from transformers import TokenClassificationPipeline class UniversalDependenciesPipeline(TokenClassificationPipeline): def _forward(self,model_input): import torch v=model_input["input_ids"][0].tolist() with torch.no_grad(): e=self.model(input_ids=torch.tensor([v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)])) return {"logits":e.logits[:,1:-2,:],**model_input} def postprocess(self,model_output,**kwargs): import numpy import ufal.chu_liu_edmonds e=model_output["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) m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan) m[1:,1:]=numpy.nanmax(e,axis=2).transpose() p=numpy.zeros(m.shape) p[1:,1:]=numpy.nanargmax(e,axis=2).transpose() for i in range(1,m.shape[0]): m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i] h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] if [0 for i in h if i==0]!=[0]: m[:,0]+=numpy.where(m[:,0]b else b-1 for a,b in enumerate(h) if i!=a] v[i-2]=(v[i-2][0],v.pop(i-1)[1]) q.pop(i) t=model_output["sentence"].replace("\n"," ") u="# text = "+t+"\n" for i,(s,e) in enumerate(v,1): u+="\t".join([str(i),t[s:e],t[s:e] if g else "_",q[i][0],"_","|".join(q[i][1:-1]),str(h[i]),q[i][-1],"_","_" if i