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hmbench-letemps-fr-hmteams-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1

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.1697538229.4aef72135bc5.1113.2 +3 -0
  5. test.tsv +0 -0
  6. training.log +242 -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 10:26:14 0.0000 0.3284 0.0977 0.5275 0.7254 0.6108 0.4449
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+ 2 10:28:36 0.0000 0.0866 0.1061 0.5293 0.8055 0.6388 0.4766
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+ 8 10:43:07 0.0000 0.0110 0.3551 0.5633 0.7941 0.6591 0.4989
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+ 9 10:45:21 0.0000 0.0068 0.3874 0.5688 0.7998 0.6648 0.5043
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+ 10 10:47:48 0.0000 0.0054 0.4004 0.5675 0.8032 0.6651 0.5050
runs/events.out.tfevents.1697538229.4aef72135bc5.1113.2 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 10:23:49,346 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:23:49,348 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 10:23:49,348 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:23:49,348 MultiCorpus: 14465 train + 1392 dev + 2432 test sentences
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+ - NER_HIPE_2022 Corpus: 14465 train + 1392 dev + 2432 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/letemps/fr/with_doc_seperator
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+ 2023-10-17 10:23:49,348 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:23:49,348 Train: 14465 sentences
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+ 2023-10-17 10:23:49,348 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 10:23:49,348 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:23:49,348 Training Params:
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+ 2023-10-17 10:23:49,348 - learning_rate: "3e-05"
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+ 2023-10-17 10:23:49,348 - mini_batch_size: "8"
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+ 2023-10-17 10:23:49,348 - max_epochs: "10"
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+ 2023-10-17 10:23:49,348 - shuffle: "True"
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+ 2023-10-17 10:23:49,348 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:23:49,348 Plugins:
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+ 2023-10-17 10:23:49,348 - TensorboardLogger
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+ 2023-10-17 10:23:49,348 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 10:23:49,348 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:23:49,348 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 10:23:49,348 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 10:23:49,348 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:23:49,349 Computation:
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+ 2023-10-17 10:23:49,349 - compute on device: cuda:0
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+ 2023-10-17 10:23:49,349 - embedding storage: none
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+ 2023-10-17 10:23:49,349 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:23:49,349 Model training base path: "hmbench-letemps/fr-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-17 10:23:49,349 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:23:49,349 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:23:49,349 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 10:24:02,854 epoch 1 - iter 180/1809 - loss 1.97077795 - time (sec): 13.50 - samples/sec: 2869.63 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-17 10:24:15,874 epoch 1 - iter 360/1809 - loss 1.13419673 - time (sec): 26.52 - samples/sec: 2872.06 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 10:24:29,572 epoch 1 - iter 540/1809 - loss 0.80989982 - time (sec): 40.22 - samples/sec: 2841.00 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 10:24:43,353 epoch 1 - iter 720/1809 - loss 0.64354116 - time (sec): 