| --- |
| 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 |
| - name: start_col |
| dtype: int64 |
| - name: start_row |
| dtype: int64 |
| - name: tex |
| dtype: string |
| - name: generated |
| struct: |
| - name: cells |
| list: |
| - name: content |
| list: string |
| - name: end_col |
| dtype: int64 |
| - name: end_row |
| dtype: int64 |
| - name: id |
| dtype: int64 |
| - name: start_col |
| dtype: int64 |
| - name: start_row |
| dtype: int64 |
| - 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. |
|
|
|
|
