create loading file

jade.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
+
+
+import pandas as pd
+import json
+import os
+import shutil
+
+import datasets
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {French Fiscal texts},
+author={Stan Bienaives
+},
+year={2022}
+}
+"""
+
+
+_DESCRIPTION = """\
+  This dataset is an extraction from the OPENDATA/JADE. A list of case laws from the French court "Conseil d'Etat".
+"""
+
+_HOMEPAGE = "echanges.dila.gouv.fr"
+
+
+_LICENSE = """/
+  This dataset is licensed under Creative Commons Attribution 4.0 International License.
+"""
+
+
+_URLS = {
+  "jade": "https://wisenlp.s3.eu-west-1.amazonaws.com/jade.parquet"
+}
+
+
+_SEED = 42
+
+class FrenchOpenFiscalTexts(datasets.GeneratorBasedBuilder):
+    """
+    This is the main class for the dataset.
+    """
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+      features = datasets.Features(
+        {
+          # "file": datasets.Value("string"),
+          "title": datasets.Value("string"),
+          "content": datasets.Value("string"),
+          "summary": datasets.Value("string"),
+          "solution": datasets.Value("string"),
+          "numero": datasets.Value("string"),
+          "publi_receuil": datasets.Value("string"),
+          "date": datasets.Value("string"),
+        }
+      )
+
+      return datasets.DatasetInfo(
+        # This is the description that will appear on the datasets page.
+        description=_DESCRIPTION,
+        features=features,
+        homepage=_HOMEPAGE,
+        license=_LICENSE,
+      )
+
+    def _split_generators(self, dl_manager):
+      self.data_dir = dl_manager.download_and_extract(_URLS["jade"])
+
+      df = pd.read_parquet(self.data_dir)
+
+      train = df.sample(frac=0.8,random_state=_SEED)
+      test = df.drop(train.index)
+
+
+      return [
+        datasets.SplitGenerator(
+          name=datasets.Split.TRAIN,
+          gen_kwargs={"dataframe": train},
+        ),
+        datasets.SplitGenerator(
+          name=datasets.Split.TEST,
+          gen_kwargs={"dataframe": test},
+        ),
+      ]
+
+    def _generate_examples(self, dataframe):
+      """
+      This function returns the examples in the raw (text) form.
+      """
+      for index, row in dataframe.iterrows():
+        yield index, {
+          # "file": row["file"],
+          "title": row["title"],
+          "content": row["content"],
+          "summary": row["summary"],
+          "solution": row["solution"],
+          "numero": row["numero"],
+          "publi_receuil": row["publi_receuil"],
+          "date": row["date"],
+        }
+
+
+
+
+
+
+