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hmbench-newseye-fi-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4

Token Classification
Flair
PyTorch
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Finnish
sequence-tagger-model
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+ 2023-10-25 21:19:59,894 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:19:59,895 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): BertModel(
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+ (embeddings): BertEmbeddings(
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+ (word_embeddings): Embedding(64001, 768)
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+ (position_embeddings): Embedding(512, 768)
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+ (token_type_embeddings): Embedding(2, 768)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (encoder): BertEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x BertLayer(
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+ (attention): BertAttention(
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+ (self): BertSelfAttention(
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+ (query): Linear(in_features=768, out_features=768, bias=True)
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+ (key): Linear(in_features=768, out_features=768, bias=True)
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+ (value): Linear(in_features=768, out_features=768, bias=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (output): BertSelfOutput(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ (intermediate): BertIntermediate(
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+ (dense): Linear(in_features=768, out_features=3072, bias=True)
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+ (intermediate_act_fn): GELUActivation()
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+ )
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+ (output): BertOutput(
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+ (dense): Linear(in_features=3072, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ )
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+ (pooler): BertPooler(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (activation): Tanh()
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+ )
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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=768, out_features=17, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-25 21:19:59,895 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:19:59,895 MultiCorpus: 1166 train + 165 dev + 415 test sentences
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+ - NER_HIPE_2022 Corpus: 1166 train + 165 dev + 415 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fi/with_doc_seperator
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+ 2023-10-25 21:19:59,895 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:19:59,895 Train: 1166 sentences
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+ 2023-10-25 21:19:59,895 (train_with_dev=False, train_with_test=False)
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+ 2023-10-25 21:19:59,895 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:19:59,895 Training Params:
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+ 2023-10-25 21:19:59,895 - learning_rate: "3e-05"
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+ 2023-10-25 21:19:59,895 - mini_batch_size: "4"
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+ 2023-10-25 21:19:59,895 - max_epochs: "10"
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+ 2023-10-25 21:19:59,895 - shuffle: "True"
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+ 2023-10-25 21:19:59,895 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:19:59,895 Plugins:
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+ 2023-10-25 21:19:59,895 - TensorboardLogger
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+ 2023-10-25 21:19:59,895 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-25 21:19:59,895 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:19:59,895 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-25 21:19:59,895 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-25 21:19:59,895 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:19:59,895 Computation:
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+ 2023-10-25 21:19:59,895 - compute on device: cuda:0
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+ 2023-10-25 21:19:59,895 - embedding storage: none
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+ 2023-10-25 21:19:59,895 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:19:59,896 Model training base path: "hmbench-newseye/fi-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
