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README.md
CHANGED
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@@ -33,19 +33,15 @@ import evaluate
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from datasets import load_dataset
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import os
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os.environ["HF_ALLOW_CODE_EVAL"] = "1"
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-
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predictions = []
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languages = []
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question_infos = []
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ds = load_dataset("gabeorlanski/bc-humaneval", split="test")
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-
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for row in ds:
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languages.append(row['language'])
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question_infos.append(row['question_info'])
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-
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# Replace this with however you generate and postprocess predictions.
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predictions.append(model.generate(row['signature_with_docstring']))
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-
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metric = evaluate.load("bc_eval")
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metrics, results = metric.compute(
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predictions=predictions, languages=languages, question_dicts=question_infos, k=[1]
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@@ -108,7 +104,6 @@ predictions = [["""def has_close_elements(numbers: List[float], threshold: float
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distance = abs(elem - elem2)
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if distance < threshold:
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return True
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-
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return False"""
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]]
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metrics, results = metric.compute(
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@@ -146,7 +141,6 @@ predictions = [["""def has_close_elements(numbers: List[float], threshold: float
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distance = elem - elem2
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if distance < threshold:
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return True
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-
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return False"""
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]]
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metrics, results = metric.compute(
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from datasets import load_dataset
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import os
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os.environ["HF_ALLOW_CODE_EVAL"] = "1"
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predictions = []
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languages = []
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question_infos = []
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ds = load_dataset("gabeorlanski/bc-humaneval", split="test")
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for row in ds:
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languages.append(row['language'])
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question_infos.append(row['question_info'])
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# Replace this with however you generate and postprocess predictions.
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predictions.append(model.generate(row['signature_with_docstring']))
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metric = evaluate.load("bc_eval")
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metrics, results = metric.compute(
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predictions=predictions, languages=languages, question_dicts=question_infos, k=[1]
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distance = abs(elem - elem2)
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if distance < threshold:
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return True
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return False"""
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]]
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metrics, results = metric.compute(
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distance = elem - elem2
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if distance < threshold:
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return True
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return False"""
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]]
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metrics, results = metric.compute(
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