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BUSTER

Tasks:
Token Classification
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finance
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BUSTER / README.md
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metadata
license: apache-2.0
task_categories:
  - token-classification
language:
  - en
tags:
  - finance
pretty_name: buster
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: FOLD_1
        path: FOLD_1.json
      - split: FOLD_2
        path: FOLD_2.json
      - split: FOLD_3
        path: FOLD_3.json
      - split: FOLD_4
        path: FOLD_4.json
      - split: FOLD_5
        path: FOLD_5.json
      - split: SILVER
        path: SILVER.json
dataset_info:
  features:
    - name: document_id
      dtype: string
    - name: tokens
      sequence: string
    - name: labels
      sequence:
        class_label:
          names:
            '0': O
            '1': B-Parties.BUYING_COMPANY
            '2': I-Parties.BUYING_COMPANY
            '3': B-Parties.SELLING_COMPANY
            '4': I-Parties.SELLING_COMPANY
            '5': B-Parties.ACQUIRED_COMPANY
            '6': I-Parties.ACQUIRED_COMPANY
            '7': B-Advisors.LEGAL_CONSULTING_COMPANY
            '8': I-Advisors.LEGAL_CONSULTING_COMPANY
            '9': B-Advisors.GENERIC_CONSULTING_COMPANY
            '10': I-Advisors.GENERIC_CONSULTING_COMPANY
            '11': B-Generic_Info.ANNUAL_REVENUES
            '12': I-Generic_Info.ANNUAL_REVENUES
  splits:
    - name: FOLD_1
      num_bytes: 11508541
      num_examples: 753
    - name: FOLD_2
      num_bytes: 11409488
      num_examples: 759
    - name: FOLD_3
      num_bytes: 11524994
      num_examples: 758
    - name: FOLD_4
      num_bytes: 11714536
      num_examples: 755
    - name: FOLD_5
      num_bytes: 11543314
      num_examples: 754
    - name: SILVER
      num_bytes: 94702584
      num_examples: 6196
  download_size: 20824877
  dataset_size: 152403457

Dataset Card for BUSTER

BUSiness Transaction Entity Recognition dataset.

BUSTER is an Entity Recognition (ER) benchmark for entities related to business transactions. It consists of a gold corpus of 3779 manually annotated documents on financial transactions that were randomly divided into 5 folds, plus an additional silver corpus of 6196 automatically annotated documents that were created by the model-optimized RoBERTa system.