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src="KoichiYasuoka/Xunzi-Qwen2-1.5B-upos" |
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tgt="KoichiYasuoka/Xunzi-Qwen2-1.5B-ud-causal" |
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url="https://github.com/UniversalDependencies/UD_Classical_Chinese-Kyoto" |
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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 UDCausalDataset(object): |
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def __init__(self,conllu,tokenizer,embeddings=None): |
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self.conllu=open(conllu,"r",encoding="utf-8") |
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self.tokenizer=tokenizer |
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self.embeddings=embeddings |
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self.max_tokens=3 |
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self.seeks=[(0,0)] |
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label=set(["SYM"]) |
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dep=set() |
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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(),0)) |
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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] if w[5]=="_" else w[3]+"|"+w[5] |
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label.add(p) |
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dep.add(p+("|" if w[6]=="0" else "|l-" if int(w[0])<int(w[6]) else "|r-")+w[7]) |
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self.seeks.append((self.seeks[-1][0],int(w[0]))) |
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self.max_tokens=max(self.max_tokens,int(w[0])*2+1) |
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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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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,t=self.seeks[i] |
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self.conllu.seek(s) |
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form,upos,deps,w=[],[],[],[""] |
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while w[0]!="\n": |
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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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deps.append((int(w[6]),w[7])) |
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v=self.tokenizer(form,add_special_tokens=False) |
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if t==0: |
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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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emb=self.embeddings |
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pad=self.tokenizer.pad_token_id |
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else: |
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import torch |
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m=[] |
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for x in v["input_ids"]: |
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if x==[]: |
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m.append(self.embeddings[self.tokenizer.unk_token_id,:]) |
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else: |
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m.append(self.embeddings[x,:].sum(axis=0)) |
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m.append(self.embeddings[self.tokenizer.sep_token_id,:]) |
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m.append(self.embeddings[self.tokenizer.pad_token_id,:]) |
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emb=torch.stack(m) |
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i,u=list(range(len(upos)+1)),upos+["SYM"] |
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i.append(t-1) |
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k,d=deps[t-1] |
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u.append(upos[t-1]+"|"+d if k==0 else upos[t-1]) |
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for j in range(t,len(upos)): |
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i.append(j) |
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a,b=deps[j] |
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u.append(upos[j]+"|r-"+b if a==t else upos[t-1]+"|l-"+d if j+1==k else upos[j]) |
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pad=-1 |
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j=self.max_tokens-len(i) |
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if j>0: |
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ids=i+[pad]*j |
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upos=u+["SYM"]*j |
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else: |
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ids=i[0:self.max_tokens] |
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upos=u[0:self.max_tokens] |
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return {"inputs_embeds":emb[ids,:],"labels":[self.label2id[p] for p in upos]} |
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from transformers import AutoTokenizer,AutoConfig,Qwen2ForTokenClassification,DefaultDataCollator,TrainingArguments,Trainer |
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tkz=AutoTokenizer.from_pretrained(src,cls_token="<|im_start|>",sep_token="<|im_end|>",mask_token="<unk>") |
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trainDS=UDCausalDataset("train.conllu",tkz) |
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devDS=UDCausalDataset("dev.conllu",tkz) |
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testDS=UDCausalDataset("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) |
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mdl=Qwen2ForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=True) |
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trainDS.embeddings=mdl.get_input_embeddings().weight |
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trainDS.max_tokens=min(trainDS.max_tokens,cfg.max_position_embeddings) |
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arg=TrainingArguments(num_train_epochs=3,per_device_train_batch_size=12,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=DefaultDataCollator(),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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