TaTA.py

# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Dataloader for TaTA: A Multilingual Table-to-Text Dataset for African Languages."""

import json
import os

import datasets
import re

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@misc{gehrmann2022TaTA,
Author = {Sebastian Gehrmann and Sebastian Ruder and Vitaly Nikolaev and Jan A. Botha and Michael Chavinda and Ankur Parikh and Clara Rivera},
Title = {TaTa: A Multilingual Table-to-Text Dataset for African Languages},
Year = {2022},
Eprint = {arXiv:2211.00142},
}
"""

# You can copy an official description
_DESCRIPTION = """\
Dataset loader for TaTA: A Multilingual Table-to-Text Dataset for African Languages
"""

_HOMEPAGE = "https://github.com/google-research/url-nlp/tree/main/tata"

_LICENSE = "CC-BY-SA 4.0"

_URLs = {
    "train": "https://raw.githubusercontent.com/google-research/url-nlp/main/tata/train.json",
    "validation": "https://raw.githubusercontent.com/google-research/url-nlp/main/tata/dev.json",
    "test": "https://raw.githubusercontent.com/google-research/url-nlp/main/tata/test.json",
    "ru": "https://raw.githubusercontent.com/google-research/url-nlp/main/tata/ru.json"
}


class TaTA(datasets.GeneratorBasedBuilder):
    """TaTA dataset builder."""

    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    # BUILDER_CONFIGS = [
    #     datasets.BuilderConfig(name="nlg_en", version=VERSION, description="NLG: Data-to-English text."),
    #     datasets.BuilderConfig(name="nlg_de", version=VERSION, description="NLG: Data-to-German text."),
    #     datasets.BuilderConfig(name="mt_en-de", version=VERSION, description="MT: English-to-German text."),
    #     datasets.BuilderConfig(name="mt_de-en", version=VERSION, description="MT: German-to-English text."),
    #     datasets.BuilderConfig(name="nlg+mt_en-de", version=VERSION, description="NLG+MT: Data+English-to-German text."),
    #     datasets.BuilderConfig(name="nlg+mt_de-en", version=VERSION, description="NLG+MT: Data+German-to-English text."),
    # ]

    def _info(self):
        # max 26 entries in each box_score field.
        features = datasets.Features(
            {
                "gem_id": datasets.Value("string"),
                "example_id": datasets.Value("string"),
                "title": datasets.Value("string"),
                "unit_of_measure": datasets.Value("string"),
                "chart_type": datasets.Value("string"),
                "was_translated": datasets.Value("string"),
                "table_data": datasets.Value("string"), # datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
                "linearized_input": datasets.Value("string"),
                # This field has all the references in a list.
                "table_text": datasets.Sequence(datasets.Value("string")),
                # Only use `target` as supervised key, not for evaluation!
                "target": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=("linearized_input", "target"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        data_dir = dl_manager.download_and_extract(_URLs)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["test"],
                    "split": "test"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["validation"],
                    "split": "validation",
                },
            ),
            datasets.SplitGenerator(
                name="ru",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["ru"],
                    "split": "ru",
                },
            ),
        ]


    def _generate_examples(
        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        """ Yields examples as (key, example) tuples. """
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.

        with open(filepath, encoding="utf-8") as f:
            all_data = json.load(f)
            for id_, data in enumerate(all_data):
                data['gem_id'] = data['example_id']
                if not data['table_text']:
                  data['target'] = ""
                else:
                  data['target'] = data['table_text'][0]
                yield id_, data