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amtam0
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timer-ner-en

Token Classification
Flair
PyTorch
English
sequence-tagger-model
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amtam0 commited on
Commit
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1 Parent(s): f63b3fd

new training -adding kywd Round

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Files changed (5) hide show
  1. dev.tsv +0 -0
  2. loss.tsv +8 -10
  3. pytorch_model.bin +1 -1
  4. test.tsv +0 -0
  5. training.log +160 -190
dev.tsv CHANGED
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loss.tsv CHANGED
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  EPOCH TIMESTAMP BAD_EPOCHS LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
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- 1 15:37:11 0 0.0001 0.16075978090894327 0.0029305333737283945 0.9992 0.9992 0.9992 0.9992
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- 4 15:44:39 2 0.0001 0.11077808575201452 0.0035813269205391407 0.9992 0.9992 0.9992 0.9992
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- 5 15:47:07 0 0.0001 0.10987376058836824 0.0010140719823539257 0.9995 0.9995 0.9995 0.9995
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- 6 15:49:35 1 0.0001 0.10985530377211841 0.0014548080507665873 0.9993 0.9993 0.9993 0.9993
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- 7 15:52:04 2 0.0001 0.11081814550640288 0.0011286081280559301 0.9994 0.9994 0.9994 0.9994
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- 8 15:54:33 0 0.0001 0.1101565688396648 0.0014515728689730167 0.9995 0.9995 0.9995 0.9995
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- 10 15:59:31 2 0.0001 0.1096125644685161 0.004609304014593363 0.9993 0.9993 0.9993 0.9993
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  EPOCH TIMESTAMP BAD_EPOCHS LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
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+ 1 23:24:06 0 0.0001 0.15910741661981953 0.002542673610150814 0.9992 0.9992 0.9992 0.9992
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+ 2 23:26:34 1 0.0001 0.10975106787141815 0.0029088123701512814 0.999 0.9987 0.9988 0.9982
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+ 8 23:41:51 1 0.0001 0.10978477235306694 0.0015213226433843374 0.9993 0.9993 0.9993 0.9993
 
 
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test.tsv CHANGED
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training.log CHANGED
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- 2021-11-17 15:34:49,923 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:34:49,924 Model: "SequenceTagger(
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  (embeddings): TransformerWordEmbeddings(
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  (model): RobertaModel(
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  (embeddings): RobertaEmbeddings(
@@ -165,200 +165,170 @@
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  (weights): None
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  (weight_tensor) None
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  )"
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- 2021-11-17 15:34:49,924 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:34:49,925 Corpus: "Corpus: 56700 train + 6300 dev + 7000 test sentences"
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- 2021-11-17 15:34:49,925 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:34:49,926 Parameters:
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- 2021-11-17 15:34:49,926 - learning_rate: "5e-05"
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- 2021-11-17 15:34:49,926 - mini_batch_size: "64"
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- 2021-11-17 15:34:49,926 - patience: "3"
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- 2021-11-17 15:34:49,927 - anneal_factor: "0.5"
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- 2021-11-17 15:34:49,927 - max_epochs: "10"
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- 2021-11-17 15:34:49,927 - shuffle: "True"
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- 2021-11-17 15:34:49,928 - train_with_dev: "False"
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- 2021-11-17 15:34:49,928 - batch_growth_annealing: "False"
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- 2021-11-17 15:34:49,928 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:34:49,929 Model training base path: "training/flair_ner/17112021_152905"
