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hmbench-icdar-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2

Token Classification
Flair
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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.1697543459.bce904bcef33.2023.5 +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 11:52:32 0.0000 0.3535 0.1160 0.7045 0.7715 0.7365 0.6014
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+ 2 11:54:07 0.0000 0.1253 0.0957 0.7392 0.7760 0.7572 0.6288
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+ 3 11:55:40 0.0000 0.0960 0.1243 0.6997 0.7749 0.7354 0.6078
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+ 4 11:57:15 0.0000 0.0763 0.1847 0.7184 0.7647 0.7408 0.6074
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+ 5 11:58:50 0.0000 0.0556 0.1827 0.7413 0.7715 0.7561 0.6297
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+ 6 12:00:23 0.0000 0.0406 0.1922 0.7412 0.7421 0.7417 0.6091
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+ 7 12:01:58 0.0000 0.0273 0.1858 0.7350 0.7783 0.7560 0.6277
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+ 8 12:03:32 0.0000 0.0196 0.2399 0.7341 0.7839 0.7582 0.6271
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+ 9 12:05:08 0.0000 0.0133 0.2514 0.7227 0.7783 0.7495 0.6209
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+ 10 12:06:51 0.0000 0.0083 0.2530 0.7419 0.7738 0.7575 0.6310
runs/events.out.tfevents.1697543459.bce904bcef33.2023.5 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 11:50:59,878 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:50:59,879 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 11:50:59,879 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:50:59,879 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 11:50:59,879 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:50:59,879 Train: 7936 sentences
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+ 2023-10-17 11:50:59,879 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 11:50:59,879 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:50:59,879 Training Params:
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+ 2023-10-17 11:50:59,879 - learning_rate: "5e-05"
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+ 2023-10-17 11:50:59,879 - mini_batch_size: "4"
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+ 2023-10-17 11:50:59,879 - max_epochs: "10"
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+ 2023-10-17 11:50:59,879 - shuffle: "True"
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+ 2023-10-17 11:50:59,879 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:50:59,879 Plugins:
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+ 2023-10-17 11:50:59,879 - TensorboardLogger
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+ 2023-10-17 11:50:59,879 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 11:50:59,879 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:50:59,879 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 11:50:59,879 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 11:50:59,879 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:50:59,879 Computation:
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+ 2023-10-17 11:50:59,880 - compute on device: cuda:0
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+ 2023-10-17 11:50:59,880 - embedding storage: none
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+ 2023-10-17 11:50:59,880 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:50:59,880 Model training base path: "hmbench-icdar/fr-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2"
