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2023-10-14 08:15:18,968 ----------------------------------------------------------------------------------------------------
2023-10-14 08:15:18,969 Model: "SequenceTagger(
(embeddings): TransformerWordEmbeddings(
(model): BertModel(
(embeddings): BertEmbeddings(
(word_embeddings): Embedding(32001, 768)
(position_embeddings): Embedding(512, 768)
(token_type_embeddings): Embedding(2, 768)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): BertEncoder(
(layer): ModuleList(
(0-11): 12 x BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): BertPooler(
(dense): Linear(in_features=768, out_features=768, bias=True)
(activation): Tanh()
)
)
)
(locked_dropout): LockedDropout(p=0.5)
(linear): Linear(in_features=768, out_features=13, bias=True)
(loss_function): CrossEntropyLoss()
)"
2023-10-14 08:15:18,969 ----------------------------------------------------------------------------------------------------
2023-10-14 08:15:18,969 MultiCorpus: 5777 train + 722 dev + 723 test sentences
- NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /root/.flair/datasets/ner_icdar_europeana/nl
2023-10-14 08:15:18,969 ----------------------------------------------------------------------------------------------------
2023-10-14 08:15:18,969 Train: 5777 sentences
2023-10-14 08:15:18,969 (train_with_dev=False, train_with_test=False)
2023-10-14 08:15:18,969 ----------------------------------------------------------------------------------------------------
2023-10-14 08:15:18,969 Training Params:
2023-10-14 08:15:18,969 - learning_rate: "5e-05"
2023-10-14 08:15:18,969 - mini_batch_size: "4"
2023-10-14 08:15:18,969 - max_epochs: "10"
2023-10-14 08:15:18,969 - shuffle: "True"
2023-10-14 08:15:18,969 ----------------------------------------------------------------------------------------------------
2023-10-14 08:15:18,969 Plugins:
2023-10-14 08:15:18,969 - LinearScheduler | warmup_fraction: '0.1'
2023-10-14 08:15:18,969 ----------------------------------------------------------------------------------------------------
2023-10-14 08:15:18,969 Final evaluation on model from best epoch (best-model.pt)
2023-10-14 08:15:18,969 - metric: "('micro avg', 'f1-score')"
2023-10-14 08:15:18,969 ----------------------------------------------------------------------------------------------------
2023-10-14 08:15:18,969 Computation:
2023-10-14 08:15:18,969 - compute on device: cuda:0
2023-10-14 08:15:18,969 - embedding storage: none
2023-10-14 08:15:18,969 ----------------------------------------------------------------------------------------------------
2023-10-14 08:15:18,969 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
2023-10-14 08:15:18,970 ----------------------------------------------------------------------------------------------------
2023-10-14 08:15:18,970 ----------------------------------------------------------------------------------------------------
2023-10-14 08:15:26,251 epoch 1 - iter 144/1445 - loss 1.64823894 - time (sec): 7.28 - samples/sec: 2327.55 - lr: 0.000005 - momentum: 0.000000
2023-10-14 08:15:33,424 epoch 1 - iter 288/1445 - loss 0.94267315 - time (sec): 14.45 - samples/sec: 2337.84 - lr: 0.000010 - momentum: 0.000000
