Upload lefff_morpho.py

lefff_morpho.py

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+# 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.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[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'filepath': downloaded_files['test']})]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # 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