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metadata
dataset_info:
  features:
    - name: id
      dtype: int32
    - name: image
      dtype: image
    - name: conversations
      list:
        - name: role
          dtype: string
        - name: content
          dtype: string
    - name: metadata
      struct:
        - name: doc_title
          dtype: string
        - name: publisher
          dtype: string
        - name: publish_year
          dtype: string
        - name: table_type
          dtype: string
        - name: table_field
          dtype: string
        - name: table_unit
          dtype: string
        - name: table_title
          dtype: string
        - name: table_header
          dtype: string
        - name: table_row_number
          dtype: int32
        - name: table_column_number
          dtype: int32
        - name: table_header_bold
          dtype: string
        - name: table_background
          dtype: string
        - name: html_path
          dtype: string
        - name: width
          dtype: int32
        - name: height
          dtype: int32
    - name: summary
      list: string
    - name: html
      dtype: string
  splits:
    - name: train
      num_bytes: 40931400228
      num_examples: 323264
    - name: validation
      num_bytes: 4185978223.25
      num_examples: 40406
  download_size: 38033928568
  dataset_size: 45117378451.25
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*

이거 HTML 데이터 Naver-OCR 이용해서 만든 데이터임. HTML 한정 부정확한 데이터가 있을 수 있음.
llava 학습 할 때의 recap 데이터로 활용할 수 있지? 있을 듯?