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2023-10-15 00:03:50,981 ----------------------------------------------------------------------------------------------------
2023-10-15 00:03:50,982 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()
)"
2023-10-15 00:03:50,982 ----------------------------------------------------------------------------------------------------
2023-10-15 00:03:50,982 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
2023-10-15 00:03:50,982 ----------------------------------------------------------------------------------------------------
2023-10-15 00:03:50,982 Train: 14465 sentences
2023-10-15 00:03:50,982 (train_with_dev=False, train_with_test=False)
2023-10-15 00:03:50,982 ----------------------------------------------------------------------------------------------------
2023-10-15 00:03:50,982 Training Params:
2023-10-15 00:03:50,982 - learning_rate: "5e-05"
2023-10-15 00:03:50,982 - mini_batch_size: "4"
2023-10-15 00:03:50,982 - max_epochs: "10"
2023-10-15 00:03:50,982 - shuffle: "True"
2023-10-15 00:03:50,982 ----------------------------------------------------------------------------------------------------
2023-10-15 00:03:50,983 Plugins:
2023-10-15 00:03:50,983 - LinearScheduler | warmup_fraction: '0.1'
2023-10-15 00:03:50,983 ----------------------------------------------------------------------------------------------------
2023-10-15 00:03:50,983 Final evaluation on model from best epoch (best-model.pt)
2023-10-15 00:03:50,983 - metric: "('micro avg', 'f1-score')"
2023-10-15 00:03:50,983 ----------------------------------------------------------------------------------------------------
2023-10-15 00:03:50,983 Computation:
2023-10-15 00:03:50,983 - compute on device: cuda:0
2023-10-15 00:03:50,983 - embedding storage: none
2023-10-15 00:03:50,983 ----------------------------------------------------------------------------------------------------
2023-10-15 00:03:50,983 Model training base path: "hmbench-letemps/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
2023-10-15 00:03:50,983 ----------------------------------------------------------------------------------------------------
2023-10-15 00:03:50,983 ----------------------------------------------------------------------------------------------------
2023-10-15 00:04:07,798 epoch 1 - iter 361/3617 - loss 1.14190309 - time (sec): 16.81 - samples/sec: 2251.69 - lr: 0.000005 - momentum: 0.000000
2023-10-15 00:04:24,690 epoch 1 - iter 722/3617 - loss 0.66100745 - time (sec): 33.71 - samples/sec: 2228.99 - lr: 0.000010 - momentum: 0.000000
2023-10-15 00:04:41,045 epoch 1 - iter 1083/3617 - loss 0.49172746 - time (sec): 50.06 - samples/sec: 2264.99 - lr: 0.000015 - momentum: 0.000000
2023-10-15 00:04:57,113 epoch 1 - iter 1444/3617 - loss 0.40440624 - time (sec): 66.13 - samples/sec: 2279.37 - lr: 0.000020 - momentum: 0.000000
2023-10-15 00:05:13,378 epoch 1 - iter 1805/3617 - loss 0.34944834 - time (sec): 82.39 - samples/sec: 2296.50 - lr: 0.000025 - momentum: 0.000000
