2023-10-17 13:24:18,291 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:24:18,292 Model: "SequenceTagger( (embeddings): TransformerWordEmbeddings( (model): ElectraModel( (embeddings): ElectraEmbeddings( (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): ElectraEncoder( (layer): ModuleList( (0-11): 12 x ElectraLayer( (attention): ElectraAttention( (self): ElectraSelfAttention( (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): ElectraSelfOutput( (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): ElectraIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): ElectraOutput( (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) ) ) ) ) ) ) (locked_dropout): LockedDropout(p=0.5) (linear): Linear(in_features=768, out_features=17, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-17 13:24:18,292 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:24:18,292 MultiCorpus: 7142 train + 698 dev + 2570 test sentences - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator 2023-10-17 13:24:18,292 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:24:18,292 Train: 7142 sentences 2023-10-17 13:24:18,292 (train_with_dev=False, train_with_test=False) 2023-10-17 13:24:18,292 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:24:18,292 Training Params: 2023-10-17 13:24:18,292 - learning_rate: "5e-05" 2023-10-17 13:24:18,292 - mini_batch_size: "8" 2023-10-17 13:24:18,292 - max_epochs: "10" 2023-10-17 13:24:18,292 - shuffle: "True" 2023-10-17 13:24:18,292 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:24:18,292 Plugins: 2023-10-17 13:24:18,292 - TensorboardLogger 2023-10-17 13:24:18,292 - LinearScheduler | warmup_fraction: '0.1' 2023-10-17 13:24:18,292 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:24:18,292 Final evaluation on model from best epoch (best-model.pt) 2023-10-17 13:24:18,292 - metric: "('micro avg', 'f1-score')" 2023-10-17 13:24:18,292 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:24:18,292 Computation: 2023-10-17 13:24:18,292 - compute on device: cuda:0 2023-10-17 13:24:18,293 - embedding storage: none 2023-10-17 13:24:18,293 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:24:18,293 Model training base path: "hmbench-newseye/fr-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2" 2023-10-17 13:24:18,293 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:24:18,293 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:24:18,293 Logging anything other than scalars to TensorBoard is currently not supported. 