Create swiss_criticality_prediction.py

swiss_criticality_prediction.py

@@ -0,0 +1,166 @@
+# 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.
+"""Dataset for the Legal Criticality Prediction task."""
+
+import json
+import lzma
+import os
+
+import datasets
+try:
+    import lzma as xz
+except ImportError:
+    import pylzma as xz
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+This dataset contains Swiss federal court decisions for the legal criticality prediction task
+"""
+
+_URLS = {
+    "full": "https://huggingface.co/datasets/rcds/swiss_criticality_prediction/resolve/main/data",
+}
+
+
+class SwissCriticalityPrediction(datasets.GeneratorBasedBuilder):
+    """This dataset contains court decision for court view generation task."""
+
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="full", description="This part covers the whole dataset"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "full"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        if self.config.name == "full":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    # Todo check if these are all
+                    "decision_id": datasets.Value("string"),
+                    "language": datasets.Value("string"),
+                    "year": datasets.Value("int32"),
+                    "chamber": datasets.Value("string"),
+                    "region": datasets.Value("string"),
+                    "origin_chamber": datasets.Value("string"),
+                    "origin_court": datasets.Value("string"),
+                    "origin_canton": datasets.Value("string"),
+                    "law_area": datasets.Value("string"),
+                    "law_sub_area": datasets.Value("string"),
+                    "bge_label": datasets.Value("string"),
+                    "citation_label": datasets.Value("string"),
+                    "facts": datasets.Value("string"),
+                    "considerations": datasets.Value("string"),
+                    "rulings": datasets.Value("string"),
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        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):
+        # 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]
+        filepath_train = dl_manager.download(os.path.join(urls, "train.jsonl.xz"))
+        filepath_validation = dl_manager.download(os.path.join(urls, "validation.jsonl.xz"))
+        filepath_test = dl_manager.download(os.path.join(urls, "test.jsonl.xz"))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": filepath_train,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": filepath_validation,
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": filepath_test,
+                    "split": "test"
+                },
+            )
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        line_counter = 0
+        try:
+            with xz.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
+                for id, line in enumerate(f):
+                    line_counter += 1
+                    if line:
+                        data = json.loads(line)
+                        if self.config.name == "full":
+                            yield id, {
+                                "decision_id": data["decision_id"],
+                                "language": data["language"],
+                                "year": data["year"],
+                                "chamber": data["chamber"],
+                                "region": data["region"],
+                                "origin_chamber": data["origin_chamber"],
+                                "origin_court": data["origin_court"],
+                                "origin_canton": data["origin_canton"],
+                                "law_area": data["law_area"],
+                                "law_sub_area": data["law_sub_area"],
+                                "citation_label": data["citation_label"],
+                                "bge_label": data["bge_label"],
+                                "facts": data["facts"],
+                                "considerations": data["considerations"],
+                                "rulings": data["rulings"],
+                            }
+        except lzma.LZMAError as e:
+            print(split, e)
+            if line_counter == 0:
+                raise e