| 2023-10-25 14:35:36,306 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:35:36,307 Model: "SequenceTagger( | |
| (embeddings): TransformerWordEmbeddings( | |
| (model): BertModel( | |
| (embeddings): BertEmbeddings( | |
| (word_embeddings): Embedding(64001, 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() | |
| )" | |
| 2023-10-25 14:35:36,307 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:35:36,307 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences | |
| - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator | |
| 2023-10-25 14:35:36,307 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:35:36,307 Train: 20847 sentences | |
| 2023-10-25 14:35:36,307 (train_with_dev=False, train_with_test=False) | |
| 2023-10-25 14:35:36,307 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:35:36,307 Training Params: | |
| 2023-10-25 14:35:36,307 - learning_rate: "5e-05" | |
| 2023-10-25 14:35:36,307 - mini_batch_size: "8" | |
| 2023-10-25 14:35:36,307 - max_epochs: "10" | |
| 2023-10-25 14:35:36,307 - shuffle: "True" | |
| 2023-10-25 14:35:36,307 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:35:36,307 Plugins: | |
| 2023-10-25 14:35:36,307 - TensorboardLogger | |
| 2023-10-25 14:35:36,307 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-25 14:35:36,307 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:35:36,307 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-25 14:35:36,307 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-25 14:35:36,307 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:35:36,307 Computation: | |
| 2023-10-25 14:35:36,307 - compute on device: cuda:0 | |
| 2023-10-25 14:35:36,307 - embedding storage: none | |
| 2023-10-25 14:35:36,308 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:35:36,308 Model training base path: "hmbench-newseye/de-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3" | |
| 2023-10-25 14:35:36,308 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:35:36,308 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:35:36,308 Logging anything other than scalars to TensorBoard is currently not supported. | |
| 2023-10-25 14:35:50,873 epoch 1 - iter 260/2606 - loss 1.43007414 - time (sec): 14.56 - samples/sec: 2541.19 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-25 14:36:05,195 epoch 1 - iter 520/2606 - loss 0.88642286 - time (sec): 28.89 - samples/sec: 2530.05 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-25 14:36:20,044 epoch 1 - iter 780/2606 - loss 0.68416171 - time (sec): 43.74 - samples/sec: 2544.39 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 14:36:34,261 epoch 1 - iter 1040/2606 - loss 0.56767487 - time (sec): 57.95 - samples/sec: 2555.77 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-25 14:36:48,977 epoch 1 - iter 1300/2606 - loss 0.49684606 - time (sec): 72.67 - samples/sec: 2593.07 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-25 14:37:03,212 epoch 1 - iter 1560/2606 - loss 0.44567246 - time (sec): 86.90 - samples/sec: 2589.23 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-25 14:37:17,495 epoch 1 - iter 1820/2606 - loss 0.41053349 - time (sec): 101.19 - samples/sec: 2585.19 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-25 14:37:31,904 epoch 1 - iter 2080/2606 - loss 0.38157875 - time (sec): 115.60 - samples/sec: 2583.32 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-25 14:37:45,329 epoch 1 - iter 2340/2606 - loss 0.36197031 - time (sec): 129.02 - samples/sec: 2572.51 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-25 14:37:59,142 epoch 1 - iter 2600/2606 - loss 0.34509274 - time (sec): 142.83 - samples/sec: 2569.22 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-25 14:37:59,394 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:37:59,395 EPOCH 1 done: loss 0.3449 - lr: 0.000050 | |
| 2023-10-25 14:38:03,168 DEV : loss 0.14861008524894714 - f1-score (micro avg) 0.3075 | |
| 2023-10-25 14:38:03,193 saving best model | |
