ind_proner.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 pathlib import Path
from typing import Dict, List, Tuple

import datasets

from seacrowd.utils import schemas
from seacrowd.utils.common_parser import load_conll_data
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks

_CITATION = """\
@INPROCEEDINGS{9212879,
  author={Akmal, Muhammad and Romadhony, Ade},
  booktitle={2020 International Conference on Data Science and Its Applications (ICoDSA)},
  title={Corpus Development for Indonesian Product Named Entity Recognition Using Semi-supervised Approach},
  year={2020},
  volume={},
  number={},
  pages={1-5},
  keywords={Feature extraction;Labeling;Buildings;Semisupervised learning;Training data;Text recognition;Manuals;proner;semi-supervised learning;crf},
  doi={10.1109/ICoDSA50139.2020.9212879}
}
"""

_DATASETNAME = "ind_proner"

_DESCRIPTION = """\
Indonesian PRONER is a corpus for Indonesian product named entity recognition . It contains data was labeled manually
and data that was labeled automatically through a semi-supervised learning approach of conditional random fields (CRF).
"""

_HOMEPAGE = "https://github.com/dziem/proner-labeled-text"

_LANGUAGES = {"ind": "id"}

_LANGUAGE_CODES = list(_LANGUAGES.values())

_LICENSE = Licenses.CC_BY_4_0.value

_LOCAL = False

_URLS = {
    "automatic": "https://raw.githubusercontent.com/dziem/proner-labeled-text/master/automatically_labeled.tsv",
    "manual": "https://raw.githubusercontent.com/dziem/proner-labeled-text/master/manually_labeled.tsv",
}

_ANNOTATION_TYPES = list(_URLS.keys())
_ANNOTATION_IDXS = {"l1": 0, "l2": 1}

_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]

_SOURCE_VERSION = "1.0.0"

_SEACROWD_VERSION = "2024.06.20"

logger = datasets.logging.get_logger(__name__)


class IndPRONERDataset(datasets.GeneratorBasedBuilder):
    """
    Indonesian PRONER is a product named entity recognition dataset from https://github.com/dziem/proner-labeled-text.
    """

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)

    BUILDER_CONFIGS = (
        [
            SEACrowdConfig(
                name=f"{_DATASETNAME}_{annotation_type}_source",
                version=datasets.Version(_SOURCE_VERSION),
                description=f"{_DATASETNAME}_{annotation_type} source schema",
                schema="source",
                subset_id=f"{_DATASETNAME}_{annotation_type}",
            )
            for annotation_type in _ANNOTATION_TYPES
        ]
        + [
            SEACrowdConfig(
                name=f"{_DATASETNAME}_{annotation_type}_l1_seacrowd_seq_label",
                version=datasets.Version(_SEACROWD_VERSION),
                description=f"{_DATASETNAME}_{annotation_type}_l1 SEACrowd schema",
                schema="seacrowd_seq_label",
                subset_id=f"{_DATASETNAME}_{annotation_type}_l1",
            )
            for annotation_type in _ANNOTATION_TYPES
        ]
        + [
            SEACrowdConfig(
                name=f"{_DATASETNAME}_{annotation_type}_l2_seacrowd_seq_label",
                version=datasets.Version(_SEACROWD_VERSION),
                description=f"{_DATASETNAME}_{annotation_type}_l2 SEACrowd schema",
                schema="seacrowd_seq_label",
                subset_id=f"{_DATASETNAME}_{annotation_type}_l2",
            )
            for annotation_type in _ANNOTATION_TYPES
        ]
    )

    label_classes = [
        "B-PRO",
        "B-BRA",
        "B-TYP",
        "I-PRO",
        "I-BRA",
        "I-TYP",
        "O",
    ]

    def _extract_label(self, text: str, idx: int) -> str:
        split = text.split("|")
        if len(split) > 1 and idx != -1:
            return split[idx]
        else:
            return text

    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.Value("string")),
                }
            )
        elif self.config.schema == "seacrowd_seq_label":
            features = schemas.seq_label_features(label_names=self.label_classes)

        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.
        """

        annotation_type = self.config.subset_id.split("_")[2]
        path = dl_manager.download_and_extract(_URLS[annotation_type])

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": path,
                    "split": "train",
                },
            )
        ]

    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
        """
        Yields examples as (key, example) tuples.
        """
        label_idx = -1
        subset_id = self.config.subset_id.split("_")
        if len(subset_id) > 3:
            if subset_id[3] in _ANNOTATION_IDXS:
                label_idx = _ANNOTATION_IDXS[subset_id[3]]

        idx = 0
        conll_dataset = load_conll_data(filepath)
        if self.config.schema == "source":
            for _, row in enumerate(conll_dataset):
                x = {"id": str(idx), "tokens": row["sentence"], "ner_tags": list(map(self._extract_label, row["label"], [label_idx] * len(row["label"])))}
                yield idx, x
                idx += 1
        elif self.config.schema == "seacrowd_seq_label":
            for _, row in enumerate(conll_dataset):
                x = {"id": str(idx), "tokens": row["sentence"], "labels": list(map(self._extract_label, row["label"], [label_idx] * len(row["label"])))}
                yield idx, x
                idx += 1