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hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5

Token Classification
Flair
PyTorch
TensorBoard
French
sequence-tagger-model
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. loss.tsv +11 -0
  4. runs/events.out.tfevents.1697553500.bce904bcef33.2023.17 +3 -0
  5. test.tsv +0 -0
  6. training.log +236 -0
best-model.pt ADDED
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dev.tsv ADDED
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loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
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+ 1 14:39:56 0.0000 0.3537 0.1149 0.6916 0.6165 0.6519 0.5046
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+ 2 14:41:32 0.0000 0.1303 0.0899 0.7533 0.7839 0.7683 0.6393
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+ 3 14:43:12 0.0000 0.0926 0.1138 0.7102 0.7873 0.7468 0.6111
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+ 4 14:44:51 0.0000 0.0736 0.1754 0.6817 0.8020 0.7370 0.6044
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+ 5 14:46:28 0.0000 0.0577 0.1741 0.7316 0.8111 0.7693 0.6471
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+ 6 14:48:07 0.0000 0.0424 0.2055 0.7467 0.7704 0.7584 0.6288
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+ 7 14:49:45 0.0000 0.0292 0.2291 0.7152 0.7896 0.7505 0.6199
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+ 8 14:51:22 0.0000 0.0204 0.2420 0.7373 0.7715 0.7540 0.6200
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+ 9 14:52:59 0.0000 0.0142 0.2532 0.7431 0.7919 0.7667 0.6387
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+ 10 14:54:37 0.0000 0.0099 0.2603 0.7409 0.7862 0.7629 0.6330
runs/events.out.tfevents.1697553500.bce904bcef33.2023.17 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ size 1108164
test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 14:38:20,069 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:20,070 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): ElectraModel(
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+ (embeddings): ElectraEmbeddings(
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+ (word_embeddings): Embedding(32001, 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): ElectraEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x ElectraLayer(
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+ (attention): ElectraAttention(
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+ (self): ElectraSelfAttention(
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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): ElectraSelfOutput(
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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): ElectraIntermediate(
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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): ElectraOutput(
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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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+ )
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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=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-17 14:38:20,070 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:20,071 MultiCorpus: 7936 train + 992 dev + 992 test sentences
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+ - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /root/.flair/datasets/ner_icdar_europeana/fr
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+ 2023-10-17 14:38:20,071 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:20,071 Train: 7936 sentences
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+ 2023-10-17 14:38:20,071 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 14:38:20,071 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:20,071 Training Params:
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+ 2023-10-17 14:38:20,071 - learning_rate: "5e-05"
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+ 2023-10-17 14:38:20,071 - mini_batch_size: "4"
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+ 2023-10-17 14:38:20,071 - max_epochs: "10"
