opusparcus.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.

"""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 doesn'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 = {
    "validation": "validation.jsonl",
    "test": "test.jsonl",
    "validation.full": "validation.jsonl",
    "test.full": "test.jsonl",
    # NB: the "train" split file is defined dynamically inside the `_split_generators` method
}

_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 < 70:
            # We need to retrieve the largest training set file
            # containing the full training set for the desired language
            _URLs["train"] = "train_{0}.60.jsonl.bz2".format(self.config.lang)
        elif self.config.quality <= 95:
            # We can do with a smaller version of the training set
            # for the desired language
            _URLs["train"] = "train_{0}.70.jsonl.bz2".format(self.config.lang)

        # Otherwise, if the desired quality is above 95, we do not
        # download any training data, because there is no matching data
            
        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",
                },
            ),
            datasets.SplitGenerator(
                name="test.full",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "lang": self.config.lang,
                    "quality": 100,
                    "filepath": data_dir["test.full"],
                    "split": "test.full"
                },
            ),
            datasets.SplitGenerator(
                name="validation.full",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "lang": self.config.lang,
                    "quality": 100,
                    "filepath": data_dir["validation.full"],
                    "split": "validation.full",
                },
            ),
        ]            

        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"],
                        "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, "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:
            keep_all = (split == "validation.full" or split == "test.full")
            with open(filepath, encoding="utf-8") as f:
                for id_, row in enumerate(f):
                    data = json.loads(row)
                    if data["lang"] == lang:
                        if keep_all or data["annot_score"] >= 3.0:
                            yield id_, {
                                "lang": data["lang"],
                                "sent1": data["sent1"],
                                "sent2": data["sent2"],
                                "annot_score": data["annot_score"],
                                "gem_id": data["gem_id"],
                                "quality": 100,
                            }