2023-10-14 19:05:16,272 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:05:16,273 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=13, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-14 19:05:16,273 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:05:16,273 MultiCorpus: 14465 train + 1392 dev + 2432 test sentences - NER_HIPE_2022 Corpus: 14465 train + 1392 dev + 2432 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/letemps/fr/with_doc_seperator 2023-10-14 19:05:16,273 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:05:16,273 Train: 14465 sentences 2023-10-14 19:05:16,273 (train_with_dev=False, train_with_test=False) 2023-10-14 19:05:16,273 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:05:16,273 Training Params: 2023-10-14 19:05:16,273 - learning_rate: "5e-05" 2023-10-14 19:05:16,273 - mini_batch_size: "4" 2023-10-14 19:05:16,273 - max_epochs: "10" 2023-10-14 19:05:16,273 - shuffle: "True" 2023-10-14 19:05:16,273 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:05:16,273 Plugins: 2023-10-14 19:05:16,273 - LinearScheduler | warmup_fraction: '0.1' 2023-10-14 19:05:16,273 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:05:16,273 Final evaluation on model from best epoch (best-model.pt) 2023-10-14 19:05:16,274 - metric: "('micro avg', 'f1-score')" 2023-10-14 19:05:16,274 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:05:16,281 Computation: 2023-10-14 19:05:16,281 - compute on device: cuda:0 2023-10-14 19:05:16,281 - embedding storage: none 2023-10-14 19:05:16,281 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:05:16,281 Model training base path: "hmbench-letemps/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1" 2023-10-14 19:05:16,281 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:05:16,281 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:05:32,627 epoch 1 - iter 361/3617 - loss 1.23920084 - time (sec): 16.34 - samples/sec: 2350.13 - lr: 0.000005 - momentum: 0.000000 2023-10-14 19:05:48,957 epoch 1 - iter 722/3617 - loss 0.71530722 - time (sec): 32.67 - samples/sec: 2334.39 - lr: 0.000010 - momentum: 0.000000 2023-10-14 19:06:05,427 epoch 1 - iter 1083/3617 - loss 0.53270967 - time (sec): 49.14 - samples/sec: 2315.81 - lr: 0.000015 - momentum: 0.000000 2023-10-14 19:06:22,334 epoch 1 - iter 1444/3617 - loss 0.42703208 - time (sec): 66.05 - samples/sec: 2316.46 - lr: 0.000020 - momentum: 0.000000 2023-10-14 19:06:38,623 epoch 1 - iter 1805/3617 - loss 0.36797501 - time (sec): 82.34 - samples/sec: 2312.38 - lr: 0.000025 - momentum: 0.000000 2023-10-14 19:06:54,895 epoch 1 - iter 2166/3617 - loss 0.32750828 - time (sec): 98.61 - samples/sec: 2310.32 - lr: 0.000030 - momentum: 0.000000 2023-10-14 19:07:11,180 epoch 1 - iter 2527/3617 - loss 0.29684754 - time (sec): 114.90 - samples/sec: 2307.26 - lr: 0.000035 - momentum: 0.000000 2023-10-14 19:07:27,493 epoch 1 - iter 2888/3617 - loss 0.27335721 - time (sec): 131.21 - samples/sec: 2323.15 - lr: 0.000040 - momentum: 0.000000 2023-10-14 19:07:43,729 epoch 1 - iter 3249/3617 - loss 0.25684398 - time (sec): 147.45 - samples/sec: 2318.20 - lr: 0.000045 - momentum: 0.000000 2023-10-14 19:08:00,221 epoch 1 - iter 3610/3617 - loss 0.24344102 - time (sec): 163.94 - samples/sec: 2313.29 - lr: 0.000050 - momentum: 0.000000 2023-10-14 19:08:00,523 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:08:00,523 