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+ 2023-10-17 14:38:20,071 - shuffle: "True"
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+ 2023-10-17 14:38:20,071 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:20,071 Plugins:
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+ 2023-10-17 14:38:20,071 - TensorboardLogger
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+ 2023-10-17 14:38:20,071 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 14:38:20,071 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:20,071 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 14:38:20,071 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 14:38:20,071 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:20,071 Computation:
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+ 2023-10-17 14:38:20,071 - compute on device: cuda:0
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+ 2023-10-17 14:38:20,071 - embedding storage: none
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+ 2023-10-17 14:38:20,071 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:20,071 Model training base path: "hmbench-icdar/fr-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-17 14:38:20,071 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:20,071 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:38:20,071 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 14:38:29,225 epoch 1 - iter 198/1984 - loss 1.95545665 - time (sec): 9.15 - samples/sec: 1762.25 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-17 14:38:38,397 epoch 1 - iter 396/1984 - loss 1.12220599 - time (sec): 18.32 - samples/sec: 1831.40 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 14:38:47,433 epoch 1 - iter 594/1984 - loss 0.83052688 - time (sec): 27.36 - samples/sec: 1800.67 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 14:38:56,632 epoch 1 - iter 792/1984 - loss 0.67249115 - time (sec): 36.56 - samples/sec: 1784.22 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 14:39:05,428 epoch 1 - iter 990/1984 - loss 0.57894481 - time (sec): 45.36 - samples/sec: 1777.03 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 14:39:13,948 epoch 1 - iter 1188/1984 - loss 0.50617737 - time (sec): 53.88 - samples/sec: 1799.57 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 14:39:23,095 epoch 1 - iter 1386/1984 - loss 0.45160565 - time (sec): 63.02 - samples/sec: 1807.03 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 14:39:32,404 epoch 1 - iter 1584/1984 - loss 0.41081686 - time (sec): 72.33 - samples/sec: 1804.38 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 14:39:42,888 epoch 1 - iter 1782/1984 - loss 0.38018847 - time (sec): 82.82 - samples/sec: 1775.37 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 14:39:53,047 epoch 1 - iter 1980/1984 - loss 0.35425717 - time (sec): 92.97 - samples/sec: 1760.16 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-17 14:39:53,226 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:39:53,227 EPOCH 1 done: loss 0.3537 - lr: 0.000050
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+ 2023-10-17 14:39:56,547 DEV : loss 0.11494190245866776 - f1-score (micro avg) 0.6519
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+ 2023-10-17 14:39:56,569 saving best model
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+ 2023-10-17 14:39:56,912 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:40:05,963 epoch 2 - iter 198/1984 - loss 0.11040149 - time (sec): 9.05 - samples/sec: 1883.05 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 14:40:14,960 epoch 2 - iter 396/1984 - loss 0.11430138 - time (sec): 18.05 - samples/sec: 1851.91 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 14:40:23,983 epoch 2 - iter 594/1984 - loss 0.12555273 - time (sec): 27.07 - samples/sec: 1837.77 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 14:40:33,040 epoch 2 - iter 792/1984 - loss 0.13261370 - time (sec): 36.13 - samples/sec: 1806.71 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 14:40:42,279 epoch 2 - iter 990/1984 - loss 0.13507266 - time (sec): 45.37 - samples/sec: 1809.43 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 14:40:51,368 epoch 2 - iter 1188/1984 - loss 0.13131044 - time (sec): 54.45 - samples/sec: 1798.92 