54.00 - samples/sec: 2825.49 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 10:24:57,139 epoch 1 - iter 900/1809 - loss 0.54278891 - time (sec): 67.79 - samples/sec: 2798.57 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 10:25:11,974 epoch 1 - iter 1080/1809 - loss 0.47491789 - time (sec): 82.62 - samples/sec: 2741.90 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 10:25:26,384 epoch 1 - iter 1260/1809 - loss 0.42566960 - time (sec): 97.03 - samples/sec: 2718.52 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 10:25:41,389 epoch 1 - iter 1440/1809 - loss 0.38559522 - time (sec): 112.04 - samples/sec: 2704.90 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 10:25:54,544 epoch 1 - iter 1620/1809 - loss 0.35386098 - time (sec): 125.19 - samples/sec: 2725.72 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 10:26:07,351 epoch 1 - iter 1800/1809 - loss 0.32934241 - time (sec): 138.00 - samples/sec: 2738.94 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 10:26:08,063 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:26:08,063 EPOCH 1 done: loss 0.3284 - lr: 0.000030
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+ 2023-10-17 10:26:14,169 DEV : loss 0.09768907725811005 - f1-score (micro avg) 0.6108
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+ 2023-10-17 10:26:14,210 saving best model
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+ 2023-10-17 10:26:14,712 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:26:27,936 epoch 2 - iter 180/1809 - loss 0.10126158 - time (sec): 13.22 - samples/sec: 2761.32 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 10:26:41,008 epoch 2 - iter 360/1809 - loss 0.09521427 - time (sec): 26.29 - samples/sec: 2883.92 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 10:26:54,116 epoch 2 - iter 540/1809 - loss 0.09155082 - time (sec): 39.40 - samples/sec: 2914.13 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 10:27:07,181 epoch 2 - iter 720/1809 - loss 0.09337842 - time (sec): 52.47 - samples/sec: 2885.06 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 10:27:21,201 epoch 2 - iter 900/1809 - loss 0.09093030 - time (sec): 66.49 - samples/sec: 2821.99 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 10:27:34,922 epoch 2 - iter 1080/1809 - loss 0.08938417 - time (sec): 80.21 - samples/sec: 2806.14 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 10:27:48,461 epoch 2 - iter 1260/1809 - loss 0.08853541 - time (sec): 93.75 - samples/sec: 2802.82 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 10:28:02,067 epoch 2 - iter 1440/1809 - loss 0.08910538 - time (sec): 107.35 - samples/sec: 2806.40 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 10:28:15,988 epoch 2 - iter 1620/1809 - loss 0.08699630 - time (sec): 121.27 - samples/sec: 2817.16 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 10:28:29,006 epoch 2 - iter 1800/1809 - loss 0.08639068 - time (sec): 134.29 - samples/sec: 2816.30 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 10:28:29,643 ----------------------------------------------------------------------------------------------------
100
+ 2023-10-17 10:28:29,644 EPOCH 2 done: loss 0.0866 - lr: 0.000027
101
+ 2023-10-17 10:28:36,718 DEV : loss 0.10609198361635208 - f1-score (micro avg) 0.6388
102
+ 2023-10-17 10:28:36,759 saving best model
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+ 2023-10-17 10:28:37,363 ----------------------------------------------------------------------------------------------------
104
