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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]<numpy.nanmax(m[:,0]),numpy.nan,0)
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
v=[(s,e) for s,e in model_output["offset_mapping"][0].tolist() if s<e]
q=[self.model.config.id2label[p[i,j]].split("|") for i,j in enumerate(h)]
if "aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]=="simple":
for i,j in reversed(list(enumerate(q[2:],2))):
if j[-1]=="goeswith":
h=[b if i>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"]
u="# text = "+t+"\n"
for i,(s,e) in enumerate(v,1):
u+="\t".join([str(i),t[s:e],"_",q[i][0],"_","|".join(q[i][1:-1]),str(h[i]),q[i][-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n"
return u+"\n"
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