RISE Humanities Data Benchmark, 0.5.3-pre1

Search Test Runs

 

A test run is a single execution of a benchmark test using a defined model configuration.
Each run represents how a particular large language model (LLM) — such as GPT-4, Claude-3, or Gemini — performed on a given task at a specific time, with specific settings.

A test run includes:

  • Prompt and role definition – what the model was asked to do and from what perspective (e.g. “as a historian”).
  • Model configuration – provider, model version, temperature, and other generation parameters.
  • Results – the model’s actual response and its evaluation (scores such as F1 or accuracy).
  • Usage and cost data – token counts and calculated API costs.
  • Metadata – information like the test date, benchmark name, and person who executed it.

Together, test runs make it possible to compare models, providers, and configurations across benchmarks in a transparent and reproducible way.

Search Results

Your search for Benchmark 'book_advert_xml__true' with Search Hidden 'False' returned 97 results, showing page 1 of 10.
Result 1 of 97

Test T1430 at 2026-07-25

{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}

Configuration
Providergenai
Modelgemini-3.6-flash
  
Temperature0.0
DataclassCorrectedAdvert
  
Normalized Score100.00 %
Test timeunknown seconds
Prompt

Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.

Results

no valid result

Scoring
Fuzzy Score F1 micro / macro Micro precision/recall Tue/False Positives
90.71 n/a n/a n/a n/a n/a n/a n/a n/a
      Micro Precision Micro Recall Instances TP FP FN
Costs / Pricing
Pricing Date: n/an/aTokens: 31.5K IT + 215.6K OT = 247.2K TTCost: 0.047$1.617$1.664$
Result 2 of 97

Test T1355 at 2026-07-25

{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}

Configuration
Provideropenai
Modelgpt-5.6-sol
  
Temperature1.0
DataclassCorrectedAdvert
  
Normalized Score100.00 %
Test timeunknown seconds
Prompt

Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.

Results

no valid result

Scoring
Fuzzy Score F1 micro / macro Micro precision/recall Tue/False Positives
98.35 n/a n/a n/a n/a n/a n/a n/a n/a
      Micro Precision Micro Recall Instances TP FP FN
Costs / Pricing
Pricing Date: n/an/aTokens: 33.6K IT + 39.6K OT = 73.2K TTCost: 0.168$1.189$1.357$
Result 3 of 97

Test T1400 at 2026-07-25

{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}

Configuration
Provideropenrouter
Modelmoonshotai/kimi-k3
  
Temperature0.0
DataclassCorrectedAdvert
  
Normalized Score100.00 %
Test timeunknown seconds
Prompt

Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.

Results

no valid result

Scoring
Fuzzy Score F1 micro / macro Micro precision/recall Tue/False Positives
98.71 n/a n/a n/a n/a n/a n/a n/a n/a
      Micro Precision Micro Recall Instances TP FP FN
Costs / Pricing
Pricing Date: n/an/aTokens: 43.2K IT + 244.5K OT = 287.6K TTCost: 0.126$3.494$3.795$
Result 4 of 97

Test T1445 at 2026-07-25

{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}

Configuration
Providergenai
Modelgemini-3.5-flash-lite
  
Temperature0.0
DataclassCorrectedAdvert
  
Normalized Score100.00 %
Test timeunknown seconds
Prompt

Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.

Results

no valid result

Scoring
Fuzzy Score F1 micro / macro Micro precision/recall Tue/False Positives
96.59 n/a n/a n/a n/a n/a n/a n/a n/a
      Micro Precision Micro Recall Instances TP FP FN
Costs / Pricing
Pricing Date: n/an/aTokens: 31.5K IT + 25.8K OT = 57.4K TTCost: 0.009$0.065$0.074$
Result 5 of 97

Test T1415 at 2026-07-25

{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}

Configuration
Provideranthropic
Modelclaude-opus-5
  
Temperature0.0
DataclassCorrectedAdvert
  
Normalized Score100.00 %
Test timeunknown seconds
Prompt

Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.

