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log.txt
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'fastai-text-classification' /home/zeio/fastai-text-classification: warning: ignoring duplicate product 'fastai-text-classification'
[1/4] Compiling fastai-text-classification DataWrangler.swift
[2/4] Compiling fastai-text-classification main.swift
[3/4] Compiling fastai-text-classification ModelWrapper.swift
[4/5] Merging module fastai-text-classification
[5/5] Linking fastai-text-classification
Omitting the description of the dataset...
███epoch train_loss valid_loss accuracy time
█0 4.502695 #na# 00:00
█1 4.511107 #na# 00:00
█2 4.515493 #na# 00:00
█3 4.508225 #na# 00:00
█4 4.503147 #na# 00:00
█5 4.501502 #na# 00:00
█6 4.507178 #na# 00:00
█7 4.511981 #na# 00:00
█8 4.513892 #na# 00:00
█9 4.514447 #na# 00:00
█10 4.513804 #na# 00:00
█11 4.514897 #na# 00:00
█12 4.515400 #na# 00:00
█13 4.510637 #na# 00:00
█14 4.505597 #na# 00:00
█15 4.507922 #na# 00:00
█16 4.497940 #na# 00:00
█17 4.482595 #na# 00:00
█18 4.455743 #na# 00:00
█19 4.414254 #na# 00:00
█20 4.360962 #na# 00:00
█21 4.285936 #na# 00:00
█22 4.198183 #na# 00:00
█23 4.099013 #na# 00:00
█24 3.991468 #na# 00:00
█25 3.882308 #na# 00:00
█26 3.766646 #na# 00:00
█27 3.651704 #na# 00:00
█28 3.541467 #na# 00:00
█29 3.473616 #na# 00:00
█30 3.561753 #na# 00:00
█31 4.082256 #na# 00:00
█32 5.724164 #na# 00:00
█33 6.617788 #na# 00:00
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 100 learning rates
Selected learning rate 0.36307805477010097 for training language model
█epoch train_loss valid_loss accuracy time
██0 4.347291 6.143998 0.147321 00:00
██1 4.631615 4.797844 0.256696 00:00
██2 4.478694 4.023266 0.320312 00:00
██3 4.171773 3.737931 0.324554 00:00
███epoch train_loss valid_loss accuracy time
█0 1.165191 #na# 00:01
█1 1.188201 #na# 00:01
█2 1.184713 #na# 00:01
█3 1.190958 #na# 00:01
█4 1.197930 #na# 00:01
█5 1.183362 #na# 00:01
█6 1.171739 #na# 00:01
█7 1.162997 #na# 00:01
█8 1.142529 #na# 00:01
█9 1.147640 #na# 00:01
█10 1.207117 #na# 00:01
█11 5.557633 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 1e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.236795 1.103185 0.333333 00:01
██1 1.205995 1.102038 0.343434 00:01
██2 1.214306 1.103007 0.343434 00:01
██3 1.210442 1.108551 0.282828 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 4 epochs and with 0 unfrozen layers:
█Most confused classes:
[(2, 0, 21), (4, 0, 17), (4, 2, 13), (0, 2, 11), (2, 4, 6), (0, 4, 3)]
Confusion matrix:
[[19 11 3]
[21 8 6]
[17 13 1]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 0, tensor(0), tensor([0.3615, 0.3573, 0.2812]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 0, tensor(0), tensor([0.3675, 0.3350, 0.2975]))
███epoch train_loss valid_loss accuracy time
█0 1.223028 #na# 00:01
█1 1.211854 #na# 00:01
█2 1.205026 #na# 00:01
█3 1.215458 #na# 00:01
█4 1.207833 #na# 00:01
█5 1.202275 #na# 00:01
█6 1.183561 #na# 00:01
█7 1.160202 #na# 00:01
█8 1.147227 #na# 00:01
█9 1.163392 #na# 00:01
█10 1.226474 #na# 00:01
█11 6.985662 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 96 learning rates
Selected learning rate 1.202264434617413e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.231978 1.096203 0.333333 00:01
██1 1.194664 1.097673 0.353535 00:01
██2 1.192934 1.099589 0.343434 00:01
██3 1.196672 1.102680 0.363636 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 4 epochs and with 1 unfrozen layers:
█Most confused classes:
[(4, 0, 18), (2, 4, 17), (2, 0, 12), (0, 4, 11), (0, 2, 3), (4, 2, 2)]
Confusion matrix:
[[19 3 11]
[12 6 17]
[18 2 11]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 4, tensor(2), tensor([0.3241, 0.3252, 0.3507]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 4, tensor(2), tensor([0.2948, 0.3150, 0.3901]))
███epoch train_loss valid_loss accuracy time
█0 1.196818 #na# 00:01
█1 1.198496 #na# 00:01
█2 1.213971 #na# 00:01
█3 1.201248 #na# 00:01
█4 1.199417 #na# 00:01
█5 1.198365 #na# 00:01
█6 1.174354 #na# 00:01
█7 1.161283 #na# 00:01
