2023-10-13 16:18:19,873 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:18:19,874 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 16:18:19,874 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:18:19,874 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 16:18:19,874 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:18:19,874 Train: 5901 sentences 2023-10-13 16:18:19,874 (train_with_dev=False, train_with_test=False) 2023-10-13 16:18:19,874 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:18:19,874 Training Params: 2023-10-13 16:18:19,874 - learning_rate: "3e-05" 2023-10-13 16:18:19,874 - mini_batch_size: "4" 2023-10-13 16:18:19,874 - max_epochs: "10" 2023-10-13 16:18:19,874 - shuffle: "True" 2023-10-13 16:18:19,874 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:18:19,874 Plugins: 2023-10-13 16:18:19,874 - LinearScheduler | warmup_fraction: '0.1' 2023-10-13 16:18:19,874 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:18:19,874 Final evaluation on model from best epoch (best-model.pt) 2023-10-13 16:18:19,874 - metric: "('micro avg', 'f1-score')" 2023-10-13 16:18:19,874 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:18:19,875 Computation: 2023-10-13 16:18:19,875 - compute on device: cuda:0 2023-10-13 16:18:19,875 - embedding storage: none 2023-10-13 16:18:19,875 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:18:19,875 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2" 2023-10-13 16:18:19,875 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:18:19,875 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:18:26,876 epoch 1 - iter 147/1476 - loss 2.68811671 - time (sec): 7.00 - samples/sec: 2530.06 - lr: 0.000003 - momentum: 0.000000 2023-10-13 16:18:33,935 epoch 1 - iter 294/1476 - loss 1.63436810 - time (sec): 14.06 - samples/sec: 2556.09 - lr: 0.000006 - momentum: 0.000000 2023-10-13 16:18:40,951 epoch 1 - iter 441/1476 - loss 1.24746196 - time (sec): 21.07 - samples/sec: 2466.58 - lr: 0.000009 - momentum: 0.000000 2023-10-13 16:18:48,209 epoch 1 - iter 588/1476 - loss 1.01857941 - time (sec): 28.33 - samples/sec: 2425.58 - lr: 0.000012 - momentum: 0.000000 2023-10-13 16:18:55,376 epoch 1 - iter 735/1476 - loss 0.88100047 - time (sec): 35.50 - samples/sec: 2410.76 - lr: 0.000015 - momentum: 0.000000 2023-10-13 16:19:02,343 epoch 1 - iter 882/1476 - loss 0.77829880 - time (sec): 42.47 - samples/sec: 2408.70 - lr: 0.000018 - momentum: 0.000000 2023-10-13 16:19:09,239 epoch 1 - iter 1029/1476 - loss 0.70303474 - time (sec): 49.36 - samples/sec: 2402.58 - lr: 0.000021 - momentum: 0.000000 2023-10-13 16:19:15,979 epoch 1 - iter 1176/1476 - loss 0.64928259 - time (sec): 56.10 - samples/sec: 2376.92 - lr: 0.000024 - momentum: 0.000000 2023-10-13 16:19:22,808 epoch 1 - iter 1323/1476 - loss 0.60030866 - time (sec): 62.93 - samples/sec: 2378.25 - lr: 0.000027 - momentum: 0.000000 2023-10-13 16:19:29,670 epoch 1 - iter 1470/1476 - loss 0.56005280 - time (sec): 69.79 - samples/sec: 2376.48 - lr: 0.000030 - momentum: 0.000000 2023-10-13 16:19:29,929 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:19:29,929 EPOCH 1 done: loss 0.5590 - lr: 0.000030 2023-10-13 16:19:36,108 DEV : loss 0.12960338592529297 - f1-score (micro avg) 0.7114 2023-10-13 16:19:36,137 saving best model 2023-10-13 16:19:36,590 