| 2023-10-15 14:35:18,166 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:35:18,167 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 14:35:18,167 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:35:18,167 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 14:35:18,167 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:35:18,167 Train: 20847 sentences | |
| 2023-10-15 14:35:18,167 (train_with_dev=False, train_with_test=False) | |
| 2023-10-15 14:35:18,167 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:35:18,167 Training Params: | |
| 2023-10-15 14:35:18,167 - learning_rate: "5e-05" | |
| 2023-10-15 14:35:18,167 - mini_batch_size: "8" | |
| 2023-10-15 14:35:18,167 - max_epochs: "10" | |
| 2023-10-15 14:35:18,167 - shuffle: "True" | |
| 2023-10-15 14:35:18,167 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:35:18,167 Plugins: | |
| 2023-10-15 14:35:18,167 - LinearScheduler | warmup_fraction: '0.1' | |
| 2023-10-15 14:35:18,167 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:35:18,167 Final evaluation on model from best epoch (best-model.pt) | |
| 2023-10-15 14:35:18,167 - metric: "('micro avg', 'f1-score')" | |
| 2023-10-15 14:35:18,167 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:35:18,167 Computation: | |
| 2023-10-15 14:35:18,168 - compute on device: cuda:0 | |
| 2023-10-15 14:35:18,168 - embedding storage: none | |
| 2023-10-15 14:35:18,168 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:35:18,168 Model training base path: "hmbench-newseye/de-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2" | |
| 2023-10-15 14:35:18,168 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:35:18,168 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:35:37,481 epoch 1 - iter 260/2606 - loss 1.70398800 - time (sec): 19.31 - samples/sec: 1954.37 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-15 14:35:56,703 epoch 1 - iter 520/2606 - loss 1.04417068 - time (sec): 38.53 - samples/sec: 1936.56 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-15 14:36:16,384 epoch 1 - iter 780/2606 - loss 0.78595655 - time (sec): 58.22 - samples/sec: 1966.31 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-15 14:36:35,005 epoch 1 - iter 1040/2606 - loss 0.66742617 - time (sec): 76.84 - samples/sec: 1944.65 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-15 14:36:53,448 epoch 1 - iter 1300/2606 - loss 0.58275769 - time (sec): 95.28 - samples/sec: 1954.32 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-15 14:37:12,505 epoch 1 - iter 1560/2606 - loss 0.52348496 - time (sec): 114.34 - samples/sec: 1950.56 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-15 14:37:31,309 epoch 1 - iter 1820/2606 - loss 0.47828591 - time (sec): 133.14 - samples/sec: 1943.65 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-15 14:37:48,903 epoch 1 - iter 2080/2606 - loss 0.44960989 - time (sec): 150.73 - samples/sec: 1945.45 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-15 14:38:07,725 epoch 1 - iter 2340/2606 - loss 0.41952533 - time (sec): 169.56 - samples/sec: 1948.33 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-15 14:38:26,224 epoch 1 - iter 2600/2606 - loss 0.39643307 - time (sec): 188.06 - samples/sec: 1948.36 - lr: 0.000050 - momentum: 0.000000 | |
| 2023-10-15 14:38:26,731 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:38:26,731 EPOCH 1 done: loss 0.3961 - lr: 0.000050 | |
| 2023-10-15 14:38:32,575 DEV : loss 0.1703825443983078 - f1-score (micro avg) 0.2723 | |
