Datasets:
File size: 5,659 Bytes
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license: apache-2.0
dataset_info:
features:
- name: id
dtype: string
- name: split
dtype: string
- name: similarity_score
dtype: float64
- name: ground_truth
struct:
- name: cells
list:
- name: content
list: string
- name: end_col
dtype: int64
- name: end_row
dtype: int64
- name: id
dtype: int64
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dtype: int64
- name: start_row
dtype: int64
- name: tex
dtype: string
- name: generated
struct:
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list:
- name: content
list: string
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dtype: int64
- name: end_row
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- name: id
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- name: start_row
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- name: tex
dtype: string
splits:
- name: train
num_bytes: 173252161
num_examples: 36066
- name: test
num_bytes: 15195072
num_examples: 3000
download_size: 48544622
dataset_size: 188447233
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Table Extraction Evaluation Dataset
A dataset for training and benchmarking **verifier models** that predict the quality of structured table extraction from documents — **without access to ground truth at inference time**.
Modern LLMs and vision-language models extract tables from PDFs impressively but inconsistently: structural recognition is largely solved, yet semantic content accuracy remains unreliable (e.g., TATR reaches GriTS ≈ 0.985 on PubTables-1M while content accuracy stays around 0.82). In high-stakes workflows — legal, financial, medical — pipelines need an automated way to judge whether an extraction can be trusted. This dataset provides supervision for exactly that task: each example pairs a machine-generated table extraction with its ground truth and a computed quality score, so a model can learn to predict extraction quality directly from the extracted output.
Built for the Stanford CS230 project **"A Neural Verifier for Structured Table Extraction"** (Ray Hu, Nofel Teldjoune, Hiva Zaad).
## Dataset Summary
| | |
|---|---|
| Total examples | 39,066 |
| Train split | 36,066 |
| Test split | 3,000 |
| Format | Parquet |
| License | Apache 2.0 |
## How It Was Built
1. **Upstream source:** [SciTSR](https://github.com/Academic-Hammer/SciTSR) — tables from scientific journals with human-annotated ground-truth JSON structure.
2. **Extraction generation:** each table image was processed with [Table Transformer (TATR)](https://github.com/microsoft/table-transformer) for structure recognition and [EasyOCR](https://github.com/JaidedAI/EasyOCR) for cell content, producing a machine-generated JSON extraction of the same table.
3. **Quality labeling:** each (ground truth, generated) pair was scored with a composite structural-similarity metric:
```
similarity_score = 0.50 * cell_detection_F1
+ 0.25 * row_accuracy
+ 0.25 * col_accuracy
```
The resulting scores span 0.04–1.0, reflecting the realistic range of extraction quality that production pipelines encounter.
## Data Fields
| Field | Type | Description |
|---|---|---|
| `id` | string | Source table identifier (SciTSR document + table index) |
| `split` | string | `train` or `test` |
| `similarity_score` | float64 | Quality label in [0, 1] — the regression target |
| `ground_truth` | dict | Human-annotated table structure: list of cells with `content`, `start_row`, `end_row`, `start_col`, `end_col`, `id`, `tex` |
| `generated` | dict | Machine-generated extraction of the same table, same schema |
## Usage
```python
from datasets import load_dataset
ds = load_dataset("rayhu/table-extraction-evaluation")
example = ds["train"][0]
print(example["similarity_score"]) # e.g. 0.5315
print(example["ground_truth"]["cells"][0])
print(example["generated"]["cells"][0])
```
A typical use: serialize `generated` (optionally with `ground_truth` during training), embed it, and train a regression model to predict `similarity_score`. At inference, the verifier scores new extractions with no ground truth available — enabling automated QA gates, model benchmarking, and extraction-pipeline monitoring.
## Baseline
A 2-layer MLP over `sentence-transformers/all-mpnet-base-v2` embeddings trained on this dataset serves as the reference baseline. Training code, data-processing scripts, and the full report: [github.com/rayhu/cs230-evaluation-model](https://github.com/rayhu/cs230-evaluation-model).
## Limitations
- Quality labels are computed from a single composite metric; other aspects of extraction quality (numerical consistency, header semantics) are not separately labeled.
- Source tables come from scientific journals (SciTSR); domain shift should be expected when applying verifiers to financial, legal, or medical documents.
- Generated extractions come from one pipeline (TATR + EasyOCR); scores reflect that pipeline's error distribution.
## Citation
If you use this dataset, please cite:
```bibtex
@misc{hu2025tableextractioneval,
title = {Table Extraction Evaluation Dataset},
author = {Hu, Ray and Teldjoune, Nofel and Zaad, Hiva},
year = {2025},
note = {Stanford CS230 project: A Neural Verifier for Structured Table Extraction},
url = {https://huggingface.co/datasets/rayhu/table-extraction-evaluation}
}
```
Upstream data derived from SciTSR: Chi et al., *Complicated Table Structure Recognition*, arXiv:1908.04729, 2019.
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