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#!/usr/bin/env bash
#
# Copyright (c) 2016-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
# This script produces the results from Table 1 in the following paper:
# Bag of Tricks for Efficient Text Classification, arXiv 1607.01759, 2016
myshuf() {
perl -MList::Util=shuffle -e 'print shuffle(<>);' "$@";
}
normalize_text() {
tr '[:upper:]' '[:lower:]' | sed -e 's/^/__label__/g' | \
sed -e "s/'/ ' /g" -e 's/"//g' -e 's/\./ \. /g' -e 's/<br \/>/ /g' \
-e 's/,/ , /g' -e 's/(/ ( /g' -e 's/)/ ) /g' -e 's/\!/ \! /g' \
-e 's/\?/ \? /g' -e 's/\;/ /g' -e 's/\:/ /g' | tr -s " " | myshuf
}
DATASET=(
ag_news
sogou_news
dbpedia
yelp_review_polarity
yelp_review_full
yahoo_answers
amazon_review_full
amazon_review_polarity
)
ID=(
0Bz8a_Dbh9QhbUDNpeUdjb0wxRms # ag_news
0Bz8a_Dbh9QhbUkVqNEszd0pHaFE # sogou_news
0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k # dbpedia
0Bz8a_Dbh9QhbNUpYQ2N3SGlFaDg # yelp_review_polarity
0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0 # yelp_review_full
0Bz8a_Dbh9Qhbd2JNdDBsQUdocVU # yahoo_answers
0Bz8a_Dbh9QhbZVhsUnRWRDhETzA # amazon_review_full
0Bz8a_Dbh9QhbaW12WVVZS2drcnM # amazon_review_polarity
)
# These learning rates were chosen by validation on a subset of the training set.
LR=( 0.25 0.5 0.5 0.1 0.1 0.1 0.05 0.05 )
RESULTDIR=result
DATADIR=data
mkdir -p "${RESULTDIR}"
mkdir -p "${DATADIR}"
# Small datasets first
for i in {0..0}
do
echo "Downloading dataset ${DATASET[i]}"
if [ ! -f "${DATADIR}/${DATASET[i]}.train" ]
then
wget -c "https://drive.google.com/uc?export=download&id=${ID[i]}" -O "${DATADIR}/${DATASET[i]}_csv.tar.gz"
tar -xzvf "${DATADIR}/${DATASET[i]}_csv.tar.gz" -C "${DATADIR}"
cat "${DATADIR}/${DATASET[i]}_csv/train.csv" | normalize_text > "${DATADIR}/${DATASET[i]}.train"
cat "${DATADIR}/${DATASET[i]}_csv/test.csv" | normalize_text > "${DATADIR}/${DATASET[i]}.test"
fi
done
# Large datasets require a bit more work due to the extra request page
for i in {1..7}
do
echo "Downloading dataset ${DATASET[i]}"
if [ ! -f "${DATADIR}/${DATASET[i]}.train" ]
then
curl -c /tmp/cookies "https://drive.google.com/uc?export=download&id=${ID[i]}" > /tmp/intermezzo.html
curl -L -b /tmp/cookies "https://drive.google.com$(cat /tmp/intermezzo.html | grep -Po 'uc-download-link" [^>]* href="\K[^"]*' | sed 's/\&amp;/\&/g')" > "${DATADIR}/${DATASET[i]}_csv.tar.gz"
tar -xzvf "${DATADIR}/${DATASET[i]}_csv.tar.gz" -C "${DATADIR}"
cat "${DATADIR}/${DATASET[i]}_csv/train.csv" | normalize_text > "${DATADIR}/${DATASET[i]}.train"
cat "${DATADIR}/${DATASET[i]}_csv/test.csv" | normalize_text > "${DATADIR}/${DATASET[i]}.test"
fi
done
make
for i in {0..7}
do
echo "Working on dataset ${DATASET[i]}"
./fasttext supervised -input "${DATADIR}/${DATASET[i]}.train" \
-output "${RESULTDIR}/${DATASET[i]}" -dim 10 -lr "${LR[i]}" -wordNgrams 2 \
-minCount 1 -bucket 10000000 -epoch 5 -thread 4 > /dev/null
./fasttext test "${RESULTDIR}/${DATASET[i]}.bin" \
"${DATADIR}/${DATASET[i]}.test"
done
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