support transformers>=4.28

ud.py

@@ -16,6 +16,8 @@ class UniversalDependenciesPipeline(TokenClassificationPipeline):
     return {"logits":e.logits[:,1:-2,:],**model_inputs}
   def postprocess(self,model_outputs,**kwargs):
     import numpy
+    if "logits" not in model_outputs:
+      return "".join(self.postprocess(x,**kwargs) for x in model_outputs)
     e=model_outputs["logits"].numpy()
     r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())]
     e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan)
@@ -73,11 +75,8 @@ class MecabPreTokenizer(MecabTokenizer):
     e=0
     for c in self.tokenize(t):
       s=t.find(c,e)
-      if s<0:
-        z.append((0,0))
-      else:
-        e=s+len(c)
-        z.append((s,e))
+      e=e if s<0 else s+len(c)
+      z.append((0,0) if s<0 else (s,e))
     return [normalized_string[s:e] for s,e in z if e>0]
   def pre_tokenize(self,pretok):
     pretok.split(self.mecab_split)