Datasets:

GEM
/

SIMPITIKI

	# 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.
	"""Data Loader for SIMPITIKI Dataset with challenge splits"""


	import csv
	import json
	import os
	import datasets
	from lxml import etree

	_CITATION = """\
	@article{tonelli2016simpitiki,
	title={SIMPITIKI: a Simplification corpus for Italian},
	author={Tonelli, Sara and Aprosio, Alessio Palmero and Saltori, Francesca},
	journal={Proceedings of CLiC-it},
	year={2016}
	}
	"""

	_DESCRIPTION = """\
	SIMPITIKI is a Simplification corpus for Italian and it consists of two sets of simplified pairs: the first one is harvested from the Italian Wikipedia in a semi-automatic way; the second one is manually annotated sentence-by-sentence from documents in the administrative domain.
	"""

	_HOMEPAGE = "https://github.com/dhfbk/simpitiki"

	_LICENSE = "CC-BY 4.0"

	_URLs = {
	"v1":{
	"random": {
	"train":"v1/random_split/train.jsonl",
	"val":"v1/random_split/val.jsonl",
	"test":"v1/random_split/test.jsonl"
	},
	"transformations": {
	"train": "v1/transformations_split/train.jsonl",
	"val": "v1/transformations_split/val.jsonl",
	"seen_transformations_test": "v1/transformations_split/seen_transformations_test.jsonl",
	"unseen_transformations_test":"v1/transformations_split/unseen_transformations_test.jsonl"
	},
	"source_dataset": {
	"itwiki_train":"v1/source_dataset_split/itwiki_train.jsonl",
	"itwiki_val": "v1/source_dataset_split/itwiki_val.jsonl",
	"itwiki_test":"v1/source_dataset_split/itwiki_test.jsonl",
	"tn_test":"v1/source_dataset_split/tn_test.jsonl"
	}
	},
	"v2":{
	"random": {
	"train":"v2/random_split/train.jsonl",
	"val":"v2/random_split/val.jsonl",
	"test":"v2/random_split/test.jsonl"
	},
	"transformations": {
	"train": "v2/transformations_split/train.jsonl",
	"val": "v2/transformations_split/val.jsonl",
	"seen_transformations_test": "v2/transformations_split/seen_transformations_test.jsonl",
	"unseen_transformations_test":"v2/transformations_split/unseen_transformations_test.jsonl"
	},
	"source_dataset": {
	"itwiki_train":"v2/source_dataset_split/itwiki_train.jsonl",
	"itwiki_val": "v2/source_dataset_split/itwiki_val.jsonl",
	"itwiki_test":"v2/source_dataset_split/itwiki_test.jsonl",
	"tn_test":"v2/source_dataset_split/tn_test.jsonl"
	}


	}

	}


	class SIMPITIKI(datasets.GeneratorBasedBuilder):
	"""SIMPITIKI is a dataset built for Sentence Simplification Task. It provides complex-to-simple sentence pairs."""

	VERSION_1 = datasets.Version("1.0.0")
	VERSION_2 = datasets.Version("2.0.0")

	# 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="v1", version=VERSION_1, description="First version"),
	datasets.BuilderConfig(name="v2", version=VERSION_2, description="Second version with better sentence boundaries."),
	]

	DEFAULT_CONFIG_NAME = "v2" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	# This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	features = datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"text": datasets.Value("string"),
	"target": datasets.Value("string"),
	"references": [datasets.Value("string")],
	"transformation_type":datasets.Value("string"),
	"source_dataset":datasets.Value("string")
	# These are the features of your dataset like images, labels ...
	}
	)
	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."""
	# 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

	my_urls = _URLs[self.config.name]
	downloaded_files = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files['random']['train'],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files['random']['val'],
	"split": "val"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files['random']['test'],
	"split": "test",
	},
	),

	datasets.SplitGenerator(
	name='challenge_seen_transformations_train',
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files['transformations']['train'],
	"split": "challenge_seen_transformations_train",
	},
	),


	datasets.SplitGenerator(
	name='challenge_seen_transformations_val',
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files['transformations']['val'],
	"split": "challenge_seen_transformations_val",
	},
	),

	datasets.SplitGenerator(
	name='challenge_seen_transformations_test',
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files['transformations']['seen_transformations_test'],
	"split": "challenge_seen_transformations_test",
	},
	),

	datasets.SplitGenerator(
	name='challenge_unseen_transformations_test',
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files['transformations']['unseen_transformations_test'],
	"split": "challenge_unseen_transformations_test",
	},
	),

	datasets.SplitGenerator(
	name='challenge_itwiki_train',
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files['source_dataset']['itwiki_train'],
	"split": "challenge_itwiki_train",
	},
	),

	datasets.SplitGenerator(
	name='challenge_itwiki_val',
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files['source_dataset']['itwiki_val'],
	"split": "challenge_itwiki_val",
	},
	),

	datasets.SplitGenerator(
	name='challenge_itwiki_test',
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files['source_dataset']['itwiki_test'],
	"split": "challenge_itwiki_test",
	},
	),

	datasets.SplitGenerator(
	name='challenge_tn_test',
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files['source_dataset']['tn_test'],
	"split": "challenge_tn_test",
	},
	),
	]

	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:
	for id_, row in enumerate(f):
	data = json.loads(row)
	# A couple of missing examples in here, skipping.
	if data["text"] == None:
	continue
	yield id_, {
	"text": data["text"],
	"target": data["simplified_text"],
	"references": [data["simplified_text"]],
	"transformation_type":data["transformation_type"],
	"source_dataset": data["source_dataset"],
	"gem_id": f"gem-SIMPITIKI-{split}-{id_}",
	}