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hmbench-icdar-fr-hmteams-bs8-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.1697555179.bce904bcef33.2023.19 +3 -0
  5. test.tsv +0 -0
  6. training.log +237 -0
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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 15:07:21 0.0000 0.4058 0.0858 0.6836 0.7455 0.7132 0.5691
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+ 2 15:08:26 0.0000 0.1063 0.0931 0.7087 0.7624 0.7346 0.5970
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+ 3 15:09:32 0.0000 0.0730 0.1031 0.7419 0.7771 0.7591 0.6274
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+ 4 15:10:37 0.0000 0.0572 0.1291 0.6859 0.7952 0.7365 0.6102
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+ 5 15:11:41 0.0000 0.0417 0.1688 0.7397 0.7907 0.7644 0.6360
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+ 6 15:12:46 0.0000 0.0311 0.1869 0.7784 0.7432 0.7604 0.6269
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+ 7 15:13:50 0.0000 0.0223 0.2008 0.7285 0.7862 0.7563 0.6307
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+ 8 15:14:54 0.0000 0.0146 0.2276 0.7438 0.7783 0.7606 0.6289
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+ 9 15:15:59 0.0000 0.0098 0.2446 0.7377 0.7828 0.7596 0.6297
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+ 10 15:17:04 0.0000 0.0064 0.2496 0.7449 0.7828 0.7634 0.6372
runs/events.out.tfevents.1697555179.bce904bcef33.2023.19 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 15:06:19,081 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:06:19,082 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 15:06:19,082 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:06:19,082 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 15:06:19,082 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:06:19,082 Train: 7936 sentences
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+ 2023-10-17 15:06:19,082 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 15:06:19,082 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:06:19,082 Training Params:
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+ 2023-10-17 15:06:19,082 - learning_rate: "5e-05"
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+ 2023-10-17 15:06:19,082 - mini_batch_size: "8"
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+ 2023-10-17 15:06:19,082 - max_epochs: "10"
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+ 2023-10-17 15:06:19,082 - shuffle: "True"
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+ 2023-10-17 15:06:19,082 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:06:19,083 Plugins:
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+ 2023-10-17 15:06:19,083 - TensorboardLogger
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+ 2023-10-17 15:06:19,083 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 15:06:19,083 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:06:19,083 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 15:06:19,083 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 15:06:19,083 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:06:19,083 Computation:
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+ 2023-10-17 15:06:19,083 - compute on device: cuda:0
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+ 2023-10-17 15:06:19,083 - embedding storage: none
