2023-10-13 12:48:22,060 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:22,061 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 12:48:22,061 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:22,062 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 12:48:22,062 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:22,062 Train: 3575 sentences 2023-10-13 12:48:22,062 (train_with_dev=False, train_with_test=False) 2023-10-13 12:48:22,062 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:22,062 Training Params: 2023-10-13 12:48:22,062 - learning_rate: "5e-05" 2023-10-13 12:48:22,062 - mini_batch_size: "8" 2023-10-13 12:48:22,062 - max_epochs: "10" 2023-10-13 12:48:22,062 - shuffle: "True" 2023-10-13 12:48:22,062 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:22,062 Plugins: 2023-10-13 12:48:22,062 - LinearScheduler | warmup_fraction: '0.1' 2023-10-13 12:48:22,062 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:22,062 Final evaluation on model from best epoch (best-model.pt) 2023-10-13 12:48:22,062 - metric: "('micro avg', 'f1-score')" 2023-10-13 12:48:22,062 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:22,062 Computation: 2023-10-13 12:48:22,062 - compute on device: cuda:0 2023-10-13 12:48:22,062 - embedding storage: none 2023-10-13 12:48:22,062 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:22,062 Model training base path: "hmbench-hipe2020/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3" 2023-10-13 12:48:22,062 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:22,062 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:24,736 epoch 1 - iter 44/447 - loss 2.72113629 - time (sec): 2.67 - samples/sec: 3086.23 - lr: 0.000005 - momentum: 0.000000 2023-10-13 12:48:27,748 epoch 1 - iter 88/447 - loss 1.76254453 - time (sec): 5.69 - samples/sec: 3008.78 - lr: 0.000010 - momentum: 0.000000 2023-10-13 12:48:30,428 epoch 1 - iter 132/447 - loss 1.36926797 - time (sec): 8.36 - samples/sec: 2990.42 - lr: 0.000015 - momentum: 0.000000 2023-10-13 12:48:33,398 epoch 1 - iter 176/447 - loss 1.09736044 - time (sec): 11.33 - samples/sec: 3050.34 - lr: 0.000020 - momentum: 0.000000 2023-10-13 12:48:36,384 epoch 1 - iter 220/447 - loss 0.93710869 - time (sec): 14.32 - samples/sec: 3024.41 - lr: 0.000024 - momentum: 0.000000 2023-10-13 12:48:39,333 epoch 1 - iter 264/447 - loss 0.82829129 - time (sec): 17.27 - samples/sec: 3009.72 - lr: 0.000029 - momentum: 0.000000 2023-10-13 12:48:42,028 epoch 1 - iter 308/447 - loss 0.75538064 - time (sec): 19.96 - samples/sec: 3005.19 - lr: 0.000034 - momentum: 0.000000 2023-10-13 12:48:44,956 epoch 1 - iter 352/447 - loss 0.70021192 - time (sec): 22.89 - samples/sec: 2974.25 - lr: 0.000039 - momentum: 0.000000 2023-10-13 12:48:48,054 epoch 1 - iter 396/447 - loss 0.64625200 - time (sec): 25.99 - samples/sec: 2968.22 - lr: 0.000044 - momentum: 0.000000 2023-10-13 12:48:50,791 epoch 1 - iter 440/447 - loss 0.60642690 - time (sec): 28.73 - samples/sec: 2972.97 - lr: 0.000049 - momentum: 0.000000 2023-10-13 12:48:51,196 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:51,196 EPOCH 1 done: loss 0.6027 - lr: 0.000049 2023-10-13 12:48:56,315 DEV : loss 0.18237876892089844 - f1-score (micro avg) 0.597 2023-10-13 12:48:56,364 saving best model 2023-10-13 