Datasets:
GEM
/
SIMPITIKI

Languages: Italian
Multilinguality: unknown
Size Categories: unknown
Language Creators: unknown
Annotations Creators: crowd-sourced
Source Datasets: original
License: cc-by-4.0
SIMPITIKI / SIMPITIKI.py
Sebastian Gehrmann
add skipping of None examples
091a47e
# 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_}",
}