def _run_crossencoder(crossencoder,
dataloader,
logger,
context_len,
device="cuda"):
crossencoder.model.eval()
accuracy = 0.0
crossencoder.to(device)
res = evaluate(crossencoder,
dataloader,
device,
logger,
context_len,
zeshel=False,
silent=False)
accuracy = res["normalized_accuracy"]
logits = res["logits"]
if accuracy > -1:
predictions = np.argsort(logits, axis=1)
else:
predictions = []
return accuracy, predictions, logits
def _run_crossencoder(crossencoder,
dataloader,
logger,
context_len,
device="cpu"):
crossencoder.model.eval()
accuracy = 0.0
crossencoder.to(device)
res = evaluate(crossencoder,
dataloader,
device,
logger,
context_len,
silent=True)
accuracy = res["normalized_accuracy"]
logits = res["logits"]
predictions = np.argsort(logits, axis=1)
return accuracy, predictions, logits
