| 2023-10-13 12:09:35,270 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:09:35,271 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 12:09:35,271 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:09:35,271 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences | |
| - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator | |
| 2023-10-13 12:09:35,271 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:09:35,271 Train: 3575 sentences | |
| 2023-10-13 12:09:35,271 (train_with_dev=False, train_with_test=False) | |
| 2023-10-13 12:09:35,271 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:09:35,271 Training Params: | |
| 2023-10-13 12:09:35,271 - learning_rate: "3e-05" | |
| 2023-10-13 12:09:35,271 - mini_batch_size: "8" | |
| 2023-10-13 12:09:35,271 - max_epochs: "10" | |
| 2023-10-13 12:09:35,271 - shuffle: "True" | |
| 2023-10-13 12:09:35,271 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:09:35,271 Plugins: | |
| 2023-10-13 12:09:35,271 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-13 12:09:35,271 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:09:35,271 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-13 12:09:35,271 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-13 12:09:35,271 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:09:35,271 Computation: | |
| 2023-10-13 12:09:35,271 - compute on device: cuda:0 | |
| 2023-10-13 12:09:35,271 - embedding storage: none | |
| 2023-10-13 12:09:35,271 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:09:35,272 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2" | |
| 2023-10-13 12:09:35,272 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:09:35,272 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:09:37,960 epoch 1 - iter 44/447 - loss 3.26423371 - time (sec): 2.69 - samples/sec: 2966.03 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 12:09:40,629 epoch 1 - iter 88/447 - loss 2.55251654 - time (sec): 5.36 - samples/sec: 2975.06 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 12:09:43,351 epoch 1 - iter 132/447 - loss 1.81465913 - time (sec): 8.08 - samples/sec: 3057.25 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 12:09:45,966 epoch 1 - iter 176/447 - loss 1.49020630 - time (sec): 10.69 - samples/sec: 3052.05 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 12:09:48,727 epoch 1 - iter 220/447 - loss 1.25720936 - time (sec): 13.45 - samples/sec: 3066.78 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 12:09:52,053 epoch 1 - iter 264/447 - loss 1.07641978 - time (sec): 16.78 - samples/sec: 3076.12 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 12:09:54,913 epoch 1 - iter 308/447 - loss 0.97218598 - time (sec): 19.64 - samples/sec: 3035.64 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 12:09:57,534 epoch 1 - iter 352/447 - loss 0.88562839 - time (sec): 22.26 - samples/sec: 3056.85 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 12:10:00,460 epoch 1 - iter 396/447 - loss 0.81842546 - time (sec): 25.19 - samples/sec: 3034.98 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 12:10:03,271 epoch 1 - iter 440/447 - loss 0.76093176 - time (sec): 28.00 - samples/sec: 3023.23 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 12:10:03,802 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:10:03,803 EPOCH 1 done: loss 0.7490 - lr: 0.000029 | |
| 2023-10-13 12:10:08,594 DEV : loss 0.1892923265695572 - f1-score (micro avg) 0.6033 | |
| 2023-10-13 12:10:08,622 saving best model | |
| 2023-10-13 12:10:08,990 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:10:11,751 epoch 2 - iter 44/447 - loss 0.22503075 - time (sec): 2.76 - samples/sec: 3089.16 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-13 12:10:14,608 epoch 2 - iter 88/447 - loss 0.21611089 - time (sec): 5.62 - samples/sec: 3004.42 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 12:10:17,239 epoch 2 - iter 132/447 - loss 0.19999503 - time (sec): 8.25 - samples/sec: 3032.70 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 12:10:20,013 epoch 2 - iter 176/447 - loss 0.19471361 - time (sec): 11.02 - samples/sec: 3064.84 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-13 12:10:22,647 epoch 2 - iter 220/447 - loss 0.18611851 - time (sec): 13.66 - samples/sec: 3044.52 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-13 12:10:25,663 epoch 2 - iter 264/447 - loss 0.18149685 - time (sec): 16.67 - samples/sec: 3054.10 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-13 12:10:28,344 epoch 2 - iter 308/447 - loss 0.17422604 - time (sec): 19.35 - samples/sec: 3062.40 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-13 12:10:31,324 epoch 2 - iter 352/447 - loss 0.16962325 - time (sec): 22.33 - samples/sec: 3071.16 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 12:10:34,299 epoch 2 - iter 396/447 - loss 0.16829992 - time (sec): 25.31 - samples/sec: 3041.33 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 12:10:37,052 epoch 2 - iter 440/447 - loss 0.16687265 - time (sec): 28.06 - samples/sec: 3037.56 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-13 12:10:37,451 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:10:37,451 EPOCH 2 done: loss 0.1664 - lr: 0.000027 | |
| 2023-10-13 12:10:45,769 DEV : loss 0.12273670732975006 - f1-score (micro avg) 0.6851 | |
| 2023-10-13 12:10:45,796 saving best model | |
| 2023-10-13 12:10:46,248 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:10:49,187 epoch 3 - iter 44/447 - loss 0.09991546 - time (sec): 2.94 - samples/sec: 2899.24 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-13 12:10:52,223 epoch 3 - iter 88/447 - loss 0.09710562 - time (sec): 5.97 - samples/sec: 3031.43 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-13 12:10:55,209 epoch 3 - iter 132/447 - loss 0.09199418 - time (sec): 8.96 - samples/sec: 3040.31 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-13 12:10:58,100 epoch 3 - iter 176/447 - loss 0.08528336 - time (sec): 11.85 - samples/sec: 3049.39 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-13 12:11:00,998 epoch 3 - iter 220/447 - loss 0.09281222 - time (sec): 14.75 - samples/sec: 3051.97 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-13 12:11:03,636 epoch 3 - iter 264/447 - loss 0.09405935 - time (sec): 17.39 - samples/sec: 3025.84 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-13 12:11:06,248 epoch 3 - iter 308/447 - loss 0.09018315 - time (sec): 20.00 - samples/sec: 3041.00 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 12:11:08,845 epoch 3 - iter 352/447 - loss 0.08995194 - time (sec): 22.60 - samples/sec: 3045.93 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 12:11:11,702 epoch 3 - iter 396/447 - loss 0.08979467 - time (sec): 25.45 - samples/sec: 3029.20 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-13 12:11:14,330 epoch 3 - iter 440/447 - loss 0.09064393 - time (sec): 28.08 - samples/sec: 3036.51 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-13 12:11:14,735 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:11:14,735 EPOCH 3 done: loss 0.0906 - lr: 0.000023 | |
| 2023-10-13 12:11:23,095 DEV : loss 0.1380746215581894 - f1-score (micro avg) 0.7368 | |
| 2023-10-13 12:11:23,122 saving best model | |
| 2023-10-13 12:11:23,561 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:11:26,132 epoch 4 - iter 44/447 - loss 0.05167342 - time (sec): 2.57 - samples/sec: 2943.67 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-13 12:11:29,209 epoch 4 - iter 88/447 - loss 0.04774725 - time (sec): 5.64 - samples/sec: 2980.68 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-13 12:11:31,981 epoch 4 - iter 132/447 - loss 0.05250541 - time (sec): 8.41 - samples/sec: 2980.78 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-13 12:11:34,711 epoch 4 - iter 176/447 - loss 0.05249414 - time (sec): 11.14 - samples/sec: 3017.69 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-13 12:11:37,381 epoch 4 - iter 220/447 - loss 0.05143646 - time (sec): 13.81 - samples/sec: 2971.83 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-13 12:11:40,611 epoch 4 - iter 264/447 - loss 0.04851668 - time (sec): 17.04 - samples/sec: 2992.98 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 12:11:43,801 epoch 4 - iter 308/447 - loss 0.04919683 - time (sec): 20.24 - samples/sec: 2954.25 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 12:11:46,537 epoch 4 - iter 352/447 - loss 0.04937125 - time (sec): 22.97 - samples/sec: 2953.29 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-13 12:11:49,442 epoch 4 - iter 396/447 - loss 0.04948050 - time (sec): 25.88 - samples/sec: 2979.68 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-13 12:11:52,225 epoch 4 - iter 440/447 - loss 0.04960176 - time (sec): 28.66 - samples/sec: 2978.70 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-13 12:11:52,628 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:11:52,629 EPOCH 4 done: loss 0.0492 - lr: 0.000020 | |