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+ 2023-10-17 11:50:59,880 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:50:59,880 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:50:59,880 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 11:51:08,865 epoch 1 - iter 198/1984 - loss 1.99658672 - time (sec): 8.98 - samples/sec: 1773.62 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-17 11:51:17,977 epoch 1 - iter 396/1984 - loss 1.11484466 - time (sec): 18.10 - samples/sec: 1803.46 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 11:51:26,677 epoch 1 - iter 594/1984 - loss 0.82031286 - time (sec): 26.80 - samples/sec: 1822.19 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 11:51:35,306 epoch 1 - iter 792/1984 - loss 0.65663170 - time (sec): 35.42 - samples/sec: 1846.14 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 11:51:44,319 epoch 1 - iter 990/1984 - loss 0.55179417 - time (sec): 44.44 - samples/sec: 1861.84 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 11:51:53,348 epoch 1 - iter 1188/1984 - loss 0.49119338 - time (sec): 53.47 - samples/sec: 1852.30 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 11:52:02,343 epoch 1 - iter 1386/1984 - loss 0.44256195 - time (sec): 62.46 - samples/sec: 1858.68 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 11:52:11,442 epoch 1 - iter 1584/1984 - loss 0.40740944 - time (sec): 71.56 - samples/sec: 1839.94 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 11:52:20,490 epoch 1 - iter 1782/1984 - loss 0.37924328 - time (sec): 80.61 - samples/sec: 1831.13 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 11:52:29,468 epoch 1 - iter 1980/1984 - loss 0.35390636 - time (sec): 89.59 - samples/sec: 1827.85 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-17 11:52:29,645 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:52:29,645 EPOCH 1 done: loss 0.3535 - lr: 0.000050
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+ 2023-10-17 11:52:32,795 DEV : loss 0.11604664474725723 - f1-score (micro avg) 0.7365
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+ 2023-10-17 11:52:32,816 saving best model
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+ 2023-10-17 11:52:33,192 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:52:42,328 epoch 2 - iter 198/1984 - loss 0.14192747 - time (sec): 9.13 - samples/sec: 1705.31 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 11:52:51,310 epoch 2 - iter 396/1984 - loss 0.12714871 - time (sec): 18.12 - samples/sec: 1747.11 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 11:53:00,111 epoch 2 - iter 594/1984 - loss 0.12855381 - time (sec): 26.92 - samples/sec: 1792.02 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 11:53:08,839 epoch 2 - iter 792/1984 - loss 0.12867843 - time (sec): 35.65 - samples/sec: 1811.84 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 11:53:17,959 epoch 2 - iter 990/1984 - loss 0.12838419 - time (sec): 44.77 - samples/sec: 1825.07 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 11:53:26,874 epoch 2 - iter 1188/1984 - loss 0.12740182 - time (sec): 53.68 - samples/sec: 1822.71 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 11:53:35,927 epoch 2 - iter 1386/1984 - loss 0.12351792 - time (sec): 62.73 - samples/sec: 1822.16 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 11:53:44,983 epoch 2 - iter 1584/1984 - loss 0.12218031 - time (sec): 71.79 - samples/sec: 1830.55 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 11:53:54,028 epoch 2 - iter 1782/1984 - loss 0.12157226 - time (sec): 80.83 - samples/sec: 1829.60 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 11:54:03,013 epoch 2 - iter 1980/1984 - loss 0.12534668 - time (sec): 89.82 - samples/sec: 1822.84 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 11:54:03,190 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:54:03,190 EPOCH 2 done: loss 0.1253 - lr: 0.000044
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+ 2023-10-17 11:54:06,991 DEV : loss 0.09573990851640701 - f1-score (micro avg) 0.7572
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+ 2023-10-17 11:54:07,012 saving best model
