| 2023-10-16 18:44:14,992 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:14,993 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 18:44:14,993 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:14,993 MultiCorpus: 1166 train + 165 dev + 415 test sentences | |
| - NER_HIPE_2022 Corpus: 1166 train + 165 dev + 415 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fi/with_doc_seperator | |
| 2023-10-16 18:44:14,993 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:14,993 Train: 1166 sentences | |
| 2023-10-16 18:44:14,993 (train_with_dev=False, train_with_test=False) | |
| 2023-10-16 18:44:14,993 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:14,993 Training Params: | |
| 2023-10-16 18:44:14,993 - learning_rate: "5e-05" | |
| 2023-10-16 18:44:14,993 - mini_batch_size: "8" | |
| 2023-10-16 18:44:14,993 - max_epochs: "10" | |
| 2023-10-16 18:44:14,993 - shuffle: "True" | |
| 2023-10-16 18:44:14,994 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:14,994 Plugins: | |
| 2023-10-16 18:44:14,994 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-16 18:44:14,994 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:14,994 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-16 18:44:14,994 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-16 18:44:14,994 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:14,994 Computation: | |
| 2023-10-16 18:44:14,994 - compute on device: cuda:0 | |
| 2023-10-16 18:44:14,994 - embedding storage: none | |
| 2023-10-16 18:44:14,994 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:14,994 Model training base path: "hmbench-newseye/fi-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4" | |
| 2023-10-16 18:44:14,994 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:14,994 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:16,450 epoch 1 - iter 14/146 - loss 2.93554078 - time (sec): 1.46 - samples/sec: 3031.39 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-16 18:44:17,598 epoch 1 - iter 28/146 - loss 2.59641910 - time (sec): 2.60 - samples/sec: 3093.50 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 18:44:19,197 epoch 1 - iter 42/146 - loss 1.98647565 - time (sec): 4.20 - samples/sec: 3046.18 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-16 18:44:20,958 epoch 1 - iter 56/146 - loss 1.65493251 - time (sec): 5.96 - samples/sec: 2908.07 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-16 18:44:22,265 epoch 1 - iter 70/146 - loss 1.48549199 - time (sec): 7.27 - samples/sec: 2906.30 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 18:44:23,957 epoch 1 - iter 84/146 - loss 1.33028113 - time (sec): 8.96 - samples/sec: 2878.44 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-16 18:44:25,337 epoch 1 - iter 98/146 - loss 1.19387586 - time (sec): 10.34 - samples/sec: 2907.41 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-16 18:44:26,724 epoch 1 - iter 112/146 - loss 1.07702304 - time (sec): 11.73 - samples/sec: 2939.16 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-16 18:44:28,150 epoch 1 - iter 126/146 - loss 0.98680341 - time (sec): 13.16 - samples/sec: 2960.74 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-16 18:44:29,393 epoch 1 - iter 140/146 - loss 0.91930004 - time (sec): 14.40 - samples/sec: 2986.90 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-16 18:44:29,886 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:29,887 EPOCH 1 done: loss 0.8989 - lr: 0.000048 | |
| 2023-10-16 18:44:30,915 DEV : loss 0.19385209679603577 - f1-score (micro avg) 0.4388 | |
| 2023-10-16 18:44:30,919 saving best model | |
