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hmbench-icdar-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1

Token Classification
Flair
PyTorch
French
sequence-tagger-model
Model card Files Community
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. loss.tsv +11 -0
  4. test.tsv +0 -0
  5. training.log +240 -0
best-model.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:fc67a8c79f6f07f54eb88743de28302ff1cf3b7e56b71a21a9a5cfed3396a368
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+ size 443311111
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 20:54:53 0.0000 0.3590 0.1057 0.6072 0.7206 0.6591 0.5226
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+ 2 20:56:32 0.0000 0.1115 0.1009 0.6697 0.7477 0.7066 0.5650
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+ 3 20:58:09 0.0000 0.0817 0.1415 0.7041 0.7941 0.7464 0.6147
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+ 4 20:59:46 0.0000 0.0639 0.1469 0.7236 0.7760 0.7489 0.6191
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+ 5 21:01:21 0.0000 0.0460 0.1644 0.7735 0.7726 0.7731 0.6492
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+ 6 21:02:57 0.0000 0.0336 0.2008 0.7583 0.7489 0.7536 0.6199
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+ 7 21:04:32 0.0000 0.0258 0.1975 0.7450 0.7930 0.7682 0.6443
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+ 8 21:06:07 0.0000 0.0177 0.2037 0.7580 0.7760 0.7669 0.6435
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+ 9 21:07:42 0.0000 0.0123 0.2158 0.7397 0.7907 0.7644 0.6378
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+ 10 21:09:19 0.0000 0.0088 0.2261 0.7457 0.7896 0.7670 0.6421
test.tsv ADDED
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training.log ADDED
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+ 2023-10-13 20:53:18,545 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:53:18,546 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(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): 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=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-13 20:53:18,546 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:53:18,547 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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+ 2023-10-13 20:53:18,547 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:53:18,547 Train: 7936 sentences
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+ 2023-10-13 20:53:18,547 (train_with_dev=False, train_with_test=False)
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+ 2023-10-13 20:53:18,547 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:53:18,547 Training Params:
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+ 2023-10-13 20:53:18,547 - learning_rate: "3e-05"
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+ 2023-10-13 20:53:18,547 - mini_batch_size: "4"
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+ 2023-10-13 20:53:18,547 - max_epochs: "10"
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+ 2023-10-13 20:53:18,547 - shuffle: "True"
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+ 2023-10-13 20:53:18,547 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:53:18,547 Plugins:
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+ 2023-10-13 20:53:18,547 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-13 20:53:18,547 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:53:18,547 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-13 20:53:18,547 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-13 20:53:18,547 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:53:18,547 Computation:
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+ 2023-10-13 20:53:18,547 - compute on device: cuda:0
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+ 2023-10-13 20:53:18,547 - embedding storage: none