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- 2021-11-17 15:34:49,930 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:34:49,930 Device: cuda
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- 2021-11-17 15:34:49,931 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:34:49,931 Embeddings storage mode: cpu
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- 2021-11-17 15:34:49,933 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:35:02,874 epoch 1 - iter 88/886 - loss 0.50644155 - samples/sec: 435.49 - lr: 0.000050
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- 2021-11-17 15:35:15,686 epoch 1 - iter 176/886 - loss 0.32420832 - samples/sec: 439.83 - lr: 0.000050
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- 2021-11-17 15:35:28,472 epoch 1 - iter 264/886 - loss 0.25984089 - samples/sec: 440.71 - lr: 0.000050
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- 2021-11-17 15:35:41,245 epoch 1 - iter 352/886 - loss 0.22670251 - samples/sec: 441.16 - lr: 0.000050
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- 2021-11-17 15:35:54,419 epoch 1 - iter 440/886 - loss 0.20579280 - samples/sec: 427.72 - lr: 0.000050
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- 2021-11-17 15:36:07,202 epoch 1 - iter 528/886 - loss 0.19081105 - samples/sec: 440.90 - lr: 0.000050
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- 2021-11-17 15:36:19,841 epoch 1 - iter 616/886 - loss 0.18055071 - samples/sec: 445.85 - lr: 0.000050
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- 2021-11-17 15:36:32,361 epoch 1 - iter 704/886 - loss 0.17219026 - samples/sec: 450.10 - lr: 0.000050
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- 2021-11-17 15:36:45,001 epoch 1 - iter 792/886 - loss 0.16603222 - samples/sec: 445.79 - lr: 0.000050
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- 2021-11-17 15:36:57,735 epoch 1 - iter 880/886 - loss 0.16102375 - samples/sec: 442.72 - lr: 0.000050
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- 2021-11-17 15:36:58,592 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:36:58,593 EPOCH 1 done: loss 0.1608 - lr 0.0000500
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- 2021-11-17 15:37:11,841 DEV : loss 0.0029305333737283945 - f1-score (micro avg) 0.9992
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- 2021-11-17 15:37:11,924 BAD EPOCHS (no improvement): 0
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- 2021-11-17 15:37:11,924 saving best model
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- 2021-11-17 15:37:12,293 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:37:25,475 epoch 2 - iter 88/886 - loss 0.11026321 - samples/sec: 427.67 - lr: 0.000050
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- 2021-11-17 15:37:38,477 epoch 2 - iter 176/886 - loss 0.11169786 - samples/sec: 433.62 - lr: 0.000050
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- 2021-11-17 15:37:51,386 epoch 2 - iter 264/886 - loss 0.11076006 - samples/sec: 436.59 - lr: 0.000050
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- 2021-11-17 15:38:04,316 epoch 2 - iter 352/886 - loss 0.11026275 - samples/sec: 435.86 - lr: 0.000050
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- 2021-11-17 15:38:17,224 epoch 2 - iter 440/886 - loss 0.11058185 - samples/sec: 436.60 - lr: 0.000050
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- 2021-11-17 15:38:30,171 epoch 2 - iter 528/886 - loss 0.11105888 - samples/sec: 435.31 - lr: 0.000050
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- 2021-11-17 15:38:43,248 epoch 2 - iter 616/886 - loss 0.11093445 - samples/sec: 431.17 - lr: 0.000050
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- 2021-11-17 15:38:56,137 epoch 2 - iter 704/886 - loss 0.11079835 - samples/sec: 437.26 - lr: 0.000050
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- 2021-11-17 15:39:09,395 epoch 2 - iter 792/886 - loss 0.11148766 - samples/sec: 425.17 - lr: 0.000050
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- 2021-11-17 15:39:22,450 epoch 2 - iter 880/886 - loss 0.11140394 - samples/sec: 431.78 - lr: 0.000050
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- 2021-11-17 15:39:23,318 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:39:23,318 EPOCH 2 done: loss 0.1113 - lr 0.0000500
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- 2021-11-17 15:39:39,217 DEV : loss 0.0013541270745918155 - f1-score (micro avg) 0.9992
