| 2023-10-20 00:21:21,464 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:21,465 Model: "SequenceTagger( | |
| (embeddings): TransformerWordEmbeddings( | |
| (model): BertModel( | |
| (embeddings): BertEmbeddings( | |
| (word_embeddings): Embedding(32001, 128) | |
| (position_embeddings): Embedding(512, 128) | |
| (token_type_embeddings): Embedding(2, 128) | |
| (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (encoder): BertEncoder( | |
| (layer): ModuleList( | |
| (0-1): 2 x BertLayer( | |
| (attention): BertAttention( | |
| (self): BertSelfAttention( | |
| (query): Linear(in_features=128, out_features=128, bias=True) | |
| (key): Linear(in_features=128, out_features=128, bias=True) | |
| (value): Linear(in_features=128, out_features=128, bias=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| (output): BertSelfOutput( | |
| (dense): Linear(in_features=128, out_features=128, bias=True) | |
| (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| (intermediate): BertIntermediate( | |
| (dense): Linear(in_features=128, out_features=512, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): BertOutput( | |
| (dense): Linear(in_features=512, out_features=128, bias=True) | |
| (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| ) | |
| ) | |
| ) | |
| ) | |
| (pooler): BertPooler( | |
| (dense): Linear(in_features=128, out_features=128, bias=True) | |
| (activation): Tanh() | |
| ) | |
| ) | |
| ) | |
| (locked_dropout): LockedDropout(p=0.5) | |
| (linear): Linear(in_features=128, out_features=17, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-20 00:21:21,465 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:21,465 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 | |
| 2023-10-20 00:21:21,465 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:21,465 Train: 1085 sentences | |
| 2023-10-20 00:21:21,465 (train_with_dev=False, train_with_test=False) | |
| 2023-10-20 00:21:21,465 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:21,465 Training Params: | |
| 2023-10-20 00:21:21,465 - learning_rate: "5e-05" | |
| 2023-10-20 00:21:21,465 - mini_batch_size: "4" | |
| 2023-10-20 00:21:21,465 - max_epochs: "10" | |
| 2023-10-20 00:21:21,465 - shuffle: "True" | |
| 2023-10-20 00:21:21,465 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:21,465 Plugins: | |
| 2023-10-20 00:21:21,465 - TensorboardLogger | |
| 2023-10-20 00:21:21,465 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-20 00:21:21,465 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:21,465 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-20 00:21:21,465 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-20 00:21:21,465 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:21,465 Computation: | |
| 2023-10-20 00:21:21,465 - compute on device: cuda:0 | |
| 2023-10-20 00:21:21,465 - embedding storage: none | |
| 2023-10-20 00:21:21,465 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:21,466 Model training base path: "hmbench-newseye/sv-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4" | |
| 2023-10-20 00:21:21,466 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:21,466 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:21,466 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-20 00:21:21,957 epoch 1 - iter 27/272 - loss 3.46711403 - time (sec): 0.49 - samples/sec: 10461.58 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-20 00:21:22,439 epoch 1 - iter 54/272 - loss 3.46171750 - time (sec): 0.97 - samples/sec: 10681.11 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-20 00:21:22,883 epoch 1 - iter 81/272 - loss 3.28534633 - time (sec): 1.42 - samples/sec: 10814.77 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-20 00:21:23,443 epoch 1 - iter 108/272 - loss 3.01861422 - time (sec): 1.98 - samples/sec: 10633.83 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-20 00:21:23,941 epoch 1 - iter 135/272 - loss 2.80909491 - time (sec): 2.48 - samples/sec: 10406.50 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-20 00:21:24,476 epoch 1 - iter 162/272 - loss 2.54231157 - time (sec): 3.01 - samples/sec: 10303.95 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-20 00:21:24,983 epoch 1 - iter 189/272 - loss 2.31826568 - time (sec): 3.52 - samples/sec: 10208.09 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-20 00:21:25,506 epoch 1 - iter 216/272 - loss 2.08682730 - time (sec): 4.04 - samples/sec: 10358.94 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-20 00:21:26,020 epoch 1 - iter 243/272 - loss 1.91212120 - time (sec): 4.55 - samples/sec: 10466.68 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-20 00:21:26,487 epoch 1 - iter 270/272 - loss 1.80967208 - time (sec): 5.02 - samples/sec: 10325.73 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-20 00:21:26,520 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:26,520 EPOCH 1 done: loss 1.8069 - lr: 0.000049 | |
