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# 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
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