pclue.py

# coding=utf-8
# 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.
"""Pclue dataset."""


import json

import py7zr

import datasets


_CITATION = """https://github.com/CLUEbenchmark/pCLUE"""

_DESCRIPTION = """https://github.com/CLUEbenchmark/pCLUE"""

_HOMEPAGE = "https://github.com/CLUEbenchmark/pCLUE"

_LICENSE = "apache-2.0"


_URL = "https://huggingface.co/datasets/wbbbbb/pclue/resolve/main/pclue.7z"


class Pclue(datasets.GeneratorBasedBuilder):
    """Pclue Corpus dataset."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="pclue"),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "input": datasets.Value("string"),
                "target": datasets.Value("string"),
                "type": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        path = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": path+"train.json",
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": path+"test.json",
                    "split": "test",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": path+"dev.json",
                    "split": "val",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        """Yields examples."""
         with open(filepath, encoding="utf-8") as f:
            for idx, row in enumerate(f):
                 yield idx, json.load(row)