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+ 2023-10-17 11:54:07,504 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:54:16,693 epoch 3 - iter 198/1984 - loss 0.09494312 - time (sec): 9.19 - samples/sec: 1831.31 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 11:54:25,610 epoch 3 - iter 396/1984 - loss 0.09329297 - time (sec): 18.10 - samples/sec: 1821.18 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 11:54:34,690 epoch 3 - iter 594/1984 - loss 0.09450932 - time (sec): 27.18 - samples/sec: 1832.48 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 11:54:43,567 epoch 3 - iter 792/1984 - loss 0.09880612 - time (sec): 36.06 - samples/sec: 1796.09 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 11:54:52,286 epoch 3 - iter 990/1984 - loss 0.09577827 - time (sec): 44.78 - samples/sec: 1815.05 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 11:55:00,816 epoch 3 - iter 1188/1984 - loss 0.09551043 - time (sec): 53.31 - samples/sec: 1829.07 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 11:55:09,757 epoch 3 - iter 1386/1984 - loss 0.09500722 - time (sec): 62.25 - samples/sec: 1852.97 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 11:55:18,875 epoch 3 - iter 1584/1984 - loss 0.09605364 - time (sec): 71.37 - samples/sec: 1848.06 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 11:55:27,832 epoch 3 - iter 1782/1984 - loss 0.09581236 - time (sec): 80.32 - samples/sec: 1834.01 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 11:55:36,807 epoch 3 - iter 1980/1984 - loss 0.09610021 - time (sec): 89.30 - samples/sec: 1832.73 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 11:55:36,985 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:55:36,986 EPOCH 3 done: loss 0.0960 - lr: 0.000039
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+ 2023-10-17 11:55:40,348 DEV : loss 0.12434609979391098 - f1-score (micro avg) 0.7354
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+ 2023-10-17 11:55:40,368 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:55:49,368 epoch 4 - iter 198/1984 - loss 0.05981882 - time (sec): 9.00 - samples/sec: 1857.83 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 11:55:58,236 epoch 4 - iter 396/1984 - loss 0.06629030 - time (sec): 17.87 - samples/sec: 1808.21 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 11:56:07,217 epoch 4 - iter 594/1984 - loss 0.07164876 - time (sec): 26.85 - samples/sec: 1770.52 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 11:56:16,630 epoch 4 - iter 792/1984 - loss 0.07351127 - time (sec): 36.26 - samples/sec: 1781.58 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 11:56:25,747 epoch 4 - iter 990/1984 - loss 0.07151819 - time (sec): 45.38 - samples/sec: 1786.64 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 11:56:34,907 epoch 4 - iter 1188/1984 - loss 0.07095217 - time (sec): 54.54 - samples/sec: 1778.05 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 11:56:43,989 epoch 4 - iter 1386/1984 - loss 0.07050035 - time (sec): 63.62 - samples/sec: 1786.16 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 11:56:53,161 epoch 4 - iter 1584/1984 - loss 0.07691480 - time (sec): 72.79 - samples/sec: 1792.55 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 11:57:02,381 epoch 4 - iter 1782/1984 - loss 0.07542490 - time (sec): 82.01 - samples/sec: 1799.04 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 11:57:11,654 epoch 4 - iter 1980/1984 - loss 0.07641851 - time (sec): 91.28 - samples/sec: 1793.27 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 11:57:11,837 ----------------------------------------------------------------------------------------------------
129
+ 2023-10-17 11:57:11,837 EPOCH 4 done: loss 0.0763 - lr: 0.000033
130
+ 2023-10-17 11:57:15,244 DEV : loss 0.18466949462890625 - f1-score (micro avg) 0.7408
