quantization-example.sh

myshuf() {
  perl -MList::Util=shuffle -e 'print shuffle(<>);' "$@";
}

normalize_text() {
  tr '[:upper:]' '[:lower:]' | sed -e 's/^/__label__/g' | \
    sed -e "s/'/ ' /g" -e 's/"//g' -e 's/\./ \. /g' -e 's/<br \/>/ /g' \
        -e 's/,/ , /g' -e 's/(/ ( /g' -e 's/)/ ) /g' -e 's/\!/ \! /g' \
        -e 's/\?/ \? /g' -e 's/\;/ /g' -e 's/\:/ /g' | tr -s " " | myshuf
}

RESULTDIR=result
DATADIR=data

mkdir -p "${RESULTDIR}"
mkdir -p "${DATADIR}"

if [ ! -f "${DATADIR}/dbpedia.train" ]
then
  wget -c "https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz" -O "${DATADIR}/dbpedia_csv.tar.gz"
  tar -xzvf "${DATADIR}/dbpedia_csv.tar.gz" -C "${DATADIR}"
  cat "${DATADIR}/dbpedia_csv/train.csv" | normalize_text > "${DATADIR}/dbpedia.train"
  cat "${DATADIR}/dbpedia_csv/test.csv" | normalize_text > "${DATADIR}/dbpedia.test"
fi

make

echo "Training..."
./fasttext supervised -input "${DATADIR}/dbpedia.train" -output "${RESULTDIR}/dbpedia" -dim 10 -lr 0.1 -wordNgrams 2 -minCount 1 -bucket 10000000 -epoch 5 -thread 4

echo "Quantizing..."
./fasttext quantize -output "${RESULTDIR}/dbpedia" -input "${DATADIR}/dbpedia.train" -qnorm -retrain -epoch 1 -cutoff 100000

echo "Testing original model..."
./fasttext test "${RESULTDIR}/dbpedia.bin" "${DATADIR}/dbpedia.test"
echo "Testing quantized model..."
./fasttext test "${RESULTDIR}/dbpedia.ftz" "${DATADIR}/dbpedia.test"

wc -c < "${RESULTDIR}/dbpedia.bin" | awk '{print "Size of the original model:\t",$1;}'
wc -c < "${RESULTDIR}/dbpedia.ftz" | awk '{print "Size of the quantized model:\t",$1;}'