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from glob import glob |
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import os |
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from pathlib import Path |
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import datasets |
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import pandas as pd |
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import requests |
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_METADATA_URL = "metadata.csv" |
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_CITATION = """\ |
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@dataset{h_novel, |
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author = {Xing Tian}, |
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title = {h_novel}, |
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month = aug, |
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year = 2023, |
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publisher = {Xing Tian}, |
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version = {1.0}, |
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} |
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""" |
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_DESCRIPTION = """\ |
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This dataset contains some SQ novel. |
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It is supposed to be used for text generation tasks. |
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""" |
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class HNovel(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="ltxsba", version=VERSION, description="ltxsba"), |
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datasets.BuilderConfig(name="ltxsba_1gb", version=VERSION, description="ltxsba_1gb"), |
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datasets.BuilderConfig(name="ltxsba_5gb", version=VERSION, description="ltxsba_5gb"), |
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datasets.BuilderConfig(name="ltxsba_100m", version=VERSION, description="ltxsba_100m"), |
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datasets.BuilderConfig(name="ltxsba_500m", version=VERSION, description="ltxsba_500m"), |
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datasets.BuilderConfig(name="yazhou", version=VERSION, description="yazhou"), |
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datasets.BuilderConfig(name="yazhou_5m", version=VERSION, description="yazhou_5m"), |
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datasets.BuilderConfig(name="yazhou_10m", version=VERSION, description="yazhou_10m"), |
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datasets.BuilderConfig(name="yazhou_20m", version=VERSION, description="yazhou_20m"), |
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datasets.BuilderConfig(name="yazhou_50m", version=VERSION, description="yazhou_50m"), |
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datasets.BuilderConfig(name="yazhou_70m", version=VERSION, description="yazhou_70m"), |
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datasets.BuilderConfig(name="all", version=VERSION, description="all"), |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"source": datasets.Value("string"), |
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"idx": datasets.Value("string"), |
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"filename": datasets.Value("string"), |
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"novel_name": datasets.Value("string"), |
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"row_idx": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage="", |
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license="", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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dl_path = dl_manager.download(_METADATA_URL) |
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archive_path = dl_path |
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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={"archive_path": archive_path, "dl_manager": dl_manager}, |
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), |
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] |
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def _generate_examples(self, archive_path, dl_manager): |
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"""Yields examples.""" |
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sample_idx = 0 |
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df = pd.read_csv(archive_path) |
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for i, row in df.iterrows(): |
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source = row["source"] |
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filename = row["filename"] |
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if self.config.name != "all" and source != self.config.name: |
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continue |
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try: |
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filename = dl_manager.download(filename) |
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except Exception: |
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continue |
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filename = Path(filename) |
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name = filename.stem |
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splits = name.split("_") |
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idx = splits[-1] |
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novel_name = "_".join(splits[:-1]) |
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row_idx = 1 |
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with open(filename.as_posix(), "r", encoding="utf-8") as f: |
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for txt_row in f: |
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txt_row = str(txt_row).strip() |
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if len(txt_row) == 0: |
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continue |
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yield sample_idx, { |
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"source": source, |
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"idx": idx, |
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"filename": "/".join(filename.parts[-3:]), |
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"novel_name": novel_name, |
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"row_idx": row_idx, |
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"text": txt_row, |
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} |
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row_idx += 1 |
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sample_idx += 1 |
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if __name__ == '__main__': |
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pass |
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