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--- |
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language: |
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- "lzh" |
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tags: |
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- "classical chinese" |
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- "literary chinese" |
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- "ancient chinese" |
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- "question-answering" |
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- "dependency-parsing" |
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datasets: |
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- "universal_dependencies" |
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license: "apache-2.0" |
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pipeline_tag: "question-answering" |
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inference: |
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parameters: |
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align_to_words: false |
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widget: |
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- text: "穴" |
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context: "不入虎穴不得虎子" |
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- text: "子" |
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context: "不入虎穴不得虎子" |
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- text: "不" |
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context: "[MASK]入虎穴不得虎子" |
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--- |
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# bert-ancient-chinese-base-ud-head |
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## Model Description |
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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](https://huggingface.co/Jihuai/bert-ancient-chinese) and [UD_Classical_Chinese-Kyoto](https://github.com/UniversalDependencies/UD_Classical_Chinese-Kyoto). Use [MASK] inside `context` to avoid ambiguity when specifying a multiple-used word as `question`. |
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## How to Use |
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```py |
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from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline |
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tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-ancient-chinese-base-ud-head") |
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model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/bert-ancient-chinese-base-ud-head") |
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qap=QuestionAnsweringPipeline(tokenizer=tokenizer,model=model,align_to_words=False) |
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print(qap(question="穴",context="不入虎穴不得虎子")) |
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``` |
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or (with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/)) |
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```py |
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class TransformersUD(object): |
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def __init__(self,bert): |
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import os |
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from transformers import (AutoTokenizer,AutoModelForQuestionAnswering, |
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AutoModelForTokenClassification,AutoConfig,TokenClassificationPipeline) |
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self.tokenizer=AutoTokenizer.from_pretrained(bert) |
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self.model=AutoModelForQuestionAnswering.from_pretrained(bert) |
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x=AutoModelForTokenClassification.from_pretrained |
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if os.path.isdir(bert): |
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d,t=x(os.path.join(bert,"deprel")),x(os.path.join(bert,"tagger")) |
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else: |
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from transformers.utils import cached_file |
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c=AutoConfig.from_pretrained(cached_file(bert,"deprel/config.json")) |
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d=x(cached_file(bert,"deprel/pytorch_model.bin"),config=c) |
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s=AutoConfig.from_pretrained(cached_file(bert,"tagger/config.json")) |
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t=x(cached_file(bert,"tagger/pytorch_model.bin"),config=s) |
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self.deprel=TokenClassificationPipeline(model=d,tokenizer=self.tokenizer, |
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aggregation_strategy="simple") |
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self.tagger=TokenClassificationPipeline(model=t,tokenizer=self.tokenizer) |
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def __call__(self,text): |
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import numpy,torch,ufal.chu_liu_edmonds |
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w=[(t["start"],t["end"],t["entity_group"]) for t in self.deprel(text)] |
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z,n={t["start"]:t["entity"].split("|") for t in self.tagger(text)},len(w) |
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r,m=[text[s:e] for s,e,p in w],numpy.full((n+1,n+1),numpy.nan) |
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v,c=self.tokenizer(r,add_special_tokens=False)["input_ids"],[] |
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for i,t in enumerate(v): |
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q=[self.tokenizer.cls_token_id]+t+[self.tokenizer.sep_token_id] |
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c.append([q]+v[0:i]+[[self.tokenizer.mask_token_id]]+v[i+1:]+[[q[-1]]]) |
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b=[[len(sum(x[0:j+1],[])) for j in range(len(x))] for x in c] |
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with torch.no_grad(): |
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d=self.model(input_ids=torch.tensor([sum(x,[]) for x in c]), |
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token_type_ids=torch.tensor([[0]*x[0]+[1]*(x[-1]-x[0]) for x in b])) |
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s,e=d.start_logits.tolist(),d.end_logits.tolist() |
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for i in range(n): |
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for j in range(n): |
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m[i+1,0 if i==j else j+1]=s[i][b[i][j]]+e[i][b[i][j+1]-1] |
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h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] |
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if [0 for i in h if i==0]!=[0]: |
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i=([p for s,e,p in w]+["root"]).index("root") |
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j=i+1 if i<n else numpy.nanargmax(m[:,0]) |
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m[0:j,0]=m[j+1:,0]=numpy.nan |
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h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] |
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u="# text = "+text.replace("\n"," ")+"\n" |
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for i,(s,e,p) in enumerate(w,1): |
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p="root" if h[i]==0 else "dep" if p=="root" else p |
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u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]), |
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str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n" |
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return u+"\n" |
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nlp=TransformersUD("KoichiYasuoka/bert-ancient-chinese-base-ud-head") |
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print(nlp("不入虎穴不得虎子")) |
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``` |
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