swiss_citation_extraction.py

# 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
"""Dataset for the Citation Extraction 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 cit ex 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 = {
    "original": "https://huggingface.co/datasets/rcds/swiss_citation_extraction/resolve/main/original/huggingface",
    "with_rules": "https://huggingface.co/datasets/rcds/swiss_citation_extraction/resolve/main/with_rules/huggingface"
}


class SwissCourtViewGeneration(datasets.GeneratorBasedBuilder):
    """This dataset contains court decision for court view generation 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"),
        datasets.BuilderConfig(name="origin", version=VERSION, description="This part of my dataset covers a subset containing only cases with origin data")
    ]

    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 == "original" or self.config.name == "with_rules":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "decision_id": datasets.Value("string"),
                    "considerations": datasets.Value("sequence"),
                    "NER_labels": datasets.Value("sequence"),
                    "law_area": datasets.Value("string"),
                    "language": datasets.Value("string"),
                    "year": datasets.Value("int64"),
                    "chamber": 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 == "original" or self.config.name == "with_rules":
                            yield id, {
                                "decision_id": data["decision_id"],
                                "considerations": data["considerations"],
                                "NER_labels": data["NER_labels"],
                                "law_area": data["law_area"],
                                "language": data["language"],
                                "year": data["year"],
                                "chamber": data["chamber"],
                                "region": data["region"]
                                }
        except lzma.LZMAError as e:
            print(split, e)
            if line_counter == 0:
                raise e