readme: ---
  language:
  - "ja"
  tags:
  - "japanese"
  - "pos"
  - "dependency-parsing"
  datasets:
  - "universal_dependencies"
  license: "cc-by-sa-4.0"
  pipeline_tag: "token-classification"
  widget:
  - text: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
  ---
  
  # roberta-base-japanese-aozora-ud-goeswith
  
  ## Model Description
  
  This is a RoBERTa model pretrained on 青空文庫 texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-base-japanese-aozora](https://huggingface.co/KoichiYasuoka/roberta-base-japanese-aozora) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW).
  
  ## 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-japanese-aozora-ud-goeswith")
  print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
  ```
  
  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-japanese-aozora-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
  print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
  ```
  
  