Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
acceptability-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
Commit
·
b6b76b7
1
Parent(s):
2dbcffe
Support streaming blimp dataset (#4016)
Browse files* Support streaming blimp dataset
* Simplify code
* Add encoding
* Fix dataset card
Commit from https://github.com/huggingface/datasets/commit/e70732a60b2cab2f23b5d582542d884ffd0357bc
README.md
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---
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languages:
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- en
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paperswithcode_id: blimp
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pretty_name: Benchmark of Linguistic Minimal Pairs
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---
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# Dataset Card for "blimp"
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---
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annotations_creators:
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- crowdsourced
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language_creators:
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- machine-generated
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languages:
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- en
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licenses:
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- unknown
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multilinguality:
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- monolingual
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pretty_name: BLiMP
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size_categories:
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- "10K<n<100K"
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source_datasets:
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- original
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task_categories:
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- text-classification
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task_ids:
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- acceptability-classification
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paperswithcode_id: blimp
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---
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# Dataset Card for "blimp"
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blimp.py
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@@ -46,21 +46,17 @@ _DOWNLOAD_URL = "https://raw.githubusercontent.com/alexwarstadt/blimp/master/"
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class BlimpConfig(datasets.BuilderConfig):
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"""BuilderConfig for Blimp."""
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def __init__(self,
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"""BuilderConfig for Blimp.
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Args:
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-
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**kwargs: keyword arguments forwarded to super.
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"""
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name = paradigm_uid
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-
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description = _DESCRIPTION
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description += f"This configuration includes the paradigm {name}."
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super(
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name=name, description=description, version=datasets.Version("0.1.0"), **kwargs
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)
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class Blimp(datasets.GeneratorBasedBuilder):
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"wh_vs_that_with_gap_long_distance",
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]
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BUILDER_CONFIGS = [BlimpConfig(
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def _info(self):
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return datasets.DatasetInfo(
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"pair_id": datasets.Value("int32"),
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}
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),
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage=_PROJECT_URL,
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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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-
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-
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downloaded_files = dl_manager.download_and_extract(download_urls)
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-
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files[cfg.name]})
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]
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def _generate_examples(self, filepath):
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"""Yields examples."""
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with open(filepath, "
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for line in f:
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line_dict = json.loads(line)
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id_ = line_dict["UID"] + "_" + line_dict["pairID"]
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class BlimpConfig(datasets.BuilderConfig):
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"""BuilderConfig for Blimp."""
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def __init__(self, name, version=datasets.Version("0.1.0"), **kwargs):
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"""BuilderConfig for Blimp.
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Args:
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name (str): UID of the linguistic paradigm
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**kwargs: keyword arguments forwarded to super.
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"""
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description = _DESCRIPTION
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description += f"This configuration includes the paradigm {name}."
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super().__init__(name=name, description=description, version=version, **kwargs)
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class Blimp(datasets.GeneratorBasedBuilder):
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"wh_vs_that_with_gap_long_distance",
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]
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BUILDER_CONFIGS = [BlimpConfig(paradigm) for paradigm in all_paradigms]
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def _info(self):
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return datasets.DatasetInfo(
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"pair_id": datasets.Value("int32"),
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}
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),
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homepage=_PROJECT_URL,
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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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download_urls = _DOWNLOAD_URL + f"data/{self.config.name}.jsonl"
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downloaded_file = dl_manager.download_and_extract(download_urls)
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file})]
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def _generate_examples(self, filepath):
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"""Yields examples."""
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with open(filepath, "r", encoding="utf-8") as f:
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for line in f:
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line_dict = json.loads(line)
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id_ = line_dict["UID"] + "_" + line_dict["pairID"]
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