| 2023-10-16 18:44:31,329 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:32,656 epoch 2 - iter 14/146 - loss 0.26646816 - time (sec): 1.33 - samples/sec: 3202.19 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-16 18:44:34,281 epoch 2 - iter 28/146 - loss 0.27245130 - time (sec): 2.95 - samples/sec: 3188.14 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-16 18:44:35,932 epoch 2 - iter 42/146 - loss 0.28481191 - time (sec): 4.60 - samples/sec: 2962.86 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-16 18:44:37,632 epoch 2 - iter 56/146 - loss 0.25721835 - time (sec): 6.30 - samples/sec: 2913.56 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-16 18:44:38,924 epoch 2 - iter 70/146 - loss 0.24376985 - time (sec): 7.59 - samples/sec: 2929.54 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-16 18:44:40,297 epoch 2 - iter 84/146 - loss 0.23798723 - time (sec): 8.97 - samples/sec: 2952.15 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-16 18:44:41,482 epoch 2 - iter 98/146 - loss 0.23172878 - time (sec): 10.15 - samples/sec: 2977.39 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-16 18:44:42,768 epoch 2 - iter 112/146 - loss 0.21818927 - time (sec): 11.44 - samples/sec: 3000.61 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-16 18:44:44,349 epoch 2 - iter 126/146 - loss 0.21149100 - time (sec): 13.02 - samples/sec: 2974.30 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-16 18:44:45,592 epoch 2 - iter 140/146 - loss 0.20682752 - time (sec): 14.26 - samples/sec: 2983.62 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-16 18:44:46,258 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:46,259 EPOCH 2 done: loss 0.2017 - lr: 0.000045 | |
| 2023-10-16 18:44:47,492 DEV : loss 0.11957067251205444 - f1-score (micro avg) 0.6291 | |
| 2023-10-16 18:44:47,503 saving best model | |
| 2023-10-16 18:44:47,979 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:44:49,571 epoch 3 - iter 14/146 - loss 0.11191478 - time (sec): 1.58 - samples/sec: 3023.30 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-16 18:44:51,442 epoch 3 - iter 28/146 - loss 0.11158050 - time (sec): 3.45 - samples/sec: 2823.35 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-16 18:44:52,817 epoch 3 - iter 42/146 - loss 0.11844144 - time (sec): 4.83 - samples/sec: 2769.61 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-16 18:44:53,989 epoch 3 - iter 56/146 - loss 0.11296688 - time (sec): 6.00 - samples/sec: 2807.39 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-16 18:44:55,247 epoch 3 - iter 70/146 - loss 0.11117130 - time (sec): 7.26 - samples/sec: 2862.75 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-16 18:44:56,780 epoch 3 - iter 84/146 - loss 0.10769328 - time (sec): 8.79 - samples/sec: 2918.14 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-16 18:44:58,413 epoch 3 - iter 98/146 - loss 0.10822287 - time (sec): 10.42 - samples/sec: 2956.84 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-16 18:44:59,893 epoch 3 - iter 112/146 - loss 0.10974325 - time (sec): 11.90 - samples/sec: 2946.24 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-16 18:45:01,233 epoch 3 - iter 126/146 - loss 0.10961191 - time (sec): 13.24 - samples/sec: 2948.19 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-16 18:45:02,762 epoch 3 - iter 140/146 - loss 0.11159692 - time (sec): 14.77 - samples/sec: 2908.76 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-16 18:45:03,277 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:45:03,277 EPOCH 3 done: loss 0.1116 - lr: 0.000039 | |
| 2023-10-16 18:45:04,525 DEV : loss 0.10484768450260162 - f1-score (micro avg) 0.7265 | |
| 2023-10-16 18:45:04,529 saving best model | |
| 2023-10-16 18:45:05,074 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:45:06,751 epoch 4 - iter 14/146 - loss 0.06773359 - time (sec): 1.67 - samples/sec: 3019.84 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-16 18:45:08,400 epoch 4 - iter 28/146 - loss 0.08808391 - time (sec): 3.32 - samples/sec: 2840.89 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-16 18:45:09,895 epoch 4 - iter 42/146 - loss 0.07321162 - time (sec): 4.82 - samples/sec: 2896.89 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-16 18:45:11,044 epoch 4 - iter 56/146 - loss 0.07713816 - time (sec): 5.97 - samples/sec: 2905.45 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-16 18:45:12,599 epoch 4 - iter 70/146 - loss 0.07754016 - time (sec): 7.52 - samples/sec: 2913.73 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-16 18:45:14,247 epoch 4 - iter 84/146 - loss 0.07736573 - time (sec): 9.17 - samples/sec: 2861.76 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-16 18:45:15,521 epoch 4 - iter 98/146 - loss 0.07596628 - time (sec): 10.44 - samples/sec: 2860.25 