deeppavlov/metrics/squad_metrics.py
def squad_v1_exact_match(y_true: List[List[str]], y_predicted: List[str]) -> float:
    """ Calculates Exact Match score between y_true and y_predicted
        EM score uses the best matching y_true answer:
            if y_pred equal at least to one answer in y_true then EM = 1, else EM = 0
        Skips examples without an answer.
    Args:
        y_true: list of correct answers (correct answers are represented by list of strings)
        y_predicted: list of predicted answers
    Returns:
        exact match score : float
    """
    EM_total = 0
    count = 0
    for ground_truth, prediction in zip(y_true, y_predicted):
        if len(ground_truth[0]) == 0:
            # skip empty answers
            continue
        count += 1
        EMs = [int(normalize_answer(gt) == normalize_answer(prediction)) for gt in ground_truth]
        EM_total += max(EMs)
    return 100 * EM_total / count if count > 0 else 0
def squad_v1_exact_match(y_true: List[List[str]], y_predicted: List[str]) -> float:
    """ Calculates Exact Match score between y_true and y_predicted
        EM score uses the best matching y_true answer:
            if y_pred equal at least to one answer in y_true then EM = 1, else EM = 0
        Skips examples without an answer.
    Args:
        y_true: list of correct answers (correct answers are represented by list of strings)
        y_predicted: list of predicted answers
    Returns:
        exact match score : float
    """
    EM_total = 0
    count = 0
    for ground_truth, prediction in zip(y_true, y_predicted):
        if len(ground_truth[0]) == 0:
            # skip empty answers
            continue
        count += 1
        EMs = [int(normalize_answer(gt) == normalize_answer(prediction)) for gt in ground_truth]
        EM_total += max(EMs)
    return 100 * EM_total / count if count > 0 else 0
def squad_v1_exact_match ( y_true : List [ List [ str ] ] , y_predicted : List [ str ] ) -> float : EM_total = 0 count = 0 for ground_truth , prediction in zip ( y_true , y_predicted ) : if len ( ground_truth [ 0 ] ) == 0 : # skip empty answers continue count += 1 EMs = [ int ( normalize_answer ( gt ) == normalize_answer ( prediction ) ) for gt in ground_truth ] EM_total += max ( EMs ) return 100 * EM_total / count if count > 0 else 0
Calculates Exact Match score between y_true and y_predicted
        EM score uses the best matching y_true answer:
            if y_pred equal at least to one answer in y_true then EM = 1, else EM = 0
        Skips examples without an answer.
    Args:
        y_true: list of correct answers (correct answers are represented by list of strings)
        y_predicted: list of predicted answers
    Returns:
        exact match score : float
Calculates Exact Match score between y_true and y_predicted EM score uses the best matching y_true answer : if y_pred equal at least to one answer in y_true then EM = 1 else EM = 0 Skips examples without an answer . Args : y_true : list of correct answers ( correct answers are represented by list of strings ) y_predicted : list of predicted answers Returns : exact match score : float