Delete BUSTER.py
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BUSTER.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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"""BUSTER: a BUSiness Transaction Entity Recognition Dataset"""
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import os
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import datasets
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from datasets import load_dataset
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_CITATION = """
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Accepted at EMNLP 2023 - Industry Track.
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TBA
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"""
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_DESCRIPTION = """
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Buster is an Entity Recognition dataset consisting of 3779 manually annotated documents on financial transactions.
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Documents were selected using EDGAR (Electronic Data Gathering, Analysis, and Retrieval system) from the
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U.S. Securities and Exchange Commission (SEC).
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The corpus focuses on the main actors involved in business transactions.
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Overall, there are three families of entities: Parties, Advisors and Generic information, for a total of 6 annotated
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entity types.
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We also released a corpus of 6196 automatically annotated documents.
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"""
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_HOMEPAGE = "https://expert.ai/buster"
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_URL = "BUSTER.zip"
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_VERSION = "1.0.0"
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logger = datasets.logging.get_logger(__name__)
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# --------------------------------------------------------------------------------------------------------
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# Tag set
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_LABELS = [
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"O", # non-entities label
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"B-Parties.BUYING_COMPANY",
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"I-Parties.BUYING_COMPANY",
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"B-Parties.SELLING_COMPANY",
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"I-Parties.SELLING_COMPANY",
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"B-Parties.ACQUIRED_COMPANY",
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"I-Parties.ACQUIRED_COMPANY",
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"B-Advisors.LEGAL_CONSULTING_COMPANY",
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"I-Advisors.LEGAL_CONSULTING_COMPANY",
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"B-Advisors.GENERIC_CONSULTING_COMPANY",
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"I-Advisors.GENERIC_CONSULTING_COMPANY",
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"B-Generic_Info.ANNUAL_REVENUES",
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"I-Generic_Info.ANNUAL_REVENUES"
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]
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class BusterConfig(datasets.BuilderConfig):
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"""BuilderConfig for the BUSTER dataset."""
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def __init__(self, **kwargs):
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"""BuilderConfig for the BUSTER dataset.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(BusterConfig, self).__init__(
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name=f"BUSTER",
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description=_DESCRIPTION,
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version=datasets.Version(_VERSION), # hf dataset script version
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**kwargs,
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)
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class Buster(datasets.GeneratorBasedBuilder):
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"""The BUSTER dataset."""
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BUILDER_CONFIGS = [
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BusterConfig()
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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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"document_id": datasets.Value("string"),
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"tokens": datasets.Sequence(datasets.Value("string")),
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"labels": datasets.Sequence(datasets.features.ClassLabel(names=_LABELS)),
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}
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),
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homepage=_HOMEPAGE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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data_dir = dl_manager.download_and_extract(_URL)
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fold_names = [f"FOLD_{i}" for i in range(1, 6)] + ["SILVER"]
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return [
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datasets.SplitGenerator(
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name=fold_name,
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gen_kwargs={"file_path": os.path.join(data_dir, f"{fold_name}.json")},
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) for fold_name in fold_names
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]
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def _generate_examples(self, file_path):
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dataset = load_dataset("json", data_files=file_path)
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logger.info(f"Generating examples from: {file_path}")
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for idx, example in enumerate(dataset["train"]):
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# example features: document_id, tokens, labels
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yield idx, example
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