█8 1.137624 #na# 00:01
█9 1.147447 #na# 00:01
█10 1.212578 #na# 00:01
█11 5.446669 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 96 learning rates
Selected learning rate 1.7378008287493754e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.228343 1.100314 0.353535 00:01
██1 1.233711 1.105051 0.363636 00:01
██2 1.235369 1.114479 0.363636 00:01
██3 1.225911 1.122506 0.323232 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 4 epochs and with 2 unfrozen layers:
█Most confused classes:
[(0, 2, 22), (4, 2, 21), (2, 0, 11), (4, 0, 9), (2, 4, 3), (0, 4, 1)]
Confusion matrix:
[[10 22 1]
[11 21 3]
[ 9 21 1]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 0, tensor(0), tensor([0.4157, 0.3515, 0.2328]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 0, tensor(0), tensor([0.3881, 0.3557, 0.2562]))
███epoch train_loss valid_loss accuracy time
█0 1.192683 #na# 00:01
█1 1.238917 #na# 00:01
█2 1.261967 #na# 00:01
█3 1.253653 #na# 00:01
█4 1.257796 #na# 00:01
█5 1.250961 #na# 00:01
█6 1.227950 #na# 00:01
█7 1.205027 #na# 00:01
█8 1.186604 #na# 00:01
█9 1.189400 #na# 00:01
█10 1.261381 #na# 00:01
█11 5.240362 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 1.7378008287493754e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.289204 1.099959 0.353535 00:01
██1 1.248860 1.104690 0.363636 00:01
██2 1.253276 1.110978 0.373737 00:01
██3 1.234155 1.117499 0.404040 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 4 epochs and with 3 unfrozen layers:
█Most confused classes:
[(0, 2, 25), (4, 2, 19), (0, 4, 8), (2, 4, 7)]
Confusion matrix:
[[ 0 25 8]
[ 0 28 7]
[ 0 19 12]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 2, tensor(1), tensor([0.2761, 0.4218, 0.3021]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 2, tensor(1), tensor([0.2715, 0.4530, 0.2756]))
███epoch train_loss valid_loss accuracy time
█0 1.197820 #na# 00:01
█1 1.207810 #na# 00:01
█2 1.218379 #na# 00:01
█3 1.219897 #na# 00:01
█4 1.206060 #na# 00:01
█5 1.202504 #na# 00:01
█6 1.192143 #na# 00:01
█7 1.185209 #na# 00:01
█8 1.165826 #na# 00:01
█9 1.162769 #na# 00:01
█10 1.261883 #na# 00:01
█11 6.476897 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 94 learning rates
Selected learning rate 1.202264434617413e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.238691 1.098822 0.333333 00:01
██1 1.241903 1.102833 0.313131 00:01
██2 1.229332 1.106657 0.323232 00:01
██3 1.228350 1.106165 0.333333 00:01
██4 1.229619 1.104215 0.313131 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 5 epochs and with 0 unfrozen layers:
█Most confused classes:
[(0, 2, 28), (2, 4, 16), (4, 2, 14), (2, 0, 5), (0, 4, 3), (4, 0, 2)]
Confusion matrix:
[[ 2 28 3]
[ 5 14 16]
[ 2 14 15]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 2, tensor(1), tensor([0.2776, 0.4393, 0.2831]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 4, tensor(2), tensor([0.1745, 0.3708, 0.4547]))
███epoch train_loss valid_loss accuracy time
█0 1.243109 #na# 00:01
█1 1.224905 #na# 00:01
█2 1.232257 #na# 00:01
█3 1.229329 #na# 00:01
█4 1.236256 #na# 00:01
█5 1.223764 #na# 00:01
█6 1.198020 #na# 00:01
█7 1.181206 #na# 00:01
█8 1.159172 #na# 00:01
█9 1.161190 #na# 00:01
█10 1.230522 #na# 00:01
█11 5.173827 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 96 learning rates
Selected learning rate 2.0892961308540395e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.217258 1.099125 0.353535 00:01
██1 1.230642 1.101164 0.363636 00:01
██2 1.209267 1.102414 0.323232 00:01
██3 1.215583 1.105128 0.323232 00:01
██4 1.212220 1.108918 0.323232 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 5 epochs and with 1 unfrozen layers:
█Most confused classes:
[(4, 2, 21), (0, 4, 18), (0, 2, 13), (2, 4, 12), (4, 0, 2), (2, 0, 1)]
Confusion matrix:
[[ 2 13 18]
[ 1 22 12]
[ 2 21 8]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 0, tensor(0), tensor([0.3627, 0.3140, 0.3234]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 2, tensor(1), tensor([0.2614, 0.5083, 0.2303]))
███epoch train_loss valid_loss accuracy time
