qqp.py

# coding=utf-8
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""The General Language Understanding Evaluation (GLUE) benchmark."""


import csv
import os
import textwrap
import json

import numpy as np

import datasets


_GLUE_CITATION = """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

_GLUE_DESCRIPTION = """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""


class GlueConfig(datasets.BuilderConfig):
    """BuilderConfig for 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 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(GlueConfig, 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


class Glue(datasets.GeneratorBasedBuilder):
    """The General Language Understanding Evaluation (GLUE) benchmark."""

    BUILDER_CONFIGS = [
        GlueConfig(
            name=bias_amplified_splits_type,
            description=textwrap.dedent(
                """\
            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",
        ) for bias_amplified_splits_type in ['minority_examples', 'partial_input']
    ]

    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=_GLUE_DESCRIPTION,
            features=datasets.Features(features),
            homepage=self.config.url,
            citation=self.config.citation + "\n" + _GLUE_CITATION,
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name="train.biased",
                gen_kwargs={
                    "filepath": dl_manager.download(os.path.join(self.config.name, "train.biased.jsonl")),
                },
            ),
            datasets.SplitGenerator(
                name="train.anti_biased",
                gen_kwargs={
                    "filepath": dl_manager.download(os.path.join(self.config.name, "train.anti_biased.jsonl")),
                },
            ),
            datasets.SplitGenerator(
                name="validation.biased",
                gen_kwargs={
                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation.biased.jsonl")),
                },
            ),
            datasets.SplitGenerator(
                name="validation.anti_biased",
                gen_kwargs={
                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation.anti_biased.jsonl")),
                },
            ),
        ]

    def _generate_examples(self, filepath):
        """Generate examples.

        Args:
          filepath: a string

        Yields:
          dictionaries containing "premise", "hypothesis" and "label" strings
        """
        process_label = self.config.process_label
        label_classes = self.config.label_classes

        for idx, line in enumerate(open(filepath, "rb")):
            if line is not None:
                line = line.strip().decode("utf-8")
                item = json.loads(line)
                example = {
                    "idx": item["idx"],
                    "question1": item["question1"],
                    "question2": item["question2"],
                }
                if self.config.label_column in item:
                    label = item[self.config.label_column]
                    example["label"] = process_label(label)
                else:
                    example["label"] = process_label(-1)

                yield example["idx"], example