load_script
Browse files- loading_script.py +109 -0
loading_script.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PsyQA dataset."""
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import json
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import os
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import datasets
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_DESCRIPTION = """ FutureWarning
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"""
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_CITATION = """ null """
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_URLs = {
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"train": "https://huggingface.co/datasets/siyangliu/PsyQA/resolve/main/train.json",
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"valid": "https://huggingface.co/datasets/siyangliu/PsyQA/resolve/main/valid.json",
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"test": "https://huggingface.co/datasets/siyangliu/PsyQA/resolve/main/test.json",
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}
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class PsyQA(datasets.GeneratorBasedBuilder):
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"""PsyQA dataset."""
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VERSION = datasets.Version("1.1.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="plain_text",
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description="Plain text",
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version=VERSION,
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)
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"question": datasets.Value("string"),
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"questionID": datasets.Value("int16"),
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"description": datasets.Value("string"),
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"keywords": datasets.Value("string"),
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"answer": datasets.Value("string"),
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"has_label": datasets.Value("bool"),
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"label_sequence":datasets.features.Sequence(
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{
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"start": datasets.Value("int16"),
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"end": datasets.Value("int16"),
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"type": datasets.Value("string"),
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}
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),
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}
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),
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supervised_keys=None,
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homepage="https://huggingface.co/datasets/siyangliu/PsyQA",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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data_dir = dl_manager.download_and_extract(_URLs)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": data_dir["train"],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"filepath": data_dir["test"],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"filepath": data_dir["valid"],
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},
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),
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]
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def _generate_examples(self, input_filepath, label_filepath=None):
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"""Yields examples."""
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with open(input_filepath, encoding="utf-8") as input_file:
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dataset = json.load(input_file)
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idx = 0
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for meta_data in dataset:
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for ans in meta_data["answers"]:
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yield idx, {"question": meta_data["question"], "description": meta_data["description"], "keywords": meta_data["keywords"], "answer": ans["answer_text"], \
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"label_sequence": ans["label_sequence"], "questionID": meta_data["questionID"], "has_label": meta_data["has_label"],}
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idx += 1
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