README.md

metadata

language:
  - ja
tags:
  - japanese
  - wikipedia
  - 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

deberta-base-japanese-wikipedia-ud-goeswith

Model Description

This is a DeBERTa(V2) model pretrained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing (using goeswith for subwords), derived from deberta-base-japanese-wikipedia and UD_Japanese-GSDLUW.

How to Use

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/deberta-base-japanese-wikipedia-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. Or without ufal.chu-liu-edmonds:

from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/deberta-base-japanese-wikipedia-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"))