| 2023-10-20 00:21:26,937 DEV : loss 0.4722510874271393 - f1-score (micro avg) 0.0 | |
| 2023-10-20 00:21:26,940 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:27,456 epoch 2 - iter 27/272 - loss 0.61178694 - time (sec): 0.52 - samples/sec: 10248.65 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-20 00:21:27,965 epoch 2 - iter 54/272 - loss 0.57067838 - time (sec): 1.02 - samples/sec: 9816.81 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-20 00:21:28,449 epoch 2 - iter 81/272 - loss 0.56029945 - time (sec): 1.51 - samples/sec: 10217.88 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-20 00:21:28,950 epoch 2 - iter 108/272 - loss 0.54873035 - time (sec): 2.01 - samples/sec: 10325.13 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-20 00:21:29,435 epoch 2 - iter 135/272 - loss 0.55233991 - time (sec): 2.49 - samples/sec: 10424.92 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-20 00:21:29,953 epoch 2 - iter 162/272 - loss 0.54109234 - time (sec): 3.01 - samples/sec: 10331.23 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-20 00:21:30,466 epoch 2 - iter 189/272 - loss 0.52309886 - time (sec): 3.53 - samples/sec: 10393.81 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-20 00:21:30,960 epoch 2 - iter 216/272 - loss 0.51818871 - time (sec): 4.02 - samples/sec: 10345.08 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-20 00:21:31,472 epoch 2 - iter 243/272 - loss 0.51911854 - time (sec): 4.53 - samples/sec: 10371.98 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-20 00:21:31,966 epoch 2 - iter 270/272 - loss 0.52018264 - time (sec): 5.03 - samples/sec: 10319.19 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-20 00:21:31,994 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:31,995 EPOCH 2 done: loss 0.5211 - lr: 0.000045 | |
| 2023-10-20 00:21:32,748 DEV : loss 0.3575037121772766 - f1-score (micro avg) 0.0408 | |
| 2023-10-20 00:21:32,752 saving best model | |
| 2023-10-20 00:21:32,779 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:33,275 epoch 3 - iter 27/272 - loss 0.40786242 - time (sec): 0.50 - samples/sec: 10258.65 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-20 00:21:33,777 epoch 3 - iter 54/272 - loss 0.42946260 - time (sec): 1.00 - samples/sec: 10855.41 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-20 00:21:34,302 epoch 3 - iter 81/272 - loss 0.41328076 - time (sec): 1.52 - samples/sec: 10338.48 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-20 00:21:34,828 epoch 3 - iter 108/272 - loss 0.41615591 - time (sec): 2.05 - samples/sec: 10115.61 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-20 00:21:35,349 epoch 3 - iter 135/272 - loss 0.41543348 - time (sec): 2.57 - samples/sec: 10345.89 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-20 00:21:35,842 epoch 3 - iter 162/272 - loss 0.41142926 - time (sec): 3.06 - samples/sec: 10218.54 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-20 00:21:36,336 epoch 3 - iter 189/272 - loss 0.40845834 - time (sec): 3.56 - samples/sec: 10363.90 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-20 00:21:36,841 epoch 3 - iter 216/272 - loss 0.42242963 - time (sec): 4.06 - samples/sec: 10316.37 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-20 00:21:37,327 epoch 3 - iter 243/272 - loss 0.42454515 - time (sec): 4.55 - samples/sec: 10234.41 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-20 00:21:37,837 epoch 3 - iter 270/272 - loss 0.42731990 - time (sec): 5.06 - samples/sec: 10207.20 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-20 00:21:37,871 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:37,871 EPOCH 3 done: loss 0.4266 - lr: 0.000039 | |
| 2023-10-20 00:21:38,629 DEV : loss 0.3016860783100128 - f1-score (micro avg) 0.1798 | |
| 2023-10-20 00:21:38,633 saving best model | |