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+ 2023-10-17 15:06:19,083 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:06:19,083 Model training base path: "hmbench-icdar/fr-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-17 15:06:19,083 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:06:19,083 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:06:19,083 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 15:06:24,942 epoch 1 - iter 99/992 - loss 2.38046963 - time (sec): 5.86 - samples/sec: 2753.36 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-17 15:06:31,168 epoch 1 - iter 198/992 - loss 1.37254739 - time (sec): 12.08 - samples/sec: 2777.31 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 15:06:37,005 epoch 1 - iter 297/992 - loss 1.00559337 - time (sec): 17.92 - samples/sec: 2749.10 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 15:06:42,788 epoch 1 - iter 396/992 - loss 0.80698866 - time (sec): 23.70 - samples/sec: 2751.86 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 15:06:48,423 epoch 1 - iter 495/992 - loss 0.68842093 - time (sec): 29.34 - samples/sec: 2747.14 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 15:06:54,254 epoch 1 - iter 594/992 - loss 0.59727087 - time (sec): 35.17 - samples/sec: 2756.67 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 15:07:00,603 epoch 1 - iter 693/992 - loss 0.52832358 - time (sec): 41.52 - samples/sec: 2742.95 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 15:07:06,517 epoch 1 - iter 792/992 - loss 0.47754192 - time (sec): 47.43 - samples/sec: 2751.53 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 15:07:12,414 epoch 1 - iter 891/992 - loss 0.43922362 - time (sec): 53.33 - samples/sec: 2756.93 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 15:07:18,496 epoch 1 - iter 990/992 - loss 0.40646285 - time (sec): 59.41 - samples/sec: 2754.47 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-17 15:07:18,609 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:07:18,609 EPOCH 1 done: loss 0.4058 - lr: 0.000050
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+ 2023-10-17 15:07:21,928 DEV : loss 0.08580786734819412 - f1-score (micro avg) 0.7132
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+ 2023-10-17 15:07:21,960 saving best model
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+ 2023-10-17 15:07:23,223 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:07:29,185 epoch 2 - iter 99/992 - loss 0.10472742 - time (sec): 5.96 - samples/sec: 2858.77 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 15:07:35,198 epoch 2 - iter 198/992 - loss 0.10274139 - time (sec): 11.97 - samples/sec: 2791.29 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 15:07:40,921 epoch 2 - iter 297/992 - loss 0.10453930 - time (sec): 17.70 - samples/sec: 2811.26 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 15:07:46,491 epoch 2 - iter 396/992 - loss 0.10618343 - time (sec): 23.27 - samples/sec: 2805.37 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 15:07:52,431 epoch 2 - iter 495/992 - loss 0.10477397 - time (sec): 29.21 - samples/sec: 2810.58 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 15:07:58,372 epoch 2 - iter 594/992 - loss 0.10424898 - time (sec): 35.15 - samples/sec: 2787.08 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 15:08:04,514 epoch 2 - iter 693/992 - loss 0.10574565 - time (sec): 41.29 - samples/sec: 2760.39 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 15:08:10,405 epoch 2 - iter 792/992 - loss 0.10502822 - time (sec): 47.18 - samples/sec: 2759.11 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 15:08:16,744 epoch 2 - iter 891/992 - loss 0.10440715 - time (sec): 53.52 - samples/sec: 2751.13 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 15:08:22,649 epoch 2 - iter 990/992 - loss 0.10635558 - time (sec): 59.42 - samples/sec: 2754.71 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 15:08:22,763 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:08:22,763 EPOCH 2 done: loss 0.1063 - lr: 0.000044
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+ 2023-10-17 15:08:26,289 DEV : loss 0.09309504926204681 - f1-score (micro avg) 0.7346
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+ 2023-10-17 15:08:26,310 saving best model
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+ 2023-10-17 15:08:26,957 ----------------------------------------------------------------------------------------------------
104
