| # 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 | |