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def brier_score_fn(items):

return items

@register_metric(metric='acc', higher_is_better=True, output_type=['loglikelihood', 'multiple_choice'], aggregation='mean')

def acc_fn(items):

return items

@register_metric(metric='acc_norm', higher_is_better=True, output_type=['loglikelihood', 'multiple_choice'], aggregation='mean')

def acc_norm_fn(items):

return items

@register_metric(metric='acc_mutual_info', higher_is_better=True, output_type='multiple_choice', aggregation='mean')

def acc_mutual_info_fn(items):

return items

def exact_match_hf_evaluate(predictions, references, regexes_to_ignore=None, ignore_case=False, ignore_punctuation=False, ignore_numbers=False):

if regexes_to_ignore is not None:

for s in regexes_to_ignore:

predictions = np.array([re.sub(s, '', x) for x in predictions])

references = np.array([re.sub(s, '', x) for x in references])

else:

predictions = np.asarray(predictions)

references = np.asarray(references)

if ignore_case:

predictions = np.char.lower(predictions)

references = np.char.lower(references)

if ignore_punctuation:

repl_table = string.punctuation.maketrans('', '', string.punctuation)

predictions = np.char.translate(predictions, table=repl_table)

references = np.char.translate(references, table=repl_table)

if ignore_numbers:

repl_table = string.digits.maketrans('', '', string.digits)

predictions = np.char.translate(predictions, table=repl_table)

references = np.char.translate(references, table=repl_table)

score_list = predictions == references

return {'exact_match': np.mean(score_list)}

@register_metric(metric='exact_match', higher_is_better=True, output_type='generate_until', aggregation='mean')

def exact_match_fn(**kwargs):

return exact_match_hf_evaluate(**kwargs)

@register_metric(metric='perplexity', higher_is_better=False, output_type='loglikelihood', aggregation='perplexity')

def perplexity_fn(items):

return items

@register_metric(metric='word_perplexity', higher_is_better=False, output_type='loglikelihood_rolling', aggregation='weighted_perplexity')

def word_perplexity_fn(items):

return items

@register_metric(metric='byte_perplexity', higher_is_better=False, output_type='loglikelihood_rolling', aggregation='weighted_perplexity')

def byte_perplexity_fn(items):

return items

@register_metric(metric='bits_per_byte', higher_is_better=False, output_type='loglikelihood_rolling', aggregation='bits_per_byte')

def bits_per_byte_fn(items):

return items

def pop_stddev(arr):

mu = mean(arr)

return math.sqrt(sum([(x - mu) ** 2 for x in arr]) / len(arr))

def sample_stddev(arr):

mu = mean(arr)

return math.sqrt(sum([(x - mu) ** 2 for x in arr]) / (len(arr) - 1))

def mean_stderr(arr):

return sample_stddev(arr) / math.sqrt(len(arr))

@register_metric(metric='bypass', higher_is_better=True, output_type=['loglikelihood', 'multiple_choice', 'generate_until'], aggregation='bypass')

def bypass(items):

return None

@register_metric(metric='mcc', higher_is_better=True, output_type='multiple_choice', aggregation='matthews_corrcoef')

def mcc_fn(items):

return items

@register_metric(metric='f1', higher_is_better=True, output_type='multiple_choice', aggregation='f1')

def f1_fn(items):

return items

@register_metric(metric='bleu', higher_is_better=True, output_type='generate_until', aggregation='bleu')

def bleu_fn(items):

return items

@register_metric(metric='chrf', higher_is_better=True, output_type='generate_until', aggregation='chrf')

def chrf_fn(items):

return items

@register_metric(metric='ter', higher_is_better=True, output_type='generate_until', aggregation='ter')

def ter_fn(items):

return items

@register_metric(metric='acc_all', higher_is_better=True, output_type='loglikelihood', aggregation='mean')

def acc_all(items):

question_scoring_dict = {}

preds = list(zip(*items))[0]

docs = list(zip(*items))[1]

for (doc, pred) in zip(docs, preds):

paragraph_id = doc['idx']['paragraph']

question_id = doc['idx']['question']