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+ 2023-10-13 20:53:18,547 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:53:18,547 Model training base path: "hmbench-icdar/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-13 20:53:18,547 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:53:18,547 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:53:28,676 epoch 1 - iter 198/1984 - loss 1.88770718 - time (sec): 10.13 - samples/sec: 1587.68 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-13 20:53:37,508 epoch 1 - iter 396/1984 - loss 1.12484439 - time (sec): 18.96 - samples/sec: 1717.72 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-13 20:53:46,295 epoch 1 - iter 594/1984 - loss 0.83522090 - time (sec): 27.75 - samples/sec: 1772.35 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-13 20:53:54,959 epoch 1 - iter 792/1984 - loss 0.67679270 - time (sec): 36.41 - samples/sec: 1793.37 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-13 20:54:03,579 epoch 1 - iter 990/1984 - loss 0.57703210 - time (sec): 45.03 - samples/sec: 1807.87 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-13 20:54:12,160 epoch 1 - iter 1188/1984 - loss 0.50510842 - time (sec): 53.61 - samples/sec: 1819.73 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-13 20:54:20,861 epoch 1 - iter 1386/1984 - loss 0.45213988 - time (sec): 62.31 - samples/sec: 1840.43 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-13 20:54:30,522 epoch 1 - iter 1584/1984 - loss 0.41118920 - time (sec): 71.97 - samples/sec: 1836.31 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-13 20:54:41,080 epoch 1 - iter 1782/1984 - loss 0.38328017 - time (sec): 82.53 - samples/sec: 1794.37 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-13 20:54:50,731 epoch 1 - iter 1980/1984 - loss 0.35941678 - time (sec): 92.18 - samples/sec: 1776.70 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-13 20:54:50,895 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:54:50,895 EPOCH 1 done: loss 0.3590 - lr: 0.000030
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+ 2023-10-13 20:54:53,891 DEV : loss 0.10573934763669968 - f1-score (micro avg) 0.6591
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+ 2023-10-13 20:54:53,910 saving best model
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+ 2023-10-13 20:54:54,647 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:55:03,748 epoch 2 - iter 198/1984 - loss 0.12106577 - time (sec): 9.10 - samples/sec: 1896.84 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-13 20:55:12,515 epoch 2 - iter 396/1984 - loss 0.11964675 - time (sec): 17.87 - samples/sec: 1814.78 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-13 20:55:21,655 epoch 2 - iter 594/1984 - loss 0.11534460 - time (sec): 27.01 - samples/sec: 1845.31 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-13 20:55:30,327 epoch 2 - iter 792/1984 - loss 0.11408993 - time (sec): 35.68 - samples/sec: 1814.80 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-13 20:55:39,134 epoch 2 - iter 990/1984 - loss 0.11433753 - time (sec): 44.49 - samples/sec: 1846.50 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-13 20:55:47,726 epoch 2 - iter 1188/1984 - loss 0.11332009 - time (sec): 53.08 - samples/sec: 1848.33 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-13 20:55:57,506 epoch 2 - iter 1386/1984 - loss 0.11366621 - time (sec): 62.86 - samples/sec: 1823.09 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-13 20:56:08,043 epoch 2 - iter 1584/1984 - loss 0.11325580 - time (sec): 73.39 - samples/sec: 1778.70 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-13 20:56:18,565 epoch 2 - iter 1782/1984 - loss 0.11283397 - time (sec): 83.92 - samples/sec: 1755.40 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-13 20:56:28,780 epoch 2 - iter 1980/1984 - loss 0.11160017 - time (sec): 94.13 - samples/sec: 1739.67 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-13 20:56:28,992 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:56:28,992 EPOCH 2 done: loss 0.1115 - lr: 0.000027
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+ 2023-10-13 20:56:32,481 DEV : loss 0.1008807048201561 - f1-score (micro avg) 0.7066
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+ 2023-10-13 20:56:32,506 saving best model
