metadata

language:
  - lzh
tags:
  - classical chinese
  - literary chinese
  - ancient chinese
  - token-classification
  - pos
  - dependency-parsing
datasets:
  - universal_dependencies
license: apache-2.0
pipeline_tag: token-classification
widget:
  - text: 孟子見梁惠王

roberta-classical-chinese-base-ud-goeswith

Model Description

This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing (using goeswith for subwords), derived from roberta-classical-chinese-base-char and UD_Classical_Chinese-Kyoto.

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"]
    n=len(v)-1
    with torch.no_grad():
      d=self.model(input_ids=torch.tensor([v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[v[i]] for i in range(1,n)]))
    e=d.logits.numpy()[:,1:n,:]
    e[:,:,0]=numpy.nan
    m=numpy.full((n,n),numpy.nan)
    m[1:,1:]=numpy.nanmax(e,axis=2).transpose()
    p=numpy.zeros((n,n))
    p[1:,1:]=numpy.nanargmax(e,axis=2).transpose()
    for i in range(1,n):
      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]
    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/roberta-classical-chinese-base-ud-goeswith")
print(nlp("孟子見梁惠王"))

ufal.chu-liu-edmonds is required.