Difference between revisions of "User:Francis Tyers/Experiments"
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==Processing== |
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===Basque→Spanish=== |
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<pre> |
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2081 cat europako_testuak_memoria_2010.tmx | iconv -f utf-16 -t utf-8 > europako_testuak_memoria_2010.tmx.u8 |
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2082 cat 2010_memo_orokorra.tmx | iconv -f utf-16 -t utf-8 > 2010_memo_orokorra.tmx.u8 |
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2088 python3 process-tmx.py europako_testuak_memoria_2010.tmx.u8 > europako_testuak_memoria_2010.txt |
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2090 python3 process-tmx.py 2010_memo_orokorra.tmx.u8 > 2010_memo_orokorra.txt |
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2091 cat 2010_memo_orokorra.txt | grep '^es' | cut -f2- > 2010_memo_orokorra.es.txt |
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2092 cat 2010_memo_orokorra.txt | grep '^eu' | cut -f2- > 2010_memo_orokorra.eu.txt |
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2094 cat europako_testuak_memoria_2010.txt | grep '^es' | cut -f2- > europako_testuak_memoria_2010.es.txt |
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2095 cat europako_testuak_memoria_2010.txt | grep '^eu' | cut -f2- > europako_testuak_memoria_2010.eu.txt |
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2099 cat europako_testuak_memoria_2010.es.txt 2010_memo_orokorra.es.txt > opendata.es |
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2100 cat europako_testuak_memoria_2010.eu.txt 2010_memo_orokorra.eu.txt > opendata.eu |
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$ wc -l opendata.e* |
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782325 opendata.es |
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782325 opendata.eu |
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2114 perl /home/fran/local/bin/scripts-20120109-1229/training/clean-corpus-n.perl opendata eu es opendata.clean 1 40 |
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2117 cat opendata.clean.eu |apertium-destxt | apertium -f none -d ~/source/apertium-eu-es/ eu-es-pretransfer > opendata.tagged.eu |
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2126 cat opendata.clean.es |apertium-destxt | apertium -f none -d ~/source/apertium-eu-es/ es-eu-pretransfer > opendata.tagged.es & |
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2132 seq 1 771238 > opendata.lines |
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2133 paste opendata.lines opendata.tagged.eu opendata.tagged.es | grep '<' | cut -f1 > opendata.lines.new |
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2134 paste opendata.lines opendata.tagged.eu opendata.tagged.es | grep '<' | cut -f2 > opendata.tagged.eu.new |
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2135 paste opendata.lines opendata.tagged.eu opendata.tagged.es | grep '<' | cut -f3 > opendata.tagged.es.new |
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2137 mv opendata.lines.new opendata.lines |
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2138 mv opendata.tagged.es.new opendata.tagged.es |
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2139 mv opendata.tagged.eu.new opendata.tagged.eu |
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2146 cat opendata.tagged.eu | lt-proc -b ~/source/apertium-eu-es/eu-es.autobil.bin >/tmp/eu-es.bil1 |
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2148 cat opendata.tagged.eu | lt-proc -b ~/source/apertium-eu-es/eu-es.autobil-noRL.bin >/tmp/eu-es.bil2 |
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$ tail -n 1 /tmp/*.poly |
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==> /tmp/eu-es.bil1.poly <== |
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1.00240014637 |
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==> /tmp/eu-es.bil2.poly <== |
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1.3015831681 |
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2191 mv /tmp/eu-es.bil2 opendata.biltrans.eu-es |
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2258 cat opendata.tagged.es | python /home/fran/source/apertium-lex-tools/scripts/process-tagger-output.py es > opendata.token.es |
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2007 cat opendata.tagged.eu | python /home/fran/source/apertium-lex-tools/scripts/process-tagger-output.py eu > opendata.token.eu |
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2014 cat opendata.biltrans.eu-es | python /home/fran/source/apertium-lex-tools/scripts/process-biltrans-output.py > opendata.token.eu-es & |
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$ nohup perl ~/local/bin/scripts-20120109-1229/training/train-model.perl -scripts-root-dir \ |
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/home/fran/local/bin/scripts-20120109-1229/ -root-dir . -corpus opendata.token -f eu -e es -alignment grow-diag-final-and \ |
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-reordering msd-bidirectional-fe -lm 0:5:/home/fran/corpora/europarl/europarl.lm:0 >log 2>&1 & |
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2011 paste opendata.lines opendata.token.eu opendata.token.es | grep -v '\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*' | cut -f1 > opendata.lines.new& |