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+ 2023-10-13 20:56:33,183 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:56:43,466 epoch 3 - iter 198/1984 - loss 0.07917549 - time (sec): 10.28 - samples/sec: 1593.09 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-13 20:56:52,745 epoch 3 - iter 396/1984 - loss 0.07784288 - time (sec): 19.56 - samples/sec: 1629.87 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-13 20:57:02,097 epoch 3 - iter 594/1984 - loss 0.07362250 - time (sec): 28.91 - samples/sec: 1709.82 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-13 20:57:11,220 epoch 3 - iter 792/1984 - loss 0.07809181 - time (sec): 38.04 - samples/sec: 1752.17 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-13 20:57:20,434 epoch 3 - iter 990/1984 - loss 0.08051703 - time (sec): 47.25 - samples/sec: 1753.43 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-13 20:57:29,676 epoch 3 - iter 1188/1984 - loss 0.08164550 - time (sec): 56.49 - samples/sec: 1752.99 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-13 20:57:38,750 epoch 3 - iter 1386/1984 - loss 0.08050737 - time (sec): 65.57 - samples/sec: 1753.84 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-13 20:57:47,724 epoch 3 - iter 1584/1984 - loss 0.08301541 - time (sec): 74.54 - samples/sec: 1764.39 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-13 20:57:56,733 epoch 3 - iter 1782/1984 - loss 0.08278631 - time (sec): 83.55 - samples/sec: 1768.46 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-13 20:58:05,774 epoch 3 - iter 1980/1984 - loss 0.08178188 - time (sec): 92.59 - samples/sec: 1766.44 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-13 20:58:05,956 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:58:05,956 EPOCH 3 done: loss 0.0817 - lr: 0.000023
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+ 2023-10-13 20:58:09,397 DEV : loss 0.14147229492664337 - f1-score (micro avg) 0.7464
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+ 2023-10-13 20:58:09,417 saving best model
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+ 2023-10-13 20:58:09,979 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:58:18,977 epoch 4 - iter 198/1984 - loss 0.05757599 - time (sec): 8.99 - samples/sec: 1838.23 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-13 20:58:28,176 epoch 4 - iter 396/1984 - loss 0.06403415 - time (sec): 18.19 - samples/sec: 1827.43 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-13 20:58:37,156 epoch 4 - iter 594/1984 - loss 0.06415865 - time (sec): 27.17 - samples/sec: 1805.48 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-13 20:58:46,425 epoch 4 - iter 792/1984 - loss 0.06303083 - time (sec): 36.44 - samples/sec: 1785.64 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-13 20:58:55,437 epoch 4 - iter 990/1984 - loss 0.06259595 - time (sec): 45.45 - samples/sec: 1791.39 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-13 20:59:04,506 epoch 4 - iter 1188/1984 - loss 0.06147075 - time (sec): 54.52 - samples/sec: 1791.86 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-13 20:59:13,860 epoch 4 - iter 1386/1984 - loss 0.06547581 - time (sec): 63.88 - samples/sec: 1784.41 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-13 20:59:23,431 epoch 4 - iter 1584/1984 - loss 0.06402360 - time (sec): 73.45 - samples/sec: 1769.42 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-13 20:59:32,955 epoch 4 - iter 1782/1984 - loss 0.06275327 - time (sec): 82.97 - samples/sec: 1766.30 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-13 20:59:42,166 epoch 4 - iter 1980/1984 - loss 0.06390400 - time (sec): 92.18 - samples/sec: 1776.30 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-13 20:59:42,347 ----------------------------------------------------------------------------------------------------
132
+ 2023-10-13 20:59:42,347 EPOCH 4 done: loss 0.0639 - lr: 0.000020
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+ 2023-10-13 20:59:46,341 DEV : loss 0.14689981937408447 - f1-score (micro avg) 0.7489
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+ 2023-10-13 20:59:46,360 saving best model
