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src="hplt_bert_base_ja" |
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tgt="KoichiYasuoka/ltgbert-base-japanese-upos" |
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import os |
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from transformers import AutoTokenizer,AutoConfig,AutoModelForTokenClassification,DataCollatorForTokenClassification,TrainingArguments,Trainer |
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os.system(f"test -d {src} || ( curl -L https://data.hplt-project.org/one/models/encoder/{src}.tar.gz | tar xvzf - )") |
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os.system("test -f ja_gsd_modern.conllu || curl -LO https://github.com/KoichiYasuoka/SuPar-UniDic/raw/main/suparunidic/suparmodels/ja_gsd_modern.conllu") |
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class UPOSFileDataset(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"]) |
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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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label.add(w[3] if w[5]=="_" else w[3]+"|"+w[5]) |
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s=self.conllu.readline() |
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lid={} |
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for i,l in enumerate(sorted(label)): |
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lid[l],lid["B-"+l],lid["I-"+l]=i*3,i*3+1,i*3+2 |
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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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self.conllu.seek(self.seeks[i]) |
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form,upos=[],[] |
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while self.conllu.tell()<self.seeks[i+1]: |
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w=self.conllu.readline().split("\t") |
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if len(w)==10: |
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form.append(w[1]) |
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if w[0].isdecimal(): |
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upos.append(w[3] if w[5]=="_" else w[3]+"|"+w[5]) |
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v=self.tokenizer(form,add_special_tokens=False) |
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i,u=[],[] |
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for j,(x,y) in enumerate(zip(v["input_ids"],upos)): |
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if x!=[]: |
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i+=x |
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u+=[y] if len(x)==1 else ["B-"+y]+["I-"+y]*(len(x)-1) |
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if len(i)<self.tokenizer.model_max_length-3: |
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ids=[self.tokenizer.cls_token_id]+i+[self.tokenizer.sep_token_id] |
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upos=["SYM"]+u+["SYM"] |
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else: |
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ids=i[0:self.tokenizer.model_max_length-2] |
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upos=u[0:self.tokenizer.model_max_length-2] |
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return {"input_ids":ids,"labels":[self.label2id[t] for t in upos]} |
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tkz=AutoTokenizer.from_pretrained(src,model_max_length=512) |
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trainDS=UPOSFileDataset("ja_gsd_modern.conllu",tkz) |
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lid=trainDS.label2id |
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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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arg=TrainingArguments(num_train_epochs=3,per_device_train_batch_size=8,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=AutoModelForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=True,trust_remote_code=True),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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