Create querie.py

querie.py

@@ -0,0 +1,121 @@
+import csv
+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 = """TODO"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# 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 = {
+    "german": "https://huggingface.co/datasets/Stern5497/querie/resolve/main/queries_de",
+    "italian": "https://huggingface.co/datasets/Stern5497/querie/resolve/main/queries_it",
+    "french": "https://huggingface.co/datasets/Stern5497/querie/resolve/main/queries_fr",
+
+}
+
+def get_url(config_name):
+    return _URLS[config_name]
+
+
+class querie(datasets.GeneratorBasedBuilder):
+    """This dataset contains court decision for law area prediction task."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="german", version=VERSION, description="This part of my dataset covers the whole dataset"),
+        datasets.BuilderConfig(name="italian", version=VERSION, description="This dataset is for predicting the sub law areas of the public law"),
+        datasets.BuilderConfig(name="french", version=VERSION, description="This dataset is for predicting the sub law areas of the civil law"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "german"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "text": 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 = get_url(self.config.name)
+        filepath = dl_manager.download(os.path.join(urls, ".jsonl"))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": filepath,
+                    "split": "train",
+                },
+            )
+        ]
+
+    # 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 == "german" or self.config.name == "italian" or self.config.name == "french":
+                            yield id,  {
+                                "id": data["id"],
+                                "text": data["text"],
+                            }
+        except lzma.LZMAError as e:
+            print(split, e)
+            if line_counter == 0:
+                raise e