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+ 2023-10-13 20:59:46,969 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 20:59:56,191 epoch 5 - iter 198/1984 - loss 0.04771070 - time (sec): 9.22 - samples/sec: 1804.52 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-13 21:00:05,681 epoch 5 - iter 396/1984 - loss 0.04649118 - time (sec): 18.71 - samples/sec: 1761.13 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-13 21:00:15,005 epoch 5 - iter 594/1984 - loss 0.04834649 - time (sec): 28.03 - samples/sec: 1757.30 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-13 21:00:24,352 epoch 5 - iter 792/1984 - loss 0.04615015 - time (sec): 37.38 - samples/sec: 1783.56 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-13 21:00:33,340 epoch 5 - iter 990/1984 - loss 0.04648506 - time (sec): 46.37 - samples/sec: 1794.36 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-13 21:00:42,270 epoch 5 - iter 1188/1984 - loss 0.04468332 - time (sec): 55.30 - samples/sec: 1794.29 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-13 21:00:51,141 epoch 5 - iter 1386/1984 - loss 0.04510172 - time (sec): 64.17 - samples/sec: 1793.83 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-13 21:01:00,022 epoch 5 - iter 1584/1984 - loss 0.04472638 - time (sec): 73.05 - samples/sec: 1801.23 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-13 21:01:09,020 epoch 5 - iter 1782/1984 - loss 0.04570004 - time (sec): 82.05 - samples/sec: 1804.48 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-13 21:01:17,633 epoch 5 - iter 1980/1984 - loss 0.04591442 - time (sec): 90.66 - samples/sec: 1804.42 - lr: 0.000017 - momentum: 0.000000
146
+ 2023-10-13 21:01:17,818 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-13 21:01:17,818 EPOCH 5 done: loss 0.0460 - lr: 0.000017
148
+ 2023-10-13 21:01:21,249 DEV : loss 0.16444256901741028 - f1-score (micro avg) 0.7731
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+ 2023-10-13 21:01:21,269 saving best model
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+ 2023-10-13 21:01:21,874 ----------------------------------------------------------------------------------------------------
151
+ 2023-10-13 21:01:30,982 epoch 6 - iter 198/1984 - loss 0.03290051 - time (sec): 9.11 - samples/sec: 1821.35 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-13 21:01:40,070 epoch 6 - iter 396/1984 - loss 0.03321950 - time (sec): 18.19 - samples/sec: 1858.59 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-13 21:01:49,239 epoch 6 - iter 594/1984 - loss 0.03311731 - time (sec): 27.36 - samples/sec: 1815.85 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-13 21:01:58,266 epoch 6 - iter 792/1984 - loss 0.03061596 - time (sec): 36.39 - samples/sec: 1801.60 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-13 21:02:07,399 epoch 6 - iter 990/1984 - loss 0.03289182 - time (sec): 45.52 - samples/sec: 1799.81 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-13 21:02:16,317 epoch 6 - iter 1188/1984 - loss 0.03218297 - time (sec): 54.44 - samples/sec: 1807.03 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-13 21:02:25,287 epoch 6 - iter 1386/1984 - loss 0.03348283 - time (sec): 63.41 - samples/sec: 1815.90 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-13 21:02:34,604 epoch 6 - iter 1584/1984 - loss 0.03361440 - time (sec): 72.73 - samples/sec: 1807.15 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-13 21:02:43,949 epoch 6 - iter 1782/1984 - loss 0.03357184 - time (sec): 82.07 - samples/sec: 1796.19 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-13 21:02:53,471 epoch 6 - iter 1980/1984 - loss 0.03361402 - time (sec): 91.59 - samples/sec: 1786.91 - lr: 0.000013 - momentum: 0.000000
161
+ 2023-10-13 21:02:53,648 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-13 21:02:53,648 EPOCH 6 done: loss 0.0336 - lr: 0.000013
163
+ 2023-10-13 21:02:57,584 DEV : loss 0.20081490278244019 - f1-score (micro avg) 0.7536
164
+ 2023-10-13 21:02:57,604 ----------------------------------------------------------------------------------------------------
165