| 2023-10-15 14:38:32,602 saving best model | |
| 2023-10-15 14:38:32,932 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:38:51,535 epoch 2 - iter 260/2606 - loss 0.15333555 - time (sec): 18.60 - samples/sec: 1881.05 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-15 14:39:10,089 epoch 2 - iter 520/2606 - loss 0.15721909 - time (sec): 37.16 - samples/sec: 1918.36 - lr: 0.000049 - momentum: 0.000000 | |
| 2023-10-15 14:39:29,112 epoch 2 - iter 780/2606 - loss 0.16754978 - time (sec): 56.18 - samples/sec: 1938.67 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-15 14:39:47,893 epoch 2 - iter 1040/2606 - loss 0.16366363 - time (sec): 74.96 - samples/sec: 1932.23 - lr: 0.000048 - momentum: 0.000000 | |
| 2023-10-15 14:40:06,293 epoch 2 - iter 1300/2606 - loss 0.15907525 - time (sec): 93.36 - samples/sec: 1946.72 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-15 14:40:25,920 epoch 2 - iter 1560/2606 - loss 0.15929231 - time (sec): 112.99 - samples/sec: 1946.11 - lr: 0.000047 - momentum: 0.000000 | |
| 2023-10-15 14:40:44,848 epoch 2 - iter 1820/2606 - loss 0.16228991 - time (sec): 131.91 - samples/sec: 1940.93 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-15 14:41:03,759 epoch 2 - iter 2080/2606 - loss 0.15903749 - time (sec): 150.83 - samples/sec: 1951.85 - lr: 0.000046 - momentum: 0.000000 | |
| 2023-10-15 14:41:22,416 epoch 2 - iter 2340/2606 - loss 0.15780880 - time (sec): 169.48 - samples/sec: 1948.20 - lr: 0.000045 - momentum: 0.000000 | |
| 2023-10-15 14:41:40,791 epoch 2 - iter 2600/2606 - loss 0.15594055 - time (sec): 187.86 - samples/sec: 1950.59 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-15 14:41:41,236 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:41:41,236 EPOCH 2 done: loss 0.1559 - lr: 0.000044 | |
| 2023-10-15 14:41:50,342 DEV : loss 0.15193568170070648 - f1-score (micro avg) 0.3847 | |
| 2023-10-15 14:41:50,371 saving best model | |
| 2023-10-15 14:41:50,831 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:42:10,499 epoch 3 - iter 260/2606 - loss 0.10386961 - time (sec): 19.66 - samples/sec: 1969.96 - lr: 0.000044 - momentum: 0.000000 | |
| 2023-10-15 14:42:29,883 epoch 3 - iter 520/2606 - loss 0.10697405 - time (sec): 39.05 - samples/sec: 1984.47 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-15 14:42:48,549 epoch 3 - iter 780/2606 - loss 0.11066127 - time (sec): 57.71 - samples/sec: 1984.71 - lr: 0.000043 - momentum: 0.000000 | |
| 2023-10-15 14:43:06,906 epoch 3 - iter 1040/2606 - loss 0.11236558 - time (sec): 76.07 - samples/sec: 1974.96 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-15 14:43:26,140 epoch 3 - iter 1300/2606 - loss 0.10971979 - time (sec): 95.31 - samples/sec: 1975.49 - lr: 0.000042 - momentum: 0.000000 | |
| 2023-10-15 14:43:44,182 epoch 3 - iter 1560/2606 - loss 0.10942417 - time (sec): 113.35 - samples/sec: 1964.00 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-15 14:44:02,842 epoch 3 - iter 1820/2606 - loss 0.11037765 - time (sec): 132.01 - samples/sec: 1952.43 - lr: 0.000041 - momentum: 0.000000 | |
| 2023-10-15 14:44:21,244 epoch 3 - iter 2080/2606 - loss 0.11002920 - time (sec): 150.41 - samples/sec: 1947.68 - lr: 0.000040 - momentum: 0.000000 | |
| 2023-10-15 14:44:40,649 epoch 3 - iter 2340/2606 - loss 0.10906780 - time (sec): 169.81 - samples/sec: 1944.70 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-15 14:44:58,744 epoch 3 - iter 2600/2606 - loss 0.10926208 - time (sec): 187.91 - samples/sec: 1947.44 - lr: 0.000039 - momentum: 0.000000 | |