12:48:56,829 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:48:59,725 epoch 2 - iter 44/447 - loss 0.19768284 - time (sec): 2.89 - samples/sec: 2808.02 - lr: 0.000049 - momentum: 0.000000 2023-10-13 12:49:02,590 epoch 2 - iter 88/447 - loss 0.18768155 - time (sec): 5.76 - samples/sec: 2855.46 - lr: 0.000049 - momentum: 0.000000 2023-10-13 12:49:05,392 epoch 2 - iter 132/447 - loss 0.17989274 - time (sec): 8.56 - samples/sec: 2902.30 - lr: 0.000048 - momentum: 0.000000 2023-10-13 12:49:08,357 epoch 2 - iter 176/447 - loss 0.16672786 - time (sec): 11.53 - samples/sec: 2885.66 - lr: 0.000048 - momentum: 0.000000 2023-10-13 12:49:11,204 epoch 2 - iter 220/447 - loss 0.16616890 - time (sec): 14.37 - samples/sec: 2884.18 - lr: 0.000047 - momentum: 0.000000 2023-10-13 12:49:14,037 epoch 2 - iter 264/447 - loss 0.16187003 - time (sec): 17.21 - samples/sec: 2896.45 - lr: 0.000047 - momentum: 0.000000 2023-10-13 12:49:16,772 epoch 2 - iter 308/447 - loss 0.16369277 - time (sec): 19.94 - samples/sec: 2908.91 - lr: 0.000046 - momentum: 0.000000 2023-10-13 12:49:19,882 epoch 2 - iter 352/447 - loss 0.15804249 - time (sec): 23.05 - samples/sec: 2909.39 - lr: 0.000046 - momentum: 0.000000 2023-10-13 12:49:22,765 epoch 2 - iter 396/447 - loss 0.15849072 - time (sec): 25.93 - samples/sec: 2958.06 - lr: 0.000045 - momentum: 0.000000 2023-10-13 12:49:25,599 epoch 2 - iter 440/447 - loss 0.15632034 - time (sec): 28.77 - samples/sec: 2964.47 - lr: 0.000045 - momentum: 0.000000 2023-10-13 12:49:26,075 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:49:26,075 EPOCH 2 done: loss 0.1560 - lr: 0.000045 2023-10-13 12:49:34,983 DEV : loss 0.13287977874279022 - f1-score (micro avg) 0.6972 2023-10-13 12:49:35,016 saving best model 2023-10-13 12:49:35,506 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:49:38,199 epoch 3 - iter 44/447 - loss 0.08731570 - time (sec): 2.68 - samples/sec: 3037.64 - lr: 0.000044 - momentum: 0.000000 2023-10-13 12:49:40,892 epoch 3 - iter 88/447 - loss 0.08438381 - time (sec): 5.38 - samples/sec: 2977.90 - lr: 0.000043 - momentum: 0.000000 2023-10-13 12:49:43,924 epoch 3 - iter 132/447 - loss 0.08570094 - time (sec): 8.41 - samples/sec: 2944.04 - lr: 0.000043 - momentum: 0.000000 2023-10-13 12:49:46,612 epoch 3 - iter 176/447 - loss 0.08756481 - time (sec): 11.10 - samples/sec: 2979.75 - lr: 0.000042 - momentum: 0.000000 2023-10-13 12:49:49,330 epoch 3 - iter 220/447 - loss 0.09082623 - time (sec): 13.82 - samples/sec: 2972.54 - lr: 0.000042 - momentum: 0.000000 2023-10-13 12:49:52,197 epoch 3 - iter 264/447 - loss 0.08856938 - time (sec): 16.68 - samples/sec: 2986.60 - lr: 0.000041 - momentum: 0.000000 2023-10-13 12:49:55,140 epoch 3 - iter 308/447 - loss 0.08687729 - time (sec): 19.63 - samples/sec: 2969.21 - lr: 0.000041 - momentum: 0.000000 2023-10-13 12:49:58,082 epoch 3 - iter 352/447 - loss 0.08587287 - time (sec): 22.57 - samples/sec: 2955.23 - lr: 0.000040 - momentum: 0.000000 2023-10-13 12:50:00,987 epoch 3 - iter 396/447 - loss 0.08320635 - time (sec): 25.47 - samples/sec: 2965.27 - lr: 0.000040 - momentum: 0.000000 2023-10-13 12:50:03,734 epoch 3 - iter 440/447 - loss 0.08286656 - time (sec): 28.22 - samples/sec: 2981.68 - lr: 0.000039 - momentum: 0.000000 2023-10-13 12:50:04,509 