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roberta-large-japanese-aozora-ud-head

Model Description

This is a RoBERTa model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from roberta-large-japanese-aozora-char and UD_Japanese-GSDLUW. Use [MASK] inside context to avoid ambiguity when specifying a multiple-used word as question.

How to Use

from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-large-japanese-aozora-ud-head")
model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/roberta-large-japanese-aozora-ud-head")
qap=QuestionAnsweringPipeline(tokenizer=tokenizer,model=model)
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)

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.file_utils import hf_bucket_url
      c=AutoConfig.from_pretrained(hf_bucket_url(bert,"deprel/config.json"))
      d=x(hf_bucket_url(bert,"deprel/pytorch_model.bin"),config=c)
      s=AutoConfig.from_pretrained(hf_bucket_url(bert,"tagger/config.json"))
      t=x(hf_bucket_url(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/roberta-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"))
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Dataset used to train KoichiYasuoka/roberta-large-japanese-aozora-ud-head