2023-10-15 00:05:29,700 epoch 1 - iter 2166/3617 - loss 0.31177626 - time (sec): 98.72 - samples/sec: 2299.63 - lr: 0.000030 - momentum: 0.000000
2023-10-15 00:05:45,946 epoch 1 - iter 2527/3617 - loss 0.28399617 - time (sec): 114.96 - samples/sec: 2296.61 - lr: 0.000035 - momentum: 0.000000
2023-10-15 00:06:02,585 epoch 1 - iter 2888/3617 - loss 0.26224385 - time (sec): 131.60 - samples/sec: 2294.38 - lr: 0.000040 - momentum: 0.000000
2023-10-15 00:06:19,442 epoch 1 - iter 3249/3617 - loss 0.24569678 - time (sec): 148.46 - samples/sec: 2296.71 - lr: 0.000045 - momentum: 0.000000
2023-10-15 00:06:36,736 epoch 1 - iter 3610/3617 - loss 0.23207698 - time (sec): 165.75 - samples/sec: 2288.12 - lr: 0.000050 - momentum: 0.000000
2023-10-15 00:06:37,043 ----------------------------------------------------------------------------------------------------
2023-10-15 00:06:37,044 EPOCH 1 done: loss 0.2318 - lr: 0.000050
2023-10-15 00:06:41,816 DEV : loss 0.14234784245491028 - f1-score (micro avg) 0.5628
2023-10-15 00:06:41,846 saving best model
2023-10-15 00:06:42,204 ----------------------------------------------------------------------------------------------------
2023-10-15 00:06:58,610 epoch 2 - iter 361/3617 - loss 0.11043076 - time (sec): 16.40 - samples/sec: 2369.45 - lr: 0.000049 - momentum: 0.000000
2023-10-15 00:07:14,868 epoch 2 - iter 722/3617 - loss 0.10307503 - time (sec): 32.66 - samples/sec: 2344.00 - lr: 0.000049 - momentum: 0.000000
2023-10-15 00:07:31,128 epoch 2 - iter 1083/3617 - loss 0.10343443 - time (sec): 48.92 - samples/sec: 2334.59 - lr: 0.000048 - momentum: 0.000000
2023-10-15 00:07:47,512 epoch 2 - iter 1444/3617 - loss 0.10211674 - time (sec): 65.31 - samples/sec: 2343.66 - lr: 0.000048 - momentum: 0.000000
2023-10-15 00:08:03,756 epoch 2 - iter 1805/3617 - loss 0.10329026 - time (sec): 81.55 - samples/sec: 2341.87 - lr: 0.000047 - momentum: 0.000000
2023-10-15 00:08:20,587 epoch 2 - iter 2166/3617 - loss 0.10243479 - time (sec): 98.38 - samples/sec: 2342.52 - lr: 0.000047 - momentum: 0.000000
2023-10-15 00:08:36,735 epoch 2 - iter 2527/3617 - loss 0.10394519 - time (sec): 114.53 - samples/sec: 2336.04 - lr: 0.000046 - momentum: 0.000000
2023-10-15 00:08:55,159 epoch 2 - iter 2888/3617 - loss 0.10245744 - time (sec): 132.95 - samples/sec: 2288.97 - lr: 0.000046 - momentum: 0.000000
2023-10-15 00:09:12,663 epoch 2 - iter 3249/3617 - loss 0.10371302 - time (sec): 150.46 - samples/sec: 2274.10 - lr: 0.000045 - momentum: 0.000000
2023-10-15 00:09:31,541 epoch 2 - iter 3610/3617 - loss 0.10357504 - time (sec): 169.34 - samples/sec: 2240.45 - lr: 0.000044 - momentum: 0.000000
2023-10-15 00:09:31,891 ----------------------------------------------------------------------------------------------------
2023-10-15 00:09:31,892 EPOCH 2 done: loss 0.1036 - lr: 0.000044
2023-10-15 00:09:38,248 DEV : loss 0.17834356427192688 - f1-score (micro avg) 0.6432
2023-10-15 00:09:38,279 saving best model
2023-10-15 00:09:38,784 ----------------------------------------------------------------------------------------------------
2023-10-15 00:09:56,999 epoch 3 - iter 361/3617 - loss 0.07560643 - time (sec): 18.21 - samples/sec: 2079.60 - lr: 0.000044 - momentum: 0.000000
