| 2023-10-13 15:56:41,973 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:56:41,974 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=21, bias=True) | |
| (loss_function): CrossEntropyLoss() | |
| )" | |
| 2023-10-13 15:56:41,975 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:56:41,975 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences | |
| - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator | |
| 2023-10-13 15:56:41,975 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:56:41,975 Train: 5901 sentences | |
| 2023-10-13 15:56:41,975 (train_with_dev=False, train_with_test=False) | |
| 2023-10-13 15:56:41,975 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:56:41,975 Training Params: | |
| 2023-10-13 15:56:41,975 - learning_rate: "3e-05" | |
| 2023-10-13 15:56:41,975 - mini_batch_size: "8" | |
| 2023-10-13 15:56:41,975 - max_epochs: "10" | |
| 2023-10-13 15:56:41,975 - shuffle: "True" | |
| 2023-10-13 15:56:41,975 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:56:41,975 Plugins: | |
| 2023-10-13 15:56:41,975 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-13 15:56:41,975 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:56:41,975 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-13 15:56:41,975 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-13 15:56:41,975 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:56:41,975 Computation: | |
| 2023-10-13 15:56:41,975 - compute on device: cuda:0 | |
| 2023-10-13 15:56:41,975 - embedding storage: none | |
| 2023-10-13 15:56:41,975 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:56:41,975 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2" | |
| 2023-10-13 15:56:41,975 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:56:41,975 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:56:47,173 epoch 1 - iter 73/738 - loss 3.05853360 - time (sec): 5.20 - samples/sec: 3385.89 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 15:56:52,161 epoch 1 - iter 146/738 - loss 2.01396427 - time (sec): 10.18 - samples/sec: 3514.39 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 15:56:56,988 epoch 1 - iter 219/738 - loss 1.53824384 - time (sec): 15.01 - samples/sec: 3444.77 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 15:57:01,882 epoch 1 - iter 292/738 - loss 1.24891774 - time (sec): 19.91 - samples/sec: 3432.48 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 15:57:06,840 epoch 1 - iter 365/738 - loss 1.07452913 - time (sec): 24.86 - samples/sec: 3418.88 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 15:57:11,658 epoch 1 - iter 438/738 - loss 0.94521822 - time (sec): 29.68 - samples/sec: 3426.34 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 15:57:16,509 epoch 1 - iter 511/738 - loss 0.85158135 - time (sec): 34.53 - samples/sec: 3412.22 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 15:57:20,999 epoch 1 - iter 584/738 - loss 0.78332215 - time (sec): 39.02 - samples/sec: 3392.42 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 15:57:25,830 epoch 1 - iter 657/738 - loss 0.72187463 - time (sec): 43.85 - samples/sec: 3388.05 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 15:57:30,631 epoch 1 - iter 730/738 - loss 0.66940959 - time (sec): 48.65 - samples/sec: 3389.93 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-13 15:57:31,100 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:57:31,100 EPOCH 1 done: loss 0.6654 - lr: 0.000030 | |
| 2023-10-13 15:57:37,172 DEV : loss 0.14468906819820404 - f1-score (micro avg) 0.7202 | |
| 2023-10-13 15:57:37,201 saving best model | |
