# 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 RotoWire English-German dataset.""" import json import os import datasets # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @article{hayashi2019findings, title={Findings of the Third Workshop on Neural Generation and Translation}, author={Hayashi, Hiroaki and Oda, Yusuke and Birch, Alexandra and Konstas, Ioannis and Finch, Andrew and Luong, Minh-Thang and Neubig, Graham and Sudoh, Katsuhito}, journal={EMNLP-IJCNLP 2019}, pages={1}, year={2019} } """ # You can copy an official description _DESCRIPTION = """\ Dataset for the WNGT 2019 DGT shared task on "Document-Level Generation and Translation”. """ _HOMEPAGE = "https://sites.google.com/view/wngt19/dgt-task" _LICENSE = "CC-BY 4.0" # The HuggingFace dataset library don't host the datasets but only point to the original files # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLs = { "train": "train.json", "validation": "validation.json", "test": "test.json" } class RotowireEnglishGerman(datasets.GeneratorBasedBuilder): """Dataset for WNGT2019 shared task on Document-level Generation and Translation.""" 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. box_score_entry = {str(i): datasets.Value("string") for i in range(26)} box_score_features = { "FIRST_NAME": box_score_entry, "MIN": box_score_entry, "FGM": box_score_entry, "REB": box_score_entry, "FG3A": box_score_entry, "PLAYER_NAME": box_score_entry, "AST": box_score_entry, "FG3M": box_score_entry, "OREB": box_score_entry, "TO": box_score_entry, "START_POSITION": box_score_entry, "PF": box_score_entry, "PTS": box_score_entry, "FGA": box_score_entry, "STL": box_score_entry, "FTA": box_score_entry, "BLK": box_score_entry, "DREB": box_score_entry, "FTM": box_score_entry, "FT_PCT": box_score_entry, "FG_PCT": box_score_entry, "FG3_PCT": box_score_entry, "SECOND_NAME": box_score_entry, "TEAM_CITY": box_score_entry, } line_features = { "TEAM-PTS_QTR2": datasets.Value("string"), "TEAM-FT_PCT": datasets.Value("string"), "TEAM-PTS_QTR1": datasets.Value("string"), "TEAM-PTS_QTR4": datasets.Value("string"), "TEAM-PTS_QTR3": datasets.Value("string"), "TEAM-CITY": datasets.Value("string"), "TEAM-PTS": datasets.Value("string"), "TEAM-AST": datasets.Value("string"), "TEAM-LOSSES": datasets.Value("string"), "TEAM-NAME": datasets.Value("string"), "TEAM-WINS": datasets.Value("string"), "TEAM-REB": datasets.Value("string"), "TEAM-TOV": datasets.Value("string"), "TEAM-FG3_PCT": datasets.Value("string"), "TEAM-FG_PCT": datasets.Value("string") } features = datasets.Features( { "id":datasets.Value("string"), "gem_id":datasets.Value("string"), "home_name": datasets.Value("string"), "box_score": box_score_features, "vis_name": datasets.Value("string"), "summary": datasets.Sequence(datasets.Value("string")), "home_line": line_features, "home_city": datasets.Value("string"), "vis_line": line_features, "vis_city": datasets.Value("string"), "day": datasets.Value("string"), "detok_summary_org": datasets.Value("string"), "detok_summary": datasets.Value("string"), "summary_en": datasets.Sequence(datasets.Value("string")), "sentence_end_index_en": datasets.Sequence(datasets.Value("int32")), "summary_de": datasets.Sequence(datasets.Value("string")), "sentence_end_index_de": datasets.Sequence(datasets.Value("int32")) } ) 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=None, # 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.""" # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # 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", }, ), ] 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): yield id_, data