EPOCH 1 done: loss 0.2432 - lr: 0.000050 2023-10-14 19:08:04,935 DEV : loss 0.1392880529165268 - f1-score (micro avg) 0.5689 2023-10-14 19:08:04,964 saving best model 2023-10-14 19:08:05,441 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:08:22,439 epoch 2 - iter 361/3617 - loss 0.10163212 - time (sec): 17.00 - samples/sec: 2243.75 - lr: 0.000049 - momentum: 0.000000 2023-10-14 19:08:38,735 epoch 2 - iter 722/3617 - loss 0.10138989 - time (sec): 33.29 - samples/sec: 2294.93 - lr: 0.000049 - momentum: 0.000000 2023-10-14 19:08:55,123 epoch 2 - iter 1083/3617 - loss 0.10563254 - time (sec): 49.68 - samples/sec: 2312.87 - lr: 0.000048 - momentum: 0.000000 2023-10-14 19:09:11,321 epoch 2 - iter 1444/3617 - loss 0.10583746 - time (sec): 65.88 - samples/sec: 2308.77 - lr: 0.000048 - momentum: 0.000000 2023-10-14 19:09:28,181 epoch 2 - iter 1805/3617 - loss 0.10742122 - time (sec): 82.74 - samples/sec: 2285.53 - lr: 0.000047 - momentum: 0.000000 2023-10-14 19:09:44,590 epoch 2 - iter 2166/3617 - loss 0.10649535 - time (sec): 99.15 - samples/sec: 2297.31 - lr: 0.000047 - momentum: 0.000000 2023-10-14 19:10:00,921 epoch 2 - iter 2527/3617 - loss 0.10728153 - time (sec): 115.48 - samples/sec: 2303.87 - lr: 0.000046 - momentum: 0.000000 2023-10-14 19:10:17,234 epoch 2 - iter 2888/3617 - loss 0.10535240 - time (sec): 131.79 - samples/sec: 2303.35 - lr: 0.000046 - momentum: 0.000000 2023-10-14 19:10:33,540 epoch 2 - iter 3249/3617 - loss 0.10454269 - time (sec): 148.10 - samples/sec: 2307.34 - lr: 0.000045 - momentum: 0.000000 2023-10-14 19:10:49,774 epoch 2 - iter 3610/3617 - loss 0.10433026 - time (sec): 164.33 - samples/sec: 2307.84 - lr: 0.000044 - momentum: 0.000000 2023-10-14 19:10:50,080 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:10:50,080 EPOCH 2 done: loss 0.1044 - lr: 0.000044 2023-10-14 19:10:55,595 DEV : loss 0.13085970282554626 - f1-score (micro avg) 0.6143 2023-10-14 19:10:55,625 saving best model 2023-10-14 19:10:56,106 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:11:12,432 epoch 3 - iter 361/3617 - loss 0.07918586 - time (sec): 16.32 - samples/sec: 2170.00 - lr: 0.000044 - momentum: 0.000000 2023-10-14 19:11:28,688 epoch 3 - iter 722/3617 - loss 0.07745191 - time (sec): 32.58 - samples/sec: 2257.80 - lr: 0.000043 - momentum: 0.000000 2023-10-14 19:11:44,987 epoch 3 - iter 1083/3617 - loss 0.07935488 - time (sec): 48.88 - samples/sec: 2279.45 - lr: 0.000043 - momentum: 0.000000 2023-10-14 19:12:01,266 epoch 3 - iter 1444/3617 - loss 0.08119260 - time (sec): 65.16 - samples/sec: 2314.24 - lr: 0.000042 - momentum: 0.000000 2023-10-14 19:12:17,499 epoch 3 - iter 1805/3617 - loss 0.08212278 - time (sec): 81.39 - samples/sec: 2316.72 - lr: 0.000042 - momentum: 0.000000 2023-10-14 19:12:33,816 epoch 3 - iter 2166/3617 - loss 0.08334289 - time (sec): 97.71 - samples/sec: 2326.30 - lr: 0.000041 - momentum: 0.000000 2023-10-14 19:12:50,145 epoch 3 - iter 2527/3617 - loss 0.08219281 - time (sec): 114.04 - samples/sec: 2331.35 - lr: 0.000041 - momentum: 0.000000 2023-10-14 19:13:06,352 epoch 3 - iter 2888/3617 - loss 0.08216017 - time (sec): 130.24 - samples/sec: 2329.63 - lr: 0.000040 - momentum: 0.000000 2023-10-14 19:13:23,473 epoch 3 - iter 3249/3617 - loss 0.08248407 - time (sec): 147.37 - samples/sec: 2313.36 - lr: 0.000039 - momentum: 0.000000 2023-10-14 19:13:39,840 