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:50:04,510 EPOCH 3 done: loss 0.0821 - lr: 0.000039 2023-10-13 12:50:13,364 DEV : loss 0.13965222239494324 - f1-score (micro avg) 0.7439 2023-10-13 12:50:13,399 saving best model 2023-10-13 12:50:13,905 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:50:16,806 epoch 4 - iter 44/447 - loss 0.06374565 - time (sec): 2.89 - samples/sec: 2818.47 - lr: 0.000038 - momentum: 0.000000 2023-10-13 12:50:19,552 epoch 4 - iter 88/447 - loss 0.06611011 - time (sec): 5.64 - samples/sec: 2940.44 - lr: 0.000038 - momentum: 0.000000 2023-10-13 12:50:22,225 epoch 4 - iter 132/447 - loss 0.05945489 - time (sec): 8.31 - samples/sec: 2982.35 - lr: 0.000037 - momentum: 0.000000 2023-10-13 12:50:25,405 epoch 4 - iter 176/447 - loss 0.05575885 - time (sec): 11.49 - samples/sec: 3029.54 - lr: 0.000037 - momentum: 0.000000 2023-10-13 12:50:28,204 epoch 4 - iter 220/447 - loss 0.05278981 - time (sec): 14.29 - samples/sec: 3024.95 - lr: 0.000036 - momentum: 0.000000 2023-10-13 12:50:31,045 epoch 4 - iter 264/447 - loss 0.05317609 - time (sec): 17.13 - samples/sec: 3014.77 - lr: 0.000036 - momentum: 0.000000 2023-10-13 12:50:33,781 epoch 4 - iter 308/447 - loss 0.05338658 - time (sec): 19.87 - samples/sec: 3025.23 - lr: 0.000035 - momentum: 0.000000 2023-10-13 12:50:36,538 epoch 4 - iter 352/447 - loss 0.05202556 - time (sec): 22.62 - samples/sec: 3023.70 - lr: 0.000035 - momentum: 0.000000 2023-10-13 12:50:39,597 epoch 4 - iter 396/447 - loss 0.05132416 - time (sec): 25.68 - samples/sec: 3010.67 - lr: 0.000034 - momentum: 0.000000 2023-10-13 12:50:42,247 epoch 4 - iter 440/447 - loss 0.05058772 - time (sec): 28.33 - samples/sec: 3013.07 - lr: 0.000033 - momentum: 0.000000 2023-10-13 12:50:42,652 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:50:42,653 EPOCH 4 done: loss 0.0507 - lr: 0.000033 2023-10-13 12:50:51,797 DEV : loss 0.15824183821678162 - f1-score (micro avg) 0.7532 2023-10-13 12:50:51,830 saving best model 2023-10-13 12:50:52,335 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:50:55,282 epoch 5 - iter 44/447 - loss 0.02999823 - time (sec): 2.94 - samples/sec: 2875.83 - lr: 0.000033 - momentum: 0.000000 2023-10-13 12:50:58,080 epoch 5 - iter 88/447 - loss 0.03142316 - time (sec): 5.74 - samples/sec: 2884.02 - lr: 0.000032 - momentum: 0.000000 2023-10-13 12:51:00,834 epoch 5 - iter 132/447 - loss 0.03153706 - time (sec): 8.49 - samples/sec: 2941.59 - lr: 0.000032 - momentum: 0.000000 2023-10-13 12:51:03,670 epoch 5 - iter 176/447 - loss 0.03146456 - time (sec): 11.33 - samples/sec: 2905.90 - lr: 0.000031 - momentum: 0.000000 2023-10-13 12:51:06,980 epoch 5 - iter 220/447 - loss 0.03208178 - time (sec): 14.64 - samples/sec: 2879.23 - lr: 0.000031 - momentum: 0.000000 2023-10-13 12:51:09,695 epoch 5 - iter 264/447 - loss 0.03368068 - time (sec): 17.36 - samples/sec: 2889.22 - lr: 0.000030 - momentum: 0.000000 2023-10-13 12:51:12,451 epoch 5 - iter 308/447 - loss 0.03426693 - time (sec): 20.11 - samples/sec: 2904.70 - lr: 0.000030 - momentum: 0.000000 2023-10-13 12:51:15,623 epoch 5 - iter 352/447 - loss 0.03584978 - time (sec): 23.28 - samples/sec: 2925.57 - lr: 0.000029 - momentum: 0.000000 2023-10-13 12:51:18,634 epoch 5 - iter 396/447 - loss 0.03559068 - time (sec): 26.29 - samples/sec: 2937.26 - lr: 