| 2023-10-13 12:12:01,176 DEV : loss 0.1485665887594223 - f1-score (micro avg) 0.7566 | |
| 2023-10-13 12:12:01,203 saving best model | |
| 2023-10-13 12:12:01,668 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:12:04,553 epoch 5 - iter 44/447 - loss 0.06274556 - time (sec): 2.88 - samples/sec: 2805.26 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-13 12:12:07,298 epoch 5 - iter 88/447 - loss 0.04536218 - time (sec): 5.63 - samples/sec: 2823.72 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-13 12:12:10,229 epoch 5 - iter 132/447 - loss 0.03980649 - time (sec): 8.56 - samples/sec: 2864.57 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-13 12:12:12,968 epoch 5 - iter 176/447 - loss 0.03919475 - time (sec): 11.30 - samples/sec: 2890.30 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-13 12:12:16,055 epoch 5 - iter 220/447 - loss 0.03737208 - time (sec): 14.38 - samples/sec: 2955.03 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 12:12:18,688 epoch 5 - iter 264/447 - loss 0.03726708 - time (sec): 17.02 - samples/sec: 2987.24 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 12:12:21,705 epoch 5 - iter 308/447 - loss 0.03688963 - time (sec): 20.03 - samples/sec: 2983.27 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-13 12:12:24,853 epoch 5 - iter 352/447 - loss 0.03722901 - time (sec): 23.18 - samples/sec: 2978.37 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-13 12:12:27,583 epoch 5 - iter 396/447 - loss 0.03667790 - time (sec): 25.91 - samples/sec: 3000.11 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-13 12:12:30,102 epoch 5 - iter 440/447 - loss 0.03624333 - time (sec): 28.43 - samples/sec: 2997.89 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-13 12:12:30,518 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:12:30,518 EPOCH 5 done: loss 0.0360 - lr: 0.000017 | |
| 2023-10-13 12:12:39,052 DEV : loss 0.16211558878421783 - f1-score (micro avg) 0.7683 | |
| 2023-10-13 12:12:39,080 saving best model | |
| 2023-10-13 12:12:39,538 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:12:42,323 epoch 6 - iter 44/447 - loss 0.01665545 - time (sec): 2.78 - samples/sec: 3085.13 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-13 12:12:45,047 epoch 6 - iter 88/447 - loss 0.01899288 - time (sec): 5.51 - samples/sec: 3046.61 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-13 12:12:47,709 epoch 6 - iter 132/447 - loss 0.02204439 - time (sec): 8.17 - samples/sec: 3038.25 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-13 12:12:50,485 epoch 6 - iter 176/447 - loss 0.02141173 - time (sec): 10.94 - samples/sec: 3031.12 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 12:12:53,395 epoch 6 - iter 220/447 - loss 0.02138525 - time (sec): 13.86 - samples/sec: 3003.83 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 12:12:56,134 epoch 6 - iter 264/447 - loss 0.02204675 - time (sec): 16.59 - samples/sec: 3010.82 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-13 12:12:58,732 epoch 6 - iter 308/447 - loss 0.02286212 - time (sec): 19.19 - samples/sec: 3012.99 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-13 12:13:01,618 epoch 6 - iter 352/447 - loss 0.02277241 - time (sec): 22.08 - samples/sec: 3009.11 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-13 12:13:04,692 epoch 6 - iter 396/447 - loss 0.02385308 - time (sec): 25.15 - samples/sec: 2983.10 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-13 12:13:07,948 epoch 6 - iter 440/447 - loss 0.02363909 - time (sec): 28.41 - samples/sec: 2992.62 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-13 12:13:08,441 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:13:08,441 EPOCH 6 done: loss 0.0233 - lr: 0.000013 | |
