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Upload glotstorybook.py with huggingface_hub
Browse files- glotstorybook.py +143 -0
glotstorybook.py
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from pathlib import Path
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from typing import Dict, List, Tuple
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import datasets
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import pandas as pd
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from seacrowd.utils.configs import SEACrowdConfig
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from seacrowd.utils.constants import Tasks, Licenses, TASK_TO_SCHEMA, SCHEMA_TO_FEATURES
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_CITATION = """\
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@inproceedings{kargaran2023glotlid,
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title = {{GlotLID: Language Identification for Low-Resource Languages}},
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author = {Kargaran, Amir Hossein and Imani, Ayyoob and Yvon, Fran{\c{c}}ois and Sch{\"u}tze, Hinrich},
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year = 2023,
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booktitle = {The 2023 Conference on Empirical Methods in Natural Language Processing},
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url = {https://openreview.net/forum?id=dl4e3EBz5j}
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}
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"""
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_DATASETNAME = "glotstorybook"
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_DESCRIPTION = """\
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The GlotStoryBook dataset is a compilation of children's storybooks from the Global
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Storybooks project, encompassing 174 languages organized for machine translation tasks. It
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features rows containing the text segment (text number), the language code, and the file
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name, which corresponds to the specific book and story segment. This structure allows for
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the comparison of texts across different languages by matching file names and text numbers
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between rows.
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"""
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_HOMEPAGE = "https://huggingface.co/datasets/cis-lmu/GlotStoryBook"
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_LICENSE = f"""{Licenses.OTHERS.value} | \
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We do not own any of the text from which these data has been extracted. All the files are
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collected from the repository located at https://github.com/global-asp/. The source
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repository for each text and file is stored in the dataset. Each file in the dataset is
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associated with one license from the CC family. The licenses include 'CC BY', 'CC BY-NC',
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'CC BY-NC-SA', 'CC-BY', 'CC-BY-NC', and 'Public Domain'. We also license the code, actual
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packaging and the metadata of these data under the cc0-1.0.
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"""
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_LOCAL=False
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_LANGUAGES = ["khg", "khm", "mya", "tet", "tha", "vie"]
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_URLS = "https://huggingface.co/datasets/cis-lmu/GlotStoryBook/resolve/main/GlotStoryBook.csv"
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_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
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_SOURCE_VERSION = "1.0.0"
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_SEACROWD_VERSION = "2024.06.20"
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class GlotStoryBookDataset(datasets.GeneratorBasedBuilder):
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"""Compilation of storybooks from the Global Storybooks project"""
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
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SEACROWD_SCHEMA_NAME = TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()
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BUILDER_CONFIGS = [
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SEACrowdConfig(
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name=f"{_DATASETNAME}_source",
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version=SOURCE_VERSION,
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description=f"{_DATASETNAME} source schema",
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schema="source",
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subset_id=f"{_DATASETNAME}",
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),
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SEACrowdConfig(
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name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
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version=SEACROWD_VERSION,
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description=f"{_DATASETNAME} SEACrowd schema",
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schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
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subset_id=f"{_DATASETNAME}",
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),
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]
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
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def _info(self) -> datasets.DatasetInfo:
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if self.config.schema == "source":
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features = datasets.Features(
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{
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"text": datasets.Value("string"),
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"text_number": datasets.Value("int64"),
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"license": datasets.Value("string"),
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"text_by": datasets.Value("string"),
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"translation_by": datasets.Value("string"),
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"language": datasets.Value("string"),
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"file_name": datasets.Value("string"),
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"source": datasets.Value("string"),
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"iso639-3": datasets.Value("string"),
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"script": datasets.Value("string"),
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}
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)
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elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
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features = SCHEMA_TO_FEATURES[self.SEACROWD_SCHEMA_NAME.upper()]
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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data_path = Path(dl_manager.download_and_extract(_URLS))
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": data_path,
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"split": "train",
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},
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),
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]
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
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"""Yields examples as (key, example) tuples."""
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df = pd.read_csv(filepath)
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df = df[df["ISO639-3"].isin(_LANGUAGES)]
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if self.config.schema == "source":
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for i, row in df.iterrows():
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yield i, {
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"text": row["Text"],
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"text_number": row["Text Number"],
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"license": row["License"],
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"text_by": row["Text By"],
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"translation_by": row["Translation By"],
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"language": row["Language"],
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"file_name": row["File Name"],
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"source": row["Source"],
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"iso639-3": row["ISO639-3"],
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"script": row["Script"],
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}
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elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
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df = df.sort_values(by=["ISO639-3", "Source", "File Name", "Text Number"])
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df = df.groupby(["ISO639-3", "Source", "File Name"]).agg({"Text": " ".join}).reset_index()
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for i, row in df.iterrows():
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yield i, {
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"id": str(i),
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"text": row["Text"],
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}
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