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from typing import Callable, List
from base_model.string_utils import lower, remove_articles, remove_punc, white_space_fix
def normalize_text(inp: str, preprocessing_functions: List[Callable[[str], str]]):
for fun in preprocessing_functions:
inp = fun(inp)
return inp
def normalize_text_default(inp: str) -> str:
"""Preprocesses the sentence string by normalizing.
Args:
s (str): the sentence
Returns:
string: normalized with default parames
"""
steps = [remove_articles, white_space_fix, remove_punc, lower]
return normalize_text(inp, steps)
def compute_exact_match(prediction: str, answer: str) -> int:
"""Computes exact match for sentences.
Args:
prediction (str): the predicted answer
answer (str): the gold answer
Returns:
int: 1 for exact match, 0 for not
"""
return int(normalize_text_default(prediction) == normalize_text_default(answer))
def compute_f1(prediction: str, answer: str) -> float:
"""Computes F1-score on token overlap for sentences.
Args:
prediction (str): the predicted answer
answer (str): the gold answer
Returns:
boolean: the f1 score
"""
pred_tokens = normalize_text_default(prediction).split()
answer_tokens = normalize_text_default(answer).split()
if len(pred_tokens) == 0 or len(answer_tokens) == 0:
return int(pred_tokens == answer_tokens)
common_tokens = set(pred_tokens) & set(answer_tokens)
if len(common_tokens) == 0:
return 0
prec = len(common_tokens) / len(pred_tokens)
rec = len(common_tokens) / len(answer_tokens)
return 2 * (prec * rec) / (prec + rec)
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