# 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_}", }