+ 2023-10-17 15:08:33,050 epoch 3 - iter 99/992 - loss 0.07641069 - time (sec): 6.09 - samples/sec: 2495.54 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 15:08:39,473 epoch 3 - iter 198/992 - loss 0.07843575 - time (sec): 12.51 - samples/sec: 2516.95 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 15:08:45,808 epoch 3 - iter 297/992 - loss 0.07377962 - time (sec): 18.85 - samples/sec: 2566.19 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 15:08:52,077 epoch 3 - iter 396/992 - loss 0.07136994 - time (sec): 25.12 - samples/sec: 2594.72 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 15:08:58,402 epoch 3 - iter 495/992 - loss 0.07162863 - time (sec): 31.44 - samples/sec: 2593.96 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 15:09:04,568 epoch 3 - iter 594/992 - loss 0.07280519 - time (sec): 37.61 - samples/sec: 2622.46 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 15:09:10,352 epoch 3 - iter 693/992 - loss 0.07280608 - time (sec): 43.39 - samples/sec: 2636.16 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 15:09:16,277 epoch 3 - iter 792/992 - loss 0.07274453 - time (sec): 49.32 - samples/sec: 2646.29 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 15:09:22,902 epoch 3 - iter 891/992 - loss 0.07234744 - time (sec): 55.94 - samples/sec: 2637.53 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 15:09:28,682 epoch 3 - iter 990/992 - loss 0.07312508 - time (sec): 61.72 - samples/sec: 2651.40 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 15:09:28,790 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:09:28,790 EPOCH 3 done: loss 0.0730 - lr: 0.000039
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+ 2023-10-17 15:09:32,577 DEV : loss 0.10305587947368622 - f1-score (micro avg) 0.7591
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+ 2023-10-17 15:09:32,610 saving best model
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+ 2023-10-17 15:09:33,090 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:09:38,942 epoch 4 - iter 99/992 - loss 0.05222278 - time (sec): 5.85 - samples/sec: 2696.28 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 15:09:45,319 epoch 4 - iter 198/992 - loss 0.05464912 - time (sec): 12.23 - samples/sec: 2664.59 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 15:09:51,435 epoch 4 - iter 297/992 - loss 0.05795324 - time (sec): 18.34 - samples/sec: 2641.93 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 15:09:57,594 epoch 4 - iter 396/992 - loss 0.05516303 - time (sec): 24.50 - samples/sec: 2658.01 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 15:10:03,797 epoch 4 - iter 495/992 - loss 0.05508194 - time (sec): 30.70 - samples/sec: 2659.79 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 15:10:09,783 epoch 4 - iter 594/992 - loss 0.05615006 - time (sec): 36.69 - samples/sec: 2665.02 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 15:10:15,945 epoch 4 - iter 693/992 - loss 0.05660499 - time (sec): 42.85 - samples/sec: 2666.84 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 15:10:21,870 epoch 4 - iter 792/992 - loss 0.05723636 - time (sec): 48.78 - samples/sec: 2675.19 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 15:10:27,762 epoch 4 - iter 891/992 - loss 0.05771745 - time (sec): 54.67 - samples/sec: 2694.56 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 15:10:33,702 epoch 4 - iter 990/992 - loss 0.05696544 - time (sec): 60.61 - samples/sec: 2700.46 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 15:10:33,832 ----------------------------------------------------------------------------------------------------
130
+ 2023-10-17 15:10:33,832 EPOCH 4 done: loss 0.0572 - lr: 0.000033
131
+ 2023-10-17 15:10:37,423 DEV : loss 0.1290876865386963 - f1-score (micro avg) 0.7365