Results

no valid result

Scoring
Fuzzy Score F1 micro / macro Micro precision/recall Tue/False Positives
97.45 n/a n/a n/a n/a n/a n/a n/a n/a
      Micro Precision Micro Recall Instances TP FP FN
Costs / Pricing
Pricing Date: n/an/aTokens: 81.2K IT + 58.2K OT = 139.4K TTCost: 0.406$1.455$1.862$
Result 6 of 97

Test T1370 at 2026-07-25

{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}

Configuration
Provideropenai
Modelgpt-5.6-terra
  
Temperature1.0
DataclassCorrectedAdvert
  
Normalized Score100.00 %
Test timeunknown seconds
Prompt

Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.

Results

no valid result

Scoring
Fuzzy Score F1 micro / macro Micro precision/recall Tue/False Positives
98.12 n/a n/a n/a n/a n/a n/a n/a n/a
      Micro Precision Micro Recall Instances TP FP FN
Costs / Pricing
Pricing Date: n/an/aTokens: 33.6K IT + 37.1K OT = 70.8K TTCost: 0.084$0.557$0.641$
Result 7 of 97

Test T1385 at 2026-07-25

{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}

Configuration
Provideropenai
Modelgpt-5.6-luna
  
Temperature1.0
DataclassCorrectedAdvert
  
Normalized Score100.00 %
Test timeunknown seconds
Prompt

Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.

Results

no valid result

Scoring
Fuzzy Score F1 micro / macro Micro precision/recall Tue/False Positives
97.66 n/a n/a n/a n/a n/a n/a n/a n/a
      Micro Precision Micro Recall Instances TP FP FN
Costs / Pricing
Pricing Date: n/an/aTokens: 33.6K IT + 62.5K OT = 96.2K TTCost: 0.034$0.375$0.409$
Result 8 of 97

Test T1200 at 2026-07-02

{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}

Configuration
Provideranthropic
Modelclaude-fable-5
  
Temperature0.0
DataclassCorrectedAdvert
  
Normalized Score100.00 %
Test timeunknown seconds
Prompt

Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.

Results

no valid result

Scoring
Fuzzy Score F1 micro / macro Micro precision/recall Tue/False Positives
97.90 n/a n/a n/a n/a n/a n/a n/a n/a
      Micro Precision Micro Recall Instances TP FP FN
Costs / Pricing
Pricing Date: n/an/aTokens: 81.4K IT + 52.9K OT = 134.3K TTCost: 0.814$2.643$3.457$
Result 9 of 97

Test T1187 at 2026-07-01

{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}

Configuration
Provideranthropic
Modelclaude-sonnet-5
  
Temperature0.0
DataclassCorrectedAdvert
  
Normalized Score100.00 %
Test timeunknown seconds
Prompt

Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.

Results

no valid result

Scoring
Fuzzy Score F1 micro / macro Micro precision/recall Tue/False Positives
96.57 n/a n/a n/a n/a n/a n/a n/a n/a
      Micro Precision Micro Recall Instances TP FP FN
Costs / Pricing
Pricing Date: n/an/aTokens: 84.6K IT + 61.3K OT = 145.9K TTCost: 0.169$0.613$0.782$
Result 10 of 97

Test T1174 at 2026-06-29

{'document-type': ['newspaper-page'], 'century': [18], 'language': ['en'], 'task': ['data-correction']}

Configuration
Providergenai
Modelgemini-3.1-flash-lite
  
Temperature0.0
DataclassCorrectedAdvert
  
Normalized Score100.00 %
Test timeunknown seconds
Prompt

Fix this xml. Add xml-tags if faulty where it makes sense.
Format your response as JSON. Use the keys 'fixed_xml', 'number_of_fixes', 'explanation'.

Results

no valid result

Scoring
Fuzzy Score F1 micro / macro Micro precision/recall Tue/False Positives
96.36 n/a n/a n/a n/a n/a n/a n/a n/a
      Micro Precision Micro Recall Instances TP FP FN
Costs / Pricing
Pricing Date: n/an/aTokens: 31.5K IT + 24.7K OT = 56.2K TTCost: 0.008$0.037$0.045$