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src="KoichiYasuoka/modernbert-large-japanese-aozora-upos" |
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tgt="KoichiYasuoka/modernbert-large-japanese-aozora-ud-triangular" |
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url="https://github.com/UniversalDependencies/UD_Japanese-GSDLUW" |
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import os |
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d=os.path.basename(url) |
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os.system("test -d "+d+" || git clone --depth=1 "+url) |
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os.system("for F in train dev test ; do cp "+d+"/*-$F.conllu $F.conllu ; done") |
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class UDTriangularDataset(object): |
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def __init__(self,conllu,tokenizer): |
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self.conllu=open(conllu,"r",encoding="utf-8") |
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self.tokenizer=tokenizer |
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self.seeks=[0] |
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label=set(["SYM|x","X|x"]) |
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dep=set(["X|x|r-goeswith"]) |
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s=self.conllu.readline() |
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while s!="": |
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if s=="\n": |
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self.seeks.append(self.conllu.tell()) |
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else: |
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w=s.split("\t") |
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if len(w)==10: |
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if w[0].isdecimal(): |
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p=w[3] |
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q="" if w[5]=="_" else "|"+w[5] |
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d=("|" if w[6]=="0" else "|l-" if int(w[0])<int(w[6]) else "|r-")+w[7] |
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label.add(p+"|o"+q) |
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label.add(p+"|x"+q) |
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dep.add(p+"|o"+q+d) |
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dep.add(p+"|x"+q+d) |
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s=self.conllu.readline() |
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lid={l:i for i,l in enumerate(sorted(label))} |
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for i,d in enumerate(sorted(dep),len(lid)): |
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lid[d]=i |
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self.label2id=lid |
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def __call__(*args): |
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lid={l:i for i,l in enumerate(sorted(set(sum([list(t.label2id) for t in args],[]))))} |
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for t in args: |
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t.label2id=lid |
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return lid |
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def __del__(self): |
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self.conllu.close() |
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__len__=lambda self:len(self.seeks)-1 |
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def __getitem__(self,i): |
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s=self.seeks[i] |
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self.conllu.seek(s) |
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c,t=[],[""] |
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while t[0]!="\n": |
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t=self.conllu.readline().split("\t") |
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if len(t)==10 and t[0].isdecimal(): |
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c.append(t) |
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v=self.tokenizer([t[1] for t in c],add_special_tokens=False)["input_ids"] |
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for i in range(len(v)-1,-1,-1): |
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for j in range(1,len(v[i])): |
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c.insert(i+1,[c[i][0],"_","_","X","_","_",c[i][0],"goeswith","_","_"]) |
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y=["0"]+[t[0] for t in c] |
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h=[i if t[6]=="0" else y.index(t[6]) for i,t in enumerate(c,1)] |
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x=["o" if k>i or sum([1 if j==i+1 else 0 for j in h[i+1:]])>0 else "x" for i,k in enumerate(h)] |
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z=[len(x)-i+1 if k=="o" else 0 for i,k in enumerate(x)] |
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w=sum(z)+1 |
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for i,j in enumerate(z): |
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if j==0 and w+len(x)-i<8192: |
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z[i]=len(x)-i+1 |
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w+=z[i] |
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p=[t[3]+"|"+x[i] if t[5]=="_" else t[3]+"|"+x[i]+"|"+t[5] for i,t in enumerate(c)] |
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d=[t[7] if t[6]=="0" else "l-"+t[7] if int(t[0])<int(t[6]) else "r-"+t[7] for t in c] |
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v=sum(v,[]) |
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ids=[self.tokenizer.cls_token_id] |
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upos=["SYM|x"] |
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for i,k in enumerate(v): |
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if z[i]>0: |
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ids.append(k) |
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upos.append(p[i]+"|"+d[i] if h[i]==i+1 else p[i]) |
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for j in range(i+1,len(v)): |
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ids.append(v[j]) |
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upos.append(p[j]+"|"+d[j] if h[j]==i+1 else p[i]+"|"+d[i] if h[i]==j+1 else p[j]) |
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ids.append(self.tokenizer.sep_token_id) |
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upos.append("SYM|x") |
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return {"input_ids":ids,"labels":[self.label2id[p] for p in upos]} |
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from transformers import AutoTokenizer,AutoConfig,AutoModelForTokenClassification,DataCollatorForTokenClassification,TrainingArguments,Trainer |
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tkz=AutoTokenizer.from_pretrained(src) |
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trainDS=UDTriangularDataset("train.conllu",tkz) |
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devDS=UDTriangularDataset("dev.conllu",tkz) |
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testDS=UDTriangularDataset("test.conllu",tkz) |
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lid=trainDS(devDS,testDS) |
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cfg=AutoConfig.from_pretrained(src,num_labels=len(lid),label2id=lid,id2label={i:l for l,i in lid.items()},ignore_mismatched_sizes=True,trust_remote_code=True) |
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mdl=AutoModelForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=True,trust_remote_code=True) |
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arg=TrainingArguments(num_train_epochs=3,per_device_train_batch_size=1,dataloader_pin_memory=False,output_dir=tgt,overwrite_output_dir=True,save_total_limit=2,learning_rate=5e-05,warmup_ratio=0.1,save_safetensors=False) |
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trn=Trainer(args=arg,data_collator=DataCollatorForTokenClassification(tkz),model=mdl,train_dataset=trainDS) |
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trn.train() |
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trn.save_model(tgt) |
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tkz.save_pretrained(tgt) |
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