readme: ---
  language:
  - "zh"
  tags:
  - "chinese"
  - "token-classification"
  - "pos"
  - "dependency-parsing"
  datasets:
  - "universal_dependencies"
  license: "apache-2.0"
  pipeline_tag: "token-classification"
  ---
  
  # deberta-large-chinese-erlangshen-ud-goeswith
  
  ## Model Description
  
  This is a DeBERTa(V2) model pre-trained on Chinese texts (both simplified and traditional) for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [deberta-large-chinese-erlangshen-upos](https://huggingface.co/KoichiYasuoka/deberta-large-chinese-erlangshen-upos).
  
  ## 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/deberta-large-chinese-erlangshen-ud-goeswith")
  print(nlp("我把这本书看完了"))
  ```
  
  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/deberta-large-chinese-erlangshen-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
  print(nlp("我把这本书看完了"))
  ```
  
  