| 2023-10-25 14:38:03,720 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:38:17,970 epoch 2 - iter 260/2606 - loss 0.16010954 - time (sec): 14.25 - samples/sec: 2672.86 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-25 14:38:32,344 epoch 2 - iter 520/2606 - loss 0.16216140 - time (sec): 28.62 - samples/sec: 2671.12 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-25 14:38:47,501 epoch 2 - iter 780/2606 - loss 0.16202218 - time (sec): 43.78 - samples/sec: 2591.71 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-25 14:39:01,279 epoch 2 - iter 1040/2606 - loss 0.16269454 - time (sec): 57.56 - samples/sec: 2603.63 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-25 14:39:15,219 epoch 2 - iter 1300/2606 - loss 0.16161422 - time (sec): 71.50 - samples/sec: 2605.20 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-25 14:39:28,712 epoch 2 - iter 1560/2606 - loss 0.16050413 - time (sec): 84.99 - samples/sec: 2609.10 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-25 14:39:43,253 epoch 2 - iter 1820/2606 - loss 0.16221687 - time (sec): 99.53 - samples/sec: 2603.35 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-25 14:39:57,463 epoch 2 - iter 2080/2606 - loss 0.16080811 - time (sec): 113.74 - samples/sec: 2606.42 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-25 14:40:11,416 epoch 2 - iter 2340/2606 - loss 0.15978570 - time (sec): 127.69 - samples/sec: 2582.57 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-25 14:40:26,509 epoch 2 - iter 2600/2606 - loss 0.15840144 - time (sec): 142.79 - samples/sec: 2565.54 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-25 14:40:26,911 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:40:26,911 EPOCH 2 done: loss 0.1582 - lr: 0.000044 | |
| 2023-10-25 14:40:34,413 DEV : loss 0.2031174749135971 - f1-score (micro avg) 0.3265 | |
| 2023-10-25 14:40:34,438 saving best model | |
| 2023-10-25 14:40:35,160 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:40:49,669 epoch 3 - iter 260/2606 - loss 0.09809148 - time (sec): 14.51 - samples/sec: 2529.64 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-25 14:41:03,475 epoch 3 - iter 520/2606 - loss 0.11472092 - time (sec): 28.31 - samples/sec: 2528.48 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-25 14:41:17,287 epoch 3 - iter 780/2606 - loss 0.11436832 - time (sec): 42.13 - samples/sec: 2553.09 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-25 14:41:31,543 epoch 3 - iter 1040/2606 - loss 0.10975702 - time (sec): 56.38 - samples/sec: 2584.12 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-25 14:41:45,618 epoch 3 - iter 1300/2606 - loss 0.10721993 - time (sec): 70.46 - samples/sec: 2599.38 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-25 14:41:59,386 epoch 3 - iter 1560/2606 - loss 0.11156032 - time (sec): 84.22 - samples/sec: 2602.20 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-25 14:42:13,163 epoch 3 - iter 1820/2606 - loss 0.11371807 - time (sec): 98.00 - samples/sec: 2602.70 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-25 14:42:27,303 epoch 3 - iter 2080/2606 - loss 0.11301284 - time (sec): 112.14 - samples/sec: 2604.51 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-25 14:42:40,919 epoch 3 - iter 2340/2606 - loss 0.11223997 - time (sec): 125.76 - samples/sec: 2599.81 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-25 14:42:55,448 epoch 3 - iter 2600/2606 - loss 0.11074835 - time (sec): 140.29 - samples/sec: 2612.40 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-25 14:42:55,774 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:42:55,774 EPOCH 3 done: loss 0.1106 - lr: 0.000039 | |
| 2023-10-25 14:43:02,639 DEV : loss 0.19291090965270996 - f1-score (micro avg) 0.3613 | |
| 2023-10-25 14:43:02,664 saving best model | |
| 2023-10-25 14:43:03,328 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:43:17,067 epoch 4 - iter 260/2606 - loss 0.09500081 - time (sec): 13.74 - samples/sec: 2632.02 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-25 14:43:31,086 epoch 4 - iter 520/2606 - loss 0.09830307 - time (sec): 27.76 - samples/sec: 2617.40 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-25 14:43:46,455 epoch 4 - iter 780/2606 - loss 0.09455546 - time (sec): 43.13 - samples/sec: 2531.37 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-25 14:44:00,720 epoch 4 - iter 1040/2606 - loss 0.09159831 - time (sec): 57.39 - samples/sec: 2495.89 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-25 14:44:14,439 epoch 4 - iter 1300/2606 - loss 0.09101211 - time (sec): 71.11 - samples/sec: 2502.90 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-25 