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2013 paste opendata.lines opendata.token.eu opendata.token.es | grep -v '\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*' | cut -f2 > opendata.eu.new & |
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2014 paste opendata.lines opendata.token.eu opendata.token.es | grep -v '\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*' | cut -f3 > opendata.es.new & |
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2017 mv opendata.lines.new opendata.lines |
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2018 mv opendata.es.new opendata.token.es |
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2019 mv opendata.eu.new opendata.token.eu |
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2032 paste opendata.lines opendata.token.eu opendata.token.es | grep -v '<sent>.*<sent>.*<sent>.*<sent>.*<sent>.*<sent>' | cut -f1 > opendata.lines.new |
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2033 paste opendata.lines opendata.token.eu opendata.token.es | grep -v '<sent>.*<sent>.*<sent>.*<sent>.*<sent>.*<sent>' | cut -f2 > opendata.eu.new & |
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2034 paste opendata.lines opendata.token.eu opendata.token.es | grep -v '<sent>.*<sent>.*<sent>.*<sent>.*<sent>.*<sent>' | cut -f3 > opendata.es.new & |
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2035 mv opendata.lines.new opendata.lines |
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2036 mv opendata.es.new opendata.token.es |
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2037 mv opendata.eu.new opendata.token.eu |
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2055 cat opendata.token.es | sed 's/ *$//g' > opendata.token.es.new |
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2056 cat opendata.token.eu | sed 's/ *$//g' > opendata.token.eu.new |
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2057 mv opendata.token.es.new opendata.token.es |
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2058 mv opendata.token.eu.new opendata.token.eu |
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</pre> |
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===English→Spanish=== |
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<pre> |
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2114 perl /home/fran/local/bin/scripts-20120109-1229/training/clean-corpus-n.perl europarl en es europarl.clean 1 40 |
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2056 cat europarl.clean.en | apertium-destxt | apertium -f none -d ~/source/apertium-en-es en-es-pretransfer > europarl.tagged.en & |
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2057 cat europarl.clean.es | apertium-destxt | apertium -f none -d ~/source/apertium-en-es es-en-pretransfer > europarl.tagged.es & |
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2073 paste europarl.lines europarl.tagged.en europarl.tagged.es | grep '<' | cut -f1 > europarl.lines.new |
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2074 paste europarl.lines europarl.tagged.en europarl.tagged.es | grep '<' | cut -f2 > europarl.tagged.en.new |
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2075 paste europarl.lines europarl.tagged.en europarl.tagged.es | grep '<' | cut -f3 > europarl.tagged.es.new |
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2087 paste europarl.lines europarl.tagged.en europarl.tagged.es | grep -v '<sent>.*<sent>.*<sent>.*<sent>.*<sent>.*<sent>' | grep -v '\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*' | cut -f1 >europarl.lines.new |
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2088 bg |
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2089 paste europarl.lines europarl.tagged.en europarl.tagged.es | grep -v '<sent>.*<sent>.*<sent>.*<sent>.*<sent>.*<sent>' | grep -v '\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*' | cut -f2 >europarl.en.new& |
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2090 paste europarl.lines europarl.tagged.en europarl.tagged.es | grep -v '<sent>.*<sent>.*<sent>.*<sent>.*<sent>.*<sent>' | grep -v '\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*.*\*' | cut -f3 >europarl.es.new& |
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2097 nohup cat europarl.tagged.en | python ~/source/apertium-lex-tools/scripts/process-tagger-output.py en > europarl.token.en & |
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2098 nohup cat europarl.tagged.es | python ~/source/apertium-lex-tools/scripts/process-tagger-output.py es > europarl.token.es & |
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2099 nohup cat europarl.biltrans.en-es | python ~/source/apertium-lex-tools/scripts/process-biltrans-output.py > europarl.token.en-es & |
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</pre> |
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===Macedonian→English=== |
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<pre> |
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:%s/еfу/еѓу/g |
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:%s/аfа/аѓа/g |
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:%s/оfа/оѓа/g |
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:%s/уfе/уѓе/g |
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:%s/нfи/нѓи/g |
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:%s/Ѓиниfиќ/Ѓинѓиќ/g |