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+ 2023-10-17 15:10:37,453 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-17 15:10:43,108 epoch 5 - iter 99/992 - loss 0.04105148 - time (sec): 5.65 - samples/sec: 2830.85 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 15:10:49,178 epoch 5 - iter 198/992 - loss 0.04148932 - time (sec): 11.72 - samples/sec: 2798.97 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 15:10:55,666 epoch 5 - iter 297/992 - loss 0.03980325 - time (sec): 18.21 - samples/sec: 2752.45 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 15:11:01,437 epoch 5 - iter 396/992 - loss 0.04154139 - time (sec): 23.98 - samples/sec: 2764.28 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 15:11:07,548 epoch 5 - iter 495/992 - loss 0.04253159 - time (sec): 30.09 - samples/sec: 2780.57 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 15:11:13,348 epoch 5 - iter 594/992 - loss 0.04313180 - time (sec): 35.89 - samples/sec: 2771.86 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 15:11:19,562 epoch 5 - iter 693/992 - loss 0.04278345 - time (sec): 42.11 - samples/sec: 2762.89 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 15:11:25,360 epoch 5 - iter 792/992 - loss 0.04253978 - time (sec): 47.91 - samples/sec: 2753.94 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 15:11:31,032 epoch 5 - iter 891/992 - loss 0.04250696 - time (sec): 53.58 - samples/sec: 2755.88 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 15:11:37,126 epoch 5 - iter 990/992 - loss 0.04181300 - time (sec): 59.67 - samples/sec: 2742.54 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 15:11:37,239 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 15:11:37,239 EPOCH 5 done: loss 0.0417 - lr: 0.000028
145
+ 2023-10-17 15:11:41,340 DEV : loss 0.1687757819890976 - f1-score (micro avg) 0.7644
146
+ 2023-10-17 15:11:41,374 saving best model
147
+ 2023-10-17 15:11:41,959 ----------------------------------------------------------------------------------------------------
148
+ 2023-10-17 15:11:48,364 epoch 6 - iter 99/992 - loss 0.02946611 - time (sec): 6.40 - samples/sec: 2586.47 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 15:11:54,568 epoch 6 - iter 198/992 - loss 0.03061488 - time (sec): 12.61 - samples/sec: 2654.29 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 15:12:00,552 epoch 6 - iter 297/992 - loss 0.02985251 - time (sec): 18.59 - samples/sec: 2698.65 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 15:12:06,580 epoch 6 - iter 396/992 - loss 0.02840164 - time (sec): 24.62 - samples/sec: 2714.73 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 15:12:12,475 epoch 6 - iter 495/992 - loss 0.02922618 - time (sec): 30.51 - samples/sec: 2725.65 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 15:12:18,608 epoch 6 - iter 594/992 - loss 0.02995890 - time (sec): 36.65 - samples/sec: 2740.43 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 15:12:24,344 epoch 6 - iter 693/992 - loss 0.03016712 - time (sec): 42.38 - samples/sec: 2735.06 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 15:12:30,413 epoch 6 - iter 792/992 - loss 0.03158247 - time (sec): 48.45 - samples/sec: 2714.37 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 15:12:36,424 epoch 6 - iter 891/992 - loss 0.03092480 - time (sec): 54.46 - samples/sec: 2713.22 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 15:12:42,336 epoch 6 - iter 990/992 - loss 0.03100699 - time (sec): 60.37 - samples/sec: 2710.72 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 15:12:42,446 ----------------------------------------------------------------------------------------------------
159
+ 2023-10-17 15:12:42,447 EPOCH 6 done: loss 0.0311 - lr: 0.000022
160
+ 2023-10-17 15:12:46,001 DEV : loss 0.18693169951438904 - f1-score (micro avg) 0.7604
161
