multifit/results/logs/tokenization/average10runs.md

TOK=sp15k NAME=e8avg for LANG in ru fr; do for i in 1 2 3 4 5 6 7 8 9; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/${TOK}/qrnn_nl4.m" --name ${NAME}$i --num-cls-epochs=8 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=18; done done TOK=sp15k NAME=avg for LANG in ru fr; do for i in 1 2 3 4 5 6 7 8 9; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/${TOK}/qrnn_nl4.m" --name ${NAME}$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=18; done done

NAME=e8avg TOK=vf60k for LANG in ru fr; do for i in 1 2 3 4 5 6 7 8 9; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/${TOK}/qrnn_nl4.m" --name ${NAME}$i --num-cls-epochs=8 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=18; done done NAME=avg TOK=vf60k for LANG in ru fr; do for i in 1 2 3 4 5 6 7 8 9; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/${TOK}/qrnn_nl4.m" --name ${NAME}$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=18; done done

for TOK in vf60k sp15k; do for LANG in ru fr; do NAME=e8avg for i in 1 2 3 4 5 6 7 8 9; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/${TOK}/qrnn_nl4.m" --name ${NAME}_$i --num-cls-epochs=8 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=18; done done done

for TOK in vf60k sp15k; do for LANG in ru fr; do NAME=avg
for i in 1 2 3 4 5 6 7 8 9; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/${TOK}/qrnn_nl4.m" --name ${NAME}$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=18; done done done LANG=es for i in 1 2 3 4 5 6 7 9; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/sp15k/qrnn_nl4.m" --name avg$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=18; done

LANG=de for i in 1 2 3 4 5 6 7 9; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/sp15k/qrnn_nl4.m" --name avg_$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=18; done

epoch 8

vf60k ds de-1 es-1 fr-1 it-1 ru-1 best 95.45 96.17 94.77 90.72 87.85 max 95.63 96.43 95.32 91.05 88.30 avg 95.42 96.05 95.07 90.59 87.80

sp15k ds de-1 es-1 fr-1 it-1 ru-1 best 96.17 95.92 94.55 90.45 86.95 max 96.28 96.03 95.10 90.72 87.45 avg 96.01 95.72 94.63 90.37 86.95

-0-- 0 data/mldoc/fr-1/models/vf60k/qrnn_e8avg_1.m 0.95325 0.211328 0.951 0.211664 1 data/mldoc/fr-1/models/vf60k/qrnn_e8avg_2.m 0.95075 0.199939 0.947 0.198606 2 data/mldoc/fr-1/models/vf60k/qrnn_e8avg_3.m 0.95125 0.217569 0.952 0.215529 3 data/mldoc/fr-1/models/vf60k/qrnn_e8avg_4.m 0.95225 0.208047 0.951 0.203784 4 data/mldoc/fr-1/models/vf60k/qrnn_e8avg_5.m 0.95025 0.206937 0.946 0.206194 5 data/mldoc/fr-1/models/vf60k/qrnn_e8avg_6.m 0.95075 0.203967 0.951 0.204809 6 data/mldoc/fr-1/models/vf60k/qrnn_e8avg_7.m 0.94775 0.211408 0.954 0.201543 7 data/mldoc/fr-1/models/vf60k/qrnn_e8avg_8.m 0.95075 0.202703 0.952 0.197218 8 data/mldoc/fr-1/models/vf60k/qrnn_e8avg_9.m 0.94925 0.207698 0.947 0.204896 9 data/mldoc/ru-1/models/vf60k/qrnn_avg_1.m 0.88300 0.375823 0.876 0.364029 10 data/mldoc/ru-1/models/vf60k/qrnn_avg_2.m 0.87675 0.386695 0.883 0.356660 11 data/mldoc/ru-1/models/vf60k/qrnn_avg_3.m 0.87750 0.372321 0.879 0.374368 12 data/mldoc/ru-1/models/vf60k/qrnn_avg_4.m 0.87400 0.379490 0.875 0.370343 13 data/mldoc/ru-1/models/vf60k/qrnn_avg_5.m 0.87725 0.380067 0.877 0.367522 14 data/mldoc/ru-1/models/vf60k/qrnn_avg_6.m 0.87525 0.393280 0.874 0.368825 15 data/mldoc/ru-1/models/vf60k/qrnn_avg_7.m 0.87900 0.380393 0.882 0.373376 16 data/mldoc/ru-1/models/vf60k/qrnn_avg_8.m 0.88025 0.376825 0.875 0.375059 17 data/mldoc/ru-1/models/vf60k/qrnn_avg_9.m 0.88125 0.380887 0.884 0.367705 18 data/mldoc/ru-1/models/vf60k/qrnn_e8avg_1.m 0.87850 0.385976 0.888 0.385302 19 data/mldoc/ru-1/models/vf60k/qrnn_e8avg_2.m 0.87600 0.384469 0.879 0.384878 20 data/mldoc/ru-1/models/vf60k/qrnn_e8avg_4.m 0.87600 0.386223 0.870 0.391646 21 data/mldoc/ru-1/models/vf60k/qrnn_e8avg_5.m 0.87950 0.385152 0.885 0.371122 22 data/mldoc/ru-1/models/vf60k/qrnn_e8avg_6.m 0.88175 0.383746 0.875 0.391054 23 data/mldoc/ru-1/models/vf60k/qrnn_e8avg_7.m 0.87850 0.392120 0.874 0.382000 24 data/mldoc/ru-1/models/vf60k/qrnn_e8avg_8.m 0.87250 0.394342 0.881 0.378663 25 data/mldoc/ru-1/models/vf60k/qrnn_e8avg_9.m 0.87950 0.387625 0.881 0.380548 ds fr-1 ru-1 best 94.77 87.85 max 95.32 88.30 avg 95.07 87.80

