File size: 5,894 Bytes
f59d332 6da92f1 f59d332 f3f71bf f59d332 f3f71bf f59d332 f3f71bf f59d332 fcb09d2 f59d332 1b47855 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
import numpy
from transformers import TokenClassificationPipeline
class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
def __init__(self,**kwargs):
super().__init__(**kwargs)
x=self.model.config.label2id
y=[k for k in x if k.startswith("B-") or not (k.startswith("I-") or k.endswith("|root") or k.find("|l-")>0 or k.find("|r-")>0)]
self.transition=numpy.full((len(x),len(x)),numpy.nan)
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
def check_model_type(self,supported_models):
pass
def postprocess(self,model_outputs,**kwargs):
if "logits" not in model_outputs:
return self.postprocess(model_outputs[0],**kwargs)
m=model_outputs["logits"][0].numpy()
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])]
for i in range(1,m.shape[0]):
k.append(numpy.nanargmax(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))):
p=t.pop("entity")
if p.startswith("I-"):
w[i-1]["score"]=min(w[i-1]["score"],t["score"])
w[i-1]["end"]=w.pop(i)["end"]
elif p.startswith("B-"):
t["entity_group"]=p[2:]
else:
t["entity_group"]=p
for t in w:
t["text"]=model_outputs["sentence"][t["start"]:t["end"]]
return w
class UniversalDependenciesCausalPipeline(BellmanFordTokenClassificationPipeline):
def __init__(self,**kwargs):
kwargs["aggregation_strategy"]="simple"
super().__init__(**kwargs)
x=self.model.config.label2id
self.root=numpy.full((len(x)),numpy.nan)
self.left_arc=numpy.full((len(x)),numpy.nan)
self.right_arc=numpy.full((len(x)),numpy.nan)
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 postprocess(self,model_outputs,**kwargs):
import torch
if "logits" not in model_outputs:
return self.postprocess(model_outputs[0],**kwargs)
m=model_outputs["logits"][0].numpy()
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])]
for i in range(1,m.shape[0]):
k.append(numpy.nanargmax(m[i]+self.transition[k[-1]]))
w=[{"entity":self.model.config.id2label[j],"start":s,"end":e} for i,((s,e),j) in enumerate(zip(model_outputs["offset_mapping"][0].tolist(),k)) if s<e]
for i,t in reversed(list(enumerate(w))):
p=t.pop("entity")
if p.startswith("I-"):
w[i-1]["end"]=max(w.pop(i)["end"],w[i-1]["end"])
elif i>0 and w[i-1]["end"]>w[i]["start"]:
w[i-1]["end"]=max(w.pop(i)["end"],w[i-1]["end"])
elif p.startswith("B-"):
t["entity_group"]=p[2:]
else:
t["entity_group"]=p
d=[model_outputs["sentence"][t["start"]:t["end"]] for t in w]
v=self.tokenizer(d,add_special_tokens=False)
e=self.model.get_input_embeddings().weight
m=[]
for x in v["input_ids"]:
if x==[]:
x=[self.tokenizer.unk_token_id]
m.append(e[x,:].sum(axis=0))
m.append(e[self.tokenizer.sep_token_id,:])
m.append(e[self.tokenizer.pad_token_id,:])
m=torch.stack(m).to(self.device)
k=list(range(len(d)+1))
e=[]
with torch.no_grad():
for i in range(len(d)):
e.append(self.model(inputs_embeds=torch.unsqueeze(m[k+list(range(i,len(d)))+[-1]*i,:],0)).logits[0,-len(d):,:])
e=torch.stack(e).cpu().numpy()
for i in range(len(d)):
for j in range(i):
e[-j-1,-i-1],e[-i-1,-j-1]=e[-i-1,i-j]+self.left_arc,e[-i-1,i-j]+self.right_arc
e[-i-1,-i-1]=e[-i-1,0]+self.root
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,h=z[numpy.nanargmax(m[z,z])],numpy.nanmin(m)-numpy.nanmax(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)
q=[self.model.config.id2label[p[j,i]].split("|") for i,j in enumerate(h)]
t=model_outputs["sentence"].replace("\n"," ")
u="# text = "+t+"\n"
for i,j in enumerate(d):
u+="\t".join([str(i+1),j,j,q[i][0],"_","_" if len(q[i])<3 else "|".join(q[i][1:-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(d) and w[i]["end"]<w[i+1]["start"] else "SpaceAfter=No"])+"\n"
return u+"\n"
def chu_liu_edmonds(self,matrix):
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,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.nanmax(matrix,axis=0)
m=numpy.block([[z[x,:][:,x],numpy.nanmax(z[x,:][:,y],axis=1).reshape(len(x),1)],[numpy.nanmax(z[y,:][:,x],axis=0),numpy.nanmax(z[y,y])]])
k=[j if i==len(x) else x[j] if j<len(x) else y[numpy.nanargmax(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.nanargmax(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
|