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import pandas as pd
import xmltodict
from sklearn.model_selection import train_test_split
import glob
import sys
filelist = glob.glob('tmx/*.tmx')
for tmxfile in filelist:
print(f"Starting to parse {tmxfile}")
verbose = 0
with open(tmxfile) as fd:
doc = xmltodict.parse(fd.read())
try:
#Use the first sentence pair to determine the target-source language
source = doc['tmx']['body']['tu'][0]['tuv'][0]['@xml:lang']
target = doc['tmx']['body']['tu'][1]['tuv'][1]['@xml:lang']
except:
source = doc['tmx']['body']['tu'][0]['tuv'][0]['@lang']
target = doc['tmx']['body']['tu'][1]['tuv'][1]['@lang']
# Extract content from xml/tmx
data=[]
errorcount = 0
for item in doc['tmx']['body']['tu'][:]:
trans = {}
valid = 1
trans[source] = item['tuv'][0]['seg']
trans[target] = item['tuv'][1]['seg']
if isinstance(trans[source],dict):
try:
trans[source] = trans[source]['#text']
trans[target] = trans[target]['#text']
except:
if verbose:
print("Dropping - Malformed XML")
valid = 0
if not trans[source] or not trans[target]:
valid = 0
if verbose:
print("Dropping source/target does not exist")
elif len(trans[source]) <= 1 or len(trans[target]) <=1:
valid = 0
if verbose:
print("Dropping - Partly empty entity")
elif '\t' in trans[source] or '\t' in trans[target]:
valid = 0
if verbose:
print("Dropping - Contains tabulator")
if valid == 1:
data.append(trans)
else:
errorcount += 1
# Create dataframe
df = pd.DataFrame(data)
# Shuffle
# df = df.sample(frac=1).reset_index(drop=True)
# Train - test - dev
#train, test = train_test_split(df, test_size=0.2)
#test, dev = train_test_split(test, test_size=0.5)
# Write the datasets to disk
#train.to_csv('train_tmp.tsv', index=False, header=False, sep='\t')
#test.to_csv('test_tmp.tsv', index=False, header=False, sep='\t')
#dev.to_csv('dev_tmp.tsv', index=False, header=False, sep='\t')
#Rename some languages
if source=="fi":
source="fin"
if target=="fi":
target="fin"
if source=="nb":
source="nob"
if target=="nb":
target="nob"
if source=="se":
source="sme"
if target=="se":
target="sme"
filename = "tsv_source_target/"+source+target+".tsv"
df.to_csv(filename, index=False, header=False, sep='\t')
#print(f"Finished writing train.tsv ({len(train)}), test.tsv ({len(test)}) and dev.tsv ({len(dev)}) to disk.")
print(f"Finished writing {filename} ({len(df)}) to disk.")
print(f"Totally {errorcount} errors")
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