2023-10-15 19:50:34,450 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:50:34,451 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=17, bias=True) (loss_function): CrossEntropyLoss() )" 2023-10-15 19:50:34,451 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:50:34,451 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator 2023-10-15 19:50:34,451 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:50:34,451 Train: 20847 sentences 2023-10-15 19:50:34,451 (train_with_dev=False, train_with_test=False) 2023-10-15 19:50:34,451 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:50:34,451 Training Params: 2023-10-15 19:50:34,451 - learning_rate: "5e-05" 2023-10-15 19:50:34,451 - mini_batch_size: "8" 2023-10-15 19:50:34,451 - max_epochs: "10" 2023-10-15 19:50:34,451 - shuffle: "True" 2023-10-15 19:50:34,451 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:50:34,451 Plugins: 2023-10-15 19:50:34,451 - LinearScheduler | warmup_fraction: '0.1' 2023-10-15 19:50:34,451 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:50:34,451 Final evaluation on model from best epoch (best-model.pt) 2023-10-15 19:50:34,451 - metric: "('micro avg', 'f1-score')" 2023-10-15 19:50:34,451 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:50:34,451 Computation: 2023-10-15 19:50:34,451 - compute on device: cuda:0 2023-10-15 19:50:34,451 - embedding storage: none 2023-10-15 19:50:34,451 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:50:34,452 Model training base path: "hmbench-newseye/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4" 2023-10-15 19:50:34,452 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:50:34,452 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:50:52,747 epoch 1 - iter 260/2606 - loss 1.59028644 - time (sec): 18.29 - samples/sec: 1909.58 - lr: 0.000005 - momentum: 0.000000 2023-10-15 19:51:12,202 epoch 1 - iter 520/2606 - loss 0.98518790 - time (sec): 37.75 - samples/sec: 1945.90 - lr: 0.000010 - momentum: 0.000000 2023-10-15 19:51:30,947 epoch 1 - iter 780/2606 - loss 0.76092996 - time (sec): 56.49 - samples/sec: 1924.76 - lr: 0.000015 - momentum: 0.000000 2023-10-15 19:51:50,886 epoch 1 - iter 1040/2606 - loss 0.63417270 - time (sec): 76.43 - samples/sec: 1893.42 - lr: 0.000020 - momentum: 0.000000 2023-10-15 19:52:10,166 epoch 1 - iter 1300/2606 - loss 0.55309956 - time (sec): 95.71 - samples/sec: 1915.70 - lr: 0.000025 - momentum: 0.000000 2023-10-15 19:52:29,070 epoch 1 - iter 1560/2606 - loss 0.49758201 - time (sec): 114.62 - samples/sec: 1905.72 - lr: 0.000030 - momentum: 0.000000 2023-10-15 19:52:48,118 epoch 1 - iter 1820/2606 - loss 0.45440578 - time (sec): 133.67 - samples/sec: 1904.65 - lr: 0.000035 - momentum: 0.000000 2023-10-15 19:53:07,758 epoch 1 - iter 2080/2606 - loss 0.41855391 - time (sec): 153.30 - samples/sec: 1900.97 - lr: 0.000040 - momentum: 0.000000 2023-10-15 19:53:26,472 epoch 1 - iter 2340/2606 - loss 0.39560327 - time (sec): 172.02 - samples/sec: 1905.23 - lr: 0.000045 - momentum: 0.000000 2023-10-15 19:53:46,488 epoch 1 - iter 2600/2606 - loss 0.37125077 - time (sec): 192.03 - samples/sec: 1909.56 - lr: 0.000050 - momentum: 0.000000 2023-10-15 19:53:46,912 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:53:46,912 EPOCH 1 done: loss 0.3708 - lr: 0.000050 2023-10-15 19:53:53,679 DEV : loss 