:sparkles: add dataset loading script

wikitext_fr.py

@@ -0,0 +1,168 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and Antoine SIMOULIN.
+#
+# 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.
+"""Wikitext-fr language modeling dataset consists of over 70 million tokens 
+extracted from the set of french Wikipedia articles that are classified as 
+"quality articles" or "good articles.". The aim is to replicate the English 
+benchmark."""
+
+
+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{simoulin:hal-03265900,
+  TITLE = {{Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le \_\_\_\_\_\_ fran{\c c}ais}},
+  AUTHOR = {Simoulin, Antoine and Crabb{\'e}, Benoit},
+  URL = {https://hal.archives-ouvertes.fr/hal-03265900},
+  BOOKTITLE = {{Traitement Automatique des Langues Naturelles}},
+  ADDRESS = {Lille, France},
+  EDITOR = {Denis, Pascal and Grabar, Natalia and Fraisse, Amel and Cardon, R{\'e}mi and Jacquemin, Bernard and Kergosien, Eric and Balvet, Antonio},
+  PUBLISHER = {{ATALA}},
+  PAGES = {246-255},
+  YEAR = {2021},
+  KEYWORDS = {fran{\c c}ais. ; GPT ; G{\'e}n{\'e}ratif ; Transformer ; Pr{\'e}-entra{\^i}n{\'e}},
+  PDF = {https://hal.archives-ouvertes.fr/hal-03265900/file/7.pdf},
+  HAL_ID = {hal-03265900},
+  HAL_VERSION = {v1},
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Wikitext-fr language modeling dataset consists of over 70 million tokens 
+extracted from the set of french Wikipedia articles that are classified as 
+"quality articles" or "good articles.". The aim is to replicate the English 
+benchmark."""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/AntoineSimoulin/gpt-fr"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "Creative Commons Attribution-ShareAlike 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 = {
+#     'wikitext-35': "./wikitext_35/",
+#     'wikitext-72': "./wikitext_72/",
+# }
+_URLs = {
+    'wikitext-35': "./wikitext_35/wiki.zip",
+    'wikitext-72': "./wikitext_72/wiki.zip",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """Wikitext-fr language modeling dataset consists of over 70 million tokens 
+    extracted from the set of french Wikipedia articles that are classified as 
+    "quality articles" or "good articles.". The aim is to replicate the English benchmark.
+    """
+
+    VERSION = datasets.Version("1.1.0")
+
+    # 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="wikitext-35", version=VERSION, description="This part covers quality articles only"),
+        datasets.BuilderConfig(name="wikitext-72", version=VERSION, description="This part covers quality articles and good articles"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "wikitext-35"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features({"paragraph": 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=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
+        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, "wiki.train.tokens"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "wiki.test.tokens"),
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "wiki.valid.tokens"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    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, 'r') as f:
+          data = f.readlines()
+          for id_, paragraph in enumerate(data):
+            yield id_, {"paragraph": paragraph, }