0.000028 - momentum: 0.000000 2023-10-13 12:51:21,576 epoch 5 - iter 440/447 - loss 0.03588276 - time (sec): 29.24 - samples/sec: 2919.05 - lr: 0.000028 - momentum: 0.000000 2023-10-13 12:51:21,988 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:51:21,989 EPOCH 5 done: loss 0.0355 - lr: 0.000028 2023-10-13 12:51:30,616 DEV : loss 0.17583982646465302 - f1-score (micro avg) 0.7625 2023-10-13 12:51:30,650 saving best model 2023-10-13 12:51:31,135 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:51:34,394 epoch 6 - iter 44/447 - loss 0.01600788 - time (sec): 3.26 - samples/sec: 2954.25 - lr: 0.000027 - momentum: 0.000000 2023-10-13 12:51:37,101 epoch 6 - iter 88/447 - loss 0.01598411 - time (sec): 5.96 - samples/sec: 2988.06 - lr: 0.000027 - momentum: 0.000000 2023-10-13 12:51:40,033 epoch 6 - iter 132/447 - loss 0.01807915 - time (sec): 8.90 - samples/sec: 2994.03 - lr: 0.000026 - momentum: 0.000000 2023-10-13 12:51:42,899 epoch 6 - iter 176/447 - loss 0.01681758 - time (sec): 11.76 - samples/sec: 3010.57 - lr: 0.000026 - momentum: 0.000000 2023-10-13 12:51:45,738 epoch 6 - iter 220/447 - loss 0.01823050 - time (sec): 14.60 - samples/sec: 3040.07 - lr: 0.000025 - momentum: 0.000000 2023-10-13 12:51:48,430 epoch 6 - iter 264/447 - loss 0.02019697 - time (sec): 17.29 - samples/sec: 3025.48 - lr: 0.000025 - momentum: 0.000000 2023-10-13 12:51:51,200 epoch 6 - iter 308/447 - loss 0.01912780 - time (sec): 20.06 - samples/sec: 3004.35 - lr: 0.000024 - momentum: 0.000000 2023-10-13 12:51:53,876 epoch 6 - iter 352/447 - loss 0.02112949 - time (sec): 22.74 - samples/sec: 3001.14 - lr: 0.000023 - momentum: 0.000000 2023-10-13 12:51:56,698 epoch 6 - iter 396/447 - loss 0.02312469 - time (sec): 25.56 - samples/sec: 3010.87 - lr: 0.000023 - momentum: 0.000000 2023-10-13 12:51:59,256 epoch 6 - iter 440/447 - loss 0.02258683 - time (sec): 28.12 - samples/sec: 3017.49 - lr: 0.000022 - momentum: 0.000000 2023-10-13 12:51:59,876 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:51:59,877 EPOCH 6 done: loss 0.0222 - lr: 0.000022 2023-10-13 12:52:08,836 DEV : loss 0.21230502426624298 - f1-score (micro avg) 0.7578 2023-10-13 12:52:08,867 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:52:11,860 epoch 7 - iter 44/447 - loss 0.02437766 - time (sec): 2.99 - samples/sec: 3073.74 - lr: 0.000022 - momentum: 0.000000 2023-10-13 12:52:15,198 epoch 7 - iter 88/447 - loss 0.01924784 - time (sec): 6.33 - samples/sec: 2997.77 - lr: 0.000021 - momentum: 0.000000 2023-10-13 12:52:18,035 epoch 7 - iter 132/447 - loss 0.01736984 - time (sec): 9.17 - samples/sec: 3015.68 - lr: 0.000021 - momentum: 0.000000 2023-10-13 12:52:20,905 epoch 7 - iter 176/447 - loss 0.01662843 - time (sec): 12.04 - samples/sec: 3055.48 - lr: 0.000020 - momentum: 0.000000 2023-10-13 12:52:24,031 epoch 7 - iter 220/447 - loss 0.01461178 - time (sec): 15.16 - samples/sec: 3011.95 - lr: 0.000020 - momentum: 0.000000 2023-10-13 12:52:26,692 epoch 7 - iter 264/447 - loss 0.01314305 - time (sec): 17.82 - samples/sec: 2990.65 - lr: 0.000019 - momentum: 0.000000 2023-10-13 12:52:29,402 epoch 7 - iter 308/447 - loss 0.01265643 - time (sec): 20.53 - samples/sec: 2973.51 - lr: 0.000018 - momentum: 0.000000 2023-10-13 12:52:32,221 epoch 7 - iter 352/447 - loss 0.01273989 - time (sec): 