0.09866511821746826 - f1-score (micro avg) 0.332 2023-10-15 19:53:53,707 saving best model 2023-10-15 19:53:54,083 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:54:13,897 epoch 2 - iter 260/2606 - loss 0.16979246 - time (sec): 19.81 - samples/sec: 1913.91 - lr: 0.000049 - momentum: 0.000000 2023-10-15 19:54:33,718 epoch 2 - iter 520/2606 - loss 0.15396119 - time (sec): 39.63 - samples/sec: 1921.04 - lr: 0.000049 - momentum: 0.000000 2023-10-15 19:54:52,579 epoch 2 - iter 780/2606 - loss 0.15278405 - time (sec): 58.49 - samples/sec: 1938.50 - lr: 0.000048 - momentum: 0.000000 2023-10-15 19:55:11,345 epoch 2 - iter 1040/2606 - loss 0.15627393 - time (sec): 77.26 - samples/sec: 1914.12 - lr: 0.000048 - momentum: 0.000000 2023-10-15 19:55:30,228 epoch 2 - iter 1300/2606 - loss 0.15531609 - time (sec): 96.14 - samples/sec: 1926.26 - lr: 0.000047 - momentum: 0.000000 2023-10-15 19:55:48,789 epoch 2 - iter 1560/2606 - loss 0.15197189 - time (sec): 114.70 - samples/sec: 1925.11 - lr: 0.000047 - momentum: 0.000000 2023-10-15 19:56:08,319 epoch 2 - iter 1820/2606 - loss 0.14969900 - time (sec): 134.23 - samples/sec: 1933.14 - lr: 0.000046 - momentum: 0.000000 2023-10-15 19:56:26,763 epoch 2 - iter 2080/2606 - loss 0.15359140 - time (sec): 152.68 - samples/sec: 1928.53 - lr: 0.000046 - momentum: 0.000000 2023-10-15 19:56:46,738 epoch 2 - iter 2340/2606 - loss 0.15309562 - time (sec): 172.65 - samples/sec: 1927.59 - lr: 0.000045 - momentum: 0.000000 2023-10-15 19:57:05,105 epoch 2 - iter 2600/2606 - loss 0.15242289 - time (sec): 191.02 - samples/sec: 1921.72 - lr: 0.000044 - momentum: 0.000000 2023-10-15 19:57:05,409 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:57:05,409 EPOCH 2 done: loss 0.1524 - lr: 0.000044 2023-10-15 19:57:15,626 DEV : loss 0.1584077775478363 - f1-score (micro avg) 0.3406 2023-10-15 19:57:15,657 saving best model 2023-10-15 19:57:16,211 ---------------------------------------------------------------------------------------------------- 2023-10-15 19:57:35,187 epoch 3 - iter 260/2606 - loss 0.11299931 - time (sec): 18.97 - samples/sec: 1921.95 - lr: 0.000044 - momentum: 0.000000 2023-10-15 19:57:54,309 epoch 3 - iter 520/2606 - loss 0.10767341 - time (sec): 38.09 - samples/sec: 1918.22 - lr: 0.000043 - momentum: 0.000000 2023-10-15 19:58:15,019 epoch 3 - iter 780/2606 - loss 0.10899205 - time (sec): 58.80 - samples/sec: 1875.68 - lr: 0.000043 - momentum: 0.000000 2023-10-15 19:58:35,731 epoch 3 - iter 1040/2606 - loss 0.11063881 - time (sec): 79.52 - samples/sec: 1865.12 - lr: 0.000042 - momentum: 0.000000 2023-10-15 19:58:55,613 epoch 3 - iter 1300/2606 - loss 0.10698292 - time (sec): 99.40 - samples/sec: 1857.48 - lr: 0.000042 - momentum: 0.000000 2023-10-15 19:59:14,663 epoch 3 - iter 1560/2606 - loss 0.10679134 - time (sec): 118.45 - samples/sec: 1858.88 - lr: 0.000041 - momentum: 0.000000 2023-10-15 19:59:33,448 epoch 3 - iter 1820/2606 - loss 0.10893641 - time (sec): 137.23 - samples/sec: 1867.67 - lr: 0.000041 - momentum: 0.000000 2023-10-15 19:59:53,465 epoch 3 - iter 2080/2606 - loss 0.10710962 - time (sec): 157.25 - samples/sec: 1877.55 - lr: 0.000040 - momentum: 0.000000 2023-10-15 20:00:12,483 epoch 3 - iter 2340/2606 - loss 0.10687887 - time (sec): 176.27 - samples/sec: 1883.24 - lr: 0.000039 - momentum: 0.000000 2023-10-15 20:00:30,949 