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from pandas import read_csv |
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from datasets import GeneratorBasedBuilder, Value, Version, BuilderConfig, Features, DatasetInfo, SplitGenerator, Split |
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_DESCRIPTION = ''' |
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This dataset contains anekdotes parsed from a few vk social network communities. The data can be useful for fine-tuning language generation models as well for tasks of automatic humour analysis. |
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''' |
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_HOMEPAGE = 'https://huggingface.co/datasets/zeio/baneks' |
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_LICENSE = 'Apache License Version 2.0' |
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_URLS = { |
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'censored': 'https://huggingface.co/datasets/zeio/baneks/resolve/main/censored.tsv', |
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'default': 'https://huggingface.co/datasets/zeio/baneks/resolve/main/default.tsv', |
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'inflated': 'https://huggingface.co/datasets/zeio/baneks/resolve/main/inflated.tsv' |
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} |
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class Baneks(GeneratorBasedBuilder): |
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VERSION = Version('10.10.2023') |
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BUILDER_CONFIGS = [ |
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BuilderConfig(name = 'censored', version = VERSION, description = 'No duplicates - entries with the same text are grouped and aggregated'), |
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BuilderConfig(name = 'default', version = VERSION, description = 'Same as "censored", but censored words are replaced with inferred values for their initial form'), |
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BuilderConfig(name = 'inflated', version = VERSION, description = 'Each entry corresponds to a post, minimal changes to the source data') |
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] |
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DEFAULT_CONFIG_NAME = 'default' |
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def _info(self): |
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return DatasetInfo( |
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description=_DESCRIPTION, |
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features = Features({ |
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'text': Value('string'), |
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'published': Value('string'), |
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'id': Value('int32'), |
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'n-likes': Value('int32'), |
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'n-views': Value('int32'), |
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'accessed': Value('string'), |
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'source': Value('string') |
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}), |
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homepage=_HOMEPAGE, |
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license=_LICENSE |
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) |
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def _split_generators(self, dl_manager): |
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name = self.config.name |
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url = _URLS[name] |
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return [ |
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SplitGenerator( |
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name = Split.TRAIN, |
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gen_kwargs = { |
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"path": dl_manager.download_and_extract(url) |
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} |
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) |
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] |
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def _generate_examples(self, path: str): |
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for _, row in read_csv(path, sep = '\t').iterrows(): |
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yield f'{row["id"]:08d}-{row["source"]}', dict(row) |
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