2023-10-17 13:24:24,918 epoch 1 - iter 89/893 - loss 2.75866120 - time (sec): 6.62 - samples/sec: 3473.86 - lr: 0.000005 - momentum: 0.000000 2023-10-17 13:24:32,428 epoch 1 - iter 178/893 - loss 1.58176161 - time (sec): 14.13 - samples/sec: 3513.32 - lr: 0.000010 - momentum: 0.000000 2023-10-17 13:24:39,299 epoch 1 - iter 267/893 - loss 1.18997196 - time (sec): 21.01 - samples/sec: 3548.14 - lr: 0.000015 - momentum: 0.000000 2023-10-17 13:24:46,281 epoch 1 - iter 356/893 - loss 0.95916455 - time (sec): 27.99 - samples/sec: 3612.14 - lr: 0.000020 - momentum: 0.000000 2023-10-17 13:24:52,902 epoch 1 - iter 445/893 - loss 0.81844743 - time (sec): 34.61 - samples/sec: 3606.55 - lr: 0.000025 - momentum: 0.000000 2023-10-17 13:24:59,404 epoch 1 - iter 534/893 - loss 0.72551967 - time (sec): 41.11 - samples/sec: 3603.88 - lr: 0.000030 - momentum: 0.000000 2023-10-17 13:25:06,164 epoch 1 - iter 623/893 - loss 0.64807688 - time (sec): 47.87 - samples/sec: 3609.93 - lr: 0.000035 - momentum: 0.000000 2023-10-17 13:25:13,480 epoch 1 - iter 712/893 - loss 0.58175950 - time (sec): 55.19 - samples/sec: 3610.39 - lr: 0.000040 - momentum: 0.000000 2023-10-17 13:25:20,051 epoch 1 - iter 801/893 - loss 0.53640711 - time (sec): 61.76 - samples/sec: 3608.51 - lr: 0.000045 - momentum: 0.000000 2023-10-17 13:25:26,675 epoch 1 - iter 890/893 - loss 0.49591919 - time (sec): 68.38 - samples/sec: 3629.34 - lr: 0.000050 - momentum: 0.000000 2023-10-17 13:25:26,847 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:25:26,847 EPOCH 1 done: loss 0.4953 - lr: 0.000050 2023-10-17 13:25:30,025 DEV : loss 0.13788369297981262 - f1-score (micro avg) 0.7176 2023-10-17 13:25:30,041 saving best model 2023-10-17 13:25:30,417 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:25:37,070 epoch 2 - iter 89/893 - loss 0.12012810 - time (sec): 6.65 - samples/sec: 3655.67 - lr: 0.000049 - momentum: 0.000000 2023-10-17 13:25:43,766 epoch 2 - iter 178/893 - loss 0.11907211 - time (sec): 13.35 - samples/sec: 3598.83 - lr: 0.000049 - momentum: 0.000000 2023-10-17 13:25:50,441 epoch 2 - iter 267/893 - loss 0.11594867 - time (sec): 20.02 - samples/sec: 3490.94 - lr: 0.000048 - momentum: 0.000000 2023-10-17 13:25:57,844 epoch 2 - iter 356/893 - loss 0.11429689 - time (sec): 27.43 - samples/sec: 3466.61 - lr: 0.000048 - momentum: 0.000000 2023-10-17 13:26:04,965 epoch 2 - iter 445/893 - loss 0.11235555 - time (sec): 34.55 - samples/sec: 3515.49 - lr: 0.000047 - momentum: 0.000000 2023-10-17 13:26:12,139 epoch 2 - iter 534/893 - loss 0.11174439 - time (sec): 41.72 - samples/sec: 3532.14 - lr: 0.000047 - momentum: 0.000000 2023-10-17 13:26:19,555 epoch 2 - iter 623/893 - loss 0.10874895 - time (sec): 49.14 - samples/sec: 3559.42 - lr: 0.000046 - momentum: 0.000000 2023-10-17 13:26:26,357 epoch 2 - iter 712/893 - loss 0.11039562 - time (sec): 55.94 - samples/sec: 3574.71 - lr: 0.000046 - momentum: 0.000000 2023-10-17 13:26:33,252 epoch 2 - iter 801/893 - loss 0.10946145 - time (sec): 62.83 - samples/sec: 3571.72 - lr: 0.000045 - momentum: 0.000000 2023-10-17 13:26:40,028 epoch 2 - iter 890/893 - loss 0.10884277 - time (sec): 69.61 - samples/sec: 3564.35 - lr: 0.000044 - momentum: 0.000000 2023-10-17 13:26:40,230 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:26:40,230 EPOCH 2 done: loss 0.1088 - lr: 0.000044 2023-10-17 13:26:44,981 DEV : loss 0.11145459860563278 - f1-score (micro avg) 0.7591 2023-10-17 13:26:44,997 saving best model 2023-10-17 13:26:45,466 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:26:52,240 epoch 3 - iter 89/893 - loss 0.06579565 - time (sec): 6.77 - samples/sec: 3623.69 - lr: 0.000044 - momentum: 0.000000 2023-10-17 13:26:59,465 epoch 3 - iter 178/893 - loss 0.06780791 - time (sec): 14.00 - samples/sec: 3529.80 - lr: 0.000043 - momentum: 0.000000 2023-10-17 13:27:06,519 epoch 3 - iter 267/893 - loss 0.06511173 - time (sec): 21.05 - samples/sec: 3546.04 - lr: 0.000043 - momentum: 0.000000 2023-10-17 13:27:13,320 epoch 3 - iter 356/893 - loss 0.06708858 - time (sec): 27.85 - samples/sec: 3533.37 - lr: 0.000042 - momentum: 0.000000 2023-10-17 13:27:20,468 epoch 3 - iter 445/893 - loss 0.06704685 - time (sec): 35.00 - samples/sec: 3543.72 - lr: 0.000042 - momentum: 0.000000 2023-10-17 13:27:27,155 epoch 3 - iter 534/893 - loss 0.06913881 - time (sec): 41.69 - samples/sec: 3549.64 - lr: 0.000041 - momentum: 0.000000 2023-10-17 13:27:33,910 epoch 3 - iter 623/893 - loss 0.07009245 - time (sec): 48.44 - samples/sec: 3557.20 - lr: 0.000041 - momentum: 0.000000 2023-10-17 13:27:41,521 epoch 3 - iter 712/893 - loss 0.07061403 - time (sec): 56.05 - samples/sec: 3538.67 - lr: 0.000040 - momentum: 0.000000 2023-10-17 13:27:48,514 epoch 3 - iter 801/893 - loss 0.07172932 - time (sec): 63.05 - samples/sec: 3535.47 - lr: 0.000039 - momentum: 0.000000 2023-10-17 13:27:55,514 epoch 3 - iter 890/893 - loss 0.07158095 - time (sec): 70.05 - samples/sec: 3535.31 - lr: 0.000039 - momentum: 0.000000 2023-10-17 13:27:55,798 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:27:55,798 EPOCH 3 done: loss 0.0718 - lr: 0.000039 2023-10-17 13:27:59,930 DEV : loss 0.11546944081783295 - f1-score (micro avg) 0.8022 2023-10-17 13:27:59,947 saving best model 2023-10-17 13:28:00,357 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:28:07,372 epoch 4 - iter 89/893 - loss 0.04412197 - time (sec): 7.01 - samples/sec: 3686.05 - lr: 0.000038 - momentum: 0.000000 2023-10-17 13:28:14,225 epoch 4 - iter 178/893 - loss 0.04610454 - time (sec): 13.87 - samples/sec: 3640.62 - lr: 0.000038 - momentum: 0.000000 2023-10-17 13:28:21,074 epoch 4 - iter 267/893 - loss 0.04657536 - time (sec): 20.72 - samples/sec: 3671.19 - lr: 0.000037 - momentum: 0.000000 2023-10-17 13:28:28,332 epoch 4 - iter 356/893 - loss 0.04801474 - time (sec): 27.97 - samples/sec: 3645.53 - lr: 0.000037 - momentum: 0.000000 2023-10-17 13:28:35,026 epoch 4 - iter 445/893 - loss 0.04910268 - time (sec): 34.67 - samples/sec: 3628.37 - lr: 0.000036 - momentum: 0.000000 2023-10-17 13:28:42,276 epoch 4 - iter 534/893 - loss 0.04764748 - time (sec): 41.92 - samples/sec: 3606.03 - lr: 0.000036 - momentum: 0.000000 2023-10-17 13:28:49,159 epoch 4 - iter 623/893 - loss 0.04869073 - time (sec): 48.80 - samples/sec: 3610.17 - lr: 0.000035 - momentum: 0.000000 2023-10-17 13:28:55,552 epoch 4 - iter 712/893 - loss 0.04835033 - time (sec): 55.19 - samples/sec: 3619.42 - lr: 0.000034 - momentum: 0.000000 2023-10-17 13:29:02,534 epoch 4 - iter 801/893 - loss 0.04802865 - time (sec): 62.18 - samples/sec: 3603.83 - lr: 0.000034 - momentum: 0.000000 2023-10-17 13:29:09,448 epoch 4 - iter 890/893 - loss 0.04862439 - time (sec): 69.09 - samples/sec: 3586.93 - lr: 0.000033 - momentum: 0.000000 2023-10-17 13:29:09,658 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:29:09,658 EPOCH 4 done: loss 0.0486 - lr: 0.000033 2023-10-17 13:29:14,333 DEV : loss 0.14231492578983307 - f1-score (micro avg) 0.7979 2023-10-17 13:29:14,349 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:29:21,265 epoch 5 - iter 89/893 - loss 0.02919832 - time (sec): 6.92 - samples/sec: 3587.21 - lr: 0.000033 - momentum: 0.000000 2023-10-17 13:29:28,578 epoch 5 - iter 178/893 - loss 0.03380540 - time (sec): 14.23 - samples/sec: 3596.81 - lr: 0.000032 - momentum: 0.000000 2023-10-17 13:29:35,749 epoch 5 - iter 267/893 - loss 0.03673622 - time (sec): 21.40 - samples/sec: 3588.93 - lr: 0.000032 - momentum: 0.000000 2023-10-17 13:29:42,735 epoch 5 - iter 356/893 - loss 0.03585695 - time (sec): 28.39 - samples/sec: 3571.78 - lr: 0.000031 - momentum: 0.000000 2023-10-17 13:29:49,409 epoch 5 - iter 445/893 - loss 0.03553433 - time (sec): 35.06 - samples/sec: 3555.51 - lr: 0.000031 - momentum: 0.000000 2023-10-17 13:29:56,393 epoch 5 - iter 534/893 - loss 0.03588921 - time (sec): 42.04 - samples/sec: 3566.40 - lr: 0.000030 - momentum: 0.000000 2023-10-17 13:30:03,143 epoch 5 - iter 623/893 - loss 0.03598839 - time (sec): 48.79 - samples/sec: 3568.67 - lr: 0.000029 - momentum: 0.000000 2023-10-17 13:30:09,807 epoch 5 - iter 712/893 - loss 0.03692474 - time (sec): 55.46 - samples/sec: 3586.34 - lr: 0.000029 - momentum: 0.000000 2023-10-17 13:30:16,339 epoch 5 - iter 801/893 - loss 0.03697731 - time (sec): 61.99 - samples/sec: 3570.11 - lr: 0.000028 - momentum: 0.000000 2023-10-17 13:30:23,926 epoch 5 - iter 890/893 - loss 0.03792902 - time (sec): 69.58 - samples/sec: 3562.29 - lr: 0.000028 - momentum: 0.000000 2023-10-17 13:30:24,182 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:30:24,182 EPOCH 5 done: loss 0.0378 - lr: 0.000028 2023-10-17 13:30:28,303 DEV : loss 0.1556406468153 - f1-score (micro avg) 0.7992 2023-10-17 13:30:28,320 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:30:35,522 epoch 6 - iter 89/893 - loss 0.03244423 - time (sec): 7.20 - samples/sec: 3480.52 - lr: 0.000027 - momentum: 0.000000 2023-10-17 13:30:42,825 epoch 6 - iter 178/893 - loss 0.02917985 - time (sec): 14.50 - samples/sec: 3535.82 - lr: 0.000027 - momentum: 0.000000 2023-10-17 13:30:49,786 epoch 6 - iter 267/893 - loss 0.02840967 - time (sec): 21.47 - samples/sec: 3499.22 - lr: 0.000026 - momentum: 0.000000 2023-10-17 13:30:56,763 epoch 6 - iter 356/893 - loss 0.02860021 - time (sec): 28.44 - samples/sec: 3506.93 - lr: 0.000026 - momentum: 0.000000 2023-10-17 13:31:03,699 