def predicted_class_index(self) -> int:
"Returns predicted class index (int) for model with last calculated `input_ids`"
if len(self.input_ids) > 0:
# we call this before _forward() so it has to be calculated twice
preds = self.model(self.input_ids)[0]
self.pred_class = torch.argmax(torch.softmax(preds, dim=0)[0])
return torch.argmax(torch.softmax(preds, dim=1)[0]).cpu().detach().numpy()
else:
raise InputIdsNotCalculatedError("input_ids have not been created yet.`")
def predicted_class_index(self):
if self.input_ids is not None:
preds = self.model(self.input_ids)[0]
self.pred_class = torch.argmax(torch.softmax(preds, dim=0)[0])
return torch.argmax(torch.softmax(preds,
dim=1)[0]).detach().numpy()
else:
raise InputIdsNotCalculatedError(
"input_ids have not been created yet. Please call `get_attributions()`"
)
def predicted_class_index(self):
if len(self.input_ids) > 0:
# we call this before _forward() so it has to be calculated twice
preds = self.model(self.input_ids)[0]
self.pred_class = torch.argmax(torch.softmax(preds, dim=0)[0])
return torch.argmax(torch.softmax(
preds, dim=1)[0]).cpu().detach().numpy()
else:
raise InputIdsNotCalculatedError(
"input_ids have not been created yet. Please call `get_attributions()`"
)
def end_pos(self):
if len(self.input_ids) > 0:
preds = self._get_preds(
self.input_ids,
self.token_type_ids,
self.position_ids,
self.attention_mask,
)
preds = preds[1]
return int(preds.argmax())
else:
raise InputIdsNotCalculatedError("input_ids have not been created yet.`")
def predicted_answer(self):
if len(self.input_ids) > 0:
preds = self._get_preds(
self.input_ids,
self.token_type_ids,
self.position_ids,
self.attention_mask,
)
start = preds[0].argmax()
end = preds[1].argmax()
return " ".join(self.decode(self.input_ids)[start : end + 1])
else:
raise InputIdsNotCalculatedError("input_ids have not been created yet.`")
def predicted_class_indexes(self) -> List[int]:
"Returns the predicted class indexes (int) for model with last calculated `input_ids`"
if len(self.input_ids) > 0:
preds = self.model(self.input_ids)
preds = preds[0]
self.pred_class = torch.softmax(preds, dim=2)[0]
return (torch.argmax(torch.softmax(preds, dim=2),
dim=2)[0].cpu().detach().numpy())
else:
raise InputIdsNotCalculatedError(
"input_ids have not been created yet.`")
def start_pos(self):
"Returns predicted start position for answer"
if len(self.input_ids) > 0:
preds = self._get_preds(
self.input_ids,
self.token_type_ids,
self.position_ids,
self.attention_mask,
)
preds = preds[0]
return int(preds.argmax())
else:
raise InputIdsNotCalculatedError(
"input_ids have not been created yet.`")