| 2023-10-13 12:13:16,934 DEV : loss 0.17185726761817932 - f1-score (micro avg) 0.7742 | |
| 2023-10-13 12:13:16,961 saving best model | |
| 2023-10-13 12:13:17,402 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:13:20,166 epoch 7 - iter 44/447 - loss 0.01916041 - time (sec): 2.76 - samples/sec: 3114.27 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-13 12:13:23,001 epoch 7 - iter 88/447 - loss 0.01510835 - time (sec): 5.60 - samples/sec: 3054.36 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-13 12:13:25,697 epoch 7 - iter 132/447 - loss 0.01730488 - time (sec): 8.29 - samples/sec: 3149.16 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 12:13:28,564 epoch 7 - iter 176/447 - loss 0.02055155 - time (sec): 11.16 - samples/sec: 3116.43 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 12:13:31,329 epoch 7 - iter 220/447 - loss 0.01884195 - time (sec): 13.93 - samples/sec: 3067.37 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-13 12:13:34,117 epoch 7 - iter 264/447 - loss 0.01845163 - time (sec): 16.71 - samples/sec: 3075.63 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-13 12:13:36,839 epoch 7 - iter 308/447 - loss 0.01868504 - time (sec): 19.44 - samples/sec: 3058.19 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-13 12:13:39,690 epoch 7 - iter 352/447 - loss 0.01820740 - time (sec): 22.29 - samples/sec: 3045.37 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-13 12:13:42,381 epoch 7 - iter 396/447 - loss 0.01837560 - time (sec): 24.98 - samples/sec: 3021.47 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-13 12:13:45,484 epoch 7 - iter 440/447 - loss 0.01746424 - time (sec): 28.08 - samples/sec: 3013.49 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-13 12:13:46,176 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:13:46,176 EPOCH 7 done: loss 0.0178 - lr: 0.000010 | |
| 2023-10-13 12:13:54,157 DEV : loss 0.1952558010816574 - f1-score (micro avg) 0.7884 | |
| 2023-10-13 12:13:54,183 saving best model | |
| 2023-10-13 12:13:54,603 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:13:57,192 epoch 8 - iter 44/447 - loss 0.00952279 - time (sec): 2.59 - samples/sec: 3226.01 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-13 12:14:00,951 epoch 8 - iter 88/447 - loss 0.01075405 - time (sec): 6.35 - samples/sec: 2813.51 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 12:14:03,638 epoch 8 - iter 132/447 - loss 0.01163951 - time (sec): 9.03 - samples/sec: 2889.35 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 12:14:06,321 epoch 8 - iter 176/447 - loss 0.01011208 - time (sec): 11.72 - samples/sec: 2947.12 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-13 12:14:08,976 epoch 8 - iter 220/447 - loss 0.01040926 - time (sec): 14.37 - samples/sec: 2951.05 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-13 12:14:11,964 epoch 8 - iter 264/447 - loss 0.01021770 - time (sec): 17.36 - samples/sec: 2950.17 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-13 12:14:14,719 epoch 8 - iter 308/447 - loss 0.01160813 - time (sec): 20.11 - samples/sec: 2987.03 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-13 12:14:17,416 epoch 8 - iter 352/447 - loss 0.01157027 - time (sec): 22.81 - samples/sec: 2990.17 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-13 12:14:20,158 epoch 8 - iter 396/447 - loss 0.01170964 - time (sec): 25.55 - samples/sec: 3004.19 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-13 12:14:22,957 epoch 8 - iter 440/447 - loss 0.01121158 - time (sec): 28.35 - samples/sec: 3006.90 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-13 12:14:23,380 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:14:23,380 EPOCH 8 done: loss 0.0112 - lr: 0.000007 | |
| 2023-10-13 12:14:31,497 DEV : loss 0.2167775183916092 - f1-score (micro avg) 0.7789 | |
