readme: ---
  language:
  - "vi"
  tags:
  - "vietnamese"
  - "token-classification"
  - "pos"
  - "dependency-parsing"
  datasets:
  - "universal_dependencies"
  license: "cc-by-sa-4.0"
  pipeline_tag: "token-classification"
  widget:
  - text: "Hai cái đầu thì tốt hơn một"
  ---
  
  # phobert-large-vietnamese-ud-goeswith
  
  ## Model Description
  
  This is a PhoBERT model pre-trained on Vietnamese texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [phobert-large](https://huggingface.co/vinai/phobert-large).
  
  ## How to Use
  
  ```py
  class UDgoeswithViNLP(object):
    def __init__(self,bert):
      from transformers import AutoTokenizer,AutoModelForTokenClassification
      from ViNLP import word_tokenize
      self.tokenizer=AutoTokenizer.from_pretrained(bert)
      self.model=AutoModelForTokenClassification.from_pretrained(bert)
      self.vinlp=word_tokenize
    def __call__(self,text):
      import numpy,torch,ufal.chu_liu_edmonds
      t=self.vinlp(text)
      w=self.tokenizer(t,add_special_tokens=False)["input_ids"]
      z=[]
      for i,j in enumerate(t):
        if j.find("_")>0 and [k for k in w[i] if k==self.tokenizer.unk_token_id]!=[]:
            w[i]=self.tokenizer(j.replace("_"," "))["input_ids"][1:-1]
        if [k for k in w[i] if k==self.tokenizer.unk_token_id]!=[]:
          w[i]=[self.tokenizer.unk_token_id]
          z.append(j)
      v=[self.tokenizer.cls_token_id]+sum(w,[])+[self.tokenizer.sep_token_id]
      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"
      q=[self.model.config.id2label[p[i,j]].split("|") for i,j in enumerate(h)]
      t=[i.replace("_"," ") for i in t]
      if len(t)!=len(v)-2:
        t=[z.pop(0) if i==self.tokenizer.unk_token else i.replace("_"," ") for i in self.tokenizer.convert_ids_to_tokens(v[1:-1])]
        for i,j in reversed(list(enumerate(q[2:],2))):
          if j[-1]=="goeswith" and set([k[-1] for k in q[h[i]+1:i+1]])=={"goeswith"}:
            h=[b if i>b else b-1 for a,b in enumerate(h) if i!=a]
            t[i-2]=(t[i-2][0:-2] if t[i-2].endswith("@@") else t[i-2]+" ")+t.pop(i-1)
            q.pop(i)
        t=[i[0:-2].strip() if i.endswith("@@") else i.strip() for i in t]
      for i,j in enumerate(t,1):
        u+="\t".join([str(i),j,"_",q[i][0],"_","|".join(q[i][1:-1]),str(h[i]),q[i][-1],"_","_"])+"\n"
      return u+"\n"
  
  nlp=UDgoeswithViNLP("KoichiYasuoka/phobert-large-vietnamese-ud-goeswith")
  print(nlp("Hai cái đầu thì tốt hơn một."))
  ```
  
  with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/) and [ViNLP](https://pypi.org/project/ViNLP/).
  Or without them:
  
  ```
  from transformers import pipeline
  nlp=pipeline("universal-dependencies","KoichiYasuoka/phobert-large-vietnamese-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
  print(nlp("Hai cái đầu thì tốt hơn một."))
  ```
  
  