23.35 - samples/sec: 2967.43 - lr: 0.000018 - momentum: 0.000000 2023-10-13 12:52:34,784 epoch 7 - iter 396/447 - loss 0.01331493 - time (sec): 25.92 - samples/sec: 2975.03 - lr: 0.000017 - momentum: 0.000000 2023-10-13 12:52:37,386 epoch 7 - iter 440/447 - loss 0.01337988 - time (sec): 28.52 - samples/sec: 2981.87 - lr: 0.000017 - momentum: 0.000000 2023-10-13 12:52:37,892 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:52:37,892 EPOCH 7 done: loss 0.0132 - lr: 0.000017 2023-10-13 12:52:46,514 DEV : loss 0.23592258989810944 - f1-score (micro avg) 0.7612 2023-10-13 12:52:46,545 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:52:49,368 epoch 8 - iter 44/447 - loss 0.01339854 - time (sec): 2.82 - samples/sec: 3064.25 - lr: 0.000016 - momentum: 0.000000 2023-10-13 12:52:52,244 epoch 8 - iter 88/447 - loss 0.01099649 - time (sec): 5.70 - samples/sec: 2945.77 - lr: 0.000016 - momentum: 0.000000 2023-10-13 12:52:55,576 epoch 8 - iter 132/447 - loss 0.00853314 - time (sec): 9.03 - samples/sec: 2964.34 - lr: 0.000015 - momentum: 0.000000 2023-10-13 12:52:58,656 epoch 8 - iter 176/447 - loss 0.00969601 - time (sec): 12.11 - samples/sec: 2906.80 - lr: 0.000015 - momentum: 0.000000 2023-10-13 12:53:01,479 epoch 8 - iter 220/447 - loss 0.01007474 - time (sec): 14.93 - samples/sec: 2918.87 - lr: 0.000014 - momentum: 0.000000 2023-10-13 12:53:04,582 epoch 8 - iter 264/447 - loss 0.01035185 - time (sec): 18.04 - samples/sec: 2902.77 - lr: 0.000013 - momentum: 0.000000 2023-10-13 12:53:07,412 epoch 8 - iter 308/447 - loss 0.01063796 - time (sec): 20.87 - samples/sec: 2919.76 - lr: 0.000013 - momentum: 0.000000 2023-10-13 12:53:10,111 epoch 8 - iter 352/447 - loss 0.00992196 - time (sec): 23.56 - samples/sec: 2924.33 - lr: 0.000012 - momentum: 0.000000 2023-10-13 12:53:12,894 epoch 8 - iter 396/447 - loss 0.00942271 - time (sec): 26.35 - samples/sec: 2938.99 - lr: 0.000012 - momentum: 0.000000 2023-10-13 12:53:15,454 epoch 8 - iter 440/447 - loss 0.00956868 - time (sec): 28.91 - samples/sec: 2952.81 - lr: 0.000011 - momentum: 0.000000 2023-10-13 12:53:15,838 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:53:15,838 EPOCH 8 done: loss 0.0095 - lr: 0.000011 2023-10-13 12:53:24,513 DEV : loss 0.2494419664144516 - f1-score (micro avg) 0.7784 2023-10-13 12:53:24,546 saving best model 2023-10-13 12:53:25,426 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:53:28,519 epoch 9 - iter 44/447 - loss 0.00818351 - time (sec): 3.09 - samples/sec: 2643.65 - lr: 0.000011 - momentum: 0.000000 2023-10-13 12:53:31,740 epoch 9 - iter 88/447 - loss 0.00467539 - time (sec): 6.31 - samples/sec: 2773.19 - lr: 0.000010 - momentum: 0.000000 2023-10-13 12:53:34,662 epoch 9 - iter 132/447 - loss 0.00483987 - time (sec): 9.23 - samples/sec: 2821.56 - lr: 0.000010 - momentum: 0.000000 2023-10-13 12:53:37,469 epoch 9 - iter 176/447 - loss 0.00526312 - time (sec): 12.04 - samples/sec: 2859.97 - lr: 0.000009 - momentum: 0.000000 2023-10-13 12:53:40,102 epoch 9 - iter 220/447 - loss 0.00658465 - time (sec): 14.67 - samples/sec: 2914.07 - lr: 0.000008 - momentum: 0.000000 2023-10-13 12:53:42,708 epoch 9 - iter 264/447 - loss 0.00837702 - time (sec): 17.28 - samples/sec: 2944.21 - lr: 0.000008 - momentum: 0.000000 2023-10-13 12:53:45,383 epoch 9 - iter 