MultilabelFbeta bug reproducible code

MultiLabelFbeta_bug.py

from fastai import *
from fastai.text import *

bs=48

path = untar_data(URLs.IMDB)
path.ls()

data_lm = (TextList.from_folder(path)
            .filter_by_folder(include=['train', 'test', 'unsup']) 
            .split_by_rand_pct(0.1, seed=42)
            .label_for_lm()           
            .databunch(bs=bs, num_workers=1))

learn_lm = language_model_learner(data_lm, AWD_LSTM, drop_mult=0.3)

data_clas = (TextList.from_folder(path, vocab=data_lm.vocab) 
    .split_by_folder(valid='test') 
    .label_from_folder(classes=['neg', 'pos']) 
    .databunch(bs=bs, num_workers=1))

learn_c = text_classifier_learner(data_clas, AWD_LSTM, drop_mult=0.3)
micro_f1 = MultiLabelFbeta(learn_c, beta=1, thresh=0.5, average="micro")

learn_c.metrics = [micro_f1]

learn_c.save('bla')
learn_c.load('bla')