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#! /usr/bin/python3
src="KoichiYasuoka/modernbert-base-japanese-wikipedia-upos"
tgt="KoichiYasuoka/modernbert-base-japanese-wikipedia-ud-triangular"
url="https://github.com/UniversalDependencies/UD_Japanese-GSDLUW"
import os
d=os.path.basename(url)
os.system("test -d "+d+" || git clone --depth=1 "+url)
os.system("for F in train dev test ; do cp "+d+"/*-$F.conllu $F.conllu ; done")
class UDTriangularDataset(object):
def __init__(self,conllu,tokenizer):
self.conllu=open(conllu,"r",encoding="utf-8")
self.tokenizer=tokenizer
self.seeks=[0]
label=set(["SYM|x","X|x"])
dep=set(["X|x|r-goeswith"])
s=self.conllu.readline()
while s!="":
if s=="\n":
self.seeks.append(self.conllu.tell())
else:
w=s.split("\t")
if len(w)==10:
if w[0].isdecimal():
p=w[3]
q="" if w[5]=="_" else "|"+w[5]
d=("|" if w[6]=="0" else "|l-" if int(w[0])<int(w[6]) else "|r-")+w[7]
label.add(p+"|o"+q)
label.add(p+"|x"+q)
dep.add(p+"|o"+q+d)
dep.add(p+"|x"+q+d)
s=self.conllu.readline()
lid={l:i for i,l in enumerate(sorted(label))}
for i,d in enumerate(sorted(dep),len(lid)):
lid[d]=i
self.label2id=lid
def __call__(*args):
lid={l:i for i,l in enumerate(sorted(set(sum([list(t.label2id) for t in args],[]))))}
for t in args:
t.label2id=lid
return lid
def __del__(self):
self.conllu.close()
__len__=lambda self:len(self.seeks)-1
def __getitem__(self,i):
s=self.seeks[i]
self.conllu.seek(s)
c,t=[],[""]
while t[0]!="\n":
t=self.conllu.readline().split("\t")
if len(t)==10 and t[0].isdecimal():
c.append(t)
v=self.tokenizer([t[1] for t in c],add_special_tokens=False)["input_ids"]
for i in range(len(v)-1,-1,-1):
for j in range(1,len(v[i])):
c.insert(i+1,[c[i][0],"_","_","X","_","_",c[i][0],"goeswith","_","_"])
y=["0"]+[t[0] for t in c]
h=[i if t[6]=="0" else y.index(t[6]) for i,t in enumerate(c,1)]
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)]
z=[len(x)-i+1 if k=="o" else 0 for i,k in enumerate(x)]
w=sum(z)+1
for i,j in enumerate(z):
if j==0 and w+len(x)-i<8192:
z[i]=len(x)-i+1
w+=z[i]
p=[t[3]+"|"+x[i] if t[5]=="_" else t[3]+"|"+x[i]+"|"+t[5] for i,t in enumerate(c)]
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]
v=sum(v,[])
ids=[self.tokenizer.cls_token_id]
upos=["SYM|x"]
for i,k in enumerate(v):
if z[i]>0:
ids.append(k)
upos.append(p[i]+"|"+d[i] if h[i]==i+1 else p[i])
for j in range(i+1,len(v)):
ids.append(v[j])
upos.append(p[j]+"|"+d[j] if h[j]==i+1 else p[i]+"|"+d[i] if h[i]==j+1 else p[j])
ids.append(self.tokenizer.sep_token_id)
upos.append("SYM|x")
return {"input_ids":ids,"labels":[self.label2id[p] for p in upos]}
from transformers import AutoTokenizer,AutoConfig,AutoModelForTokenClassification,DataCollatorForTokenClassification,TrainingArguments,Trainer
tkz=AutoTokenizer.from_pretrained(src)
trainDS=UDTriangularDataset("train.conllu",tkz)
devDS=UDTriangularDataset("dev.conllu",tkz)
testDS=UDTriangularDataset("test.conllu",tkz)
lid=trainDS(devDS,testDS)
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)
mdl=AutoModelForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=True,trust_remote_code=True)
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)
trn=Trainer(args=arg,data_collator=DataCollatorForTokenClassification(tkz),model=mdl,train_dataset=trainDS)
trn.train()
trn.save_model(tgt)
tkz.save_pretrained(tgt)
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