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2023-10-19 23:54:46,265 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:46,265 Model: "SequenceTagger(
(embeddings): TransformerWordEmbeddings(
(model): BertModel(
(embeddings): BertEmbeddings(
(word_embeddings): Embedding(32001, 128)
(position_embeddings): Embedding(512, 128)
(token_type_embeddings): Embedding(2, 128)
(LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): BertEncoder(
(layer): ModuleList(
(0-1): 2 x BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=128, out_features=128, bias=True)
(key): Linear(in_features=128, out_features=128, bias=True)
(value): Linear(in_features=128, out_features=128, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=128, out_features=128, bias=True)
(LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=128, out_features=512, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): BertOutput(
(dense): Linear(in_features=512, out_features=128, bias=True)
(LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): BertPooler(
(dense): Linear(in_features=128, out_features=128, bias=True)
(activation): Tanh()
)
)
)
(locked_dropout): LockedDropout(p=0.5)
(linear): Linear(in_features=128, out_features=17, bias=True)
(loss_function): CrossEntropyLoss()
)"
2023-10-19 23:54:46,265 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:46,265 MultiCorpus: 1166 train + 165 dev + 415 test sentences
- NER_HIPE_2022 Corpus: 1166 train + 165 dev + 415 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fi/with_doc_seperator
2023-10-19 23:54:46,265 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:46,265 Train: 1166 sentences
2023-10-19 23:54:46,265 (train_with_dev=False, train_with_test=False)
2023-10-19 23:54:46,265 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:46,265 Training Params:
2023-10-19 23:54:46,265 - learning_rate: "5e-05"
2023-10-19 23:54:46,265 - mini_batch_size: "4"
2023-10-19 23:54:46,265 - max_epochs: "10"
2023-10-19 23:54:46,265 - shuffle: "True"
2023-10-19 23:54:46,265 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:46,265 Plugins:
2023-10-19 23:54:46,265 - TensorboardLogger
2023-10-19 23:54:46,265 - LinearScheduler | warmup_fraction: '0.1'
2023-10-19 23:54:46,265 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:46,266 Final evaluation on model from best epoch (best-model.pt)
2023-10-19 23:54:46,266 - metric: "('micro avg', 'f1-score')"
2023-10-19 23:54:46,266 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:46,266 Computation:
2023-10-19 23:54:46,266 - compute on device: cuda:0
2023-10-19 23:54:46,266 - embedding storage: none
2023-10-19 23:54:46,266 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:46,266 Model training base path: "hmbench-newseye/fi-dbmdz/bert-tiny-historic-multilingual-cased-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
2023-10-19 23:54:46,266 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:46,266 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:46,266 Logging anything other than scalars to TensorBoard is currently not supported.
