| 2023-10-16 19:31:53,657 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:31:53,658 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=17, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-16 19:31:53,658 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:31:53,658 MultiCorpus: 1085 train + 148 dev + 364 test sentences | |
| - NER_HIPE_2022 Corpus: 1085 train + 148 dev + 364 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/sv/with_doc_seperator | |
| 2023-10-16 19:31:53,658 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:31:53,659 Train: 1085 sentences | |
| 2023-10-16 19:31:53,659 (train_with_dev=False, train_with_test=False) | |
| 2023-10-16 19:31:53,659 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:31:53,659 Training Params: | |
| 2023-10-16 19:31:53,659 - learning_rate: "5e-05" | |
| 2023-10-16 19:31:53,659 - mini_batch_size: "4" | |
| 2023-10-16 19:31:53,659 - max_epochs: "10" | |
| 2023-10-16 19:31:53,659 - shuffle: "True" | |
| 2023-10-16 19:31:53,659 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:31:53,659 Plugins: | |
| 2023-10-16 19:31:53,659 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-16 19:31:53,659 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:31:53,659 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-16 19:31:53,659 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-16 19:31:53,659 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:31:53,659 Computation: | |
| 2023-10-16 19:31:53,659 - compute on device: cuda:0 | |
| 2023-10-16 19:31:53,659 - embedding storage: none | |
| 2023-10-16 19:31:53,659 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:31:53,659 Model training base path: "hmbench-newseye/sv-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1" | |
| 2023-10-16 19:31:53,659 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:31:53,659 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:31:55,223 epoch 1 - iter 27/272 - loss 2.95328015 - time (sec): 1.56 - samples/sec: 3068.64 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-16 19:31:56,836 epoch 1 - iter 54/272 - loss 2.16693759 - time (sec): 3.18 - samples/sec: 3231.27 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-16 19:31:58,277 epoch 1 - iter 81/272 - loss 1.65058382 - time (sec): 4.62 - samples/sec: 3248.92 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 19:31:59,693 epoch 1 - iter 108/272 - loss 1.41300806 - time (sec): 6.03 - samples/sec: 3246.96 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-16 19:32:01,227 epoch 1 - iter 135/272 - loss 1.16296640 - time (sec): 7.57 - samples/sec: 3364.72 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-16 19:32:02,735 epoch 1 - iter 162/272 - loss 1.02940910 - time (sec): 9.07 - samples/sec: 3352.61 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-16 19:32:04,318 epoch 1 - iter 189/272 - loss 0.91107036 - time (sec): 10.66 - samples/sec: 3348.23 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-16 19:32:05,793 epoch 1 - iter 216/272 - loss 0.83286521 - time (sec): 12.13 - samples/sec: 3351.30 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-16 19:32:07,395 epoch 1 - iter 243/272 - loss 0.76057220 - time (sec): 13.73 - samples/sec: 3355.03 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-16 19:32:08,993 epoch 1 - iter 270/272 - loss 0.69365828 - time (sec): 15.33 - samples/sec: 3371.47 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-16 19:32:09,106 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:32:09,106 EPOCH 1 done: loss 0.6924 - lr: 0.000049 | |
| 2023-10-16 19:32:10,110 DEV : loss 0.19004914164543152 - f1-score (micro avg) 0.5426 | |
| 2023-10-16 19:32:10,114 saving best model | |