epoch 3 - iter 2600/2606 - loss 0.10778076 - time (sec): 194.74 - samples/sec: 1882.60 - lr: 0.000039 - momentum: 0.000000 2023-10-15 20:00:31,324 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:00:31,324 EPOCH 3 done: loss 0.1078 - lr: 0.000039 2023-10-15 20:00:40,591 DEV : loss 0.15240149199962616 - f1-score (micro avg) 0.4084 2023-10-15 20:00:40,626 saving best model 2023-10-15 20:00:41,208 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:01:00,629 epoch 4 - iter 260/2606 - loss 0.08035620 - time (sec): 19.41 - samples/sec: 1887.95 - lr: 0.000038 - momentum: 0.000000 2023-10-15 20:01:18,964 epoch 4 - iter 520/2606 - loss 0.07715406 - time (sec): 37.75 - samples/sec: 1884.39 - lr: 0.000038 - momentum: 0.000000 2023-10-15 20:01:37,159 epoch 4 - iter 780/2606 - loss 0.08039817 - time (sec): 55.94 - samples/sec: 1918.31 - lr: 0.000037 - momentum: 0.000000 2023-10-15 20:01:56,554 epoch 4 - iter 1040/2606 - loss 0.07833131 - time (sec): 75.34 - samples/sec: 1914.94 - lr: 0.000037 - momentum: 0.000000 2023-10-15 20:02:14,628 epoch 4 - iter 1300/2606 - loss 0.07761709 - time (sec): 93.41 - samples/sec: 1924.78 - lr: 0.000036 - momentum: 0.000000 2023-10-15 20:02:32,876 epoch 4 - iter 1560/2606 - loss 0.07793021 - time (sec): 111.66 - samples/sec: 1930.25 - lr: 0.000036 - momentum: 0.000000 2023-10-15 20:02:52,296 epoch 4 - iter 1820/2606 - loss 0.07911638 - time (sec): 131.08 - samples/sec: 1936.60 - lr: 0.000035 - momentum: 0.000000 2023-10-15 20:03:10,776 epoch 4 - iter 2080/2606 - loss 0.07888385 - time (sec): 149.56 - samples/sec: 1932.16 - lr: 0.000034 - momentum: 0.000000 2023-10-15 20:03:29,996 epoch 4 - iter 2340/2606 - loss 0.07940196 - time (sec): 168.78 - samples/sec: 1941.28 - lr: 0.000034 - momentum: 0.000000 2023-10-15 20:03:50,163 epoch 4 - iter 2600/2606 - loss 0.07772595 - time (sec): 188.95 - samples/sec: 1939.73 - lr: 0.000033 - momentum: 0.000000 2023-10-15 20:03:50,679 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:03:50,679 EPOCH 4 done: loss 0.0776 - lr: 0.000033 2023-10-15 20:03:59,788 DEV : loss 0.24223537743091583 - f1-score (micro avg) 0.3895 2023-10-15 20:03:59,816 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:04:17,856 epoch 5 - iter 260/2606 - loss 0.05320156 - time (sec): 18.04 - samples/sec: 1901.00 - lr: 0.000033 - momentum: 0.000000 2023-10-15 20:04:36,525 epoch 5 - iter 520/2606 - loss 0.06004399 - time (sec): 36.71 - samples/sec: 1892.24 - lr: 0.000032 - momentum: 0.000000 2023-10-15 20:04:55,064 epoch 5 - iter 780/2606 - loss 0.06361788 - time (sec): 55.25 - samples/sec: 1900.04 - lr: 0.000032 - momentum: 0.000000 2023-10-15 20:05:13,980 epoch 5 - iter 1040/2606 - loss 0.06308322 - time (sec): 74.16 - samples/sec: 1916.92 - lr: 0.000031 - momentum: 0.000000 2023-10-15 20:05:33,591 epoch 5 - iter 1300/2606 - loss 0.06120360 - time (sec): 93.77 - samples/sec: 1917.50 - lr: 0.000031 - momentum: 0.000000 2023-10-15 20:05:52,267 epoch 5 - iter 1560/2606 - loss 0.06016703 - time (sec): 112.45 - samples/sec: 1913.29 - lr: 0.000030 - momentum: 0.000000 2023-10-15 20:06:11,407 epoch 5 - iter 1820/2606 - loss 0.06035800 - time (sec): 131.59 - samples/sec: 1920.61 - lr: 0.000029 - momentum: 0.000000 2023-10-15 20:06:30,790 epoch 5 - iter 2080/2606 - loss 0.05910496 - time (sec): 150.97 - samples/sec: 1928.30 - lr: 0.000029 - momentum: 0.000000 2023-10-15 20:06:50,194 epoch 5 - iter 2340/2606 - loss 0.05814657 - time (sec): 170.38 - samples/sec: 1929.74 - lr: 0.000028 - momentum: 0.000000 2023-10-15 20:07:09,957 epoch 5 - iter 2600/2606 - loss 0.05838664 - time (sec): 190.14 - samples/sec: 1927.76 - lr: 0.000028 - momentum: 0.000000 2023-10-15 20:07:10,400 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:07:10,401 EPOCH 5 done: loss 0.0584 - lr: 0.000028 2023-10-15 20:07:18,725 DEV : loss 0.3234298527240753 - f1-score (micro avg) 0.3501 2023-10-15 20:07:18,755 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:07:38,043 epoch 6 - iter 260/2606 - loss 0.04968909 - time (sec): 19.29 - samples/sec: 1943.44 - lr: 0.000027 - momentum: 0.000000 2023-10-15 20:07:58,295 epoch 6 - iter 520/2606 - loss 0.04591580 - time (sec): 39.54 - samples/sec: 1911.03 - lr: 0.000027 - momentum: 0.000000 2023-10-15 20:08:16,820 epoch 6 - iter 780/2606 - loss 0.04497115 - time (sec): 58.06 - samples/sec: 1917.34 - lr: 0.000026 - momentum: 0.000000 2023-10-15 20:08:36,677 epoch 6 - iter 1040/2606 - loss 0.04222920 - time (sec): 77.92 - samples/sec: 1932.37 - lr: 0.000026 - momentum: 0.000000 2023-10-15 20:08:55,221 epoch 6 - iter 1300/2606 - loss 0.04176238 - time (sec): 96.46 - samples/sec: 1935.86 - lr: 0.000025 - momentum: 0.000000 2023-10-15 20:09:14,166 epoch 6 - iter 1560/2606 - loss 0.04201919 - time (sec): 115.41 - samples/sec: 1930.35 - lr: 0.000024 - momentum: 0.000000 2023-10-15 20:09:32,322 epoch 6 - iter 1820/2606 - loss 0.04211214 - time (sec): 133.57 - samples/sec: 1928.39 - lr: 0.000024 - momentum: 0.000000 2023-10-15 20:09:51,338 epoch 6 - iter 2080/2606 - loss 0.04276177 - time (sec): 152.58 - samples/sec: 1919.57 - lr: 0.000023 - momentum: 0.000000 2023-10-15 20:10:09,800 epoch 6 - iter 2340/2606 - loss 0.04345221 - time (sec): 171.04 - samples/sec: 1915.62 - lr: 0.000023 - momentum: 0.000000 2023-10-15 20:10:29,580 epoch 6 - iter 2600/2606 - loss 0.04434131 - time (sec): 190.82 - samples/sec: 1918.41 - lr: 0.000022 - momentum: 0.000000 2023-10-15 20:10:30,220 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:10:30,220 EPOCH 6 done: loss 0.0442 - lr: 0.000022 2023-10-15 20:10:38,461 DEV : loss 0.360347181558609 - f1-score (micro avg) 0.3831 2023-10-15 20:10:38,489 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:10:57,550 epoch 7 - iter 260/2606 - loss 0.03605703 - time (sec): 19.06 - samples/sec: 1967.84 - lr: 0.000022 - momentum: 0.000000 2023-10-15 20:11:17,462 epoch 7 - iter 520/2606 - loss 0.03233024 - time (sec): 38.97 - samples/sec: 1942.69 - lr: 0.000021 - momentum: 0.000000 2023-10-15 20:11:36,061 epoch 7 - iter 780/2606 - loss 0.03422854 - time (sec): 57.57 - samples/sec: 1930.25 - lr: 0.000021 - momentum: 0.000000 2023-10-15 20:11:55,421 epoch 7 - iter 1040/2606 - loss 0.03509936 - time (sec): 76.93 - samples/sec: 1887.52 - lr: 0.000020 - momentum: 0.000000 2023-10-15 20:12:15,268 epoch 7 - iter 1300/2606 - loss 0.03481716 - time (sec): 96.78 - samples/sec: 1892.73 - lr: 0.000019 - momentum: 0.000000 2023-10-15 20:12:33,527 epoch 7 - iter 1560/2606 - loss 0.03453842 - time (sec): 115.04 - samples/sec: 1897.80 - lr: 0.000019 - momentum: 0.000000 2023-10-15 20:12:53,305 epoch 7 - iter 1820/2606 - loss 0.03453028 - time (sec): 134.81 - samples/sec: 1910.27 - lr: 0.000018 - momentum: 0.000000 2023-10-15 20:13:11,312 epoch 7 - iter 2080/2606 - loss 0.03351666 - time (sec): 152.82 - samples/sec: 1910.11 - lr: 0.000018 - momentum: 0.000000 2023-10-15 20:13:30,700 epoch 7 - iter 2340/2606 - loss 0.03294077 - time (sec): 172.21 - samples/sec: 1917.56 - lr: 0.000017 - momentum: 0.000000 2023-10-15 20:13:49,306 epoch 7 - iter 2600/2606 - loss 0.03257111 - time (sec): 190.82 - samples/sec: 1920.47 - lr: 0.000017 - momentum: 0.000000 2023-10-15 20:13:49,775 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:13:49,775 EPOCH 7 done: loss 0.0326 - lr: 0.000017 2023-10-15 20:13:57,989 DEV : loss 0.37199294567108154 - f1-score (micro avg) 0.3858 2023-10-15 20:13:58,018 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:14:16,046 epoch 8 - iter 260/2606 - loss 0.02342308 - time (sec): 18.03 - samples/sec: 1889.83 - lr: 0.000016 - momentum: 0.000000 2023-10-15 20:14:35,427 epoch 8 - iter 520/2606 - loss 0.02364228 - time (sec): 37.41 - samples/sec: 1945.44 - lr: 0.000016 - momentum: 0.000000 2023-10-15 20:14:54,448 epoch 8 - iter 780/2606 - loss 0.02356941 - time (sec): 56.43 - samples/sec: 1926.14 - lr: 0.000015 - momentum: 0.000000 2023-10-15 20:15:13,454 epoch 8 - iter 1040/2606 - loss 0.02491977 - time (sec): 75.44 - samples/sec: 1918.27 - lr: 0.000014 - momentum: 0.000000 2023-10-15 20:15:32,549 epoch 8 - iter 1300/2606 - loss 0.02428659 - time (sec): 94.53 - samples/sec: 1932.58 - lr: 0.000014 - momentum: 0.000000 2023-10-15 20:15:52,160 epoch 8 - iter 1560/2606 - loss 0.02366101 - time (sec): 114.14 - samples/sec: 1931.16 - lr: 0.000013 - momentum: 0.000000 2023-10-15 20:16:10,784 epoch 8 - iter 1820/2606 - loss 0.02343244 - time (sec): 132.77 - samples/sec: 1938.37 - lr: 0.000013 - momentum: 0.000000 2023-10-15 20:16:30,328 epoch 8 - iter 2080/2606 - loss 0.02351442 - time (sec): 152.31 - samples/sec: 1925.81 - lr: 0.000012 - momentum: 0.000000 2023-10-15 20:16:49,792 epoch 8 - iter 2340/2606 - loss 0.02300749 - time (sec): 171.77 - samples/sec: 1923.52 - lr: 0.000012 - momentum: 0.000000 2023-10-15 20:17:08,499 epoch 8 - iter 2600/2606 - loss 0.02311972 - time (sec): 190.48 - samples/sec: 1924.21 - lr: 0.000011 - momentum: 0.000000 2023-10-15 20:17:08,968 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:17:08,968 EPOCH 8 done: loss 0.0231 - lr: 0.000011 2023-10-15 20:17:17,209 DEV : loss 0.38435670733451843 - f1-score (micro avg) 0.3878 2023-10-15 20:17:17,237 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:17:36,880 epoch 9 - iter 260/2606 - loss 0.01429002 - time (sec): 19.64 - samples/sec: 2021.11 - lr: 0.000011 - momentum: 0.000000 2023-10-15 20:17:56,464 epoch 9 - iter 520/2606 - loss 0.01483258 - time (sec): 39.23 - samples/sec: 1987.77 - lr: 0.000010 - momentum: 0.000000 2023-10-15 20:18:15,715 epoch 9 - iter 780/2606 - loss 0.01447465 - time (sec): 58.48 - samples/sec: 1973.65 - lr: 0.000009 - momentum: 0.000000 2023-10-15 20:18:34,051 epoch 9 - iter 1040/2606 - loss 0.01472942 - time (sec): 76.81 - samples/sec: 1971.06 - lr: 0.000009 - momentum: 0.000000 2023-10-15 20:18:52,832 epoch 9 - iter 1300/2606 - loss 0.01528011 - time (sec): 95.59 - samples/sec: 1965.67 - lr: 0.000008 - momentum: 0.000000 2023-10-15 20:19:10,916 epoch 9 - iter 1560/2606 - loss 0.01518566 - time (sec): 113.68 - samples/sec: 1940.69 - lr: 0.000008 - momentum: 0.000000 2023-10-15 20:19:30,000 epoch 9 - iter 1820/2606 - loss 0.01575329 - time (sec): 132.76 - samples/sec: 1940.91 - lr: 0.000007 - momentum: 0.000000 2023-10-15 20:19:48,598 epoch 9 - iter 2080/2606 - loss 0.01545732 - time (sec): 151.36 - samples/sec: 1943.70 - lr: 0.000007 - momentum: 0.000000 2023-10-15 20:20:07,608 epoch 9 - iter 2340/2606 - loss 0.01535373 - time (sec): 170.37 - samples/sec: 1939.88 - lr: 0.000006 - momentum: 0.000000 2023-10-15 20:20:26,963 epoch 9 - iter 2600/2606 - loss 0.01534271 - time (sec): 189.72 - samples/sec: 1933.83 - lr: 0.000006 - momentum: 0.000000 2023-10-15 20:20:27,294 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:20:27,295 EPOCH 9 done: loss 0.0153 - lr: 0.000006 2023-10-15 20:20:35,652 DEV : loss 0.44299206137657166 - f1-score (micro avg) 0.3749 2023-10-15 20:20:35,697 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:20:53,742 epoch 10 - iter 260/2606 - loss 0.01007017 - time (sec): 18.04 - samples/sec: 1966.73 - lr: 0.000005 - momentum: 0.000000 2023-10-15 20:21:11,849 epoch 10 - iter 520/2606 - loss 0.00872718 - time (sec): 36.15 - samples/sec: 1916.23 - lr: 0.000004 - momentum: 0.000000 2023-10-15 20:21:30,620 epoch 10 - iter 780/2606 - loss 0.00877023 - time (sec): 54.92 - samples/sec: 1938.58 - lr: 0.000004 - momentum: 0.000000 2023-10-15 20:21:48,942 epoch 10 - iter 1040/2606 - loss 0.00900546 - time (sec): 73.24 - samples/sec: 1944.56 - lr: 0.000003 - momentum: 0.000000 2023-10-15 20:22:07,349 epoch 10 - iter 1300/2606 - loss 0.00925451 - time (sec): 91.65 - samples/sec: 1947.96 - lr: 0.000003 - momentum: 0.000000 2023-10-15 20:22:26,240 epoch 10 - iter 1560/2606 - loss 0.00955119 - time (sec): 110.54 - samples/sec: 1945.38 - lr: 0.000002 - momentum: 0.000000 2023-10-15 20:22:45,017 epoch 10 - iter 1820/2606 - loss 0.00958418 - time (sec): 129.32 - samples/sec: 1949.64 - lr: 0.000002 - momentum: 0.000000 2023-10-15 20:23:04,810 epoch 10 - iter 2080/2606 - loss 0.00978376 - time (sec): 149.11 - samples/sec: 1952.87 - lr: 0.000001 - momentum: 0.000000 2023-10-15 20:23:23,622 epoch 10 - iter 2340/2606 - loss 0.00995251 - time (sec): 167.92 - samples/sec: 1946.93 - lr: 0.000001 - momentum: 0.000000 2023-10-15 20:23:43,809 epoch 10 - iter 2600/2606 - loss 0.01001595 - time (sec): 188.11 - samples/sec: 1948.65 - lr: 0.000000 - momentum: 0.000000 2023-10-15 20:23:44,199 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:23:44,200 EPOCH 10 done: loss 0.0100 - lr: 0.000000 2023-10-15 20:23:53,258 DEV : loss 0.4734904170036316 - f1-score (micro avg) 0.3815 2023-10-15 20:23:53,688 ---------------------------------------------------------------------------------------------------- 2023-10-15 20:23:53,689 Loading model from best epoch ... 2023-10-15 20:23:55,224 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd 2023-10-15 20:24:11,965 Results: - F-score (micro) 0.446 - F-score (macro) 0.2767 - Accuracy 0.2916 By class: precision recall f1-score support LOC 0.4970 0.6063 0.5462 1214 PER 0.4008 0.3651 0.3821 808 ORG 0.2091 0.1558 0.1786 353 HumanProd 0.0000 0.0000 0.0000 15 micro avg 0.4379 0.4544 0.4460 2390 macro avg 0.2767 0.2818 0.2767 2390 weighted avg 0.4188 0.4544 0.4330 2390 2023-10-15 20:24:11,966 ----------------------------------------------------------------------------------------------------