#!/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(<>);' "$@"; } normalize_text() { tr '[:upper:]' '[:lower:]' | sed -e 's/^/__label__/g' | \ sed -e "s/'/ ' /g" -e 's/"//g' -e 's/\./ \. /g' -e 's/
/ /g' \ -e 's/,/ , /g' -e 's/(/ ( /g' -e 's/)/ ) /g' -e 's/\!/ \! /g' \ -e 's/\?/ \? /g' -e 's/\;/ /g' -e 's/\:/ /g' | tr -s " " | myshuf } DATASET=( ag_news sogou_news dbpedia yelp_review_polarity yelp_review_full yahoo_answers amazon_review_full amazon_review_polarity ) ID=( 0Bz8a_Dbh9QhbUDNpeUdjb0wxRms # ag_news 0Bz8a_Dbh9QhbUkVqNEszd0pHaFE # sogou_news 0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k # dbpedia 0Bz8a_Dbh9QhbNUpYQ2N3SGlFaDg # yelp_review_polarity 0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0 # yelp_review_full 0Bz8a_Dbh9Qhbd2JNdDBsQUdocVU # yahoo_answers 0Bz8a_Dbh9QhbZVhsUnRWRDhETzA # amazon_review_full 0Bz8a_Dbh9QhbaW12WVVZS2drcnM # amazon_review_polarity ) # These learning rates were chosen by validation on a subset of the training set. LR=( 0.25 0.5 0.5 0.1 0.1 0.1 0.05 0.05 ) RESULTDIR=result DATADIR=data mkdir -p "${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$(cat /tmp/intermezzo.html | grep -Po 'uc-download-link" [^>]* href="\K[^"]*' | sed 's/\&/\&/g')" > "${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