
# 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](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-aozora-char) 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/roberta-large-japanese-aozora-ud-head")
model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/roberta-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/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"))
```


{"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "question-answering", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "question-answering", "inference": {"parameters": {"align_to_words": false}}, "widget": [{"text": "\u56fd\u8a9e", "context": "\u5168\u5b66\u5e74\u306b\u308f\u305f\u3063\u3066\u5c0f\u5b66\u6821\u306e\u56fd\u8a9e\u306e\u6559\u79d1\u66f8\u306b\u633f\u3057\u7d75\u304c\u7528\u3044\u3089\u308c\u3066\u3044\u308b"}, {"text": "\u6559\u79d1\u66f8", "context": "\u5168\u5b66\u5e74\u306b\u308f\u305f\u3063\u3066\u5c0f\u5b66\u6821\u306e\u56fd\u8a9e\u306e\u6559\u79d1\u66f8\u306b\u633f\u3057\u7d75\u304c\u7528\u3044\u3089\u308c\u3066\u3044\u308b"}, {"text": "\u306e", "context": "\u5168\u5b66\u5e74\u306b\u308f\u305f\u3063\u3066\u5c0f\u5b66\u6821\u306e\u56fd\u8a9e[MASK]\u6559\u79d1\u66f8\u306b\u633f\u3057\u7d75\u304c\u7528\u3044\u3089\u308c\u3066\u3044\u308b"}]}
transformers pytorch roberta question-answering japanese dependency-parsing ja dataset:universal_dependencies license:cc-by-sa-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us 


# 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



or (with URL-liu-edmonds)




# 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



or (with URL-liu-edmonds)
TAGS
#transformers #pytorch #roberta #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us 
 # 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



or (with URL-liu-edmonds)
passage: TAGS
#transformers #pytorch #roberta #question-answering #japanese #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #endpoints_compatible #region-us 
# 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



or (with URL-liu-edmonds)