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 14:41:00,548 epoch 2 - iter 1386/1984 - loss 0.13302616 - time (sec): 63.63 - samples/sec: 1791.07 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 14:41:09,476 epoch 2 - iter 1584/1984 - loss 0.13118869 - time (sec): 72.56 - samples/sec: 1793.98 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 14:41:18,829 epoch 2 - iter 1782/1984 - loss 0.13064899 - time (sec): 81.92 - samples/sec: 1797.43 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 14:41:28,044 epoch 2 - iter 1980/1984 - loss 0.13048013 - time (sec): 91.13 - samples/sec: 1796.27 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 14:41:28,226 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:41:28,226 EPOCH 2 done: loss 0.1303 - lr: 0.000044
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+ 2023-10-17 14:41:32,192 DEV : loss 0.08985628187656403 - f1-score (micro avg) 0.7683
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+ 2023-10-17 14:41:32,213 saving best model
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+ 2023-10-17 14:41:32,698 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:41:41,476 epoch 3 - iter 198/1984 - loss 0.09836698 - time (sec): 8.78 - samples/sec: 1731.66 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 14:41:50,622 epoch 3 - iter 396/1984 - loss 0.09517513 - time (sec): 17.92 - samples/sec: 1757.29 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 14:41:59,657 epoch 3 - iter 594/1984 - loss 0.09416783 - time (sec): 26.96 - samples/sec: 1794.17 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 14:42:08,870 epoch 3 - iter 792/1984 - loss 0.09163933 - time (sec): 36.17 - samples/sec: 1801.78 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 14:42:18,170 epoch 3 - iter 990/1984 - loss 0.09142898 - time (sec): 45.47 - samples/sec: 1793.64 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 14:42:28,214 epoch 3 - iter 1188/1984 - loss 0.09272082 - time (sec): 55.51 - samples/sec: 1776.56 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 14:42:38,994 epoch 3 - iter 1386/1984 - loss 0.09277418 - time (sec): 66.29 - samples/sec: 1725.49 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 14:42:49,340 epoch 3 - iter 1584/1984 - loss 0.09372745 - time (sec): 76.64 - samples/sec: 1702.85 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 14:42:58,868 epoch 3 - iter 1782/1984 - loss 0.09155199 - time (sec): 86.17 - samples/sec: 1712.32 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 14:43:08,051 epoch 3 - iter 1980/1984 - loss 0.09272496 - time (sec): 95.35 - samples/sec: 1716.29 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 14:43:08,231 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:43:08,231 EPOCH 3 done: loss 0.0926 - lr: 0.000039
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+ 2023-10-17 14:43:12,013 DEV : loss 0.11382433772087097 - f1-score (micro avg) 0.7468
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+ 2023-10-17 14:43:12,043 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:43:21,936 epoch 4 - iter 198/1984 - loss 0.07511519 - time (sec): 9.89 - samples/sec: 1594.72 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 14:43:31,544 epoch 4 - iter 396/1984 - loss 0.07526871 - time (sec): 19.50 - samples/sec: 1670.87 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 14:43:41,930 epoch 4 - iter 594/1984 - loss 0.07569617 - time (sec): 29.89 - samples/sec: 1621.54 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 14:43:51,570 epoch 4 - iter 792/1984 - loss 0.07339105 - time (sec): 39.53 - samples/sec: 1647.73 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 14:44:00,490 epoch 4 - iter 990/1984 - loss 0.07413341 - time (sec): 48.45 - samples/sec: 1685.78 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 14:44:09,434 epoch 4 - iter 1188/1984 - loss 0.07541001 - time (sec): 57.39 - samples/sec: 1703.83 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 14:44:18,710 epoch 4 - iter 1386/1984 - loss 0.07462380 - time (sec): 66.66 - samples/sec: 1714.27 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 14:44:28,053 epoch 4 - iter 1584/1984 - loss 0.07558972 - time (sec): 76.01 - samples/sec: 1716.80 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 14:44:37,952 epoch 4 - iter 1782/1984 - loss 0.07546181 - time (sec): 85.91 - samples/sec: 1714.79 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 14:44:48,067 epoch 4 - iter 1980/1984 - loss 0.07339469 - time (sec): 96.02 - samples/sec: 1704.56 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 14:44:48,248 ----------------------------------------------------------------------------------------------------