2023-10-15 00:10:13,534 epoch 3 - iter 722/3617 - loss 0.08065039 - time (sec): 34.75 - samples/sec: 2214.64 - lr: 0.000043 - momentum: 0.000000
2023-10-15 00:10:29,627 epoch 3 - iter 1083/3617 - loss 0.08073724 - time (sec): 50.84 - samples/sec: 2235.86 - lr: 0.000043 - momentum: 0.000000
2023-10-15 00:10:46,011 epoch 3 - iter 1444/3617 - loss 0.08653947 - time (sec): 67.22 - samples/sec: 2250.71 - lr: 0.000042 - momentum: 0.000000
2023-10-15 00:11:02,212 epoch 3 - iter 1805/3617 - loss 0.08584021 - time (sec): 83.42 - samples/sec: 2249.12 - lr: 0.000042 - momentum: 0.000000
2023-10-15 00:11:19,576 epoch 3 - iter 2166/3617 - loss 0.08541915 - time (sec): 100.79 - samples/sec: 2241.64 - lr: 0.000041 - momentum: 0.000000
2023-10-15 00:11:36,404 epoch 3 - iter 2527/3617 - loss 0.08471573 - time (sec): 117.62 - samples/sec: 2245.13 - lr: 0.000041 - momentum: 0.000000
2023-10-15 00:11:53,556 epoch 3 - iter 2888/3617 - loss 0.08579463 - time (sec): 134.77 - samples/sec: 2241.78 - lr: 0.000040 - momentum: 0.000000
2023-10-15 00:12:11,257 epoch 3 - iter 3249/3617 - loss 0.08564418 - time (sec): 152.47 - samples/sec: 2226.98 - lr: 0.000039 - momentum: 0.000000
2023-10-15 00:12:30,255 epoch 3 - iter 3610/3617 - loss 0.08383490 - time (sec): 171.47 - samples/sec: 2212.68 - lr: 0.000039 - momentum: 0.000000
2023-10-15 00:12:30,615 ----------------------------------------------------------------------------------------------------
2023-10-15 00:12:30,615 EPOCH 3 done: loss 0.0842 - lr: 0.000039
2023-10-15 00:12:36,289 DEV : loss 0.21627244353294373 - f1-score (micro avg) 0.6109
2023-10-15 00:12:36,341 ----------------------------------------------------------------------------------------------------
2023-10-15 00:12:55,272 epoch 4 - iter 361/3617 - loss 0.06671701 - time (sec): 18.93 - samples/sec: 2004.88 - lr: 0.000038 - momentum: 0.000000
2023-10-15 00:13:15,637 epoch 4 - iter 722/3617 - loss 0.06317418 - time (sec): 39.29 - samples/sec: 1904.94 - lr: 0.000038 - momentum: 0.000000
2023-10-15 00:13:34,686 epoch 4 - iter 1083/3617 - loss 0.06492905 - time (sec): 58.34 - samples/sec: 1969.17 - lr: 0.000037 - momentum: 0.000000
2023-10-15 00:13:52,578 epoch 4 - iter 1444/3617 - loss 0.06403201 - time (sec): 76.24 - samples/sec: 2000.12 - lr: 0.000037 - momentum: 0.000000
2023-10-15 00:14:11,684 epoch 4 - iter 1805/3617 - loss 0.06566880 - time (sec): 95.34 - samples/sec: 1994.66 - lr: 0.000036 - momentum: 0.000000
2023-10-15 00:14:30,587 epoch 4 - iter 2166/3617 - loss 0.06340193 - time (sec): 114.24 - samples/sec: 1994.97 - lr: 0.000036 - momentum: 0.000000
2023-10-15 00:14:49,596 epoch 4 - iter 2527/3617 - loss 0.06409664 - time (sec): 133.25 - samples/sec: 1995.84 - lr: 0.000035 - momentum: 0.000000
2023-10-15 00:15:08,843 epoch 4 - iter 2888/3617 - loss 0.06400556 - time (sec): 152.50 - samples/sec: 1989.21 - lr: 0.000034 - momentum: 0.000000
2023-10-15 00:15:27,862 epoch 4 - iter 3249/3617 - loss 0.06304313 - time (sec): 171.52 - samples/sec: 1989.02 - lr: 0.000034 - momentum: 0.000000
