wehub-resource-sync
142 lines
4.8 KiB
Python
142 lines
4.8 KiB
Python
# coding=utf-8
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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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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# Lint as: python3
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import os
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import json
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import hashlib
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import datasets
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logger = datasets.logging.get_logger(__name__)
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_CITATION = """\
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@article{Jaume2019FUNSDAD,
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title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
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author={Guillaume Jaume and H. K. Ekenel and J. Thiran},
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journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
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year={2019},
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volume={2},
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pages={1-6}
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}
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"""
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_DESCRIPTION = """\
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https://guillaumejaume.github.io/FUNSD/
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"""
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_URL = "https://bj.bcebos.com/paddlenlp/datasets/funsd.tar.gz"
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def _get_md5(string):
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"""Get md5 value for string"""
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hl = hashlib.md5()
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hl.update(string.encode(encoding="utf-8"))
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return hl.hexdigest()
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class FUNSDConfig(datasets.BuilderConfig):
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"""funsd dataset config"""
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target_size: int = 1000
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max_size: int = 1000
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def __init__(self, **kwargs):
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super(FUNSDConfig, self).__init__(**kwargs)
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class FUNSD(datasets.GeneratorBasedBuilder):
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"""funsd dataset builder"""
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BUILDER_CONFIGS = [
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FUNSDConfig(
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name="funsd",
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version=datasets.Version("1.0.0", ""),
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description="Plain text",
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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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"name": datasets.Value("string"),
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"page_no": datasets.Value("int32"),
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"text": datasets.features.Sequence(datasets.Value("string")),
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"bbox": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("int32"))),
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"segment_bbox": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("int32"))),
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"segment_id": datasets.features.Sequence(datasets.Value("int32")),
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"image": datasets.Value("string"),
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"width": datasets.Value("int32"),
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"height": datasets.Value("int32"),
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"md5sum": datasets.Value("string"),
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"qas": datasets.features.Sequence(
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{
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"question_id": datasets.Value("int32"),
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"question": datasets.Value("string"),
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"answers": datasets.features.Sequence(
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{
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"text": datasets.Value("string"),
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"answer_start": datasets.Value("int32"),
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"answer_end": datasets.Value("int32"),
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}
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),
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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://guillaumejaume.github.io/FUNSD/",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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dl_dir = dl_manager.download_and_extract(_URL)
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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={"filepath": os.path.join(dl_dir, "funsd", "train.json")},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={"filepath": os.path.join(dl_dir, "funsd", "dev.json")},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={"filepath": os.path.join(dl_dir, "funsd", "test.json")},
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),
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]
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def _generate_examples(self, filepath):
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"""This function returns the examples in the raw (text) form."""
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logger.info("Generating examples from = {}".format(filepath))
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idx = 0
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with open(filepath, "r") as fin:
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for line in fin:
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data = json.loads(line)
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if "page_no" not in data:
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data["page_no"] = 0
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for item in data["qas"]:
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if "question_id" not in item:
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item["question_id"] = -1
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data["md5sum"] = _get_md5(data["image"])
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yield idx, data
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idx += 1
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