indocollex.py

# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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
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from itertools import chain
from pathlib import Path
from typing import Dict, List, Tuple

import datasets

from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks

_CITATION = """\
@inproceedings{wibowo-etal-2021-indocollex,
    title = "{I}ndo{C}ollex: A Testbed for Morphological Transformation of {I}ndonesian Word Colloquialism",
    author = {Wibowo, Haryo Akbarianto  and Nityasya, Made Nindyatama  and Aky{\"u}rek, Afra Feyza  and Fitriany, Suci  and Aji, Alham Fikri  and Prasojo, Radityo Eko  and Wijaya, Derry Tanti},
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.280",
    doi = "10.18653/v1/2021.findings-acl.280",
    pages = "3170--3183",
}"""

_LANGUAGES = ["ind"]
_LOCAL = False

_DATASETNAME = "indocollex"

_DESCRIPTION = """\
IndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words
"""

_HOMEPAGE = "https://github.com/haryoa/indo-collex"

_LICENSE = "CC BY-SA 4.0"

_URLS = {
    _DATASETNAME: {
        "train": "https://github.com/haryoa/indo-collex/raw/main/data/full.csv",
    },
    f"{_DATASETNAME}_f2i": {
        "train": "https://github.com/haryoa/indo-collex/raw/main/data/formal_to_informal/train.csv",
        "dev": "https://github.com/haryoa/indo-collex/raw/main/data/formal_to_informal/dev.csv",
        "test": "https://github.com/haryoa/indo-collex/raw/main/data/formal_to_informal/test.csv",
    },
    f"{_DATASETNAME}_i2f": {
        "train": "https://github.com/haryoa/indo-collex/raw/main/data/informal_to_formal/train.csv",
        "dev": "https://github.com/haryoa/indo-collex/raw/main/data/informal_to_formal/dev.csv",
        "test": "https://github.com/haryoa/indo-collex/raw/main/data/informal_to_formal/test.csv",
    },
}

_SUPPORTED_TASKS = [Tasks.MORPHOLOGICAL_INFLECTION]

_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"


class NewDataset(datasets.GeneratorBasedBuilder):
    """IndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words"""

    label_classes = ["acronym", "affixation", "disemvoweling", "rev", "shorten", "sound-alter", "space-dash"]

    BUILDER_CONFIGS = list(
        chain(
            *[
                [
                    SEACrowdConfig(
                        name=f"{_DATASETNAME}{suffix}_source",
                        version=datasets.Version(_SOURCE_VERSION),
                        description=f"{_DATASETNAME} source schema",
                        schema="source",
                        subset_id=f"{_DATASETNAME}{suffix}",
                    ),
                    SEACrowdConfig(
                        name=f"{_DATASETNAME}{suffix}_seacrowd_pairs_multi",
                        version=datasets.Version(_SEACROWD_VERSION),
                        description=f"{_DATASETNAME} Nusantara schema",
                        schema="seacrowd_pairs_multi",
                        subset_id=f"{_DATASETNAME}{suffix}",
                    ),
                ]
                for suffix in ["", "_f2i", "_i2f"]
            ]
        )
    )

    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "no": datasets.Value("string"),
                    "transformed": datasets.Value("string"),
                    "original-for": datasets.Value("string"),
                    "transformation": datasets.Value("string"),
                }
            )

        elif self.config.schema == "seacrowd_pairs_multi":
            features = schemas.pairs_multi_features(self.label_classes)

        else:
            raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.")

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        """Returns SplitGenerators."""

        urls = _URLS[self.config.subset_id]
        data_paths = dl_manager.download(urls)

        ret = [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": data_paths["train"]},
            )
        ]

        if len(data_paths) > 1:
            ret.extend(
                [
                    datasets.SplitGenerator(
                        name=datasets.Split.TEST,
                        gen_kwargs={"filepath": data_paths["test"]},
                    ),
                    datasets.SplitGenerator(
                        name=datasets.Split.VALIDATION,
                        gen_kwargs={"filepath": data_paths["dev"]},
                    ),
                ]
            )

        return ret

    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""

        with open(filepath, "r", encoding="utf8") as f:
            dataset = list(map(lambda l: l.rstrip("\r\n").split(","), f))

        _assert = set(map(len, dataset))
        if _assert != {4}:
            raise AssertionError(f"Expecting exactly 4 fields (no, transformed, base, label), but found: {_assert}")

        _assert = dataset[0]
        source_columns = ["no", "transformed", "original-for", "transformation"]
        if _assert != source_columns:
            raise AssertionError(f"The expected header is not found. {_assert}")

        dataset = dataset[1:]

        if self.config.schema == "source":
            for key, ex in enumerate(dataset):
                yield key, dict(zip(source_columns, ex))

        elif self.config.schema == "seacrowd_pairs_multi":
            for key, ex in enumerate(dataset):
                yield key, {
                    "id": str(key),
                    "text_1": ex[2],
                    "text_2": ex[1],
                    "label": [ex[3]],
                }