2023-10-19 23:54:46,793 epoch 1 - iter 29/292 - loss 3.55752914 - time (sec): 0.53 - samples/sec: 7908.79 - lr: 0.000005 - momentum: 0.000000
2023-10-19 23:54:47,324 epoch 1 - iter 58/292 - loss 3.51797914 - time (sec): 1.06 - samples/sec: 8808.85 - lr: 0.000010 - momentum: 0.000000
2023-10-19 23:54:47,833 epoch 1 - iter 87/292 - loss 3.38770600 - time (sec): 1.57 - samples/sec: 8612.55 - lr: 0.000015 - momentum: 0.000000
2023-10-19 23:54:48,346 epoch 1 - iter 116/292 - loss 3.15720938 - time (sec): 2.08 - samples/sec: 8491.67 - lr: 0.000020 - momentum: 0.000000
2023-10-19 23:54:48,870 epoch 1 - iter 145/292 - loss 2.91319087 - time (sec): 2.60 - samples/sec: 8456.38 - lr: 0.000025 - momentum: 0.000000
2023-10-19 23:54:49,365 epoch 1 - iter 174/292 - loss 2.68788876 - time (sec): 3.10 - samples/sec: 8374.19 - lr: 0.000030 - momentum: 0.000000
2023-10-19 23:54:49,915 epoch 1 - iter 203/292 - loss 2.45249775 - time (sec): 3.65 - samples/sec: 8479.75 - lr: 0.000035 - momentum: 0.000000
2023-10-19 23:54:50,473 epoch 1 - iter 232/292 - loss 2.21945254 - time (sec): 4.21 - samples/sec: 8501.77 - lr: 0.000040 - momentum: 0.000000
2023-10-19 23:54:50,953 epoch 1 - iter 261/292 - loss 2.05466586 - time (sec): 4.69 - samples/sec: 8518.18 - lr: 0.000045 - momentum: 0.000000
2023-10-19 23:54:51,428 epoch 1 - iter 290/292 - loss 1.91851237 - time (sec): 5.16 - samples/sec: 8529.52 - lr: 0.000049 - momentum: 0.000000
2023-10-19 23:54:51,462 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:51,462 EPOCH 1 done: loss 1.9040 - lr: 0.000049
2023-10-19 23:54:51,728 DEV : loss 0.45997393131256104 - f1-score (micro avg) 0.0
2023-10-19 23:54:51,732 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:52,162 epoch 2 - iter 29/292 - loss 0.58656023 - time (sec): 0.43 - samples/sec: 7909.71 - lr: 0.000049 - momentum: 0.000000
2023-10-19 23:54:52,603 epoch 2 - iter 58/292 - loss 0.63439941 - time (sec): 0.87 - samples/sec: 9252.63 - lr: 0.000049 - momentum: 0.000000
2023-10-19 23:54:53,049 epoch 2 - iter 87/292 - loss 0.63149781 - time (sec): 1.32 - samples/sec: 9293.51 - lr: 0.000048 - momentum: 0.000000
2023-10-19 23:54:53,493 epoch 2 - iter 116/292 - loss 0.62294731 - time (sec): 1.76 - samples/sec: 9411.80 - lr: 0.000048 - momentum: 0.000000
2023-10-19 23:54:53,982 epoch 2 - iter 145/292 - loss 0.67190983 - time (sec): 2.25 - samples/sec: 9782.63 - lr: 0.000047 - momentum: 0.000000
2023-10-19 23:54:54,587 epoch 2 - iter 174/292 - loss 0.65041157 - time (sec): 2.85 - samples/sec: 9342.32 - lr: 0.000047 - momentum: 0.000000
2023-10-19 23:54:55,159 epoch 2 - iter 203/292 - loss 0.62124438 - time (sec): 3.43 - samples/sec: 9211.13 - lr: 0.000046 - momentum: 0.000000
2023-10-19 23:54:55,714 epoch 2 - iter 232/292 - loss 0.61325278 - time (sec): 3.98 - samples/sec: 8902.60 - lr: 0.000046 - momentum: 0.000000
2023-10-19 23:54:56,273 epoch 2 - iter 261/292 - loss 0.60750010 - time (sec): 4.54 - samples/sec: 8763.90 - lr: 0.000045 - momentum: 0.000000