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+ 2023-10-17 11:57:15,266 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 11:57:24,362 epoch 5 - iter 198/1984 - loss 0.04859297 - time (sec): 9.10 - samples/sec: 1863.76 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 11:57:33,455 epoch 5 - iter 396/1984 - loss 0.04635554 - time (sec): 18.19 - samples/sec: 1835.22 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 11:57:42,513 epoch 5 - iter 594/1984 - loss 0.04871548 - time (sec): 27.25 - samples/sec: 1821.40 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 11:57:51,623 epoch 5 - iter 792/1984 - loss 0.05252835 - time (sec): 36.36 - samples/sec: 1805.94 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 11:58:00,844 epoch 5 - iter 990/1984 - loss 0.05388850 - time (sec): 45.58 - samples/sec: 1792.29 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 11:58:09,855 epoch 5 - iter 1188/1984 - loss 0.05515150 - time (sec): 54.59 - samples/sec: 1778.02 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 11:58:19,340 epoch 5 - iter 1386/1984 - loss 0.05623579 - time (sec): 64.07 - samples/sec: 1779.81 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 11:58:28,807 epoch 5 - iter 1584/1984 - loss 0.05445857 - time (sec): 73.54 - samples/sec: 1779.22 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 11:58:37,858 epoch 5 - iter 1782/1984 - loss 0.05549020 - time (sec): 82.59 - samples/sec: 1778.92 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 11:58:46,900 epoch 5 - iter 1980/1984 - loss 0.05573079 - time (sec): 91.63 - samples/sec: 1786.04 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 11:58:47,077 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-17 11:58:47,077 EPOCH 5 done: loss 0.0556 - lr: 0.000028
144
+ 2023-10-17 11:58:50,444 DEV : loss 0.1826779842376709 - f1-score (micro avg) 0.7561
145
+ 2023-10-17 11:58:50,465 ----------------------------------------------------------------------------------------------------
146
+ 2023-10-17 11:58:59,462 epoch 6 - iter 198/1984 - loss 0.03393163 - time (sec): 9.00 - samples/sec: 1795.87 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 11:59:08,616 epoch 6 - iter 396/1984 - loss 0.03547336 - time (sec): 18.15 - samples/sec: 1805.34 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 11:59:17,653 epoch 6 - iter 594/1984 - loss 0.03602657 - time (sec): 27.19 - samples/sec: 1787.83 - lr: 0.000026 - momentum: 0.000000
149
+ 2023-10-17 11:59:26,822 epoch 6 - iter 792/1984 - loss 0.03952462 - time (sec): 36.35 - samples/sec: 1804.95 - lr: 0.000026 - momentum: 0.000000
150
+ 2023-10-17 11:59:36,151 epoch 6 - iter 990/1984 - loss 0.03912622 - time (sec): 45.68 - samples/sec: 1810.09 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 11:59:45,232 epoch 6 - iter 1188/1984 - loss 0.03870866 - time (sec): 54.77 - samples/sec: 1813.32 - lr: 0.000024 - momentum: 0.000000
152
+ 2023-10-17 11:59:53,643 epoch 6 - iter 1386/1984 - loss 0.04046966 - time (sec): 63.18 - samples/sec: 1815.61 - lr: 0.000024 - momentum: 0.000000
153
+ 2023-10-17 12:00:02,231 epoch 6 - iter 1584/1984 - loss 0.03983182 - time (sec): 71.76 - samples/sec: 1827.05 - lr: 0.000023 - momentum: 0.000000
154
+ 2023-10-17 12:00:10,739 epoch 6 - iter 1782/1984 - loss 0.03988919 - time (sec): 80.27 - samples/sec: 1834.23 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 12:00:19,695 epoch 6 - iter 1980/1984 - loss 0.04066461 - time (sec): 89.23 - samples/sec: 1834.70 - lr: 0.000022 - momentum: 0.000000
156
+ 2023-10-17 12:00:19,885 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-17 12:00:19,885 EPOCH 6 done: loss 0.0406 - lr: 0.000022
158
+ 2023-10-17 12:00:23,280 DEV : loss 0.1922488510608673 - f1-score (micro avg) 0.7417
159
+ 2023-10-17 12:00:23,303 ----------------------------------------------------------------------------------------------------
160
