| from typing import Callable, List | |
| from src.utils.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 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 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) | |