█0 1.202146 #na# 00:01
█1 1.232380 #na# 00:01
█2 1.246059 #na# 00:01
█3 1.247993 #na# 00:01
█4 1.253818 #na# 00:01
█5 1.237259 #na# 00:01
█6 1.223181 #na# 00:01
█7 1.207421 #na# 00:01
█8 1.183155 #na# 00:01
█9 1.178959 #na# 00:01
█10 1.265563 #na# 00:01
█11 4.359889 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 96 learning rates
Selected learning rate 1.7378008287493754e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.399173 1.101251 0.323232 00:01
██1 1.373300 1.107349 0.282828 00:01
██2 1.382073 1.119945 0.303030 00:01
██3 1.387379 1.139177 0.262626 00:01
██4 1.389900 1.165358 0.282828 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 5 epochs and with 2 unfrozen layers:
█Most confused classes:
[(2, 0, 17), (0, 2, 16), (4, 0, 15), (2, 4, 10), (4, 2, 7), (0, 4, 6)]
Confusion matrix:
[[11 16 6]
[17 8 10]
[15 7 9]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 4, tensor(2), tensor([0.3342, 0.2521, 0.4137]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 4, tensor(2), tensor([0.3003, 0.2170, 0.4827]))
███epoch train_loss valid_loss accuracy time
█0 1.227125 #na# 00:01
█1 1.219589 #na# 00:01
█2 1.224713 #na# 00:01
█3 1.235029 #na# 00:01
█4 1.238215 #na# 00:01
█5 1.224430 #na# 00:01
█6 1.211781 #na# 00:01
█7 1.185960 #na# 00:01
█8 1.165105 #na# 00:01
█9 1.160837 #na# 00:01
█10 1.211295 #na# 00:01
█11 5.216842 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 94 learning rates
Selected learning rate 1.202264434617413e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.230800 1.101996 0.292929 00:01
██1 1.229218 1.105530 0.323232 00:01
██2 1.225057 1.117972 0.333333 00:01
██3 1.232746 1.130377 0.313131 00:01
██4 1.217245 1.148369 0.323232 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 5 epochs and with 3 unfrozen layers:
█Most confused classes:
[(2, 0, 28), (4, 0, 26), (2, 4, 5), (4, 2, 5), (0, 2, 2), (0, 4, 1)]
Confusion matrix:
[[30 2 1]
[28 2 5]
[26 5 0]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 2, tensor(1), tensor([0.3858, 0.4331, 0.1811]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 2, tensor(1), tensor([0.3395, 0.3614, 0.2991]))
███epoch train_loss valid_loss accuracy time
█0 1.221712 #na# 00:01
█1 1.238085 #na# 00:01
█2 1.220683 #na# 00:01
█3 1.218584 #na# 00:01
█4 1.222623 #na# 00:01
█5 1.208394 #na# 00:01
█6 1.186396 #na# 00:01
█7 1.162160 #na# 00:01
█8 1.145307 #na# 00:01
█9 1.159863 #na# 00:01
█10 1.242052 #na# 00:01
█11 5.227242 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 96 learning rates
Selected learning rate 5.248074602497725e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.153125 1.099784 0.333333 00:01
██1 1.193006 1.102507 0.373737 00:01
██2 1.197763 1.105514 0.333333 00:01
██3 1.199324 1.106039 0.353535 00:01
██4 1.199452 1.106362 0.353535 00:01
██5 1.189084 1.106455 0.353535 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 6 epochs and with 0 unfrozen layers:
█Most confused classes:
[(0, 4, 20), (2, 4, 20), (2, 0, 8), (4, 0, 7), (4, 2, 7), (0, 2, 2)]
Confusion matrix:
[[11 2 20]
[ 8 7 20]
[ 7 7 17]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 0, tensor(0), tensor([0.4002, 0.3709, 0.2290]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 2, tensor(1), tensor([0.4063, 0.4109, 0.1828]))
███epoch train_loss valid_loss accuracy time
█0 1.229543 #na# 00:01
█1 1.211711 #na# 00:01
█2 1.219647 #na# 00:01
█3 1.220186 #na# 00:01
█4 1.219212 #na# 00:01
█5 1.205625 #na# 00:01
█6 1.182863 #na# 00:01
█7 1.156157 #na# 00:01
█8 1.136685 #na# 00:01
█9 1.145447 #na# 00:01
█10 1.223062 #na# 00:01
█11 4.799697 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 1.4454397707459274e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.251907 1.099365 0.353535 00:01
██1 1.239667 1.107496 0.353535 00:01
██2 1.252611 1.119807 0.343434 00:01
██3 1.247598 1.129019 0.303030 00:01
██4 1.251540 1.139892 0.292929 00:01
██5 1.254921 1.155509 0.272727 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 6 epochs and with 1 unfrozen layers:
█Most confused classes:
