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README.md

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----
-title: Table Markdown Metrics
-emoji: 📊
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 5.29.0
-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)