README.md

---
language:
- "ja"
tags:
- "japanese"
- "wikipedia"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
---

# deberta-base-japanese-wikipedia-ud-goeswith

## Model Description

This is a DeBERTa(V2) model pretrained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [deberta-base-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW).

## 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"]
    x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)]
    with torch.no_grad():
      e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:]
    r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())]
    e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan)
    g=self.model.config.label2id["X|_|goeswith"]
    r=numpy.tri(e.shape[0])
    for i in range(e.shape[0]):
      for j in range(i+2,e.shape[1]):
        r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1
    e[:,:,g]+=numpy.where(r==0,0,numpy.nan)
    m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan)
    m[1:,1:]=numpy.nanmax(e,axis=2).transpose()
    p=numpy.zeros(m.shape)
    p[1:,1:]=numpy.nanargmax(e,axis=2).transpose()
    for i in range(1,m.shape[0]):
      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]
    if [0 for i in h if i==0]!=[0]:
      m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan)
      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)]
      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/deberta-base-japanese-wikipedia-ud-goeswith")
print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
```

with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/).
Or without ufal.chu-liu-edmonds:

```
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/deberta-base-japanese-wikipedia-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
```