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danidarko
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docs

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def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
processed_docs = list(map(self._process_doc, self.dataset['train']))
processed_docs = [item for sublist in processed_docs for item in sublist]
processed_dict = {key: [d[key] for d in processed_docs] for key in processed_docs[0]}
return Dataset.from_dict(processed_dict)
def validation_docs(self):
processed_docs = list(map(self._process_doc, self.dataset['validation']))
processed_docs = [item for sublist in processed_docs for item in sublist]
processed_dict = {key: [d[key] for d in processed_docs] for key in processed_docs[0]}
return Dataset.from_dict(processed_dict)
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc['input']
def download(self, *args, **kwargs):
super().download(*args, **kwargs)
del self.dataset['test']
for split in self.dataset:
self.dataset[split] = _drop_duplicates_in_input(self.dataset[split])
if self.PRUNE_TOKENIZERS is not None:
self.prune()
def _get_prune_text(self, sample):
return self.doc_to_text(self._process_doc(sample)[0])
def prune(self):
tokenizers = [AutoTokenizer.from_pretrained(tokenizer) for tokenizer in self.PRUNE_TOKENIZERS]
cache = {}
def _filter(sample):
text = self._get_prune_text(sample)
cached = cache.get(text, None)
if cached is None:
for tokenizer in tokenizers:
if len(tokenizer(text).input_ids) > self.PRUNE_MAX_TOKENS:
cache[text] = False
return False
cache[text] = True
return True
else:
return cached
self.dataset = self.dataset.filter(_filter, num_proc=self.PRUNE_NUM_PROC)
def doc_to_target(self, doc):
return ' ' + ', '.join(doc['outputs'])
def doc_to_text(self, doc):
return f"{doc['text']}\n\nQuestion: {doc['question']}\nAnswer:"
def higher_is_better(self):
return {x: True for x in self._scrolls_metrics().keys()}
@abstractmethod
def _scrolls_metrics(self):
pass
def _make_compute_metrics(self, value):
def compute_metrics(samples):
(predictions, references) = zip(*samples)
computed = self.metric.compute(predictions=predictions, references=references)
return computed[value]
return compute_metrics
def aggregation(self):
return {key: self._make_compute_metrics(value) for (key, value) in self._scrolls_metrics().items()}
class _SCROLLSMultipleChoiceTask(_SCROLLSTask):
def __post_init__(self):
self.metric = None
def _scrolls_metrics(self):
return None
def aggregation(self):
return {'em': mean, 'acc': mean, 'acc_norm': mean}
def higher_is_better(self):
return {'em': True, 'acc': True, 'acc_norm': True}
def process_results(self, doc, results):
gold = doc['gold']
(lls, _) = zip(*results)
acc = 1.0 if np.argmax(lls) == gold else 0.0
completion_len = np.array([float(len(i)) for i in doc['choices']])
acc_norm = 1.0 if np.argmax(lls / completion_len) == gold else 0.0