14:44:29,101 epoch 4 - iter 1560/2606 - loss 0.08649454 - time (sec): 85.77 - samples/sec: 2547.17 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-25 14:44:42,716 epoch 4 - iter 1820/2606 - loss 0.08545536 - time (sec): 99.39 - samples/sec: 2538.89 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-25 14:44:57,358 epoch 4 - iter 2080/2606 - loss 0.08615483 - time (sec): 114.03 - samples/sec: 2542.81 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-25 14:45:11,633 epoch 4 - iter 2340/2606 - loss 0.08630774 - time (sec): 128.30 - samples/sec: 2544.39 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-25 14:45:26,422 epoch 4 - iter 2600/2606 - loss 0.08626198 - time (sec): 143.09 - samples/sec: 2559.67 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-25 14:45:26,820 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:45:26,820 EPOCH 4 done: loss 0.0863 - lr: 0.000033 | |
| 2023-10-25 14:45:33,781 DEV : loss 0.2698976397514343 - f1-score (micro avg) 0.3764 | |
| 2023-10-25 14:45:33,806 saving best model | |
| 2023-10-25 14:45:34,465 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:45:48,639 epoch 5 - iter 260/2606 - loss 0.06464820 - time (sec): 14.17 - samples/sec: 2626.43 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-25 14:46:03,035 epoch 5 - iter 520/2606 - loss 0.06110421 - time (sec): 28.57 - samples/sec: 2543.20 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-25 14:46:17,861 epoch 5 - iter 780/2606 - loss 0.06016891 - time (sec): 43.39 - samples/sec: 2548.67 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-25 14:46:32,445 epoch 5 - iter 1040/2606 - loss 0.06173170 - time (sec): 57.98 - samples/sec: 2555.72 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-25 14:46:46,809 epoch 5 - iter 1300/2606 - loss 0.06133749 - time (sec): 72.34 - samples/sec: 2522.61 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-25 14:47:01,071 epoch 5 - iter 1560/2606 - loss 0.06007684 - time (sec): 86.60 - samples/sec: 2530.55 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-25 14:47:15,449 epoch 5 - iter 1820/2606 - loss 0.06078408 - time (sec): 100.98 - samples/sec: 2533.31 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-25 14:47:29,645 epoch 5 - iter 2080/2606 - loss 0.06059075 - time (sec): 115.18 - samples/sec: 2552.45 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-25 14:47:44,001 epoch 5 - iter 2340/2606 - loss 0.06046212 - time (sec): 129.53 - samples/sec: 2566.87 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-25 14:47:57,841 epoch 5 - iter 2600/2606 - loss 0.06127827 - time (sec): 143.37 - samples/sec: 2556.47 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-25 14:47:58,210 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:47:58,210 EPOCH 5 done: loss 0.0613 - lr: 0.000028 | |
| 2023-10-25 14:48:05,406 DEV : loss 0.3795294165611267 - f1-score (micro avg) 0.3326 | |
| 2023-10-25 14:48:05,434 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:48:20,290 epoch 6 - iter 260/2606 - loss 0.03491277 - time (sec): 14.86 - samples/sec: 2652.35 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 14:48:35,779 epoch 6 - iter 520/2606 - loss 0.04096153 - time (sec): 30.34 - samples/sec: 2591.06 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-25 14:48:49,582 epoch 6 - iter 780/2606 - loss 0.04201657 - time (sec): 44.15 - samples/sec: 2561.47 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-25 14:49:03,570 epoch 6 - iter 1040/2606 - loss 0.04304112 - time (sec): 58.13 - samples/sec: 2565.68 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-25 14:49:17,029 epoch 6 - iter 1300/2606 - loss 0.04336291 - time (sec): 71.59 - samples/sec: 2550.43 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-25 14:49:31,156 epoch 6 - iter 1560/2606 - loss 0.04379894 - time (sec): 85.72 - samples/sec: 2550.97 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 14:49:45,284 epoch 6 - iter 1820/2606 - loss 0.04398689 - time (sec): 99.85 - samples/sec: 2566.43 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-25 14:49:59,537 epoch 6 - iter 2080/2606 - loss 0.04462424 - time (sec): 114.10 - samples/sec: 2575.83 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-25 14:50:13,887 epoch 6 - iter 2340/2606 - loss 0.04389891 - time (sec): 128.45 - samples/sec: 2578.71 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-25 14:50:27,662 epoch 6 - iter 2600/2606 - loss 0.04366628 - time (sec): 142.23 - samples/sec: 2572.78 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-25 14:50:28,029 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:50:28,030 EPOCH 6 done: loss 0.0437 - lr: 0.000022 | |
