mnli.py

# coding=utf-8
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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.
"""

_MNLI_BASE_KWARGS = dict(
    text_features={
        "premise": "sentence1",
        "hypothesis": "sentence2",
    },
    label_classes=["entailment", "neutral", "contradiction"],
    label_column="gold_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/",
)


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 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,
        ) 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_matched.biased",
                gen_kwargs={
                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation_matched.biased.jsonl")),
                },
            ),
            datasets.SplitGenerator(
                name="validation_matched.anti_biased",
                gen_kwargs={
                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation_matched.anti_biased.jsonl")),
                },
            ),
            datasets.SplitGenerator(
                name="validation_mismatched.biased",
                gen_kwargs={
                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation_mismatched.biased.jsonl")),
                },
            ),
            datasets.SplitGenerator(
                name="validation_mismatched.anti_biased",
                gen_kwargs={
                    "filepath": dl_manager.download(os.path.join(self.config.name, "validation_mismatched.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"],
                    "premise": item["premise"],
                    "hypothesis": item["hypothesis"],
                }
                if self.config.label_column in item:
                    label = item[self.config.label_column]
                    # For some tasks, the label is represented as 0 and 1 in the tsv
                    # files and needs to be cast to integer to work with the feature.
                    if label_classes and label not in label_classes:
                        label = int(label) if label else None
                    example["label"] = process_label(label)
                else:
                    example["label"] = process_label(-1)

                yield example["idx"], example