epoch 3 - iter 3610/3617 - loss 0.08153553 - time (sec): 163.73 - samples/sec: 2316.89 - lr: 0.000039 - momentum: 0.000000 2023-10-14 19:13:40,153 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:13:40,153 EPOCH 3 done: loss 0.0815 - lr: 0.000039 2023-10-14 19:13:46,359 DEV : loss 0.18807156383991241 - f1-score (micro avg) 0.6297 2023-10-14 19:13:46,388 saving best model 2023-10-14 19:13:46,968 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:14:03,475 epoch 4 - iter 361/3617 - loss 0.06057895 - time (sec): 16.50 - samples/sec: 2377.22 - lr: 0.000038 - momentum: 0.000000 2023-10-14 19:14:19,869 epoch 4 - iter 722/3617 - loss 0.06057279 - time (sec): 32.90 - samples/sec: 2329.53 - lr: 0.000038 - momentum: 0.000000 2023-10-14 19:14:36,163 epoch 4 - iter 1083/3617 - loss 0.06014925 - time (sec): 49.19 - samples/sec: 2321.32 - lr: 0.000037 - momentum: 0.000000 2023-10-14 19:14:52,280 epoch 4 - iter 1444/3617 - loss 0.06060661 - time (sec): 65.31 - samples/sec: 2305.72 - lr: 0.000037 - momentum: 0.000000 2023-10-14 19:15:08,428 epoch 4 - iter 1805/3617 - loss 0.06034321 - time (sec): 81.46 - samples/sec: 2323.14 - lr: 0.000036 - momentum: 0.000000 2023-10-14 19:15:24,537 epoch 4 - iter 2166/3617 - loss 0.06055420 - time (sec): 97.57 - samples/sec: 2322.80 - lr: 0.000036 - momentum: 0.000000 2023-10-14 19:15:40,905 epoch 4 - iter 2527/3617 - loss 0.06148213 - time (sec): 113.93 - samples/sec: 2325.01 - lr: 0.000035 - momentum: 0.000000 2023-10-14 19:15:57,092 epoch 4 - iter 2888/3617 - loss 0.06126538 - time (sec): 130.12 - samples/sec: 2331.57 - lr: 0.000034 - momentum: 0.000000 2023-10-14 19:16:13,259 epoch 4 - iter 3249/3617 - loss 0.06194798 - time (sec): 146.29 - samples/sec: 2334.61 - lr: 0.000034 - momentum: 0.000000 2023-10-14 19:16:29,462 epoch 4 - iter 3610/3617 - loss 0.06151356 - time (sec): 162.49 - samples/sec: 2333.50 - lr: 0.000033 - momentum: 0.000000 2023-10-14 19:16:29,772 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:16:29,773 EPOCH 4 done: loss 0.0614 - lr: 0.000033 2023-10-14 19:16:35,943 DEV : loss 0.2195165902376175 - f1-score (micro avg) 0.6197 2023-10-14 19:16:35,973 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:16:52,484 epoch 5 - iter 361/3617 - loss 0.04617918 - time (sec): 16.51 - samples/sec: 2385.58 - lr: 0.000033 - momentum: 0.000000 2023-10-14 19:17:08,608 epoch 5 - iter 722/3617 - loss 0.04463891 - time (sec): 32.63 - samples/sec: 2362.80 - lr: 0.000032 - momentum: 0.000000 2023-10-14 19:17:24,908 epoch 5 - iter 1083/3617 - loss 0.04774820 - time (sec): 48.93 - samples/sec: 2352.31 - lr: 0.000032 - momentum: 0.000000 2023-10-14 19:17:41,128 epoch 5 - iter 1444/3617 - loss 0.04829543 - time (sec): 65.15 - samples/sec: 2331.91 - lr: 0.000031 - momentum: 0.000000 2023-10-14 19:17:57,525 epoch 5 - iter 1805/3617 - loss 0.04629774 - time (sec): 81.55 - samples/sec: 2341.74 - lr: 0.000031 - momentum: 0.000000 2023-10-14 19:18:13,880 epoch 5 - iter 2166/3617 - loss 0.04539649 - time (sec): 97.91 - samples/sec: 2342.96 - lr: 0.000030 - momentum: 0.000000 2023-10-14 19:18:30,165 epoch 5 - iter 2527/3617 - loss 0.04517893 - time (sec): 114.19 - samples/sec: 2345.27 - lr: 0.000029 - momentum: 0.000000 2023-10-14 19:18:46,313 epoch 5 - iter 2888/3617 - loss 0.04431891 - time (sec): 130.34 - samples/sec: 2337.69 - lr: 0.000029 - momentum: 0.000000 2023-10-14 