2023-10-14 08:15:40,844 epoch 1 - iter 432/1445 - loss 0.67856790 - time (sec): 21.87 - samples/sec: 2388.14 - lr: 0.000015 - momentum: 0.000000
2023-10-14 08:15:48,104 epoch 1 - iter 576/1445 - loss 0.55406917 - time (sec): 29.13 - samples/sec: 2397.44 - lr: 0.000020 - momentum: 0.000000
2023-10-14 08:15:55,240 epoch 1 - iter 720/1445 - loss 0.47981540 - time (sec): 36.27 - samples/sec: 2391.61 - lr: 0.000025 - momentum: 0.000000
2023-10-14 08:16:02,180 epoch 1 - iter 864/1445 - loss 0.43508566 - time (sec): 43.21 - samples/sec: 2365.86 - lr: 0.000030 - momentum: 0.000000
2023-10-14 08:16:09,469 epoch 1 - iter 1008/1445 - loss 0.39439415 - time (sec): 50.50 - samples/sec: 2392.47 - lr: 0.000035 - momentum: 0.000000
2023-10-14 08:16:16,914 epoch 1 - iter 1152/1445 - loss 0.36462348 - time (sec): 57.94 - samples/sec: 2396.36 - lr: 0.000040 - momentum: 0.000000
2023-10-14 08:16:24,794 epoch 1 - iter 1296/1445 - loss 0.34134840 - time (sec): 65.82 - samples/sec: 2383.68 - lr: 0.000045 - momentum: 0.000000
2023-10-14 08:16:32,940 epoch 1 - iter 1440/1445 - loss 0.31936100 - time (sec): 73.97 - samples/sec: 2373.43 - lr: 0.000050 - momentum: 0.000000
2023-10-14 08:16:33,232 ----------------------------------------------------------------------------------------------------
2023-10-14 08:16:33,232 EPOCH 1 done: loss 0.3185 - lr: 0.000050
2023-10-14 08:16:36,699 DEV : loss 0.14940427243709564 - f1-score (micro avg) 0.5985
2023-10-14 08:16:36,717 saving best model
2023-10-14 08:16:37,103 ----------------------------------------------------------------------------------------------------
2023-10-14 08:16:44,662 epoch 2 - iter 144/1445 - loss 0.12942045 - time (sec): 7.56 - samples/sec: 2297.91 - lr: 0.000049 - momentum: 0.000000
2023-10-14 08:16:52,560 epoch 2 - iter 288/1445 - loss 0.12243824 - time (sec): 15.45 - samples/sec: 2227.86 - lr: 0.000049 - momentum: 0.000000
2023-10-14 08:16:59,832 epoch 2 - iter 432/1445 - loss 0.12297936 - time (sec): 22.73 - samples/sec: 2291.85 - lr: 0.000048 - momentum: 0.000000
2023-10-14 08:17:06,999 epoch 2 - iter 576/1445 - loss 0.11660000 - time (sec): 29.89 - samples/sec: 2318.21 - lr: 0.000048 - momentum: 0.000000
2023-10-14 08:17:14,567 epoch 2 - iter 720/1445 - loss 0.11717628 - time (sec): 37.46 - samples/sec: 2340.11 - lr: 0.000047 - momentum: 0.000000
2023-10-14 08:17:21,760 epoch 2 - iter 864/1445 - loss 0.11504791 - time (sec): 44.65 - samples/sec: 2345.59 - lr: 0.000047 - momentum: 0.000000
2023-10-14 08:17:29,231 epoch 2 - iter 1008/1445 - loss 0.11715840 - time (sec): 52.13 - samples/sec: 2354.63 - lr: 0.000046 - momentum: 0.000000
2023-10-14 08:17:36,191 epoch 2 - iter 1152/1445 - loss 0.11408128 - time (sec): 59.09 - samples/sec: 2346.22 - lr: 0.000046 - momentum: 0.000000
2023-10-14 08:17:43,759 epoch 2 - iter 1296/1445 - loss 0.11282938 - time (sec): 66.65 - samples/sec: 2367.71 - lr: 0.000045 - momentum: 0.000000
2023-10-14 08:17:51,144 epoch 2 - iter 1440/1445 - loss 0.11126139 - time (sec): 74.04 - samples/sec: 2372.49 - lr: 0.000044 - momentum: 0.000000
2023-10-14 08:17:51,422 ----------------------------------------------------------------------------------------------------