129
+ 2023-10-17 14:44:48,248 EPOCH 4 done: loss 0.0736 - lr: 0.000033
130
+ 2023-10-17 14:44:51,859 DEV : loss 0.17536717653274536 - f1-score (micro avg) 0.737
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+ 2023-10-17 14:44:51,882 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:45:00,911 epoch 5 - iter 198/1984 - loss 0.05079454 - time (sec): 9.03 - samples/sec: 1773.03 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 14:45:10,378 epoch 5 - iter 396/1984 - loss 0.05612183 - time (sec): 18.49 - samples/sec: 1774.34 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 14:45:20,089 epoch 5 - iter 594/1984 - loss 0.05327248 - time (sec): 28.21 - samples/sec: 1777.20 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 14:45:29,458 epoch 5 - iter 792/1984 - loss 0.05519592 - time (sec): 37.57 - samples/sec: 1764.35 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 14:45:38,763 epoch 5 - iter 990/1984 - loss 0.05814401 - time (sec): 46.88 - samples/sec: 1784.98 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 14:45:48,192 epoch 5 - iter 1188/1984 - loss 0.05928625 - time (sec): 56.31 - samples/sec: 1766.93 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 14:45:57,790 epoch 5 - iter 1386/1984 - loss 0.05868169 - time (sec): 65.91 - samples/sec: 1765.22 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 14:46:06,665 epoch 5 - iter 1584/1984 - loss 0.05899803 - time (sec): 74.78 - samples/sec: 1764.21 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 14:46:15,619 epoch 5 - iter 1782/1984 - loss 0.05870097 - time (sec): 83.73 - samples/sec: 1763.35 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 14:46:24,650 epoch 5 - iter 1980/1984 - loss 0.05779940 - time (sec): 92.77 - samples/sec: 1764.14 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 14:46:24,834 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-17 14:46:24,834 EPOCH 5 done: loss 0.0577 - lr: 0.000028
144
+ 2023-10-17 14:46:28,553 DEV : loss 0.1740676909685135 - f1-score (micro avg) 0.7693
145
+ 2023-10-17 14:46:28,576 saving best model
146
+ 2023-10-17 14:46:29,022 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-17 14:46:38,264 epoch 6 - iter 198/1984 - loss 0.03690461 - time (sec): 9.24 - samples/sec: 1792.31 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 14:46:47,589 epoch 6 - iter 396/1984 - loss 0.03910030 - time (sec): 18.57 - samples/sec: 1802.43 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 14:46:56,854 epoch 6 - iter 594/1984 - loss 0.03925035 - time (sec): 27.83 - samples/sec: 1802.69 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 14:47:07,108 epoch 6 - iter 792/1984 - loss 0.03792416 - time (sec): 38.08 - samples/sec: 1754.85 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 14:47:16,414 epoch 6 - iter 990/1984 - loss 0.03876486 - time (sec): 47.39 - samples/sec: 1755.06 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 14:47:25,956 epoch 6 - iter 1188/1984 - loss 0.03957422 - time (sec): 56.93 - samples/sec: 1764.00 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 14:47:36,075 epoch 6 - iter 1386/1984 - loss 0.04093735 - time (sec): 67.05 - samples/sec: 1728.86 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 14:47:45,389 epoch 6 - iter 1584/1984 - loss 0.04172085 - time (sec): 76.36 - samples/sec: 1722.22 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 14:47:54,555 epoch 6 - iter 1782/1984 - loss 0.04124955 - time (sec): 85.53 - samples/sec: 1727.67 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 14:48:03,703 epoch 6 - iter 1980/1984 - loss 0.04232429 - time (sec): 94.68 - samples/sec: 1728.57 - lr: 0.000022 - momentum: 0.000000
157
+ 2023-10-17 14:48:03,877 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-17 14:48:03,877 EPOCH 6 done: loss 0.0424 - lr: 0.000022
159