| 2023-10-13 15:57:37,657 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:57:41,990 epoch 2 - iter 73/738 - loss 0.14759708 - time (sec): 4.33 - samples/sec: 3490.78 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-13 15:57:46,742 epoch 2 - iter 146/738 - loss 0.14672610 - time (sec): 9.08 - samples/sec: 3447.45 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 15:57:51,446 epoch 2 - iter 219/738 - loss 0.14523852 - time (sec): 13.79 - samples/sec: 3442.51 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 15:57:56,392 epoch 2 - iter 292/738 - loss 0.14107499 - time (sec): 18.73 - samples/sec: 3369.54 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 15:58:01,243 epoch 2 - iter 365/738 - loss 0.14216356 - time (sec): 23.58 - samples/sec: 3333.33 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-13 15:58:06,232 epoch 2 - iter 438/738 - loss 0.13781464 - time (sec): 28.57 - samples/sec: 3340.70 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-13 15:58:11,636 epoch 2 - iter 511/738 - loss 0.13577481 - time (sec): 33.98 - samples/sec: 3350.22 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-13 15:58:16,471 epoch 2 - iter 584/738 - loss 0.13005589 - time (sec): 38.81 - samples/sec: 3352.85 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 15:58:21,461 epoch 2 - iter 657/738 - loss 0.12956762 - time (sec): 43.80 - samples/sec: 3362.65 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 15:58:26,771 epoch 2 - iter 730/738 - loss 0.12951791 - time (sec): 49.11 - samples/sec: 3353.97 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 15:58:27,274 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:58:27,274 EPOCH 2 done: loss 0.1294 - lr: 0.000027 | |
| 2023-10-13 15:58:38,435 DEV : loss 0.11062650382518768 - f1-score (micro avg) 0.7675 | |
| 2023-10-13 15:58:38,464 saving best model | |
| 2023-10-13 15:58:39,090 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:58:43,863 epoch 3 - iter 73/738 - loss 0.06065277 - time (sec): 4.77 - samples/sec: 3236.91 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-13 15:58:48,700 epoch 3 - iter 146/738 - loss 0.07316894 - time (sec): 9.61 - samples/sec: 3338.82 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-13 15:58:54,048 epoch 3 - iter 219/738 - loss 0.07863303 - time (sec): 14.95 - samples/sec: 3233.00 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-13 15:58:58,383 epoch 3 - iter 292/738 - loss 0.07743735 - time (sec): 19.29 - samples/sec: 3281.31 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-13 15:59:03,909 epoch 3 - iter 365/738 - loss 0.07606154 - time (sec): 24.81 - samples/sec: 3262.96 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-13 15:59:09,129 epoch 3 - iter 438/738 - loss 0.07463572 - time (sec): 30.03 - samples/sec: 3309.18 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-13 15:59:13,987 epoch 3 - iter 511/738 - loss 0.07177934 - time (sec): 34.89 - samples/sec: 3306.95 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 15:59:18,942 epoch 3 - iter 584/738 - loss 0.07324880 - time (sec): 39.85 - samples/sec: 3320.00 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 15:59:24,146 epoch 3 - iter 657/738 - loss 0.07181062 - time (sec): 45.05 - samples/sec: 3315.65 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 15:59:28,957 epoch 3 - iter 730/738 - loss 0.07338875 - time (sec): 49.86 - samples/sec: 3305.07 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-13 15:59:29,439 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:59:29,439 EPOCH 3 done: loss 0.0738 - lr: 0.000023 | |
| 2023-10-13 15:59:40,672 DEV : loss 0.10864270478487015 - f1-score (micro avg) 0.8175 | |
| 2023-10-13 15:59:40,703 saving best model | |
| 2023-10-13 15:59:41,239 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 15:59:46,028 epoch 4 - iter 73/738 - loss 0.04491633 - time (sec): 4.79 - samples/sec: 3162.59 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-13 15:59:50,685 epoch 4 - iter 146/738 - loss 0.04351311 - time (sec): 9.44 - samples/sec: 3264.79 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-13 15:59:55,499 epoch 4 - iter 219/738 - loss 0.04436716 - time (sec): 14.26 - samples/sec: 3321.07 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-13 16:00:00,085 epoch 4 - iter 292/738 - loss 0.04559344 - time (sec): 18.84 - samples/sec: 3331.80 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-13 16:00:05,124 epoch 4 - iter 365/738 - loss 0.04601732 - time (sec): 23.88 - samples/sec: 3329.15 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-13 16:00:10,439 epoch 4 - iter 438/738 - loss 0.04450045 - time (sec): 29.20 - samples/sec: 3316.64 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 16:00:16,073 epoch 4 - iter 511/738 - loss 0.04465368 - time (sec): 34.83 - samples/sec: 3316.58 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 16:00:20,833 epoch 4 - iter 584/738 - loss 0.04598766 - time (sec): 39.59 - samples/sec: 3332.91 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 16:00:25,931 epoch 4 - iter 657/738 - loss 0.05017371 - time (sec): 44.69 - samples/sec: 3328.90 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-13 16:00:30,598 epoch 4 - iter 730/738 - loss 0.04880929 - time (sec): 49.36 - samples/sec: 3338.99 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-13 16:00:31,086 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:00:31,086 EPOCH 4 done: loss 0.0490 - lr: 0.000020 | |