+ 2023-10-17 10:28:52,654 epoch 3 - iter 180/1809 - loss 0.06507567 - time (sec): 15.29 - samples/sec: 2455.09 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 10:29:07,332 epoch 3 - iter 360/1809 - loss 0.06327625 - time (sec): 29.97 - samples/sec: 2571.73 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 10:29:22,132 epoch 3 - iter 540/1809 - loss 0.06394436 - time (sec): 44.77 - samples/sec: 2564.70 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 10:29:36,446 epoch 3 - iter 720/1809 - loss 0.06297439 - time (sec): 59.08 - samples/sec: 2604.56 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 10:29:49,726 epoch 3 - iter 900/1809 - loss 0.06209821 - time (sec): 72.36 - samples/sec: 2647.74 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 10:30:02,972 epoch 3 - iter 1080/1809 - loss 0.06128483 - time (sec): 85.61 - samples/sec: 2688.49 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 10:30:16,883 epoch 3 - iter 1260/1809 - loss 0.06138333 - time (sec): 99.52 - samples/sec: 2691.44 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 10:30:31,017 epoch 3 - iter 1440/1809 - loss 0.06203921 - time (sec): 113.65 - samples/sec: 2679.12 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 10:30:43,946 epoch 3 - iter 1620/1809 - loss 0.06242292 - time (sec): 126.58 - samples/sec: 2700.92 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 10:30:55,919 epoch 3 - iter 1800/1809 - loss 0.06256995 - time (sec): 138.55 - samples/sec: 2729.61 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 10:30:56,446 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:30:56,446 EPOCH 3 done: loss 0.0626 - lr: 0.000023
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+ 2023-10-17 10:31:02,865 DEV : loss 0.12596164643764496 - f1-score (micro avg) 0.6405
117
+ 2023-10-17 10:31:02,910 saving best model
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+ 2023-10-17 10:31:03,530 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-17 10:31:17,877 epoch 4 - iter 180/1809 - loss 0.04168279 - time (sec): 14.34 - samples/sec: 2707.50 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 10:31:31,828 epoch 4 - iter 360/1809 - loss 0.03962130 - time (sec): 28.30 - samples/sec: 2707.68 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 10:31:46,220 epoch 4 - iter 540/1809 - loss 0.04238080 - time (sec): 42.69 - samples/sec: 2714.96 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 10:31:59,189 epoch 4 - iter 720/1809 - loss 0.04283943 - time (sec): 55.66 - samples/sec: 2743.08 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 10:32:12,306 epoch 4 - iter 900/1809 - loss 0.04535463 - time (sec): 68.77 - samples/sec: 2782.02 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 10:32:25,119 epoch 4 - iter 1080/1809 - loss 0.04573226 - time (sec): 81.59 - samples/sec: 2784.61 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 10:32:38,107 epoch 4 - iter 1260/1809 - loss 0.04529284 - time (sec): 94.57 - samples/sec: 2803.28 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 10:32:51,646 epoch 4 - iter 1440/1809 - loss 0.04597535 - time (sec): 108.11 - samples/sec: 2807.64 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 10:33:05,014 epoch 4 - iter 1620/1809 - loss 0.04539057 - time (sec): 121.48 - samples/sec: 2801.07 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 10:33:18,630 epoch 4 - iter 1800/1809 - loss 0.04608749 - time (sec): 135.10 - samples/sec: 2798.18 - lr: 0.000020 - momentum: 0.000000
129
+ 2023-10-17 10:33:19,371 ----------------------------------------------------------------------------------------------------
130
+ 2023-10-17 10:33:19,371 EPOCH 4 done: loss 0.0461 - lr: 0.000020
131
+ 2023-10-17 10:33:26,556 DEV : loss 0.17203289270401 - f1-score (micro avg) 0.6513
132
+ 2023-10-17 10:33:26,598 saving best model
133
+ 2023-10-17 10:33:27,228 ----------------------------------------------------------------------------------------------------
134
+ 2023-10-17 10:33:41,243 epoch 5 - iter 180/1809 - loss 0.02768648 - time (sec): 14.01 - samples/sec: 2708.81 - lr: 0.000020 - momentum: 0.000000
135
+ 2023-10-17 10:33:54,303 epoch 5 - iter 360/1809 - loss 0.03102599 - time (sec): 27.07 - samples/sec: 2738.17 - lr: 0.000019 - momentum: 0.000000
136
+ 2023-10-17 10:34:08,359 epoch 5 - iter 540/1809 - loss 0.03185567 - time (sec): 41.13 - samples/sec: 2734.73 - lr: 0.000019 - momentum: 0.000000
137
+ 2023-10-17 10:34:21,848 epoch 5 - iter 720/1809 - loss 0.03061780 - time (sec): 54.62 - samples/sec: 2737.94 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-10-17 10:34:36,525 epoch 5 - iter 900/1809 - loss 0.03119659 - time (sec): 69.29 - samples/sec: 2723.36 - lr: 0.000018 - momentum: 0.000000