| 2023-10-25 14:50:34,281 DEV : loss 0.3496846556663513 - f1-score (micro avg) 0.3677 | |
| 2023-10-25 14:50:34,307 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:50:49,498 epoch 7 - iter 260/2606 - loss 0.03844051 - time (sec): 15.19 - samples/sec: 2291.49 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-25 14:51:04,819 epoch 7 - iter 520/2606 - loss 0.03763631 - time (sec): 30.51 - samples/sec: 2355.19 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 14:51:19,867 epoch 7 - iter 780/2606 - loss 0.04085011 - time (sec): 45.56 - samples/sec: 2305.17 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-25 14:51:35,277 epoch 7 - iter 1040/2606 - loss 0.04072254 - time (sec): 60.97 - samples/sec: 2328.77 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-25 14:51:49,777 epoch 7 - iter 1300/2606 - loss 0.04275275 - time (sec): 75.47 - samples/sec: 2367.75 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-25 14:52:04,500 epoch 7 - iter 1560/2606 - loss 0.04412206 - time (sec): 90.19 - samples/sec: 2437.18 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-25 14:52:19,146 epoch 7 - iter 1820/2606 - loss 0.04560131 - time (sec): 104.84 - samples/sec: 2483.64 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 14:52:33,348 epoch 7 - iter 2080/2606 - loss 0.04383087 - time (sec): 119.04 - samples/sec: 2498.57 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-25 14:52:47,443 epoch 7 - iter 2340/2606 - loss 0.04287800 - time (sec): 133.13 - samples/sec: 2508.05 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-25 14:53:00,987 epoch 7 - iter 2600/2606 - loss 0.04293301 - time (sec): 146.68 - samples/sec: 2499.36 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-25 14:53:01,273 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:53:01,273 EPOCH 7 done: loss 0.0430 - lr: 0.000017 | |
| 2023-10-25 14:53:07,539 DEV : loss 0.36888352036476135 - f1-score (micro avg) 0.3547 | |
| 2023-10-25 14:53:07,566 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:53:22,206 epoch 8 - iter 260/2606 - loss 0.02816273 - time (sec): 14.64 - samples/sec: 2654.17 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-25 14:53:36,106 epoch 8 - iter 520/2606 - loss 0.02706506 - time (sec): 28.54 - samples/sec: 2565.72 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-25 14:53:50,421 epoch 8 - iter 780/2606 - loss 0.03077908 - time (sec): 42.85 - samples/sec: 2559.81 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-25 14:54:04,697 epoch 8 - iter 1040/2606 - loss 0.03177140 - time (sec): 57.13 - samples/sec: 2549.73 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-25 14:54:19,291 epoch 8 - iter 1300/2606 - loss 0.03241797 - time (sec): 71.72 - samples/sec: 2560.08 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-25 14:54:34,241 epoch 8 - iter 1560/2606 - loss 0.04472240 - time (sec): 86.67 - samples/sec: 2569.48 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-25 14:54:48,203 epoch 8 - iter 1820/2606 - loss 0.06082510 - time (sec): 100.64 - samples/sec: 2564.29 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-25 14:55:03,356 epoch 8 - iter 2080/2606 - loss 0.07237813 - time (sec): 115.79 - samples/sec: 2590.36 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 14:55:17,114 epoch 8 - iter 2340/2606 - loss 0.07735544 - time (sec): 129.55 - samples/sec: 2577.12 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-25 14:55:30,594 epoch 8 - iter 2600/2606 - loss 0.08074278 - time (sec): 143.03 - samples/sec: 2562.17 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-25 14:55:31,015 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:55:31,015 EPOCH 8 done: loss 0.0806 - lr: 0.000011 | |
| 2023-10-25 14:55:37,310 DEV : loss 0.3248702585697174 - f1-score (micro avg) 0.2139 | |
