|
import pandas as pd |
|
import xmltodict |
|
from sklearn.model_selection import train_test_split |
|
import glob |
|
import sys |
|
import os |
|
|
|
filelist = glob.glob('sentences/*.txt') |
|
|
|
data = pd.DataFrame() |
|
|
|
for tsvfile in filelist: |
|
print(f"Processing {tsvfile}") |
|
data = pd.read_csv(tsvfile, sep='\t',on_bad_lines='skip',engine='python',encoding='utf8') |
|
lang=tsvfile.split('/')[1][0:3] |
|
if len(data.columns)==1: |
|
data.insert(0,'id','') |
|
|
|
data.columns=['id','source'] |
|
data['target']=lang |
|
|
|
data['source'] = "lang: "+data['source'] |
|
data['source'] = data['source'].str.replace('\t',' ') |
|
data = data.sample(frac=1).reset_index(drop=True) |
|
|
|
data = data[['source','target']] |
|
|
|
|
|
train, test = train_test_split(data, test_size=0.2) |
|
test, dev = train_test_split(test, test_size=0.5) |
|
|
|
|
|
train.to_csv('langid_datafiles/'+lang+'_train.tsv', index=False, header=False, sep='\t') |
|
test.to_csv('langid_datafiles/'+lang+'_test.tsv', index=False, header=False, sep='\t') |
|
dev.to_csv('langid_datafiles/'+lang+'_dev.tsv', index=False, header=False, sep='\t') |
|
|
|
|
|
print("Finished") |
|
|
|
|
|
|
|
|