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-16 18:45:17,303 epoch 4 - iter 112/146 - loss 0.07335282 - time (sec): 12.23 - samples/sec: 2866.41 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-16 18:45:18,600 epoch 4 - iter 126/146 - loss 0.07440373 - time (sec): 13.52 - samples/sec: 2899.44 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-16 18:45:19,917 epoch 4 - iter 140/146 - loss 0.07551967 - time (sec): 14.84 - samples/sec: 2900.64 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-16 18:45:20,359 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:45:20,359 EPOCH 4 done: loss 0.0754 - lr: 0.000034 | |
| 2023-10-16 18:45:21,637 DEV : loss 0.106049545109272 - f1-score (micro avg) 0.7489 | |
| 2023-10-16 18:45:21,642 saving best model | |
| 2023-10-16 18:45:22,190 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:45:23,719 epoch 5 - iter 14/146 - loss 0.04848651 - time (sec): 1.53 - samples/sec: 2833.76 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-16 18:45:25,226 epoch 5 - iter 28/146 - loss 0.04053376 - time (sec): 3.03 - samples/sec: 2740.38 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-16 18:45:26,737 epoch 5 - iter 42/146 - loss 0.04042929 - time (sec): 4.54 - samples/sec: 2797.98 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-16 18:45:28,427 epoch 5 - iter 56/146 - loss 0.04755111 - time (sec): 6.23 - samples/sec: 2885.92 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-16 18:45:30,011 epoch 5 - iter 70/146 - loss 0.04653289 - time (sec): 7.82 - samples/sec: 2907.59 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-16 18:45:31,331 epoch 5 - iter 84/146 - loss 0.05035831 - time (sec): 9.14 - samples/sec: 2918.02 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-16 18:45:32,949 epoch 5 - iter 98/146 - loss 0.04699916 - time (sec): 10.76 - samples/sec: 2946.14 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-16 18:45:34,106 epoch 5 - iter 112/146 - loss 0.04895681 - time (sec): 11.91 - samples/sec: 2962.47 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-16 18:45:35,479 epoch 5 - iter 126/146 - loss 0.05041908 - time (sec): 13.29 - samples/sec: 2959.37 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-16 18:45:36,721 epoch 5 - iter 140/146 - loss 0.05285443 - time (sec): 14.53 - samples/sec: 2974.98 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-16 18:45:37,198 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:45:37,198 EPOCH 5 done: loss 0.0529 - lr: 0.000028 | |
| 2023-10-16 18:45:38,518 DEV : loss 0.11109450459480286 - f1-score (micro avg) 0.7431 | |
| 2023-10-16 18:45:38,525 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:45:39,890 epoch 6 - iter 14/146 - loss 0.05005263 - time (sec): 1.36 - samples/sec: 3099.52 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 18:45:41,470 epoch 6 - iter 28/146 - loss 0.04617285 - time (sec): 2.94 - samples/sec: 3109.63 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-16 18:45:42,781 epoch 6 - iter 42/146 - loss 0.03762395 - time (sec): 4.25 - samples/sec: 3087.76 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-16 18:45:44,214 epoch 6 - iter 56/146 - loss 0.03821664 - time (sec): 5.69 - samples/sec: 3009.65 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-16 18:45:45,976 epoch 6 - iter 70/146 - loss 0.03792029 - time (sec): 7.45 - samples/sec: 2884.01 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-16 18:45:47,431 epoch 6 - iter 84/146 - loss 0.03947725 - time (sec): 8.90 - samples/sec: 2851.63 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-16 18:45:48,916 epoch 6 - iter 98/146 - loss 0.03763630 - time (sec): 10.39 - samples/sec: 2885.48 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 18:45:50,323 epoch 6 - iter 112/146 - loss 0.03716618 - time (sec): 11.80 - samples/sec: 2911.43 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-16 18:45:51,783 epoch 6 - iter 126/146 - loss 0.03715143 - time (sec): 13.26 - samples/sec: 2934.88 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-16 18:45:53,097 epoch 6 - iter 140/146 - loss 0.03776802 - time (sec): 14.57 - samples/sec: 2928.64 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-16 18:45:53,735 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:45:53,735 EPOCH 6 done: loss 0.0377 - lr: 0.000023 | |
