wikiner_fr.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.
"""Wikiner dataset for NER in french language """

from __future__ import absolute_import, division, print_function

import csv
import json
import os

import datasets
from datasets import Dataset


_NER_LABEL_NAMES = [
    "O",
    "LOC",
    "PER",
    "MISC",
    "ORG"
]



# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Wikiner dataset for NER task in french},
authors={Created by Nothman et al. at 2013},
year={2013}
}
"""






# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
Dataset can be used to train on NER task for french langugage.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://www.sciencedirect.com/science/article/pii/S0004370212000276?via%3Dihub"

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

# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don'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 = {
     "NER": "https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip"
}


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class WikinerFr(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.0.1")

    # 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="NER", version="0.0.1", description="Dataset for entity recognition")
    ]

    DEFAULT_CONFIG_NAME = "NER"  # 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 == "NER":  # This is the name of the configuration selected in BUILDER_CONFIGS above 
            features = datasets.Features(
                {
                    "id": datasets.Value("int32"),
                    "ner_tags": datasets.Sequence(
                        feature=datasets.ClassLabel(num_classes=len(_NER_LABEL_NAMES), names=_NER_LABEL_NAMES)
                    ),
                    "tokens": datasets.Sequence(feature=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,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=("id", "ner_tags"),
            # 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.data_dir:
            data_dir = self.config.data_dir
        else:
            my_urls = _URLs[self.config.name]
            data_dir = 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": os.path.join(data_dir, "data","train"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "data", "test"),
                    "split": "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.
        dataset = Dataset.load_from_disk(filepath)
        for i in range(0, len(dataset)):
#             print(dataset[i])
            yield i, {"id": str(dataset[i]["id"]),"tokens": dataset[i]["tokens"], "ner_tags": dataset[i]["ner_tags"]}