2023-10-15 00:15:46,803 epoch 4 - iter 3610/3617 - loss 0.06409593 - time (sec): 190.46 - samples/sec: 1991.45 - lr: 0.000033 - momentum: 0.000000
2023-10-15 00:15:47,168 ----------------------------------------------------------------------------------------------------
2023-10-15 00:15:47,168 EPOCH 4 done: loss 0.0641 - lr: 0.000033
2023-10-15 00:15:52,843 DEV : loss 0.2644401490688324 - f1-score (micro avg) 0.608
2023-10-15 00:15:52,874 ----------------------------------------------------------------------------------------------------
2023-10-15 00:16:09,928 epoch 5 - iter 361/3617 - loss 0.05868204 - time (sec): 17.05 - samples/sec: 2185.12 - lr: 0.000033 - momentum: 0.000000
2023-10-15 00:16:26,528 epoch 5 - iter 722/3617 - loss 0.05449946 - time (sec): 33.65 - samples/sec: 2261.15 - lr: 0.000032 - momentum: 0.000000
2023-10-15 00:16:42,928 epoch 5 - iter 1083/3617 - loss 0.04942409 - time (sec): 50.05 - samples/sec: 2291.71 - lr: 0.000032 - momentum: 0.000000
2023-10-15 00:16:59,251 epoch 5 - iter 1444/3617 - loss 0.04938677 - time (sec): 66.38 - samples/sec: 2293.76 - lr: 0.000031 - momentum: 0.000000
2023-10-15 00:17:15,645 epoch 5 - iter 1805/3617 - loss 0.04852069 - time (sec): 82.77 - samples/sec: 2302.53 - lr: 0.000031 - momentum: 0.000000
2023-10-15 00:17:31,936 epoch 5 - iter 2166/3617 - loss 0.04830852 - time (sec): 99.06 - samples/sec: 2312.12 - lr: 0.000030 - momentum: 0.000000
2023-10-15 00:17:48,190 epoch 5 - iter 2527/3617 - loss 0.04877510 - time (sec): 115.31 - samples/sec: 2329.25 - lr: 0.000029 - momentum: 0.000000
2023-10-15 00:18:04,491 epoch 5 - iter 2888/3617 - loss 0.04805849 - time (sec): 131.62 - samples/sec: 2325.19 - lr: 0.000029 - momentum: 0.000000
2023-10-15 00:18:20,840 epoch 5 - iter 3249/3617 - loss 0.04778150 - time (sec): 147.96 - samples/sec: 2316.92 - lr: 0.000028 - momentum: 0.000000
2023-10-15 00:18:37,081 epoch 5 - iter 3610/3617 - loss 0.04737949 - time (sec): 164.21 - samples/sec: 2309.75 - lr: 0.000028 - momentum: 0.000000
2023-10-15 00:18:37,380 ----------------------------------------------------------------------------------------------------
2023-10-15 00:18:37,380 EPOCH 5 done: loss 0.0473 - lr: 0.000028
2023-10-15 00:18:44,647 DEV : loss 0.30833280086517334 - f1-score (micro avg) 0.6202
2023-10-15 00:18:44,680 ----------------------------------------------------------------------------------------------------
2023-10-15 00:19:04,511 epoch 6 - iter 361/3617 - loss 0.03472543 - time (sec): 19.83 - samples/sec: 1893.54 - lr: 0.000027 - momentum: 0.000000
2023-10-15 00:19:20,916 epoch 6 - iter 722/3617 - loss 0.03710473 - time (sec): 36.23 - samples/sec: 2099.04 - lr: 0.000027 - momentum: 0.000000
2023-10-15 00:19:37,412 epoch 6 - iter 1083/3617 - loss 0.03705415 - time (sec): 52.73 - samples/sec: 2162.30 - lr: 0.000026 - momentum: 0.000000
2023-10-15 00:19:54,771 epoch 6 - iter 1444/3617 - loss 0.03567499 - time (sec): 70.09 - samples/sec: 2181.21 - lr: 0.000026 - momentum: 0.000000
2023-10-15 00:20:13,851 epoch 6 - iter 1805/3617 - loss 0.03467301 - time (sec): 89.17 - samples/sec: 2131.96 - lr: 0.000025 - momentum: 0.000000