139
+ 2023-10-17 10:34:50,562 epoch 5 - iter 1080/1809 - loss 0.03101663 - time (sec): 83.33 - samples/sec: 2720.38 - lr: 0.000018 - momentum: 0.000000
140
+ 2023-10-17 10:35:04,954 epoch 5 - iter 1260/1809 - loss 0.03070119 - time (sec): 97.72 - samples/sec: 2718.56 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-10-17 10:35:19,209 epoch 5 - iter 1440/1809 - loss 0.03061179 - time (sec): 111.98 - samples/sec: 2706.42 - lr: 0.000017 - momentum: 0.000000
142
+ 2023-10-17 10:35:33,200 epoch 5 - iter 1620/1809 - loss 0.03213924 - time (sec): 125.97 - samples/sec: 2700.86 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 10:35:46,610 epoch 5 - iter 1800/1809 - loss 0.03232409 - time (sec): 139.38 - samples/sec: 2712.26 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-10-17 10:35:47,324 ----------------------------------------------------------------------------------------------------
145
+ 2023-10-17 10:35:47,324 EPOCH 5 done: loss 0.0324 - lr: 0.000017
146
+ 2023-10-17 10:35:53,774 DEV : loss 0.2933090031147003 - f1-score (micro avg) 0.6564
147
+ 2023-10-17 10:35:53,824 saving best model
148
+ 2023-10-17 10:35:54,466 ----------------------------------------------------------------------------------------------------
149
+ 2023-10-17 10:36:08,260 epoch 6 - iter 180/1809 - loss 0.02526723 - time (sec): 13.79 - samples/sec: 2751.63 - lr: 0.000016 - momentum: 0.000000
150
+ 2023-10-17 10:36:21,717 epoch 6 - iter 360/1809 - loss 0.02266318 - time (sec): 27.25 - samples/sec: 2791.79 - lr: 0.000016 - momentum: 0.000000
151
+ 2023-10-17 10:36:35,235 epoch 6 - iter 540/1809 - loss 0.02250374 - time (sec): 40.77 - samples/sec: 2768.29 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-10-17 10:36:48,648 epoch 6 - iter 720/1809 - loss 0.02114013 - time (sec): 54.18 - samples/sec: 2787.83 - lr: 0.000015 - momentum: 0.000000
153
+ 2023-10-17 10:37:02,292 epoch 6 - iter 900/1809 - loss 0.02208737 - time (sec): 67.82 - samples/sec: 2787.14 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-10-17 10:37:15,201 epoch 6 - iter 1080/1809 - loss 0.02271038 - time (sec): 80.73 - samples/sec: 2800.43 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-10-17 10:37:29,400 epoch 6 - iter 1260/1809 - loss 0.02373796 - time (sec): 94.93 - samples/sec: 2783.97 - lr: 0.000014 - momentum: 0.000000
156
+ 2023-10-17 10:37:43,390 epoch 6 - iter 1440/1809 - loss 0.02351275 - time (sec): 108.92 - samples/sec: 2769.13 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-10-17 10:37:56,318 epoch 6 - iter 1620/1809 - loss 0.02349186 - time (sec): 121.85 - samples/sec: 2791.97 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-10-17 10:38:09,258 epoch 6 - iter 1800/1809 - loss 0.02321458 - time (sec): 134.79 - samples/sec: 2801.94 - lr: 0.000013 - momentum: 0.000000
159
+ 2023-10-17 10:38:09,960 ----------------------------------------------------------------------------------------------------
160
+ 2023-10-17 10:38:09,961 EPOCH 6 done: loss 0.0231 - lr: 0.000013
161
+ 2023-10-17 10:38:16,483 DEV : loss 0.317108690738678 - f1-score (micro avg) 0.6555
162
+ 2023-10-17 10:38:16,533 ----------------------------------------------------------------------------------------------------
163
+ 2023-10-17 10:38:30,845 epoch 7 - iter 180/1809 - loss 0.01666041 - time (sec): 14.31 - samples/sec: 2797.44 - lr: 0.000013 - momentum: 0.000000
164
+ 2023-10-17 10:38:45,010 epoch 7 - iter 360/1809 - loss 0.01535944 - time (sec): 28.48 - samples/sec: 2763.71 - lr: 0.000013 - momentum: 0.000000
165
+ 2023-10-17 10:38:59,190 epoch 7 - iter 540/1809 - loss 0.01585990 - time (sec): 42.66 - samples/sec: 2712.38 - lr: 0.000012 - momentum: 0.000000
166
+ 2023-10-17 10:39:12,131 epoch 7 - iter 720/1809 - loss 0.01588240 - time (sec): 55.60 - samples/sec: 2743.87 - lr: 0.000012 - momentum: 0.000000
167