2023-10-14 08:17:51,422 EPOCH 2 done: loss 0.1111 - lr: 0.000044
2023-10-14 08:17:54,946 DEV : loss 0.13982899487018585 - f1-score (micro avg) 0.6855
2023-10-14 08:17:54,961 saving best model
2023-10-14 08:17:55,492 ----------------------------------------------------------------------------------------------------
2023-10-14 08:18:03,092 epoch 3 - iter 144/1445 - loss 0.07642927 - time (sec): 7.60 - samples/sec: 2331.85 - lr: 0.000044 - momentum: 0.000000
2023-10-14 08:18:10,587 epoch 3 - iter 288/1445 - loss 0.07342995 - time (sec): 15.09 - samples/sec: 2356.25 - lr: 0.000043 - momentum: 0.000000
2023-10-14 08:18:17,841 epoch 3 - iter 432/1445 - loss 0.07324502 - time (sec): 22.35 - samples/sec: 2380.66 - lr: 0.000043 - momentum: 0.000000
2023-10-14 08:18:25,239 epoch 3 - iter 576/1445 - loss 0.07198888 - time (sec): 29.75 - samples/sec: 2381.02 - lr: 0.000042 - momentum: 0.000000
2023-10-14 08:18:32,571 epoch 3 - iter 720/1445 - loss 0.07329604 - time (sec): 37.08 - samples/sec: 2387.91 - lr: 0.000042 - momentum: 0.000000
2023-10-14 08:18:39,580 epoch 3 - iter 864/1445 - loss 0.07223058 - time (sec): 44.09 - samples/sec: 2406.77 - lr: 0.000041 - momentum: 0.000000
2023-10-14 08:18:47,122 epoch 3 - iter 1008/1445 - loss 0.07418564 - time (sec): 51.63 - samples/sec: 2382.83 - lr: 0.000041 - momentum: 0.000000
2023-10-14 08:18:54,438 epoch 3 - iter 1152/1445 - loss 0.07370676 - time (sec): 58.94 - samples/sec: 2396.16 - lr: 0.000040 - momentum: 0.000000
2023-10-14 08:19:01,641 epoch 3 - iter 1296/1445 - loss 0.07403015 - time (sec): 66.15 - samples/sec: 2402.18 - lr: 0.000039 - momentum: 0.000000
2023-10-14 08:19:09,044 epoch 3 - iter 1440/1445 - loss 0.07386810 - time (sec): 73.55 - samples/sec: 2390.08 - lr: 0.000039 - momentum: 0.000000
2023-10-14 08:19:09,261 ----------------------------------------------------------------------------------------------------
2023-10-14 08:19:09,261 EPOCH 3 done: loss 0.0739 - lr: 0.000039
2023-10-14 08:19:13,617 DEV : loss 0.11367038637399673 - f1-score (micro avg) 0.7623
2023-10-14 08:19:13,642 saving best model
2023-10-14 08:19:14,163 ----------------------------------------------------------------------------------------------------
2023-10-14 08:19:21,776 epoch 4 - iter 144/1445 - loss 0.04581362 - time (sec): 7.61 - samples/sec: 2370.16 - lr: 0.000038 - momentum: 0.000000
2023-10-14 08:19:29,103 epoch 4 - iter 288/1445 - loss 0.04337311 - time (sec): 14.94 - samples/sec: 2450.96 - lr: 0.000038 - momentum: 0.000000
2023-10-14 08:19:36,314 epoch 4 - iter 432/1445 - loss 0.04860217 - time (sec): 22.15 - samples/sec: 2401.90 - lr: 0.000037 - momentum: 0.000000
2023-10-14 08:19:43,653 epoch 4 - iter 576/1445 - loss 0.04972909 - time (sec): 29.49 - samples/sec: 2417.58 - lr: 0.000037 - momentum: 0.000000
2023-10-14 08:19:50,773 epoch 4 - iter 720/1445 - loss 0.05476643 - time (sec): 36.61 - samples/sec: 2420.98 - lr: 0.000036 - momentum: 0.000000
2023-10-14 08:19:57,817 epoch 4 - iter 864/1445 - loss 0.05460145 - time (sec): 43.65 - samples/sec: 2411.04 - lr: 0.000036 - momentum: 0.000000