19:19:02,438 epoch 5 - iter 3249/3617 - loss 0.04447990 - time (sec): 146.46 - samples/sec: 2341.40 - lr: 0.000028 - momentum: 0.000000 2023-10-14 19:19:18,521 epoch 5 - iter 3610/3617 - loss 0.04436946 - time (sec): 162.55 - samples/sec: 2333.42 - lr: 0.000028 - momentum: 0.000000 2023-10-14 19:19:18,823 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:19:18,823 EPOCH 5 done: loss 0.0443 - lr: 0.000028 2023-10-14 19:19:25,023 DEV : loss 0.3201915919780731 - f1-score (micro avg) 0.6218 2023-10-14 19:19:25,052 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:19:41,359 epoch 6 - iter 361/3617 - loss 0.02847515 - time (sec): 16.31 - samples/sec: 2328.31 - lr: 0.000027 - momentum: 0.000000 2023-10-14 19:19:57,743 epoch 6 - iter 722/3617 - loss 0.03162381 - time (sec): 32.69 - samples/sec: 2301.42 - lr: 0.000027 - momentum: 0.000000 2023-10-14 19:20:13,935 epoch 6 - iter 1083/3617 - loss 0.03126177 - time (sec): 48.88 - samples/sec: 2288.28 - lr: 0.000026 - momentum: 0.000000 2023-10-14 19:20:30,140 epoch 6 - iter 1444/3617 - loss 0.03237564 - time (sec): 65.09 - samples/sec: 2296.72 - lr: 0.000026 - momentum: 0.000000 2023-10-14 19:20:46,426 epoch 6 - iter 1805/3617 - loss 0.03357153 - time (sec): 81.37 - samples/sec: 2314.60 - lr: 0.000025 - momentum: 0.000000 2023-10-14 19:21:02,702 epoch 6 - iter 2166/3617 - loss 0.03474249 - time (sec): 97.65 - samples/sec: 2321.83 - lr: 0.000024 - momentum: 0.000000 2023-10-14 19:21:18,924 epoch 6 - iter 2527/3617 - loss 0.03416368 - time (sec): 113.87 - samples/sec: 2318.63 - lr: 0.000024 - momentum: 0.000000 2023-10-14 19:21:35,300 epoch 6 - iter 2888/3617 - loss 0.03478335 - time (sec): 130.25 - samples/sec: 2333.53 - lr: 0.000023 - momentum: 0.000000 2023-10-14 19:21:51,568 epoch 6 - iter 3249/3617 - loss 0.03429456 - time (sec): 146.51 - samples/sec: 2326.16 - lr: 0.000023 - momentum: 0.000000 2023-10-14 19:22:07,803 epoch 6 - iter 3610/3617 - loss 0.03403117 - time (sec): 162.75 - samples/sec: 2330.17 - lr: 0.000022 - momentum: 0.000000 2023-10-14 19:22:08,110 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:22:08,110 EPOCH 6 done: loss 0.0340 - lr: 0.000022 2023-10-14 19:22:13,840 DEV : loss 0.295797199010849 - f1-score (micro avg) 0.6353 2023-10-14 19:22:13,873 saving best model 2023-10-14 19:22:15,198 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:22:31,709 epoch 7 - iter 361/3617 - loss 0.01782055 - time (sec): 16.51 - samples/sec: 2349.80 - lr: 0.000022 - momentum: 0.000000 2023-10-14 19:22:48,225 epoch 7 - iter 722/3617 - loss 0.02038942 - time (sec): 33.02 - samples/sec: 2334.00 - lr: 0.000021 - momentum: 0.000000 2023-10-14 19:23:04,594 epoch 7 - iter 1083/3617 - loss 0.02125683 - time (sec): 49.39 - samples/sec: 2328.23 - lr: 0.000021 - momentum: 0.000000 2023-10-14 19:23:20,803 epoch 7 - iter 1444/3617 - loss 0.02186389 - time (sec): 65.60 - samples/sec: 2329.56 - lr: 0.000020 - momentum: 0.000000 2023-10-14 19:23:37,105 epoch 7 - iter 1805/3617 - loss 0.02286446 - time (sec): 81.90 - samples/sec: 2327.67 - lr: 0.000019 - momentum: 0.000000 2023-10-14 19:23:53,394 epoch 7 - iter 2166/3617 - loss 0.02265517 - time (sec): 98.19 - samples/sec: 2327.00 - lr: 0.000019 - momentum: 0.000000 2023-10-14 19:24:09,594 epoch 7 - iter 2527/3617 - loss 0.02328181 - time (sec): 114.39 - samples/sec: 2325.06 - lr: 0.000018 - momentum: 0.000000 2023-10-14 