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"""The General Language Understanding Evaluation (GLUE) benchmark.""" |
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import csv |
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import os |
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import textwrap |
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import json |
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import numpy as np |
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import datasets |
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_GLUE_CITATION = """\ |
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@inproceedings{wang2019glue, |
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title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding}, |
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author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.}, |
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note={In the Proceedings of ICLR.}, |
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year={2019} |
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} |
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""" |
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_GLUE_DESCRIPTION = """\ |
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GLUE, the General Language Understanding Evaluation benchmark |
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(https://gluebenchmark.com/) is a collection of resources for training, |
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evaluating, and analyzing natural language understanding systems. |
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""" |
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class GlueConfig(datasets.BuilderConfig): |
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"""BuilderConfig for GLUE.""" |
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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 GLUE. |
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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(GlueConfig, 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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class Glue(datasets.GeneratorBasedBuilder): |
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"""The General Language Understanding Evaluation (GLUE) benchmark.""" |
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BUILDER_CONFIGS = [ |
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GlueConfig( |
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name=bias_amplified_splits_type, |
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description=textwrap.dedent( |
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"""\ |
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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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) for bias_amplified_splits_type in ['minority_examples', 'partial_input'] |
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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=_GLUE_DESCRIPTION, |
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features=datasets.Features(features), |
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homepage=self.config.url, |
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citation=self.config.citation + "\n" + _GLUE_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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return [ |
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datasets.SplitGenerator( |
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name="train.biased", |
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gen_kwargs={ |
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"filepath": dl_manager.download(os.path.join(self.config.name, "train.biased.jsonl")), |
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}, |
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), |
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datasets.SplitGenerator( |
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name="train.anti_biased", |
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gen_kwargs={ |
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"filepath": dl_manager.download(os.path.join(self.config.name, "train.anti_biased.jsonl")), |
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}, |
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), |
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datasets.SplitGenerator( |
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name="validation.biased", |
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gen_kwargs={ |
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"filepath": dl_manager.download(os.path.join(self.config.name, "validation.biased.jsonl")), |
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}, |
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), |
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datasets.SplitGenerator( |
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name="validation.anti_biased", |
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gen_kwargs={ |
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"filepath": dl_manager.download(os.path.join(self.config.name, "validation.anti_biased.jsonl")), |
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}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""Generate examples. |
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Args: |
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filepath: a string |
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Yields: |
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dictionaries containing "premise", "hypothesis" and "label" strings |
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""" |
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process_label = self.config.process_label |
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label_classes = self.config.label_classes |
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for idx, line in enumerate(open(filepath, "rb")): |
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if line is not None: |
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line = line.strip().decode("utf-8") |
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item = json.loads(line) |
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example = { |
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"idx": item["idx"], |
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"question1": item["question1"], |
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"question2": item["question2"], |
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} |
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if self.config.label_column in item: |
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label = item[self.config.label_column] |
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example["label"] = process_label(label) |
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else: |
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example["label"] = process_label(-1) |
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yield example["idx"], example |
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