[(0, 2, 28), (4, 2, 21), (2, 4, 11), (2, 0, 6), (4, 0, 4), (0, 4, 2)]
Confusion matrix:
[[ 3 28 2]
[ 6 18 11]
[ 4 21 6]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 0, tensor(0), tensor([0.4073, 0.3150, 0.2777]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 0, tensor(0), tensor([0.5949, 0.2320, 0.1731]))
███epoch train_loss valid_loss accuracy time
█0 1.204767 #na# 00:01
█1 1.227957 #na# 00:01
█2 1.222437 #na# 00:01
█3 1.223400 #na# 00:01
█4 1.226105 #na# 00:01
█5 1.217555 #na# 00:01
█6 1.194966 #na# 00:01
█7 1.172811 #na# 00:01
█8 1.155582 #na# 00:01
█9 1.158871 #na# 00:01
█10 1.212720 #na# 00:01
█11 4.752730 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 94 learning rates
Selected learning rate 1e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.230735 1.102821 0.333333 00:01
██1 1.201337 1.114552 0.333333 00:01
██2 1.197001 1.125729 0.323232 00:01
██3 1.195184 1.124164 0.353535 00:01
██4 1.202230 1.111661 0.353535 00:01
██5 1.195443 1.110023 0.333333 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 6 epochs and with 2 unfrozen layers:
█Most confused classes:
[(4, 0, 21), (2, 0, 20), (2, 4, 8), (4, 2, 7), (0, 2, 6), (0, 4, 4)]
Confusion matrix:
[[23 6 4]
[20 7 8]
[21 7 3]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 0, tensor(0), tensor([0.5336, 0.2630, 0.2034]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 0, tensor(0), tensor([0.4855, 0.2244, 0.2901]))
███epoch train_loss valid_loss accuracy time
█0 1.226831 #na# 00:01
█1 1.251168 #na# 00:01
█2 1.250401 #na# 00:01
█3 1.237134 #na# 00:01
█4 1.217187 #na# 00:01
█5 1.204458 #na# 00:01
█6 1.181223 #na# 00:01
█7 1.168131 #na# 00:01
█8 1.151156 #na# 00:01
█9 1.152428 #na# 00:01
█10 1.217204 #na# 00:01
█11 7.178283 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 96 learning rates
Selected learning rate 1e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.237080 1.110091 0.313131 00:01
██1 1.224590 1.118243 0.313131 00:01
██2 1.236139 1.126112 0.323232 00:01
██3 1.236511 1.133084 0.313131 00:01
██4 1.231638 1.133482 0.353535 00:01
██5 1.235454 1.134336 0.353535 00:01
Interpretation of the results given by model having encoder trained during 4 epochs, classifier during 6 epochs and with 3 unfrozen layers:
█Most confused classes:
[(0, 4, 25), (2, 4, 21), (2, 0, 7), (0, 2, 5), (4, 0, 4), (4, 2, 2)]
Confusion matrix:
[[ 3 5 25]
[ 7 7 21]
[ 4 2 25]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 4, tensor(2), tensor([0.3254, 0.2206, 0.4540]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 0, tensor(0), tensor([0.4395, 0.2554, 0.3051]))
███epoch train_loss valid_loss accuracy time
█0 4.512152 #na# 00:00
█1 4.518033 #na# 00:00
█2 4.524119 #na# 00:00
█3 4.515772 #na# 00:00
█4 4.508965 #na# 00:00
█5 4.502408 #na# 00:00
█6 4.505103 #na# 00:00
█7 4.509205 #na# 00:00
█8 4.510522 #na# 00:00
█9 4.510532 #na# 00:00
█10 4.510661 #na# 00:00
█11 4.519208 #na# 00:00
█12 4.519639 #na# 00:00
█13 4.513743 #na# 00:00
█14 4.511100 #na# 00:00
█15 4.503556 #na# 00:00
█16 4.493880 #na# 00:00
█17 4.478192 #na# 00:00
█18 4.451132 #na# 00:00
█19 4.412338 #na# 00:00
█20 4.357008 #na# 00:00
█21 4.282712 #na# 00:00
█22 4.195765 #na# 00:00
█23 4.096710 #na# 00:00
█24 3.989541 #na# 00:00
█25 3.877428 #na# 00:00
█26 3.763654 #na# 00:00
█27 3.647449 #na# 00:00
█28 3.547477 #na# 00:00
█29 3.493020 #na# 00:00
█30 3.614445 #na# 00:00
█31 4.406263 #na# 00:00
█32 6.161151 #na# 00:00
█33 6.916849 #na# 00:00
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 100 learning rates
Selected learning rate 0.36307805477010097 for training language model
█epoch train_loss valid_loss accuracy time
██0 4.214622 4.742460 0.204688 00:00
██1 4.182008 4.624360 0.216964 00:00
██2 4.046873 4.020425 0.276786 00:00
██3 3.736370 3.594758 0.317187 00:00
██4 3.448071 3.530092 0.324777 00:00
███epoch train_loss valid_loss accuracy time
█0 1.237365 #na# 00:01
█1 1.220286 #na# 00:01
█2 1.217320 #na# 00:01
█3 1.217432 #na# 00:01
█4 1.201872 #na# 00:01
█5 1.200505 #na# 00:01
█6 1.187517 #na# 00:01