| 2023-10-13 16:00:42,240 DEV : loss 0.1474699079990387 - f1-score (micro avg) 0.7874 | |
| 2023-10-13 16:00:42,273 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:00:47,244 epoch 5 - iter 73/738 - loss 0.04483958 - time (sec): 4.97 - samples/sec: 3309.95 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-13 16:00:52,261 epoch 5 - iter 146/738 - loss 0.03940692 - time (sec): 9.99 - samples/sec: 3306.44 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-13 16:00:57,081 epoch 5 - iter 219/738 - loss 0.03823729 - time (sec): 14.81 - samples/sec: 3372.73 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-13 16:01:01,852 epoch 5 - iter 292/738 - loss 0.03570809 - time (sec): 19.58 - samples/sec: 3372.43 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-13 16:01:06,660 epoch 5 - iter 365/738 - loss 0.03611074 - time (sec): 24.39 - samples/sec: 3366.09 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 16:01:11,422 epoch 5 - iter 438/738 - loss 0.03549566 - time (sec): 29.15 - samples/sec: 3345.91 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 16:01:16,434 epoch 5 - iter 511/738 - loss 0.03481242 - time (sec): 34.16 - samples/sec: 3329.04 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 16:01:21,988 epoch 5 - iter 584/738 - loss 0.03612795 - time (sec): 39.71 - samples/sec: 3312.86 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-13 16:01:27,611 epoch 5 - iter 657/738 - loss 0.03541289 - time (sec): 45.34 - samples/sec: 3304.32 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-13 16:01:32,079 epoch 5 - iter 730/738 - loss 0.03603975 - time (sec): 49.80 - samples/sec: 3305.92 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-13 16:01:32,629 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:01:32,630 EPOCH 5 done: loss 0.0359 - lr: 0.000017 | |
| 2023-10-13 16:01:43,734 DEV : loss 0.15243035554885864 - f1-score (micro avg) 0.8137 | |
| 2023-10-13 16:01:43,764 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:01:48,291 epoch 6 - iter 73/738 - loss 0.02753601 - time (sec): 4.53 - samples/sec: 3278.39 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-13 16:01:53,834 epoch 6 - iter 146/738 - loss 0.02412278 - time (sec): 10.07 - samples/sec: 3370.68 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-13 16:01:58,832 epoch 6 - iter 219/738 - loss 0.02730955 - time (sec): 15.07 - samples/sec: 3372.32 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-13 16:02:03,512 epoch 6 - iter 292/738 - loss 0.02914052 - time (sec): 19.75 - samples/sec: 3355.51 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 16:02:08,994 epoch 6 - iter 365/738 - loss 0.02963576 - time (sec): 25.23 - samples/sec: 3347.08 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 16:02:13,940 epoch 6 - iter 438/738 - loss 0.03069200 - time (sec): 30.18 - samples/sec: 3357.87 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 16:02:18,409 epoch 6 - iter 511/738 - loss 0.02902534 - time (sec): 34.64 - samples/sec: 3367.48 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-13 16:02:23,343 epoch 6 - iter 584/738 - loss 0.02744013 - time (sec): 39.58 - samples/sec: 3365.62 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-13 16:02:28,153 epoch 6 - iter 657/738 - loss 0.02726539 - time (sec): 44.39 - samples/sec: 3353.62 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-13 16:02:33,016 epoch 6 - iter 730/738 - loss 0.02719915 - time (sec): 49.25 - samples/sec: 3346.24 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-13 16:02:33,486 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:02:33,487 EPOCH 6 done: loss 0.0272 - lr: 0.000013 | |