| 2023-10-16 19:32:10,471 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:32:12,167 epoch 2 - iter 27/272 - loss 0.19414973 - time (sec): 1.69 - samples/sec: 3479.42 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-16 19:32:13,697 epoch 2 - iter 54/272 - loss 0.17106126 - time (sec): 3.22 - samples/sec: 3302.48 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-16 19:32:15,263 epoch 2 - iter 81/272 - loss 0.16459301 - time (sec): 4.79 - samples/sec: 3368.49 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-16 19:32:16,827 epoch 2 - iter 108/272 - loss 0.18139997 - time (sec): 6.35 - samples/sec: 3303.45 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-16 19:32:18,369 epoch 2 - iter 135/272 - loss 0.17164269 - time (sec): 7.90 - samples/sec: 3282.85 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-16 19:32:19,835 epoch 2 - iter 162/272 - loss 0.16439995 - time (sec): 9.36 - samples/sec: 3238.76 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-16 19:32:21,448 epoch 2 - iter 189/272 - loss 0.15959924 - time (sec): 10.98 - samples/sec: 3291.21 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-16 19:32:22,926 epoch 2 - iter 216/272 - loss 0.15587286 - time (sec): 12.45 - samples/sec: 3330.90 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-16 19:32:24,472 epoch 2 - iter 243/272 - loss 0.15185873 - time (sec): 14.00 - samples/sec: 3313.00 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-16 19:32:26,009 epoch 2 - iter 270/272 - loss 0.14816339 - time (sec): 15.54 - samples/sec: 3331.01 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-16 19:32:26,108 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:32:26,108 EPOCH 2 done: loss 0.1480 - lr: 0.000045 | |
| 2023-10-16 19:32:27,518 DEV : loss 0.10820505768060684 - f1-score (micro avg) 0.7463 | |
| 2023-10-16 19:32:27,522 saving best model | |
| 2023-10-16 19:32:27,965 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:32:29,500 epoch 3 - iter 27/272 - loss 0.08155325 - time (sec): 1.53 - samples/sec: 3281.37 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-16 19:32:30,876 epoch 3 - iter 54/272 - loss 0.08409408 - time (sec): 2.91 - samples/sec: 3254.26 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-16 19:32:32,578 epoch 3 - iter 81/272 - loss 0.08139739 - time (sec): 4.61 - samples/sec: 3376.79 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-16 19:32:34,076 epoch 3 - iter 108/272 - loss 0.08180166 - time (sec): 6.11 - samples/sec: 3332.59 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-16 19:32:35,533 epoch 3 - iter 135/272 - loss 0.07974796 - time (sec): 7.57 - samples/sec: 3281.81 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-16 19:32:37,161 epoch 3 - iter 162/272 - loss 0.08213381 - time (sec): 9.19 - samples/sec: 3327.65 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-16 19:32:38,724 epoch 3 - iter 189/272 - loss 0.08010112 - time (sec): 10.76 - samples/sec: 3301.80 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-16 19:32:40,302 epoch 3 - iter 216/272 - loss 0.08185408 - time (sec): 12.34 - samples/sec: 3301.59 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-16 19:32:41,794 epoch 3 - iter 243/272 - loss 0.08367674 - time (sec): 13.83 - samples/sec: 3290.53 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-16 19:32:43,430 epoch 3 - iter 270/272 - loss 0.08124787 - time (sec): 15.46 - samples/sec: 3336.37 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-16 19:32:43,548 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:32:43,548 EPOCH 3 done: loss 0.0809 - lr: 0.000039 | |
| 2023-10-16 19:32:44,984 DEV : loss 0.10929891467094421 - f1-score (micro avg) 0.7446 | |
| 2023-10-16 19:32:44,988 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:32:46,576 epoch 4 - iter 27/272 - loss 0.05437417 - time (sec): 1.59 - samples/sec: 3533.96 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-16 19:32:48,099 epoch 4 - iter 54/272 - loss 0.04384694 - time (sec): 3.11 - samples/sec: 3554.17 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-16 19:32:49,691 epoch 4 - iter 81/272 - loss 0.04594154 - time (sec): 4.70 - samples/sec: 3477.23 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-16 19:32:51,240 epoch 4 - iter 108/272 - loss 0.05424351 - time (sec): 6.25 - samples/sec: 3424.77 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-16 19:32:52,810 epoch 4 - iter 135/272 - loss 0.04989679 - time (sec): 7.82 - samples/sec: 3412.90 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-16 19:32:54,386 epoch 4 - iter 162/272 - loss 0.04978778 - time (sec): 9.40 - samples/sec: 3389.52 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-16 19:32:56,065 epoch 4 - iter 189/272 - loss 0.05027613 - time (sec): 11.08 - samples/sec: 3392.18 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-16 19:32:57,704 epoch 4 - iter 216/272 - loss 0.05205747 - time (sec): 12.71 - samples/sec: 3307.15 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-16 19:32:59,252 epoch 4 - iter 243/272 - loss 0.05199972 - time (sec): 14.26 - samples/sec: 3315.73 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-16 19:33:00,770 epoch 4 - iter 270/272 - loss 0.05396688 - time (sec): 15.78 - samples/sec: 3289.80 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-16 19:33:00,848 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:33:00,849 EPOCH 4 done: loss 0.0539 - lr: 0.000033 | |
| 2023-10-16 19:33:02,295 DEV : loss 0.12052459269762039 - f1-score (micro avg) 0.8095 | |
| 2023-10-16 19:33:02,299 saving best model | |
| 2023-10-16 19:33:02,747 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:33:04,363 epoch 5 - iter 27/272 - loss 0.04257767 - time (sec): 1.61 - samples/sec: 3332.16 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-16 19:33:05,927 epoch 5 - iter 54/272 - loss 0.03104477 - time (sec): 3.17 - samples/sec: 3234.01 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-16 19:33:07,592 epoch 5 - iter 81/272 - loss 0.02769835 - time (sec): 4.84 - samples/sec: 3363.99 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-16 19:33:09,122 epoch 5 - iter 108/272 - loss 0.03682368 - time (sec): 6.37 - samples/sec: 3352.56 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-16 19:33:10,626 epoch 5 - iter 135/272 - loss 0.04182425 - time (sec): 7.87 - samples/sec: 3379.02 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-16 19:33:12,140 epoch 5 - iter 162/272 - loss 0.04003463 - time (sec): 9.38 - samples/sec: 3316.19 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-16 19:33:13,737 epoch 5 - iter 189/272 - loss 0.03933014 - time (sec): 10.98 - samples/sec: 3353.80 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-16 19:33:15,308 epoch 5 - iter 216/272 - loss 0.03933942 - time (sec): 12.55 - samples/sec: 3338.80 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-16 19:33:16,911 epoch 5 - iter 243/272 - loss 0.03741570 - time (sec): 14.16 - samples/sec: 3358.22 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-16 19:33:18,380 epoch 5 - iter 270/272 - loss 0.03884246 - time (sec): 15.62 - samples/sec: 3322.35 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-16 19:33:18,463 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:33:18,463 EPOCH 5 done: loss 0.0388 - lr: 0.000028 | |
| 2023-10-16 19:33:19,898 DEV : loss 0.1621648073196411 - f1-score (micro avg) 0.7891 | |
| 2023-10-16 19:33:19,901 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:33:21,401 epoch 6 - iter 27/272 - loss 0.01912861 - time (sec): 1.50 - samples/sec: 3262.46 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 19:33:22,800 epoch 6 - iter 54/272 - loss 0.02221352 - time (sec): 2.90 - samples/sec: 3284.19 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 19:33:24,348 epoch 6 - iter 81/272 - loss 0.02732011 - time (sec): 4.45 - samples/sec: 3290.85 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-16 19:33:26,088 epoch 6 - iter 108/272 - loss 0.02665238 - time (sec): 6.19 - samples/sec: 3331.01 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-16 19:33:27,525 epoch 6 - iter 135/272 - loss 0.03098490 - time (sec): 7.62 - samples/sec: 3374.30 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-16 19:33:29,079 epoch 6 - iter 162/272 - loss 0.03126089 - time (sec): 9.18 - samples/sec: 3286.18 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 19:33:30,706 epoch 6 - iter 189/272 - loss 0.03235828 - time (sec): 10.80 - samples/sec: 3258.39 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 19:33:32,321 epoch 6 - iter 216/272 - loss 0.03110078 - time (sec): 12.42 - samples/sec: 3283.92 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-16 19:33:33,890 epoch 6 - iter 243/272 - loss 0.02991518 - time (sec): 13.99 - samples/sec: 3270.91 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-16 19:33:35,585 epoch 6 - iter 270/272 - loss 0.02933351 - time (sec): 15.68 - samples/sec: 3309.80 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-16 19:33:35,675 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:33:35,675 EPOCH 6 done: loss 0.0293 - lr: 0.000022 | |