+ 2023-10-17 12:00:32,599 epoch 7 - iter 198/1984 - loss 0.03507544 - time (sec): 9.29 - samples/sec: 1779.65 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 12:00:41,907 epoch 7 - iter 396/1984 - loss 0.03033014 - time (sec): 18.60 - samples/sec: 1789.70 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 12:00:51,166 epoch 7 - iter 594/1984 - loss 0.02968150 - time (sec): 27.86 - samples/sec: 1802.14 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 12:01:00,212 epoch 7 - iter 792/1984 - loss 0.02789631 - time (sec): 36.91 - samples/sec: 1810.72 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 12:01:08,802 epoch 7 - iter 990/1984 - loss 0.02776484 - time (sec): 45.50 - samples/sec: 1838.23 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 12:01:17,842 epoch 7 - iter 1188/1984 - loss 0.02661241 - time (sec): 54.54 - samples/sec: 1822.87 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 12:01:26,856 epoch 7 - iter 1386/1984 - loss 0.02640200 - time (sec): 63.55 - samples/sec: 1809.99 - lr: 0.000018 - momentum: 0.000000
167
+ 2023-10-17 12:01:35,967 epoch 7 - iter 1584/1984 - loss 0.02663353 - time (sec): 72.66 - samples/sec: 1806.05 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 12:01:44,950 epoch 7 - iter 1782/1984 - loss 0.02669130 - time (sec): 81.64 - samples/sec: 1798.68 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 12:01:54,310 epoch 7 - iter 1980/1984 - loss 0.02730554 - time (sec): 91.00 - samples/sec: 1798.73 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 12:01:54,485 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:01:54,485 EPOCH 7 done: loss 0.0273 - lr: 0.000017
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+ 2023-10-17 12:01:58,292 DEV : loss 0.18583810329437256 - f1-score (micro avg) 0.756
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+ 2023-10-17 12:01:58,313 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:02:07,377 epoch 8 - iter 198/1984 - loss 0.01962946 - time (sec): 9.06 - samples/sec: 1832.57 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 12:02:16,417 epoch 8 - iter 396/1984 - loss 0.02070604 - time (sec): 18.10 - samples/sec: 1860.49 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 12:02:25,338 epoch 8 - iter 594/1984 - loss 0.02069916 - time (sec): 27.02 - samples/sec: 1830.33 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 12:02:34,277 epoch 8 - iter 792/1984 - loss 0.02086583 - time (sec): 35.96 - samples/sec: 1822.60 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 12:02:43,312 epoch 8 - iter 990/1984 - loss 0.01924450 - time (sec): 45.00 - samples/sec: 1831.26 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 12:02:52,365 epoch 8 - iter 1188/1984 - loss 0.01949509 - time (sec): 54.05 - samples/sec: 1819.84 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 12:03:01,315 epoch 8 - iter 1386/1984 - loss 0.01960191 - time (sec): 63.00 - samples/sec: 1804.63 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 12:03:10,558 epoch 8 - iter 1584/1984 - loss 0.02035400 - time (sec): 72.24 - samples/sec: 1811.71 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 12:03:19,519 epoch 8 - iter 1782/1984 - loss 0.01981080 - time (sec): 81.20 - samples/sec: 1809.61 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 12:03:28,832 epoch 8 - iter 1980/1984 - loss 0.01959358 - time (sec): 90.52 - samples/sec: 1808.27 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 12:03:29,026 ----------------------------------------------------------------------------------------------------
185
+ 2023-10-17 12:03:29,027 EPOCH 8 done: loss 0.0196 - lr: 0.000011
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+ 2023-10-17 12:03:32,455 DEV : loss 0.23994652926921844 - f1-score (micro avg) 0.7582
187
+ 2023-10-17 12:03:32,476 saving best model
188
+ 2023-10-17 12:03:32,872 ----------------------------------------------------------------------------------------------------
189
+ 2023-10-17 12:03:42,075 epoch 9 - iter 198/1984 - loss 0.01369371 - time (sec): 9.20 - samples/sec: 1698.60 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 12:03:51,142 epoch 9 - iter 396/1984 - loss 0.01542831 - time (sec): 18.27 - samples/sec: 1744.84 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 12:04:00,413 epoch 9 - iter 594/1984 - loss 0.01701451 - time (sec): 27.54 - samples/sec: 1752.27 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 12:04:09,646 epoch 9 - iter 792/1984 - loss 0.01617942 - time (sec): 36.77 - samples/sec: 1769.71 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 12:04:18,868 epoch 9 - iter 990/1984 - loss 0.01513742 - time (sec): 45.99 - samples/sec: 1764.51 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 12:04:28,200 epoch 9 - iter 1188/1984 - loss 0.01474000 - time (sec): 55.33 - samples/sec: 1778.49 - lr: 0.000008 - momentum: 0.000000