+ 2023-10-13 21:03:06,697 epoch 7 - iter 198/1984 - loss 0.02416311 - time (sec): 9.09 - samples/sec: 1689.96 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-13 21:03:15,884 epoch 7 - iter 396/1984 - loss 0.02612239 - time (sec): 18.28 - samples/sec: 1761.83 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-13 21:03:24,877 epoch 7 - iter 594/1984 - loss 0.02503909 - time (sec): 27.27 - samples/sec: 1752.06 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-13 21:03:33,834 epoch 7 - iter 792/1984 - loss 0.02544103 - time (sec): 36.23 - samples/sec: 1765.44 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-13 21:03:42,811 epoch 7 - iter 990/1984 - loss 0.02481477 - time (sec): 45.21 - samples/sec: 1795.09 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-13 21:03:52,055 epoch 7 - iter 1188/1984 - loss 0.02586965 - time (sec): 54.45 - samples/sec: 1808.49 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-13 21:04:01,424 epoch 7 - iter 1386/1984 - loss 0.02624069 - time (sec): 63.82 - samples/sec: 1781.30 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-13 21:04:10,658 epoch 7 - iter 1584/1984 - loss 0.02564693 - time (sec): 73.05 - samples/sec: 1788.89 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-13 21:04:19,928 epoch 7 - iter 1782/1984 - loss 0.02628502 - time (sec): 82.32 - samples/sec: 1786.06 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-13 21:04:29,051 epoch 7 - iter 1980/1984 - loss 0.02588685 - time (sec): 91.45 - samples/sec: 1788.99 - lr: 0.000010 - momentum: 0.000000
175
+ 2023-10-13 21:04:29,240 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-13 21:04:29,240 EPOCH 7 done: loss 0.0258 - lr: 0.000010
177
+ 2023-10-13 21:04:32,591 DEV : loss 0.19746360182762146 - f1-score (micro avg) 0.7682
178
+ 2023-10-13 21:04:32,611 ----------------------------------------------------------------------------------------------------
179
+ 2023-10-13 21:04:42,128 epoch 8 - iter 198/1984 - loss 0.01260002 - time (sec): 9.52 - samples/sec: 1716.88 - lr: 0.000010 - momentum: 0.000000
180
+ 2023-10-13 21:04:51,190 epoch 8 - iter 396/1984 - loss 0.01660345 - time (sec): 18.58 - samples/sec: 1773.92 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-10-13 21:05:00,514 epoch 8 - iter 594/1984 - loss 0.01395853 - time (sec): 27.90 - samples/sec: 1750.34 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-10-13 21:05:09,660 epoch 8 - iter 792/1984 - loss 0.01619325 - time (sec): 37.05 - samples/sec: 1776.28 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-10-13 21:05:18,644 epoch 8 - iter 990/1984 - loss 0.01676123 - time (sec): 46.03 - samples/sec: 1778.63 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-10-13 21:05:27,517 epoch 8 - iter 1188/1984 - loss 0.01715802 - time (sec): 54.91 - samples/sec: 1771.93 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-10-13 21:05:36,488 epoch 8 - iter 1386/1984 - loss 0.01772604 - time (sec): 63.88 - samples/sec: 1784.87 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-10-13 21:05:45,810 epoch 8 - iter 1584/1984 - loss 0.01778083 - time (sec): 73.20 - samples/sec: 1797.68 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-10-13 21:05:54,770 epoch 8 - iter 1782/1984 - loss 0.01759045 - time (sec): 82.16 - samples/sec: 1794.86 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-10-13 21:06:04,075 epoch 8 - iter 1980/1984 - loss 0.01768193 - time (sec): 91.46 - samples/sec: 1790.18 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-10-13 21:06:04,255 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-13 21:06:04,255 EPOCH 8 done: loss 0.0177 - lr: 0.000007
191
+ 2023-10-13 21:06:07,624 DEV : loss 0.2036585807800293 - f1-score (micro avg) 0.7669
192
+ 2023-10-13 21:06:07,644 ----------------------------------------------------------------------------------------------------
193