█7 1.173238 #na# 00:01
█8 1.158247 #na# 00:01
█9 1.159816 #na# 00:01
█10 1.219593 #na# 00:01
█11 5.655685 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 96 learning rates
Selected learning rate 1.202264434617413e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.238944 1.104491 0.363636 00:01
██1 1.246768 1.121697 0.292929 00:01
██2 1.265202 1.139551 0.292929 00:01
██3 1.275142 1.151537 0.313131 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 4 epochs and with 0 unfrozen layers:
█Most confused classes:
[(2, 4, 32), (0, 4, 30), (2, 0, 3), (4, 0, 3)]
Confusion matrix:
[[ 3 0 30]
[ 3 0 32]
[ 3 0 28]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 2, tensor(1), tensor([0.3342, 0.3488, 0.3170]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 0, tensor(0), tensor([0.4276, 0.2284, 0.3441]))
███epoch train_loss valid_loss accuracy time
█0 1.222339 #na# 00:01
█1 1.212497 #na# 00:01
█2 1.205083 #na# 00:01
█3 1.202118 #na# 00:01
█4 1.195439 #na# 00:01
█5 1.177341 #na# 00:01
█6 1.168231 #na# 00:01
█7 1.149554 #na# 00:01
█8 1.133826 #na# 00:01
█9 1.139892 #na# 00:01
█10 1.201963 #na# 00:01
█11 4.607167 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 94 learning rates
Selected learning rate 1.202264434617413e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.123754 1.103096 0.313131 00:01
██1 1.150639 1.110894 0.323232 00:01
██2 1.137413 1.120108 0.323232 00:01
██3 1.138138 1.122519 0.313131 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 4 epochs and with 1 unfrozen layers:
█Most confused classes:
[(0, 4, 31), (2, 4, 31), (4, 2, 4), (0, 2, 2)]
Confusion matrix:
[[ 0 2 31]
[ 0 4 31]
[ 0 4 27]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 2, tensor(1), tensor([0.2796, 0.3940, 0.3263]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 4, tensor(2), tensor([0.2592, 0.3310, 0.4098]))
███epoch train_loss valid_loss accuracy time
█0 1.210966 #na# 00:01
█1 1.221789 #na# 00:01
█2 1.220261 #na# 00:01
█3 1.227662 #na# 00:01
█4 1.231038 #na# 00:01
█5 1.228552 #na# 00:01
█6 1.217980 #na# 00:01
█7 1.190984 #na# 00:01
█8 1.162197 #na# 00:01
█9 1.170536 #na# 00:01
█10 1.216565 #na# 00:01
█11 6.381035 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 96 learning rates
Selected learning rate 2.0892961308540395e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.273081 1.095737 0.353535 00:01
██1 1.271605 1.095385 0.353535 00:01
██2 1.271883 1.096935 0.414141 00:01
██3 1.278896 1.093899 0.414141 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 4 epochs and with 2 unfrozen layers:
█Most confused classes:
[(0, 2, 19), (4, 0, 16), (4, 2, 13), (2, 0, 9), (2, 4, 1)]
Confusion matrix:
[[14 19 0]
[ 9 25 1]
[16 13 2]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 2, tensor(1), tensor([0.3024, 0.3719, 0.3257]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 0, tensor(0), tensor([0.3583, 0.3105, 0.3312]))
███epoch train_loss valid_loss accuracy time
█0 1.234074 #na# 00:01
█1 1.227968 #na# 00:01
█2 1.219731 #na# 00:01
█3 1.215776 #na# 00:01
█4 1.199947 #na# 00:01
█5 1.194977 #na# 00:01
█6 1.172124 #na# 00:01
█7 1.156316 #na# 00:01
█8 1.141733 #na# 00:01
█9 1.152375 #na# 00:01
█10 1.188464 #na# 00:01
█11 5.267361 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 6.309573444801933e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.193120 1.109803 0.313131 00:01
██1 1.205626 1.119056 0.313131 00:01
██2 1.203285 1.123625 0.313131 00:01
██3 1.200858 1.120544 0.343434 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 4 epochs and with 3 unfrozen layers:
█Most confused classes:
[(0, 4, 32), (2, 4, 30), (4, 2, 2), (0, 2, 1)]
Confusion matrix:
[[ 0 1 32]
[ 0 5 30]
[ 0 2 29]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 2, tensor(1), tensor([0.2642, 0.3745, 0.3613]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 4, tensor(2), tensor([0.2442, 0.3083, 0.4476]))
███epoch train_loss valid_loss accuracy time
█0 1.233164 #na# 00:01
█1 1.221100 #na# 00:01
█2 1.224536 #na# 00:01
█3 1.231173 #na# 00:01
█4 1.221801 #na# 00:01