+ 2023-10-17 10:39:24,799 epoch 7 - iter 900/1809 - loss 0.01567846 - time (sec): 68.26 - samples/sec: 2772.04 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-10-17 10:39:37,658 epoch 7 - iter 1080/1809 - loss 0.01573739 - time (sec): 81.12 - samples/sec: 2782.75 - lr: 0.000011 - momentum: 0.000000
169
+ 2023-10-17 10:39:51,060 epoch 7 - iter 1260/1809 - loss 0.01492397 - time (sec): 94.53 - samples/sec: 2794.91 - lr: 0.000011 - momentum: 0.000000
170
+ 2023-10-17 10:40:04,433 epoch 7 - iter 1440/1809 - loss 0.01512705 - time (sec): 107.90 - samples/sec: 2804.37 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 10:40:18,776 epoch 7 - iter 1620/1809 - loss 0.01537735 - time (sec): 122.24 - samples/sec: 2781.25 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 10:40:33,065 epoch 7 - iter 1800/1809 - loss 0.01577194 - time (sec): 136.53 - samples/sec: 2771.15 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 10:40:33,734 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 10:40:33,735 EPOCH 7 done: loss 0.0157 - lr: 0.000010
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+ 2023-10-17 10:40:40,913 DEV : loss 0.35636281967163086 - f1-score (micro avg) 0.6586
176
+ 2023-10-17 10:40:40,989 saving best model
177
+ 2023-10-17 10:40:41,627 ----------------------------------------------------------------------------------------------------
178
+ 2023-10-17 10:40:55,327 epoch 8 - iter 180/1809 - loss 0.01233300 - time (sec): 13.70 - samples/sec: 2714.96 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 10:41:08,947 epoch 8 - iter 360/1809 - loss 0.01007325 - time (sec): 27.32 - samples/sec: 2768.24 - lr: 0.000009 - momentum: 0.000000
180
+ 2023-10-17 10:41:22,905 epoch 8 - iter 540/1809 - loss 0.01079234 - time (sec): 41.28 - samples/sec: 2745.17 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 10:41:36,976 epoch 8 - iter 720/1809 - loss 0.01286652 - time (sec): 55.35 - samples/sec: 2726.46 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 10:41:51,431 epoch 8 - iter 900/1809 - loss 0.01195537 - time (sec): 69.80 - samples/sec: 2706.92 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 10:42:05,932 epoch 8 - iter 1080/1809 - loss 0.01176322 - time (sec): 84.30 - samples/sec: 2683.32 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-10-17 10:42:20,154 epoch 8 - iter 1260/1809 - loss 0.01134419 - time (sec): 98.52 - samples/sec: 2682.88 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-10-17 10:42:33,804 epoch 8 - iter 1440/1809 - loss 0.01129014 - time (sec): 112.18 - samples/sec: 2696.96 - lr: 0.000007 - momentum: 0.000000
186
+ 2023-10-17 10:42:47,117 epoch 8 - iter 1620/1809 - loss 0.01089610 - time (sec): 125.49 - samples/sec: 2708.67 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-10-17 10:43:00,413 epoch 8 - iter 1800/1809 - loss 0.01091399 - time (sec): 138.78 - samples/sec: 2725.05 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-10-17 10:43:01,033 ----------------------------------------------------------------------------------------------------
189
+ 2023-10-17 10:43:01,033 EPOCH 8 done: loss 0.0110 - lr: 0.000007
190
+ 2023-10-17 10:43:07,612 DEV : loss 0.35512134432792664 - f1-score (micro avg) 0.6591
191
+ 2023-10-17 10:43:07,659 saving best model
192
+ 2023-10-17 10:43:08,266 ----------------------------------------------------------------------------------------------------
193
+ 2023-10-17 10:43:21,490 epoch 9 - iter 180/1809 - loss 0.00903196 - time (sec): 13.22 - samples/sec: 2927.11 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-10-17 10:43:33,246 epoch 9 - iter 360/1809 - loss 0.00779450 - time (sec): 24.98 - samples/sec: 2999.25 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-10-17 10:43:45,896 epoch 9 - iter 540/1809 - loss 0.00679913 - time (sec): 37.63 - samples/sec: 2998.42 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-10-17 10:43:57,445 epoch 9 - iter 720/1809 - loss 0.00667016 - time (sec): 49.18 - samples/sec: 3080.76 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-10-17 10:44:09,143 epoch 9 - iter 900/1809 - loss 0.00738014 - time (sec): 60.88 - samples/sec: 3111.49 