+ 2023-10-13 21:06:16,928 epoch 9 - iter 198/1984 - loss 0.01121945 - time (sec): 9.28 - samples/sec: 1803.90 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-10-13 21:06:25,941 epoch 9 - iter 396/1984 - loss 0.00942507 - time (sec): 18.30 - samples/sec: 1791.88 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-10-13 21:06:34,795 epoch 9 - iter 594/1984 - loss 0.01115411 - time (sec): 27.15 - samples/sec: 1791.55 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-10-13 21:06:43,917 epoch 9 - iter 792/1984 - loss 0.01115229 - time (sec): 36.27 - samples/sec: 1813.41 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-10-13 21:06:53,144 epoch 9 - iter 990/1984 - loss 0.01139535 - time (sec): 45.50 - samples/sec: 1813.79 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-10-13 21:07:02,159 epoch 9 - iter 1188/1984 - loss 0.01109025 - time (sec): 54.51 - samples/sec: 1799.43 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-10-13 21:07:11,471 epoch 9 - iter 1386/1984 - loss 0.01120806 - time (sec): 63.83 - samples/sec: 1788.63 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-10-13 21:07:20,727 epoch 9 - iter 1584/1984 - loss 0.01155852 - time (sec): 73.08 - samples/sec: 1792.50 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-10-13 21:07:29,843 epoch 9 - iter 1782/1984 - loss 0.01241535 - time (sec): 82.20 - samples/sec: 1798.57 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-10-13 21:07:38,723 epoch 9 - iter 1980/1984 - loss 0.01230984 - time (sec): 91.08 - samples/sec: 1796.01 - lr: 0.000003 - momentum: 0.000000
203
+ 2023-10-13 21:07:38,909 ----------------------------------------------------------------------------------------------------
204
+ 2023-10-13 21:07:38,909 EPOCH 9 done: loss 0.0123 - lr: 0.000003
205
+ 2023-10-13 21:07:42,702 DEV : loss 0.21578332781791687 - f1-score (micro avg) 0.7644
206
+ 2023-10-13 21:07:42,722 ----------------------------------------------------------------------------------------------------
207
+ 2023-10-13 21:07:52,175 epoch 10 - iter 198/1984 - loss 0.01257622 - time (sec): 9.45 - samples/sec: 1803.26 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-13 21:08:01,164 epoch 10 - iter 396/1984 - loss 0.01088765 - time (sec): 18.44 - samples/sec: 1777.14 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-13 21:08:10,229 epoch 10 - iter 594/1984 - loss 0.00982544 - time (sec): 27.51 - samples/sec: 1774.23 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-13 21:08:19,951 epoch 10 - iter 792/1984 - loss 0.00945824 - time (sec): 37.23 - samples/sec: 1758.02 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-13 21:08:29,388 epoch 10 - iter 990/1984 - loss 0.00969098 - time (sec): 46.66 - samples/sec: 1746.07 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-13 21:08:38,684 epoch 10 - iter 1188/1984 - loss 0.00953138 - time (sec): 55.96 - samples/sec: 1750.67 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-13 21:08:47,952 epoch 10 - iter 1386/1984 - loss 0.00928403 - time (sec): 65.23 - samples/sec: 1752.64 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-13 21:08:56,924 epoch 10 - iter 1584/1984 - loss 0.00941987 - time (sec): 74.20 - samples/sec: 1764.57 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-10-13 21:09:06,116 epoch 10 - iter 1782/1984 - loss 0.00912027 - time (sec): 83.39 - samples/sec: 1774.72 - lr: 0.000000 - momentum: 0.000000
216
+ 2023-10-13 21:09:15,364 epoch 10 - iter 1980/1984 - loss 0.00877729 - time (sec): 92.64 - samples/sec: 1766.45 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-10-13 21:09:15,565 ----------------------------------------------------------------------------------------------------
218
+ 2023-10-13 21:09:15,565 EPOCH 10 done: loss 0.0088 - lr: 0.000000
219
+ 2023-10-13 21:09:18,982 DEV : loss 0.22608202695846558 - f1-score (micro avg) 0.767
220
+ 2023-10-13 21:09:19,478 ----------------------------------------------------------------------------------------------------
221
+ 2023-10-13 21:09:19,479 Loading model from best epoch ...
222
+ 2023-10-13 21:09:21,398 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
223
+ 2023-10-13 21:09:24,698
224
+ Results:
225
+ - F-score (micro) 0.7846
226
+ - F-score (macro) 0.6793
227
+ - Accuracy 0.6641
228
+
229
+ By class:
230
+ precision recall f1-score support
231
+
232
+ LOC 0.8554 0.8489 0.8521 655
233
+ PER 0.7449 0.8117 0.7768 223
234
+ ORG 0.4694 0.3622 0.4089 127
235
+
236
+ micro avg 0.7901 0.7791 0.7846 1005
237
+ macro avg 0.6899 0.6742 0.6793 1005
238
+ weighted avg 0.7821 0.7791 0.7794 1005
239
+
240
+ 2023-10-13 21:09:24,699 ----------------------------------------------------------------------------------------------------