| 2023-10-13 16:02:44,645 DEV : loss 0.17250441014766693 - f1-score (micro avg) 0.8204 | |
| 2023-10-13 16:02:44,676 saving best model | |
| 2023-10-13 16:02:45,316 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:02:50,958 epoch 7 - iter 73/738 - loss 0.01634149 - time (sec): 5.64 - samples/sec: 3018.98 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-13 16:02:55,302 epoch 7 - iter 146/738 - loss 0.01514888 - time (sec): 9.98 - samples/sec: 3189.06 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-13 16:03:00,715 epoch 7 - iter 219/738 - loss 0.01838780 - time (sec): 15.40 - samples/sec: 3270.41 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 16:03:06,168 epoch 7 - iter 292/738 - loss 0.01756859 - time (sec): 20.85 - samples/sec: 3303.19 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 16:03:10,622 epoch 7 - iter 365/738 - loss 0.01866420 - time (sec): 25.30 - samples/sec: 3307.34 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 16:03:15,201 epoch 7 - iter 438/738 - loss 0.01851098 - time (sec): 29.88 - samples/sec: 3315.07 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-13 16:03:19,804 epoch 7 - iter 511/738 - loss 0.01926756 - time (sec): 34.48 - samples/sec: 3336.22 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-13 16:03:24,445 epoch 7 - iter 584/738 - loss 0.01977881 - time (sec): 39.13 - samples/sec: 3335.09 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-13 16:03:29,619 epoch 7 - iter 657/738 - loss 0.01907489 - time (sec): 44.30 - samples/sec: 3305.95 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-13 16:03:35,450 epoch 7 - iter 730/738 - loss 0.01894560 - time (sec): 50.13 - samples/sec: 3288.28 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-13 16:03:35,947 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:03:35,948 EPOCH 7 done: loss 0.0190 - lr: 0.000010 | |
| 2023-10-13 16:03:47,105 DEV : loss 0.20412878692150116 - f1-score (micro avg) 0.8154 | |
| 2023-10-13 16:03:47,134 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:03:52,161 epoch 8 - iter 73/738 - loss 0.01344325 - time (sec): 5.03 - samples/sec: 3320.25 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-13 16:03:57,698 epoch 8 - iter 146/738 - loss 0.01748030 - time (sec): 10.56 - samples/sec: 3185.06 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 16:04:04,280 epoch 8 - iter 219/738 - loss 0.01866343 - time (sec): 17.14 - samples/sec: 3081.13 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 16:04:09,445 epoch 8 - iter 292/738 - loss 0.01990917 - time (sec): 22.31 - samples/sec: 3033.63 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 16:04:14,015 epoch 8 - iter 365/738 - loss 0.01831245 - time (sec): 26.88 - samples/sec: 3065.14 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-13 16:04:18,968 epoch 8 - iter 438/738 - loss 0.01923268 - time (sec): 31.83 - samples/sec: 3095.99 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-13 16:04:23,888 epoch 8 - iter 511/738 - loss 0.01795623 - time (sec): 36.75 - samples/sec: 3121.73 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-13 16:04:28,303 epoch 8 - iter 584/738 - loss 0.01764510 - time (sec): 41.17 - samples/sec: 3142.12 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-13 16:04:33,109 epoch 8 - iter 657/738 - loss 0.01668878 - time (sec): 45.97 - samples/sec: 3159.38 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-13 16:04:38,749 epoch 8 - iter 730/738 - loss 0.01600711 - time (sec): 51.61 - samples/sec: 3191.33 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-13 16:04:39,252 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:04:39,252 EPOCH 8 done: loss 0.0158 - lr: 0.000007 | |
| 2023-10-13 16:04:50,416 DEV : loss 0.19149629771709442 - f1-score (micro avg) 0.826 | |
| 2023-10-13 16:04:50,445 saving best model | |
