babi_nli.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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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# Lint as: python3
"""bAbI_nli datasets"""

from __future__ import absolute_import, division, print_function

import csv
import os
import textwrap

import six

import datasets


bAbI_nli_CITATION = r"""@article{weston2015towards,
  title={Towards ai-complete question answering: A set of prerequisite toy tasks},
  author={Weston, Jason and Bordes, Antoine and Chopra, Sumit and Rush, Alexander M and Van Merri{\"e}nboer, Bart and Joulin, Armand and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1502.05698},
  year={2015}
}

"""

_babi_nli_DESCRIPTION = """\
bAbi tasks recasted as natural language inference.
"""

DATA_URL = "https://www.dropbox.com/s/0b98tbrv2mej3cu/babi_nli.zip?dl=1"

LABELS=["not-entailed", "entailed"]

CONFIGS=['single-supporting-fact',
         'two-supporting-facts',
         'three-supporting-facts',
         'two-arg-relations',
         'three-arg-relations',
         'yes-no-questions',
         'counting',
         'lists-sets',
         'simple-negation',
         'indefinite-knowledge',
         'basic-coreference',
         'conjunction',
         'compound-coreference',
         'time-reasoning',
         'basic-deduction',
         'basic-induction',
         'positional-reasoning',
         'size-reasoning',
         'path-finding',
         'agents-motivations']

class bAbI_nli_Config(datasets.BuilderConfig):
    """BuilderConfig for bAbI_nli."""

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

        self.text_features = text_features
        self.label_column = "label"
        self.label_classes = LABELS
        self.data_url = DATA_URL
        self.data_dir = self.name #os.path.join("babi_nli", self.name)
        self.citation = textwrap.dedent(bAbI_nli_CITATION)
        self.process_label = lambda x: str(x)
        self.description = ""
        self.url = "https://github.com/facebookarchive/bAbI-tasks/blob/master/LICENSE.md"
        

class bAbI_nli(datasets.GeneratorBasedBuilder):

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

    BUILDER_CONFIG_CLASS = bAbI_nli_Config

    BUILDER_CONFIGS = [
        bAbI_nli_Config(
            name=name,
            text_features={"premise": "premise", "hypothesis": "hypothesis"},
        ) for name in CONFIGS
    ]

    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=_babi_nli_DESCRIPTION,
            features=datasets.Features(features),
            homepage=self.config.url,
            citation=self.config.citation + "\n" + bAbI_nli_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 "", "validation.tsv"),
                    "split": "validation",
                },
            ),
            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