Delete BUSTER.py

removal of loading script

BUSTER.py

@@ -1,114 +0,0 @@
-# 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.
-"""BUSTER: a BUSiness Transaction Entity Recognition Dataset"""
-
-import os
-import datasets
-from datasets import load_dataset
-
-_CITATION = """
-Accepted at EMNLP 2023 - Industry Track.
-TBA
-"""
-
-_DESCRIPTION = """
-Buster is an Entity Recognition dataset consisting of 3779 manually annotated documents on financial transactions.
-Documents were selected using EDGAR (Electronic Data Gathering, Analysis, and Retrieval system) from the 
-U.S. Securities and Exchange Commission (SEC). 
-The corpus focuses on the main actors involved in business transactions. 
-Overall, there are three families of entities: Parties, Advisors and Generic information, for a total of 6 annotated
-entity types.
-We also released a corpus of 6196 automatically annotated documents. 
-"""
-
-_HOMEPAGE = "https://expert.ai/buster"
-_URL = "BUSTER.zip"
-_VERSION = "1.0.0"
-
-logger = datasets.logging.get_logger(__name__)
-
-
-# --------------------------------------------------------------------------------------------------------
-# Tag set
-_LABELS = [
-    "O",  # non-entities label
-    "B-Parties.BUYING_COMPANY",
-    "I-Parties.BUYING_COMPANY",
-    "B-Parties.SELLING_COMPANY",
-    "I-Parties.SELLING_COMPANY",
-    "B-Parties.ACQUIRED_COMPANY",
-    "I-Parties.ACQUIRED_COMPANY",
-    "B-Advisors.LEGAL_CONSULTING_COMPANY",
-    "I-Advisors.LEGAL_CONSULTING_COMPANY",
-    "B-Advisors.GENERIC_CONSULTING_COMPANY",
-    "I-Advisors.GENERIC_CONSULTING_COMPANY",
-    "B-Generic_Info.ANNUAL_REVENUES",
-    "I-Generic_Info.ANNUAL_REVENUES"
-]
-
-
-class BusterConfig(datasets.BuilderConfig):
-    """BuilderConfig for the BUSTER dataset."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for the BUSTER dataset.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(BusterConfig, self).__init__(
-            name=f"BUSTER",
-            description=_DESCRIPTION,
-            version=datasets.Version(_VERSION),  # hf dataset script version
-            **kwargs,
-        )
-
-
-class Buster(datasets.GeneratorBasedBuilder):
-    """The BUSTER dataset."""
-
-    BUILDER_CONFIGS = [
-        BusterConfig()
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "document_id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "labels": datasets.Sequence(datasets.features.ClassLabel(names=_LABELS)),
-                }
-            ),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URL)
-        fold_names = [f"FOLD_{i}" for i in range(1, 6)] + ["SILVER"]
-        return [
-            datasets.SplitGenerator(
-                name=fold_name,
-                gen_kwargs={"file_path": os.path.join(data_dir, f"{fold_name}.json")},
-            ) for fold_name in fold_names
-        ]
-
-    def _generate_examples(self, file_path):
-        dataset = load_dataset("json", data_files=file_path)
-        logger.info(f"Generating examples from: {file_path}")
-        for idx, example in enumerate(dataset["train"]):
-            # example features: document_id, tokens, labels
-            yield idx, example