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---
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](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-base-char) and [UD_Classical_Chinese-Kyoto](https://github.com/UniversalDependencies/UD_Classical_Chinese-Kyoto).
## How to Use
```py
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](https://pypi.org/project/ufal.chu-liu-edmonds/) is required.
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