| 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 | |
| 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) | |
| g=self.model.config.label2id["X|_|goeswith"] | |
| r=numpy.tri(e.shape[0]) | |
| for i in range(e.shape[0]): | |
| for j in range(i+2,e.shape[1]): | |
| r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1 | |
| e[:,:,g]+=numpy.where(r==0,0,numpy.nan) | |
| m,p=numpy.nanmax(e,axis=2),numpy.nanargmax(e,axis=2) | |
| h=self.chu_liu_edmonds(m) | |
| z=[i for i,j in enumerate(h) if i==j] | |
| if len(z)>1: | |
| k=z[numpy.nanargmax(m[z,z])] | |
| m[:,z]+=[[0 if j in z and (i!=j or i==k) else numpy.nan for i in z] for j in range(m.shape[0])] | |
| h=self.chu_liu_edmonds(m) | |
| v=[(s,e) for s,e in model_output["offset_mapping"][0].tolist() if s<e] | |
| q=[self.model.config.id2label[p[j,i]].split("|") for i,j in enumerate(h)] | |
| if "aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]!="none": | |
| for i,j in reversed(list(enumerate(q[1:],1))): | |
| if j[-1]=="goeswith" and set([t[-1] for t in q[h[i]+1:i+1]])=={"goeswith"}: | |
| h=[b if i>b else b-1 for a,b in enumerate(h) if i!=a] | |
| v[i-1]=(v[i-1][0],v.pop(i)[1]) | |
| q.pop(i) | |
| t=model_output["sentence"].replace("\n"," ") | |
| u="# text = "+t+"\n" | |
| for i,(s,e) in enumerate(v): | |
| u+="\t".join([str(i+1),t[s:e],"_",q[i][0],"_","|".join(q[i][1:-1]),str(0 if h[i]==i else h[i]+1),q[i][-1],"_","_" if i+1<len(v) and e<v[i+1][0] else "SpaceAfter=No"])+"\n" | |
| return u+"\n" | |
| def chu_liu_edmonds(self,matrix): | |
| import numpy | |
| h=numpy.nanargmax(matrix,axis=0) | |
| x=[-1 if i==j else j for i,j in enumerate(h)] | |
| for b in [lambda x,i,j:-1 if i not in x else x[i],lambda x,i,j:-1 if j<0 else x[j]]: | |
| y=[] | |
| while x!=y: | |
| y=list(x) | |
| for i,j in enumerate(x): | |
| x[i]=b(x,i,j) | |
| if max(x)<0: | |
| return h | |
| y=[i for i,j in enumerate(x) if j==max(x)] | |
| m=numpy.full((matrix.shape[0]+1,matrix.shape[1]+1),numpy.nan) | |
| m[0:-1,0:-1]=z=matrix-numpy.nanmax(matrix,axis=0) | |
| m[0:-1,-1],m[-1,0:-1],m[-1,-1]=numpy.nanmax(z[:,y],axis=1),numpy.nanmax(z[y,:],axis=0),numpy.nanmax(z[y,y]) | |
| m[y,:]=m[:,y]=numpy.nan | |
| m[y,y]=0 | |
| k=self.chu_liu_edmonds(m) | |
| j=y[numpy.nanargmax(z[k[-1],y] if k[-1]<z.shape[0] else z[y,y])] | |
| i=k[-1] if k[-1]<z.shape[0] else j | |
| z[0:i,j]=z[i+1:,j]=numpy.nan | |
| return self.chu_liu_edmonds(z) | |