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glotstorybook.py

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