| 2023-10-16 18:45:55,064 DEV : loss 0.10371122509241104 - f1-score (micro avg) 0.7702 | |
| 2023-10-16 18:45:55,070 saving best model | |
| 2023-10-16 18:45:55,594 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:45:56,996 epoch 7 - iter 14/146 - loss 0.02717694 - time (sec): 1.40 - samples/sec: 3059.75 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-16 18:45:58,279 epoch 7 - iter 28/146 - loss 0.02170902 - time (sec): 2.68 - samples/sec: 3094.36 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 18:45:59,710 epoch 7 - iter 42/146 - loss 0.01935375 - time (sec): 4.11 - samples/sec: 3129.38 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-16 18:46:01,234 epoch 7 - iter 56/146 - loss 0.02181448 - time (sec): 5.64 - samples/sec: 3090.41 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-16 18:46:02,563 epoch 7 - iter 70/146 - loss 0.02092834 - time (sec): 6.97 - samples/sec: 3056.05 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-16 18:46:04,330 epoch 7 - iter 84/146 - loss 0.02507712 - time (sec): 8.73 - samples/sec: 2999.06 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-16 18:46:05,695 epoch 7 - iter 98/146 - loss 0.02423133 - time (sec): 10.10 - samples/sec: 3005.29 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-16 18:46:07,233 epoch 7 - iter 112/146 - loss 0.02532614 - time (sec): 11.64 - samples/sec: 2964.77 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 18:46:08,741 epoch 7 - iter 126/146 - loss 0.02581130 - time (sec): 13.15 - samples/sec: 2945.18 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-16 18:46:10,281 epoch 7 - iter 140/146 - loss 0.02646009 - time (sec): 14.69 - samples/sec: 2926.02 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-16 18:46:10,770 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:46:10,770 EPOCH 7 done: loss 0.0263 - lr: 0.000017 | |
| 2023-10-16 18:46:12,029 DEV : loss 0.12524546682834625 - f1-score (micro avg) 0.7613 | |
| 2023-10-16 18:46:12,034 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:46:13,421 epoch 8 - iter 14/146 - loss 0.01540096 - time (sec): 1.39 - samples/sec: 3120.75 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-16 18:46:14,944 epoch 8 - iter 28/146 - loss 0.01738761 - time (sec): 2.91 - samples/sec: 3063.83 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-16 18:46:16,589 epoch 8 - iter 42/146 - loss 0.02301455 - time (sec): 4.55 - samples/sec: 3009.18 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 18:46:17,768 epoch 8 - iter 56/146 - loss 0.02368094 - time (sec): 5.73 - samples/sec: 2956.04 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-16 18:46:19,225 epoch 8 - iter 70/146 - loss 0.02328872 - time (sec): 7.19 - samples/sec: 2990.68 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-16 18:46:20,897 epoch 8 - iter 84/146 - loss 0.02214130 - time (sec): 8.86 - samples/sec: 2969.74 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-16 18:46:22,215 epoch 8 - iter 98/146 - loss 0.02037658 - time (sec): 10.18 - samples/sec: 3002.75 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-16 18:46:23,724 epoch 8 - iter 112/146 - loss 0.02107436 - time (sec): 11.69 - samples/sec: 2975.20 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-16 18:46:25,210 epoch 8 - iter 126/146 - loss 0.02013553 - time (sec): 13.18 - samples/sec: 2976.87 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 18:46:26,439 epoch 8 - iter 140/146 - loss 0.01954804 - time (sec): 14.40 - samples/sec: 2984.73 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-16 18:46:26,930 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:46:26,930 EPOCH 8 done: loss 0.0194 - lr: 0.000012 | |
| 2023-10-16 18:46:28,209 DEV : loss 0.1471555233001709 - f1-score (micro avg) 0.7468 | |
