readme: ---
  language:
  - "ja"
  tags:
  - "japanese"
  - "wikipedia"
  - "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"
  ---
  
  # bert-base-japanese-wikipedia-ud-head
  
  ## Model Description
  
  This is a BERT model pretrained on Japanese Wikipedia texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [bert-base-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-base-japanese-char-extended) 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/bert-base-japanese-wikipedia-ud-head")
  model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/bert-base-japanese-wikipedia-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/bert-base-japanese-wikipedia-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"))
  ```
  
  