2023-10-19 23:54:56,780 epoch 2 - iter 290/292 - loss 0.59371287 - time (sec): 5.05 - samples/sec: 8713.85 - lr: 0.000045 - momentum: 0.000000
2023-10-19 23:54:56,811 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:56,811 EPOCH 2 done: loss 0.5942 - lr: 0.000045
2023-10-19 23:54:57,612 DEV : loss 0.34521862864494324 - f1-score (micro avg) 0.0
2023-10-19 23:54:57,616 ----------------------------------------------------------------------------------------------------
2023-10-19 23:54:58,150 epoch 3 - iter 29/292 - loss 0.43874260 - time (sec): 0.53 - samples/sec: 8885.33 - lr: 0.000044 - momentum: 0.000000
2023-10-19 23:54:58,660 epoch 3 - iter 58/292 - loss 0.45959734 - time (sec): 1.04 - samples/sec: 8864.19 - lr: 0.000043 - momentum: 0.000000
2023-10-19 23:54:59,178 epoch 3 - iter 87/292 - loss 0.46972901 - time (sec): 1.56 - samples/sec: 8336.04 - lr: 0.000043 - momentum: 0.000000
2023-10-19 23:54:59,714 epoch 3 - iter 116/292 - loss 0.45936071 - time (sec): 2.10 - samples/sec: 8357.63 - lr: 0.000042 - momentum: 0.000000
2023-10-19 23:55:00,257 epoch 3 - iter 145/292 - loss 0.48567862 - time (sec): 2.64 - samples/sec: 8187.12 - lr: 0.000042 - momentum: 0.000000
2023-10-19 23:55:00,798 epoch 3 - iter 174/292 - loss 0.47727066 - time (sec): 3.18 - samples/sec: 8207.62 - lr: 0.000041 - momentum: 0.000000
2023-10-19 23:55:01,351 epoch 3 - iter 203/292 - loss 0.48496424 - time (sec): 3.73 - samples/sec: 8427.15 - lr: 0.000041 - momentum: 0.000000
2023-10-19 23:55:01,873 epoch 3 - iter 232/292 - loss 0.48084606 - time (sec): 4.26 - samples/sec: 8398.02 - lr: 0.000040 - momentum: 0.000000
2023-10-19 23:55:02,397 epoch 3 - iter 261/292 - loss 0.47958169 - time (sec): 4.78 - samples/sec: 8305.75 - lr: 0.000040 - momentum: 0.000000
2023-10-19 23:55:02,917 epoch 3 - iter 290/292 - loss 0.47094732 - time (sec): 5.30 - samples/sec: 8310.97 - lr: 0.000039 - momentum: 0.000000
2023-10-19 23:55:02,952 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:02,953 EPOCH 3 done: loss 0.4685 - lr: 0.000039
2023-10-19 23:55:03,596 DEV : loss 0.31381186842918396 - f1-score (micro avg) 0.1303
2023-10-19 23:55:03,600 saving best model
2023-10-19 23:55:03,629 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:04,156 epoch 4 - iter 29/292 - loss 0.43177072 - time (sec): 0.53 - samples/sec: 8615.07 - lr: 0.000038 - momentum: 0.000000
2023-10-19 23:55:04,683 epoch 4 - iter 58/292 - loss 0.41853015 - time (sec): 1.05 - samples/sec: 8960.14 - lr: 0.000038 - momentum: 0.000000
2023-10-19 23:55:05,216 epoch 4 - iter 87/292 - loss 0.39904768 - time (sec): 1.59 - samples/sec: 8975.40 - lr: 0.000037 - momentum: 0.000000
2023-10-19 23:55:05,693 epoch 4 - iter 116/292 - loss 0.39486048 - time (sec): 2.06 - samples/sec: 8683.45 - lr: 0.000037 - momentum: 0.000000
2023-10-19 23:55:06,182 epoch 4 - iter 145/292 - loss 0.38926525 - time (sec): 2.55 - samples/sec: 8552.06 - lr: 0.000036 - momentum: 0.000000