2023-10-15 00:20:31,490 epoch 6 - iter 2166/3617 - loss 0.03401480 - time (sec): 106.81 - samples/sec: 2132.64 - lr: 0.000024 - momentum: 0.000000
2023-10-15 00:20:48,141 epoch 6 - iter 2527/3617 - loss 0.03417127 - time (sec): 123.46 - samples/sec: 2148.18 - lr: 0.000024 - momentum: 0.000000
2023-10-15 00:21:04,805 epoch 6 - iter 2888/3617 - loss 0.03417472 - time (sec): 140.12 - samples/sec: 2162.24 - lr: 0.000023 - momentum: 0.000000
2023-10-15 00:21:23,559 epoch 6 - iter 3249/3617 - loss 0.03431393 - time (sec): 158.88 - samples/sec: 2149.16 - lr: 0.000023 - momentum: 0.000000
2023-10-15 00:21:40,415 epoch 6 - iter 3610/3617 - loss 0.03406611 - time (sec): 175.73 - samples/sec: 2157.29 - lr: 0.000022 - momentum: 0.000000
2023-10-15 00:21:40,717 ----------------------------------------------------------------------------------------------------
2023-10-15 00:21:40,718 EPOCH 6 done: loss 0.0341 - lr: 0.000022
2023-10-15 00:21:47,770 DEV : loss 0.29267942905426025 - f1-score (micro avg) 0.6138
2023-10-15 00:21:47,811 ----------------------------------------------------------------------------------------------------
2023-10-15 00:22:04,371 epoch 7 - iter 361/3617 - loss 0.02492550 - time (sec): 16.56 - samples/sec: 2240.71 - lr: 0.000022 - momentum: 0.000000
2023-10-15 00:22:20,782 epoch 7 - iter 722/3617 - loss 0.02586181 - time (sec): 32.97 - samples/sec: 2232.97 - lr: 0.000021 - momentum: 0.000000
2023-10-15 00:22:37,204 epoch 7 - iter 1083/3617 - loss 0.02579214 - time (sec): 49.39 - samples/sec: 2267.41 - lr: 0.000021 - momentum: 0.000000
2023-10-15 00:22:54,241 epoch 7 - iter 1444/3617 - loss 0.02631518 - time (sec): 66.43 - samples/sec: 2245.58 - lr: 0.000020 - momentum: 0.000000
2023-10-15 00:23:10,610 epoch 7 - iter 1805/3617 - loss 0.02825187 - time (sec): 82.80 - samples/sec: 2262.71 - lr: 0.000019 - momentum: 0.000000
2023-10-15 00:23:27,120 epoch 7 - iter 2166/3617 - loss 0.02772231 - time (sec): 99.31 - samples/sec: 2272.34 - lr: 0.000019 - momentum: 0.000000
2023-10-15 00:23:43,593 epoch 7 - iter 2527/3617 - loss 0.02743720 - time (sec): 115.78 - samples/sec: 2287.95 - lr: 0.000018 - momentum: 0.000000
2023-10-15 00:23:59,953 epoch 7 - iter 2888/3617 - loss 0.02583311 - time (sec): 132.14 - samples/sec: 2284.62 - lr: 0.000018 - momentum: 0.000000
2023-10-15 00:24:16,683 epoch 7 - iter 3249/3617 - loss 0.02569944 - time (sec): 148.87 - samples/sec: 2299.18 - lr: 0.000017 - momentum: 0.000000
2023-10-15 00:24:34,095 epoch 7 - iter 3610/3617 - loss 0.02550174 - time (sec): 166.28 - samples/sec: 2281.13 - lr: 0.000017 - momentum: 0.000000
2023-10-15 00:24:34,434 ----------------------------------------------------------------------------------------------------
2023-10-15 00:24:34,434 EPOCH 7 done: loss 0.0255 - lr: 0.000017
2023-10-15 00:24:41,164 DEV : loss 0.32456058263778687 - f1-score (micro avg) 0.6318
2023-10-15 00:24:41,200 ----------------------------------------------------------------------------------------------------
