bert-ancient-chinese-base-ud-head

Model Description

This is a BERT model pre-trained on Classical Chinese texts for dependency-parsing (head-detection on Universal Dependencies) as question-answering, derived from bert-ancient-chinese and UD_Classical_Chinese-Kyoto. Use [MASK] inside context to avoid ambiguity when specifying a multiple-used word as question.

How to Use

from transformers import AutoTokenizer,AutoModelForQuestionAnswering
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-ancient-chinese-base-ud-head")
model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/bert-ancient-chinese-base-ud-head")
question="穴"
context="不入虎穴不得虎子"
inputs=tokenizer(question,context,return_tensors="pt",return_offsets_mapping=True)
offsets=inputs.pop("offset_mapping").tolist()[0]
outputs=model(**inputs)
start,end=outputs.start_logits.argmax(),outputs.end_logits.argmax()
print(context[offsets[start][0]:offsets[end][-1]])

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.utils import cached_file
      c=cached_file(bert,"deprel/config.json")
      d=x(os.path.dirname(cached_file(bert,"deprel/pytorch_model.bin")))
      s=cached_file(bert,"tagger/config.json")
      t=x(os.path.dirname(cached_file(bert,"tagger/pytorch_model.bin")))
    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-ancient-chinese-base-ud-head")
print(nlp("不入虎穴不得虎子"))