/
back_translation.py
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back_translation.py
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import argparse
import torch
import os
import json
import pickle
from nltk.tokenize import sent_tokenize
import nltk
nltk.download('punkt')
import datasets
from tqdm import tqdm
import numpy as np
import random
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.device_count() > 0:
torch.cuda.manual_seed_all(seed)
def clean_web_text(st):
"""
Adapted from UDA official code
https://github.com/google-research/uda/blob/master/text/utils/imdb_format.py
"""
st = st.replace("<br />", " ")
st = st.replace(""", '"')
st = st.replace("<p>", " ")
if "<a href=" in st:
while "<a href=" in st:
start_pos = st.find("<a href=")
end_pos = st.find(">", start_pos)
if end_pos != -1:
st = st[:start_pos] + st[end_pos + 1 :]
else:
st = st[:start_pos] + st[start_pos + len("<a href=")]
st = st.replace("</a>", "")
st = st.replace("\\n", " ")
return st
def split_sent_by_punc(sent, punc_list, max_len):
"""
Adapted from UDA official code
https://github.com/google-research/uda/blob/master/back_translate/split_paragraphs.py
"""
if len(punc_list) == 0 or len(sent) <= max_len:
return [sent]
punc = punc_list[0]
if punc == " " or not punc:
offset = 100
else:
offset = 5
sent_list = []
start = 0
while start < len(sent):
if punc:
pos = sent.find(punc, start + offset)
else:
pos = start + offset
if pos != -1:
sent_list += [sent[start: pos + 1]]
start = pos + 1
else:
sent_list += [sent[start:]]
break
new_sent_list = []
for temp_sent in sent_list:
new_sent_list += split_sent_by_punc(temp_sent, punc_list[1:], max_len)
return new_sent_list
def split_sent(content, max_len):
"""
Adapted from UDA Official code
https://github.com/google-research/uda/blob/master/back_translate/split_paragraphs.py
"""
sent_list = sent_tokenize(content)
new_sent_list = []
split_punc_list = [".", ";", ",", " ", ""]
for sent in sent_list:
new_sent_list += split_sent_by_punc(sent, split_punc_list, max_len)
return new_sent_list, len(new_sent_list)
## batch iteration
def batch(iterable, n=1):
l = len(iterable)
for ndx in range(0, l, n):
yield iterable[ndx:min(ndx + n, l)]
## save pickle
def save_pickle(path, data):
with open(path, 'wb') as handle:
pickle.dump(data, handle)
def run(args):
## load translator
src2tgt = torch.hub.load("pytorch/fairseq", args.src2tgt_model, tokenizer=args.tokenizer, bpe=args.bpe).to(
args.device).eval()
tgt2src = torch.hub.load("pytorch/fairseq", args.tgt2src_model, tokenizer=args.tokenizer, bpe=args.bpe).to(
args.device).eval()
## load Dataset
imdb_data = datasets.load_dataset('imdb')
data_list = ['train', 'test', 'unsupervised']
for dataname in tqdm(data_list, desc='data name'):
temp_dataset = imdb_data[dataname]
temp_docs = temp_dataset['text']
temp_label = temp_dataset['label']
## clean web tag from text
temp_docs = [clean_web_text(temp_sent) for temp_sent in temp_docs]
new_contents = []
new_contents_length = []
for temp_doc in temp_docs:
new_sents, new_sents_length = split_sent(temp_doc, args.max_len)
new_contents += new_sents
new_contents_length += [new_sents_length]
backtranslated_contents = []
for contents in tqdm(batch(new_contents, args.batch_size), total=int(len(new_contents)/args.batch_size)):
with torch.no_grad():
translated_data = src2tgt.translate(
contents,
sampling=True if args.temperature is not None else False,
temperature=args.temperature,
)
back_translated_data = tgt2src.translate(
translated_data,
sampling=True if args.temperature is not None else False,
temperature=args.temperature,
)
backtranslated_contents += back_translated_data
merge_backtranslated_contents=[]
merge_new_contents = []
cumulate_length = 0
for temp_length in new_contents_length:
merge_backtranslated_contents += [" ".join(backtranslated_contents[cumulate_length:cumulate_length + temp_length])]
merge_new_contents += [" ".join(new_contents[cumulate_length:cumulate_length + temp_length])]
cumulate_length += temp_length
save_data = {
'raw_text' : temp_docs,
'label' : temp_label,
'clean_text' : merge_new_contents,
'backtranslated_text' : merge_backtranslated_contents,
}
save_path = os.path.join(args.save_dir, "{}.p".format(dataname))
save_pickle(save_path, save_data)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--save_dir",
default=None,
type=str,
required=True,
help="Save Directory",
)
parser.add_argument(
"--src2tgt_model",
default='transformer.wmt19.en-de.single_model',
type=str,
help="torch HUB translation Model(source->target)",
)
parser.add_argument(
"--tgt2src_model",
default='transformer.wmt19.de-en.single_model',
type=str,
help="torch HUB translation Model(target->source)",
)
parser.add_argument(
"--bpe",
default='fastbpe',
type=str,
help="torch HUB translation bpe option",
)
parser.add_argument(
"--tokenizer",
default='moses',
type=str,
help="torch HUB translation tokenizer",
)
parser.add_argument(
"--no_cuda",
action="store_true",
help="if you don't want to use CUDA"
)
parser.add_argument(
"--batch_size",
default=16,
type=int,
help="Back-translation Batch size"
)
parser.add_argument(
"--max_len",
default=300,
type=int,
help="Translation Available length"
)
parser.add_argument(
"--temperature",
default=0.9,
type=float,
help="Translation Available length"
)
parser.add_argument(
"--seed",
default=1,
type=int,
help="Translation Available length"
)
args = parser.parse_args()
args.device = 'cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'
## make save path
os.makedirs(args.save_dir, exist_ok=True)
## set Seed
set_seed(args.seed)
def _print_config(config):
import pprint
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(vars(config))
_print_config(args)
run(args)