Datasets:
hans

Task Categories: text-classification
Languages: English
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: expert-generated
Annotations Creators: expert-generated
Source Datasets: original
Licenses: unknown
system commited on
Commit
a6404e4
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Update files from the datasets library (from 1.0.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

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dataset_infos.json ADDED
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+ {"plain_text": {"description": "The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn.\n", "citation": "@article{DBLP:journals/corr/abs-1902-01007,\n author = {R. Thomas McCoy and\n Ellie Pavlick and\n Tal Linzen},\n title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural\n Language Inference},\n journal = {CoRR},\n volume = {abs/1902.01007},\n year = {2019},\n url = {http://arxiv.org/abs/1902.01007},\n archivePrefix = {arXiv},\n eprint = {1902.01007},\n timestamp = {Tue, 21 May 2019 18:03:36 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-1902-01007.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://github.com/tommccoy1/hans", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["entailment", "non-entailment"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "validation": {}}}, "supervised_keys": null, "builder_name": "hans", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3024446, "num_examples": 30000, "dataset_name": "hans"}, "validation": {"name": "validation", "num_bytes": 3019374, "num_examples": 30000, "dataset_name": "hans"}}, "download_checksums": {"https://raw.githubusercontent.com/tommccoy1/hans/master/heuristics_train_set.txt": {"num_bytes": 15485296, "checksum": "49245bd5fdb0b185dcbfbf48f0f16513c62ad5bc9fad0b8800dc48d6818ee5cf"}, "https://raw.githubusercontent.com/tommccoy1/hans/master/heuristics_evaluation_set.txt": {"num_bytes": 15462062, "checksum": "c55b62feef9913070e88f38938dc2492018c945ac81f70139346472494124e79"}}, "download_size": 30947358, "post_processing_size": 0, "dataset_size": 6043820, "size_in_bytes": 36991178}}
dummy/plain_text/1.0.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:6aa173a553448e1f186b8cede9e0755f2aaa5234c76704401655f755844270e6
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+ size 226
hans.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+
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+ # Lint as: python3
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+ """Heuristic Analysis for NLI Systems"""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import datasets
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+
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+
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+ _CITATION = """\
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+ @article{DBLP:journals/corr/abs-1902-01007,
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+ author = {R. Thomas McCoy and
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+ Ellie Pavlick and
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+ Tal Linzen},
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+ title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural
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+ Language Inference},
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+ journal = {CoRR},
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+ volume = {abs/1902.01007},
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+ year = {2019},
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+ url = {http://arxiv.org/abs/1902.01007},
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+ archivePrefix = {arXiv},
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+ eprint = {1902.01007},
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+ timestamp = {Tue, 21 May 2019 18:03:36 +0200},
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+ biburl = {https://dblp.org/rec/journals/corr/abs-1902-01007.bib},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn.
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+ """
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+
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+
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+ class HansConfig(datasets.BuilderConfig):
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+ """BuilderConfig for HANS."""
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+
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+ def __init__(self, **kwargs):
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+ """BuilderConfig for HANS.
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+
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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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+ super(HansConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
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+
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+
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+ class Hans(datasets.GeneratorBasedBuilder):
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+ """Hans: Heuristic Analysis for NLI Systems."""
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+
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+ BUILDER_CONFIGS = [
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+ HansConfig(
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+ name="plain_text",
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+ description="Plain text",
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+ ),
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "premise": datasets.Value("string"),
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+ "hypothesis": datasets.Value("string"),
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+ "label": datasets.features.ClassLabel(names=["entailment", "non-entailment"]),
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+ }
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+ ),
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+ # No default supervised_keys (as we have to pass both premise
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+ # and hypothesis as input).
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+ supervised_keys=None,
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+ homepage="https://github.com/tommccoy1/hans",
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+ citation=_CITATION,
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+ )
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+
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+ def _vocab_text_gen(self, filepath):
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+ for _, ex in self._generate_examples(filepath):
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+ yield " ".join([ex["premise"], ex["hypothesis"]])
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+
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+ def _split_generators(self, dl_manager):
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+
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+ train_path = dl_manager.download_and_extract(
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+ "https://raw.githubusercontent.com/tommccoy1/hans/master/heuristics_train_set.txt"
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+ )
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+ valid_path = dl_manager.download_and_extract(
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+ "https://raw.githubusercontent.com/tommccoy1/hans/master/heuristics_evaluation_set.txt"
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+ )
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+
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
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+ datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ """Generate hans examples.
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+
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+ Args:
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+ filepath: a string
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+
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+ Yields:
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+ dictionaries containing "premise", "hypothesis" and "label" strings
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+ """
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+ for idx, line in enumerate(open(filepath, "rb")):
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+ if idx == 0:
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+ continue # skip header
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+ line = line.strip().decode("utf-8")
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+ split_line = line.split("\t")
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+ # Examples not marked with a three out of five consensus are marked with
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+ # "-" and should not be used in standard evaluations.
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+ if split_line[0] == "-":
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+ continue
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+ # Works for both splits even though dev has some extra human labels.
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+ yield idx, {"premise": split_line[5], "hypothesis": split_line[6], "label": split_line[0]}