19:24:25,973 epoch 7 - iter 2888/3617 - loss 0.02329662 - time (sec): 130.77 - samples/sec: 2325.37 - lr: 0.000018 - momentum: 0.000000 2023-10-14 19:24:42,223 epoch 7 - iter 3249/3617 - loss 0.02335070 - time (sec): 147.02 - samples/sec: 2321.86 - lr: 0.000017 - momentum: 0.000000 2023-10-14 19:24:58,552 epoch 7 - iter 3610/3617 - loss 0.02367072 - time (sec): 163.35 - samples/sec: 2321.55 - lr: 0.000017 - momentum: 0.000000 2023-10-14 19:24:58,858 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:24:58,858 EPOCH 7 done: loss 0.0237 - lr: 0.000017 2023-10-14 19:25:04,434 DEV : loss 0.2667960822582245 - f1-score (micro avg) 0.6398 2023-10-14 19:25:04,468 saving best model 2023-10-14 19:25:05,056 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:25:21,333 epoch 8 - iter 361/3617 - loss 0.01420959 - time (sec): 16.27 - samples/sec: 2271.05 - lr: 0.000016 - momentum: 0.000000 2023-10-14 19:25:37,646 epoch 8 - iter 722/3617 - loss 0.01438499 - time (sec): 32.59 - samples/sec: 2300.97 - lr: 0.000016 - momentum: 0.000000 2023-10-14 19:25:54,010 epoch 8 - iter 1083/3617 - loss 0.01612726 - time (sec): 48.95 - samples/sec: 2324.11 - lr: 0.000015 - momentum: 0.000000 2023-10-14 19:26:10,324 epoch 8 - iter 1444/3617 - loss 0.01522163 - time (sec): 65.26 - samples/sec: 2323.82 - lr: 0.000014 - momentum: 0.000000 2023-10-14 19:26:26,522 epoch 8 - iter 1805/3617 - loss 0.01523890 - time (sec): 81.46 - samples/sec: 2319.15 - lr: 0.000014 - momentum: 0.000000 2023-10-14 19:26:42,609 epoch 8 - iter 2166/3617 - loss 0.01636181 - time (sec): 97.55 - samples/sec: 2301.16 - lr: 0.000013 - momentum: 0.000000 2023-10-14 19:26:59,146 epoch 8 - iter 2527/3617 - loss 0.01647260 - time (sec): 114.09 - samples/sec: 2313.61 - lr: 0.000013 - momentum: 0.000000 2023-10-14 19:27:15,466 epoch 8 - iter 2888/3617 - loss 0.01588100 - time (sec): 130.41 - samples/sec: 2311.13 - lr: 0.000012 - momentum: 0.000000 2023-10-14 19:27:31,824 epoch 8 - iter 3249/3617 - loss 0.01566046 - time (sec): 146.76 - samples/sec: 2319.16 - lr: 0.000012 - momentum: 0.000000 2023-10-14 19:27:48,117 epoch 8 - iter 3610/3617 - loss 0.01532074 - time (sec): 163.06 - samples/sec: 2325.05 - lr: 0.000011 - momentum: 0.000000 2023-10-14 19:27:48,435 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:27:48,435 EPOCH 8 done: loss 0.0153 - lr: 0.000011 2023-10-14 19:27:54,685 DEV : loss 0.3515782952308655 - f1-score (micro avg) 0.6337 2023-10-14 19:27:54,715 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:28:11,336 epoch 9 - iter 361/3617 - loss 0.01078017 - time (sec): 16.62 - samples/sec: 2283.55 - lr: 0.000011 - momentum: 0.000000 2023-10-14 19:28:27,693 epoch 9 - iter 722/3617 - loss 0.01382949 - time (sec): 32.98 - samples/sec: 2335.85 - lr: 0.000010 - momentum: 0.000000 2023-10-14 19:28:43,933 epoch 9 - iter 1083/3617 - loss 0.01233271 - time (sec): 49.22 - samples/sec: 2346.52 - lr: 0.000009 - momentum: 0.000000 2023-10-14 19:29:00,350 epoch 9 - iter 1444/3617 - loss 0.01223895 - time (sec): 65.63 - samples/sec: 2317.17 - lr: 0.000009 - momentum: 0.000000 2023-10-14 19:29:16,569 epoch 9 - iter 1805/3617 - loss 0.01153695 - time (sec): 81.85 - samples/sec: 2317.35 - lr: 0.000008 - momentum: 0.000000 2023-10-14 19:29:32,978 epoch 9 - iter 2166/3617 - loss 0.01075403 - time (sec): 98.26 - samples/sec: 2311.68 - lr: 0.000008 - momentum: 0.000000 