+ 2023-10-17 14:48:07,504 DEV : loss 0.20554155111312866 - f1-score (micro avg) 0.7584
160
+ 2023-10-17 14:48:07,528 ----------------------------------------------------------------------------------------------------
161
+ 2023-10-17 14:48:16,741 epoch 7 - iter 198/1984 - loss 0.02950479 - time (sec): 9.21 - samples/sec: 1724.69 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 14:48:26,412 epoch 7 - iter 396/1984 - loss 0.03032516 - time (sec): 18.88 - samples/sec: 1722.14 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 14:48:35,780 epoch 7 - iter 594/1984 - loss 0.03120726 - time (sec): 28.25 - samples/sec: 1730.28 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 14:48:45,211 epoch 7 - iter 792/1984 - loss 0.03102425 - time (sec): 37.68 - samples/sec: 1762.04 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 14:48:54,671 epoch 7 - iter 990/1984 - loss 0.03038851 - time (sec): 47.14 - samples/sec: 1788.78 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 14:49:03,867 epoch 7 - iter 1188/1984 - loss 0.02992404 - time (sec): 56.34 - samples/sec: 1783.21 - lr: 0.000019 - momentum: 0.000000
167
+ 2023-10-17 14:49:12,915 epoch 7 - iter 1386/1984 - loss 0.02931818 - time (sec): 65.39 - samples/sec: 1777.03 - lr: 0.000018 - momentum: 0.000000
168
+ 2023-10-17 14:49:22,413 epoch 7 - iter 1584/1984 - loss 0.02916391 - time (sec): 74.88 - samples/sec: 1765.34 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 14:49:31,394 epoch 7 - iter 1782/1984 - loss 0.03026648 - time (sec): 83.87 - samples/sec: 1767.12 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 14:49:40,661 epoch 7 - iter 1980/1984 - loss 0.02921112 - time (sec): 93.13 - samples/sec: 1756.70 - lr: 0.000017 - momentum: 0.000000
171
+ 2023-10-17 14:49:40,851 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:49:40,851 EPOCH 7 done: loss 0.0292 - lr: 0.000017
173
+ 2023-10-17 14:49:45,123 DEV : loss 0.2290586233139038 - f1-score (micro avg) 0.7505
174
+ 2023-10-17 14:49:45,146 ----------------------------------------------------------------------------------------------------
175
+ 2023-10-17 14:49:54,826 epoch 8 - iter 198/1984 - loss 0.01513390 - time (sec): 9.68 - samples/sec: 1680.82 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 14:50:04,078 epoch 8 - iter 396/1984 - loss 0.01803984 - time (sec): 18.93 - samples/sec: 1706.25 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 14:50:13,291 epoch 8 - iter 594/1984 - loss 0.01882815 - time (sec): 28.14 - samples/sec: 1733.21 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 14:50:22,747 epoch 8 - iter 792/1984 - loss 0.01927639 - time (sec): 37.60 - samples/sec: 1736.55 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 14:50:32,072 epoch 8 - iter 990/1984 - loss 0.01968799 - time (sec): 46.92 - samples/sec: 1735.11 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 14:50:41,324 epoch 8 - iter 1188/1984 - loss 0.01995151 - time (sec): 56.18 - samples/sec: 1729.33 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 14:50:50,348 epoch 8 - iter 1386/1984 - loss 0.02024781 - time (sec): 65.20 - samples/sec: 1743.15 - lr: 0.000013 - momentum: 0.000000
182
+ 2023-10-17 14:50:59,511 epoch 8 - iter 1584/1984 - loss 0.02068668 - time (sec): 74.36 - samples/sec: 1753.81 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 14:51:09,432 epoch 8 - iter 1782/1984 - loss 0.02062173 - time (sec): 84.28 - samples/sec: 1735.43 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 14:51:18,868 epoch 8 - iter 1980/1984 - loss 0.02039143 - time (sec): 93.72 - samples/sec: 1745.87 - lr: 0.000011 - momentum: 0.000000
185
+ 2023-10-17 14:51:19,051 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-17 14:51:19,051 EPOCH 8 done: loss 0.0204 - lr: 0.000011
187
+ 2023-10-17 14:51:22,854 DEV : loss 0.24203334748744965 - f1-score (micro avg) 0.754
188
+ 2023-10-17 14:51:22,894 ----------------------------------------------------------------------------------------------------
189
+ 2023-10-17 14:51:32,128 epoch 9 - iter 198/1984 - loss 0.01084987 - time (sec): 9.23 - samples/sec: 1823.34 - lr: 0.000011 - momentum: 0.000000
190
+ 2023-10-17 14:51:41,212 epoch 9 - iter 396/1984 - loss 0.01141896 - time (sec): 18.32 - samples/sec: 1843.65 - lr: 0.000010 - momentum: 0.000000