epoch 6 - iter 445/893 - loss 0.02825516 - time (sec): 35.38 - samples/sec: 3497.03 - lr: 0.000025 - momentum: 0.000000 2023-10-17 13:31:10,471 epoch 6 - iter 534/893 - loss 0.02743104 - time (sec): 42.15 - samples/sec: 3508.88 - lr: 0.000024 - momentum: 0.000000 2023-10-17 13:31:17,404 epoch 6 - iter 623/893 - loss 0.02858377 - time (sec): 49.08 - samples/sec: 3519.50 - lr: 0.000024 - momentum: 0.000000 2023-10-17 13:31:24,472 epoch 6 - iter 712/893 - loss 0.02891426 - time (sec): 56.15 - samples/sec: 3536.30 - lr: 0.000023 - momentum: 0.000000 2023-10-17 13:31:31,162 epoch 6 - iter 801/893 - loss 0.02921549 - time (sec): 62.84 - samples/sec: 3545.04 - lr: 0.000023 - momentum: 0.000000 2023-10-17 13:31:37,875 epoch 6 - iter 890/893 - loss 0.02861389 - time (sec): 69.55 - samples/sec: 3563.39 - lr: 0.000022 - momentum: 0.000000 2023-10-17 13:31:38,099 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:31:38,099 EPOCH 6 done: loss 0.0286 - lr: 0.000022 2023-10-17 13:31:42,231 DEV : loss 0.19660857319831848 - f1-score (micro avg) 0.8014 2023-10-17 13:31:42,248 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:31:49,341 epoch 7 - iter 89/893 - loss 0.01678255 - time (sec): 7.09 - samples/sec: 3621.51 - lr: 0.000022 - momentum: 0.000000 2023-10-17 13:31:56,211 epoch 7 - iter 178/893 - loss 0.01553467 - time (sec): 13.96 - samples/sec: 3639.54 - lr: 0.000021 - momentum: 0.000000 2023-10-17 13:32:02,722 epoch 7 - iter 267/893 - loss 0.01925221 - time (sec): 20.47 - samples/sec: 3658.02 - lr: 0.000021 - momentum: 0.000000 2023-10-17 13:32:09,570 epoch 7 - iter 356/893 - loss 0.02042759 - time (sec): 27.32 - samples/sec: 3676.51 - lr: 0.000020 - momentum: 0.000000 2023-10-17 13:32:16,679 epoch 7 - iter 445/893 - loss 0.02070852 - time (sec): 34.43 - samples/sec: 3652.01 - lr: 0.000019 - momentum: 0.000000 2023-10-17 13:32:23,162 epoch 7 - iter 534/893 - loss 0.02114971 - time (sec): 40.91 - samples/sec: 3654.12 - lr: 0.000019 - momentum: 0.000000 2023-10-17 13:32:30,075 epoch 7 - iter 623/893 - loss 0.02086037 - time (sec): 47.83 - samples/sec: 3667.87 - lr: 0.000018 - momentum: 0.000000 2023-10-17 13:32:36,985 epoch 7 - iter 712/893 - loss 0.02012071 - time (sec): 54.74 - samples/sec: 3654.83 - lr: 0.000018 - momentum: 0.000000 2023-10-17 13:32:43,800 epoch 7 - iter 801/893 - loss 0.02038745 - time (sec): 61.55 - samples/sec: 3617.12 - lr: 0.000017 - momentum: 0.000000 2023-10-17 13:32:50,696 epoch 7 - iter 890/893 - loss 0.02046484 - time (sec): 68.45 - samples/sec: 3615.30 - lr: 0.000017 - momentum: 0.000000 2023-10-17 13:32:50,929 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:32:50,930 EPOCH 7 done: loss 0.0204 - lr: 0.000017 2023-10-17 13:32:55,592 DEV : loss 0.2123110592365265 - f1-score (micro avg) 0.8067 2023-10-17 13:32:55,610 saving best model 2023-10-17 13:32:56,074 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:33:03,283 epoch 8 - iter 89/893 - loss 0.01777584 - time (sec): 