| 2023-10-16 18:46:28,214 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:46:29,632 epoch 9 - iter 14/146 - loss 0.02032134 - time (sec): 1.42 - samples/sec: 3324.63 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-16 18:46:31,349 epoch 9 - iter 28/146 - loss 0.01946398 - time (sec): 3.13 - samples/sec: 2988.38 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-16 18:46:32,688 epoch 9 - iter 42/146 - loss 0.01654113 - time (sec): 4.47 - samples/sec: 2913.92 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-16 18:46:34,080 epoch 9 - iter 56/146 - loss 0.01628118 - time (sec): 5.86 - samples/sec: 2922.13 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 18:46:35,284 epoch 9 - iter 70/146 - loss 0.01650357 - time (sec): 7.07 - samples/sec: 2956.01 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-16 18:46:36,626 epoch 9 - iter 84/146 - loss 0.01631769 - time (sec): 8.41 - samples/sec: 2971.67 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-16 18:46:38,373 epoch 9 - iter 98/146 - loss 0.01409268 - time (sec): 10.16 - samples/sec: 2910.90 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-16 18:46:39,652 epoch 9 - iter 112/146 - loss 0.01357809 - time (sec): 11.44 - samples/sec: 2907.10 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-16 18:46:41,271 epoch 9 - iter 126/146 - loss 0.01361320 - time (sec): 13.06 - samples/sec: 2887.92 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-16 18:46:42,819 epoch 9 - iter 140/146 - loss 0.01401163 - time (sec): 14.60 - samples/sec: 2903.52 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-16 18:46:43,393 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:46:43,393 EPOCH 9 done: loss 0.0137 - lr: 0.000006 | |
| 2023-10-16 18:46:44,724 DEV : loss 0.14237351715564728 - f1-score (micro avg) 0.7425 | |
| 2023-10-16 18:46:44,729 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:46:46,002 epoch 10 - iter 14/146 - loss 0.01164471 - time (sec): 1.27 - samples/sec: 2898.78 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-16 18:46:47,276 epoch 10 - iter 28/146 - loss 0.00769121 - time (sec): 2.55 - samples/sec: 3100.67 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-16 18:46:48,584 epoch 10 - iter 42/146 - loss 0.01041592 - time (sec): 3.85 - samples/sec: 3128.38 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-16 18:46:50,364 epoch 10 - iter 56/146 - loss 0.01063731 - time (sec): 5.63 - samples/sec: 3008.74 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-16 18:46:51,888 epoch 10 - iter 70/146 - loss 0.01116153 - time (sec): 7.16 - samples/sec: 3038.46 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 18:46:53,303 epoch 10 - iter 84/146 - loss 0.01395303 - time (sec): 8.57 - samples/sec: 3047.73 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-16 18:46:54,633 epoch 10 - iter 98/146 - loss 0.01344263 - time (sec): 9.90 - samples/sec: 3046.71 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-16 18:46:56,080 epoch 10 - iter 112/146 - loss 0.01227917 - time (sec): 11.35 - samples/sec: 3019.23 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-16 18:46:57,603 epoch 10 - iter 126/146 - loss 0.01125604 - time (sec): 12.87 - samples/sec: 3017.04 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-16 18:46:59,081 epoch 10 - iter 140/146 - loss 0.01038127 - time (sec): 14.35 - samples/sec: 3020.01 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-16 18:46:59,533 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:46:59,533 EPOCH 10 done: loss 0.0104 - lr: 0.000000 | |
| 2023-10-16 18:47:00,784 DEV : loss 0.14098528027534485 - f1-score (micro avg) 0.7532 | |
| 2023-10-16 18:47:01,250 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-16 18:47:01,252 Loading model from best epoch ... | |
| 2023-10-16 18:47:02,920 SequenceTagger predicts: Dictionary with 17 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, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd | |
| 2023-10-16 18:47:05,389 | |
| Results: | |
| - F-score (micro) 0.7535 | |
| - F-score (macro) 0.6922 | |
| - Accuracy 0.6267 | |
| By class: | |
| precision recall f1-score support | |
| PER 0.7902 0.8333 0.8112 348 | |
| LOC 0.6719 0.8161 0.7370 261 | |
| ORG 0.4340 0.4423 0.4381 52 | |
| HumanProd 0.7500 0.8182 0.7826 22 | |
| micro avg 0.7148 0.7965 0.7535 683 | |
| macro avg 0.6615 0.7275 0.6922 683 | |
| weighted avg 0.7166 0.7965 0.7535 683 | |
| 2023-10-16 18:47:05,390 ---------------------------------------------------------------------------------------------------- | |