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return {'acc': acc, 'acc_norm': acc_norm, 'em': acc_norm * 100.0}
def construct_requests(self, doc, ctx, **kwargs):
request_list = [Instance(request_type='loglikelihood', doc=doc, arguments=(ctx, ' {}'.format(choice)), idx=i, **kwargs) for (i, choice) in enumerate(doc['choices'])]
return request_list
class _SCROLLSSummaryTask(_SCROLLSTask):
def _process_doc(self, doc):
return [doc]
def _scrolls_metrics(self):
return {'rouge1': 'rouge/rouge1', 'rouge2': 'rouge/rouge2', 'rougeL': 'rouge/rougeL'}
def process_results(self, doc, results):
return {'rouge1': (results[0], doc['outputs']), 'rouge2': (results[0], doc['outputs']), 'rougeL': (results[0], doc['outputs'])}
def construct_requests(self, doc, ctx, **kwargs):
return Instance(request_type='generate_until', doc=doc, arguments=(ctx, {'until': ['\n']}), idx=0, **kwargs)
def doc_to_text(self, doc):
return f"{doc['input']}\n\nQuestion: What is a summary of the preceding text?\nAnswer:"
class Qasper(_SCROLLSTask):
DATASET_NAME = 'qasper'
def _process_doc(self, doc):
doc = _process_doc_prepended_question(doc)
doc['is_yes_no'] = reduce(lambda prev, cur: prev and squad_metrics.normalize_answer(cur) in ['yes', 'no'], doc['outputs'], True)
return [doc]
def _scrolls_metrics(self):
return {'f1': 'f1'}
def process_results(self, doc, results):
if doc['is_yes_no']:
prediction = ' yes' if results[0] > results[1] else ' no'
elif len(results[0].strip()) == 0:
prediction = 'Unanswerable'
else:
prediction = results[0]
return {'f1': (prediction, doc['outputs'])}
def construct_requests(self, doc, ctx, **kwargs):
if doc['is_yes_no']:
return [Instance(request_type='loglikelihood', doc=doc, arguments=(ctx, ' yes'), idx=0, **kwargs), Instance(request_type='loglikelihood', doc=doc, arguments=(ctx, ' no'), idx=1, **kwargs)]
else:
return Instance(request_type='generate_until', doc=doc, arguments=(ctx, {'until': ['\n']}), idx=0, **kwargs)
class QuALITY(_SCROLLSMultipleChoiceTask):
DATASET_NAME = 'quality'
_multiple_choice_pattern = re.compile(' *\\([A-D]\\) *')
@staticmethod
def _normalize_answer(text):
return ' '.join(text.split()).strip()
def _process_doc(self, doc):
doc = _process_doc_prepended_question(doc)
split = doc['text'].find('\n\n', doc['text'].find('(D)'))
choices_text = doc['text'][:split]
doc['text'] = doc['text'][split:].strip()
doc['choices'] = [QuALITY._normalize_answer(choice) for choice in re.split(QuALITY._multiple_choice_pattern, choices_text)[1:]]
doc['gold'] = doc['choices'].index(QuALITY._normalize_answer(doc['outputs'][0]))
return [doc]
class NarrativeQA(_SCROLLSTask):
DATASET_NAME = 'narrative_qa'
def _process_doc(self, doc):
return [_process_doc_prepended_question(doc)]
def _scrolls_metrics(self):
return {'f1': 'f1'}
def _get_prune_text(self, doc):
return self._process_doc(doc)[0]['text']
def process_results(self, doc, results):
return {'f1': (results[0], doc['outputs'])}
def construct_requests(self, doc, ctx, **kwargs):
return Instance(request_type='generate_until', doc=doc, arguments=(ctx, {'until': ['\n']}), idx=0, **kwargs)
class ContractNLI(_SCROLLSMultipleChoiceTask):
DATASET_NAME = 'contract_nli'
CHOICES = ['Not mentioned', 'Entailment', 'Contradiction']
def _process_doc(self, doc):
doc = _process_doc_prepended_question(doc)
doc['choices'] = ContractNLI.CHOICES
doc['gold'] = ContractNLI.CHOICES.index(doc['outputs'][0])
return [doc]
def doc_to_text(self, doc):
return f"{doc['text']}\n\nHypothesis: {doc['question']}\nConclusion:"
class GovReport(_SCROLLSSummaryTask):
DATASET_NAME = 'gov_report'