yezdata
/

EmCoder

Text Classification

emotion-recognition

bayesian-deep-learning

uncertainty-quantification

multi-label-classification

Eval Results (legacy)

Model card Files Files and versions

yezdata commited on May 17

Commit

ff42f17

·

verified ·

1 Parent(s): 14f6a86

update V1.5 README

Files changed (1) hide show

README.md +6 -1

README.md CHANGED Viewed

@@ -90,7 +90,12 @@ inputs = tokenizer("I am so happy you are here!", return_tensors="pt")
 model.eval()
 with torch.no_grad():
     # Automatically keeps Dropout active, even when in model.eval
-    mc_logits = model.mc_forward(inputs['input_ids'], inputs['attention_mask'], n_samples=N_SAMPLES, max_batch_size=MAX_BATCH_SIZE)
 # Bayesian Post-processing
 all_probs = torch.sigmoid(mc_logits) # (n_samples, B, 28)

 model.eval()
 with torch.no_grad():
     # Automatically keeps Dropout active, even when in model.eval
+    mc_logits = model.mc_forward(
+        inputs['input_ids'],
+        inputs['attention_mask'],
+        n_samples=N_SAMPLES,
+        max_batch_size=MAX_BATCH_SIZE
+    )
 # Bayesian Post-processing
 all_probs = torch.sigmoid(mc_logits) # (n_samples, B, 28)