█5 1.217438 #na# 00:01
█6 1.197105 #na# 00:01
█7 1.179062 #na# 00:01
█8 1.160559 #na# 00:01
█9 1.150664 #na# 00:01
█10 1.235316 #na# 00:01
█11 6.446673 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 2.51188643150958e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.267803 1.105413 0.313131 00:01
██1 1.249094 1.111958 0.363636 00:01
██2 1.261369 1.119020 0.373737 00:01
██3 1.253727 1.126420 0.373737 00:01
██4 1.251405 1.137290 0.363636 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 5 epochs and with 0 unfrozen layers:
█Most confused classes:
[(2, 0, 20), (0, 4, 16), (2, 4, 13), (4, 0, 12), (0, 2, 1), (4, 2, 1)]
Confusion matrix:
[[16 1 16]
[20 2 13]
[12 1 18]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 4, tensor(2), tensor([0.3468, 0.2536, 0.3996]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 4, tensor(2), tensor([0.3453, 0.2154, 0.4394]))
███epoch train_loss valid_loss accuracy time
█0 1.247548 #na# 00:01
█1 1.247152 #na# 00:01
█2 1.244660 #na# 00:01
█3 1.255233 #na# 00:01
█4 1.242822 #na# 00:01
█5 1.236443 #na# 00:01
█6 1.208726 #na# 00:01
█7 1.183017 #na# 00:01
█8 1.158383 #na# 00:01
█9 1.156458 #na# 00:01
█10 1.225232 #na# 00:01
█11 6.300654 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 1.202264434617413e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.148130 1.109826 0.333333 00:01
██1 1.152645 1.115450 0.333333 00:01
██2 1.146880 1.117709 0.323232 00:01
██3 1.162021 1.117151 0.373737 00:01
██4 1.165601 1.113371 0.393939 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 5 epochs and with 1 unfrozen layers:
█Most confused classes:
[(2, 0, 26), (4, 0, 25), (2, 4, 6), (0, 4, 3)]
Confusion matrix:
[[30 0 3]
[26 3 6]
[25 0 6]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 0, tensor(0), tensor([0.4761, 0.1790, 0.3448]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 0, tensor(0), tensor([0.4876, 0.2598, 0.2526]))
███epoch train_loss valid_loss accuracy time
█0 1.248487 #na# 00:01
█1 1.244347 #na# 00:01
█2 1.226165 #na# 00:01
█3 1.222266 #na# 00:01
█4 1.225075 #na# 00:01
█5 1.216830 #na# 00:01
█6 1.193122 #na# 00:01
█7 1.172604 #na# 00:01
█8 1.154024 #na# 00:01
█9 1.158175 #na# 00:01
█10 1.223507 #na# 00:01
█11 5.301956 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 3.019951720402016e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.295002 1.106145 0.343434 00:01
██1 1.275227 1.114136 0.313131 00:01
██2 1.279736 1.124381 0.343434 00:01
██3 1.271481 1.133139 0.313131 00:01
██4 1.269628 1.147090 0.343434 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 5 epochs and with 2 unfrozen layers:
█Most confused classes:
[(2, 4, 19), (0, 2, 18), (0, 4, 13), (4, 2, 13), (2, 0, 2)]
Confusion matrix:
[[ 2 18 13]
[ 2 14 19]
[ 0 13 18]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 4, tensor(2), tensor([0.2355, 0.2487, 0.5158]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 0, tensor(0), tensor([0.3780, 0.2823, 0.3397]))
███epoch train_loss valid_loss accuracy time
█0 1.261043 #na# 00:01
█1 1.235994 #na# 00:01
█2 1.245299 #na# 00:01
█3 1.244120 #na# 00:01
█4 1.241469 #na# 00:01
█5 1.230624 #na# 00:01
█6 1.211220 #na# 00:01
█7 1.194042 #na# 00:01
█8 1.165574 #na# 00:01
█9 1.170034 #na# 00:01
█10 1.244204 #na# 00:01
█11 6.187933 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 1.7378008287493754e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.211474 1.099188 0.353535 00:01
██1 1.218926 1.105882 0.353535 00:01
██2 1.242481 1.112255 0.383838 00:01
██3 1.231155 1.115405 0.343434 00:01
██4 1.231460 1.124489 0.363636 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 5 epochs and with 3 unfrozen layers:
█Most confused classes:
[(0, 2, 24), (4, 2, 23), (2, 4, 8), (0, 4, 4), (2, 0, 2), (4, 0, 2)]
Confusion matrix:
[[ 5 24 4]
[ 2 25 8]
[ 2 23 6]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 2, tensor(1), tensor([0.2547, 0.4955, 0.2498]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 2, tensor(1), tensor([0.1784, 0.5598, 0.2618]))
███epoch train_loss valid_loss accuracy time
