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docs: fix benchmark dataset spacing (closes #196)
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@@ -437,6 +437,7 @@ This benchmark tests **LLM comprehension and data retrieval accuracy** across di
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Eleven datasets designed to test different structural patterns and validation capabilities:
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Eleven datasets designed to test different structural patterns and validation capabilities:
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**Primary datasets:**
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**Primary datasets:**
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1. **Tabular** (100 employee records): Uniform objects with identical fields – optimal for TOON's tabular format.
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1. **Tabular** (100 employee records): Uniform objects with identical fields – optimal for TOON's tabular format.
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2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
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2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
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3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
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3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
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@@ -445,6 +446,7 @@ Eleven datasets designed to test different structural patterns and validation ca
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6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility.
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6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility.
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**Structural validation datasets:**
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**Structural validation datasets:**
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7. **Control**: Valid complete dataset (baseline for validation)
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7. **Control**: Valid complete dataset (baseline for validation)
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8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection)
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8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection)
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9. **Extra rows**: Array with 3 additional rows beyond declared length
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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
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Eleven datasets designed to test different structural patterns and validation capabilities:
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Eleven datasets designed to test different structural patterns and validation capabilities:
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**Primary datasets:**
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**Primary datasets:**
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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.
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2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
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2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
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3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
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3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
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@@ -286,6 +287,7 @@ Eleven datasets designed to test different structural patterns and validation ca
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6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility.
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6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility.
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**Structural validation datasets:**
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**Structural validation datasets:**
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7. **Control**: Valid complete dataset (baseline for validation)
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7. **Control**: Valid complete dataset (baseline for validation)
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8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection)
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8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection)
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9. **Extra rows**: Array with 3 additional rows beyond declared length
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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
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Eleven datasets designed to test different structural patterns and validation capabilities:
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Eleven datasets designed to test different structural patterns and validation capabilities:
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**Primary datasets:**
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**Primary datasets:**
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1. **Tabular** (${tabularSize} employee records): Uniform objects with identical fields – optimal for TOON's tabular format.
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1. **Tabular** (${tabularSize} employee records): Uniform objects with identical fields – optimal for TOON's tabular format.
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2. **Nested** (${nestedSize} e-commerce orders): Complex structures with nested customer objects and item arrays.
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2. **Nested** (${nestedSize} e-commerce orders): Complex structures with nested customer objects and item arrays.
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3. **Analytics** (${analyticsSize} days of metrics): Time-series data with dates and numeric values.
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3. **Analytics** (${analyticsSize} days of metrics): Time-series data with dates and numeric values.
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@@ -292,6 +293,7 @@ Eleven datasets designed to test different structural patterns and validation ca
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6. **Nested Config** (${nestedConfigSize} configuration): Deeply nested configuration with minimal tabular eligibility.
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6. **Nested Config** (${nestedConfigSize} configuration): Deeply nested configuration with minimal tabular eligibility.
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**Structural validation datasets:**
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**Structural validation datasets:**
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7. **Control**: Valid complete dataset (baseline for validation)
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7. **Control**: Valid complete dataset (baseline for validation)
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8. **Truncated**: Array with 3 rows removed from end (tests \`[N]\` length detection)
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8. **Truncated**: Array with 3 rows removed from end (tests \`[N]\` length detection)
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9. **Extra rows**: Array with 3 additional rows beyond declared length
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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
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Eleven datasets designed to test different structural patterns and validation capabilities:
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Eleven datasets designed to test different structural patterns and validation capabilities:
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|
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**Primary datasets:**
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**Primary datasets:**
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|
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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.
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||||||
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.
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3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
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3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
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@@ -302,6 +303,7 @@ Eleven datasets designed to test different structural patterns and validation ca
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6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility.
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6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility.
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**Structural validation datasets:**
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**Structural validation datasets:**
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7. **Control**: Valid complete dataset (baseline for validation)
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7. **Control**: Valid complete dataset (baseline for validation)
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8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection)
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8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection)
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9. **Extra rows**: Array with 3 additional rows beyond declared length
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9. **Extra rows**: Array with 3 additional rows beyond declared length
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