2023-10-14 08:20:04,856 epoch 4 - iter 1008/1445 - loss 0.05255163 - time (sec): 50.69 - samples/sec: 2405.89 - lr: 0.000035 - momentum: 0.000000
2023-10-14 08:20:12,336 epoch 4 - iter 1152/1445 - loss 0.05337710 - time (sec): 58.17 - samples/sec: 2415.11 - lr: 0.000034 - momentum: 0.000000
2023-10-14 08:20:19,753 epoch 4 - iter 1296/1445 - loss 0.05469107 - time (sec): 65.59 - samples/sec: 2398.02 - lr: 0.000034 - momentum: 0.000000
2023-10-14 08:20:27,109 epoch 4 - iter 1440/1445 - loss 0.05505521 - time (sec): 72.94 - samples/sec: 2409.15 - lr: 0.000033 - momentum: 0.000000
2023-10-14 08:20:27,343 ----------------------------------------------------------------------------------------------------
2023-10-14 08:20:27,343 EPOCH 4 done: loss 0.0558 - lr: 0.000033
2023-10-14 08:20:30,916 DEV : loss 0.12969306111335754 - f1-score (micro avg) 0.7744
2023-10-14 08:20:30,940 saving best model
2023-10-14 08:20:31,444 ----------------------------------------------------------------------------------------------------
2023-10-14 08:20:39,113 epoch 5 - iter 144/1445 - loss 0.04612436 - time (sec): 7.67 - samples/sec: 2315.46 - lr: 0.000033 - momentum: 0.000000
2023-10-14 08:20:46,441 epoch 5 - iter 288/1445 - loss 0.04420133 - time (sec): 14.99 - samples/sec: 2374.89 - lr: 0.000032 - momentum: 0.000000
2023-10-14 08:20:53,540 epoch 5 - iter 432/1445 - loss 0.04018260 - time (sec): 22.09 - samples/sec: 2375.54 - lr: 0.000032 - momentum: 0.000000
2023-10-14 08:21:00,700 epoch 5 - iter 576/1445 - loss 0.04133613 - time (sec): 29.25 - samples/sec: 2390.64 - lr: 0.000031 - momentum: 0.000000
2023-10-14 08:21:08,009 epoch 5 - iter 720/1445 - loss 0.04029888 - time (sec): 36.56 - samples/sec: 2397.70 - lr: 0.000031 - momentum: 0.000000
2023-10-14 08:21:15,905 epoch 5 - iter 864/1445 - loss 0.03888505 - time (sec): 44.46 - samples/sec: 2381.62 - lr: 0.000030 - momentum: 0.000000
2023-10-14 08:21:22,792 epoch 5 - iter 1008/1445 - loss 0.03857434 - time (sec): 51.34 - samples/sec: 2390.28 - lr: 0.000029 - momentum: 0.000000
2023-10-14 08:21:30,009 epoch 5 - iter 1152/1445 - loss 0.03791905 - time (sec): 58.56 - samples/sec: 2400.71 - lr: 0.000029 - momentum: 0.000000
2023-10-14 08:21:37,524 epoch 5 - iter 1296/1445 - loss 0.03924086 - time (sec): 66.08 - samples/sec: 2398.78 - lr: 0.000028 - momentum: 0.000000
2023-10-14 08:21:44,508 epoch 5 - iter 1440/1445 - loss 0.03949965 - time (sec): 73.06 - samples/sec: 2402.13 - lr: 0.000028 - momentum: 0.000000
2023-10-14 08:21:44,804 ----------------------------------------------------------------------------------------------------
2023-10-14 08:21:44,805 EPOCH 5 done: loss 0.0394 - lr: 0.000028
2023-10-14 08:21:48,276 DEV : loss 0.21078866720199585 - f1-score (micro avg) 0.7504
2023-10-14 08:21:48,291 ----------------------------------------------------------------------------------------------------
2023-10-14 08:21:55,561 epoch 6 - iter 144/1445 - loss 0.03043142 - time (sec): 7.27 - samples/sec: 2473.28 - lr: 0.000027 - momentum: 0.000000
2023-10-14 08:22:03,148 epoch 6 - iter 288/1445 - loss 0.02495404 - time (sec): 14.86 - samples/sec: 2433.69 - lr: 0.000027 - momentum: 0.000000
2023-10-14 08:22:10,421 epoch 6 - iter 432/1445 - loss 0.02660960 - time (sec): 22.13 - samples/sec: 2439.55 - lr: 0.000026 - momentum: 0.000000
