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chore: import upstream snapshot with attribution
2026-07-13 13:36:15 +08:00

61 lines
1.8 KiB
Python

"""TableBench benchmark processor."""
from .base import BenchmarkProcessor
class TableBenchProcessor(BenchmarkProcessor):
"""TableBench: Table QA with different subtasks."""
CORE_METRICS = {
"fact": "accuracy",
"numerical": "accuracy",
"analysis": "accuracy",
"visualization": "Pass@1", # TableBench visualization uses Pass@1 as core metric
}
# TableBench-specific metrics where higher is better
HIGHER_IS_BETTER = BenchmarkProcessor.HIGHER_IS_BETTER | {
"Pass@1",
"ECR@1",
"Parse@1",
}
@classmethod
def match(cls, benchmark_name: str) -> bool:
return "tablebench" in benchmark_name.lower()
@classmethod
def get_core_metric(cls, accuracy_summary: dict) -> tuple[str, float, bool] | None:
scores = []
metrics_used = []
for ds, metrics in accuracy_summary.items():
if not isinstance(metrics, dict):
continue
ds_lower = ds.lower()
# Find matching core metric
core_metric = "accuracy" # fallback
for pattern, metric in cls.CORE_METRICS.items():
if pattern in ds_lower:
core_metric = metric
break
if core_metric in metrics:
scores.append(float(metrics[core_metric]))
metrics_used.append(core_metric)
if not scores:
return None
avg = sum(scores) / len(scores)
unique = list(set(metrics_used))
if len(scores) == 1:
metric_name = unique[0]
elif len(unique) == 1:
metric_name = f"{unique[0]} (average)"
else:
metric_name = "mixed (average)"
return (metric_name, avg, cls.is_higher_better(metric_name))