| 2023-10-16 19:33:37,090 DEV : loss 0.14851412177085876 - f1-score (micro avg) 0.8433 | |
| 2023-10-16 19:33:37,094 saving best model | |
| 2023-10-16 19:33:37,537 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:33:39,088 epoch 7 - iter 27/272 - loss 0.03104110 - time (sec): 1.55 - samples/sec: 3804.25 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-16 19:33:40,630 epoch 7 - iter 54/272 - loss 0.02299984 - time (sec): 3.09 - samples/sec: 3494.92 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 19:33:42,130 epoch 7 - iter 81/272 - loss 0.02190619 - time (sec): 4.59 - samples/sec: 3523.84 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 19:33:43,696 epoch 7 - iter 108/272 - loss 0.02242868 - time (sec): 6.16 - samples/sec: 3465.79 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-16 19:33:45,260 epoch 7 - iter 135/272 - loss 0.02177196 - time (sec): 7.72 - samples/sec: 3405.58 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-16 19:33:46,851 epoch 7 - iter 162/272 - loss 0.02050069 - time (sec): 9.31 - samples/sec: 3408.90 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-16 19:33:48,584 epoch 7 - iter 189/272 - loss 0.01812006 - time (sec): 11.04 - samples/sec: 3382.28 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 19:33:50,044 epoch 7 - iter 216/272 - loss 0.01834730 - time (sec): 12.50 - samples/sec: 3345.05 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 19:33:51,565 epoch 7 - iter 243/272 - loss 0.02112046 - time (sec): 14.02 - samples/sec: 3358.62 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-16 19:33:53,115 epoch 7 - iter 270/272 - loss 0.02135027 - time (sec): 15.57 - samples/sec: 3319.75 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-16 19:33:53,200 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:33:53,200 EPOCH 7 done: loss 0.0212 - lr: 0.000017 | |
| 2023-10-16 19:33:54,810 DEV : loss 0.16008441150188446 - f1-score (micro avg) 0.8207 | |
| 2023-10-16 19:33:54,814 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:33:56,499 epoch 8 - iter 27/272 - loss 0.01358068 - time (sec): 1.68 - samples/sec: 3467.90 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-16 19:33:58,092 epoch 8 - iter 54/272 - loss 0.01355086 - time (sec): 3.28 - samples/sec: 3411.86 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-16 19:33:59,532 epoch 8 - iter 81/272 - loss 0.01197700 - time (sec): 4.72 - samples/sec: 3422.20 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 19:34:01,011 epoch 8 - iter 108/272 - loss 0.01122890 - time (sec): 6.20 - samples/sec: 3386.16 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-16 19:34:02,554 epoch 8 - iter 135/272 - loss 0.01385527 - time (sec): 7.74 - samples/sec: 3427.09 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-16 19:34:04,081 epoch 8 - iter 162/272 - loss 0.01323604 - time (sec): 9.27 - samples/sec: 3411.87 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-16 19:34:05,881 epoch 8 - iter 189/272 - loss 0.01363279 - time (sec): 11.07 - samples/sec: 3430.21 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-16 19:34:07,477 epoch 8 - iter 216/272 - loss 0.01337961 - time (sec): 12.66 - samples/sec: 3382.58 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 19:34:08,939 epoch 8 - iter 243/272 - loss 0.01371133 - time (sec): 14.12 - samples/sec: 3351.16 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 19:34:10,433 epoch 8 - iter 270/272 - loss 0.01409468 - time (sec): 15.62 - samples/sec: 3321.67 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-16 19:34:10,520 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:34:10,521 EPOCH 8 done: loss 0.0141 - lr: 0.000011 | |
| 2023-10-16 19:34:11,958 DEV : loss 0.17060722410678864 - f1-score (micro avg) 0.8088 | |