195
+ 2023-10-17 12:04:37,255 epoch 9 - iter 1386/1984 - loss 0.01453604 - time (sec): 64.38 - samples/sec: 1788.32 - lr: 0.000007 - momentum: 0.000000
196
+ 2023-10-17 12:04:46,295 epoch 9 - iter 1584/1984 - loss 0.01380448 - time (sec): 73.42 - samples/sec: 1786.61 - lr: 0.000007 - momentum: 0.000000
197
+ 2023-10-17 12:04:55,527 epoch 9 - iter 1782/1984 - loss 0.01369184 - time (sec): 82.65 - samples/sec: 1786.61 - lr: 0.000006 - momentum: 0.000000
198
+ 2023-10-17 12:05:04,642 epoch 9 - iter 1980/1984 - loss 0.01333927 - time (sec): 91.77 - samples/sec: 1784.22 - lr: 0.000006 - momentum: 0.000000
199
+ 2023-10-17 12:05:04,808 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-17 12:05:04,808 EPOCH 9 done: loss 0.0133 - lr: 0.000006
201
+ 2023-10-17 12:05:08,300 DEV : loss 0.25141069293022156 - f1-score (micro avg) 0.7495
202
+ 2023-10-17 12:05:08,331 ----------------------------------------------------------------------------------------------------
203
+ 2023-10-17 12:05:19,095 epoch 10 - iter 198/1984 - loss 0.00791381 - time (sec): 10.76 - samples/sec: 1555.82 - lr: 0.000005 - momentum: 0.000000
204
+ 2023-10-17 12:05:28,038 epoch 10 - iter 396/1984 - loss 0.00907342 - time (sec): 19.70 - samples/sec: 1675.12 - lr: 0.000004 - momentum: 0.000000
205
+ 2023-10-17 12:05:37,094 epoch 10 - iter 594/1984 - loss 0.00841106 - time (sec): 28.76 - samples/sec: 1736.20 - lr: 0.000004 - momentum: 0.000000
206
+ 2023-10-17 12:05:46,297 epoch 10 - iter 792/1984 - loss 0.00778224 - time (sec): 37.96 - samples/sec: 1732.33 - lr: 0.000003 - momentum: 0.000000
207
+ 2023-10-17 12:05:55,632 epoch 10 - iter 990/1984 - loss 0.00790979 - time (sec): 47.30 - samples/sec: 1754.39 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-17 12:06:06,086 epoch 10 - iter 1188/1984 - loss 0.00810074 - time (sec): 57.75 - samples/sec: 1728.90 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-17 12:06:16,433 epoch 10 - iter 1386/1984 - loss 0.00812282 - time (sec): 68.10 - samples/sec: 1708.90 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 12:06:26,896 epoch 10 - iter 1584/1984 - loss 0.00827390 - time (sec): 78.56 - samples/sec: 1683.18 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-17 12:06:37,153 epoch 10 - iter 1782/1984 - loss 0.00865749 - time (sec): 88.82 - samples/sec: 1664.10 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 12:06:47,233 epoch 10 - iter 1980/1984 - loss 0.00828889 - time (sec): 98.90 - samples/sec: 1655.75 - lr: 0.000000 - momentum: 0.000000
213
+ 2023-10-17 12:06:47,441 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-17 12:06:47,441 EPOCH 10 done: loss 0.0083 - lr: 0.000000
215
+ 2023-10-17 12:06:51,115 DEV : loss 0.2530412971973419 - f1-score (micro avg) 0.7575
216
+ 2023-10-17 12:06:51,546 ----------------------------------------------------------------------------------------------------
217
+ 2023-10-17 12:06:51,547 Loading model from best epoch ...
218
+ 2023-10-17 12:06:52,995 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 12:06:56,574
220
+ Results:
221
+ - F-score (micro) 0.7556
222
+ - F-score (macro) 0.6677
223
+ - Accuracy 0.6401
224
+
225
+ By class:
226
+ precision recall f1-score support
227
+
228
+ LOC 0.8259 0.8183 0.8221 655
229
+ PER 0.6522 0.8072 0.7214 223
230
+ ORG 0.5000 0.4252 0.4596 127
231
+
232
+ micro avg 0.7454 0.7662 0.7556 1005
233
+ macro avg 0.6594 0.6836 0.6677 1005
234
+ weighted avg 0.7462 0.7662 0.7539 1005
235
+
236
+ 2023-10-17 12:06:56,574 ----------------------------------------------------------------------------------------------------