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