| """Dataset for the Judgment Prediction task.""" | |
| 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 = """\ | |
| This dataset contains court decision for judgment prediction task. | |
| """ | |
| # 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 = { | |
| "full": "https://huggingface.co/datasets/rcds/judgment_prediction/resolve/main/data/huggingface" | |
| } | |
| class JudgmentPrediction(datasets.GeneratorBasedBuilder): | |
| """This dataset contains court decision for judgment prediction task.""" | |
| 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="full", version=VERSION, description="This part of my dataset 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( | |
| { | |
| "decision_id": datasets.Value("string"), | |
| "facts": datasets.Value("string"), | |
| "considerations": datasets.Value("string"), | |
| "label": datasets.Value("string"), | |
| "law_area": datasets.Value("string"), | |
| "language": datasets.Value("string"), | |
| "year": datasets.Value("int32"), | |
| "court": datasets.Value("string"), | |
| "chamber": datasets.Value("string"), | |
| "canton": datasets.Value("string"), | |
| "region": 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"], | |
| "facts": data["facts"], | |
| "considerations": data["considerations"], | |
| "label": data["label"], | |
| "law_area": data["law_area"], | |
| "language": data["language"], | |
| "year": data["year"], | |
| "court": data["court"], | |
| "chamber": data["chamber"], | |
| "canton": data["canton"], | |
| "region": data["region"] | |
| } | |
| except lzma.LZMAError as e: | |
| print(split, e) | |
| if line_counter == 0: | |
| raise e | |