2023-10-15 00:24:57,722 epoch 8 - iter 361/3617 - loss 0.01548764 - time (sec): 16.52 - samples/sec: 2256.70 - lr: 0.000016 - momentum: 0.000000
2023-10-15 00:25:14,113 epoch 8 - iter 722/3617 - loss 0.01434764 - time (sec): 32.91 - samples/sec: 2300.40 - lr: 0.000016 - momentum: 0.000000
2023-10-15 00:25:31,676 epoch 8 - iter 1083/3617 - loss 0.01437637 - time (sec): 50.47 - samples/sec: 2234.89 - lr: 0.000015 - momentum: 0.000000
2023-10-15 00:25:49,450 epoch 8 - iter 1444/3617 - loss 0.01581867 - time (sec): 68.25 - samples/sec: 2227.74 - lr: 0.000014 - momentum: 0.000000
2023-10-15 00:26:05,762 epoch 8 - iter 1805/3617 - loss 0.01496411 - time (sec): 84.56 - samples/sec: 2234.48 - lr: 0.000014 - momentum: 0.000000
2023-10-15 00:26:21,684 epoch 8 - iter 2166/3617 - loss 0.01497828 - time (sec): 100.48 - samples/sec: 2256.01 - lr: 0.000013 - momentum: 0.000000
2023-10-15 00:26:38,210 epoch 8 - iter 2527/3617 - loss 0.01610702 - time (sec): 117.01 - samples/sec: 2258.71 - lr: 0.000013 - momentum: 0.000000
2023-10-15 00:26:55,148 epoch 8 - iter 2888/3617 - loss 0.01643447 - time (sec): 133.95 - samples/sec: 2263.00 - lr: 0.000012 - momentum: 0.000000
2023-10-15 00:27:12,712 epoch 8 - iter 3249/3617 - loss 0.01674537 - time (sec): 151.51 - samples/sec: 2249.77 - lr: 0.000012 - momentum: 0.000000
2023-10-15 00:27:29,206 epoch 8 - iter 3610/3617 - loss 0.01703835 - time (sec): 168.00 - samples/sec: 2256.24 - lr: 0.000011 - momentum: 0.000000
2023-10-15 00:27:29,523 ----------------------------------------------------------------------------------------------------
2023-10-15 00:27:29,523 EPOCH 8 done: loss 0.0170 - lr: 0.000011
2023-10-15 00:27:35,276 DEV : loss 0.3864128291606903 - f1-score (micro avg) 0.6289
2023-10-15 00:27:35,310 ----------------------------------------------------------------------------------------------------
2023-10-15 00:27:51,747 epoch 9 - iter 361/3617 - loss 0.01450096 - time (sec): 16.44 - samples/sec: 2304.81 - lr: 0.000011 - momentum: 0.000000
2023-10-15 00:28:08,204 epoch 9 - iter 722/3617 - loss 0.01064632 - time (sec): 32.89 - samples/sec: 2320.76 - lr: 0.000010 - momentum: 0.000000
2023-10-15 00:28:24,784 epoch 9 - iter 1083/3617 - loss 0.01026489 - time (sec): 49.47 - samples/sec: 2310.82 - lr: 0.000009 - momentum: 0.000000
2023-10-15 00:28:41,232 epoch 9 - iter 1444/3617 - loss 0.01130151 - time (sec): 65.92 - samples/sec: 2296.54 - lr: 0.000009 - momentum: 0.000000
2023-10-15 00:28:58,560 epoch 9 - iter 1805/3617 - loss 0.01065243 - time (sec): 83.25 - samples/sec: 2282.03 - lr: 0.000008 - momentum: 0.000000
2023-10-15 00:29:14,860 epoch 9 - iter 2166/3617 - loss 0.01016027 - time (sec): 99.55 - samples/sec: 2288.00 - lr: 0.000008 - momentum: 0.000000
2023-10-15 00:29:31,188 epoch 9 - iter 2527/3617 - loss 0.01022229 - time (sec): 115.88 - samples/sec: 2288.63 - lr: 0.000007 - momentum: 0.000000
2023-10-15 00:29:47,607 epoch 9 - iter 2888/3617 - loss 0.01081834 - time (sec): 132.30 - samples/sec: 2296.70 - lr: 0.000007 - momentum: 0.000000
2023-10-15 00:30:04,031 epoch 9 - iter 3249/3617 - loss 0.01056924 - time (sec): 148.72 - samples/sec: 2297.77 - lr: 0.000006 - momentum: 0.000000