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:19:43,259 epoch 2 - iter 147/1476 - loss 0.14371163 - time (sec): 6.67 - samples/sec: 2282.89 - lr: 0.000030 - momentum: 0.000000 2023-10-13 16:19:50,126 epoch 2 - iter 294/1476 - loss 0.14687695 - time (sec): 13.53 - samples/sec: 2321.77 - lr: 0.000029 - momentum: 0.000000 2023-10-13 16:19:56,916 epoch 2 - iter 441/1476 - loss 0.14324943 - time (sec): 20.33 - samples/sec: 2350.40 - lr: 0.000029 - momentum: 0.000000 2023-10-13 16:20:03,828 epoch 2 - iter 588/1476 - loss 0.13800148 - time (sec): 27.24 - samples/sec: 2341.03 - lr: 0.000029 - momentum: 0.000000 2023-10-13 16:20:10,771 epoch 2 - iter 735/1476 - loss 0.14220914 - time (sec): 34.18 - samples/sec: 2310.96 - lr: 0.000028 - momentum: 0.000000 2023-10-13 16:20:17,816 epoch 2 - iter 882/1476 - loss 0.14051112 - time (sec): 41.22 - samples/sec: 2330.35 - lr: 0.000028 - momentum: 0.000000 2023-10-13 16:20:25,068 epoch 2 - iter 1029/1476 - loss 0.13606559 - time (sec): 48.48 - samples/sec: 2361.19 - lr: 0.000028 - momentum: 0.000000 2023-10-13 16:20:31,924 epoch 2 - iter 1176/1476 - loss 0.13105034 - time (sec): 55.33 - samples/sec: 2366.25 - lr: 0.000027 - momentum: 0.000000 2023-10-13 16:20:38,969 epoch 2 - iter 1323/1476 - loss 0.13230912 - time (sec): 62.38 - samples/sec: 2373.34 - lr: 0.000027 - momentum: 0.000000 2023-10-13 16:20:46,152 epoch 2 - iter 1470/1476 - loss 0.13197270 - time (sec): 69.56 - samples/sec: 2380.76 - lr: 0.000027 - momentum: 0.000000 2023-10-13 16:20:46,430 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:20:46,431 EPOCH 2 done: loss 0.1321 - lr: 0.000027 2023-10-13 16:20:57,635 DEV : loss 0.14676305651664734 - f1-score (micro avg) 0.7483 2023-10-13 16:20:57,663 saving best model 2023-10-13 16:20:58,251 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:21:05,130 epoch 3 - iter 147/1476 - loss 0.07375163 - time (sec): 6.88 - samples/sec: 2256.94 - lr: 0.000026 - momentum: 0.000000 2023-10-13 16:21:12,085 epoch 3 - iter 294/1476 - loss 0.08555357 - time (sec): 13.83 - samples/sec: 2339.15 - lr: 0.000026 - momentum: 0.000000 2023-10-13 16:21:18,976 epoch 3 - iter 441/1476 - loss 0.08707338 - time (sec): 20.72 - samples/sec: 2346.38 - lr: 0.000026 - momentum: 0.000000 2023-10-13 16:21:25,687 epoch 3 - iter 588/1476 - loss 0.08703905 - time (sec): 27.43 - samples/sec: 2337.30 - lr: 0.000025 - momentum: 0.000000 2023-10-13 16:21:32,780 epoch 3 - iter 735/1476 - loss 0.08725903 - time (sec): 34.53 - samples/sec: 2362.45 - lr: 0.000025 - momentum: 0.000000 2023-10-13 16:21:39,920 epoch 3 - iter 882/1476 - loss 0.08502530 - time (sec): 41.67 - samples/sec: 2401.73 - lr: 0.000025 - momentum: 0.000000 2023-10-13 16:21:46,866 epoch 3 - iter 1029/1476 - loss 0.08313622 - time (sec): 48.61 - samples/sec: 2388.85 - lr: 0.000024 - momentum: 0.000000 2023-10-13 16:21:53,843 epoch 3 - iter 1176/1476 - loss 0.08509026 - time (sec): 55.59 - samples/sec: 2410.45 - lr: 0.000024 - momentum: 0.000000 2023-10-13 16:22:00,456 epoch 3 - iter 1323/1476 - loss 0.08340603 - time (sec): 62.20 - samples/sec: 2415.55 - lr: 0.000024 - momentum: 0.000000 2023-10-13 16:22:07,228 epoch 3 - iter 1470/1476 - loss 0.08513356 - time (sec): 68.97 - samples/sec: 2404.08 - lr: 0.000023 - momentum: 0.000000 2023-10-13 16:22:07,489 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:22:07,489 