2023-10-14 08:22:18,087 epoch 6 - iter 576/1445 - loss 0.02912055 - time (sec): 29.79 - samples/sec: 2412.69 - lr: 0.000026 - momentum: 0.000000
2023-10-14 08:22:25,105 epoch 6 - iter 720/1445 - loss 0.02608450 - time (sec): 36.81 - samples/sec: 2407.86 - lr: 0.000025 - momentum: 0.000000
2023-10-14 08:22:32,477 epoch 6 - iter 864/1445 - loss 0.02559760 - time (sec): 44.19 - samples/sec: 2390.04 - lr: 0.000024 - momentum: 0.000000
2023-10-14 08:22:39,837 epoch 6 - iter 1008/1445 - loss 0.02922170 - time (sec): 51.54 - samples/sec: 2393.10 - lr: 0.000024 - momentum: 0.000000
2023-10-14 08:22:47,278 epoch 6 - iter 1152/1445 - loss 0.03029099 - time (sec): 58.99 - samples/sec: 2401.56 - lr: 0.000023 - momentum: 0.000000
2023-10-14 08:22:54,411 epoch 6 - iter 1296/1445 - loss 0.03010830 - time (sec): 66.12 - samples/sec: 2394.91 - lr: 0.000023 - momentum: 0.000000
2023-10-14 08:23:01,494 epoch 6 - iter 1440/1445 - loss 0.03033135 - time (sec): 73.20 - samples/sec: 2399.89 - lr: 0.000022 - momentum: 0.000000
2023-10-14 08:23:01,741 ----------------------------------------------------------------------------------------------------
2023-10-14 08:23:01,741 EPOCH 6 done: loss 0.0304 - lr: 0.000022
2023-10-14 08:23:05,565 DEV : loss 0.24649791419506073 - f1-score (micro avg) 0.7402
2023-10-14 08:23:05,580 ----------------------------------------------------------------------------------------------------
2023-10-14 08:23:12,971 epoch 7 - iter 144/1445 - loss 0.01657038 - time (sec): 7.39 - samples/sec: 2355.54 - lr: 0.000022 - momentum: 0.000000
2023-10-14 08:23:20,172 epoch 7 - iter 288/1445 - loss 0.02025614 - time (sec): 14.59 - samples/sec: 2339.42 - lr: 0.000021 - momentum: 0.000000
2023-10-14 08:23:27,560 epoch 7 - iter 432/1445 - loss 0.02071666 - time (sec): 21.98 - samples/sec: 2387.00 - lr: 0.000021 - momentum: 0.000000
2023-10-14 08:23:34,837 epoch 7 - iter 576/1445 - loss 0.02132575 - time (sec): 29.26 - samples/sec: 2410.09 - lr: 0.000020 - momentum: 0.000000
2023-10-14 08:23:42,121 epoch 7 - iter 720/1445 - loss 0.02120114 - time (sec): 36.54 - samples/sec: 2408.82 - lr: 0.000019 - momentum: 0.000000
2023-10-14 08:23:49,772 epoch 7 - iter 864/1445 - loss 0.02176288 - time (sec): 44.19 - samples/sec: 2409.67 - lr: 0.000019 - momentum: 0.000000
2023-10-14 08:23:56,878 epoch 7 - iter 1008/1445 - loss 0.02230750 - time (sec): 51.30 - samples/sec: 2402.09 - lr: 0.000018 - momentum: 0.000000
2023-10-14 08:24:04,185 epoch 7 - iter 1152/1445 - loss 0.02201668 - time (sec): 58.60 - samples/sec: 2413.44 - lr: 0.000018 - momentum: 0.000000
2023-10-14 08:24:11,294 epoch 7 - iter 1296/1445 - loss 0.02270576 - time (sec): 65.71 - samples/sec: 2404.77 - lr: 0.000017 - momentum: 0.000000
2023-10-14 08:24:18,460 epoch 7 - iter 1440/1445 - loss 0.02316075 - time (sec): 72.88 - samples/sec: 2410.80 - lr: 0.000017 - momentum: 0.000000
2023-10-14 08:24:18,691 ----------------------------------------------------------------------------------------------------
2023-10-14 08:24:18,692 EPOCH 7 done: loss 0.0232 - lr: 0.000017
2023-10-14 08:24:22,285 DEV : loss 0.18435220420360565 - f1-score (micro avg) 0.8055
