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"""The Adversarial GLUE (AdvGLUE) benchmark.
Homepage: https://adversarialglue.github.io/
"""
import json
import os
import textwrap
import datasets
_ADV_GLUE_CITATION = """\
@article{Wang2021AdversarialGA,
title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},
author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},
journal={ArXiv},
year={2021},
volume={abs/2111.02840}
}
"""
_ADV_GLUE_DESCRIPTION = """\
Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark
that focuses on the adversarial robustness evaluation of language models. It covers five
natural language understanding tasks from the famous GLUE tasks and is an adversarial
version of GLUE benchmark.
"""
_MNLI_BASE_KWARGS = dict(
text_features={
"premise": "premise",
"hypothesis": "hypothesis",
},
label_classes=["entailment", "neutral", "contradiction"],
label_column="label",
data_url="https://dl.fbaipublicfiles.com/glue/data/MNLI.zip",
data_dir="MNLI",
citation=textwrap.dedent(
"""\
@InProceedings{N18-1101,
author = "Williams, Adina
and Nangia, Nikita
and Bowman, Samuel",
title = "A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference",
booktitle = "Proceedings of the 2018 Conference of
the North American Chapter of the
Association for Computational Linguistics:
Human Language Technologies, Volume 1 (Long
Papers)",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "1112--1122",
location = "New Orleans, Louisiana",
url = "http://aclweb.org/anthology/N18-1101"
}
@article{bowman2015large,
title={A large annotated corpus for learning natural language inference},
author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},
journal={arXiv preprint arXiv:1508.05326},
year={2015}
}"""
),
url="http://www.nyu.edu/projects/bowman/multinli/",
)
ADVGLUE_DEV_URL = "https://adversarialglue.github.io/dataset/dev.zip"
class AdvGlueConfig(datasets.BuilderConfig):
"""BuilderConfig for Adversarial GLUE."""
def __init__(
self,
text_features,
label_column,
data_url,
data_dir,
citation,
url,
label_classes=None,
process_label=lambda x: x,
**kwargs,
):
"""BuilderConfig for Adversarial GLUE.
Args:
text_features: `dict[string, string]`, map from the name of the feature
dict for each text field to the name of the column in the tsv file
label_column: `string`, name of the column in the tsv file corresponding
to the label
data_url: `string`, url to download the zip file from
data_dir: `string`, the path to the folder containing the tsv files in the
downloaded zip
citation: `string`, citation for the data set
url: `string`, url for information about the data set
label_classes: `list[string]`, the list of classes if the label is
categorical. If not provided, then the label will be of type
`datasets.Value('float32')`.
process_label: `Function[string, any]`, function taking in the raw value
of the label and processing it to the form required by the label feature
**kwargs: keyword arguments forwarded to super.
"""
super(AdvGlueConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.text_features = text_features
self.label_column = label_column
self.label_classes = label_classes
self.data_url = data_url
self.data_dir = data_dir
self.citation = citation
self.url = url
self.process_label = process_label
ADVGLUE_BUILDER_CONFIGS = [
AdvGlueConfig(
name="adv_sst2",
description=textwrap.dedent(
"""Adversarial version of SST-2.
The Stanford Sentiment Treebank consists of sentences from movie reviews and
human annotations of their sentiment. The task is to predict the sentiment of a
given sentence. We use the two-way (positive/negative) class split, and use only
sentence-level labels."""
),
text_features={"sentence": "sentence"},
label_classes=["negative", "positive"],
label_column="label",
data_url="https://dl.fbaipublicfiles.com/glue/data/SST-2.zip",
data_dir="SST-2",
citation=textwrap.dedent(
"""\
@inproceedings{socher2013recursive,
title={Recursive deep models for semantic compositionality over a sentiment treebank},
author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},
booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},
pages={1631--1642},
year={2013}
}"""
),
url="https://datasets.stanford.edu/sentiment/index.html",
),
AdvGlueConfig(
name="adv_qqp",
description=textwrap.dedent(
"""Adversarial version of QQP.
The Quora Question Pairs2 dataset is a collection of question pairs from the
community question-answering website Quora. The task is to determine whether a
pair of questions are semantically equivalent."""
),
text_features={
"question1": "question1",
"question2": "question2",
},
label_classes=["not_duplicate", "duplicate"],
label_column="label",
data_url="https://dl.fbaipublicfiles.com/glue/data/QQP-clean.zip",
data_dir="QQP",
citation=textwrap.dedent(
"""\
@online{WinNT,
author = {Iyer, Shankar and Dandekar, Nikhil and Csernai, Kornel},
title = {First Quora Dataset Release: Question Pairs},
year = {2017},
url = {https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs},
urldate = {2019-04-03}
}"""
),
url="https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs",
),
AdvGlueConfig(
name="adv_mnli",
description=textwrap.dedent(
"""Adversarial version of MNLI.
The Multi-Genre Natural Language Inference Corpus is a crowdsourced
collection of sentence pairs with textual entailment annotations. Given a premise sentence
and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis
(entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are
gathered from ten different sources, including transcribed speech, fiction, and government reports.
We use the standard test set, for which we obtained private labels from the authors, and evaluate
on both the matched (in-domain) and mismatched (cross-domain) section. We also use and recommend
the SNLI corpus as 550k examples of auxiliary training data."""
