apply_sentence_piece.sh

#!/bin/bash

# This script tokenizes the preprocessed train and dev set using the trained spm models.


echo `date`
exp_dir=$1                      # path to the experiment directory
data_dir=$2                     # path to the data directory where all lang pairs are concatenated
bpe_dir=$3                      # path to the tokenized data directory
src_lang=$4                     # source language
tgt_lang=$5                     # target language
split=$6                        # name of the split
parallel_installed=${7:-false}  # If GNU Parallel is installed or not

in_split_dir=$data_dir/$split
out_split_dir=$bpe_dir/$split

echo "Apply Sentence Piece tokenization to SRC corpus"
# for very large datasets, it is recommended to use gnu-parallel to speed up applying bpe

if $parallel_installed; then
    parallel --pipe --keep-order \
    spm_encode --model=$exp_dir/vocab/model.SRC \
        --output_format=piece \
        < $in_split_dir.$src_lang \
        > $out_split_dir.$src_lang
else
    spm_encode --model=$exp_dir/vocab/model.SRC \
        --output_format=piece \
        < $in_split_dir.$src_lang \
        > $out_split_dir.$src_lang
fi

echo "Apply Sentence Piece tokenization to TGT corpus"
# for very large datasets, it is recommended to use gnu-parallel to speed up applying bpe

if $parallel_installed; then
    parallel --pipe --keep-order \
    spm_encode --model=$exp_dir/vocab/model.TGT \
        --output_format=piece \
        < $in_split_dir.$tgt_lang \
        > $out_split_dir.$tgt_lang
else
    spm_encode --model=$exp_dir/vocab/model.TGT \
        --output_format=piece \
        < $in_split_dir.$tgt_lang \
        > $out_split_dir.$tgt_lang
fi