2023-10-19 23:55:06,693 epoch 4 - iter 174/292 - loss 0.38841178 - time (sec): 3.06 - samples/sec: 8439.81 - lr: 0.000036 - momentum: 0.000000
2023-10-19 23:55:07,187 epoch 4 - iter 203/292 - loss 0.38717609 - time (sec): 3.56 - samples/sec: 8323.23 - lr: 0.000035 - momentum: 0.000000
2023-10-19 23:55:07,715 epoch 4 - iter 232/292 - loss 0.39343263 - time (sec): 4.09 - samples/sec: 8440.52 - lr: 0.000035 - momentum: 0.000000
2023-10-19 23:55:08,255 epoch 4 - iter 261/292 - loss 0.41230712 - time (sec): 4.63 - samples/sec: 8572.69 - lr: 0.000034 - momentum: 0.000000
2023-10-19 23:55:08,816 epoch 4 - iter 290/292 - loss 0.41910163 - time (sec): 5.19 - samples/sec: 8487.60 - lr: 0.000033 - momentum: 0.000000
2023-10-19 23:55:08,856 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:08,856 EPOCH 4 done: loss 0.4154 - lr: 0.000033
2023-10-19 23:55:09,491 DEV : loss 0.3016578257083893 - f1-score (micro avg) 0.2328
2023-10-19 23:55:09,495 saving best model
2023-10-19 23:55:09,530 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:10,074 epoch 5 - iter 29/292 - loss 0.43229368 - time (sec): 0.54 - samples/sec: 8265.43 - lr: 0.000033 - momentum: 0.000000
2023-10-19 23:55:10,601 epoch 5 - iter 58/292 - loss 0.37539253 - time (sec): 1.07 - samples/sec: 8654.01 - lr: 0.000032 - momentum: 0.000000
2023-10-19 23:55:11,118 epoch 5 - iter 87/292 - loss 0.40406274 - time (sec): 1.59 - samples/sec: 8605.36 - lr: 0.000032 - momentum: 0.000000
2023-10-19 23:55:11,636 epoch 5 - iter 116/292 - loss 0.40222319 - time (sec): 2.11 - samples/sec: 8412.63 - lr: 0.000031 - momentum: 0.000000
2023-10-19 23:55:12,149 epoch 5 - iter 145/292 - loss 0.39690686 - time (sec): 2.62 - samples/sec: 8624.99 - lr: 0.000031 - momentum: 0.000000
2023-10-19 23:55:12,669 epoch 5 - iter 174/292 - loss 0.39595604 - time (sec): 3.14 - samples/sec: 8461.59 - lr: 0.000030 - momentum: 0.000000
2023-10-19 23:55:13,173 epoch 5 - iter 203/292 - loss 0.39014674 - time (sec): 3.64 - samples/sec: 8592.12 - lr: 0.000030 - momentum: 0.000000
2023-10-19 23:55:13,667 epoch 5 - iter 232/292 - loss 0.39145583 - time (sec): 4.14 - samples/sec: 8507.40 - lr: 0.000029 - momentum: 0.000000
2023-10-19 23:55:14,174 epoch 5 - iter 261/292 - loss 0.38172039 - time (sec): 4.64 - samples/sec: 8575.01 - lr: 0.000028 - momentum: 0.000000
2023-10-19 23:55:14,665 epoch 5 - iter 290/292 - loss 0.37542897 - time (sec): 5.13 - samples/sec: 8597.45 - lr: 0.000028 - momentum: 0.000000
2023-10-19 23:55:14,700 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:14,700 EPOCH 5 done: loss 0.3775 - lr: 0.000028
2023-10-19 23:55:15,336 DEV : loss 0.2973732054233551 - f1-score (micro avg) 0.2654
2023-10-19 23:55:15,340 saving best model
2023-10-19 23:55:15,372 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:15,873 epoch 6 - iter 29/292 - loss 0.37312102 - time (sec): 0.50 - samples/sec: 9364.57 - lr: 0.000027 - momentum: 0.000000
2023-10-19 23:55:16,391 epoch 6 - iter 58/292 - loss 0.36942596 - time (sec): 1.02 - samples/sec: 8762.88 - lr: 0.000027 - momentum: 0.000000
