lefff_morpho.py

# 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: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{sagot:inria-00521242,
  TITLE = {{The Lefff, a freely available and large-coverage morphological and syntactic lexicon for French}},
  AUTHOR = {Sagot, Beno{\^i}t},
  URL = {https://hal.inria.fr/inria-00521242},
  BOOKTITLE = {{7th international conference on Language Resources and Evaluation (LREC 2010)}},
  ADDRESS = {Valletta, Malta},
  YEAR = {2010},
  MONTH = May,
  PDF = {https://hal.inria.fr/inria-00521242/file/lrec10lefff.pdf},
  HAL_ID = {inria-00521242},
  HAL_VERSION = {v1},
}"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
The lefff-morpho dataset gives access to the morphological information, in both its original format and the UniMorph format.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "http://almanach.inria.fr/software_and_resources/custom/Alexina-en.html"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "LGPL-LR"

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "all": "lefff_morpho-3.5.json",
}


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class LefffMorpho(datasets.GeneratorBasedBuilder):
    """The lefff-morpho dataset gives access to the morphological information, in both its original format and the UniMorph format."""

    VERSION = datasets.Version("3.5.0")

    # 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')

    DEFAULT_CONFIG_NAME = "all"  # 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
        features = datasets.Features({
            "form": datasets.Value("string"),
            "lemma": datasets.Value("string"),
            "category": datasets.Value("string"),
            "type": datasets.Value("string"),
            "msfeatures": datasets.Value("string"),
            "unimorph": datasets.Value("string"),
            "expansion": datasets.Value("string")
            })
        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, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # 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):
        # 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
        urls = _URLS["all"]
        downloaded_files = dl_manager.download_and_extract(_URLS)
        return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'filepath': downloaded_files['all']})]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            lefff_morpho = json.load(f)
            for key, row in enumerate(lefff_morpho):
                yield key, row