epoch 4

SP15k

                                     name  tst_accuracy  tst_loss  val_accuracy  val_loss

0 data/mldoc/de-1/models/sp15k/qrnn_avg_1.m 0.95700 0.142444 0.945 0.206329 1 data/mldoc/de-1/models/sp15k/qrnn_avg_2.m 0.95975 0.141225 0.955 0.189209 2 data/mldoc/de-1/models/sp15k/qrnn_avg_3.m 0.95925 0.140172 0.946 0.198159 3 data/mldoc/de-1/models/sp15k/qrnn_avg_4.m 0.95650 0.145889 0.942 0.193202 4 data/mldoc/de-1/models/sp15k/qrnn_avg_5.m 0.96000 0.137710 0.953 0.192368 5 data/mldoc/de-1/models/sp15k/qrnn_avg_6.m 0.95975 0.145318 0.954 0.196413 6 data/mldoc/de-1/models/sp15k/qrnn_avg_7.m 0.95925 0.141719 0.943 0.199226 7 data/mldoc/de-1/models/sp15k/qrnn_avg_8.m 0.96100 0.140644 0.949 0.204398 8 data/mldoc/de-1/models/sp15k/qrnn_avg_9.m 0.96050 0.143330 0.949 0.189472 9 data/mldoc/es-1/models/sp15k/qrnn_avg_1.m 0.95700 0.149081 0.963 0.162808 10 data/mldoc/es-1/models/sp15k/qrnn_avg_2.m 0.95875 0.141572 0.963 0.150970 11 data/mldoc/es-1/models/sp15k/qrnn_avg_3.m 0.95900 0.151387 0.962 0.157047 12 data/mldoc/es-1/models/sp15k/qrnn_avg_4.m 0.95375 0.165935 0.956 0.182161 13 data/mldoc/es-1/models/sp15k/qrnn_avg_5.m 0.95850 0.151109 0.960 0.156376 14 data/mldoc/es-1/models/sp15k/qrnn_avg_6.m 0.95800 0.150724 0.961 0.152761 15 data/mldoc/es-1/models/sp15k/qrnn_avg_7.m 0.95875 0.142476 0.963 0.151585 16 data/mldoc/es-1/models/sp15k/qrnn_avg_8.m 0.95525 0.165120 0.957 0.164723 17 data/mldoc/es-1/models/sp15k/qrnn_avg_9.m 0.95725 0.151323 0.960 0.156123 18 data/mldoc/it-1/models/sp15k/qrnn_avg_1.m 0.90175 0.312996 0.900 0.297118 19 data/mldoc/it-1/models/sp15k/qrnn_avg_2.m 0.90250 0.316763 0.903 0.274423 20 data/mldoc/it-1/models/sp15k/qrnn_avg_3.m 0.89900 0.329157 0.915 0.290098 21 data/mldoc/it-1/models/sp15k/qrnn_avg_4.m 0.90100 0.322112 0.907 0.285727 22 data/mldoc/it-1/models/sp15k/qrnn_avg_5.m 0.90100 0.308545 0.910 0.276683 23 data/mldoc/it-1/models/sp15k/qrnn_avg_6.m 0.90275 0.323594 0.915 0.287004 24 data/mldoc/it-1/models/sp15k/qrnn_avg_7.m 0.89925 0.295158 0.910 0.269167 25 data/mldoc/it-1/models/sp15k/qrnn_avg_9.m 0.90325 0.312664 0.908 0.297471 ds de-1 es-1 it-1 best 95.97 95.70 89.90 max 96.10 95.90 90.32 avg 95.92 95.74 90.13

