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danidarko
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docs

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def validation_docs(self):
return self.dataset['validation']
def doc_to_text(self, doc):
return 'Title: ' + doc['title'] + '\n\n' + 'Background: ' + doc['context'] + '\n\n' + 'Question: ' + doc['question'] + '\n\n' + 'Answer:'
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc['context']
def doc_to_target(self, doc):
answer_list = doc['answers']['text']
if len(answer_list) > 0:
answer = answer_list[0]
else:
answer = 'unanswerable'
return ' ' + answer
def construct_requests(self, doc, ctx, **kwargs):
return [Instance(request_type='generate_until', doc=doc, arguments=(ctx, {'until': ['\n']}), idx=0, **kwargs), Instance(request_type='loglikelihood', doc=doc, arguments=(ctx, ' ' + 'unanswerable'), idx=0, **kwargs)]
def process_results(self, doc, results):
(continuation, (logprob_unanswerable, _)) = results
no_answer_probability = exp(logprob_unanswerable)
predictions = {'id': doc['id'], 'prediction_text': continuation, 'no_answer_probability': no_answer_probability}
references = {'id': doc['id'], 'answers': doc['answers']}
return {'exact': (predictions, references), 'f1': (predictions, references), 'HasAns_exact': (predictions, references), 'HasAns_f1': (predictions, references), 'NoAns_exact': (predictions, references), 'NoAns_f1': (predictions, references), 'best_exact': (predictions, references), 'best_f1': (predictions, references)}
def aggregation(self):
return {'exact': partial(_squad_agg, 'exact'), 'f1': partial(_squad_agg, 'f1'), 'HasAns_exact': partial(_squad_agg, 'HasAns_exact'), 'HasAns_f1': partial(_squad_agg, 'HasAns_f1'), 'NoAns_exact': partial(_squad_agg, 'NoAns_exact'), 'NoAns_f1': partial(_squad_agg, 'NoAns_f1'), 'best_exact': partial(_squad_agg, 'best_exact'), 'best_f1': partial(_squad_agg, 'best_f1')}
def higher_is_better(self):
return {'exact': True, 'f1': True, 'HasAns_exact': True, 'HasAns_f1': True, 'NoAns_exact': True, 'NoAns_f1': True, 'best_exact': True, 'best_f1': True}
# File: lm-evaluation-harness-main/lm_eval/tasks/super_glue/cb/aggregate.py
import numpy as np
import sklearn
def cb_multi_fi(items):
(preds, golds) = zip(*items)
preds = np.array(preds)
golds = np.array(golds)
f11 = sklearn.metrics.f1_score(y_true=golds == 0, y_pred=preds == 0)
f12 = sklearn.metrics.f1_score(y_true=golds == 1, y_pred=preds == 1)
f13 = sklearn.metrics.f1_score(y_true=golds == 2, y_pred=preds == 2)
avg_f1 = np.mean([f11, f12, f13])
return avg_f1
# File: lm-evaluation-harness-main/lm_eval/tasks/super_glue/cb/t5_utils.py
import sklearn.metrics
def mean_3class_f1(predictions, references):
string_label = ['entailment', 'contradiction', 'neutral']
predictions = string_label.index(predictions[0]) if predictions[0] in string_label else 0
references = string_label.index(references[0])
return (predictions, references)
def agg_mean_3class_f1(items):
(predictions, references) = zip(*items)
''
metric_str = 'fbeta_score'
metric_fn_kwargs = {'beta': 1, 'labels': range(3), 'average': 'macro'}
def _fn(predictions, references):
metric_fn = getattr(sklearn.metrics, metric_str)
metric_val = metric_fn(references, predictions, **metric_fn_kwargs)
return metric_val
return _fn(predictions, references)
# File: lm-evaluation-harness-main/lm_eval/tasks/super_glue/copa/utils.py
def convert_choice(choice):
return choice[0].lower() + choice[1:]
def doc_to_text(doc):
connector = {'cause': 'because', 'effect': 'therefore'}[doc['question']]
return doc['premise'].strip()[:-1] + f' {connector}'
def doc_to_target(doc):
correct_choice = doc['choice1'] if doc['label'] == 0 else doc['choice2']
return ' ' + convert_choice(correct_choice)
def doc_to_choice(doc):
return [' ' + convert_choice(doc['choice1']), ' ' + convert_choice(doc['choice2'])]
# File: lm-evaluation-harness-main/lm_eval/tasks/super_glue/multirc/t5_utils.py
import collections
import numpy as np
import sklearn.metrics
def f1(predictions, references):
_prediction = predictions[0]
_reference = references[0].split('_')[-1]
string_label = ['False', 'True']
reference = string_label.index(_reference)
prediction = string_label.index(_prediction) if _prediction in string_label else not bool(reference)
return (prediction, reference)
def agg_f1(items):