Datasets:

GEM
/

opusparcus

	# 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.

	"""TODO: Add a description here."""

	import csv
	import json
	import os
	import datasets
	import bz2

	# Add BibTeX citation

	_CITATION = """\

	@InProceedings{huggingface:dataset,
	title = {A great new dataset},
	author={huggingface, Inc.
	},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	Test adding a dataset with challenge set to GEM benchmark .
	"""

	_HOMEPAGE = ""

	_LICENSE = ""

	# 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": None, # actual value set in the `_split_generators` method
	"validation": "validation.jsonl",
	"test": "test.jsonl"

	}

	_VERSION = datasets.Version("1.0.0", "")

	class OpusparcusConfig(datasets.BuilderConfig):
	"""BuilderConfig for Opusparcus."""

	def __init__(self, lang=None, quality=100, **kwargs):
	"""BuilderConfig for Wikipedia.
	Args:
	language: string, the language code for the Wikipedia dump to use.
	date: string, date of the Wikipedia dump in YYYYMMDD format. A list of
	available dates can be found at https://dumps.wikimedia.org/enwiki/.
	**kwargs: keyword arguments forwarded to super.
	"""
	super(OpusparcusConfig, self).__init__(
	name="{0}.{1}".format(lang, quality),
	description="Opusparcus dataset for {0}".format(lang),
	**kwargs,
	)
	self.lang = lang
	self.quality = quality

	LANGS = [ "de", "en", "fi", "fr", "ru", "sv" ]

	QUALITIES = [ 100, 95, 90, 85, 80, 75, 70, 65, 60 ]

	class Opusparcus(datasets.GeneratorBasedBuilder):

	"""TODO: Short description of my dataset."""

	# 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 = OpusparcusConfig
	# 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 = [
	OpusparcusConfig(lang=lang, quality=quality, version=_VERSION) for lang in LANGS for quality in QUALITIES
	]

	#DEFAULT_CONFIG_NAME = "test" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	#if self.config.name == "test": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"lang": datasets.Value("string"),
	"sent1": datasets.Value("string"),
	"sent2": datasets.Value("string"),
	"annot_score": datasets.Value("float"),
	"gem_id": datasets.Value("string"),
	"quality": datasets.Value("uint8")
	}
	)

	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

	if self.config.quality > 95:
	# No training data matches this quality criterion
	del _URLs["train"]
	else:
	_URLs["train"] = "train_{0}.jsonl.bz2".format(self.config.lang)

	data_dir = dl_manager.download_and_extract(_URLs)

	splits = [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"lang": self.config.lang,
	"quality": 100,
	"filepath": data_dir["test"],
	"split": "test"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"lang": self.config.lang,
	"quality": 100,
	"filepath": data_dir["validation"],
	"split": "validation",
	},
	)
	]

	if self.config.quality <= 95:
	# We do have training data as well
	splits.append(
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"lang": self.config.lang,
	"quality": self.config.quality,
	"filepath": [data_dir["train"], data_dir["train"], data_dir["train"]],
	"split": "train",
	},
	)
	)

	return splits

	def _generate_examples(
	self, lang, quality, 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.
	if split == datasets.Split.TRAIN:
	with bz2.open(filepath[0], "rt", encoding="utf-8") as f:
	# We know that this file only contains the desired language,
	# because for the training sets the languages are in separate
	# files, and only the desired language has been downloaded
	for id_, row in enumerate(f):
	data = json.loads(row)
	if data["quality"] < quality:
	# The rest of this file contains too low quality data
	break
	yield id_, {
	"lang": data["lang"],
	"sent1": data["sent1"],
	"sent2": data["sent2"],
	"annot_score": 0.0,
	"gem_id": data["gem_id"],
	"quality": data["quality"],
	}
	else:
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	if data["lang"] == lang:
	yield id_, {
	"lang": data["lang"],
	"sent1": data["sent1"],
	"sent2": data["sent2"],
	"annot_score": data["annot_score"],
	"gem_id": data["gem_id"],
	"quality": 100,
	}