| 2023-10-15 14:44:59,407 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:44:59,407 EPOCH 3 done: loss 0.1090 - lr: 0.000039 | |
| 2023-10-15 14:45:08,482 DEV : loss 0.25894129276275635 - f1-score (micro avg) 0.3382 | |
| 2023-10-15 14:45:08,509 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:45:28,158 epoch 4 - iter 260/2606 - loss 0.07515214 - time (sec): 19.65 - samples/sec: 1967.37 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-15 14:45:46,029 epoch 4 - iter 520/2606 - loss 0.07733261 - time (sec): 37.52 - samples/sec: 1927.84 - lr: 0.000038 - momentum: 0.000000 | |
| 2023-10-15 14:46:04,834 epoch 4 - iter 780/2606 - loss 0.07656362 - time (sec): 56.32 - samples/sec: 1944.62 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-15 14:46:23,015 epoch 4 - iter 1040/2606 - loss 0.07700269 - time (sec): 74.50 - samples/sec: 1947.83 - lr: 0.000037 - momentum: 0.000000 | |
| 2023-10-15 14:46:41,548 epoch 4 - iter 1300/2606 - loss 0.07567123 - time (sec): 93.04 - samples/sec: 1954.80 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-15 14:47:00,617 epoch 4 - iter 1560/2606 - loss 0.07597083 - time (sec): 112.11 - samples/sec: 1956.79 - lr: 0.000036 - momentum: 0.000000 | |
| 2023-10-15 14:47:19,328 epoch 4 - iter 1820/2606 - loss 0.07763210 - time (sec): 130.82 - samples/sec: 1952.80 - lr: 0.000035 - momentum: 0.000000 | |
| 2023-10-15 14:47:37,968 epoch 4 - iter 2080/2606 - loss 0.07854227 - time (sec): 149.46 - samples/sec: 1945.80 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-15 14:47:57,679 epoch 4 - iter 2340/2606 - loss 0.07872444 - time (sec): 169.17 - samples/sec: 1944.39 - lr: 0.000034 - momentum: 0.000000 | |
| 2023-10-15 14:48:16,986 epoch 4 - iter 2600/2606 - loss 0.07824603 - time (sec): 188.48 - samples/sec: 1945.35 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-15 14:48:17,398 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:48:17,398 EPOCH 4 done: loss 0.0782 - lr: 0.000033 | |
| 2023-10-15 14:48:26,390 DEV : loss 0.2620859742164612 - f1-score (micro avg) 0.3624 | |
| 2023-10-15 14:48:26,417 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:48:44,955 epoch 5 - iter 260/2606 - loss 0.05400958 - time (sec): 18.54 - samples/sec: 1982.06 - lr: 0.000033 - momentum: 0.000000 | |
| 2023-10-15 14:49:05,767 epoch 5 - iter 520/2606 - loss 0.05211167 - time (sec): 39.35 - samples/sec: 1984.42 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-15 14:49:24,232 epoch 5 - iter 780/2606 - loss 0.05301335 - time (sec): 57.81 - samples/sec: 1966.71 - lr: 0.000032 - momentum: 0.000000 | |
| 2023-10-15 14:49:43,982 epoch 5 - iter 1040/2606 - loss 0.05569211 - time (sec): 77.56 - samples/sec: 1963.80 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-15 14:50:02,076 epoch 5 - iter 1300/2606 - loss 0.05528294 - time (sec): 95.66 - samples/sec: 1962.93 - lr: 0.000031 - momentum: 0.000000 | |
| 2023-10-15 14:50:21,420 epoch 5 - iter 1560/2606 - loss 0.05556370 - time (sec): 115.00 - samples/sec: 1962.65 - lr: 0.000030 - momentum: 0.000000 | |
| 2023-10-15 14:50:40,077 epoch 5 - iter 1820/2606 - loss 0.05564560 - time (sec): 133.66 - samples/sec: 1948.82 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-15 14:50:58,218 epoch 5 - iter 2080/2606 - loss 0.05629388 - time (sec): 151.80 - samples/sec: 1948.56 - lr: 0.000029 - momentum: 0.000000 | |
