| import numpy |
| from transformers import TokenClassificationPipeline |
|
|
| class UniversalDependenciesPipeline(TokenClassificationPipeline): |
| def __init__(self,**kwargs): |
| super().__init__(**kwargs) |
| x=self.model.config.label2id |
| self.root=numpy.full((len(x)),-numpy.inf) |
| self.left_arc=numpy.full((len(x)),-numpy.inf) |
| self.right_arc=numpy.full((len(x)),-numpy.inf) |
| for k,v in x.items(): |
| if k.endswith("|root"): |
| self.root[v]=0 |
| elif k.find("|l-")>0: |
| self.left_arc[v]=0 |
| elif k.find("|r-")>0: |
| self.right_arc[v]=0 |
| def check_model_type(self,supported_models): |
| pass |
| def postprocess(self,model_outputs,**kwargs): |
| import torch |
| if "logits" not in model_outputs: |
| return "".join(self.postprocess(x,**kwargs) for x in model_outputs) |
| m=model_outputs["logits"][0].cpu().numpy() |
| k=numpy.argmax(m,axis=1).tolist() |
| x=[self.model.config.id2label[i].split("|")[1]=="o" for i in k[1:-1]] |
| v=model_outputs["input_ids"][0].tolist() |
| off=model_outputs["offset_mapping"][0].tolist() |
| for i,(s,e) in reversed(list(enumerate(off))): |
| if s<e: |
| d=model_outputs["sentence"][s:e] |
| j=len(d)-len(d.lstrip()) |
| if j>0: |
| d=d.lstrip() |
| off[i][0]+=j |
| j=len(d)-len(d.rstrip()) |
| if j>0: |
| d=d.rstrip() |
| off[i][1]-=j |
| if d.strip()=="": |
| off.pop(i) |
| v.pop(i) |
| x.pop(i-1) |
| if len(x)<127: |
| x=[True]*len(x) |
| else: |
| w=sum([len(x)-i+1 if b else 0 for i,b in enumerate(x)])+1 |
| for i in numpy.argsort(numpy.max(m,axis=1)[1:-1]): |
| if x[i]==False and w+len(x)-i<8192: |
| x[i]=True |
| w+=len(x)-i+1 |
| w=[self.tokenizer.cls_token_id] |
| for i,j in enumerate(x): |
| if j: |
| w+=v[i+1:] |
| with torch.no_grad(): |
| e=self.model(input_ids=torch.tensor([w]).to(self.device)) |
| m=e.logits[0].cpu().numpy() |
| w=len(v)-2 |
| e=numpy.full((w,w,m.shape[-1]),m.min()) |
| k=1 |
| for i in range(w): |
| if x[i]: |
| e[i,i]=m[k]+self.root |
| k+=1 |
| for j in range(1,w-i): |
| e[i+j,i]=m[k]+self.left_arc |
| e[i,i+j]=m[k]+self.right_arc |
| k+=1 |
| k+=1 |
| g=self.model.config.label2id["X|x|r-goeswith"] |
| m,r=numpy.max(e,axis=2),numpy.tri(e.shape[0]) |
| for i in range(e.shape[0]): |
| for j in range(i+2,e.shape[1]): |
| r[i,j]=1 |
| if numpy.argmax(e[i,j-1])==g and numpy.argmax(m[:,j-1])==i: |
| r[i,j]=r[i,j-1] |
| e[:,:,g]+=numpy.where(r==0,0,-numpy.inf) |
| m,p=numpy.max(e,axis=2),numpy.argmax(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,h=z[numpy.argmax(m[z,z])],numpy.min(m)-numpy.max(m) |
| m[:,z]+=[[0 if j in z and (i!=j or i==k) else h for i in z] for j in range(m.shape[0])] |
| h=self.chu_liu_edmonds(m) |
| v=[(s,e) for s,e in off 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]=="r-goeswith" and set([t[-1] for t in q[h[i]+1:i+1]])=={"r-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) |
| elif v[i-1][1]>v[i][0]: |
| 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_outputs["sentence"].replace("\n"," ") |
| u="# text = "+t+"\n" |
| for i,(s,e) in enumerate(v): |
| u+="\t".join([str(i+1),t[s:e],t[s:e],q[i][0],"_","_" if len(q[i])<4 else "|".join(q[i][2:-1]),str(0 if h[i]==i else h[i]+1),"root" if q[i][-1]=="root" else q[i][-1][2:],"_","_" if i+1<len(v) and e<v[i+1][0] else "SpaceAfter=No"])+"\n" |
| return u+"\n" |
| def chu_liu_edmonds(self,matrix): |
| h=numpy.argmax(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,x=[i for i,j in enumerate(x) if j==max(x)],[i for i,j in enumerate(x) if j<max(x)] |
| z=matrix-numpy.max(matrix,axis=0) |
| m=numpy.block([[z[x,:][:,x],numpy.max(z[x,:][:,y],axis=1).reshape(len(x),1)],[numpy.max(z[y,:][:,x],axis=0),numpy.max(z[y,y])]]) |
| k=[j if i==len(x) else x[j] if j<len(x) else y[numpy.argmax(z[y,x[i]])] for i,j in enumerate(self.chu_liu_edmonds(m))] |
| h=[j if i in y else k[x.index(i)] for i,j in enumerate(h)] |
| i=y[numpy.argmax(z[x[k[-1]],y] if k[-1]<len(x) else z[y,y])] |
| h[i]=x[k[-1]] if k[-1]<len(x) else i |
| return h |
|
|
