without ufal.chu-liu.edmonds

README.md

@@ -24,52 +24,6 @@ This is a RoBERTa model pre-trained on CC-100 Latin texts for POS-tagging and de
 ## How to Use
 
 ```py
-class UDgoeswith(object):
-  def __init__(self,bert):
-    from transformers import AutoTokenizer,AutoModelForTokenClassification
-    self.tokenizer=AutoTokenizer.from_pretrained(bert)
-    self.model=AutoModelForTokenClassification.from_pretrained(bert)
-  def __call__(self,text):
-    import numpy,torch,ufal.chu_liu_edmonds
-    w=self.tokenizer(text,return_offsets_mapping=True)
-    v=w["input_ids"]
-    x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)]
-    with torch.no_grad():
-      e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:]
-    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=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[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan)
-      m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)]
-      h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
-    u="# text = "+text+"\n"
-    v=[(s,e) for s,e in w["offset_mapping"] if s<e]
-    for i,(s,e) in enumerate(v,1):
-      q=self.model.config.id2label[p[i,h[i]]].split("|")
-      u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n"
-    return u+"\n"
-
-nlp=UDgoeswith("KoichiYasuoka/roberta-base-latin-ud-goeswith")
-print(nlp("deus videt te non sentientem"))
-```
-
-with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/).
-Or without ufal.chu-liu-edmonds:
-
-```
 from transformers import pipeline
 nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-base-latin-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
 print(nlp("deus videt te non sentientem"))