Datasets:

Jean-Baptiste
/

wikiner_fr

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.ipynb_checkpoints/wikiner_fr-checkpoint.py DELETED Viewed

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-# 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("1.0.0")
-    # 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="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"]}