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
hans / hans.py
# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Heuristic Analysis for NLI Systems"""
import datasets
_CITATION = """\
@article{DBLP:journals/corr/abs-1902-01007,
author = {R. Thomas McCoy and
Ellie Pavlick and
Tal Linzen},
title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural
Language Inference},
journal = {CoRR},
volume = {abs/1902.01007},
year = {2019},
url = {http://arxiv.org/abs/1902.01007},
archivePrefix = {arXiv},
eprint = {1902.01007},
timestamp = {Tue, 21 May 2019 18:03:36 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1902-01007.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DESCRIPTION = """\
The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn.
"""
class HansConfig(datasets.BuilderConfig):
"""BuilderConfig for HANS."""
def __init__(self, **kwargs):
"""BuilderConfig for HANS.
Args:
.
**kwargs: keyword arguments forwarded to super.
"""
super(HansConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class Hans(datasets.GeneratorBasedBuilder):
"""Hans: Heuristic Analysis for NLI Systems."""
BUILDER_CONFIGS = [
HansConfig(
name="plain_text",
description="Plain text",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["entailment", "non-entailment"]),
"parse_premise": datasets.Value("string"),
"parse_hypothesis": datasets.Value("string"),
"binary_parse_premise": datasets.Value("string"),
"binary_parse_hypothesis": datasets.Value("string"),
"heuristic": datasets.Value("string"),
"subcase": datasets.Value("string"),
"template": datasets.Value("string"),
}
),
# No default supervised_keys (as we have to pass both premise
# and hypothesis as input).
supervised_keys=None,
homepage="https://github.com/tommccoy1/hans",
citation=_CITATION,
)
def _vocab_text_gen(self, filepath):
for _, ex in self._generate_examples(filepath):
yield " ".join([ex["premise"], ex["hypothesis"]])
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(
"https://raw.githubusercontent.com/tommccoy1/hans/master/heuristics_train_set.txt"
)
valid_path = dl_manager.download_and_extract(
"https://raw.githubusercontent.com/tommccoy1/hans/master/heuristics_evaluation_set.txt"
)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
]
def _generate_examples(self, filepath):
"""Generate hans examples.
Args:
filepath: a string
Yields:
dictionaries containing "premise", "hypothesis" and "label" strings
"""
for idx, line in enumerate(open(filepath, "r", encoding="utf-8")):
if idx == 0:
continue # skip header
line = line.strip()
split_line = line.split("\t")
# Examples not marked with a three out of five consensus are marked with
# "-" and should not be used in standard evaluations.
if split_line[0] == "-":
continue
# Works for both splits even though dev has some extra human labels.
yield idx, {
"premise": split_line[5],
"hypothesis": split_line[6],
"label": split_line[0],
"binary_parse_premise": split_line[1],
"binary_parse_hypothesis": split_line[2],
"parse_premise": split_line[3],
"parse_hypothesis": split_line[4],
"heuristic": split_line[8],
"subcase": split_line[9],
"template": split_line[10],
}