dataset_info:
features:
- name: id
dtype: string
- name: table_type
dtype: string
- name: domain
dtype: string
- name: difficulty
dtype: string
- name: sample_index
dtype: int32
- name: query
dtype: string
- name: answer
dtype: string
- name: positive_image
dtype: image
- name: negative_image
dtype: image
- name: target_cell
dtype: string
- name: negative_type
dtype: string
splits:
- name: train
num_bytes: 430733374
num_examples: 2000
download_size: 427805046
dataset_size: 430733374
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Table retrieval training data
2,000 synthetic hard table retrieval pairs generated with GPT-6 Luna: 500 each of Vertical, Matrix, Hierarchy, and Nested.
Each train row contains a query, its answer, a positive image, a style-matched hard negative image, the table type and domain, target cell metadata (JSON string), and negative transformation type. Both image columns embed PNG bytes in Parquet shards.
Negative rules: Vertical swaps distinct values in one column; Matrix moves the queried entities onto the same row or column axis; Hierarchy swaps parent groups; Nested swaps outer groups. Rendering uses randomized Matplotlib, HTML/CSS, and LaTeX styles, preserved between each positive and negative.
metadata/ preserves source table specifications, query templates and answers, original render styles, and hard-negative records. Paths inside these original records describe the source layout; use the embedded image columns when loading the dataset.
from datasets import load_dataset
ds = load_dataset("NLPRETGOOD/table_train", split="train")