█0 1.203194 #na# 00:01
█1 1.181227 #na# 00:01
█2 1.197414 #na# 00:01
█3 1.203949 #na# 00:01
█4 1.206335 #na# 00:01
█5 1.195048 #na# 00:01
█6 1.167117 #na# 00:01
█7 1.150704 #na# 00:01
█8 1.134930 #na# 00:01
█9 1.144467 #na# 00:01
█10 1.206081 #na# 00:01
█11 6.265078 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 94 learning rates
Selected learning rate 1e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.245232 1.109231 0.313131 00:01
██1 1.240782 1.113714 0.313131 00:01
██2 1.247811 1.116791 0.323232 00:01
██3 1.254699 1.121678 0.303030 00:01
██4 1.258930 1.127900 0.333333 00:01
██5 1.246864 1.130980 0.383838 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 6 epochs and with 0 unfrozen layers:
█Most confused classes:
[(0, 4, 21), (2, 4, 20), (0, 2, 8), (2, 0, 5), (4, 2, 5), (4, 0, 2)]
Confusion matrix:
[[ 4 8 21]
[ 5 10 20]
[ 2 5 24]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 2, tensor(1), tensor([0.4005, 0.4119, 0.1876]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 0, tensor(0), tensor([0.3919, 0.2334, 0.3747]))
███epoch train_loss valid_loss accuracy time
█0 1.200900 #na# 00:01
█1 1.175866 #na# 00:01
█2 1.168159 #na# 00:01
█3 1.180190 #na# 00:01
█4 1.181903 #na# 00:01
█5 1.181894 #na# 00:01
█6 1.166261 #na# 00:01
█7 1.146240 #na# 00:01
█8 1.134205 #na# 00:01
█9 1.151151 #na# 00:01
█10 1.211599 #na# 00:01
█11 4.802361 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 96 learning rates
Selected learning rate 1.4454397707459274e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.235312 1.099682 0.343434 00:01
██1 1.208069 1.096362 0.343434 00:01
██2 1.200575 1.096396 0.363636 00:01
██3 1.207808 1.097699 0.363636 00:01
██4 1.203151 1.100384 0.404040 00:01
██5 1.194951 1.104077 0.414141 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 6 epochs and with 1 unfrozen layers:
█Most confused classes:
[(2, 4, 15), (0, 4, 9), (2, 0, 9), (4, 0, 9), (0, 2, 8), (4, 2, 8)]
Confusion matrix:
[[16 8 9]
[ 9 11 15]
[ 9 8 14]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 4, tensor(2), tensor([0.2214, 0.3228, 0.4557]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 4, tensor(2), tensor([0.3345, 0.2806, 0.3849]))
███epoch train_loss valid_loss accuracy time
█0 1.175426 #na# 00:01
█1 1.190673 #na# 00:01
█2 1.188093 #na# 00:01
█3 1.197078 #na# 00:01
█4 1.188866 #na# 00:01
█5 1.178807 #na# 00:01
█6 1.165759 #na# 00:01
█7 1.150370 #na# 00:01
█8 1.137647 #na# 00:01
█9 1.143535 #na# 00:01
█10 1.237250 #na# 00:01
█11 5.168668 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 1e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.159221 1.095372 0.333333 00:01
██1 1.180417 1.090966 0.353535 00:01
██2 1.187391 1.087265 0.323232 00:01
██3 1.183280 1.086829 0.353535 00:01
██4 1.190022 1.088459 0.373737 00:01
██5 1.188474 1.094908 0.353535 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 6 epochs and with 2 unfrozen layers:
█Most confused classes:
[(4, 0, 13), (0, 2, 11), (2, 0, 11), (4, 2, 11), (0, 4, 10), (2, 4, 8)]
Confusion matrix:
[[12 11 10]
[11 16 8]
[13 11 7]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 0, tensor(0), tensor([0.3530, 0.3319, 0.3151]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 0, tensor(0), tensor([0.3920, 0.2884, 0.3196]))
███epoch train_loss valid_loss accuracy time
█0 1.121423 #na# 00:01
█1 1.146013 #na# 00:01
█2 1.145563 #na# 00:01
█3 1.157675 #na# 00:01
█4 1.160586 #na# 00:01
█5 1.159535 #na# 00:01
█6 1.139682 #na# 00:01
█7 1.132839 #na# 00:01
█8 1.128253 #na# 00:01
█9 1.143565 #na# 00:01
█10 1.211256 #na# 00:01
█11 4.914131 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 1e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.179392 1.099074 0.303030 00:01
██1 1.198554 1.103706 0.343434 00:01
██2 1.212932 1.109308 0.333333 00:01
██3 1.199020 1.112861 0.262626 00:01
██4 1.200171 1.117876 0.272727 00:01
██5 1.204757 1.125506 0.303030 00:01
Interpretation of the results given by model having encoder trained during 5 epochs, classifier during 6 epochs and with 3 unfrozen layers:
█Most confused classes:
[(0, 2, 20), (4, 2, 17), (2, 0, 14), (4, 0, 10), (0, 4, 4), (2, 4, 4)]
Confusion matrix:
[[ 9 20 4]
[14 17 4]
[10 17 4]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 0, tensor(0), tensor([0.4670, 0.2240, 0.3090]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 4, tensor(2), tensor([0.3558, 0.2731, 0.3711]))
███epoch train_loss valid_loss accuracy time
█0 4.507874 #na# 00:00
█1 4.516766 #na# 00:00
█2 4.525942 #na# 00:00
█3 4.521689 #na# 00:00
█4 4.518176 #na# 00:00
█5 4.513752 #na# 00:00
█6 4.513875 #na# 00:00
█7 4.517342 #na# 00:00
█8 4.518344 #na# 00:00
█9 4.519014 #na# 00:00
█10 4.521449 #na# 00:00
█11 4.521437 #na# 00:00
█12 4.521554 #na# 00:00
█13 4.516238 #na# 00:00
█14 4.510921 #na# 00:00
█15 4.503390 #na# 00:00
█16 4.494423 #na# 00:00
█17 4.478190 #na# 00:00
█18 4.450451 #na# 00:00
█19 4.411125 #na# 00:00
█20 4.355531 #na# 00:00
█21 4.280551 #na# 00:00
█22 4.192331 #na# 00:00
█23 4.093952 #na# 00:00
█24 3.987053 #na# 00:00
█25 3.874823 #na# 00:00
█26 3.758440 #na# 00:00
█27 3.641326 #na# 00:00
█28 3.533523 #na# 00:00
█29 3.473961 #na# 00:00
█30 3.622495 #na# 00:00
█31 4.383571 #na# 00:00
█32 6.111361 #na# 00:00
█33 6.970829 #na# 00:00
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 100 learning rates
Selected learning rate 0.36307805477010097 for training language model
█epoch train_loss valid_loss accuracy time
██0 4.137086 4.036283 0.223214 00:00
██1 3.939004 4.798164 0.188839 00:00
██2 3.929213 4.342981 0.271429 00:00
██3 3.703672 3.768699 0.333929 00:00
██4 3.419255 3.604480 0.332366 00:00
██5 3.186797 3.584019 0.332589 00:00
███epoch train_loss valid_loss accuracy time
█0 1.157380 #na# 00:01
█1 1.177552 #na# 00:01
█2 1.171068 #na# 00:01
█3 1.170525 #na# 00:01
█4 1.172836 #na# 00:01
█5 1.182714 #na# 00:01
█6 1.173436 #na# 00:01
█7 1.156983 #na# 00:01
█8 1.138203 #na# 00:01
█9 1.143810 #na# 00:01
█10 1.205058 #na# 00:01
█11 5.416551 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 1.4454397707459274e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.219807 1.106867 0.303030 00:01
██1 1.201931 1.111668 0.313131 00:01
██2 1.215235 1.115234 0.333333 00:01
██3 1.201255 1.115810 0.353535 00:01
Interpretation of the results given by model having encoder trained during 6 epochs, classifier during 4 epochs and with 0 unfrozen layers:
█Most confused classes:
[(0, 4, 26), (2, 4, 26), (2, 0, 4), (4, 0, 4), (0, 2, 3), (4, 2, 1)]
Confusion matrix:
[[ 4 3 26]
[ 4 5 26]
[ 4 1 26]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 4, tensor(2), tensor([0.2579, 0.3005, 0.4416]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 2, tensor(1), tensor([0.3226, 0.3510, 0.3265]))
███epoch train_loss valid_loss accuracy time
█0 1.170650 #na# 00:01
█1 1.176970 #na# 00:01
█2 1.174855 #na# 00:01
█3 1.184264 #na# 00:01
█4 1.185273 #na# 00:01
█5 1.180329 #na# 00:01
█6 1.165016 #na# 00:01
█7 1.160421 #na# 00:01
█8 1.150880 #na# 00:01
█9 1.164643 #na# 00:01
█10 1.259014 #na# 00:01
█11 5.646818 #na# 00:01
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Tested 95 learning rates
Selected learning rate 1e-07 for training classifier
█epoch train_loss valid_loss accuracy time
██0 1.192027 1.094449 0.363636 00:01
██1 1.196406 1.091344 0.353535 00:01
██2 1.199481 1.090194 0.353535 00:01
██3 1.193969 1.087810 0.343434 00:01
Interpretation of the results given by model having encoder trained during 6 epochs, classifier during 4 epochs and with 1 unfrozen layers:
█Most confused classes:
[(4, 2, 29), (0, 2, 26), (2, 0, 6), (4, 0, 2), (0, 4, 1), (2, 4, 1)]
Confusion matrix:
[[ 6 26 1]
[ 6 28 1]
[ 2 29 0]]
Prediction on sentence "This movie is really nice!!!!! haven't seen anything like this before :3":
(Category 2, tensor(1), tensor([0.3574, 0.3684, 0.2741]))
Prediction on sentence "OMG WHAT WAS THAT???":
(Category 2, tensor(1), tensor([0.2645, 0.3946, 0.3409]))
███epoch train_loss valid_loss accuracy time
█0 1.253398 #na# 00:01
█1 1.244074 #na# 00:01
█2 1.234970 #na# 00:01
█3 1.235302 #na# 00:01