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buster.py

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+# 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 116 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(5)] + ["SILVER"]
+        return [
+            datasets.SplitGenerator(
+                name=fold_name,
+                gen_kwargs={"file_path": os.path.join(data_dir, fold_name)},
+            ) 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

buster.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e5aa6bc863e87893b1279742a2925b7e1cced1718cf3db3774bdeefdab18b61
+size 37795744