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:%s/еfе/еѓе/g |
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:%s/уfм/уѓм/g |
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:%s/рfи/рѓи/g |
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:%s/ fе / ѓе /g |
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:%s/рfе/рѓе/g |
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:%s/уfи/уѓи/g |
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:%s/ fу/ ѓу/g |
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:%s/Караfорѓевиќ/Караѓорѓевиќ/g |
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:%s/Холанfанец/Холанѓанец/g |
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:%s/реfаваат/реѓаваат/g |
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:%s/Швеfанката/Швеѓанката/g |
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:%s/Новозеланfани/Новозеланѓани/g |
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:%s/Мрfан/Мрѓан/g |
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:%s/Анfелка/Анѓелка/g |
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:%s/рfосаната/рѓосаната/g |
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:%s/оттуfуваоето/оттуѓуваоето/g |
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:%s/Енfел/Енѓел/g |
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:%s/Караfорѓевиќ/Караѓорѓевиќ/g |
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:%s/маfународната/маѓународната/g |
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:%s/Пеfа/Пеѓа/g |
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:%s/маfепсник/маѓепсник/g |
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:%s/Караfорѓе/Караѓорѓе/g |
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:%s/Граfевинар/Граѓевинар/g |
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:%s/Меfаши/Меѓаши/g |
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:%s/Ванfел/Ванѓел/g |
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:%s/Караfиќ/Караѓиќ/g |
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:%s/Анfели/Анѓели/g |
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:%s/саfи/саѓи/g |
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:%s/маfионичарски/маѓионичарски/g |
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:%s/Караfорѓевиќ/Караѓорѓевиќ/g |
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:%s/панаfур/панаѓур/g |
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:%s/Ѓерf/Ѓерѓ/g |
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:%s/Ѓинѓиf/Ѓинѓиѓ/g |
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2042 paste setimes.mk setimes.en| grep -v '^(' | cut -f1 > setimes.mk.new |
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2043 paste setimes.mk setimes.en| grep -v '^(' | cut -f2 > setimes.en.new |
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2044 paste setimes.en.new setimes.mk.new | grep -v '^(' | cut -f1 > setimes.en |
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2045 paste setimes.en.new setimes.mk.new | grep -v '^(' | cut -f2 > setimes.mk |
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perl /home/fran/local/bin/scripts-20120109-1229/training/clean-corpus-n.perl setimes mk en setimes.clean 1 40 |
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2052 cat setimes.clean.mk | apertium-destxt | apertium -f none -d ~/source/apertium-mk-en/ mk-en-pretransfer > setimes.tagged.mk& |
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2054 cat setimes.clean.en | apertium-destxt | apertium -f none -d ~/source/apertium-mk-en/ en-mk-pretransfer > setimes.tagged.en& |
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2063 seq 1 190503 > setimes.lines |
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2064 paste setimes.lines setimes.tagged.mk setimes.tagged.en | grep '<' | cut -f1 > setimes.lines.new |
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2065 paste setimes.lines setimes.tagged.mk setimes.tagged.en | grep '<' | cut -f2 > setimes.mk.new |
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2066 paste setimes.lines setimes.tagged.mk setimes.tagged.en | grep '<' | cut -f3 > setimes.en.new |
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2067 mv setimes.en.new setimes.tagged.en |
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2068 mv setimes.mk.new setimes.tagged.mk |
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2069 mv setimes.lines.new setimes.lines |
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2077 nohup cat setimes.tagged.mk | lt-proc -b ~/source/apertium-mk-en/mk-en.autobil.bin > setimes.biltrans.mk-en & |
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2122 cat setimes.tagged.mk | python ~/source/apertium-lex-tools/scripts/process-tagger-output.py mk > setimes.token.mk & |
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2123 cat setimes.tagged.en | python ~/source/apertium-lex-tools/scripts/process-tagger-output.py en > setimes.token.en & |
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</pre> |
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Revision as of 13:48, 13 September 2012
TODO
Do LER in/out domain testing for the en-es setup with news commentary.Do BLEU in/out domain testing for the en-es setup with news commentary.mk-en: why is TLM LER/BLEU so much better ?- (partial) answer: 0-context rules (e.g. defaults) not applying properly. Fixed by running in series. This "solves" the LER issue.