2023-10-14 19:29:49,302 epoch 9 - iter 2527/3617 - loss 0.01022522 - time (sec): 114.59 - samples/sec: 2302.94 - lr: 0.000007 - momentum: 0.000000 2023-10-14 19:30:05,794 epoch 9 - iter 2888/3617 - loss 0.01001724 - time (sec): 131.08 - samples/sec: 2302.46 - lr: 0.000007 - momentum: 0.000000 2023-10-14 19:30:22,232 epoch 9 - iter 3249/3617 - loss 0.01004538 - time (sec): 147.52 - samples/sec: 2308.06 - lr: 0.000006 - momentum: 0.000000 2023-10-14 19:30:38,601 epoch 9 - iter 3610/3617 - loss 0.00979458 - time (sec): 163.88 - samples/sec: 2314.28 - lr: 0.000006 - momentum: 0.000000 2023-10-14 19:30:38,911 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:30:38,911 EPOCH 9 done: loss 0.0098 - lr: 0.000006 2023-10-14 19:30:45,203 DEV : loss 0.365791916847229 - f1-score (micro avg) 0.6403 2023-10-14 19:30:45,260 saving best model 2023-10-14 19:30:45,882 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:31:02,260 epoch 10 - iter 361/3617 - loss 0.00465788 - time (sec): 16.38 - samples/sec: 2343.20 - lr: 0.000005 - momentum: 0.000000 2023-10-14 19:31:18,604 epoch 10 - iter 722/3617 - loss 0.00449815 - time (sec): 32.72 - samples/sec: 2361.91 - lr: 0.000004 - momentum: 0.000000 2023-10-14 19:31:34,930 epoch 10 - iter 1083/3617 - loss 0.00507346 - time (sec): 49.05 - samples/sec: 2330.64 - lr: 0.000004 - momentum: 0.000000 2023-10-14 19:31:51,267 epoch 10 - iter 1444/3617 - loss 0.00557339 - time (sec): 65.38 - samples/sec: 2328.26 - lr: 0.000003 - momentum: 0.000000 2023-10-14 19:32:07,730 epoch 10 - iter 1805/3617 - loss 0.00531563 - time (sec): 81.85 - samples/sec: 2328.59 - lr: 0.000003 - momentum: 0.000000 2023-10-14 19:32:24,093 epoch 10 - iter 2166/3617 - loss 0.00641317 - time (sec): 98.21 - samples/sec: 2330.04 - lr: 0.000002 - momentum: 0.000000 2023-10-14 19:32:40,290 epoch 10 - iter 2527/3617 - loss 0.00595602 - time (sec): 114.41 - samples/sec: 2318.77 - lr: 0.000002 - momentum: 0.000000 2023-10-14 19:32:56,448 epoch 10 - iter 2888/3617 - loss 0.00598682 - time (sec): 130.56 - samples/sec: 2304.67 - lr: 0.000001 - momentum: 0.000000 2023-10-14 19:33:12,731 epoch 10 - iter 3249/3617 - loss 0.00596379 - time (sec): 146.85 - samples/sec: 2312.77 - lr: 0.000001 - momentum: 0.000000 2023-10-14 19:33:29,112 epoch 10 - iter 3610/3617 - loss 0.00609464 - time (sec): 163.23 - samples/sec: 2323.98 - lr: 0.000000 - momentum: 0.000000 2023-10-14 19:33:29,412 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:33:29,412 EPOCH 10 done: loss 0.0061 - lr: 0.000000 2023-10-14 19:33:35,636 DEV : loss 0.3828698396682739 - f1-score (micro avg) 0.6321 2023-10-14 19:33:36,042 ---------------------------------------------------------------------------------------------------- 2023-10-14 19:33:36,043 Loading model from best epoch ... 2023-10-14 19:33:37,423 SequenceTagger predicts: Dictionary with 13 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 2023-10-14 19:33:44,142 Results: - F-score (micro) 0.6452 - F-score (macro) 0.4857 - Accuracy 0.4899 By class: precision recall f1-score support loc 0.6283 0.8037 0.7053 591 pers 0.5526 0.7507 0.6366 357 org 0.1333 0.1013 0.1151 79 micro avg 0.5772 0.7313 0.6452 1027 macro avg 0.4381 0.5519 0.4857 1027 weighted avg 0.5639 0.7313 0.6360 1027 2023-10-14 19:33:44,142 ----------------------------------------------------------------------------------------------------