2023-10-19 23:55:16,914 epoch 6 - iter 87/292 - loss 0.34805366 - time (sec): 1.54 - samples/sec: 8399.49 - lr: 0.000026 - momentum: 0.000000
2023-10-19 23:55:17,447 epoch 6 - iter 116/292 - loss 0.37301859 - time (sec): 2.07 - samples/sec: 8795.53 - lr: 0.000026 - momentum: 0.000000
2023-10-19 23:55:17,968 epoch 6 - iter 145/292 - loss 0.38869681 - time (sec): 2.60 - samples/sec: 8817.51 - lr: 0.000025 - momentum: 0.000000
2023-10-19 23:55:18,485 epoch 6 - iter 174/292 - loss 0.36418246 - time (sec): 3.11 - samples/sec: 8987.93 - lr: 0.000025 - momentum: 0.000000
2023-10-19 23:55:19,005 epoch 6 - iter 203/292 - loss 0.36949889 - time (sec): 3.63 - samples/sec: 8807.31 - lr: 0.000024 - momentum: 0.000000
2023-10-19 23:55:19,515 epoch 6 - iter 232/292 - loss 0.36160845 - time (sec): 4.14 - samples/sec: 8753.61 - lr: 0.000023 - momentum: 0.000000
2023-10-19 23:55:20,009 epoch 6 - iter 261/292 - loss 0.36352966 - time (sec): 4.64 - samples/sec: 8640.60 - lr: 0.000023 - momentum: 0.000000
2023-10-19 23:55:20,507 epoch 6 - iter 290/292 - loss 0.35857782 - time (sec): 5.13 - samples/sec: 8588.66 - lr: 0.000022 - momentum: 0.000000
2023-10-19 23:55:20,538 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:20,538 EPOCH 6 done: loss 0.3575 - lr: 0.000022
2023-10-19 23:55:21,183 DEV : loss 0.29466673731803894 - f1-score (micro avg) 0.2937
2023-10-19 23:55:21,187 saving best model
2023-10-19 23:55:21,221 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:21,729 epoch 7 - iter 29/292 - loss 0.38819137 - time (sec): 0.51 - samples/sec: 8190.47 - lr: 0.000022 - momentum: 0.000000
2023-10-19 23:55:22,263 epoch 7 - iter 58/292 - loss 0.34242214 - time (sec): 1.04 - samples/sec: 8732.14 - lr: 0.000021 - momentum: 0.000000
2023-10-19 23:55:22,798 epoch 7 - iter 87/292 - loss 0.35717419 - time (sec): 1.58 - samples/sec: 8812.53 - lr: 0.000021 - momentum: 0.000000
2023-10-19 23:55:23,307 epoch 7 - iter 116/292 - loss 0.37602817 - time (sec): 2.09 - samples/sec: 8721.64 - lr: 0.000020 - momentum: 0.000000
2023-10-19 23:55:23,820 epoch 7 - iter 145/292 - loss 0.36879662 - time (sec): 2.60 - samples/sec: 8665.03 - lr: 0.000020 - momentum: 0.000000
2023-10-19 23:55:24,342 epoch 7 - iter 174/292 - loss 0.36046721 - time (sec): 3.12 - samples/sec: 8544.70 - lr: 0.000019 - momentum: 0.000000
2023-10-19 23:55:24,835 epoch 7 - iter 203/292 - loss 0.35584894 - time (sec): 3.61 - samples/sec: 8468.06 - lr: 0.000018 - momentum: 0.000000
2023-10-19 23:55:25,332 epoch 7 - iter 232/292 - loss 0.35765109 - time (sec): 4.11 - samples/sec: 8520.60 - lr: 0.000018 - momentum: 0.000000
2023-10-19 23:55:25,837 epoch 7 - iter 261/292 - loss 0.34589803 - time (sec): 4.62 - samples/sec: 8620.32 - lr: 0.000017 - momentum: 0.000000
2023-10-19 23:55:26,370 epoch 7 - iter 290/292 - loss 0.33965089 - time (sec): 5.15 - samples/sec: 8590.24 - lr: 0.000017 - momentum: 0.000000
2023-10-19 23:55:26,397 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:26,397 EPOCH 7 done: loss 0.3397 - lr: 0.000017