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+ 2023-10-25 21:19:59,896 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:19:59,896 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:19:59,896 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-25 21:20:01,315 epoch 1 - iter 29/292 - loss 3.17590895 - time (sec): 1.42 - samples/sec: 3376.53 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-25 21:20:02,583 epoch 1 - iter 58/292 - loss 2.43702185 - time (sec): 2.69 - samples/sec: 3311.36 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 21:20:03,832 epoch 1 - iter 87/292 - loss 1.95169503 - time (sec): 3.94 - samples/sec: 3312.69 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 21:20:05,105 epoch 1 - iter 116/292 - loss 1.62169131 - time (sec): 5.21 - samples/sec: 3296.01 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 21:20:06,393 epoch 1 - iter 145/292 - loss 1.40877887 - time (sec): 6.50 - samples/sec: 3234.73 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 21:20:07,807 epoch 1 - iter 174/292 - loss 1.20206482 - time (sec): 7.91 - samples/sec: 3319.44 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 21:20:09,100 epoch 1 - iter 203/292 - loss 1.07963154 - time (sec): 9.20 - samples/sec: 3305.80 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 21:20:10,469 epoch 1 - iter 232/292 - loss 0.97153816 - time (sec): 10.57 - samples/sec: 3301.30 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 21:20:11,772 epoch 1 - iter 261/292 - loss 0.87789158 - time (sec): 11.88 - samples/sec: 3333.01 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 21:20:13,122 epoch 1 - iter 290/292 - loss 0.80297568 - time (sec): 13.23 - samples/sec: 3338.93 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-25 21:20:13,206 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:13,207 EPOCH 1 done: loss 0.8001 - lr: 0.000030
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+ 2023-10-25 21:20:13,864 DEV : loss 0.16615526378154755 - f1-score (micro avg) 0.5011
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+ 2023-10-25 21:20:13,868 saving best model
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+ 2023-10-25 21:20:14,344 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:15,649 epoch 2 - iter 29/292 - loss 0.22299871 - time (sec): 1.30 - samples/sec: 3330.56 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-25 21:20:16,981 epoch 2 - iter 58/292 - loss 0.17875882 - time (sec): 2.64 - samples/sec: 3515.59 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 21:20:18,227 epoch 2 - iter 87/292 - loss 0.17193383 - time (sec): 3.88 - samples/sec: 3453.99 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 21:20:19,545 epoch 2 - iter 116/292 - loss 0.18206582 - time (sec): 5.20 - samples/sec: 3451.77 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 21:20:20,848 epoch 2 - iter 145/292 - loss 0.17741981 - time (sec): 6.50 - samples/sec: 3404.44 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 21:20:22,094 epoch 2 - iter 174/292 - loss 0.17506302 - time (sec): 7.75 - samples/sec: 3334.92 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 21:20:23,379 epoch 2 - iter 203/292 - loss 0.17541687 - time (sec): 9.03 - samples/sec: 3311.77 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 21:20:24,707 epoch 2 - iter 232/292 - loss 0.17510377 - time (sec): 10.36 - samples/sec: 3327.47 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 21:20:26,046 epoch 2 - iter 261/292 - loss 0.16765312 - time (sec): 11.70 - samples/sec: 3370.33 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 21:20:27,363 epoch 2 - iter 290/292 - loss 0.16482145 - time (sec): 13.02 - samples/sec: 3404.67 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 21:20:27,452 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:27,452 EPOCH 2 done: loss 0.1647 - lr: 0.000027
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+ 2023-10-25 21:20:28,362 DEV : loss 0.11898940056562424 - f1-score (micro avg) 0.7314
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+ 2023-10-25 21:20:28,366 saving best model
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+ 2023-10-25 21:20:28,978 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:30,260 epoch 3 - iter 29/292 - loss 0.08120991 - time (sec): 1.28 - samples/sec: 3746.82 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 21:20:31,528 epoch 3 - iter 58/292 - loss 0.08860077 - time (sec): 2.55 - samples/sec: 3614.53 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 21:20:32,824 epoch 3 - iter 87/292 - loss 0.09344842 - time (sec): 3.84 - samples/sec: 3526.47 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 21:20:34,123 epoch 3 - iter 116/292 - loss 0.10393542 - time (sec): 5.14 - samples/sec: 3433.06 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 21:20:35,428 epoch 3 - iter 145/292 - loss 0.11337875 - time (sec): 6.45 - samples/sec: 3415.71 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 21:20:36,671 epoch 3 - iter 174/292 - loss 0.10761336 - time (sec): 7.69 - samples/sec: 3336.96 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 21:20:37,957 epoch 3 - iter 203/292 - loss 0.10181384 - time (sec): 8.98 - samples/sec: 3356.72 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 21:20:39,290 epoch 3 - iter 232/292 - loss 0.10275434 - time (sec): 10.31 - samples/sec: 3364.72 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 21:20:40,687 epoch 3 - iter 261/292 - loss 0.10297989 - time (sec): 11.71 - samples/sec: 3372.52 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 21:20:41,981 epoch 3 - iter 290/292 - loss 0.09953734 - time (sec): 13.00 - samples/sec: 3408.36 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 21:20:42,060 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-25 21:20:42,060 EPOCH 3 done: loss 0.0995 - lr: 0.000023
