self.config.name}/{_TRAINING_FILE}",
            "dev": f"{_URL}{self.config.name}/{_DEV_FILE}",
            "test": f"{_URL}{self.config.name}/{_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"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        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:
                    # Masakhaner tokens are space separated
                    splits = line.split(" ")
                    tokens.append(splits[0])
                    ner_tags.append(splits[1].rstrip())
            # last example
            if tokens:
                yield guid, {
                    "id": str(guid),
                    "tokens": tokens,
                    "ner_tags": ner_tags,
                }