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--- |
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language: |
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- "en" |
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tags: |
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- "english" |
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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: "cc-by-sa-4.0" |
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pipeline_tag: "token-classification" |
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--- |
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# roberta-base-english-ud-goeswith |
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## Model Description |
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This is a RoBERTa model for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-base](https://huggingface.co/roberta-base). |
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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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n=len(v)-1 |
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with torch.no_grad(): |
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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)])) |
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e=d.logits.numpy()[:,1:n,:] |
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e[:,:,0]=numpy.nan |
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m=numpy.full((n,n),numpy.nan) |
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m[1:,1:]=numpy.nanmax(e,axis=2).transpose() |
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p=numpy.zeros((n,n)) |
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p[1:,1:]=numpy.nanargmax(e,axis=2).transpose() |
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for i in range(1,n): |
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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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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-base-english-ud-goeswith") |
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print(nlp("I saw a horse yesterday which had no name")) |
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``` |
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[ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/) is required. |
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