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- 2021-11-17 15:39:39,304 BAD EPOCHS (no improvement): 0
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- 2021-11-17 15:39:39,305 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:39:52,661 epoch 3 - iter 88/886 - loss 0.10886323 - samples/sec: 422.03 - lr: 0.000050
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- 2021-11-17 15:40:05,912 epoch 3 - iter 176/886 - loss 0.10787832 - samples/sec: 425.49 - lr: 0.000050
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- 2021-11-17 15:40:19,212 epoch 3 - iter 264/886 - loss 0.11035842 - samples/sec: 423.74 - lr: 0.000050
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- 2021-11-17 15:40:32,505 epoch 3 - iter 352/886 - loss 0.11104986 - samples/sec: 424.15 - lr: 0.000050
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- 2021-11-17 15:40:45,782 epoch 3 - iter 440/886 - loss 0.11091610 - samples/sec: 424.49 - lr: 0.000050
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- 2021-11-17 15:40:59,163 epoch 3 - iter 528/886 - loss 0.11110444 - samples/sec: 421.17 - lr: 0.000050
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- 2021-11-17 15:41:12,392 epoch 3 - iter 616/886 - loss 0.11146392 - samples/sec: 426.23 - lr: 0.000050
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- 2021-11-17 15:41:25,673 epoch 3 - iter 704/886 - loss 0.11154272 - samples/sec: 424.34 - lr: 0.000050
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- 2021-11-17 15:41:38,940 epoch 3 - iter 792/886 - loss 0.11160924 - samples/sec: 424.88 - lr: 0.000050
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- 2021-11-17 15:41:52,243 epoch 3 - iter 880/886 - loss 0.11176415 - samples/sec: 423.61 - lr: 0.000050
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- 2021-11-17 15:41:53,139 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:41:53,141 EPOCH 3 done: loss 0.1118 - lr 0.0000500
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- 2021-11-17 15:42:09,290 DEV : loss 0.0017125594895333052 - f1-score (micro avg) 0.9992
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- 2021-11-17 15:42:09,373 BAD EPOCHS (no improvement): 1
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- 2021-11-17 15:42:09,374 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:42:22,858 epoch 4 - iter 88/886 - loss 0.10978185 - samples/sec: 418.00 - lr: 0.000050
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- 2021-11-17 15:42:36,074 epoch 4 - iter 176/886 - loss 0.10973528 - samples/sec: 426.43 - lr: 0.000050
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- 2021-11-17 15:42:49,423 epoch 4 - iter 264/886 - loss 0.11060583 - samples/sec: 422.19 - lr: 0.000050
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- 2021-11-17 15:43:02,798 epoch 4 - iter 352/886 - loss 0.11082956 - samples/sec: 421.55 - lr: 0.000050
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- 2021-11-17 15:43:16,118 epoch 4 - iter 440/886 - loss 0.11054231 - samples/sec: 423.16 - lr: 0.000050
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- 2021-11-17 15:43:29,471 epoch 4 - iter 528/886 - loss 0.11108359 - samples/sec: 422.07 - lr: 0.000050
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- 2021-11-17 15:43:42,869 epoch 4 - iter 616/886 - loss 0.11117851 - samples/sec: 420.64 - lr: 0.000050
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- 2021-11-17 15:43:56,526 epoch 4 - iter 704/886 - loss 0.11137181 - samples/sec: 412.67 - lr: 0.000050
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- 2021-11-17 15:44:10,054 epoch 4 - iter 792/886 - loss 0.11142306 - samples/sec: 416.60 - lr: 0.000050
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- 2021-11-17 15:44:23,264 epoch 4 - iter 880/886 - loss 0.11088636 - samples/sec: 426.62 - lr: 0.000050
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- 2021-11-17 15:44:24,146 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:44:24,146 EPOCH 4 done: loss 0.1108 - lr 0.0000500
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- 2021-11-17 15:44:39,706 DEV : loss 0.0035813269205391407 - f1-score (micro avg) 0.9992
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- 2021-11-17 15:44:39,791 BAD EPOCHS (no improvement): 2
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- 2021-11-17 15:44:39,791 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:44:53,041 epoch 5 - iter 88/886 - loss 0.10802392 - samples/sec: 425.46 - lr: 0.000050
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- 2021-11-17 15:45:06,325 epoch 5 - iter 176/886 - loss 0.10760262 - samples/sec: 424.24 - lr: 0.000050