| 2023-10-16 19:34:11,962 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:34:13,452 epoch 9 - iter 27/272 - loss 0.02253323 - time (sec): 1.49 - samples/sec: 3402.69 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-16 19:34:15,039 epoch 9 - iter 54/272 - loss 0.02096247 - time (sec): 3.08 - samples/sec: 3492.39 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-16 19:34:16,608 epoch 9 - iter 81/272 - loss 0.01767350 - time (sec): 4.65 - samples/sec: 3550.63 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 19:34:18,108 epoch 9 - iter 108/272 - loss 0.01812866 - time (sec): 6.15 - samples/sec: 3553.70 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 19:34:19,612 epoch 9 - iter 135/272 - loss 0.01657397 - time (sec): 7.65 - samples/sec: 3446.40 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-16 19:34:21,155 epoch 9 - iter 162/272 - loss 0.01586682 - time (sec): 9.19 - samples/sec: 3350.79 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-16 19:34:22,715 epoch 9 - iter 189/272 - loss 0.01440847 - time (sec): 10.75 - samples/sec: 3383.69 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-16 19:34:24,274 epoch 9 - iter 216/272 - loss 0.01282252 - time (sec): 12.31 - samples/sec: 3381.02 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-16 19:34:25,956 epoch 9 - iter 243/272 - loss 0.01219213 - time (sec): 13.99 - samples/sec: 3364.21 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-16 19:34:27,400 epoch 9 - iter 270/272 - loss 0.01184073 - time (sec): 15.44 - samples/sec: 3354.34 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-16 19:34:27,499 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:34:27,499 EPOCH 9 done: loss 0.0118 - lr: 0.000006 | |
| 2023-10-16 19:34:28,911 DEV : loss 0.17339861392974854 - f1-score (micro avg) 0.7993 | |
| 2023-10-16 19:34:28,915 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:34:30,641 epoch 10 - iter 27/272 - loss 0.00454535 - time (sec): 1.72 - samples/sec: 3068.80 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-16 19:34:32,245 epoch 10 - iter 54/272 - loss 0.00270348 - time (sec): 3.33 - samples/sec: 3229.23 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-16 19:34:33,797 epoch 10 - iter 81/272 - loss 0.00830662 - time (sec): 4.88 - samples/sec: 3230.52 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-16 19:34:35,204 epoch 10 - iter 108/272 - loss 0.00919675 - time (sec): 6.29 - samples/sec: 3246.34 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 19:34:36,732 epoch 10 - iter 135/272 - loss 0.00958562 - time (sec): 7.82 - samples/sec: 3213.56 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 19:34:38,281 epoch 10 - iter 162/272 - loss 0.00906453 - time (sec): 9.37 - samples/sec: 3227.96 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-16 19:34:39,835 epoch 10 - iter 189/272 - loss 0.00785760 - time (sec): 10.92 - samples/sec: 3247.03 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-16 19:34:41,312 epoch 10 - iter 216/272 - loss 0.00743252 - time (sec): 12.40 - samples/sec: 3274.69 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-16 19:34:42,878 epoch 10 - iter 243/272 - loss 0.00781445 - time (sec): 13.96 - samples/sec: 3353.35 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-16 19:34:44,315 epoch 10 - iter 270/272 - loss 0.00738827 - time (sec): 15.40 - samples/sec: 3365.36 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-16 19:34:44,394 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:34:44,394 EPOCH 10 done: loss 0.0074 - lr: 0.000000 | |
| 2023-10-16 19:34:45,829 DEV : loss 0.17555038630962372 - f1-score (micro avg) 0.811 | |
| 2023-10-16 19:34:46,196 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 19:34:46,197 Loading model from best epoch ... | |
| 2023-10-16 19:34:47,567 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd, S-ORG, B-ORG, E-ORG, I-ORG | |
| 2023-10-16 19:34:49,746 | |
| Results: | |
| - F-score (micro) 0.7744 | |
| - F-score (macro) 0.7533 | |
| - Accuracy 0.6521 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.7982 0.8494 0.8230 312 | |
| PER 0.6679 0.8606 0.7521 208 | |
| ORG 0.5417 0.4727 0.5049 55 | |
| HumanProd 0.9130 0.9545 0.9333 22 | |
| micro avg 0.7317 0.8224 0.7744 597 | |
| macro avg 0.7302 0.7843 0.7533 597 | |
| weighted avg 0.7334 0.8224 0.7730 597 | |
| 2023-10-16 19:34:49,746 ---------------------------------------------------------------------------------------------------- | |