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?