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- 2021-11-17 15:45:19,569 epoch 5 - iter 264/886 - loss 0.10806256 - samples/sec: 425.73 - lr: 0.000050
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- 2021-11-17 15:45:32,761 epoch 5 - iter 352/886 - loss 0.10865681 - samples/sec: 427.42 - lr: 0.000050
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- 2021-11-17 15:45:45,855 epoch 5 - iter 440/886 - loss 0.10912184 - samples/sec: 430.61 - lr: 0.000050
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- 2021-11-17 15:45:59,034 epoch 5 - iter 528/886 - loss 0.10891177 - samples/sec: 427.65 - lr: 0.000050
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- 2021-11-17 15:46:12,303 epoch 5 - iter 616/886 - loss 0.10963959 - samples/sec: 424.90 - lr: 0.000050
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- 2021-11-17 15:46:25,367 epoch 5 - iter 704/886 - loss 0.10977588 - samples/sec: 431.42 - lr: 0.000050
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- 2021-11-17 15:46:38,535 epoch 5 - iter 792/886 - loss 0.10983991 - samples/sec: 427.99 - lr: 0.000050
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- 2021-11-17 15:46:52,036 epoch 5 - iter 880/886 - loss 0.10983081 - samples/sec: 417.44 - lr: 0.000050
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- 2021-11-17 15:46:52,981 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:46:52,981 EPOCH 5 done: loss 0.1099 - lr 0.0000500
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- 2021-11-17 15:47:07,506 DEV : loss 0.0010140719823539257 - f1-score (micro avg) 0.9995
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- 2021-11-17 15:47:07,591 BAD EPOCHS (no improvement): 0
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- 2021-11-17 15:47:07,592 saving best model
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- 2021-11-17 15:47:08,183 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:47:21,511 epoch 6 - iter 88/886 - loss 0.10567650 - samples/sec: 422.90 - lr: 0.000050
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- 2021-11-17 15:47:34,509 epoch 6 - iter 176/886 - loss 0.10887869 - samples/sec: 433.61 - lr: 0.000050
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- 2021-11-17 15:47:47,528 epoch 6 - iter 264/886 - loss 0.10842350 - samples/sec: 432.88 - lr: 0.000050
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- 2021-11-17 15:48:00,526 epoch 6 - iter 352/886 - loss 0.10983462 - samples/sec: 433.80 - lr: 0.000050
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- 2021-11-17 15:48:13,643 epoch 6 - iter 440/886 - loss 0.10883770 - samples/sec: 429.63 - lr: 0.000050
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- 2021-11-17 15:48:26,632 epoch 6 - iter 528/886 - loss 0.10926475 - samples/sec: 434.11 - lr: 0.000050
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- 2021-11-17 15:48:39,864 epoch 6 - iter 616/886 - loss 0.10987226 - samples/sec: 425.93 - lr: 0.000050
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- 2021-11-17 15:48:52,954 epoch 6 - iter 704/886 - loss 0.11003466 - samples/sec: 430.54 - lr: 0.000050
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- 2021-11-17 15:49:06,114 epoch 6 - iter 792/886 - loss 0.11000339 - samples/sec: 428.26 - lr: 0.000050
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- 2021-11-17 15:49:19,283 epoch 6 - iter 880/886 - loss 0.10986999 - samples/sec: 427.94 - lr: 0.000050
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- 2021-11-17 15:49:20,160 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:49:20,161 EPOCH 6 done: loss 0.1099 - lr 0.0000500
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- 2021-11-17 15:49:35,569 DEV : loss 0.0014548080507665873 - f1-score (micro avg) 0.9993
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- 2021-11-17 15:49:35,652 BAD EPOCHS (no improvement): 1
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- 2021-11-17 15:49:35,653 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:49:48,878 epoch 7 - iter 88/886 - loss 0.10951206 - samples/sec: 426.18 - lr: 0.000050
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- 2021-11-17 15:50:01,971 epoch 7 - iter 176/886 - loss 0.11032338 - samples/sec: 430.47 - lr: 0.000050
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- 2021-11-17 15:50:15,172 epoch 7 - iter 264/886 - loss 0.11045747 - samples/sec: 426.91 - lr: 0.000050
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- 2021-11-17 15:50:28,317 epoch 7 - iter 352/886 - loss 0.11071942 - samples/sec: 428.73 - lr: 0.000050