2023-10-14 08:24:22,305 saving best model
2023-10-14 08:24:22,836 ----------------------------------------------------------------------------------------------------
2023-10-14 08:24:30,556 epoch 8 - iter 144/1445 - loss 0.01173685 - time (sec): 7.72 - samples/sec: 2259.84 - lr: 0.000016 - momentum: 0.000000
2023-10-14 08:24:38,092 epoch 8 - iter 288/1445 - loss 0.01201500 - time (sec): 15.25 - samples/sec: 2313.14 - lr: 0.000016 - momentum: 0.000000
2023-10-14 08:24:45,557 epoch 8 - iter 432/1445 - loss 0.01104701 - time (sec): 22.72 - samples/sec: 2329.39 - lr: 0.000015 - momentum: 0.000000
2023-10-14 08:24:53,317 epoch 8 - iter 576/1445 - loss 0.01394833 - time (sec): 30.48 - samples/sec: 2302.53 - lr: 0.000014 - momentum: 0.000000
2023-10-14 08:25:00,792 epoch 8 - iter 720/1445 - loss 0.01349697 - time (sec): 37.95 - samples/sec: 2347.18 - lr: 0.000014 - momentum: 0.000000
2023-10-14 08:25:07,946 epoch 8 - iter 864/1445 - loss 0.01327706 - time (sec): 45.11 - samples/sec: 2347.90 - lr: 0.000013 - momentum: 0.000000
2023-10-14 08:25:15,059 epoch 8 - iter 1008/1445 - loss 0.01560794 - time (sec): 52.22 - samples/sec: 2365.91 - lr: 0.000013 - momentum: 0.000000
2023-10-14 08:25:22,151 epoch 8 - iter 1152/1445 - loss 0.01577549 - time (sec): 59.31 - samples/sec: 2362.24 - lr: 0.000012 - momentum: 0.000000
2023-10-14 08:25:29,734 epoch 8 - iter 1296/1445 - loss 0.01538313 - time (sec): 66.90 - samples/sec: 2368.49 - lr: 0.000012 - momentum: 0.000000
2023-10-14 08:25:36,951 epoch 8 - iter 1440/1445 - loss 0.01529247 - time (sec): 74.11 - samples/sec: 2372.81 - lr: 0.000011 - momentum: 0.000000
2023-10-14 08:25:37,177 ----------------------------------------------------------------------------------------------------
2023-10-14 08:25:37,177 EPOCH 8 done: loss 0.0153 - lr: 0.000011
2023-10-14 08:25:40,654 DEV : loss 0.21136066317558289 - f1-score (micro avg) 0.7935
2023-10-14 08:25:40,669 ----------------------------------------------------------------------------------------------------
2023-10-14 08:25:48,159 epoch 9 - iter 144/1445 - loss 0.01001014 - time (sec): 7.49 - samples/sec: 2434.15 - lr: 0.000011 - momentum: 0.000000
2023-10-14 08:25:55,953 epoch 9 - iter 288/1445 - loss 0.01043171 - time (sec): 15.28 - samples/sec: 2405.81 - lr: 0.000010 - momentum: 0.000000
2023-10-14 08:26:03,628 epoch 9 - iter 432/1445 - loss 0.00854045 - time (sec): 22.96 - samples/sec: 2436.37 - lr: 0.000009 - momentum: 0.000000
2023-10-14 08:26:10,997 epoch 9 - iter 576/1445 - loss 0.00869129 - time (sec): 30.33 - samples/sec: 2378.55 - lr: 0.000009 - momentum: 0.000000
2023-10-14 08:26:18,488 epoch 9 - iter 720/1445 - loss 0.00926320 - time (sec): 37.82 - samples/sec: 2401.19 - lr: 0.000008 - momentum: 0.000000
2023-10-14 08:26:25,414 epoch 9 - iter 864/1445 - loss 0.00890667 - time (sec): 44.74 - samples/sec: 2393.98 - lr: 0.000008 - momentum: 0.000000
2023-10-14 08:26:32,785 epoch 9 - iter 1008/1445 - loss 0.00902072 - time (sec): 52.12 - samples/sec: 2381.96 - lr: 0.000007 - momentum: 0.000000
2023-10-14 08:26:40,012 epoch 9 - iter 1152/1445 - loss 0.00935748 - time (sec): 59.34 - samples/sec: 2361.68 - lr: 0.000007 - momentum: 0.000000