),
**_MNLI_BASE_KWARGS,
),
AdvGlueConfig(
name="adv_mnli_mismatched",
description=textwrap.dedent(
"""Adversarial version of MNLI-mismatched.
The mismatched validation and test splits from MNLI.
See the "mnli" BuilderConfig for additional information."""
),
**_MNLI_BASE_KWARGS,
),
AdvGlueConfig(
name="adv_qnli",
description=textwrap.dedent(
"""Adversarial version of QNLI.
The Stanford Question Answering Dataset is a question-answering
dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn
from Wikipedia) contains the answer to the corresponding question (written by an annotator). We
convert the task into sentence pair classification by forming a pair between each question and each
sentence in the corresponding context, and filtering out pairs with low lexical overlap between the
question and the context sentence. The task is to determine whether the context sentence contains
the answer to the question. This modified version of the original task removes the requirement that
the model select the exact answer, but also removes the simplifying assumptions that the answer
is always present in the input and that lexical overlap is a reliable cue."""
), # pylint: disable=line-too-long
text_features={
"question": "question",
"sentence": "sentence",
},
label_classes=["entailment", "not_entailment"],
label_column="label",
data_url="https://dl.fbaipublicfiles.com/glue/data/QNLIv2.zip",
data_dir="QNLI",
citation=textwrap.dedent(
"""\
@article{rajpurkar2016squad,
title={Squad: 100,000+ questions for machine comprehension of text},
author={Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy},
journal={arXiv preprint arXiv:1606.05250},
year={2016}
}"""
),
url="https://rajpurkar.github.io/SQuAD-explorer/",
),
AdvGlueConfig(
name="adv_rte",
description=textwrap.dedent(
"""Adversarial version of RTE.
The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual
entailment challenges. We combine the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim
et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009).4 Examples are
constructed based on news and Wikipedia text. We convert all datasets to a two-class split, where
for three-class datasets we collapse neutral and contradiction into not entailment, for consistency."""
), # pylint: disable=line-too-long
text_features={
"sentence1": "sentence1",
"sentence2": "sentence2",
},
label_classes=["entailment", "not_entailment"],
label_column="label",
data_url="https://dl.fbaipublicfiles.com/glue/data/RTE.zip",
data_dir="RTE",
citation=textwrap.dedent(
"""\
@inproceedings{dagan2005pascal,
title={The PASCAL recognising textual entailment challenge},
author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo},
booktitle={Machine Learning Challenges Workshop},
pages={177--190},
year={2005},
organization={Springer}
}
@inproceedings{bar2006second,
title={The second pascal recognising textual entailment challenge},
author={Bar-Haim, Roy and Dagan, Ido and Dolan, Bill and Ferro, Lisa and Giampiccolo, Danilo and Magnini, Bernardo and Szpektor, Idan},
booktitle={Proceedings of the second PASCAL challenges workshop on recognising textual entailment},
volume={6},
number={1},
pages={6--4},
year={2006},
organization={Venice}
}
@inproceedings{giampiccolo2007third,
title={The third pascal recognizing textual entailment challenge},
author={Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill},
booktitle={Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing},
pages={1--9},
year={2007},
organization={Association for Computational Linguistics}
}
@inproceedings{bentivogli2009fifth,
title={The Fifth PASCAL Recognizing Textual Entailment Challenge.},
author={Bentivogli, Luisa and Clark, Peter and Dagan, Ido and Giampiccolo, Danilo},
booktitle={TAC},
year={2009}
}"""
),
url="https://aclweb.org/aclwiki/Recognizing_Textual_Entailment",
),
]
class AdvGlue(datasets.GeneratorBasedBuilder):
"""The General Language Understanding Evaluation (GLUE) benchmark."""
DATASETS = ["adv_sst2", "adv_qqp", "adv_mnli", "adv_mnli_mismatched", "adv_qnli", "adv_rte"]
BUILDER_CONFIGS = ADVGLUE_BUILDER_CONFIGS
def _info(self):
features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
if self.config.label_classes:
features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
else:
features["label"] = datasets.Value("float32")
features["idx"] = datasets.Value("int32")
return datasets.DatasetInfo(
description=_ADV_GLUE_DESCRIPTION,
features=datasets.Features(features),
homepage="https://adversarialglue.github.io/",
citation=_ADV_GLUE_CITATION,
)
def _split_generators(self, dl_manager):
assert self.config.name in AdvGlue.DATASETS
data_dir = dl_manager.download_and_extract(ADVGLUE_DEV_URL)
data_file = os.path.join(data_dir, "dev", "dev.json")
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_file": data_file,
},
)
]
def _generate_examples(self, data_file):
# We name splits 'adv_sst2' instead of 'sst2' so as not to be confused
# with the original SST-2. Here they're named like 'sst2' so we have to
# remove the 'adv_' prefix.
config_key = self.config.name.replace("adv_", "")
if config_key == "mnli_mismatched":
# and they name this split differently.
config_key = "mnli-mm"
data = json.loads(open(data_file).read())
for row in data[config_key]:
example = {feat: row[col] for feat, col in self.config.text_features.items()}
example["label"] = self.config.process_label(row[self.config.label_column])
example["idx"] = row["idx"]
yield example["idx"], example
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