readme: ---
  language:
  - "ja"
  tags:
  - "japanese"
  - "question-answering"
  - "dependency-parsing"
  datasets:
  - "universal_dependencies"
  license: "cc-by-sa-4.0"
  pipeline_tag: "question-answering"
  widget:
  - text: "国語"
    context: "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"
  - text: "教科書"
    context: "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"
  - text: "の"
    context: "全学年にわたって小学校の国語[MASK]教科書に挿し絵が用いられている"
  ---
  
  # deberta-large-japanese-aozora-ud-head
  
  ## Model Description
  
  This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-aozora) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW). Use [MASK] inside `context` to avoid ambiguity when specifying a multiple-used word as `question`.
  
  ## How to Use
  
  ```py
  from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline
  tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-aozora-ud-head")
  model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-large-japanese-aozora-ud-head")
  qap=QuestionAnsweringPipeline(tokenizer=tokenizer,model=model,align_to_words=False)
  print(qap(question="国語",context="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"))
  ```
  
  or (with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/))
  
  ```py
  class TransformersUD(object):
    def __init__(self,bert):
      import os
      from transformers import (AutoTokenizer,AutoModelForQuestionAnswering,
        AutoModelForTokenClassification,AutoConfig,TokenClassificationPipeline)
      self.tokenizer=AutoTokenizer.from_pretrained(bert)
      self.model=AutoModelForQuestionAnswering.from_pretrained(bert)
      x=AutoModelForTokenClassification.from_pretrained
      if os.path.isdir(bert):
        d,t=x(os.path.join(bert,"deprel")),x(os.path.join(bert,"tagger"))
      else:
        from transformers.utils import cached_file
        c=AutoConfig.from_pretrained(cached_file(bert,"deprel/config.json"))
        d=x(cached_file(bert,"deprel/pytorch_model.bin"),config=c)
        s=AutoConfig.from_pretrained(cached_file(bert,"tagger/config.json"))
        t=x(cached_file(bert,"tagger/pytorch_model.bin"),config=s)
      self.deprel=TokenClassificationPipeline(model=d,tokenizer=self.tokenizer,
        aggregation_strategy="simple")
      self.tagger=TokenClassificationPipeline(model=t,tokenizer=self.tokenizer)
    def __call__(self,text):
      import numpy,torch,ufal.chu_liu_edmonds
      w=[(t["start"],t["end"],t["entity_group"]) for t in self.deprel(text)]
      z,n={t["start"]:t["entity"].split("|") for t in self.tagger(text)},len(w)
      r,m=[text[s:e] for s,e,p in w],numpy.full((n+1,n+1),numpy.nan)
      v,c=self.tokenizer(r,add_special_tokens=False)["input_ids"],[]
      for i,t in enumerate(v):
        q=[self.tokenizer.cls_token_id]+t+[self.tokenizer.sep_token_id]
        c.append([q]+v[0:i]+[[self.tokenizer.mask_token_id]]+v[i+1:]+[[q[-1]]])
      b=[[len(sum(x[0:j+1],[])) for j in range(len(x))] for x in c]
      with torch.no_grad():
        d=self.model(input_ids=torch.tensor([sum(x,[]) for x in c]),
          token_type_ids=torch.tensor([[0]*x[0]+[1]*(x[-1]-x[0]) for x in b]))
      s,e=d.start_logits.tolist(),d.end_logits.tolist()
      for i in range(n):
        for j in range(n):
          m[i+1,0 if i==j else j+1]=s[i][b[i][j]]+e[i][b[i][j+1]-1]
      h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
      if [0 for i in h if i==0]!=[0]:
        i=([p for s,e,p in w]+["root"]).index("root")
        j=i+1 if i<n else numpy.nanargmax(m[:,0])
        m[0:j,0]=m[j+1:,0]=numpy.nan
        h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
      u="# text = "+text.replace("\n"," ")+"\n"
      for i,(s,e,p) in enumerate(w,1):
        p="root" if h[i]==0 else "dep" if p=="root" else p
        u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]),
          str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n"
      return u+"\n"
  
  nlp=TransformersUD("KoichiYasuoka/deberta-large-japanese-aozora-ud-head")
  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"))
  ```
  
  ## Reference
  
  安岡孝一: [青空文庫DeBERTaモデルによる国語研長単位係り受け解析](http://hdl.handle.net/2433/275409), 東洋学へのコンピュータ利用, 第35回研究セミナー (2022年7月), pp.29-43.
  
  