EPOCH 3 done: loss 0.0851 - lr: 0.000023 2023-10-13 16:22:18,604 DEV : loss 0.1558631807565689 - f1-score (micro avg) 0.8021 2023-10-13 16:22:18,632 saving best model 2023-10-13 16:22:19,132 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:22:25,971 epoch 4 - iter 147/1476 - loss 0.05321490 - time (sec): 6.84 - samples/sec: 2228.72 - lr: 0.000023 - momentum: 0.000000 2023-10-13 16:22:32,717 epoch 4 - iter 294/1476 - loss 0.05088317 - time (sec): 13.58 - samples/sec: 2289.73 - lr: 0.000023 - momentum: 0.000000 2023-10-13 16:22:39,592 epoch 4 - iter 441/1476 - loss 0.05582560 - time (sec): 20.46 - samples/sec: 2337.16 - lr: 0.000022 - momentum: 0.000000 2023-10-13 16:22:46,278 epoch 4 - iter 588/1476 - loss 0.05853689 - time (sec): 27.14 - samples/sec: 2329.23 - lr: 0.000022 - momentum: 0.000000 2023-10-13 16:22:53,495 epoch 4 - iter 735/1476 - loss 0.05804754 - time (sec): 34.36 - samples/sec: 2325.70 - lr: 0.000022 - momentum: 0.000000 2023-10-13 16:23:00,651 epoch 4 - iter 882/1476 - loss 0.05583338 - time (sec): 41.52 - samples/sec: 2346.35 - lr: 0.000021 - momentum: 0.000000 2023-10-13 16:23:07,936 epoch 4 - iter 1029/1476 - loss 0.05481177 - time (sec): 48.80 - samples/sec: 2384.46 - lr: 0.000021 - momentum: 0.000000 2023-10-13 16:23:14,776 epoch 4 - iter 1176/1476 - loss 0.05475217 - time (sec): 55.64 - samples/sec: 2389.60 - lr: 0.000021 - momentum: 0.000000 2023-10-13 16:23:21,862 epoch 4 - iter 1323/1476 - loss 0.05730543 - time (sec): 62.73 - samples/sec: 2386.95 - lr: 0.000020 - momentum: 0.000000 2023-10-13 16:23:28,584 epoch 4 - iter 1470/1476 - loss 0.05764999 - time (sec): 69.45 - samples/sec: 2386.25 - lr: 0.000020 - momentum: 0.000000 2023-10-13 16:23:28,863 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:23:28,864 EPOCH 4 done: loss 0.0578 - lr: 0.000020 2023-10-13 16:23:40,076 DEV : loss 0.18168844282627106 - f1-score (micro avg) 0.803 2023-10-13 16:23:40,104 saving best model 2023-10-13 16:23:40,595 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:23:47,639 epoch 5 - iter 147/1476 - loss 0.05535559 - time (sec): 7.04 - samples/sec: 2350.97 - lr: 0.000020 - momentum: 0.000000 2023-10-13 16:23:54,732 epoch 5 - iter 294/1476 - loss 0.04177522 - time (sec): 14.14 - samples/sec: 2354.73 - lr: 0.000019 - momentum: 0.000000 2023-10-13 16:24:01,904 epoch 5 - iter 441/1476 - loss 0.04295503 - time (sec): 21.31 - samples/sec: 2362.66 - lr: 0.000019 - momentum: 0.000000 2023-10-13 16:24:09,007 epoch 5 - iter 588/1476 - loss 0.03870174 - time (sec): 28.41 - samples/sec: 2337.31 - lr: 0.000019 - momentum: 0.000000 2023-10-13 16:24:15,872 epoch 5 - iter 735/1476 - loss 0.03947753 - time (sec): 35.28 - samples/sec: 2340.03 - lr: 0.000018 - momentum: 0.000000 2023-10-13 16:24:22,622 epoch 5 - iter 882/1476 - loss 0.04215224 - time (sec): 42.03 - samples/sec: 2333.54 - lr: 0.000018 - momentum: 0.000000 2023-10-13 16:24:29,595 epoch 5 - iter 1029/1476 - loss 0.04176081 - time (sec): 49.00 - samples/sec: 2334.66 - lr: 0.000018 - momentum: 0.000000 2023-10-13 16:24:36,904 epoch 5 - iter 1176/1476 - loss 0.04176508 - time (sec): 56.31 - samples/sec: 2359.18 - lr: 0.000017 - momentum: 0.000000 2023-10-13 16:24:44,014 epoch 5 - iter 1323/1476 - loss 0.04105383 - time (sec): 63.42 - samples/sec: 2376.53 - lr: 0.000017 - momentum: 0.000000 