2023-10-14 08:26:47,327 epoch 9 - iter 1296/1445 - loss 0.00932214 - time (sec): 66.66 - samples/sec: 2362.95 - lr: 0.000006 - momentum: 0.000000
2023-10-14 08:26:54,741 epoch 9 - iter 1440/1445 - loss 0.00919275 - time (sec): 74.07 - samples/sec: 2371.68 - lr: 0.000006 - momentum: 0.000000
2023-10-14 08:26:54,970 ----------------------------------------------------------------------------------------------------
2023-10-14 08:26:54,970 EPOCH 9 done: loss 0.0094 - lr: 0.000006
2023-10-14 08:26:58,830 DEV : loss 0.20292457938194275 - f1-score (micro avg) 0.8054
2023-10-14 08:26:58,845 ----------------------------------------------------------------------------------------------------
2023-10-14 08:27:06,259 epoch 10 - iter 144/1445 - loss 0.00347635 - time (sec): 7.41 - samples/sec: 2478.68 - lr: 0.000005 - momentum: 0.000000
2023-10-14 08:27:13,129 epoch 10 - iter 288/1445 - loss 0.00368842 - time (sec): 14.28 - samples/sec: 2428.24 - lr: 0.000004 - momentum: 0.000000
2023-10-14 08:27:20,581 epoch 10 - iter 432/1445 - loss 0.00658264 - time (sec): 21.73 - samples/sec: 2422.22 - lr: 0.000004 - momentum: 0.000000
2023-10-14 08:27:27,940 epoch 10 - iter 576/1445 - loss 0.00628559 - time (sec): 29.09 - samples/sec: 2437.35 - lr: 0.000003 - momentum: 0.000000
2023-10-14 08:27:34,939 epoch 10 - iter 720/1445 - loss 0.00657734 - time (sec): 36.09 - samples/sec: 2433.97 - lr: 0.000003 - momentum: 0.000000
2023-10-14 08:27:42,516 epoch 10 - iter 864/1445 - loss 0.00684358 - time (sec): 43.67 - samples/sec: 2452.32 - lr: 0.000002 - momentum: 0.000000
2023-10-14 08:27:49,636 epoch 10 - iter 1008/1445 - loss 0.00708911 - time (sec): 50.79 - samples/sec: 2443.68 - lr: 0.000002 - momentum: 0.000000
2023-10-14 08:27:56,748 epoch 10 - iter 1152/1445 - loss 0.00717665 - time (sec): 57.90 - samples/sec: 2437.61 - lr: 0.000001 - momentum: 0.000000
2023-10-14 08:28:03,759 epoch 10 - iter 1296/1445 - loss 0.00674554 - time (sec): 64.91 - samples/sec: 2431.08 - lr: 0.000001 - momentum: 0.000000
2023-10-14 08:28:11,172 epoch 10 - iter 1440/1445 - loss 0.00645507 - time (sec): 72.33 - samples/sec: 2431.56 - lr: 0.000000 - momentum: 0.000000
2023-10-14 08:28:11,392 ----------------------------------------------------------------------------------------------------
2023-10-14 08:28:11,392 EPOCH 10 done: loss 0.0065 - lr: 0.000000
2023-10-14 08:28:14,844 DEV : loss 0.22300778329372406 - f1-score (micro avg) 0.7971
2023-10-14 08:28:15,247 ----------------------------------------------------------------------------------------------------
2023-10-14 08:28:15,248 Loading model from best epoch ...
2023-10-14 08:28:16,910 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG
2023-10-14 08:28:20,000
Results:
- F-score (micro) 0.7811
- F-score (macro) 0.6709
- Accuracy 0.6559
By class:
precision recall f1-score support
PER 0.8044 0.7510 0.7768 482
LOC 0.8859 0.7969 0.8391 458
ORG 0.4615 0.3478 0.3967 69
micro avg 0.8217 0.7443 0.7811 1009
macro avg 0.7173 0.6319 0.6709 1009
weighted avg 0.8180 0.7443 0.7791 1009
2023-10-14 08:28:20,000 ----------------------------------------------------------------------------------------------------