| 2023-10-15 14:51:17,603 epoch 5 - iter 2340/2606 - loss 0.05874671 - time (sec): 171.18 - samples/sec: 1947.78 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-15 14:51:35,349 epoch 5 - iter 2600/2606 - loss 0.05895855 - time (sec): 188.93 - samples/sec: 1941.44 - lr: 0.000028 - momentum: 0.000000 | |
| 2023-10-15 14:51:35,736 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:51:35,737 EPOCH 5 done: loss 0.0589 - lr: 0.000028 | |
| 2023-10-15 14:51:43,968 DEV : loss 0.3398889899253845 - f1-score (micro avg) 0.3363 | |
| 2023-10-15 14:51:43,998 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:52:04,548 epoch 6 - iter 260/2606 - loss 0.03389900 - time (sec): 20.55 - samples/sec: 1875.95 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-15 14:52:22,918 epoch 6 - iter 520/2606 - loss 0.04212432 - time (sec): 38.92 - samples/sec: 1912.60 - lr: 0.000027 - momentum: 0.000000 | |
| 2023-10-15 14:52:41,566 epoch 6 - iter 780/2606 - loss 0.04368331 - time (sec): 57.57 - samples/sec: 1937.32 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-15 14:52:59,861 epoch 6 - iter 1040/2606 - loss 0.04514083 - time (sec): 75.86 - samples/sec: 1920.95 - lr: 0.000026 - momentum: 0.000000 | |
| 2023-10-15 14:53:19,505 epoch 6 - iter 1300/2606 - loss 0.04442191 - time (sec): 95.51 - samples/sec: 1925.06 - lr: 0.000025 - momentum: 0.000000 | |
| 2023-10-15 14:53:38,680 epoch 6 - iter 1560/2606 - loss 0.04517608 - time (sec): 114.68 - samples/sec: 1930.25 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-15 14:53:59,139 epoch 6 - iter 1820/2606 - loss 0.04366513 - time (sec): 135.14 - samples/sec: 1934.69 - lr: 0.000024 - momentum: 0.000000 | |
| 2023-10-15 14:54:17,058 epoch 6 - iter 2080/2606 - loss 0.04407085 - time (sec): 153.06 - samples/sec: 1930.85 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-15 14:54:35,685 epoch 6 - iter 2340/2606 - loss 0.04345890 - time (sec): 171.69 - samples/sec: 1930.29 - lr: 0.000023 - momentum: 0.000000 | |
| 2023-10-15 14:54:54,040 epoch 6 - iter 2600/2606 - loss 0.04335818 - time (sec): 190.04 - samples/sec: 1928.19 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-15 14:54:54,516 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:54:54,517 EPOCH 6 done: loss 0.0432 - lr: 0.000022 | |
| 2023-10-15 14:55:03,040 DEV : loss 0.3315297067165375 - f1-score (micro avg) 0.3722 | |
| 2023-10-15 14:55:03,072 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:55:21,053 epoch 7 - iter 260/2606 - loss 0.02363314 - time (sec): 17.98 - samples/sec: 1876.09 - lr: 0.000022 - momentum: 0.000000 | |
| 2023-10-15 14:55:39,507 epoch 7 - iter 520/2606 - loss 0.02903783 - time (sec): 36.43 - samples/sec: 1901.90 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-15 14:55:59,059 epoch 7 - iter 780/2606 - loss 0.03146854 - time (sec): 55.99 - samples/sec: 1930.42 - lr: 0.000021 - momentum: 0.000000 | |
| 2023-10-15 14:56:17,964 epoch 7 - iter 1040/2606 - loss 0.03278017 - time (sec): 74.89 - samples/sec: 1903.68 - lr: 0.000020 - momentum: 0.000000 | |
| 2023-10-15 14:56:35,796 epoch 7 - iter 1300/2606 - loss 0.03388944 - time (sec): 92.72 - samples/sec: 1906.56 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-15 14:56:54,161 epoch 7 - iter 1560/2606 - loss 0.03255298 - time (sec): 111.09 - samples/sec: 1924.34 - lr: 0.000019 - momentum: 0.000000 | |
| 2023-10-15 14:57:13,152 epoch 7 - iter 1820/2606 - loss 0.03196894 - time (sec): 130.08 - samples/sec: 1927.44 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-15 14:57:32,652 epoch 7 - iter 2080/2606 - loss 0.03065448 - time (sec): 149.58 - samples/sec: 1936.22 - lr: 0.000018 - momentum: 0.000000 | |