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+ 2023-10-25 21:20:43,120 DEV : loss 0.11886752396821976 - f1-score (micro avg) 0.7387
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+ 2023-10-25 21:20:43,124 saving best model
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+ 2023-10-25 21:20:43,739 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 21:20:45,076 epoch 4 - iter 29/292 - loss 0.06595635 - time (sec): 1.34 - samples/sec: 3335.83 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 21:20:46,414 epoch 4 - iter 58/292 - loss 0.07118520 - time (sec): 2.67 - samples/sec: 3534.73 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 21:20:47,757 epoch 4 - iter 87/292 - loss 0.06789459 - time (sec): 4.02 - samples/sec: 3575.75 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 21:20:49,027 epoch 4 - iter 116/292 - loss 0.06211278 - time (sec): 5.29 - samples/sec: 3547.81 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 21:20:50,305 epoch 4 - iter 145/292 - loss 0.06537711 - time (sec): 6.56 - samples/sec: 3522.30 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 21:20:51,507 epoch 4 - iter 174/292 - loss 0.06560146 - time (sec): 7.77 - samples/sec: 3445.44 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 21:20:52,761 epoch 4 - iter 203/292 - loss 0.06457142 - time (sec): 9.02 - samples/sec: 3419.07 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 21:20:54,091 epoch 4 - iter 232/292 - loss 0.06534614 - time (sec): 10.35 - samples/sec: 3443.05 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 21:20:55,340 epoch 4 - iter 261/292 - loss 0.06405753 - time (sec): 11.60 - samples/sec: 3419.94 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 21:20:56,667 epoch 4 - iter 290/292 - loss 0.06209047 - time (sec): 12.93 - samples/sec: 3423.10 - lr: 0.000020 - momentum: 0.000000
133
+ 2023-10-25 21:20:56,757 ----------------------------------------------------------------------------------------------------
134
+ 2023-10-25 21:20:56,757 EPOCH 4 done: loss 0.0619 - lr: 0.000020
135
+ 2023-10-25 21:20:57,676 DEV : loss 0.11910221725702286 - f1-score (micro avg) 0.7722
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+ 2023-10-25 21:20:57,680 saving best model
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+ 2023-10-25 21:20:58,296 ----------------------------------------------------------------------------------------------------
138
+ 2023-10-25 21:20:59,656 epoch 5 - iter 29/292 - loss 0.02417236 - time (sec): 1.36 - samples/sec: 3453.26 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 21:21:00,977 epoch 5 - iter 58/292 - loss 0.04141239 - time (sec): 2.68 - samples/sec: 3487.81 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 21:21:02,248 epoch 5 - iter 87/292 - loss 0.03995571 - time (sec): 3.95 - samples/sec: 3508.83 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 21:21:03,535 epoch 5 - iter 116/292 - loss 0.04020180 - time (sec): 5.24 - samples/sec: 3553.70 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 21:21:04,810 epoch 5 - iter 145/292 - loss 0.03664786 - time (sec): 6.51 - samples/sec: 3607.13 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 21:21:06,022 epoch 5 - iter 174/292 - loss 0.03846683 - time (sec): 7.72 - samples/sec: 3515.50 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 21:21:07,249 epoch 5 - iter 203/292 - loss 0.03939697 - time (sec): 8.95 - samples/sec: 3532.53 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 21:21:08,412 epoch 5 - iter 232/292 - loss 0.04260414 - time (sec): 10.11 - samples/sec: 3502.45 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-25 21:21:09,613 epoch 5 - iter 261/292 - loss 0.04150922 - time (sec): 11.31 - samples/sec: 3508.40 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-25 21:21:10,816 epoch 5 - iter 290/292 - loss 0.04161576 - time (sec): 12.52 - samples/sec: 3534.13 - lr: 0.000017 - momentum: 0.000000
148
+ 2023-10-25 21:21:10,893 ----------------------------------------------------------------------------------------------------
149
+ 2023-10-25 21:21:10,893 EPOCH 5 done: loss 0.0415 - lr: 0.000017
150
+ 2023-10-25 21:21:11,809 DEV : loss 0.13558463752269745 - f1-score (micro avg) 0.7706
151
+ 2023-10-25 21:21:11,814 ----------------------------------------------------------------------------------------------------
152
