Datasets:
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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 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
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