| 2023-10-15 14:57:52,137 epoch 7 - iter 2340/2606 - loss 0.02973814 - time (sec): 169.06 - samples/sec: 1942.74 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-15 14:58:11,366 epoch 7 - iter 2600/2606 - loss 0.03090218 - time (sec): 188.29 - samples/sec: 1943.06 - lr: 0.000017 - momentum: 0.000000 | |
| 2023-10-15 14:58:12,154 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:58:12,154 EPOCH 7 done: loss 0.0309 - lr: 0.000017 | |
| 2023-10-15 14:58:20,444 DEV : loss 0.4454517364501953 - f1-score (micro avg) 0.362 | |
| 2023-10-15 14:58:20,472 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 14:58:39,045 epoch 8 - iter 260/2606 - loss 0.01715014 - time (sec): 18.57 - samples/sec: 2053.83 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-15 14:58:57,659 epoch 8 - iter 520/2606 - loss 0.01871143 - time (sec): 37.19 - samples/sec: 2016.24 - lr: 0.000016 - momentum: 0.000000 | |
| 2023-10-15 14:59:15,612 epoch 8 - iter 780/2606 - loss 0.02054380 - time (sec): 55.14 - samples/sec: 1967.69 - lr: 0.000015 - momentum: 0.000000 | |
| 2023-10-15 14:59:34,079 epoch 8 - iter 1040/2606 - loss 0.02016721 - time (sec): 73.61 - samples/sec: 1969.24 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-15 14:59:51,734 epoch 8 - iter 1300/2606 - loss 0.02166010 - time (sec): 91.26 - samples/sec: 1959.92 - lr: 0.000014 - momentum: 0.000000 | |
| 2023-10-15 15:00:10,857 epoch 8 - iter 1560/2606 - loss 0.02170611 - time (sec): 110.38 - samples/sec: 1952.41 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-15 15:00:31,030 epoch 8 - iter 1820/2606 - loss 0.02156229 - time (sec): 130.56 - samples/sec: 1959.10 - lr: 0.000013 - momentum: 0.000000 | |
| 2023-10-15 15:00:50,568 epoch 8 - iter 2080/2606 - loss 0.02342631 - time (sec): 150.09 - samples/sec: 1959.29 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-15 15:01:08,797 epoch 8 - iter 2340/2606 - loss 0.02326894 - time (sec): 168.32 - samples/sec: 1958.13 - lr: 0.000012 - momentum: 0.000000 | |
| 2023-10-15 15:01:27,579 epoch 8 - iter 2600/2606 - loss 0.02326670 - time (sec): 187.11 - samples/sec: 1958.63 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-15 15:01:28,005 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 15:01:28,005 EPOCH 8 done: loss 0.0233 - lr: 0.000011 | |
| 2023-10-15 15:01:36,293 DEV : loss 0.5098573565483093 - f1-score (micro avg) 0.3418 | |
| 2023-10-15 15:01:36,321 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 15:01:53,995 epoch 9 - iter 260/2606 - loss 0.01668464 - time (sec): 17.67 - samples/sec: 1958.94 - lr: 0.000011 - momentum: 0.000000 | |
| 2023-10-15 15:02:11,655 epoch 9 - iter 520/2606 - loss 0.01529131 - time (sec): 35.33 - samples/sec: 1937.99 - lr: 0.000010 - momentum: 0.000000 | |
| 2023-10-15 15:02:31,960 epoch 9 - iter 780/2606 - loss 0.01417848 - time (sec): 55.64 - samples/sec: 1944.47 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-15 15:02:51,522 epoch 9 - iter 1040/2606 - loss 0.01420376 - time (sec): 75.20 - samples/sec: 1930.86 - lr: 0.000009 - momentum: 0.000000 | |
| 2023-10-15 15:03:11,085 epoch 9 - iter 1300/2606 - loss 0.01442784 - time (sec): 94.76 - samples/sec: 1931.52 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-15 15:03:30,402 epoch 9 - iter 1560/2606 - loss 0.01622053 - time (sec): 114.08 - samples/sec: 1925.97 - lr: 0.000008 - momentum: 0.000000 | |
