(
    model,
    *,
    X_test: Sequence[str],
    Y_test : thinc.types.Array2d,
    batch_size: int,
)
return float(correct / total)
Args:
        model
        X_test
        Y_test
        batch_size
Args:
        model
        X_test
        Y_test
        batch_size
Args : model X_test Y_test batch_size
def evaluate_model(
    model,
    *,
    X_test: Sequence[str],
    Y_test : thinc.types.Array2d,
    batch_size: int,
) -> float:
    """
    Args:
        model
        X_test
        Y_test
        batch_size
    """
    correct = 0
    total = 0
    for X, Y in model.ops.multibatch(batch_size, X_test, Y_test):
        Yh = model.predict(X)
        for yh, y in zip(Yh, Y):
            correct += (y.argmax(axis=0) == yh.argmax(axis=0)).sum()
        total += len(Y)
    return float(correct / total)
def evaluate_model ( model , * , X_test : Sequence [ str ] , Y_test : thinc . types . Array2d , batch_size : int , ) -> float : correct = 0 total = 0 for X , Y in model . ops . multibatch ( batch_size , X_test , Y_test ) : Yh = model . predict ( X ) for yh , y in zip ( Yh , Y ) : correct += ( y . argmax ( axis = 0 ) == yh . argmax ( axis = 0 ) ) . sum ( ) total += len ( Y ) return float ( correct / total )