VF60k

                                     name  tst_accuracy  tst_loss  val_accuracy  val_loss

0 data/mldoc/de-1/models/vf60k/qrnn_avg.m 0.95250 0.193797 0.946 0.225316 1 data/mldoc/de-1/models/vf60k/qrnn_avg_1.m 0.95575 0.157327 0.947 0.189885 2 data/mldoc/de-1/models/vf60k/qrnn_avg_2.m 0.95400 0.174519 0.947 0.201792 3 data/mldoc/de-1/models/vf60k/qrnn_avg_3.m 0.95325 0.180489 0.947 0.208106 4 data/mldoc/de-1/models/vf60k/qrnn_avg_4.m 0.95425 0.161056 0.949 0.199169 5 data/mldoc/de-1/models/vf60k/qrnn_avg_5.m 0.94775 0.182012 0.941 0.210262 6 data/mldoc/de-1/models/vf60k/qrnn_avg_6.m 0.95375 0.164578 0.947 0.198632 7 data/mldoc/de-1/models/vf60k/qrnn_avg_7.m 0.95575 0.152596 0.947 0.196844 8 data/mldoc/de-1/models/vf60k/qrnn_avg_8.m 0.95350 0.167661 0.942 0.203538 9 data/mldoc/es-1/models/vf60k/qrnn_avg.m 0.95950 0.146121 0.961 0.161852 10 data/mldoc/es-1/models/vf60k/qrnn_avg_1.m 0.95500 0.154836 0.960 0.176217 11 data/mldoc/es-1/models/vf60k/qrnn_avg_2.m 0.95850 0.154539 0.961 0.163008 12 data/mldoc/es-1/models/vf60k/qrnn_avg_3.m 0.96100 0.151916 0.966 0.169869 13 data/mldoc/es-1/models/vf60k/qrnn_avg_4.m 0.95825 0.144630 0.962 0.144410 14 data/mldoc/es-1/models/vf60k/qrnn_avg_5.m 0.95675 0.155685 0.960 0.175439 15 data/mldoc/es-1/models/vf60k/qrnn_avg_6.m 0.95900 0.143995 0.959 0.164156 16 data/mldoc/es-1/models/vf60k/qrnn_avg_7.m 0.95800 0.144662 0.962 0.162957 17 data/mldoc/es-1/models/vf60k/qrnn_avg_8.m 0.95850 0.149185 0.962 0.163159 18 data/mldoc/it-1/models/vf60k/qrnn_avg.m 0.89925 0.320389 0.912 0.272104 19 data/mldoc/it-1/models/vf60k/qrnn_avg_1.m 0.90525 0.305978 0.920 0.255507 20 data/mldoc/it-1/models/vf60k/qrnn_avg_2.m 0.90725 0.287647 0.917 0.245568 21 data/mldoc/it-1/models/vf60k/qrnn_avg_3.m 0.89925 0.313870 0.910 0.271480 22 data/mldoc/it-1/models/vf60k/qrnn_avg_4.m 0.91125 0.285618 0.915 0.255942 23 data/mldoc/it-1/models/vf60k/qrnn_avg_5.m 0.91100 0.288841 0.911 0.255724 24 data/mldoc/it-1/models/vf60k/qrnn_avg_6.m 0.90525 0.287412 0.914 0.253394 25 data/mldoc/it-1/models/vf60k/qrnn_avg_7.m 0.90000 0.308104 0.910 0.256991 26 data/mldoc/it-1/models/vf60k/qrnn_avg_8.m 0.90450 0.301262 0.918 0.251368 ds de-1 es-1 it-1 best 95.42 96.10 90.53 max 95.57 96.10 91.12 avg 95.34 95.83 90.48

IT

VF60k - 9 runs eval

for i in 1 2 3 4 5 6 7 8; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/vf60k/qrnn_nl4.m" --name avg_$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=10; done python -m ulmfit eval --glob="mldoc/${LANG}-1/models/vf60k/qrnn_nl4*.m" --train=False

