Datasets:

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