- (partial) answer: preposition selection is much better. We could try running with ling-default preps.
- Do pairwise bootstrap resampling for each of best baseline + best rules
- why do breton numbers for monolingual rules not approach TLM ?
- why do eu-es rules not improve over freq ?
- (partial) answer: some rules do not apply because of tag wankery. See line #129774 in the test corpus. Need to define better how tags work. Perhaps only include tags where ambiguous ?
Corpus stats
| Pair | Corpus | Lines | W. (src) | SL cov. | Extracted | Extracted (%) | L. (train) | L. (test) | L (dev) | Uniq. tokens >1 trad. | Avg. trad / word | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| br-fr | oab | 57,305 | 702,328 | 94.47% | 4,668 | 8.32 | 2,668 | 1,000 | 1,000 | 603 | 1.07 | |
| en-es | europarl | 1,467,708 | 30,154,098 | 98.08% | 312,162 | 22.18 | 310,162 | 1,000 | 1,000 | 2,082 | 1.08 | |
| eu-es | opendata.euskadi.net | 765,115 | 10,190,079 | 91.70% | 87,907 | 11.48 | 85,907 | 1,000 | 1,000 | 1,806 | 1.30 | |
| mk-en | setimes | 190,493 | 4,259,338 | 92.17% | 19,747 | 10.94 | 17,747 | 1,000 | 1,000 | 13,134 | 1.86 | |
| sh-mk | setimes |
Evaluation corpus
Out of domain
| Pair | Lines | Words (L1) | Words (L2) | Ambig. tokens | Ambig. types | Ambig token/type | % ambig | Av. trad/word |
|---|---|---|---|---|---|---|---|---|
| en-es | 434 | 9,463 | 10,280 | 619 | 303 | 2.04 | 6.54% | - |
In domain
| Pair | Lines | Words (L1) | Words (L2) | Ambig. tokens | Ambig. types | Ambig token/type | % ambig | Av. trad/word |
|---|---|---|---|---|---|---|---|---|
| br-fr | 1,000 | 13,854 | 13,878 | 1,163 | 372 | 3.13 | 8.39% | - |
| en-es | 1,000 | 19,882 | 20,944 | 1,469 | 337 | 4.35 | 7.38% | - |
| eu-es | 1,000 | 7,967 | 11,476 | 1,360 | 412 | 3.30 | 17.07% | - |
| mk-en | 1,000 | 13,441 | 14,228 | 3,872 | 1,289 | 3.00 | 28.80% | - |
- % ambig = number of SL tokens with >1 translation
EAMT-style results
Out of domain
LER
| Pair | freq | tlm | ling | alig | rules (c>1.5) |
rules (c>2.0) |
rules (c>2.5) |
rules (c>3.0) |
rules (c>3.5) |
rules (c>4.0) |
|---|---|---|---|---|---|---|---|---|---|---|
| en-es | — [44.5, 52.0] |
— [34.7, 41.9] |
667 [24.7, 31.9] |
630 [ 21.4 , 28.4 ] |
2881 [20.2, 27.2] |
2728 [20.2, 27.2] |
1683 [20.7, 27.6] |
1578 [20.7, 27.6] |
1242 [20.7, 27.6] |
1197 [20.7, 27.6] |
BLEU
| Pair | freq | tlm | ling | alig | rules (c>1.5) |
rules (c>2.0) |
rules (c>2.5) |
rules (c>3.0) |
rules (c>3.5) |
rules (c>4.0) |
|---|---|---|---|---|---|---|---|---|---|---|
| en-es | [0.1885, 0.2133] | [0.1953, 0.2201] | [0.1832, 0.2067] | [0.1832, 0.2067] | [0.1831, 0.2067] | [0.1830, 0.2067] | [ [0.1828, 0.2063] | [0.1828, 0.2063] | [0.1828, 0.2063] |
In domain
LER
is the "crispiness" ratio, the amount of times an alternative translation is seen in a given context compared to the default translation. So, a of 2.0 means that the translation appears twice as frequently as the default.
