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from typing import List |
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import numpy as np |
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try: |
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import tinyBenchmarks as tb |
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except ModuleNotFoundError: |
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raise ModuleNotFoundError( |
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"`tinyBenchmarks` is required for tinyBenchmarks task metric calculation, install via \ |
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`pip install git+https://github.com/felipemaiapolo/tinyBenchmarks`" |
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) |
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def agg_pirt(items: List[float], benchmark: str) -> float: |
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items = np.array(items) |
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predictions = tb.evaluate(items, benchmark) |
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return predictions[benchmark]["pirt"] |
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def agg_gpirt_arc(items: List[float], benchmark: str = "arc") -> float: |
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items = np.array(items) |
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predictions = tb.evaluate(items, benchmark) |
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return predictions[benchmark]["gpirt"] |
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def agg_gpirt_gsm8k(items: List[float], benchmark: str = "gsm8k") -> float: |
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items = np.array(items) |
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predictions = tb.evaluate(items, benchmark) |
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return predictions[benchmark]["gpirt"] |
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def agg_gpirt_hellaswag(items: List[float], benchmark: str = "hellaswag") -> float: |
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items = np.array(items) |
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predictions = tb.evaluate(items, benchmark) |
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return predictions[benchmark]["gpirt"] |
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def agg_gpirt_mmlu(items: List[float], benchmark: str = "mmlu") -> float: |
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items = np.array(items) |
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predictions = tb.evaluate(items, benchmark) |
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return predictions[benchmark]["gpirt"] |
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def agg_gpirt_truthfulqa(items: List[float], benchmark: str = "truthfulqa") -> float: |
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items = np.array(items) |
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predictions = tb.evaluate(items, benchmark) |
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return predictions[benchmark]["gpirt"] |
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def agg_gpirt_winogrande(items: List[float], benchmark: str = "winogrande") -> float: |
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items = np.array(items) |
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predictions = tb.evaluate(items, benchmark) |
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return predictions[benchmark]["gpirt"] |
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