+ 2023-10-25 21:21:13,067 epoch 6 - iter 29/292 - loss 0.02684862 - time (sec): 1.25 - samples/sec: 3466.34 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 21:21:14,380 epoch 6 - iter 58/292 - loss 0.02943289 - time (sec): 2.57 - samples/sec: 3272.73 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 21:21:15,709 epoch 6 - iter 87/292 - loss 0.02812073 - time (sec): 3.89 - samples/sec: 3249.93 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 21:21:16,969 epoch 6 - iter 116/292 - loss 0.03204096 - time (sec): 5.15 - samples/sec: 3224.49 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 21:21:18,298 epoch 6 - iter 145/292 - loss 0.02999566 - time (sec): 6.48 - samples/sec: 3268.79 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 21:21:19,687 epoch 6 - iter 174/292 - loss 0.03172843 - time (sec): 7.87 - samples/sec: 3286.22 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 21:21:20,979 epoch 6 - iter 203/292 - loss 0.03002027 - time (sec): 9.16 - samples/sec: 3300.22 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 21:21:22,397 epoch 6 - iter 232/292 - loss 0.03059791 - time (sec): 10.58 - samples/sec: 3369.61 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 21:21:23,678 epoch 6 - iter 261/292 - loss 0.02908626 - time (sec): 11.86 - samples/sec: 3344.93 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 21:21:25,027 epoch 6 - iter 290/292 - loss 0.02740269 - time (sec): 13.21 - samples/sec: 3352.52 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 21:21:25,113 ----------------------------------------------------------------------------------------------------
163
+ 2023-10-25 21:21:25,113 EPOCH 6 done: loss 0.0274 - lr: 0.000013
164
+ 2023-10-25 21:21:26,037 DEV : loss 0.15638913214206696 - f1-score (micro avg) 0.7679
165
+ 2023-10-25 21:21:26,042 ----------------------------------------------------------------------------------------------------
166
+ 2023-10-25 21:21:27,313 epoch 7 - iter 29/292 - loss 0.01451795 - time (sec): 1.27 - samples/sec: 2997.89 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 21:21:28,737 epoch 7 - iter 58/292 - loss 0.03043042 - time (sec): 2.69 - samples/sec: 3010.82 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 21:21:30,145 epoch 7 - iter 87/292 - loss 0.02516600 - time (sec): 4.10 - samples/sec: 3245.65 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 21:21:31,430 epoch 7 - iter 116/292 - loss 0.02158147 - time (sec): 5.39 - samples/sec: 3157.27 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 21:21:32,717 epoch 7 - iter 145/292 - loss 0.02062496 - time (sec): 6.67 - samples/sec: 3169.32 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 21:21:34,061 epoch 7 - iter 174/292 - loss 0.01787927 - time (sec): 8.02 - samples/sec: 3250.37 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 21:21:35,385 epoch 7 - iter 203/292 - loss 0.01848142 - time (sec): 9.34 - samples/sec: 3297.63 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 21:21:36,709 epoch 7 - iter 232/292 - loss 0.01855890 - time (sec): 10.67 - samples/sec: 3308.28 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 21:21:38,062 epoch 7 - iter 261/292 - loss 0.01791828 - time (sec): 12.02 - samples/sec: 3265.53 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-25 21:21:39,423 epoch 7 - iter 290/292 - loss 0.01785400 - time (sec): 13.38 - samples/sec: 3297.27 - lr: 0.000010 - momentum: 0.000000
176
+ 2023-10-25 21:21:39,515 ----------------------------------------------------------------------------------------------------
177
+ 2023-10-25 21:21:39,516 EPOCH 7 done: loss 0.0180 - lr: 0.000010
178
+ 2023-10-25 21:21:40,597 DEV : loss 0.18467850983142853 - f1-score (micro avg) 0.7632
179
+ 2023-10-25 21:21:40,601 ----------------------------------------------------------------------------------------------------
180
+ 2023-10-25 21:21:41,860 epoch 8 - iter 29/292 - loss 0.00890727 - time (sec): 1.26 - samples/sec: 3364.75 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-25 21:21:43,217 epoch 8 - iter 58/292 - loss 0.01626205 - time (sec): 2.61 - samples/sec: 3650.22 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 21:21:44,484 epoch 8 - iter 87/292 - loss 0.01503426 - time (sec): 3.88 - samples/sec: 3519.28 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-10-25 21:21:45,740 epoch 8 - iter 116/292 - loss 0.01367778 - time (sec): 5.14 - samples/sec: 3430.14 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 21:21:47,040 epoch 8 - iter 145/292 - loss 0.01407450 - time (sec): 6.44 - samples/sec: 3444.56 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 21:21:48,396 epoch 8 - iter 174/292 - loss 0.01584846 - time (sec): 7.79 - samples/sec: 3459.37 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 21:21:49,716 epoch 8 - iter 203/292 - loss 0.01528952 - time (sec): 9.11 - samples/sec: 3405.25 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 21:21:51,000 epoch 8 - iter 232/292 - loss 0.01505405 - time (sec): 10.40 - samples/sec: 3346.87 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 21:21:52,312 epoch 8 - iter 261/292 - loss 0.01625469 - time (sec): 11.71 - samples/sec: 3367.76 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 21:21:53,621 epoch 8 - iter 290/292 - loss 0.01598943 - time (sec): 13.02 - samples/sec: 3400.48 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 21:21:53,697 ----------------------------------------------------------------------------------------------------