2023-10-19 23:55:27,049 DEV : loss 0.29104095697402954 - f1-score (micro avg) 0.3193
2023-10-19 23:55:27,052 saving best model
2023-10-19 23:55:27,085 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:27,615 epoch 8 - iter 29/292 - loss 0.29154439 - time (sec): 0.53 - samples/sec: 8832.63 - lr: 0.000016 - momentum: 0.000000
2023-10-19 23:55:28,142 epoch 8 - iter 58/292 - loss 0.34250586 - time (sec): 1.06 - samples/sec: 9218.94 - lr: 0.000016 - momentum: 0.000000
2023-10-19 23:55:28,629 epoch 8 - iter 87/292 - loss 0.32930383 - time (sec): 1.54 - samples/sec: 8830.03 - lr: 0.000015 - momentum: 0.000000
2023-10-19 23:55:29,160 epoch 8 - iter 116/292 - loss 0.32840663 - time (sec): 2.07 - samples/sec: 8764.31 - lr: 0.000015 - momentum: 0.000000
2023-10-19 23:55:29,636 epoch 8 - iter 145/292 - loss 0.32284264 - time (sec): 2.55 - samples/sec: 8542.07 - lr: 0.000014 - momentum: 0.000000
2023-10-19 23:55:30,078 epoch 8 - iter 174/292 - loss 0.32209124 - time (sec): 2.99 - samples/sec: 8424.20 - lr: 0.000013 - momentum: 0.000000
2023-10-19 23:55:30,578 epoch 8 - iter 203/292 - loss 0.33155736 - time (sec): 3.49 - samples/sec: 8675.07 - lr: 0.000013 - momentum: 0.000000
2023-10-19 23:55:31,241 epoch 8 - iter 232/292 - loss 0.32136242 - time (sec): 4.16 - samples/sec: 8525.82 - lr: 0.000012 - momentum: 0.000000
2023-10-19 23:55:31,714 epoch 8 - iter 261/292 - loss 0.32137603 - time (sec): 4.63 - samples/sec: 8544.97 - lr: 0.000012 - momentum: 0.000000
2023-10-19 23:55:32,222 epoch 8 - iter 290/292 - loss 0.32337631 - time (sec): 5.14 - samples/sec: 8605.69 - lr: 0.000011 - momentum: 0.000000
2023-10-19 23:55:32,257 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:32,257 EPOCH 8 done: loss 0.3226 - lr: 0.000011
2023-10-19 23:55:32,918 DEV : loss 0.29328519105911255 - f1-score (micro avg) 0.3129
2023-10-19 23:55:32,923 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:33,419 epoch 9 - iter 29/292 - loss 0.29822543 - time (sec): 0.50 - samples/sec: 8134.79 - lr: 0.000011 - momentum: 0.000000
2023-10-19 23:55:33,914 epoch 9 - iter 58/292 - loss 0.34739399 - time (sec): 0.99 - samples/sec: 8415.03 - lr: 0.000010 - momentum: 0.000000
2023-10-19 23:55:34,382 epoch 9 - iter 87/292 - loss 0.34634734 - time (sec): 1.46 - samples/sec: 8010.70 - lr: 0.000010 - momentum: 0.000000
2023-10-19 23:55:34,892 epoch 9 - iter 116/292 - loss 0.34386116 - time (sec): 1.97 - samples/sec: 8071.38 - lr: 0.000009 - momentum: 0.000000
2023-10-19 23:55:35,411 epoch 9 - iter 145/292 - loss 0.34301480 - time (sec): 2.49 - samples/sec: 8269.81 - lr: 0.000008 - momentum: 0.000000
2023-10-19 23:55:35,926 epoch 9 - iter 174/292 - loss 0.33151984 - time (sec): 3.00 - samples/sec: 8533.95 - lr: 0.000008 - momentum: 0.000000
2023-10-19 23:55:36,436 epoch 9 - iter 203/292 - loss 0.33282371 - time (sec): 3.51 - samples/sec: 8455.15 - lr: 0.000007 - momentum: 0.000000
2023-10-19 23:55:36,974 epoch 9 - iter 232/292 - loss 0.33502228 - time (sec): 4.05 - samples/sec: 8611.47 - lr: 0.000007 - momentum: 0.000000
