hpo_anno.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.
import json
import os

import datasets
import pandas as pd


logger = datasets.logging.get_logger(__name__)

_CITATION = """\
CITATION 
"""

_DESCRIPTION = """\
DESCRIPTION
"""

_HOMEPAGE = "HOMEPAGE"

_LICENSE = ""

_DATA_URL = r"https://huggingface.co/datasets/yourui/hpo_anno/resolve/main/data/"

task_list = [
    "GeneReviews",
    "GSC+",
    "ID-68",
    "val"
]


class HPOAnnoConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super().__init__(version=datasets.Version("1.0.0"), **kwargs)


class HPOAnno(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        HPOAnnoConfig(
            name=task_name,
        )
        for task_name in task_list
    ]

    def _info(self):
        features = datasets.Features(
            {
                "id":datasets.Value("string"),
                "corpus": datasets.Value("string"),
                "ann": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        task_name = self.config.name
        local_file = dl_manager.download_and_extract(os.path.join(_DATA_URL, f"{task_name}.json"))
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": local_file}),
        ]

    def _generate_examples(self, filepath):
        logger.info("generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            obj = json.load(f)
            for item in obj:
                id_ = item["id"]
                yield id_, {
                    "id": id_,
                    "corpus":  item["corpus"],
                    "ann": item["ann"]
                }