Upload README.md

README.md

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+---
+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="|  | 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}
+```
+
+### 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  - 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={Research on Chinese Chart Data Extraction Methods},
+  author={Qiuping Ma,Hangshuo Bi,Qi Zhang,Xiaofan Zhao},
+  journal={None},
+  volume={0},
+  pages={0--0},
+  year={2025}
+}
+```
+
+## 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)