KoichiYasuoka
/

Swallow-MS-7b-upos

Token Classification

text-generation-inference

Inference Endpoints

Model card Files Files and versions Community

KoichiYasuoka commited on Mar 28

Commit

6cf32b2

•

1 Parent(s): e829ec5

bug fix

Files changed (2) hide show

maker.sh +1 -1
upos.py +1 -1

maker.sh CHANGED Viewed

@@ -83,7 +83,7 @@ class MistralForTokenClassification(MistralPreTrainedModel):
       loss_fct=nn.CrossEntropyLoss()
       loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
     if not return_dict:
-      output=(logits,)+transformer_outputs[1:]
       return ((loss,)+output) if loss is not None else output
     return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)

       loss_fct=nn.CrossEntropyLoss()
       loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
     if not return_dict:
+      output=(logits,)+transformer_outputs[2:]
       return ((loss,)+output) if loss is not None else output
     return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)

upos.py CHANGED Viewed

@@ -71,6 +71,6 @@ class MistralForTokenClassification(MistralPreTrainedModel):
       loss_fct=nn.CrossEntropyLoss()
       loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
     if not return_dict:
-      output=(logits,)+transformer_outputs[1:]
       return ((loss,)+output) if loss is not None else output
     return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)

       loss_fct=nn.CrossEntropyLoss()
       loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
     if not return_dict:
+      output=(logits,)+transformer_outputs[2:]
       return ((loss,)+output) if loss is not None else output
     return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)