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Update files from the datasets library (from 1.0.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.0.0
- .gitattributes +27 -0
- dataset_infos.json +1 -0
- dummy/multi_nli/1.0.0/dummy_data.zip +3 -0
- dummy/snli/1.0.0/dummy_data.zip +3 -0
- kor_nli.py +121 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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dataset_infos.json
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{"multi_nli": {"description": " Korean Natural Language Inference datasets\n", "citation": "@article{ham2020kornli,\n title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},\n author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},\n journal={arXiv preprint arXiv:2004.03289},\n year={2020}\n}\n", "homepage": "https://github.com/kakaobrain/KorNLUDatasets", "license": "", "features": {"sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}, "gold_label": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "kor_nli", "config_name": "multi_nli", "version": {"version_str": "1.0.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 87169599, "num_examples": 385494, "dataset_name": "kor_nli"}}, "download_checksums": {"https://github.com/kakaobrain/KorNLUDatasets/archive/master.zip": {"num_bytes": 42113232, "checksum": "b1184d5e78a7d988400eabe3374b8a7e2abf182896f54e6e311c5173bb2c9bf5"}}, "download_size": 42113232, "dataset_size": 87169599, "size_in_bytes": 129282831}}
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dummy/multi_nli/1.0.0/dummy_data.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:63ea1d99733461e2228db79f7cbe91f13d65a43235ab01b27100b1fa08079ead
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size 1565
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dummy/snli/1.0.0/dummy_data.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:4dd0e7f1ce2e481a25344b31bcd5e4c69dcd900ec3d5fb74bf449caa99ae5259
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size 1259
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kor_nli.py
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"""TODO(kor_nli): Add a description here."""
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from __future__ import absolute_import, division, print_function
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import csv
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import os
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import datasets
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# TODO(kor_nli): BibTeX citation
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_CITATION = """\
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@article{ham2020kornli,
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title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
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author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
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journal={arXiv preprint arXiv:2004.03289},
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year={2020}
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}
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"""
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# TODO(kor_nli):
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_DESCRIPTION = """ Korean Natural Language Inference datasets
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"""
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_URL = "https://github.com/kakaobrain/KorNLUDatasets/archive/master.zip"
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class KorNLIConfig(datasets.BuilderConfig):
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"""BuilderConfig for KorNLI."""
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def __init__(self, **kwargs):
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"""BuilderConfig for KorNLI.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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# Version 1.1.0 remove empty document and summary strings.
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super(KorNLIConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
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class KorNli(datasets.GeneratorBasedBuilder):
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"""TODO(kor_nli): Short description of my dataset."""
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# TODO(kor_nli): Set up version.
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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KorNLIConfig(name="multi_nli", description="Korean multi NLI datasets"),
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KorNLIConfig(name="snli", description="Korean SNLI dataset"),
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KorNLIConfig(name="xnli", description="Korean XNLI dataset"),
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]
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def _info(self):
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# TODO(kor_nli): Specifies the datasets.DatasetInfo object
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# datasets.features.FeatureConnectors
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features=datasets.Features(
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{
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# These are the features of your dataset like images, labels ...
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"sentence1": datasets.Value("string"),
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"sentence2": datasets.Value("string"),
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"gold_label": datasets.Value("string"),
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}
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),
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# If there's a common (input, target) tuple from the features,
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# specify them here. They'll be used if as_supervised=True in
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# builder.as_dataset.
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage="https://github.com/kakaobrain/KorNLUDatasets",
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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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# TODO(kor_nli): Downloads the data and defines the splits
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# dl_manager is a datasets.download.DownloadManager that can be used to
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# download and extract URLs
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dl_dir = dl_manager.download_and_extract(_URL)
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dl_dir = os.path.join(dl_dir, "KorNLUDatasets-master", "KorNLI")
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if self.config.name == "multi_nli":
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": os.path.join(dl_dir, "multinli.train.ko.tsv")},
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),
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]
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elif self.config.name == "snli":
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": os.path.join(dl_dir, "snli_1.0_train.ko.tsv")},
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),
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]
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else:
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return [
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": os.path.join(dl_dir, "xnli.dev.ko.tsv")},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": os.path.join(dl_dir, "xnli.test.ko.tsv")},
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),
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]
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def _generate_examples(self, filepath):
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"""Yields examples."""
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# TODO(kor_nli): Yields (key, example) tuples from the dataset
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with open(filepath, encoding="utf-8") as f:
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data = csv.DictReader(f, dialect="excel-tab")
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for id_, row in enumerate(data):
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if len(row) != 3:
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continue
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yield id_, row
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