2023-10-19 23:55:37,492 epoch 9 - iter 261/292 - loss 0.32271061 - time (sec): 4.57 - samples/sec: 8680.15 - lr: 0.000006 - momentum: 0.000000
2023-10-19 23:55:38,050 epoch 9 - iter 290/292 - loss 0.31960374 - time (sec): 5.13 - samples/sec: 8640.65 - lr: 0.000006 - momentum: 0.000000
2023-10-19 23:55:38,079 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:38,079 EPOCH 9 done: loss 0.3192 - lr: 0.000006
2023-10-19 23:55:38,728 DEV : loss 0.2915344834327698 - f1-score (micro avg) 0.3067
2023-10-19 23:55:38,731 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:39,234 epoch 10 - iter 29/292 - loss 0.25691072 - time (sec): 0.50 - samples/sec: 9550.60 - lr: 0.000005 - momentum: 0.000000
2023-10-19 23:55:39,752 epoch 10 - iter 58/292 - loss 0.32264057 - time (sec): 1.02 - samples/sec: 9710.22 - lr: 0.000005 - momentum: 0.000000
2023-10-19 23:55:40,262 epoch 10 - iter 87/292 - loss 0.29381973 - time (sec): 1.53 - samples/sec: 9342.81 - lr: 0.000004 - momentum: 0.000000
2023-10-19 23:55:40,745 epoch 10 - iter 116/292 - loss 0.29276188 - time (sec): 2.01 - samples/sec: 9055.61 - lr: 0.000003 - momentum: 0.000000
2023-10-19 23:55:41,215 epoch 10 - iter 145/292 - loss 0.30739420 - time (sec): 2.48 - samples/sec: 8724.85 - lr: 0.000003 - momentum: 0.000000
2023-10-19 23:55:41,719 epoch 10 - iter 174/292 - loss 0.30017946 - time (sec): 2.99 - samples/sec: 8931.07 - lr: 0.000002 - momentum: 0.000000
2023-10-19 23:55:42,236 epoch 10 - iter 203/292 - loss 0.29891419 - time (sec): 3.50 - samples/sec: 8834.20 - lr: 0.000002 - momentum: 0.000000
2023-10-19 23:55:42,768 epoch 10 - iter 232/292 - loss 0.30792335 - time (sec): 4.04 - samples/sec: 8901.96 - lr: 0.000001 - momentum: 0.000000
2023-10-19 23:55:43,258 epoch 10 - iter 261/292 - loss 0.31694630 - time (sec): 4.53 - samples/sec: 8787.52 - lr: 0.000001 - momentum: 0.000000
2023-10-19 23:55:43,793 epoch 10 - iter 290/292 - loss 0.31667346 - time (sec): 5.06 - samples/sec: 8740.26 - lr: 0.000000 - momentum: 0.000000
2023-10-19 23:55:43,823 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:43,823 EPOCH 10 done: loss 0.3158 - lr: 0.000000
2023-10-19 23:55:44,474 DEV : loss 0.2926194965839386 - f1-score (micro avg) 0.307
2023-10-19 23:55:44,506 ----------------------------------------------------------------------------------------------------
2023-10-19 23:55:44,507 Loading model from best epoch ...
2023-10-19 23:55:44,580 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
2023-10-19 23:55:45,486
Results:
- F-score (micro) 0.3714
- F-score (macro) 0.196
- Accuracy 0.237
By class:
precision recall f1-score support
PER 0.3965 0.3908 0.3936 348
LOC 0.3316 0.4751 0.3906 261
ORG 0.0000 0.0000 0.0000 52
HumanProd 0.0000 0.0000 0.0000 22
micro avg 0.3626 0.3807 0.3714 683
macro avg 0.1820 0.2165 0.1960 683
weighted avg 0.3287 0.3807 0.3498 683
2023-10-19 23:55:45,486 ----------------------------------------------------------------------------------------------------
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