| 2023-10-13 12:14:31,525 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:14:34,360 epoch 9 - iter 44/447 - loss 0.00561482 - time (sec): 2.83 - samples/sec: 3020.24 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 12:14:37,025 epoch 9 - iter 88/447 - loss 0.00613922 - time (sec): 5.50 - samples/sec: 3060.61 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 12:14:39,869 epoch 9 - iter 132/447 - loss 0.00641510 - time (sec): 8.34 - samples/sec: 3001.76 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-13 12:14:42,645 epoch 9 - iter 176/447 - loss 0.00730428 - time (sec): 11.12 - samples/sec: 3027.86 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-13 12:14:46,449 epoch 9 - iter 220/447 - loss 0.00735542 - time (sec): 14.92 - samples/sec: 2903.03 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-13 12:14:49,357 epoch 9 - iter 264/447 - loss 0.00755574 - time (sec): 17.83 - samples/sec: 2901.71 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-13 12:14:52,247 epoch 9 - iter 308/447 - loss 0.00820703 - time (sec): 20.72 - samples/sec: 2885.30 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-13 12:14:55,197 epoch 9 - iter 352/447 - loss 0.00811542 - time (sec): 23.67 - samples/sec: 2904.86 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-13 12:14:57,809 epoch 9 - iter 396/447 - loss 0.00836396 - time (sec): 26.28 - samples/sec: 2915.48 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-13 12:15:00,699 epoch 9 - iter 440/447 - loss 0.00820656 - time (sec): 29.17 - samples/sec: 2916.14 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 12:15:01,337 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:15:01,337 EPOCH 9 done: loss 0.0082 - lr: 0.000003 | |
| 2023-10-13 12:15:09,668 DEV : loss 0.21906858682632446 - f1-score (micro avg) 0.7831 | |
| 2023-10-13 12:15:09,697 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:15:12,660 epoch 10 - iter 44/447 - loss 0.00224005 - time (sec): 2.96 - samples/sec: 3090.02 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 12:15:15,340 epoch 10 - iter 88/447 - loss 0.00566886 - time (sec): 5.64 - samples/sec: 3042.50 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-13 12:15:18,179 epoch 10 - iter 132/447 - loss 0.00777346 - time (sec): 8.48 - samples/sec: 2990.28 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-13 12:15:21,408 epoch 10 - iter 176/447 - loss 0.00673985 - time (sec): 11.71 - samples/sec: 3011.92 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-13 12:15:24,050 epoch 10 - iter 220/447 - loss 0.00725669 - time (sec): 14.35 - samples/sec: 3032.21 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-13 12:15:26,607 epoch 10 - iter 264/447 - loss 0.00740123 - time (sec): 16.91 - samples/sec: 3062.02 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-13 12:15:29,245 epoch 10 - iter 308/447 - loss 0.00855628 - time (sec): 19.55 - samples/sec: 3045.94 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-13 12:15:32,354 epoch 10 - iter 352/447 - loss 0.00792544 - time (sec): 22.66 - samples/sec: 3010.68 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-13 12:15:35,115 epoch 10 - iter 396/447 - loss 0.00837304 - time (sec): 25.42 - samples/sec: 3003.23 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-13 12:15:38,576 epoch 10 - iter 440/447 - loss 0.00803818 - time (sec): 28.88 - samples/sec: 2957.32 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-13 12:15:39,010 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:15:39,010 EPOCH 10 done: loss 0.0079 - lr: 0.000000 | |
| 2023-10-13 12:15:47,210 DEV : loss 0.21315275132656097 - f1-score (micro avg) 0.7884 | |
| 2023-10-13 12:15:47,592 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-13 12:15:47,594 Loading model from best epoch ... | |
| 2023-10-13 12:15:49,064 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-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time | |
| 2023-10-13 12:15:54,036 | |
| Results: | |
| - F-score (micro) 0.7463 | |
| - F-score (macro) 0.6505 | |
| - Accuracy 0.6143 | |
| By class: | |
| precision recall f1-score support | |
| loc 0.8507 0.8607 0.8557 596 | |
| pers 0.6477 0.7508 0.6954 333 | |
| org 0.5182 0.4318 0.4711 132 | |
| prod 0.7045 0.4697 0.5636 66 | |
| time 0.6600 0.6735 0.6667 49 | |
| micro avg 0.7410 0.7517 0.7463 1176 | |
| macro avg 0.6762 0.6373 0.6505 1176 | |
| weighted avg 0.7398 0.7517 0.7429 1176 | |
| 2023-10-13 12:15:54,036 ---------------------------------------------------------------------------------------------------- | |