| 2023-10-13 16:04:50,981 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:04:55,805 epoch 9 - iter 73/738 - loss 0.00698935 - time (sec): 4.82 - samples/sec: 3214.71 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 16:05:00,887 epoch 9 - iter 146/738 - loss 0.00952456 - time (sec): 9.91 - samples/sec: 3262.85 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 16:05:05,508 epoch 9 - iter 219/738 - loss 0.00949156 - time (sec): 14.53 - samples/sec: 3325.48 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 16:05:10,381 epoch 9 - iter 292/738 - loss 0.01022930 - time (sec): 19.40 - samples/sec: 3341.20 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-13 16:05:15,595 epoch 9 - iter 365/738 - loss 0.01120093 - time (sec): 24.61 - samples/sec: 3363.39 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-13 16:05:20,184 epoch 9 - iter 438/738 - loss 0.01043318 - time (sec): 29.20 - samples/sec: 3365.10 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-13 16:05:25,342 epoch 9 - iter 511/738 - loss 0.01017895 - time (sec): 34.36 - samples/sec: 3352.34 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-13 16:05:29,969 epoch 9 - iter 584/738 - loss 0.01017742 - time (sec): 38.99 - samples/sec: 3339.96 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-13 16:05:34,734 epoch 9 - iter 657/738 - loss 0.00985239 - time (sec): 43.75 - samples/sec: 3352.03 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-13 16:05:40,086 epoch 9 - iter 730/738 - loss 0.01123444 - time (sec): 49.10 - samples/sec: 3352.47 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 16:05:40,697 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:05:40,698 EPOCH 9 done: loss 0.0117 - lr: 0.000003 | |
| 2023-10-13 16:05:51,786 DEV : loss 0.19480924308300018 - f1-score (micro avg) 0.8267 | |
| 2023-10-13 16:05:51,816 saving best model | |
| 2023-10-13 16:05:52,417 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:05:57,660 epoch 10 - iter 73/738 - loss 0.01186703 - time (sec): 5.24 - samples/sec: 3361.08 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 16:06:02,305 epoch 10 - iter 146/738 - loss 0.00836274 - time (sec): 9.89 - samples/sec: 3390.77 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 16:06:06,585 epoch 10 - iter 219/738 - loss 0.01000260 - time (sec): 14.17 - samples/sec: 3456.57 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-13 16:06:11,468 epoch 10 - iter 292/738 - loss 0.00903905 - time (sec): 19.05 - samples/sec: 3427.99 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-13 16:06:16,389 epoch 10 - iter 365/738 - loss 0.00871698 - time (sec): 23.97 - samples/sec: 3393.43 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-13 16:06:21,868 epoch 10 - iter 438/738 - loss 0.00896638 - time (sec): 29.45 - samples/sec: 3398.02 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-13 16:06:26,485 epoch 10 - iter 511/738 - loss 0.00840529 - time (sec): 34.07 - samples/sec: 3368.60 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-13 16:06:32,017 epoch 10 - iter 584/738 - loss 0.00852755 - time (sec): 39.60 - samples/sec: 3327.66 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-13 16:06:37,311 epoch 10 - iter 657/738 - loss 0.00884580 - time (sec): 44.89 - samples/sec: 3302.90 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-13 16:06:42,635 epoch 10 - iter 730/738 - loss 0.00832851 - time (sec): 50.22 - samples/sec: 3284.25 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-13 16:06:43,054 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:06:43,054 EPOCH 10 done: loss 0.0083 - lr: 0.000000 | |
| 2023-10-13 16:06:54,526 DEV : loss 0.19598710536956787 - f1-score (micro avg) 0.8274 | |
| 2023-10-13 16:06:54,557 saving best model | |
| 2023-10-13 16:06:55,518 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 16:06:55,520 Loading model from best epoch ... | |
| 2023-10-13 16:06:57,087 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-time, B-time, E-time, I-time, S-prod, B-prod, E-prod, I-prod | |
| 2023-10-13 16:07:03,859 | |
| Results: | |
| - F-score (micro) 0.7929 | |
| - F-score (macro) 0.6926 | |
| - Accuracy 0.6785 | |
| By class: | |
| precision recall f1-score support | |
| loc 0.8673 0.8683 0.8678 858 | |
| pers 0.7402 0.8119 0.7744 537 | |
| org 0.5067 0.5758 0.5390 132 | |
| prod 0.6885 0.6885 0.6885 61 | |
| time 0.5469 0.6481 0.5932 54 | |
| micro avg 0.7742 0.8124 0.7929 1642 | |
| macro avg 0.6699 0.7185 0.6926 1642 | |
| weighted avg 0.7796 0.8124 0.7951 1642 | |
| 2023-10-13 16:07:03,859 ---------------------------------------------------------------------------------------------------- | |