---
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: 430733374.0
    num_examples: 2000
  download_size: 427805046
  dataset_size: 430733374.0
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.

```python
from datasets import load_dataset
ds = load_dataset("NLPRETGOOD/table_train", split="train")
```