                                    name  tst_accuracy  tst_loss  val_accuracy  val_loss

0 data/mldoc/it-1/models/vf60k/qrnn_nl4.m 0.89925 0.320389 0.912 0.272104 1 data/mldoc/it-1/models/vf60k/qrnn_nl4_1.m 0.90525 0.305978 0.920 0.255507 2 data/mldoc/it-1/models/vf60k/qrnn_nl4_2.m 0.90725 0.287647 0.917 0.245568 3 data/mldoc/it-1/models/vf60k/qrnn_nl4_3.m 0.89925 0.313870 0.910 0.271480 4 data/mldoc/it-1/models/vf60k/qrnn_nl4_4.m 0.91125 0.285618 0.915 0.255942 5 data/mldoc/it-1/models/vf60k/qrnn_nl4_5.m 0.91100 0.288841 0.911 0.255724 6 data/mldoc/it-1/models/vf60k/qrnn_nl4_6.m 0.90525 0.287412 0.914 0.253394 7 data/mldoc/it-1/models/vf60k/qrnn_nl4_7.m 0.90000 0.308104 0.910 0.256991 8 data/mldoc/it-1/models/vf60k/qrnn_nl4_8.m 0.90450 0.301262 0.918 0.251368 ds it-1 best 90.53 max 91.12 avg 90.48

sp15k - 9 runs eval

LANG=it for i in 1 2 3 4 5 6 7 9; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/sp15k/qrnn_nl4.m" --name avg_$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=18; done python -m ulmfit eval --glob="mldoc/${LANG}-1/models/sp15k/qrnn_nl4_a*.m" --train=False name tst_accuracy tst_loss val_accuracy val_loss 0 data/mldoc/it-1/models/sp15k/qrnn_avg_1.m 0.90175 0.312996 0.900 0.297118 1 data/mldoc/it-1/models/sp15k/qrnn_avg_2.m 0.90250 0.316763 0.903 0.274423 2 data/mldoc/it-1/models/sp15k/qrnn_avg_3.m 0.89900 0.329157 0.915 0.290098 3 data/mldoc/it-1/models/sp15k/qrnn_avg_4.m 0.90100 0.322112 0.907 0.285727 4 data/mldoc/it-1/models/sp15k/qrnn_avg_5.m 0.90100 0.308545 0.910 0.276683 5 data/mldoc/it-1/models/sp15k/qrnn_avg_6.m 0.90275 0.323594 0.915 0.287004 6 data/mldoc/it-1/models/sp15k/qrnn_avg_7.m 0.89925 0.295158 0.910 0.269167 7 data/mldoc/it-1/models/sp15k/qrnn_avg_9.m 0.90325 0.312664 0.908 0.297471 ds it-1 best 89.90 max 90.32 avg 90.13

ES

VF60k - 8 runs eval

for i in 1 2 3 4 5 6 7 8; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/vf60k/qrnn_nl4.m" --name avg_$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=10; done python -m ulmfit eval --glob="mldoc/${LANG}-1/models/vf60k/qrnn_nl4*.m" --train=False

                                    name  tst_accuracy  tst_loss  val_accuracy  val_loss

0 data/mldoc/es-1/models/vf60k/qrnn_nl4.m 0.95950 0.146121 0.961 0.161852 1 data/mldoc/es-1/models/vf60k/qrnn_nl4_1.m 0.95500 0.154836 0.960 0.176217 2 data/mldoc/es-1/models/vf60k/qrnn_nl4_2.m 0.95850 0.154539 0.961 0.163008 3 data/mldoc/es-1/models/vf60k/qrnn_nl4_3.m 0.96100 0.151916 0.966 0.169869 4 data/mldoc/es-1/models/vf60k/qrnn_nl4_4.m 0.95825 0.144630 0.962 0.144410 5 data/mldoc/es-1/models/vf60k/qrnn_nl4_5.m 0.95675 0.155685 0.960 0.175439 6 data/mldoc/es-1/models/vf60k/qrnn_nl4_6.m 0.95900 0.143995 0.959 0.164156 7 data/mldoc/es-1/models/vf60k/qrnn_nl4_7.m 0.95800 0.144662 0.962 0.162957 8 data/mldoc/es-1/models/vf60k/qrnn_nl4_8.m 0.95850 0.149185 0.962 0.163159 ds es-1 best 96.10 max 96.10 avg 95.83