| 2023-10-20 00:21:38,664 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:39,141 epoch 4 - iter 27/272 - loss 0.39020834 - time (sec): 0.48 - samples/sec: 9628.52 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-20 00:21:39,583 epoch 4 - iter 54/272 - loss 0.38305660 - time (sec): 0.92 - samples/sec: 9183.83 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-20 00:21:40,088 epoch 4 - iter 81/272 - loss 0.38108401 - time (sec): 1.42 - samples/sec: 9963.30 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-20 00:21:40,568 epoch 4 - iter 108/272 - loss 0.37152231 - time (sec): 1.90 - samples/sec: 10128.14 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-20 00:21:41,090 epoch 4 - iter 135/272 - loss 0.37083389 - time (sec): 2.43 - samples/sec: 9998.37 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-20 00:21:41,640 epoch 4 - iter 162/272 - loss 0.37693631 - time (sec): 2.97 - samples/sec: 10410.48 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-20 00:21:42,164 epoch 4 - iter 189/272 - loss 0.37660175 - time (sec): 3.50 - samples/sec: 10495.63 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-20 00:21:42,659 epoch 4 - iter 216/272 - loss 0.38496790 - time (sec): 3.99 - samples/sec: 10395.82 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-20 00:21:43,184 epoch 4 - iter 243/272 - loss 0.38842763 - time (sec): 4.52 - samples/sec: 10340.72 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-20 00:21:43,684 epoch 4 - iter 270/272 - loss 0.38100025 - time (sec): 5.02 - samples/sec: 10307.69 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-20 00:21:43,717 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:43,717 EPOCH 4 done: loss 0.3806 - lr: 0.000033 | |
| 2023-10-20 00:21:44,623 DEV : loss 0.28507402539253235 - f1-score (micro avg) 0.3513 | |
| 2023-10-20 00:21:44,626 saving best model | |
| 2023-10-20 00:21:44,658 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:45,175 epoch 5 - iter 27/272 - loss 0.30924673 - time (sec): 0.52 - samples/sec: 10765.09 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-20 00:21:45,704 epoch 5 - iter 54/272 - loss 0.34869346 - time (sec): 1.04 - samples/sec: 9934.41 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-20 00:21:46,261 epoch 5 - iter 81/272 - loss 0.34272748 - time (sec): 1.60 - samples/sec: 9845.49 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-20 00:21:46,824 epoch 5 - iter 108/272 - loss 0.33624812 - time (sec): 2.16 - samples/sec: 9475.98 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-20 00:21:47,321 epoch 5 - iter 135/272 - loss 0.34768000 - time (sec): 2.66 - samples/sec: 9471.14 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-20 00:21:47,841 epoch 5 - iter 162/272 - loss 0.34735833 - time (sec): 3.18 - samples/sec: 9453.30 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-20 00:21:48,385 epoch 5 - iter 189/272 - loss 0.34992901 - time (sec): 3.73 - samples/sec: 9530.67 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-20 00:21:48,899 epoch 5 - iter 216/272 - loss 0.34923380 - time (sec): 4.24 - samples/sec: 9511.06 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-20 00:21:49,410 epoch 5 - iter 243/272 - loss 0.34680989 - time (sec): 4.75 - samples/sec: 9621.44 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-20 00:21:49,910 epoch 5 - iter 270/272 - loss 0.34720474 - time (sec): 5.25 - samples/sec: 9831.66 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-20 00:21:49,942 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:49,942 EPOCH 5 done: loss 0.3483 - lr: 0.000028 | |
| 2023-10-20 00:21:50,740 DEV : loss 0.27170366048812866 - f1-score (micro avg) 0.4177 | |
| 2023-10-20 00:21:50,744 saving best model | |
| 2023-10-20 00:21:50,777 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:51,258 epoch 6 - iter 27/272 - loss 0.34835965 - time (sec): 0.48 - samples/sec: 10640.77 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-20 00:21:51,754 epoch 6 - iter 54/272 - loss 0.35236544 - time (sec): 0.98 - samples/sec: 10347.26 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-20 00:21:52,225 epoch 6 - iter 81/272 - loss 0.34898421 - time (sec): 1.45 - samples/sec: 10363.93 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-20 00:21:52,743 epoch 6 - iter 108/272 - loss 0.34103544 - time (sec): 1.97 - samples/sec: 10239.48 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-20 00:21:53,267 epoch 6 - iter 135/272 - loss 0.34258782 - time (sec): 2.49 - samples/sec: 10292.90 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-20 00:21:53,748 epoch 6 - iter 162/272 - loss 0.34092986 - time (sec): 2.97 - samples/sec: 10268.06 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-20 00:21:54,282 epoch 6 - iter 189/272 - loss 0.33967518 - time (sec): 3.50 - samples/sec: 10554.34 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-20 00:21:54,766 epoch 6 - iter 216/272 - loss 0.34503779 - time (sec): 3.99 - samples/sec: 10493.61 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-20 00:21:55,274 epoch 6 - iter 243/272 - loss 0.33713653 - time (sec): 4.50 - samples/sec: 10472.18 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-20 00:21:55,750 epoch 6 - iter 270/272 - loss 0.33500603 - time (sec): 4.97 - samples/sec: 10390.02 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-20 00:21:55,782 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:55,783 EPOCH 6 done: loss 0.3345 - lr: 0.000022 | |
| 2023-10-20 00:21:56,555 DEV : loss 0.2611343264579773 - f1-score (micro avg) 0.4743 | |
| 2023-10-20 00:21:56,559 saving best model | |
| 2023-10-20 00:21:56,590 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:21:57,104 epoch 7 - iter 27/272 - loss 0.26414586 - time (sec): 0.51 - samples/sec: 10785.15 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-20 00:21:57,653 epoch 7 - iter 54/272 - loss 0.28507475 - time (sec): 1.06 - samples/sec: 10182.43 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-20 00:21:58,174 epoch 7 - iter 81/272 - loss 0.32465150 - time (sec): 1.58 - samples/sec: 10141.41 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-20 00:21:58,684 epoch 7 - iter 108/272 - loss 0.33301144 - time (sec): 2.09 - samples/sec: 9808.71 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-20 00:21:59,201 epoch 7 - iter 135/272 - loss 0.31522540 - time (sec): 2.61 - samples/sec: 9762.86 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-20 00:21:59,708 epoch 7 - iter 162/272 - loss 0.31244099 - time (sec): 3.12 - samples/sec: 9829.27 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-20 00:22:00,198 epoch 7 - iter 189/272 - loss 0.30460946 - time (sec): 3.61 - samples/sec: 9951.47 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-20 00:22:00,681 epoch 7 - iter 216/272 - loss 0.30823837 - time (sec): 4.09 - samples/sec: 9918.18 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-20 00:22:01,195 epoch 7 - iter 243/272 - loss 0.31442376 - time (sec): 4.60 - samples/sec: 10046.22 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-20 00:22:01,687 epoch 7 - iter 270/272 - loss 0.31608757 - time (sec): 5.10 - samples/sec: 10151.04 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-20 00:22:01,717 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:22:01,717 EPOCH 7 done: loss 0.3156 - lr: 0.000017 | |
| 2023-10-20 00:22:02,477 DEV : loss 0.25722736120224 - f1-score (micro avg) 0.4912 | |
| 2023-10-20 00:22:02,481 saving best model | |
| 2023-10-20 00:22:02,511 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:22:03,016 epoch 8 - iter 27/272 - loss 0.35861152 - time (sec): 0.50 - samples/sec: 9876.68 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-20 00:22:03,506 epoch 8 - iter 54/272 - loss 0.32562148 - time (sec): 0.99 - samples/sec: 10259.78 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-20 00:22:04,042 epoch 8 - iter 81/272 - loss 0.29992941 - time (sec): 1.53 - samples/sec: 10360.30 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-20 00:22:04,546 epoch 8 - iter 108/272 - loss 0.30456466 - time (sec): 2.03 - samples/sec: 10210.32 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-20 00:22:05,050 epoch 8 - iter 135/272 - loss 0.30766248 - time (sec): 2.54 - samples/sec: 10207.99 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-20 00:22:05,558 epoch 8 - iter 162/272 - loss 0.30300256 - time (sec): 3.05 - samples/sec: 10223.82 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-20 00:22:06,057 epoch 8 - iter 189/272 - loss 0.29664404 - time (sec): 3.55 - samples/sec: 10395.75 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-20 00:22:06,577 epoch 8 - iter 216/272 - loss 0.29539590 - time (sec): 4.07 - samples/sec: 10466.18 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-20 00:22:07,059 epoch 8 - iter 243/272 - loss 0.29947757 - time (sec): 4.55 - samples/sec: 10330.59 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-20 00:22:07,541 epoch 8 - iter 270/272 - loss 0.30317529 - time (sec): 5.03 - samples/sec: 10286.12 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-20 00:22:07,570 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:22:07,570 EPOCH 8 done: loss 0.3029 - lr: 0.000011 | |
| 2023-10-20 00:22:08,345 DEV : loss 0.25111961364746094 - f1-score (micro avg) 0.5085 | |