2023-10-15 00:30:20,223 epoch 9 - iter 3610/3617 - loss 0.01077879 - time (sec): 164.91 - samples/sec: 2300.24 - lr: 0.000006 - momentum: 0.000000
2023-10-15 00:30:20,526 ----------------------------------------------------------------------------------------------------
2023-10-15 00:30:20,526 EPOCH 9 done: loss 0.0108 - lr: 0.000006
2023-10-15 00:30:26,188 DEV : loss 0.3467862010002136 - f1-score (micro avg) 0.6337
2023-10-15 00:30:26,221 ----------------------------------------------------------------------------------------------------
2023-10-15 00:30:43,182 epoch 10 - iter 361/3617 - loss 0.00565146 - time (sec): 16.96 - samples/sec: 2278.19 - lr: 0.000005 - momentum: 0.000000
2023-10-15 00:30:59,790 epoch 10 - iter 722/3617 - loss 0.00592381 - time (sec): 33.57 - samples/sec: 2289.25 - lr: 0.000004 - momentum: 0.000000
2023-10-15 00:31:16,416 epoch 10 - iter 1083/3617 - loss 0.00612734 - time (sec): 50.19 - samples/sec: 2263.89 - lr: 0.000004 - momentum: 0.000000
2023-10-15 00:31:32,708 epoch 10 - iter 1444/3617 - loss 0.00593170 - time (sec): 66.49 - samples/sec: 2296.56 - lr: 0.000003 - momentum: 0.000000
2023-10-15 00:31:48,980 epoch 10 - iter 1805/3617 - loss 0.00622588 - time (sec): 82.76 - samples/sec: 2295.89 - lr: 0.000003 - momentum: 0.000000
2023-10-15 00:32:05,209 epoch 10 - iter 2166/3617 - loss 0.00640911 - time (sec): 98.99 - samples/sec: 2291.44 - lr: 0.000002 - momentum: 0.000000
2023-10-15 00:32:23,502 epoch 10 - iter 2527/3617 - loss 0.00605974 - time (sec): 117.28 - samples/sec: 2269.96 - lr: 0.000002 - momentum: 0.000000
2023-10-15 00:32:41,563 epoch 10 - iter 2888/3617 - loss 0.00603640 - time (sec): 135.34 - samples/sec: 2247.59 - lr: 0.000001 - momentum: 0.000000
2023-10-15 00:32:57,825 epoch 10 - iter 3249/3617 - loss 0.00584887 - time (sec): 151.60 - samples/sec: 2253.59 - lr: 0.000001 - momentum: 0.000000
2023-10-15 00:33:14,082 epoch 10 - iter 3610/3617 - loss 0.00661902 - time (sec): 167.86 - samples/sec: 2258.04 - lr: 0.000000 - momentum: 0.000000
2023-10-15 00:33:14,400 ----------------------------------------------------------------------------------------------------
2023-10-15 00:33:14,400 EPOCH 10 done: loss 0.0066 - lr: 0.000000
2023-10-15 00:33:20,856 DEV : loss 0.40056759119033813 - f1-score (micro avg) 0.6307
2023-10-15 00:33:21,342 ----------------------------------------------------------------------------------------------------
2023-10-15 00:33:21,343 Loading model from best epoch ...
2023-10-15 00:33:23,092 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
2023-10-15 00:33:29,961
Results:
- F-score (micro) 0.6314
- F-score (macro) 0.4273
- Accuracy 0.4768
By class:
precision recall f1-score support
loc 0.6242 0.7868 0.6961 591
pers 0.5294 0.6555 0.5857 357
org 0.0000 0.0000 0.0000 79
micro avg 0.5889 0.6806 0.6314 1027
macro avg 0.3845 0.4808 0.4273 1027
weighted avg 0.5432 0.6806 0.6042 1027
2023-10-15 00:33:29,961 ----------------------------------------------------------------------------------------------------
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