recast.py

# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""Recast datasets"""

from __future__ import absolute_import, division, print_function

import csv
import os
import textwrap

import six

import datasets


_Recast_CITATION = r"""@inproceedings{poliak-etal-2018-collecting,
    title = "Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation",
    author = "Poliak, Adam  and
      Haldar, Aparajita  and
      Rudinger, Rachel  and
      Hu, J. Edward  and
      Pavlick, Ellie  and
      White, Aaron Steven  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D18-1007",
    doi = "10.18653/v1/D18-1007",
    pages = "67--81",
    abstract = "We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at \url{https://www.decomp.net}, and will grow over time as additional resources are recast and added from novel sources.",
}
"""

_Recast_DESCRIPTION = """\
A diverse collection of tasks recasted as natural language inference tasks.
"""

DATA_URL = "https://www.dropbox.com/s/z1mcq6ygfsae0wj/recast.zip?dl=1"

TASK_TO_LABELS = {
    "recast_kg_relations": ["1", "2", "3", "4", "5", "6"],
    "recast_puns": ["not-entailed", "entailed"],
    "recast_factuality": ["not-entailed", "entailed"],
    "recast_verbnet": ["not-entailed", "entailed"],
    "recast_verbcorner": ["not-entailed", "entailed"],
    "recast_sentiment": ["not-entailed", "entailed"],
    "recast_megaveridicality": ["not-entailed", "entailed"],
    "recast_ner": ["not-entailed", "entailed"],
    "recast_winogender": ["not-entailed", "entailed"],
    "recast_ner": ["not-entailed", "entailed"],
}


def get_labels(task):
    return TASK_TO_LABELS[task]


class RecastConfig(datasets.BuilderConfig):
    """BuilderConfig for Recast."""

    def __init__(
        self,
        text_features,
        label_classes=None,
        process_label=lambda x: x,
        **kwargs,
    ):
        """BuilderConfig for Recast.
        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(RecastConfig, self).__init__(
            version=datasets.Version("1.0.0", ""), **kwargs
        )

        self.text_features = text_features
        self.label_column = "label"
        self.label_classes = get_labels(self.name)
        self.data_url = DATA_URL
        self.data_dir = os.path.join("recast", self.name)
        self.citation = textwrap.dedent(_Recast_CITATION)
        self.process_label = lambda x: str(x)
        self.description = ""
        self.url = ""


class Recast(datasets.GeneratorBasedBuilder):

    """The General Language Understanding Evaluation (Recast) benchmark."""

    BUILDER_CONFIG_CLASS = RecastConfig

    BUILDER_CONFIGS = [
        RecastConfig(
            name="recast_kg_relations",
            text_features={"context": "context", "hypothesis": "hypothesis"},
        ),
        RecastConfig(
            name="recast_puns",
            text_features={"context": "context", "hypothesis": "hypothesis"},
        ),
        RecastConfig(
            name="recast_factuality",
            text_features={"context": "context", "hypothesis": "hypothesis"},
        ),
        RecastConfig(
            name="recast_verbnet",
            text_features={"context": "context", "hypothesis": "hypothesis"},
        ),
        RecastConfig(
            name="recast_verbcorner",
            text_features={"context": "context", "hypothesis": "hypothesis"},
        ),
        RecastConfig(
            name="recast_ner",
            text_features={"context": "context", "hypothesis": "hypothesis"},
        ),
        RecastConfig(
            name="recast_sentiment",
            text_features={"context": "context", "hypothesis": "hypothesis"},
        ),
        RecastConfig(
            name="recast_megaveridicality",
            text_features={"context": "context", "hypothesis": "hypothesis"},
        ),
    ]

    def _info(self):
        features = {
            text_feature: datasets.Value("string")
            for text_feature in six.iterkeys(self.config.text_features)
        }
        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=_Recast_DESCRIPTION,
            features=datasets.Features(features),
            homepage=self.config.url,
            citation=self.config.citation + "\n" + _Recast_CITATION,
        )

    def _split_generators(self, dl_manager):
        dl_dir = dl_manager.download_and_extract(self.config.data_url)
        data_dir = os.path.join(dl_dir, self.config.data_dir)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data_file": os.path.join(data_dir or "", "train.tsv"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "data_file": os.path.join(data_dir or "", "dev.tsv"),
                    "split": "dev",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "data_file": os.path.join(data_dir or "", "test.tsv"),
                    "split": "test",
                },
            ),
        ]

    def _generate_examples(self, data_file, split):

        process_label = self.config.process_label
        label_classes = self.config.label_classes

        with open(data_file, encoding="utf8") as f:
            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)

            for n, row in enumerate(reader):

                example = {
                    feat: row[col]
                    for feat, col in six.iteritems(self.config.text_features)
                }
                example["idx"] = n

                if self.config.label_column in row:
                    label = row[self.config.label_column]
                    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