| Pair | freq | tlm | ling | alig | rules (c>1.5) |
rules (c>2.0) |
rules (c>2.5) |
rules (c>3.0) |
rules (c>3.5) |
rules (c>4.0) |
|---|---|---|---|---|---|---|---|---|---|---|
| br-fr | — [58.9, 64.8] |
— [44.2, 50.5] |
168 [54.8, 60.7] |
115 [28.5, 34.1] |
221 [27.8, 33.3] |
213 [27.6, 33.0] |
159 [26.3, 31.8] |
150 [26.1, 31.6] |
135 [27.2, 32.8] |
135 [27.2, 32.8] |
| en-es | — [21.0, 25.3] |
— [15.1, 18.9] |
667 [20.7, 25.1 |
630 [7.2, 10.0] |
2881 [5.9, 8.6] |
2728 [6.0, 8.6] |
1683 [5.7, 8.3] |
1578 [5.7, 8.3] |
1242 [6.0, 8.5] |
1197 [5.9, 8.6] |
| eu-es | — [41.1, 46.6] |
— [38.8, 44.2] |
697 [47.8, 53.0] |
598 [16.5, 20.8] |
2253 [20.2, 24.7] |
2088 [17.2, 21.7] |
1382 [16.8, 21.0] |
1266 [16.1, 20.4] |
1022 [15.9, 20.2] |
995 [16.0, 20.3] |
| mk-en | — [42.4, 46.3] |
— [27.1, 30.8] |
1385 [28.8, 32.6] |
1079 [19.0, 22.2] |
1684 [18.5, 21.5] |
1635 [18.6, 21.6] |
1323 [19.1, 22.2] |
1271 [19.0, 22.0] |
1198 [19.1, 22.1] |
1079 [19.1, 22.1] |
BLEU
| Pair | freq | tlm | ling | alig | rules (c>1.5) |
rules (c>2.0) |
rules (c>2.5) |
rules (c>3.0) |
rules (c>3.5) |
rules (c>4.0) |
|---|---|---|---|---|---|---|---|---|---|---|
| br-fr | — [0.1247, 0.1420] |
— [0.1397, 0.1572] |
168 [0.1325, 0.1503] |
115 [0.1344, 0.1526] |
221 [0.1367, 0.1551] |
213 [0.1367, 0.1549] |
159 [0.1374, 0.1554] |
150 [0.1364, 0.1543] |
135 [0.1352, 0.1535] |
135 [0.1352, 0.1535] |
| en-es | — [0.2151, 0.2340] |
— [0.2197, 0.2384] |
667 [0.2148, 0.2337] |
630 [0.2208, 0.2398] |
2881 [0.2217, 0.2405] |
2728 [0.2217, 0.2406] |
1683 [0.2217, 0.2407] |
1578 [0.2217, 0.2407] |
1242 [0.2217, 0.2407] |
1197 [0.2217, 0.2408] |
| eu-es | — [0.0873, 0.1038] |
— [0.0921, 0.1093] |
697 [0.0870, 0.1030] |
598 [0.0972, 0.1149] |
2253 [0.0965, 0.1142] |
2088 [0.0971, 0.1147] |
1382 [0.0971, 0.1148] |
1266 [0.0971, 0.1148] |
1022 [0.0973, 0.1150] |
995 [0.0973, 0.1150] |
| mk-en | — [0.2300, 0.2511] |
— [0.2976, 0.3230] |
1385 [0.2337, 0.2563] |
1079 [0.2829, 0.3064] |
1684 [0.2838, 0.3071] |
1635 [0.2834, 0.3067] |
1323 [0.2825, 0.3058] |
1271 [0.2827, 0.3059] |
1198 [0.2827, 0.3059] |
1079 |
Learning monolingually
Setup:
- SL side of the training corpus
- All possibilities translated and scored
- Absolute winners taken
- Rules generated by counting ngrams in the same way as with the parallel corpus, only no alignment needed as it works like an annotated corpus.