7.21 - samples/sec: 3367.87 - lr: 0.000016 - momentum: 0.000000 2023-10-17 13:33:10,120 epoch 8 - iter 178/893 - loss 0.01370706 - time (sec): 14.04 - samples/sec: 3476.86 - lr: 0.000016 - momentum: 0.000000 2023-10-17 13:33:16,860 epoch 8 - iter 267/893 - loss 0.01393815 - time (sec): 20.78 - samples/sec: 3522.88 - lr: 0.000015 - momentum: 0.000000 2023-10-17 13:33:23,827 epoch 8 - iter 356/893 - loss 0.01526503 - time (sec): 27.75 - samples/sec: 3514.42 - lr: 0.000014 - momentum: 0.000000 2023-10-17 13:33:30,667 epoch 8 - iter 445/893 - loss 0.01611125 - time (sec): 34.59 - samples/sec: 3537.20 - lr: 0.000014 - momentum: 0.000000 2023-10-17 13:33:37,620 epoch 8 - iter 534/893 - loss 0.01582779 - time (sec): 41.54 - samples/sec: 3521.50 - lr: 0.000013 - momentum: 0.000000 2023-10-17 13:33:44,461 epoch 8 - iter 623/893 - loss 0.01549864 - time (sec): 48.38 - samples/sec: 3546.15 - lr: 0.000013 - momentum: 0.000000 2023-10-17 13:33:52,073 epoch 8 - iter 712/893 - loss 0.01573317 - time (sec): 56.00 - samples/sec: 3543.71 - lr: 0.000012 - momentum: 0.000000 2023-10-17 13:33:58,734 epoch 8 - iter 801/893 - loss 0.01601116 - time (sec): 62.66 - samples/sec: 3552.01 - lr: 0.000012 - momentum: 0.000000 2023-10-17 13:34:05,638 epoch 8 - iter 890/893 - loss 0.01543464 - time (sec): 69.56 - samples/sec: 3567.03 - lr: 0.000011 - momentum: 0.000000 2023-10-17 13:34:05,852 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:34:05,852 EPOCH 8 done: loss 0.0155 - lr: 0.000011 2023-10-17 13:34:10,003 DEV : loss 0.22176583111286163 - f1-score (micro avg) 0.8003 2023-10-17 13:34:10,021 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:34:18,103 epoch 9 - iter 89/893 - loss 0.01083963 - time (sec): 8.08 - samples/sec: 3159.68 - lr: 0.000011 - momentum: 0.000000 2023-10-17 13:34:25,070 epoch 9 - iter 178/893 - loss 0.00956570 - time (sec): 15.05 - samples/sec: 3388.71 - lr: 0.000010 - momentum: 0.000000 2023-10-17 13:34:31,928 epoch 9 - iter 267/893 - loss 0.01138130 - time (sec): 21.91 - samples/sec: 3434.27 - lr: 0.000009 - momentum: 0.000000 2023-10-17 13:34:38,528 epoch 9 - iter 356/893 - loss 0.01058467 - time (sec): 28.51 - samples/sec: 3469.81 - lr: 0.000009 - momentum: 0.000000 2023-10-17 13:34:45,321 epoch 9 - iter 445/893 - loss 0.00921370 - time (sec): 35.30 - samples/sec: 3500.48 - lr: 0.000008 - momentum: 0.000000 2023-10-17 13:34:51,981 epoch 9 - iter 534/893 - loss 0.00953823 - time (sec): 41.96 - samples/sec: 3527.17 - lr: 0.000008 - momentum: 0.000000 2023-10-17 13:34:58,609 epoch 9 - iter 623/893 - loss 0.00962444 - time (sec): 48.59 - samples/sec: 3523.06 - lr: 0.000007 - momentum: 0.000000 2023-10-17 13:35:05,539 epoch 9 - iter 712/893 - loss 0.01033271 - time (sec): 55.52 - samples/sec: 3533.02 - lr: 0.000007 - momentum: 0.000000 2023-10-17 13:35:12,598 epoch 9 - iter 801/893 - loss 0.01059466 - time (sec): 62.58 - samples/sec: 3540.07 - lr: 0.000006 - momentum: 0.000000 2023-10-17 13:35:19,637 