bornholmsk_parallel.py

# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3
"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""

import os

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{derczynski-kjeldsen-2019-bornholmsk,
    title = "Bornholmsk Natural Language Processing: Resources and Tools",
    author = "Derczynski, Leon  and
      Kjeldsen, Alex Speed",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://aclanthology.org/W19-6138",
    pages = "338--344",
    abstract = {This paper introduces language processing resources and tools for Bornholmsk, a language spoken on the island of Bornholm, with roots in Danish and closely related to Scanian. This presents an overview of the language and available data, and the first NLP models for this living, minority Nordic language. Sammenfattnijng p{\aa} borrijnholmst: D{\ae}jnna artikkelijn introduserer naturspr{\aa}gsresurser {\aa} varktoi for borrijnholmst, ed spr{\aa}g a d{\ae}r snakkes p{\aa} {\"o}n Borrijnholm me r{\o}dder i danst {\aa} i n{\ae}r familia me sk{\aa}nst. Artikkelijn gjer ed {\^a}uersyn {\^a}uer spr{\aa}ged {\aa} di datan som fijnnes, {\aa} di fosste NLP mod{\ae}llarna for d{\ae}tta l{\ae}wenes nordiska minnret{\^a}lsspr{\aa}ged.},
}

"""

_DESCRIPTION = """\
This dataset is parallel text for Bornholmsk and Danish. 

For more details, see the paper [Bornholmsk Natural Language Processing: Resources and Tools](https://aclanthology.org/W19-6138/).
"""

_URL_BASE = "https://raw.githubusercontent.com/StrombergNLP/bornholmsk/0bdb51bf7522c1d154bcc9c54f6ffd4c5125a121/parallel."


class BornholmskParallelConfig(datasets.BuilderConfig):
    """BuilderConfig for Bornholmsk"""

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

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


class BornholmskParallel(datasets.GeneratorBasedBuilder):
    """Bornholmsk dataset."""

    BUILDER_CONFIGS = [
        BornholmskParallelConfig(name="BornholmskParallel", version=datasets.Version("1.0.0"), description="Bornholmsk/Danish Parallel Texts"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "da_bornholm": datasets.Value("string"),
                    "da": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/StrombergNLP/bornholmsk",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_files = {}
        for lang in ('da', 'da-bornholm'):
            for partition in ('train', 'val', 'test'):
                part = f"{lang}.{partition}"
                downloaded_files[part] = dl_manager.download_and_extract(_URL_BASE + part)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, 
                gen_kwargs={"filepath": 
                    {
                        'da': downloaded_files['da.train'], 
                        'da-bornholm':downloaded_files['da-bornholm.train']
                    }
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, 
                gen_kwargs={"filepath": 
                    {
                        'da': downloaded_files['da.val'], 
                        'da-bornholm':downloaded_files['da-bornholm.val']
                    }
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": 
                    {
                        'da': downloaded_files['da.test'], 
                        'da-bornholm':downloaded_files['da-bornholm.test']
                    }
                }
            ),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s and %s", (filepath['da'], filepath['da-bornholm']))
        guid = 0
        with open(filepath['da-bornholm'], encoding="utf-8") as f_da:
            with open(filepath['da'], encoding="utf-8") as f_bo:
                bo = f_bo.readlines()
                da = f_da.readlines()
                for instance in zip(bo, da):
                    yield guid, {
                        "id": str(guid),
                        "da_bornholm": instance[0].strip(),
                        "da": instance[1].strip(),
                    }
                    guid += 1