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src="KoichiYasuoka/roberta-base-ainu-upos" |
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tgt="KoichiYasuoka/roberta-base-ainu-ud-goeswith" |
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import os |
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url="https://github.com/KoichiYasuoka/UD-Ainu" |
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d=os.path.basename(url) |
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os.system("test -d {} || git clone --depth=1 {}".format(d,url)) |
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s='{if($0==""){if(u~/\\t0\\troot\\t/)print u;u=""}else u=u$0"\\n"}' |
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os.system("nawk -F'\\t' '{}' {}/ain_*-ud-*.conllu > train.conllu".format(s,d)) |
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class UDgoeswithDataset(object): |
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def __init__(self,conllu,tokenizer): |
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self.ids,self.tags,label=[],[],set() |
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with open(conllu,"r",encoding="utf-8") as r: |
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cls,sep,msk=tokenizer.cls_token_id,tokenizer.sep_token_id,tokenizer.mask_token_id |
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dep,c="-|_|dep",[] |
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for s in r: |
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t=s.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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elif c!=[]: |
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for x in [1,2]: |
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d=list(c) |
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v=tokenizer([t[x] for t in d],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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d.insert(i+1,[d[i][0],"_","_","X","_","_",d[i][0],"goeswith","_","_"]) |
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y=["0"]+[t[0] for t in d] |
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h=[i if t[6]=="0" else y.index(t[6]) for i,t in enumerate(d,1)] |
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p,v=[t[3]+"|"+t[4]+"|"+t[7] for t in d],sum(v,[]) |
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if len(v)<tokenizer.model_max_length-3: |
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self.ids.append([cls]+v+[sep]) |
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self.tags.append([dep]+p+[dep]) |
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label=set(sum([self.tags[-1],list(label)],[])) |
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for i,k in enumerate(v): |
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self.ids.append([cls]+v[0:i]+[msk]+v[i+1:]+[sep,k]) |
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self.tags.append([dep]+[t if h[j]==i+1 else dep for j,t in enumerate(p)]+[dep,dep]) |
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c=[] |
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self.label2id={l:i for i,l in enumerate(sorted(label))} |
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__len__=lambda self:len(self.ids) |
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__getitem__=lambda self,i:{"input_ids":self.ids[i],"labels":[self.label2id[t] for t in self.tags[i]]} |
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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=UDgoeswithDataset("train.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) |
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arg=TrainingArguments(num_train_epochs=3,per_device_train_batch_size=64,output_dir="/tmp",overwrite_output_dir=True,save_total_limit=2,learning_rate=5e-05,warmup_ratio=0.1) |
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trn=Trainer(args=arg,data_collator=DataCollatorForTokenClassification(tkz),model=AutoModelForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=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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