Patch documentation

transformers/modeling_bert.py

@@ -891,7 +891,7 @@ class BertForMaskedLM(BertPreTrainedModel):
         # 2. If `lm_labels` is provided we are in a causal scenario where we
         #    try to predict the next token for each input in the decoder.
         if masked_lm_labels is not None:
-            loss_fct = CrossEntropyLoss()  # -1 index = padding token
+            loss_fct = CrossEntropyLoss()  # -100 index = padding token
             masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
             outputs = (masked_lm_loss,) + outputs