skills/hugging-face-evaluation/examples/example_readme_tables.md

# Example Evaluation Table Formats

This file shows various formats of evaluation tables that can be extracted from model README files.

## Format 1: Benchmarks as Rows (Most Common)

```markdown
| Benchmark | Score |
|-----------|-------|
| MMLU      | 85.2  |
| HumanEval | 72.5  |
| GSM8K     | 91.3  |
| HellaSwag | 88.9  |
```

## Format 2: Multiple Metric Columns

```markdown
| Benchmark | Accuracy | F1 Score |
|-----------|----------|----------|
| MMLU      | 85.2     | 0.84     |
| GSM8K     | 91.3     | 0.91     |
| DROP      | 78.5     | 0.77     |
```

## Format 3: Benchmarks as Columns

```markdown
| MMLU | HumanEval | GSM8K | HellaSwag |
|------|-----------|-------|-----------|
| 85.2 | 72.5      | 91.3  | 88.9      |
```

## Format 4: Percentage Values

```markdown
| Benchmark     | Score    |
|---------------|----------|
| MMLU          | 85.2%    |
| HumanEval     | 72.5%    |
| GSM8K         | 91.3%    |
| TruthfulQA    | 68.7%    |
```

## Format 5: Mixed Format with Categories

```markdown
### Reasoning

| Benchmark | Score |
|-----------|-------|
| MMLU      | 85.2  |
| BBH       | 82.4  |
| GPQA      | 71.3  |

### Coding

| Benchmark | Score |
|-----------|-------|
| HumanEval | 72.5  |
| MBPP      | 78.9  |

### Math

| Benchmark | Score |
|-----------|-------|
| GSM8K     | 91.3  |
| MATH      | 65.8  |
```

## Format 6: With Additional Columns

```markdown
| Benchmark | Score | Rank | Notes              |
|-----------|-------|------|--------------------|
| MMLU      | 85.2  | #5   | 5-shot             |
| HumanEval | 72.5  | #8   | pass@1             |
| GSM8K     | 91.3  | #3   | 8-shot, maj@1      |
```

## How the Extractor Works

The script will:
1. Find all markdown tables in the README
2. Identify which tables contain evaluation results
3. Parse the table structure (rows vs columns)
4. Extract numeric values as scores
5. Convert to model-index YAML format

## Tips for README Authors

To ensure your evaluation tables are properly extracted:

1. **Use clear headers**: Include "Benchmark", "Score", or similar terms
2. **Keep it simple**: Stick to benchmark name + score columns
3. **Use standard formats**: Follow markdown table syntax
4. **Include numeric values**: Ensure scores are parseable numbers
5. **Be consistent**: Use the same format across multiple tables

## Example Complete README Section

```markdown
# Model Card for MyModel-7B

## Evaluation Results

Our model was evaluated on several standard benchmarks:

| Benchmark     | Score |
|---------------|-------|
| MMLU          | 85.2  |
| HumanEval     | 72.5  |
| GSM8K         | 91.3  |
| HellaSwag     | 88.9  |
| ARC-Challenge | 81.7  |
| TruthfulQA    | 68.7  |

### Detailed Results

For more detailed results and methodology, see our [paper](link).
```

## Running the Extractor

```bash
# Extract from this example
python scripts/evaluation_manager.py extract-readme \
  --repo-id "your-username/your-model" \
  --dry-run

# Apply to your model card
python scripts/evaluation_manager.py extract-readme \
  --repo-id "your-username/your-model" \
  --task-type "text-generation"
```