algorithm improved

upos.py

@@ -6,7 +6,7 @@ class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
     super().__init__(**kwargs)
     x=self.model.config.label2id
     y=[k for k in x if not k.startswith("I-")]
-    self.transition=numpy.full((len(x),len(x)),numpy.nan)
+    self.transition=numpy.full((len(x),len(x)),-numpy.inf)
     for k,v in x.items():
       for j in ["I-"+k[2:]] if k.startswith("B-") else [k]+y if k.startswith("I-") else y:
         self.transition[v,x[j]]=0
@@ -20,10 +20,10 @@ class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
     e=numpy.exp(m-numpy.max(m,axis=-1,keepdims=True))
     z=e/e.sum(axis=-1,keepdims=True)
     for i in range(m.shape[0]-1,0,-1):
-      m[i-1]+=numpy.nanmax(m[i]+self.transition,axis=1)
-    k=[numpy.nanargmax(m[0]+self.transition[0])]
+      m[i-1]+=numpy.max(m[i]+self.transition,axis=1)
+    k=[self.model.config.label2id["SYM"]]
     for i in range(1,m.shape[0]):
-      k.append(numpy.nanargmax(m[i]+self.transition[k[-1]]))
+      k.append(numpy.argmax(m[i]+self.transition[k[-1]]))
     w=[{"entity":self.model.config.id2label[j],"start":s,"end":e,"score":z[i,j]} for i,((s,e),j) in enumerate(zip(model_outputs["offset_mapping"][0].tolist(),k)) if s<e]
     if "aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]!="none":
       for i,t in reversed(list(enumerate(w))):