Out of domain
LER
| Pair | freq | tlm | ling | alig | rules (c>1.5) |
rules (c>2.0) |
rules (c>2.5) |
rules (c>3.0) |
rules (c>3.5) |
rules (c>4.0) |
|---|---|---|---|---|---|---|---|---|---|---|
| en-es | [44.5, 52.0] | [34.7, 41.9] | [24.7, 31.9] | [30.2, 37.9] | [30.2, 37.9] | [29.2, 37.0] | [29.3, 36.8] | [29.0, 36.4] | [29.1, 36.5] |
BLEU
| Pair | freq | tlm | ling | alig | rules (c>1.5) |
rules (c>2.0) |
rules (c>2.5) |
rules (c>3.0) |
rules (c>3.5) |
rules (c>4.0) |
|---|---|---|---|---|---|---|---|---|---|---|
| en-es | [0.1885, 0.2133] | [0.1953, 0.2201] | [0.1832, 0.2067] | [0.1806, 0.2042] | [0.1806, 0.2042] | [0.1808, 0.2043] | [0.1810, 0.2046] | [0.1809, 0.2045] | [0.1809, 0.2045] |
In domain
LER
| Pair | freq | tlm | ling | alig | rules (c>1.5) |
rules (c>2.0) |
rules (c>2.5) |
rules (c>3.0) |
rules (c>3.5) |
rules (c>4.0) |
|---|---|---|---|---|---|---|---|---|---|---|
| br-fr | — [58.9, 64.8] |
— [44.2, 50.5] |
168 [54.8, 60.7] |
115 |
261 [53.5, 59.2] |
247 [52.1, 58.2] |
172 [54.3, 60.2] |
165 [52.7, 58.4] |
138 [50.5, 56.3] |
136 [50.6, 56.6] |
| en-es | — [21.0, 25.3] |
— [15.1, 18.9] |
667 [20.7, 25.1] |
? ? |
2595 [15.0, 19.0] |
2436 [15.1, 19.1] |
1520 [13.7, 17.6] |
1402 [13.6, 17.3] |
1065 [13.9, 17.7] |
1024 [13.9, 17.8] |
| eu-es | — [41.1, 46.6] |
— [38.8, 44.2] |
? [47.8, 53.0] |
? |
2631 [40.9, 46.4] |
2427 [40.9, 46.5] |
1186 [40.7, 46.1] |
1025 [40.7, 46.2] |
685 [40.5, 45.9] |
641 [40.5, 45.9] |
| mk-en | — [42.4, 46.3] |
— [27.1, 30.8] |
1385 [28.8, 32.6] |
? |
1698 [27.8, 31.5] |
1662 [27.8, 31.4] |
1321 [27.8, 31.4] |
1285 [27.8, 31.4] |
1186 [27.7, 31.4] |
1180 [27.7, 31.4] |
BLEU
| Pair | freq | tlm | ling | alig | rules (c>1.5) |
rules (c>2.0) |
rules (c>2.5) |
rules (c>3.0) |
rules (c>3.5) |
rules (c>4.0) |
|---|---|---|---|---|---|---|---|---|---|---|
| br-fr | — [0.1247, 0.1420] |
— [0.1397, 0.1572] |
168 [0.1325, 0.1503] |
115 |
261 [0.1250, 0.1425] |
247 [0.1252, 0.1429] |
172 [0.1240, 0.1412] |
165 [0.1243, 0.1416] |
138 [0.1255, 0.1429] |
136 [0.1255, 0.1429] |
| en-es | — [0.2151, 0.2340] |
— [0.2197, 0.2384] |
667 [0.2148, 0.2337] |
? |
2595 [0.2180, 0.2371] |
2436 [0.2180, 0.2372] |
1520 [0.2190, 0.2380] |
1402 [0.2190, 0.2381] |
1065 [0.2189, 0.2380] |
1024 [0.2189, 0.2380] |
| eu-es | — [0.0873, 0.1038] |
— [0.0921, 0.1093] |
? [0.0870, 0.1030] |
? |
2631 [0.0875, 0.1040] |
2427 [0.0878, 0.1042] |
1186 [0.0878, 0.1043] |
1025 [0.0878, 0.1043] |
685 [0.0879, 0.1043] |
641 [0.0879, 0.1043] |
| mk-en | — [0.2300, 0.2511] |
— [0.2976, 0.3230] |
1385 [0.2567, 0.2798] |
1698 [0.2694, 0.2930] |
1662 [0.2695, 0.2931] |
1321 [0.2696, 0.2935] |
1285 [0.2696, 0.2935] |
1186 [0.2696, 0.2934] |
1180 [0.2696, 0.2934] |