2023-10-13 16:24:50,709 epoch 5 - iter 1470/1476 - loss 0.04223612 - time (sec): 70.11 - samples/sec: 2365.53 - lr: 0.000017 - momentum: 0.000000 2023-10-13 16:24:50,971 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:24:50,972 EPOCH 5 done: loss 0.0421 - lr: 0.000017 2023-10-13 16:25:02,159 DEV : loss 0.17941001057624817 - f1-score (micro avg) 0.8126 2023-10-13 16:25:02,189 saving best model 2023-10-13 16:25:02,695 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:25:09,753 epoch 6 - iter 147/1476 - loss 0.02653020 - time (sec): 7.06 - samples/sec: 2115.54 - lr: 0.000016 - momentum: 0.000000 2023-10-13 16:25:16,999 epoch 6 - iter 294/1476 - loss 0.02862818 - time (sec): 14.30 - samples/sec: 2386.80 - lr: 0.000016 - momentum: 0.000000 2023-10-13 16:25:23,876 epoch 6 - iter 441/1476 - loss 0.03132242 - time (sec): 21.18 - samples/sec: 2410.71 - lr: 0.000016 - momentum: 0.000000 2023-10-13 16:25:30,766 epoch 6 - iter 588/1476 - loss 0.03209656 - time (sec): 28.07 - samples/sec: 2401.90 - lr: 0.000015 - momentum: 0.000000 2023-10-13 16:25:37,800 epoch 6 - iter 735/1476 - loss 0.03269220 - time (sec): 35.10 - samples/sec: 2419.37 - lr: 0.000015 - momentum: 0.000000 2023-10-13 16:25:44,929 epoch 6 - iter 882/1476 - loss 0.03367211 - time (sec): 42.23 - samples/sec: 2419.30 - lr: 0.000015 - momentum: 0.000000 2023-10-13 16:25:51,539 epoch 6 - iter 1029/1476 - loss 0.03249115 - time (sec): 48.84 - samples/sec: 2403.54 - lr: 0.000014 - momentum: 0.000000 2023-10-13 16:25:58,447 epoch 6 - iter 1176/1476 - loss 0.03140458 - time (sec): 55.75 - samples/sec: 2402.72 - lr: 0.000014 - momentum: 0.000000 2023-10-13 16:26:05,272 epoch 6 - iter 1323/1476 - loss 0.03117712 - time (sec): 62.58 - samples/sec: 2393.92 - lr: 0.000014 - momentum: 0.000000 2023-10-13 16:26:12,144 epoch 6 - iter 1470/1476 - loss 0.03087811 - time (sec): 69.45 - samples/sec: 2389.87 - lr: 0.000013 - momentum: 0.000000 2023-10-13 16:26:12,398 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:26:12,399 EPOCH 6 done: loss 0.0308 - lr: 0.000013 2023-10-13 16:26:23,570 DEV : loss 0.1737550050020218 - f1-score (micro avg) 0.8073 2023-10-13 16:26:23,599 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:26:30,817 epoch 7 - iter 147/1476 - loss 0.01587476 - time (sec): 7.22 - samples/sec: 2370.42 - lr: 0.000013 - momentum: 0.000000 2023-10-13 16:26:37,420 epoch 7 - iter 294/1476 - loss 0.01435257 - time (sec): 13.82 - samples/sec: 2325.55 - lr: 0.000013 - momentum: 0.000000 2023-10-13 16:26:44,799 epoch 7 - iter 441/1476 - loss 0.02157759 - time (sec): 21.20 - samples/sec: 2384.74 - lr: 0.000012 - momentum: 0.000000 2023-10-13 16:26:52,071 epoch 7 - iter 588/1476 - loss 0.02281126 - time (sec): 28.47 - samples/sec: 2434.79 - lr: 0.000012 - momentum: 0.000000 2023-10-13 16:26:58,793 epoch 7 - iter 735/1476 - loss 0.02402209 - time (sec): 35.19 - samples/sec: 2398.19 - lr: 0.000012 - momentum: 0.000000 2023-10-13 16:27:05,497 epoch 7 - iter 882/1476 - loss 0.02296830 - time (sec): 41.90 - samples/sec: 2381.17 - lr: 0.000011 - momentum: 0.000000 2023-10-13 16:27:12,184 epoch 7 - iter 1029/1476 - loss 0.02320940 - time (sec): 48.58 - samples/sec: 2384.78 - lr: 0.000011 - momentum: 0.000000 2023-10-13 16:27:18,947 epoch 7 - iter 1176/1476 - loss 0.02303719 - time (sec): 55.35 - samples/sec: 2376.63 - lr: 0.000011 - momentum: 0.000000 2023-10-13 16:27:25,778 epoch 7 - iter 1323/1476 - loss 0.02138011 - time (sec): 62.18 - samples/sec: 2373.80 - lr: 0.000010 - momentum: 0.000000 2023-10-13 16:27:32,996 epoch 7 - iter 1470/1476 - loss 0.02155465 - time (sec): 69.40 - samples/sec: 2390.58 - lr: 0.000010 - momentum: 0.000000 2023-10-13 16:27:33,255 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:27:33,255 EPOCH 7 done: loss 0.0215 - lr: 0.000010 2023-10-13 16:27:44,418 DEV : loss 0.20156921446323395 - f1-score (micro avg) 0.8156 2023-10-13 16:27:44,448 saving best model 2023-10-13 16:27:45,057 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:27:52,066 epoch 8 - iter 147/1476 - loss 0.01727771 - time (sec): 7.00 - samples/sec: 2398.66 - lr: 0.000010 - momentum: 0.000000 2023-10-13 16:27:59,060 epoch 8 - iter 294/1476 - loss 0.01903390 - time (sec): 14.00 - samples/sec: 2412.30 - lr: 0.000009 - momentum: 0.000000 2023-10-13 16:28:06,483 epoch 8 - iter 441/1476 - loss 0.01852869 - time (sec): 21.42 - samples/sec: 2482.55 - lr: 0.000009 - momentum: 0.000000 2023-10-13 16:28:13,207 epoch 8 - iter 588/1476 - loss 0.01894146 - time (sec): 28.14 - samples/sec: 2415.93 - lr: 0.000009 - momentum: 0.000000 2023-10-13 16:28:20,113 epoch 8 - iter 735/1476 - loss 0.01775006 - time (sec): 35.05 - samples/sec: 2390.37 - lr: 0.000008 - momentum: 0.000000 2023-10-13 16:28:26,932 epoch 8 - iter 882/1476 - loss 0.01706730 - time (sec): 41.87 - samples/sec: 2369.20 - lr: 0.000008 - momentum: 0.000000 2023-10-13 16:28:33,847 epoch 8 - iter 1029/1476 - loss 0.01593317 - time (sec): 48.78 - samples/sec: 2361.06 - lr: 0.000008 - momentum: 0.000000 2023-10-13 16:28:40,797 epoch 8 - iter 1176/1476 - loss 0.01516748 - time (sec): 55.73 - samples/sec: 2338.33 - lr: 0.000007 - momentum: 0.000000 2023-10-13 16:28:47,956 epoch 8 - iter 1323/1476 - loss 0.01460520 - time (sec): 62.89 - samples/sec: 2354.33 - lr: 0.000007 - momentum: 0.000000 2023-10-13 16:28:54,942 epoch 8 - iter 1470/1476 - loss 0.01394633 - time (sec): 69.88 - samples/sec: 2372.44 - lr: 0.000007 - momentum: 0.000000 2023-10-13 16:28:55,205 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:28:55,205 EPOCH 8 done: loss 0.0139 - lr: 0.000007 2023-10-13 16:29:06,318 DEV : loss 0.21695148944854736 - f1-score (micro avg) 0.8318 2023-10-13 16:29:06,348 saving best model 2023-10-13 16:29:06,888 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:29:13,839 epoch 9 - iter 147/1476 - loss 0.01149428 - time (sec): 6.95 - samples/sec: 2241.86 - lr: 0.000006 - momentum: 0.000000 2023-10-13 16:29:20,892 epoch 9 - iter 294/1476 - loss 0.01051718 - time (sec): 14.00 - samples/sec: 2354.76 - lr: 0.000006 - momentum: 0.000000 2023-10-13 16:29:27,592 epoch 9 - iter 441/1476 - loss 0.01109949 - time (sec): 20.70 - samples/sec: 2346.93 - lr: 0.000006 - momentum: 0.000000 2023-10-13 16:29:34,486 epoch 9 - iter 588/1476 - loss 0.01034791 - time (sec): 27.59 - samples/sec: 2365.06 - lr: 0.000005 - momentum: 0.000000 2023-10-13 16:29:41,777 epoch 9 - iter 735/1476 - loss 0.01038753 - time (sec): 34.88 - samples/sec: 2389.31 - lr: 0.000005 - momentum: 0.000000 2023-10-13 16:29:48,663 epoch 9 - iter 882/1476 - loss 0.00977770 - time (sec): 41.77 - samples/sec: 2369.64 - lr: 0.000005 - momentum: 0.000000 2023-10-13 16:29:55,755 epoch 9 - iter 1029/1476 - loss 0.00886349 - time (sec): 48.86 - samples/sec: 2375.51 - lr: 0.000004 - momentum: 0.000000 2023-10-13 16:30:02,562 epoch 9 - iter 1176/1476 - loss 0.00868716 - time (sec): 55.67 - samples/sec: 2364.56 - lr: 0.000004 - momentum: 0.000000 2023-10-13 16:30:09,255 epoch 9 - iter 1323/1476 - loss 0.00810351 - time (sec): 62.36 - samples/sec: 2374.52 - lr: 0.000004 - momentum: 0.000000 2023-10-13 16:30:16,370 epoch 9 - iter 1470/1476 - loss 0.01065603 - time (sec): 69.48 - samples/sec: 2383.02 - lr: 0.000003 - momentum: 0.000000 2023-10-13 16:30:16,664 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:30:16,664 EPOCH 9 done: loss 0.0110 - lr: 0.000003 2023-10-13 16:30:27,816 DEV : loss 0.20820745825767517 - f1-score (micro avg) 0.8377 2023-10-13 16:30:27,845 saving best model 2023-10-13 16:30:28,406 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:30:35,677 epoch 10 - iter 147/1476 - loss 0.00895335 - time (sec): 7.27 - samples/sec: 2440.59 - lr: 0.000003 - momentum: 0.000000 2023-10-13 16:30:42,470 epoch 10 - iter 294/1476 - loss 0.00653471 - time (sec): 14.06 - samples/sec: 2395.44 - lr: 0.000003 - momentum: 0.000000 2023-10-13 16:30:49,027 epoch 10 - iter 441/1476 - loss 0.00745494 - time (sec): 20.62 - samples/sec: 2388.45 - lr: 0.000002 - momentum: 0.000000 2023-10-13 16:30:56,197 epoch 10 - iter 588/1476 - loss 0.00712878 - time (sec): 27.79 - samples/sec: 2366.71 - lr: 0.000002 - momentum: 0.000000 2023-10-13 16:31:03,271 epoch 10 - iter 735/1476 - loss 0.00682681 - time (sec): 34.86 - samples/sec: 2347.22 - lr: 0.000002 - momentum: 0.000000 2023-10-13 16:31:10,608 epoch 10 - iter 882/1476 - loss 0.00780092 - time (sec): 42.20 - samples/sec: 2386.20 - lr: 0.000001 - momentum: 0.000000 2023-10-13 16:31:17,347 epoch 10 - iter 1029/1476 - loss 0.00743818 - time (sec): 48.94 - samples/sec: 2359.12 - lr: 0.000001 - momentum: 0.000000 2023-10-13 16:31:24,541 epoch 10 - iter 1176/1476 - loss 0.00763687 - time (sec): 56.13 - samples/sec: 2360.28 - lr: 0.000001 - momentum: 0.000000 2023-10-13 16:31:31,528 epoch 10 - iter 1323/1476 - loss 0.00786299 - time (sec): 63.12 - samples/sec: 2367.54 - lr: 0.000000 - momentum: 0.000000 2023-10-13 16:31:38,440 epoch 10 - iter 1470/1476 - loss 0.00744089 - time (sec): 70.03 - samples/sec: 2368.59 - lr: 0.000000 - momentum: 0.000000 2023-10-13 16:31:38,703 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:31:38,703 EPOCH 10 done: loss 0.0074 - lr: 0.000000 2023-10-13 16:31:50,374 DEV : loss 0.2123088389635086 - f1-score (micro avg) 0.8335 2023-10-13 16:31:50,806 ---------------------------------------------------------------------------------------------------- 2023-10-13 16:31:50,807 Loading model from best epoch ... 2023-10-13 16:31:52,210 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 16:31:58,089 Results: - F-score (micro) 0.7975 - F-score (macro) 0.703 - Accuracy 0.6891 By class: precision recall f1-score support loc 0.8449 0.8823 0.8632 858 pers 0.7500 0.8045 0.7763 537 org 0.5968 0.5606 0.5781 132 prod 0.6935 0.7049 0.6992 61 time 0.5556 0.6481 0.5983 54 micro avg 0.7792 0.8167 0.7975 1642 macro avg 0.6881 0.7201 0.7030 1642 weighted avg 0.7788 0.8167 0.7970 1642 2023-10-13 16:31:58,089 ----------------------------------------------------------------------------------------------------