| 2023-10-25 14:55:37,335 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:55:51,369 epoch 9 - iter 260/2606 - loss 0.09042002 - time (sec): 14.03 - samples/sec: 2440.70 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-25 14:56:05,964 epoch 9 - iter 520/2606 - loss 0.10574243 - time (sec): 28.63 - samples/sec: 2529.19 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-25 14:56:20,271 epoch 9 - iter 780/2606 - loss 0.10510265 - time (sec): 42.93 - samples/sec: 2535.61 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 14:56:34,213 epoch 9 - iter 1040/2606 - loss 0.11581644 - time (sec): 56.88 - samples/sec: 2537.07 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-25 14:56:48,138 epoch 9 - iter 1300/2606 - loss 0.13853151 - time (sec): 70.80 - samples/sec: 2534.52 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-25 14:57:02,718 epoch 9 - iter 1560/2606 - loss 0.15621653 - time (sec): 85.38 - samples/sec: 2538.87 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-25 14:57:16,241 epoch 9 - iter 1820/2606 - loss 0.16924463 - time (sec): 98.90 - samples/sec: 2563.69 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-25 14:57:31,403 epoch 9 - iter 2080/2606 - loss 0.17046855 - time (sec): 114.07 - samples/sec: 2567.41 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-25 14:57:45,821 epoch 9 - iter 2340/2606 - loss 0.17400048 - time (sec): 128.48 - samples/sec: 2576.01 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-25 14:57:59,796 epoch 9 - iter 2600/2606 - loss 0.17816634 - time (sec): 142.46 - samples/sec: 2574.61 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-25 14:58:00,124 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:58:00,124 EPOCH 9 done: loss 0.1782 - lr: 0.000006 | |
| 2023-10-25 14:58:06,453 DEV : loss 0.22723859548568726 - f1-score (micro avg) 0.0329 | |
| 2023-10-25 14:58:06,479 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 14:58:20,284 epoch 10 - iter 260/2606 - loss 0.19008451 - time (sec): 13.80 - samples/sec: 2551.98 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-25 14:58:34,669 epoch 10 - iter 520/2606 - loss 0.19479366 - time (sec): 28.19 - samples/sec: 2599.77 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-25 14:58:48,736 epoch 10 - iter 780/2606 - loss 0.19834988 - time (sec): 42.26 - samples/sec: 2530.31 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-25 14:59:02,964 epoch 10 - iter 1040/2606 - loss 0.19699333 - time (sec): 56.48 - samples/sec: 2567.60 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 14:59:17,539 epoch 10 - iter 1300/2606 - loss 0.18959408 - time (sec): 71.06 - samples/sec: 2580.81 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-25 14:59:32,058 epoch 10 - iter 1560/2606 - loss 0.18754436 - time (sec): 85.58 - samples/sec: 2585.89 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-25 14:59:46,141 epoch 10 - iter 1820/2606 - loss 0.19219019 - time (sec): 99.66 - samples/sec: 2586.46 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-25 15:00:00,445 epoch 10 - iter 2080/2606 - loss 0.19419016 - time (sec): 113.96 - samples/sec: 2574.81 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-25 15:00:14,275 epoch 10 - iter 2340/2606 - loss 0.19370314 - time (sec): 127.79 - samples/sec: 2571.63 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-25 15:00:29,133 epoch 10 - iter 2600/2606 - loss 0.19280325 - time (sec): 142.65 - samples/sec: 2569.33 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-25 15:00:29,439 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 15:00:29,439 EPOCH 10 done: loss 0.1929 - lr: 0.000000 | |
| 2023-10-25 15:00:36,359 DEV : loss 0.25543084740638733 - f1-score (micro avg) 0.0519 | |
| 2023-10-25 15:00:37,019 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-25 15:00:37,020 Loading model from best epoch ... | |
| 2023-10-25 15:00:39,021 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 | |
| 2023-10-25 15:00:49,051 | |
| Results: | |
| - F-score (micro) 0.4518 | |
| - F-score (macro) 0.3002 | |
| - Accuracy 0.2956 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.4964 0.5700 0.5307 1214 | |
| PER 0.3949 0.4394 0.4159 808 | |
| ORG 0.2628 0.2465 0.2544 353 | |
| HumanProd 0.0000 0.0000 0.0000 15 | |
| micro avg 0.4312 0.4745 0.4518 2390 | |
| macro avg 0.2885 0.3140 0.3002 2390 | |
| weighted avg 0.4245 0.4745 0.4477 2390 | |
| 2023-10-25 15:00:49,051 ---------------------------------------------------------------------------------------------------- | |