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-10-17 10:44:21,055 epoch 9 - iter 1080/1809 - loss 0.00679923 - time (sec): 72.79 - samples/sec: 3125.62 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-10-17 10:44:33,455 epoch 9 - iter 1260/1809 - loss 0.00660081 - time (sec): 85.19 - samples/sec: 3109.31 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-10-17 10:44:47,147 epoch 9 - iter 1440/1809 - loss 0.00659073 - time (sec): 98.88 - samples/sec: 3062.56 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-10-17 10:45:01,502 epoch 9 - iter 1620/1809 - loss 0.00679880 - time (sec): 113.23 - samples/sec: 3015.68 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-10-17 10:45:14,150 epoch 9 - iter 1800/1809 - loss 0.00670581 - time (sec): 125.88 - samples/sec: 3006.68 - lr: 0.000003 - momentum: 0.000000
203
+ 2023-10-17 10:45:14,787 ----------------------------------------------------------------------------------------------------
204
+ 2023-10-17 10:45:14,787 EPOCH 9 done: loss 0.0068 - lr: 0.000003
205
+ 2023-10-17 10:45:21,264 DEV : loss 0.38741225004196167 - f1-score (micro avg) 0.6648
206
+ 2023-10-17 10:45:21,310 saving best model
207
+ 2023-10-17 10:45:21,908 ----------------------------------------------------------------------------------------------------
208
+ 2023-10-17 10:45:35,707 epoch 10 - iter 180/1809 - loss 0.00757151 - time (sec): 13.80 - samples/sec: 2784.91 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-17 10:45:49,725 epoch 10 - iter 360/1809 - loss 0.00689107 - time (sec): 27.82 - samples/sec: 2720.52 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-10-17 10:46:04,659 epoch 10 - iter 540/1809 - loss 0.00613861 - time (sec): 42.75 - samples/sec: 2647.42 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-17 10:46:18,293 epoch 10 - iter 720/1809 - loss 0.00588222 - time (sec): 56.38 - samples/sec: 2663.59 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-17 10:46:32,068 epoch 10 - iter 900/1809 - loss 0.00544332 - time (sec): 70.16 - samples/sec: 2658.14 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-10-17 10:46:45,517 epoch 10 - iter 1080/1809 - loss 0.00530158 - time (sec): 83.61 - samples/sec: 2688.74 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-17 10:46:59,225 epoch 10 - iter 1260/1809 - loss 0.00497582 - time (sec): 97.32 - samples/sec: 2719.02 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-10-17 10:47:12,932 epoch 10 - iter 1440/1809 - loss 0.00553063 - time (sec): 111.02 - samples/sec: 2716.47 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-10-17 10:47:27,001 epoch 10 - iter 1620/1809 - loss 0.00533905 - time (sec): 125.09 - samples/sec: 2716.76 - lr: 0.000000 - momentum: 0.000000
217
+ 2023-10-17 10:47:41,204 epoch 10 - iter 1800/1809 - loss 0.00529427 - time (sec): 139.29 - samples/sec: 2715.81 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-10-17 10:47:41,818 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-17 10:47:41,818 EPOCH 10 done: loss 0.0054 - lr: 0.000000
220
+ 2023-10-17 10:47:48,079 DEV : loss 0.40038371086120605 - f1-score (micro avg) 0.6651
221
+ 2023-10-17 10:47:48,122 saving best model
222
+ 2023-10-17 10:47:49,288 ----------------------------------------------------------------------------------------------------
223
+ 2023-10-17 10:47:49,290 Loading model from best epoch ...
224
+ 2023-10-17 10:47:51,019 SequenceTagger predicts: Dictionary with 13 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org
225
+ 2023-10-17 10:48:00,015
226
+ Results:
227
+ - F-score (micro) 0.6719
228
+ - F-score (macro) 0.5432
229
+ - Accuracy 0.5187
230
+
231
+ By class:
232
+ precision recall f1-score support
233
+
234
+ loc 0.6531 0.8156 0.7254 591
235
+ pers 0.5876 0.7423 0.6559 357
236
+ org 0.2931 0.2152 0.2482 79
237
+
238
+ micro avg 0.6127 0.7439 0.6719 1027
239
+ macro avg 0.5113 0.5910 0.5432 1027
240
+ weighted avg 0.6026 0.7439 0.6645 1027
241
+
242
+ 2023-10-17 10:48:00,015 ----------------------------------------------------------------------------------------------------