README.md

---
title: Table Markdown Metrics
emoji: 📊 
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
tags:
- evaluate
- metric
- table
- markdown
description: >-
  Table evaluation metrics for assessing the matching degree between predicted and reference tables.
  It calculates precision, recall, and F1 score for table data extraction or generation tasks.
---

# Metric Card for Table Markdown Metrics

## Metric Description

This metric evaluates the accuracy of table data extraction or generation by comparing predicted tables with reference tables. It calculates:

1. Precision: The ratio of correctly predicted cells to the total number of cells in the predicted table
2. Recall: The ratio of correctly predicted cells to the total number of cells in the reference table
3. F1 Score: The harmonic mean of precision and recall

## How to Use

This metric requires predictions and references as inputs in Markdown table format.

```python
>>> table_metric = evaluate.load("table_markdown")
>>> results = table_metric.compute(
...     predictions="|A|B|\n|1|2|",
...     references="|A|B|\n|1|3|"
... )
>>> print(results)
{'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'true_positives': 1, 'false_positives': 1, 'false_negatives': 1}
```

### Inputs
- **predictions** (`str`): Predicted table in Markdown format.
- **references** (`str`): Reference table in Markdown format.

### Output Values
- **precision** (`float`): Precision score. Range: [0,1]
- **recall** (`float`): Recall score. Range: [0,1]
- **f1** (`float`): F1 score. Range: [0,1]
- **true_positives** (`int`): Number of correctly predicted cells
- **false_positives** (`int`): Number of incorrectly predicted cells
- **false_negatives** (`int`): Number of cells that were not predicted

### Examples

Example 1 - Simple table comparison:
```python
>>> table_metric = evaluate.load("table_markdown")
>>> results = table_metric.compute(
...     predictions="|  | lobby | search | band | charge | chain ||--|--|--|--|--|--|| desire | 5 | 8 | 7 | 5 | 9 || wage | 1 | 5 | 3 | 8 | 5 |",
...     references="|  | lobby | search | band | charge | chain ||--|--|--|--|--|--|| desire | 1 | 6 | 7 | 5 | 9 || wage | 1 | 5 | 2 | 8 | 5 |"
... )
>>> print(results)
{'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'true_positives': 7, 'false_positives': 3, 'false_negatives': 3}
```

Example 2 - Complex table comparison:
```python
>>> table_metric = evaluate.load("table_markdown")
>>> results = table_metric.compute(
...     predictions="""
... |  | lobby | search | band |
... |--|-------|--------|------|
... | desire | 5 | 8 | 7 |
... | wage | 1 | 5 | 3 |
... """,
...     references="""
... |  | lobby | search | band |
... |--|-------|--------|------|
... | desire | 5 | 8 | 7 |
... | wage | 1 | 5 | 3 |
... """
... )
>>> print(results)
{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'true_positives': 6, 'false_positives': 0, 'false_negatives': 0}
```

## Limitations and Bias

1. The metric assumes that tables are well-formed in Markdown format
2. The comparison is case-sensitive
3. The metric does not handle merged cells or complex table structures
4. The metric treats each cell as a separate unit and does not consider the semantic meaning of the content

## Citation(s)
```bibtex
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
```

## Further References

- [Markdown Tables](https://www.markdownguide.org/extended-syntax/#tables)
- [Table Structure Recognition](https://paperswithcode.com/task/table-structure-recognition)
- [Table Extraction](https://paperswithcode.com/task/table-extraction)