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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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- "token-classification" |
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- "pos" |
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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: "token-classification" |
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widget: |
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- text: "孟子見梁惠王" |
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--- |
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# roberta-classical-chinese-base-ud-goeswith |
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## Model Description |
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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](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-base-char) and [UD_Classical_Chinese-Kyoto](https://github.com/UniversalDependencies/UD_Classical_Chinese-Kyoto). |
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## How to Use |
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```py |
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class UDgoeswith(object): |
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def __init__(self,bert): |
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from transformers import AutoTokenizer,AutoModelForTokenClassification |
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self.tokenizer=AutoTokenizer.from_pretrained(bert) |
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self.model=AutoModelForTokenClassification.from_pretrained(bert) |
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def __call__(self,text): |
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import numpy,torch,ufal.chu_liu_edmonds |
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w=self.tokenizer(text,return_offsets_mapping=True) |
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v=w["input_ids"] |
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x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)] |
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with torch.no_grad(): |
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e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:] |
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r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())] |
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e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan) |
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g=self.model.config.label2id["X|_|goeswith"] |
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r=numpy.tri(e.shape[0]) |
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for i in range(e.shape[0]): |
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for j in range(i+2,e.shape[1]): |
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r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1 |
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e[:,:,g]+=numpy.where(r==0,0,numpy.nan) |
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m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan) |
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m[1:,1:]=numpy.nanmax(e,axis=2).transpose() |
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p=numpy.zeros(m.shape) |
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p[1:,1:]=numpy.nanargmax(e,axis=2).transpose() |
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for i in range(1,m.shape[0]): |
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m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i] |
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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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m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan) |
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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)] |
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h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] |
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u="# text = "+text+"\n" |
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v=[(s,e) for s,e in w["offset_mapping"] if s<e] |
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for i,(s,e) in enumerate(v,1): |
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q=self.model.config.id2label[p[i,h[i]]].split("|") |
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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" |
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return u+"\n" |
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nlp=UDgoeswith("KoichiYasuoka/roberta-classical-chinese-base-ud-goeswith") |
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print(nlp("孟子見梁惠王")) |
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``` |
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with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/). |
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Or without ufal.chu-liu-edmonds: |
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``` |
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from transformers import pipeline |
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nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-classical-chinese-base-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple") |
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print(nlp("孟子見梁惠王")) |
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``` |
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## Reference |
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Koichi Yasuoka: [Sequence-Labeling RoBERTa Model for Dependency-Parsing in Classical Chinese and Its Application to Vietnamese and Thai](https://doi.org/10.1109/ICBIR57571.2023.10147628), ICBIR 2023: 8th International Conference on Business and Industrial Research (May 2023), pp.169-173. |
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