+ 2023-10-17 15:12:46,022 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-17 15:12:51,972 epoch 7 - iter 99/992 - loss 0.02441708 - time (sec): 5.95 - samples/sec: 2670.95 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 15:12:58,124 epoch 7 - iter 198/992 - loss 0.02229387 - time (sec): 12.10 - samples/sec: 2687.51 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 15:13:04,348 epoch 7 - iter 297/992 - loss 0.02315922 - time (sec): 18.32 - samples/sec: 2667.51 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 15:13:10,631 epoch 7 - iter 396/992 - loss 0.02270701 - time (sec): 24.61 - samples/sec: 2698.30 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 15:13:17,126 epoch 7 - iter 495/992 - loss 0.02221474 - time (sec): 31.10 - samples/sec: 2711.28 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 15:13:22,985 epoch 7 - iter 594/992 - loss 0.02215952 - time (sec): 36.96 - samples/sec: 2718.05 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 15:13:28,764 epoch 7 - iter 693/992 - loss 0.02222955 - time (sec): 42.74 - samples/sec: 2718.53 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 15:13:34,713 epoch 7 - iter 792/992 - loss 0.02198392 - time (sec): 48.69 - samples/sec: 2715.09 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 15:13:40,679 epoch 7 - iter 891/992 - loss 0.02263738 - time (sec): 54.66 - samples/sec: 2711.54 - lr: 0.000017 - momentum: 0.000000
171
+ 2023-10-17 15:13:46,476 epoch 7 - iter 990/992 - loss 0.02209691 - time (sec): 60.45 - samples/sec: 2706.35 - lr: 0.000017 - momentum: 0.000000
172
+ 2023-10-17 15:13:46,588 ----------------------------------------------------------------------------------------------------
173
+ 2023-10-17 15:13:46,588 EPOCH 7 done: loss 0.0223 - lr: 0.000017
174
+ 2023-10-17 15:13:50,192 DEV : loss 0.20075371861457825 - f1-score (micro avg) 0.7563
175
+ 2023-10-17 15:13:50,226 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-17 15:13:56,194 epoch 8 - iter 99/992 - loss 0.01434494 - time (sec): 5.97 - samples/sec: 2726.76 - lr: 0.000016 - momentum: 0.000000
177
+ 2023-10-17 15:14:01,983 epoch 8 - iter 198/992 - loss 0.01221880 - time (sec): 11.75 - samples/sec: 2747.78 - lr: 0.000016 - momentum: 0.000000
178
+ 2023-10-17 15:14:08,169 epoch 8 - iter 297/992 - loss 0.01358537 - time (sec): 17.94 - samples/sec: 2718.92 - lr: 0.000015 - momentum: 0.000000
179
+ 2023-10-17 15:14:14,284 epoch 8 - iter 396/992 - loss 0.01240926 - time (sec): 24.06 - samples/sec: 2714.16 - lr: 0.000014 - momentum: 0.000000
180
+ 2023-10-17 15:14:20,536 epoch 8 - iter 495/992 - loss 0.01395736 - time (sec): 30.31 - samples/sec: 2686.43 - lr: 0.000014 - momentum: 0.000000
181
+ 2023-10-17 15:14:26,357 epoch 8 - iter 594/992 - loss 0.01412399 - time (sec): 36.13 - samples/sec: 2688.93 - lr: 0.000013 - momentum: 0.000000
182
+ 2023-10-17 15:14:32,373 epoch 8 - iter 693/992 - loss 0.01400427 - time (sec): 42.15 - samples/sec: 2696.74 - lr: 0.000013 - momentum: 0.000000
183
+ 2023-10-17 15:14:38,421 epoch 8 - iter 792/992 - loss 0.01472146 - time (sec): 48.19 - samples/sec: 2706.17 - lr: 0.000012 - momentum: 0.000000
184
+ 2023-10-17 15:14:44,134 epoch 8 - iter 891/992 - loss 0.01436421 - time (sec): 53.91 - samples/sec: 2713.39 - lr: 0.000012 - momentum: 0.000000
185
+ 2023-10-17 15:14:50,402 epoch 8 - iter 990/992 - loss 0.01442694 - time (sec): 60.17 - samples/sec: 2719.16 - lr: 0.000011 - momentum: 0.000000
186
+ 2023-10-17 15:14:50,524 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-17 15:14:50,525 EPOCH 8 done: loss 0.0146 - lr: 0.000011
188
+ 2023-10-17 15:14:54,214 DEV : loss 0.22761167585849762 - f1-score (micro avg) 0.7606
189
+ 2023-10-17 15:14:54,240 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-17 15:15:00,161 epoch 9 - iter 99/992 - loss 0.00799092 - time (sec): 5.92 - samples/sec: 2843.66 - lr: 0.000011 - momentum: 0.000000
191
+ 2023-10-17 15:15:06,079 epoch 9 - iter 198/992 - loss 0.00828030 - time (sec): 11.84 - samples/sec: 2852.74 - lr: 0.000010 - momentum: 0.000000
192
+ 2023-10-17 15:15:12,439 epoch 9 - iter 297/992 - loss 0.00916347 - time (sec): 18.20 - samples/sec: 2757.11 - lr: 0.000009 - momentum: 0.000000
193