sp15k - 8 runs eval

LANG=es for i in 1 2 3 4 5 6 7 9; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/sp15k/qrnn_nl4.m" --name avg_$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=18; done python -m ulmfit eval --glob="mldoc/${LANG}-1/models/sp15k/qrnn_avg*.m" --train=False name tst_accuracy tst_loss val_accuracy val_loss 0 data/mldoc/es-1/models/sp15k/qrnn_avg_1.m 0.95700 0.149081 0.963 0.162808 1 data/mldoc/es-1/models/sp15k/qrnn_avg_2.m 0.95875 0.141572 0.963 0.150970 2 data/mldoc/es-1/models/sp15k/qrnn_avg_3.m 0.95900 0.151387 0.962 0.157047 3 data/mldoc/es-1/models/sp15k/qrnn_avg_4.m 0.95375 0.165935 0.956 0.182161 4 data/mldoc/es-1/models/sp15k/qrnn_avg_5.m 0.95850 0.151109 0.960 0.156376 5 data/mldoc/es-1/models/sp15k/qrnn_avg_6.m 0.95800 0.150724 0.961 0.152761 6 data/mldoc/es-1/models/sp15k/qrnn_avg_7.m 0.95875 0.142476 0.963 0.151585 7 data/mldoc/es-1/models/sp15k/qrnn_avg_8.m 0.95525 0.165120 0.957 0.164723 8 data/mldoc/es-1/models/sp15k/qrnn_avg_9.m 0.95725 0.151323 0.960 0.156123 ds es-1 best 95.70 max 95.90 avg 95.74

DE

VF60k - 9 runs eval

for i in 1 2 3 4 5 6 7 8; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/vf60k/qrnn_nl4.m" --name avg_$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=10; done python -m ulmfit eval --glob="mldoc/${LANG}-1/models/vf60k/qrnn_nl4*.m" --train=False name tst_accuracy tst_loss val_accuracy val_loss 0 data/mldoc/de-1/models/vf60k/qrnn_nl4.m 0.95250 0.193797 0.946 0.225316 1 data/mldoc/de-1/models/vf60k/qrnn_nl4_1.m 0.95575 0.157327 0.947 0.189885 2 data/mldoc/de-1/models/vf60k/qrnn_nl4_2.m 0.95400 0.174519 0.947 0.201792 3 data/mldoc/de-1/models/vf60k/qrnn_nl4_3.m 0.95325 0.180489 0.947 0.208106 4 data/mldoc/de-1/models/vf60k/qrnn_nl4_4.m 0.95425 0.161056 0.949 0.199169 5 data/mldoc/de-1/models/vf60k/qrnn_nl4_5.m 0.94775 0.182012 0.941 0.210262 6 data/mldoc/de-1/models/vf60k/qrnn_nl4_6.m 0.95375 0.164578 0.947 0.198632 7 data/mldoc/de-1/models/vf60k/qrnn_nl4_7.m 0.95575 0.152596 0.947 0.196844 8 data/mldoc/de-1/models/vf60k/qrnn_nl4_8.m 0.95350 0.167661 0.942 0.203538 ds de-1 best 95.42 max 95.57 avg 95.34

sp15k - 8 runs eval

for i in 1 2 3 4 5 6 7 9; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/sp15k/qrnn_nl4.m" --name avg_$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=18; done python -m ulmfit eval --glob="mldoc/${LANG}-1/models/sp15k/qrnn_avg*.m" --train=False name tst_accuracy tst_loss val_accuracy val_loss 0 data/mldoc/de-1/models/sp15k/qrnn_avg_1.m 0.95700 0.142444 0.945 0.206329 1 data/mldoc/de-1/models/sp15k/qrnn_avg_2.m 0.95975 0.141225 0.955 0.189209 2 data/mldoc/de-1/models/sp15k/qrnn_avg_3.m 0.95925 0.140172 0.946 0.198159 3 data/mldoc/de-1/models/sp15k/qrnn_avg_4.m 0.95650 0.145889 0.942 0.193202 4 data/mldoc/de-1/models/sp15k/qrnn_avg_5.m 0.96000 0.137710 0.953 0.192368 5 data/mldoc/de-1/models/sp15k/qrnn_avg_6.m 0.95975 0.145318 0.954 0.196413 6 data/mldoc/de-1/models/sp15k/qrnn_avg_7.m 0.95925 0.141719 0.943 0.199226 7 data/mldoc/de-1/models/sp15k/qrnn_avg_8.m 0.96100 0.140644 0.949 0.204398 8 data/mldoc/de-1/models/sp15k/qrnn_avg_9.m 0.96050 0.143330 0.949 0.189472 ds de-1 best 95.97 max 96.10 avg 95.92

RU

VF60k - 9 runs eval

for i in 1 2 3 4 5 6 7 8; do python -m ulmfit eval --glob="mldoc/${LANG}-1/models/vf60k/qrnn_nl4.m" --name nl4_$i --num-cls-epochs=4 --label-smoothing-eps=0.1 --lr_sched=1cycle --bs=10; done python -m ulmfit eval --glob="mldoc/${LANG}-1/models/vf60k/qrnn_nl4*.m" --train=False