def rescore(self, query: Query, texts: List[Text]) -> List[Text]:
texts = deepcopy(texts)
doc_pairs = list(permutations(texts, 2))
scores = defaultdict(float)
batch_input = DuoQueryDocumentBatch(query=query, doc_pairs=doc_pairs)
for batch in self.tokenizer.traverse_duo_query_document(batch_input):
with torch.cuda.amp.autocast(enabled=self.use_amp):
input_ids = batch.output['input_ids'].to(self.device)
attn_mask = batch.output['attention_mask'].to(self.device)
_, batch_scores = greedy_decode(self.model,
input_ids,
length=1,
attention_mask=attn_mask,
return_last_logits=True)
# 6136 and 1176 are the indexes of the tokens false and true in T5.
batch_scores = batch_scores[:, [6136, 1176]]
batch_scores = torch.nn.functional.softmax(batch_scores, dim=1)
batch_probs = batch_scores[:, 1].tolist()
for doc, score in zip(batch.doc_pairs, batch_probs):
scores[doc[0].metadata['docid']] += score
scores[doc[1].metadata['docid']] += (1 - score)
for text in texts:
text.score = scores[text.metadata['docid']]
return texts
def rerank(self, query: Query, texts: List[Text]) -> List[Text]:
texts = deepcopy(texts)
batch_input = QueryDocumentBatch(query=query, documents=texts)
for batch in self.tokenizer.traverse_query_document(batch_input):
input_ids = batch.output['input_ids']
attn_mask = batch.output['attention_mask']
_, batch_scores = greedy_decode(self.model,
input_ids.to(self.device),
length=2,
attention_mask=attn_mask.to(self.device),
return_last_logits=True)
# 6136 and 1176 are the indexes of the tokens false and true in T5.
batch_scores = batch_scores[:, [6136, 1176]]
batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
batch_log_probs = batch_scores[:, 1].tolist()
for doc, score in zip(batch.documents, batch_log_probs):
doc.score = score
return texts
def rescore(self, query: Query, texts: List[Text]) -> List[Text]:
texts = deepcopy(texts)
batch_input = QueryDocumentBatch(query=query, documents=texts)
for batch in self.tokenizer.traverse_query_document(batch_input):
with torch.cuda.amp.autocast(enabled=self.use_amp):
input_ids = batch.output['input_ids'].to(self.device)
attn_mask = batch.output['attention_mask'].to(self.device)
_, batch_scores = greedy_decode(self.model,
input_ids,
length=1,
attention_mask=attn_mask,
return_last_logits=True)
batch_scores = batch_scores[:, [
self.token_false_id, self.token_true_id
]]
batch_scores = torch.nn.functional.log_softmax(batch_scores,
dim=1)
batch_log_probs = batch_scores[:, 1].tolist()
for doc, score in zip(batch.documents, batch_log_probs):
doc.score = score
return texts