191
+ 2023-10-17 14:51:50,845 epoch 9 - iter 594/1984 - loss 0.01119116 - time (sec): 27.95 - samples/sec: 1795.12 - lr: 0.000009 - momentum: 0.000000
192
+ 2023-10-17 14:52:00,215 epoch 9 - iter 792/1984 - loss 0.01278662 - time (sec): 37.32 - samples/sec: 1777.27 - lr: 0.000009 - momentum: 0.000000
193
+ 2023-10-17 14:52:09,971 epoch 9 - iter 990/1984 - loss 0.01250870 - time (sec): 47.08 - samples/sec: 1768.96 - lr: 0.000008 - momentum: 0.000000
194
+ 2023-10-17 14:52:19,310 epoch 9 - iter 1188/1984 - loss 0.01336412 - time (sec): 56.41 - samples/sec: 1757.02 - lr: 0.000008 - momentum: 0.000000
195
+ 2023-10-17 14:52:28,724 epoch 9 - iter 1386/1984 - loss 0.01389645 - time (sec): 65.83 - samples/sec: 1756.38 - lr: 0.000007 - momentum: 0.000000
196
+ 2023-10-17 14:52:37,914 epoch 9 - iter 1584/1984 - loss 0.01428654 - time (sec): 75.02 - samples/sec: 1756.92 - lr: 0.000007 - momentum: 0.000000
197
+ 2023-10-17 14:52:47,117 epoch 9 - iter 1782/1984 - loss 0.01382085 - time (sec): 84.22 - samples/sec: 1754.81 - lr: 0.000006 - momentum: 0.000000
198
+ 2023-10-17 14:52:56,208 epoch 9 - iter 1980/1984 - loss 0.01418063 - time (sec): 93.31 - samples/sec: 1754.30 - lr: 0.000006 - momentum: 0.000000
199
+ 2023-10-17 14:52:56,394 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-17 14:52:56,394 EPOCH 9 done: loss 0.0142 - lr: 0.000006
201
+ 2023-10-17 14:52:59,962 DEV : loss 0.25322186946868896 - f1-score (micro avg) 0.7667
202
+ 2023-10-17 14:52:59,990 ----------------------------------------------------------------------------------------------------
203
+ 2023-10-17 14:53:09,490 epoch 10 - iter 198/1984 - loss 0.01078046 - time (sec): 9.50 - samples/sec: 1780.74 - lr: 0.000005 - momentum: 0.000000
204
+ 2023-10-17 14:53:18,596 epoch 10 - iter 396/1984 - loss 0.01032270 - time (sec): 18.61 - samples/sec: 1793.76 - lr: 0.000004 - momentum: 0.000000
205
+ 2023-10-17 14:53:28,045 epoch 10 - iter 594/1984 - loss 0.01021630 - time (sec): 28.05 - samples/sec: 1788.38 - lr: 0.000004 - momentum: 0.000000
206
+ 2023-10-17 14:53:38,161 epoch 10 - iter 792/1984 - loss 0.00952196 - time (sec): 38.17 - samples/sec: 1717.08 - lr: 0.000003 - momentum: 0.000000
207
+ 2023-10-17 14:53:47,799 epoch 10 - iter 990/1984 - loss 0.01024037 - time (sec): 47.81 - samples/sec: 1726.62 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-17 14:53:57,540 epoch 10 - iter 1188/1984 - loss 0.01046950 - time (sec): 57.55 - samples/sec: 1708.01 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-17 14:54:06,228 epoch 10 - iter 1386/1984 - loss 0.01083885 - time (sec): 66.24 - samples/sec: 1730.07 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 14:54:15,164 epoch 10 - iter 1584/1984 - loss 0.01034329 - time (sec): 75.17 - samples/sec: 1735.55 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-17 14:54:23,815 epoch 10 - iter 1782/1984 - loss 0.00998575 - time (sec): 83.82 - samples/sec: 1742.01 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 14:54:33,267 epoch 10 - iter 1980/1984 - loss 0.00987809 - time (sec): 93.28 - samples/sec: 1755.56 - lr: 0.000000 - momentum: 0.000000
213
+ 2023-10-17 14:54:33,445 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-17 14:54:33,445 EPOCH 10 done: loss 0.0099 - lr: 0.000000
215
+ 2023-10-17 14:54:37,004 DEV : loss 0.2603410482406616 - f1-score (micro avg) 0.7629
216
+ 2023-10-17 14:54:37,394 ----------------------------------------------------------------------------------------------------
217
+ 2023-10-17 14:54:37,395 Loading model from best epoch ...
218
+ 2023-10-17 14:54:38,770 SequenceTagger predicts: Dictionary with 13 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG
219
+ 2023-10-17 14:54:42,106
220
+ Results:
221
+ - F-score (micro) 0.7645
222
+ - F-score (macro) 0.6852
223
+ - Accuracy 0.6492
224
+
225
+ By class:
226
+ precision recall f1-score support
227
+
228
+ LOC 0.7888 0.8611 0.8234 655
229
+ PER 0.7399 0.7399 0.7399 223
230
+ ORG 0.4884 0.4961 0.4922 127
231
+
232
+ micro avg 0.7423 0.7881 0.7645 1005
233
+ macro avg 0.6724 0.6990 0.6852 1005
234
+ weighted avg 0.7400 0.7881 0.7630 1005
235
+
236
+ 2023-10-17 14:54:42,106 ----------------------------------------------------------------------------------------------------