ik-nlp-22_transqe.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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# Lint as: python3
"""Dutch translation of the e-SNLI corpus with added quality estimation scores"""


import csv
csv.register_dialect("tsv", delimiter="\t")

import datasets


_CITATION = """
@incollection{NIPS2018_8163,
title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {9539--9549},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
}
"""

_DESCRIPTION = """
The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
include human-annotated natural language explanations of the entailment
relations. This version includes an automatic translation to Dutch and two quality estimation annotations
for each translated field.
"""

_HOMEPAGE = "https://www.rug.nl/masters/information-science/?lang=en"

_URLS = {
    "train": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/resolve/main/data/train.tsv.gz",
    "validation": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/resolve/main/data/validation.tsv.gz",
    "test": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/resolve/main/data/test.tsv.gz",
}

class IkNlp22ExpNLIConfig(datasets.GeneratorBasedBuilder):
    """e-SNLI corpus with added translation and quality estimation scores"""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="plain_text",
            version=datasets.Version("0.0.2"),
            description="Plain text import of e-SNLI",
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "premise_en": datasets.Value("string"),
                    "premise_nl": datasets.Value("string"),
                    "hypothesis_en": datasets.Value("string"),
                    "hypothesis_nl": datasets.Value("string"),
                    "label": datasets.Value("int32"),
                    "explanation_1_en": datasets.Value("string"),
                    "explanation_1_nl": datasets.Value("string"),
                    "explanation_2_en": datasets.Value("string"),
                    "explanation_2_nl": datasets.Value("string"),
                    "explanation_3_en": datasets.Value("string"),
                    "explanation_3_nl": datasets.Value("string"),
                    "da_premise": datasets.Value("string"),
                    "mqm_premise": datasets.Value("string"),
                    "da_hypothesis": datasets.Value("string"),
                    "mqm_hypothesis": datasets.Value("string"),
                    "da_explanation_1": datasets.Value("string"),
                    "mqm_explanation_1": datasets.Value("string"),
                    "da_explanation_2": datasets.Value("string"),
                    "mqm_explanation_2": datasets.Value("string"),
                    "da_explanation_3": datasets.Value("string"),
                    "mqm_explanation_3": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        files = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(
                name=name,
                gen_kwargs={"filepath": filepath},
            )
            for name, filepath in files.items()
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f, dialect="tsv")
            for i, row in enumerate(reader):
                yield i, {
                        "premise_en": row["premise_en"],
                        "premise_nl": row["premise_nl"],
                        "hypothesis_en": row["hypothesis_en"],
                        "hypothesis_nl": row["hypothesis_nl"],
                        "label": row["label"],
                        "explanation_1_en": row["explanation_1_en"],
                        "explanation_1_nl": row["explanation_1_nl"],
                        "explanation_2_en": row.get("explanation_2_en", ""),
                        "explanation_2_nl": row.get("explanation_2_nl", ""),
                        "explanation_3_en": row.get("explanation_3_en", ""),
                        "explanation_3_nl": row.get("explanation_3_nl", ""),
                        "da_premise": row["da_premise"],
                        "mqm_premise": row["mqm_premise"],
                        "da_hypothesis": row["da_hypothesis"],
                        "mqm_hypothesis": row["mqm_hypothesis"],
                        "da_explanation_1": row["da_explanation_1"],
                        "mqm_explanation_1": row["mqm_explanation_1"],
                         "da_explanation_2": row.get("da_explanation_2", ""),
                        "mqm_explanation_2": row.get("mqm_explanation_2", ""),
                         "da_explanation_3": row.get("da_explanation_3", ""),
                        "mqm_explanation_3": row.get("mqm_explanation_3", ""),
                    }