191
+ 2023-10-25 21:21:53,697 EPOCH 8 done: loss 0.0160 - lr: 0.000007
192
+ 2023-10-25 21:21:54,623 DEV : loss 0.18894101679325104 - f1-score (micro avg) 0.7505
193
+ 2023-10-25 21:21:54,628 ----------------------------------------------------------------------------------------------------
194
+ 2023-10-25 21:21:55,912 epoch 9 - iter 29/292 - loss 0.00813305 - time (sec): 1.28 - samples/sec: 3571.13 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-10-25 21:21:57,150 epoch 9 - iter 58/292 - loss 0.00977612 - time (sec): 2.52 - samples/sec: 3468.56 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-10-25 21:21:58,365 epoch 9 - iter 87/292 - loss 0.01322378 - time (sec): 3.74 - samples/sec: 3555.01 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 21:21:59,642 epoch 9 - iter 116/292 - loss 0.01295467 - time (sec): 5.01 - samples/sec: 3637.29 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-10-25 21:22:00,834 epoch 9 - iter 145/292 - loss 0.01203134 - time (sec): 6.20 - samples/sec: 3567.96 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-10-25 21:22:02,159 epoch 9 - iter 174/292 - loss 0.01179165 - time (sec): 7.53 - samples/sec: 3515.78 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-10-25 21:22:03,457 epoch 9 - iter 203/292 - loss 0.01078956 - time (sec): 8.83 - samples/sec: 3470.82 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-10-25 21:22:04,698 epoch 9 - iter 232/292 - loss 0.00998269 - time (sec): 10.07 - samples/sec: 3414.02 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-10-25 21:22:06,032 epoch 9 - iter 261/292 - loss 0.00887687 - time (sec): 11.40 - samples/sec: 3443.18 - lr: 0.000004 - momentum: 0.000000
203
+ 2023-10-25 21:22:07,378 epoch 9 - iter 290/292 - loss 0.01054438 - time (sec): 12.75 - samples/sec: 3454.51 - lr: 0.000003 - momentum: 0.000000
204
+ 2023-10-25 21:22:07,467 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-25 21:22:07,467 EPOCH 9 done: loss 0.0105 - lr: 0.000003
206
+ 2023-10-25 21:22:08,374 DEV : loss 0.19431500136852264 - f1-score (micro avg) 0.757
207
+ 2023-10-25 21:22:08,379 ----------------------------------------------------------------------------------------------------
208
+ 2023-10-25 21:22:09,708 epoch 10 - iter 29/292 - loss 0.01082939 - time (sec): 1.33 - samples/sec: 3453.25 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-25 21:22:10,945 epoch 10 - iter 58/292 - loss 0.00934277 - time (sec): 2.57 - samples/sec: 3316.40 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-10-25 21:22:12,197 epoch 10 - iter 87/292 - loss 0.00832350 - time (sec): 3.82 - samples/sec: 3224.24 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-25 21:22:13,561 epoch 10 - iter 116/292 - loss 0.01106584 - time (sec): 5.18 - samples/sec: 3359.73 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-25 21:22:14,806 epoch 10 - iter 145/292 - loss 0.00926850 - time (sec): 6.43 - samples/sec: 3398.47 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-10-25 21:22:16,184 epoch 10 - iter 174/292 - loss 0.00795886 - time (sec): 7.80 - samples/sec: 3429.82 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-25 21:22:17,554 epoch 10 - iter 203/292 - loss 0.00803485 - time (sec): 9.17 - samples/sec: 3467.14 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-10-25 21:22:18,867 epoch 10 - iter 232/292 - loss 0.00888351 - time (sec): 10.49 - samples/sec: 3395.29 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-10-25 21:22:20,193 epoch 10 - iter 261/292 - loss 0.00841492 - time (sec): 11.81 - samples/sec: 3373.34 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-10-25 21:22:21,478 epoch 10 - iter 290/292 - loss 0.00810355 - time (sec): 13.10 - samples/sec: 3371.86 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-10-25 21:22:21,573 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-25 21:22:21,573 EPOCH 10 done: loss 0.0081 - lr: 0.000000
220
+ 2023-10-25 21:22:22,489 DEV : loss 0.19873516261577606 - f1-score (micro avg) 0.7592
221
+ 2023-10-25 21:22:22,963 ----------------------------------------------------------------------------------------------------
222
+ 2023-10-25 21:22:22,964 Loading model from best epoch ...
223
+ 2023-10-25 21:22:24,545 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
224
+ 2023-10-25 21:22:26,090
225
+ Results:
226
+ - F-score (micro) 0.7596
227
+ - F-score (macro) 0.6635
228
+ - Accuracy 0.6407
229
+
230
+ By class:
231
+ precision recall f1-score support
232
+
233
+ PER 0.8050 0.8305 0.8175 348
234
+ LOC 0.7096 0.8238 0.7624 261
235
+ ORG 0.3929 0.4231 0.4074 52
236
+ HumanProd 0.6154 0.7273 0.6667 22
237
+
238
+ micro avg 0.7285 0.7936 0.7596 683
239
+ macro avg 0.6307 0.7011 0.6635 683
240
+ weighted avg 0.7311 0.7936 0.7604 683
241
+
242
+ 2023-10-25 21:22:26,090 ----------------------------------------------------------------------------------------------------