epoch 9 - iter 890/893 - loss 0.01016129 - time (sec): 69.61 - samples/sec: 3565.62 - lr: 0.000006 - momentum: 0.000000 2023-10-17 13:35:19,813 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:35:19,813 EPOCH 9 done: loss 0.0101 - lr: 0.000006 2023-10-17 13:35:23,967 DEV : loss 0.2231239527463913 - f1-score (micro avg) 0.8124 2023-10-17 13:35:23,985 saving best model 2023-10-17 13:35:24,449 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:35:31,551 epoch 10 - iter 89/893 - loss 0.00425626 - time (sec): 7.10 - samples/sec: 3586.43 - lr: 0.000005 - momentum: 0.000000 2023-10-17 13:35:38,709 epoch 10 - iter 178/893 - loss 0.00582628 - time (sec): 14.25 - samples/sec: 3585.95 - lr: 0.000004 - momentum: 0.000000 2023-10-17 13:35:45,652 epoch 10 - iter 267/893 - loss 0.00793966 - time (sec): 21.20 - samples/sec: 3592.75 - lr: 0.000004 - momentum: 0.000000 2023-10-17 13:35:52,247 epoch 10 - iter 356/893 - loss 0.00702058 - time (sec): 27.79 - samples/sec: 3578.58 - lr: 0.000003 - momentum: 0.000000 2023-10-17 13:35:59,564 epoch 10 - iter 445/893 - loss 0.00689434 - time (sec): 35.11 - samples/sec: 3565.57 - lr: 0.000003 - momentum: 0.000000 2023-10-17 13:36:06,713 epoch 10 - iter 534/893 - loss 0.00633107 - time (sec): 42.26 - samples/sec: 3543.91 - lr: 0.000002 - momentum: 0.000000 2023-10-17 13:36:13,298 epoch 10 - iter 623/893 - loss 0.00613990 - time (sec): 48.84 - samples/sec: 3557.23 - lr: 0.000002 - momentum: 0.000000 2023-10-17 13:36:20,364 epoch 10 - iter 712/893 - loss 0.00603713 - time (sec): 55.91 - samples/sec: 3563.05 - lr: 0.000001 - momentum: 0.000000 2023-10-17 13:36:27,000 epoch 10 - iter 801/893 - loss 0.00645980 - time (sec): 62.55 - samples/sec: 3571.76 - lr: 0.000001 - momentum: 0.000000 2023-10-17 13:36:34,138 epoch 10 - iter 890/893 - loss 0.00649064 - time (sec): 69.68 - samples/sec: 3557.52 - lr: 0.000000 - momentum: 0.000000 2023-10-17 13:36:34,324 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:36:34,324 EPOCH 10 done: loss 0.0065 - lr: 0.000000 2023-10-17 13:36:39,073 DEV : loss 0.23280413448810577 - f1-score (micro avg) 0.8165 2023-10-17 13:36:39,090 saving best model 2023-10-17 13:36:39,905 ---------------------------------------------------------------------------------------------------- 2023-10-17 13:36:39,907 Loading model from best epoch ... 2023-10-17 13:36:41,238 SequenceTagger predicts: Dictionary with 17 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd 2023-10-17 13:36:50,854 Results: - F-score (micro) 0.6959 - F-score (macro) 0.6247 - Accuracy 0.5492 By class: precision recall f1-score support LOC 0.6964 0.6995 0.6979 1095 PER 0.7849 0.7826 0.7838 1012 ORG 0.4352 0.5742 0.4952 357 HumanProd 0.4068 0.7273 0.5217 33 micro avg 0.6772 0.7157 0.6959 2497 macro avg 0.5808 0.6959 0.6247 2497 weighted avg 0.6911 0.7157 0.7014 2497 2023-10-17 13:36:50,854 ----------------------------------------------------------------------------------------------------