| 2023-10-20 00:22:08,348 saving best model | |
| 2023-10-20 00:22:08,384 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:22:08,889 epoch 9 - iter 27/272 - loss 0.29468874 - time (sec): 0.50 - samples/sec: 10627.07 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-20 00:22:09,377 epoch 9 - iter 54/272 - loss 0.28721000 - time (sec): 0.99 - samples/sec: 10287.41 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-20 00:22:09,867 epoch 9 - iter 81/272 - loss 0.30800265 - time (sec): 1.48 - samples/sec: 10078.94 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-20 00:22:10,363 epoch 9 - iter 108/272 - loss 0.30773883 - time (sec): 1.98 - samples/sec: 10143.35 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-20 00:22:10,880 epoch 9 - iter 135/272 - loss 0.30764331 - time (sec): 2.49 - samples/sec: 10622.48 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-20 00:22:11,333 epoch 9 - iter 162/272 - loss 0.30584889 - time (sec): 2.95 - samples/sec: 10761.15 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-20 00:22:11,816 epoch 9 - iter 189/272 - loss 0.30001539 - time (sec): 3.43 - samples/sec: 10584.33 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-20 00:22:12,318 epoch 9 - iter 216/272 - loss 0.29759046 - time (sec): 3.93 - samples/sec: 10478.54 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-20 00:22:12,847 epoch 9 - iter 243/272 - loss 0.29325136 - time (sec): 4.46 - samples/sec: 10481.70 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-20 00:22:13,356 epoch 9 - iter 270/272 - loss 0.29409257 - time (sec): 4.97 - samples/sec: 10419.18 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-20 00:22:13,385 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:22:13,385 EPOCH 9 done: loss 0.2944 - lr: 0.000006 | |
| 2023-10-20 00:22:14,303 DEV : loss 0.24987336993217468 - f1-score (micro avg) 0.5047 | |
| 2023-10-20 00:22:14,307 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:22:14,773 epoch 10 - iter 27/272 - loss 0.32141785 - time (sec): 0.47 - samples/sec: 10482.29 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-20 00:22:15,272 epoch 10 - iter 54/272 - loss 0.31702834 - time (sec): 0.96 - samples/sec: 10922.56 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-20 00:22:15,739 epoch 10 - iter 81/272 - loss 0.29843000 - time (sec): 1.43 - samples/sec: 10748.54 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-20 00:22:16,241 epoch 10 - iter 108/272 - loss 0.29486713 - time (sec): 1.93 - samples/sec: 10407.84 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-20 00:22:16,724 epoch 10 - iter 135/272 - loss 0.30815187 - time (sec): 2.42 - samples/sec: 10305.36 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-20 00:22:17,232 epoch 10 - iter 162/272 - loss 0.30545146 - time (sec): 2.92 - samples/sec: 10301.34 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-20 00:22:17,717 epoch 10 - iter 189/272 - loss 0.29883950 - time (sec): 3.41 - samples/sec: 10355.45 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-20 00:22:18,228 epoch 10 - iter 216/272 - loss 0.29494759 - time (sec): 3.92 - samples/sec: 10502.29 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-20 00:22:18,754 epoch 10 - iter 243/272 - loss 0.28921084 - time (sec): 4.45 - samples/sec: 10547.46 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-20 00:22:19,264 epoch 10 - iter 270/272 - loss 0.28845308 - time (sec): 4.96 - samples/sec: 10461.24 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-20 00:22:19,292 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:22:19,292 EPOCH 10 done: loss 0.2903 - lr: 0.000000 | |
| 2023-10-20 00:22:20,053 DEV : loss 0.2502773106098175 - f1-score (micro avg) 0.4971 | |
| 2023-10-20 00:22:20,083 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-20 00:22:20,083 Loading model from best epoch ... | |
| 2023-10-20 00:22:20,162 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 | |
| 2023-10-20 00:22:20,978 | |
| Results: | |
| - F-score (micro) 0.3892 | |
| - F-score (macro) 0.199 | |
| - Accuracy 0.2555 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.4795 0.5609 0.5170 312 | |
| PER 0.2359 0.3413 0.2790 208 | |
| ORG 0.0000 0.0000 0.0000 55 | |
| HumanProd 0.0000 0.0000 0.0000 22 | |
| micro avg 0.3688 0.4121 0.3892 597 | |
| macro avg 0.1788 0.2256 0.1990 597 | |
| weighted avg 0.3328 0.4121 0.3674 597 | |
| 2023-10-20 00:22:20,979 ---------------------------------------------------------------------------------------------------- | |