| 2023-10-15 15:03:49,697 epoch 9 - iter 1820/2606 - loss 0.01569716 - time (sec): 133.37 - samples/sec: 1929.46 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-15 15:04:08,230 epoch 9 - iter 2080/2606 - loss 0.01571005 - time (sec): 151.91 - samples/sec: 1933.51 - lr: 0.000007 - momentum: 0.000000 | |
| 2023-10-15 15:04:28,322 epoch 9 - iter 2340/2606 - loss 0.01553269 - time (sec): 172.00 - samples/sec: 1921.80 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-15 15:04:47,584 epoch 9 - iter 2600/2606 - loss 0.01532827 - time (sec): 191.26 - samples/sec: 1919.20 - lr: 0.000006 - momentum: 0.000000 | |
| 2023-10-15 15:04:47,880 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 15:04:47,880 EPOCH 9 done: loss 0.0153 - lr: 0.000006 | |
| 2023-10-15 15:04:56,163 DEV : loss 0.4624152183532715 - f1-score (micro avg) 0.3618 | |
| 2023-10-15 15:04:56,192 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 15:05:15,338 epoch 10 - iter 260/2606 - loss 0.00882181 - time (sec): 19.14 - samples/sec: 1930.56 - lr: 0.000005 - momentum: 0.000000 | |
| 2023-10-15 15:05:34,619 epoch 10 - iter 520/2606 - loss 0.01164627 - time (sec): 38.43 - samples/sec: 1921.16 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-15 15:05:54,353 epoch 10 - iter 780/2606 - loss 0.01191395 - time (sec): 58.16 - samples/sec: 1945.73 - lr: 0.000004 - momentum: 0.000000 | |
| 2023-10-15 15:06:13,183 epoch 10 - iter 1040/2606 - loss 0.01088257 - time (sec): 76.99 - samples/sec: 1947.12 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-15 15:06:30,946 epoch 10 - iter 1300/2606 - loss 0.01110488 - time (sec): 94.75 - samples/sec: 1943.64 - lr: 0.000003 - momentum: 0.000000 | |
| 2023-10-15 15:06:49,375 epoch 10 - iter 1560/2606 - loss 0.01164780 - time (sec): 113.18 - samples/sec: 1944.76 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-15 15:07:09,170 epoch 10 - iter 1820/2606 - loss 0.01131708 - time (sec): 132.98 - samples/sec: 1941.93 - lr: 0.000002 - momentum: 0.000000 | |
| 2023-10-15 15:07:27,445 epoch 10 - iter 2080/2606 - loss 0.01073956 - time (sec): 151.25 - samples/sec: 1940.29 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-15 15:07:46,629 epoch 10 - iter 2340/2606 - loss 0.01037212 - time (sec): 170.44 - samples/sec: 1938.05 - lr: 0.000001 - momentum: 0.000000 | |
| 2023-10-15 15:08:05,232 epoch 10 - iter 2600/2606 - loss 0.01025554 - time (sec): 189.04 - samples/sec: 1937.16 - lr: 0.000000 - momentum: 0.000000 | |
| 2023-10-15 15:08:05,747 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 15:08:05,747 EPOCH 10 done: loss 0.0102 - lr: 0.000000 | |
| 2023-10-15 15:08:14,814 DEV : loss 0.4725865125656128 - f1-score (micro avg) 0.3743 | |
| 2023-10-15 15:08:15,190 ---------------------------------------------------------------------------------------------------- | |
| 2023-10-15 15:08:15,191 Loading model from best epoch ... | |
| 2023-10-15 15:08:16,646 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 15:08:31,788 | |
| Results: | |
| - F-score (micro) 0.4127 | |
| - F-score (macro) 0.2811 | |
| - Accuracy 0.2633 | |
| By class: | |
| precision recall f1-score support | |
| LOC 0.5280 0.4745 0.4998 1214 | |
| PER 0.3436 0.4295 0.3817 808 | |
| ORG 0.2143 0.2805 0.2429 353 | |
| HumanProd 0.0000 0.0000 0.0000 15 | |
| micro avg 0.3988 0.4276 0.4127 2390 | |
| macro avg 0.2715 0.2961 0.2811 2390 | |
| weighted avg 0.4160 0.4276 0.4188 2390 | |
| 2023-10-15 15:08:31,788 ---------------------------------------------------------------------------------------------------- | |