+ 2023-10-17 15:15:18,232 epoch 9 - iter 396/992 - loss 0.01105875 - time (sec): 23.99 - samples/sec: 2764.75 - lr: 0.000009 - momentum: 0.000000
194
+ 2023-10-17 15:15:24,475 epoch 9 - iter 495/992 - loss 0.01018621 - time (sec): 30.23 - samples/sec: 2754.32 - lr: 0.000008 - momentum: 0.000000
195
+ 2023-10-17 15:15:30,268 epoch 9 - iter 594/992 - loss 0.01068382 - time (sec): 36.03 - samples/sec: 2751.33 - lr: 0.000008 - momentum: 0.000000
196
+ 2023-10-17 15:15:36,445 epoch 9 - iter 693/992 - loss 0.01008544 - time (sec): 42.20 - samples/sec: 2739.54 - lr: 0.000007 - momentum: 0.000000
197
+ 2023-10-17 15:15:42,355 epoch 9 - iter 792/992 - loss 0.00998382 - time (sec): 48.11 - samples/sec: 2739.36 - lr: 0.000007 - momentum: 0.000000
198
+ 2023-10-17 15:15:48,413 epoch 9 - iter 891/992 - loss 0.00988621 - time (sec): 54.17 - samples/sec: 2728.23 - lr: 0.000006 - momentum: 0.000000
199
+ 2023-10-17 15:15:54,351 epoch 9 - iter 990/992 - loss 0.00985186 - time (sec): 60.11 - samples/sec: 2723.30 - lr: 0.000006 - momentum: 0.000000
200
+ 2023-10-17 15:15:54,459 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-17 15:15:54,459 EPOCH 9 done: loss 0.0098 - lr: 0.000006
202
+ 2023-10-17 15:15:58,980 DEV : loss 0.24459530413150787 - f1-score (micro avg) 0.7596
203
+ 2023-10-17 15:15:59,006 ----------------------------------------------------------------------------------------------------
204
+ 2023-10-17 15:16:05,391 epoch 10 - iter 99/992 - loss 0.00479346 - time (sec): 6.38 - samples/sec: 2649.86 - lr: 0.000005 - momentum: 0.000000
205
+ 2023-10-17 15:16:11,650 epoch 10 - iter 198/992 - loss 0.00564539 - time (sec): 12.64 - samples/sec: 2639.92 - lr: 0.000004 - momentum: 0.000000
206
+ 2023-10-17 15:16:17,940 epoch 10 - iter 297/992 - loss 0.00670906 - time (sec): 18.93 - samples/sec: 2650.03 - lr: 0.000004 - momentum: 0.000000
207
+ 2023-10-17 15:16:24,031 epoch 10 - iter 396/992 - loss 0.00703610 - time (sec): 25.02 - samples/sec: 2619.19 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-17 15:16:30,132 epoch 10 - iter 495/992 - loss 0.00702067 - time (sec): 31.12 - samples/sec: 2652.10 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-17 15:16:36,083 epoch 10 - iter 594/992 - loss 0.00679611 - time (sec): 37.07 - samples/sec: 2651.23 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 15:16:41,761 epoch 10 - iter 693/992 - loss 0.00690832 - time (sec): 42.75 - samples/sec: 2680.33 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-17 15:16:47,817 epoch 10 - iter 792/992 - loss 0.00687466 - time (sec): 48.81 - samples/sec: 2673.00 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 15:16:53,876 epoch 10 - iter 891/992 - loss 0.00684382 - time (sec): 54.87 - samples/sec: 2661.32 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-17 15:17:00,571 epoch 10 - iter 990/992 - loss 0.00637386 - time (sec): 61.56 - samples/sec: 2659.92 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-17 15:17:00,687 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-17 15:17:00,687 EPOCH 10 done: loss 0.0064 - lr: 0.000000
216
+ 2023-10-17 15:17:04,344 DEV : loss 0.24962207674980164 - f1-score (micro avg) 0.7634
217
+ 2023-10-17 15:17:04,822 ----------------------------------------------------------------------------------------------------
218
+ 2023-10-17 15:17:04,823 Loading model from best epoch ...
219
+ 2023-10-17 15:17:06,444 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
220
+ 2023-10-17 15:17:10,062
221
+ Results:
222
+ - F-score (micro) 0.7653
223
+ - F-score (macro) 0.665
224
+ - Accuracy 0.6489
225
+
226
+ By class:
227
+ precision recall f1-score support
228
+
229
+ LOC 0.7975 0.8779 0.8358 655
230
+ PER 0.6768 0.7982 0.7325 223
231
+ ORG 0.4554 0.4016 0.4268 127
232
+
233
+ micro avg 0.7336 0.8000 0.7653 1005
234
+ macro avg 0.6432 0.6925 0.6650 1005
235
+ weighted avg 0.7275 0.8000 0.7612 1005
236
+
237
+ 2023-10-17 15:17:10,062 ----------------------------------------------------------------------------------------------------