308/447 - loss 0.00730189 - time (sec): 19.96 - samples/sec: 2955.19 - lr: 0.000007 - momentum: 0.000000 2023-10-13 12:53:48,016 epoch 9 - iter 352/447 - loss 0.00753736 - time (sec): 22.59 - samples/sec: 2973.51 - lr: 0.000007 - momentum: 0.000000 2023-10-13 12:53:51,129 epoch 9 - iter 396/447 - loss 0.00741956 - time (sec): 25.70 - samples/sec: 2997.38 - lr: 0.000006 - momentum: 0.000000 2023-10-13 12:53:53,973 epoch 9 - iter 440/447 - loss 0.00707842 - time (sec): 28.55 - samples/sec: 2989.06 - lr: 0.000006 - momentum: 0.000000 2023-10-13 12:53:54,369 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:53:54,369 EPOCH 9 done: loss 0.0073 - lr: 0.000006 2023-10-13 12:54:02,736 DEV : loss 0.2321784794330597 - f1-score (micro avg) 0.7825 2023-10-13 12:54:02,767 saving best model 2023-10-13 12:54:03,165 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:54:06,127 epoch 10 - iter 44/447 - loss 0.00185807 - time (sec): 2.96 - samples/sec: 3045.90 - lr: 0.000005 - momentum: 0.000000 2023-10-13 12:54:08,834 epoch 10 - iter 88/447 - loss 0.00465358 - time (sec): 5.67 - samples/sec: 3052.43 - lr: 0.000005 - momentum: 0.000000 2023-10-13 12:54:12,066 epoch 10 - iter 132/447 - loss 0.00358618 - time (sec): 8.90 - samples/sec: 2892.03 - lr: 0.000004 - momentum: 0.000000 2023-10-13 12:54:14,976 epoch 10 - iter 176/447 - loss 0.00391395 - time (sec): 11.81 - samples/sec: 2932.40 - lr: 0.000003 - momentum: 0.000000 2023-10-13 12:54:17,595 epoch 10 - iter 220/447 - loss 0.00416413 - time (sec): 14.43 - samples/sec: 2956.30 - lr: 0.000003 - momentum: 0.000000 2023-10-13 12:54:20,350 epoch 10 - iter 264/447 - loss 0.00456129 - time (sec): 17.18 - samples/sec: 2956.21 - lr: 0.000002 - momentum: 0.000000 2023-10-13 12:54:23,283 epoch 10 - iter 308/447 - loss 0.00441630 - time (sec): 20.12 - samples/sec: 2953.83 - lr: 0.000002 - momentum: 0.000000 2023-10-13 12:54:26,710 epoch 10 - iter 352/447 - loss 0.00512117 - time (sec): 23.54 - samples/sec: 2958.63 - lr: 0.000001 - momentum: 0.000000 2023-10-13 12:54:29,451 epoch 10 - iter 396/447 - loss 0.00514772 - time (sec): 26.28 - samples/sec: 2948.94 - lr: 0.000001 - momentum: 0.000000 2023-10-13 12:54:32,059 epoch 10 - iter 440/447 - loss 0.00469391 - time (sec): 28.89 - samples/sec: 2951.55 - lr: 0.000000 - momentum: 0.000000 2023-10-13 12:54:32,458 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:54:32,458 EPOCH 10 done: loss 0.0047 - lr: 0.000000 2023-10-13 12:54:40,938 DEV : loss 0.23675945401191711 - f1-score (micro avg) 0.7819 2023-10-13 12:54:41,310 ---------------------------------------------------------------------------------------------------- 2023-10-13 12:54:41,311 Loading model from best epoch ... 2023-10-13 12:54:42,795 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 12:54:47,671 Results: - F-score (micro) 0.7527 - F-score (macro) 0.6738 - Accuracy 0.624 By class: precision recall f1-score support loc 0.8248 0.8607 0.8424 596 pers 0.6852 0.7387 0.7110 333 org 0.5635 0.5379 0.5504 132 prod 0.5962 0.4697 0.5254 66 time 0.7255 0.7551 0.7400 49 micro avg 0.7421 0.7636 0.7527 1176 macro avg 0.6790 0.6724 0.6738 1176 weighted avg 0.7390 0.7636 0.7503 1176 2023-10-13 12:54:47,671 ----------------------------------------------------------------------------------------------------