spacy-alignments

0aa1640 6 months ago

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	---
	language:
	- "ja"
	tags:
	- "japanese"
	- "question-answering"
	- "dependency-parsing"
	datasets:
	- "universal_dependencies"
	license: "cc-by-sa-4.0"
	pipeline_tag: "question-answering"
	inference:
	parameters:
	align_to_words: false
	widget:
	- text: "国語"
	context: "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"
	- text: "教科書"
	context: "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"
	- text: "の"
	context: "全学年にわたって小学校の国語[MASK]教科書に挿し絵が用いられている"
	---

	# deberta-large-japanese-unidic-ud-head

	## Model Description

	This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-large-japanese-unidic](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-unidic) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW). Use [MASK] inside `context` to avoid ambiguity when specifying a multiple-used word as `question`.

	## How to Use

	```py
	import torch
	from transformers import AutoTokenizer,AutoModelForQuestionAnswering
	tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-unidic-ud-head")
	model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-large-japanese-unidic-ud-head")
	question="国語"
	context="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"
	inputs=tokenizer(question,context,return_tensors="pt")
	outputs=model(**inputs)
	start,end=torch.argmax(outputs.start_logits),torch.argmax(outputs.end_logits)
	print(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0,start:end+1]))
	```

	or

	```py
	from transformers import (AutoTokenizer,AutoModelForQuestionAnswering,
	AutoModelForTokenClassification,AutoConfig,TokenClassificationPipeline)
	class TaggerPipeline(TokenClassificationPipeline):
	def __call__(self,text):
	d=super().__call__(text)
	if len(d)>0 and ("start" not in d[0] or d[0]["start"]==None):
	import spacy_alignments as tokenizations
	v=[x["word"].replace(" ","") for x in d]
	a2b,b2a=tokenizations.get_alignments(v,text)
	for i,t in enumerate(a2b):
	s,e=(0,0) if t==[] else (t[0],t[-1]+1)
	if v[i].startswith(self.tokenizer.unk_token):
	s=([[-1]]+[x for x in a2b[0:i] if x>[]])[-1][-1]+1
	if v[i].endswith(self.tokenizer.unk_token):
	e=([x for x in a2b[i+1:] if x>[]]+[[len(text)]])[0][0]
	d[i]["start"],d[i]["end"]=s,e
	return d
	class TransformersSlowUD(object):
	def __init__(self,bert):
	import os
	self.tokenizer=AutoTokenizer.from_pretrained(bert)
	self.model=AutoModelForQuestionAnswering.from_pretrained(bert)
	x=AutoModelForTokenClassification.from_pretrained
	if os.path.isdir(bert):
	d,t=x(os.path.join(bert,"deprel")),x(os.path.join(bert,"tagger"))
	else:
	from transformers.utils import cached_file
	c=AutoConfig.from_pretrained(cached_file(bert,"deprel/config.json"))
	d=x(cached_file(bert,"deprel/pytorch_model.bin"),config=c)
	s=AutoConfig.from_pretrained(cached_file(bert,"tagger/config.json"))
	t=x(cached_file(bert,"tagger/pytorch_model.bin"),config=s)
	self.deprel=TaggerPipeline(model=d,tokenizer=self.tokenizer,
	aggregation_strategy="simple")
	self.tagger=TaggerPipeline(model=t,tokenizer=self.tokenizer)
	def __call__(self,text):
	import numpy,torch,ufal.chu_liu_edmonds
	w=[(t["start"],t["end"],t["entity_group"]) for t in self.deprel(text)]
	z,n={t["start"]:t["entity"].split("\|") for t in self.tagger(text)},len(w)
	r,m=[text[s:e] for s,e,p in w],numpy.full((n+1,n+1),numpy.nan)
	v,c=self.tokenizer(r,add_special_tokens=False)["input_ids"],[]
	for i,t in enumerate(v):
	q=[self.tokenizer.cls_token_id]+t+[self.tokenizer.sep_token_id]
	c.append([q]+v[0:i]+[[self.tokenizer.mask_token_id]]+v[i+1:]+[[q[-1]]])
	b=[[len(sum(x[0:j+1],[])) for j in range(len(x))] for x in c]
	with torch.no_grad():
	d=self.model(input_ids=torch.tensor([sum(x,[]) for x in c]),
	token_type_ids=torch.tensor([[0]x[0]+[1](x[-1]-x[0]) for x in b]))
	s,e=d.start_logits.tolist(),d.end_logits.tolist()
	for i in range(n):
	for j in range(n):
	m[i+1,0 if i==j else j+1]=s[i][b[i][j]]+e[i][b[i][j+1]-1]
	h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
	if [0 for i in h if i==0]!=[0]:
	i=([p for s,e,p in w]+["root"]).index("root")
	j=i+1 if i<n else numpy.nanargmax(m[:,0])
	m[0:j,0]=m[j+1:,0]=numpy.nan
	h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
	u="# text = "+text.replace("\n"," ")+"\n"
	for i,(s,e,p) in enumerate(w,1):
	p="root" if h[i]==0 else "dep" if p=="root" else p
	u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","\|".join(z[s][1:]),
	str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n"
	return u+"\n"

	nlp=TransformersSlowUD("KoichiYasuoka/deberta-large-japanese-unidic-ud-head")
	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"))
	```

	[fugashi](https://pypi.org/project/fugashi) [unidic-lite](https://pypi.org/project/unidic-lite) [spacy-alignments](https://pypi.org/project/spacy-alignments) and [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/) required.