docs: fix benchmark dataset spacing (closes #196)

This commit is contained in:
Johann Schopplich
2025-11-19 22:06:23 +01:00
parent 9968cd2521
commit 796b333e75
4 changed files with 8 additions and 0 deletions

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@@ -437,6 +437,7 @@ This benchmark tests **LLM comprehension and data retrieval accuracy** across di
Eleven datasets designed to test different structural patterns and validation capabilities: Eleven datasets designed to test different structural patterns and validation capabilities:
**Primary datasets:** **Primary datasets:**
1. **Tabular** (100 employee records): Uniform objects with identical fields optimal for TOON's tabular format. 1. **Tabular** (100 employee records): Uniform objects with identical fields optimal for TOON's tabular format.
2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays. 2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values. 3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
@@ -445,6 +446,7 @@ Eleven datasets designed to test different structural patterns and validation ca
6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility. 6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility.
**Structural validation datasets:** **Structural validation datasets:**
7. **Control**: Valid complete dataset (baseline for validation) 7. **Control**: Valid complete dataset (baseline for validation)
8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection) 8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection)
9. **Extra rows**: Array with 3 additional rows beyond declared length 9. **Extra rows**: Array with 3 additional rows beyond declared length

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@@ -278,6 +278,7 @@ This benchmark tests **LLM comprehension and data retrieval accuracy** across di
Eleven datasets designed to test different structural patterns and validation capabilities: Eleven datasets designed to test different structural patterns and validation capabilities:
**Primary datasets:** **Primary datasets:**
1. **Tabular** (100 employee records): Uniform objects with identical fields optimal for TOON's tabular format. 1. **Tabular** (100 employee records): Uniform objects with identical fields optimal for TOON's tabular format.
2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays. 2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values. 3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
@@ -286,6 +287,7 @@ Eleven datasets designed to test different structural patterns and validation ca
6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility. 6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility.
**Structural validation datasets:** **Structural validation datasets:**
7. **Control**: Valid complete dataset (baseline for validation) 7. **Control**: Valid complete dataset (baseline for validation)
8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection) 8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection)
9. **Extra rows**: Array with 3 additional rows beyond declared length 9. **Extra rows**: Array with 3 additional rows beyond declared length

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@@ -284,6 +284,7 @@ This benchmark tests **LLM comprehension and data retrieval accuracy** across di
Eleven datasets designed to test different structural patterns and validation capabilities: Eleven datasets designed to test different structural patterns and validation capabilities:
**Primary datasets:** **Primary datasets:**
1. **Tabular** (${tabularSize} employee records): Uniform objects with identical fields optimal for TOON's tabular format. 1. **Tabular** (${tabularSize} employee records): Uniform objects with identical fields optimal for TOON's tabular format.
2. **Nested** (${nestedSize} e-commerce orders): Complex structures with nested customer objects and item arrays. 2. **Nested** (${nestedSize} e-commerce orders): Complex structures with nested customer objects and item arrays.
3. **Analytics** (${analyticsSize} days of metrics): Time-series data with dates and numeric values. 3. **Analytics** (${analyticsSize} days of metrics): Time-series data with dates and numeric values.
@@ -292,6 +293,7 @@ Eleven datasets designed to test different structural patterns and validation ca
6. **Nested Config** (${nestedConfigSize} configuration): Deeply nested configuration with minimal tabular eligibility. 6. **Nested Config** (${nestedConfigSize} configuration): Deeply nested configuration with minimal tabular eligibility.
**Structural validation datasets:** **Structural validation datasets:**
7. **Control**: Valid complete dataset (baseline for validation) 7. **Control**: Valid complete dataset (baseline for validation)
8. **Truncated**: Array with 3 rows removed from end (tests \`[N]\` length detection) 8. **Truncated**: Array with 3 rows removed from end (tests \`[N]\` length detection)
9. **Extra rows**: Array with 3 additional rows beyond declared length 9. **Extra rows**: Array with 3 additional rows beyond declared length

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@@ -294,6 +294,7 @@ This benchmark tests **LLM comprehension and data retrieval accuracy** across di
Eleven datasets designed to test different structural patterns and validation capabilities: Eleven datasets designed to test different structural patterns and validation capabilities:
**Primary datasets:** **Primary datasets:**
1. **Tabular** (100 employee records): Uniform objects with identical fields optimal for TOON's tabular format. 1. **Tabular** (100 employee records): Uniform objects with identical fields optimal for TOON's tabular format.
2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays. 2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values. 3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
@@ -302,6 +303,7 @@ Eleven datasets designed to test different structural patterns and validation ca
6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility. 6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility.
**Structural validation datasets:** **Structural validation datasets:**
7. **Control**: Valid complete dataset (baseline for validation) 7. **Control**: Valid complete dataset (baseline for validation)
8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection) 8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection)
9. **Extra rows**: Array with 3 additional rows beyond declared length 9. **Extra rows**: Array with 3 additional rows beyond declared length