SIMPITIKI.py

# 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_}",
                        }