【深度学习】sentencepiece工具之BPE训练使用

深度学习】sentencepiece工具之BPE训练使用"/>

【深度学习】sentencepiece工具之BPE训练使用

为什么要使用BPE,BPE是什么

BPE：迭代的将字符串里出现频率最高的子串进行合并
训练过程

使用教程

代码使用的语料在这里

# -*- coding: utf-8 -*-
#/usr/bin/python3import os
import errno
import sentencepiece as spm
import re
import logginglogging.basicConfig(level=logging.INFO)def prepro(hp):print("# Check if raw files exist")train1 = "iwslt2016/de-en/train.tags.de-en.de"train2 = "iwslt2016/de-en/train.tags.de-en.en"eval1 = "iwslt2016/de-en/IWSLT16.TED.tst2013.de-en.de.xml"eval2 = "iwslt2016/de-en/IWSLT16.TED.tst2013.de-en.en.xml"test1 = "iwslt2016/de-en/IWSLT16.TED.tst2014.de-en.de.xml"test2 = "iwslt2016/de-en/IWSLT16.TED.tst2014.de-en.en.xml"for f in (train1, train2, eval1, eval2, test1, test2):if not os.path.isfile(f):raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), f)print("# Preprocessing")# train_prepro = lambda x:  [line.strip() for line in open(x, mode='r',encoding="utf-8").read().split("\n") \if not line.startswith("<")]prepro_train1, prepro_train2 = _prepro(train1), _prepro(train2)assert len(prepro_train1)==len(prepro_train2), "Check if train source and target files match."# eval_prepro = lambda x: [re.sub("<[^>]+>", "", line).strip() \for line in open(x, mode='r',encoding="utf-8").read().split("\n") \if line.startswith("<seg id")]prepro_eval1, prepro_eval2 = _prepro(eval1), _prepro(eval2)assert len(prepro_eval1) == len(prepro_eval2), "Check if eval source and target files match."# testprepro_test1, prepro_test2 = _prepro(test1), _prepro(test2)assert len(prepro_test1) == len(prepro_test2), "Check if test source and target files match."print("Let's see how preprocessed data look like")print("prepro_train1:", prepro_train1[0])print("prepro_train2:", prepro_train2[0])print("prepro_eval1:", prepro_eval1[0])print("prepro_eval2:", prepro_eval2[0])print("prepro_test1:", prepro_test1[0])print("prepro_test2:", prepro_test2[0])print("# write preprocessed files to disk")os.makedirs("iwslt2016/prepro", exist_ok=True)def _write(sents, fname):with open(fname, mode='w',encoding="utf-8") as fout:fout.write("\n".join(sents))_write(prepro_train1, "iwslt2016/prepro/train.de")_write(prepro_train2, "iwslt2016/prepro/train.en")_write(prepro_train1+prepro_train2, "iwslt2016/prepro/train")_write(prepro_eval1, "iwslt2016/prepro/eval.de")_write(prepro_eval2, "iwslt2016/prepro/eval.en")_write(prepro_test1, "iwslt2016/prepro/test.de")_write(prepro_test2, "iwslt2016/prepro/test.en")print("# Train a joint BPE model with sentencepiece")os.makedirs("iwslt2016/segmented", exist_ok=True)train = '--input=iwslt2016/prepro/train --pad_id=0 --unk_id=1 \--bos_id=2 --eos_id=3\--model_prefix=iwslt2016/segmented/bpe --vocab_size={} \--model_type=bpe'.format(hp.vocab_size)spm.SentencePieceTrainer.Train(train)print("# Load trained bpe model")sp = spm.SentencePieceProcessor()sp.Load("iwslt2016/segmented/bpe.model")print("# Segment")def _segment_and_write(sents, fname):with open(fname,mode= "w",encoding="utf-8") as fout:for sent in sents:pieces = sp.EncodeAsPieces(sent)fout.write(" ".join(pieces) + "\n")_segment_and_write(prepro_train1, "iwslt2016/segmented/train.de.bpe")_segment_and_write(prepro_train2, "iwslt2016/segmented/train.en.bpe")_segment_and_write(prepro_eval1, "iwslt2016/segmented/eval.de.bpe")_segment_and_write(prepro_eval2, "iwslt2016/segmented/eval.en.bpe")_segment_and_write(prepro_test1, "iwslt2016/segmented/test.de.bpe")print("Let's see how segmented data look like")print("train1:", open("iwslt2016/segmented/train.de.bpe",mode='r',encoding="utf-8").readline())print("train2:", open("iwslt2016/segmented/train.en.bpe", mode='r',encoding="utf-8").readline())print("eval1:", open("iwslt2016/segmented/eval.de.bpe", mode='r',encoding="utf-8").readline())print("eval2:", open("iwslt2016/segmented/eval.en.bpe", mode='r',encoding="utf-8").readline())print("test1:", open("iwslt2016/segmented/test.de.bpe", mode='r',encoding="utf-8").readline())if __name__ == '__main__':hparams = Hparams()parser = hparams.parserhp = parser.parse_args()prepro(hp)print("Done")