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- 2021-11-17 15:50:41,502 epoch 7 - iter 440/886 - loss 0.11000396 - samples/sec: 427.62 - lr: 0.000050
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- 2021-11-17 15:50:54,735 epoch 7 - iter 528/886 - loss 0.11036286 - samples/sec: 425.91 - lr: 0.000050
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- 2021-11-17 15:51:08,179 epoch 7 - iter 616/886 - loss 0.11044996 - samples/sec: 419.40 - lr: 0.000050
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- 2021-11-17 15:51:21,435 epoch 7 - iter 704/886 - loss 0.11062300 - samples/sec: 425.15 - lr: 0.000050
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- 2021-11-17 15:51:34,569 epoch 7 - iter 792/886 - loss 0.11050441 - samples/sec: 429.10 - lr: 0.000050
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- 2021-11-17 15:51:47,616 epoch 7 - iter 880/886 - loss 0.11081751 - samples/sec: 432.02 - lr: 0.000050
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- 2021-11-17 15:51:48,504 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:51:48,504 EPOCH 7 done: loss 0.1108 - lr 0.0000500
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- 2021-11-17 15:52:04,138 DEV : loss 0.0011286081280559301 - f1-score (micro avg) 0.9994
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- 2021-11-17 15:52:04,221 BAD EPOCHS (no improvement): 2
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- 2021-11-17 15:52:04,221 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:52:17,523 epoch 8 - iter 88/886 - loss 0.10894525 - samples/sec: 423.73 - lr: 0.000050
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- 2021-11-17 15:52:30,625 epoch 8 - iter 176/886 - loss 0.11013192 - samples/sec: 430.14 - lr: 0.000050
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- 2021-11-17 15:52:43,834 epoch 8 - iter 264/886 - loss 0.11008158 - samples/sec: 426.69 - lr: 0.000050
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- 2021-11-17 15:52:57,028 epoch 8 - iter 352/886 - loss 0.11060585 - samples/sec: 427.15 - lr: 0.000050
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- 2021-11-17 15:53:10,298 epoch 8 - iter 440/886 - loss 0.11058677 - samples/sec: 424.70 - lr: 0.000050
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- 2021-11-17 15:53:23,599 epoch 8 - iter 528/886 - loss 0.11039821 - samples/sec: 423.70 - lr: 0.000050
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- 2021-11-17 15:53:36,716 epoch 8 - iter 616/886 - loss 0.11030582 - samples/sec: 429.67 - lr: 0.000050
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- 2021-11-17 15:53:49,982 epoch 8 - iter 704/886 - loss 0.10977816 - samples/sec: 424.83 - lr: 0.000050
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- 2021-11-17 15:54:03,181 epoch 8 - iter 792/886 - loss 0.11012337 - samples/sec: 426.98 - lr: 0.000050
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- 2021-11-17 15:54:16,462 epoch 8 - iter 880/886 - loss 0.11017103 - samples/sec: 424.37 - lr: 0.000050
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- 2021-11-17 15:54:17,329 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:54:17,329 EPOCH 8 done: loss 0.1102 - lr 0.0000500
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- 2021-11-17 15:54:32,948 DEV : loss 0.0014515728689730167 - f1-score (micro avg) 0.9995
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- 2021-11-17 15:54:33,031 BAD EPOCHS (no improvement): 0
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- 2021-11-17 15:54:33,032 saving best model
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- 2021-11-17 15:54:33,637 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:54:46,858 epoch 9 - iter 88/886 - loss 0.10922566 - samples/sec: 426.35 - lr: 0.000050
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- 2021-11-17 15:54:59,965 epoch 9 - iter 176/886 - loss 0.11082640 - samples/sec: 429.99 - lr: 0.000050
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- 2021-11-17 15:55:13,176 epoch 9 - iter 264/886 - loss 0.11164660 - samples/sec: 426.60 - lr: 0.000050
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- 2021-11-17 15:55:26,289 epoch 9 - iter 352/886 - loss 0.11113663 - samples/sec: 429.99 - lr: 0.000050
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- 2021-11-17 15:55:40,047 epoch 9 - iter 440/886 - loss 0.11075153 - samples/sec: 409.63 - lr: 0.000050
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- 2021-11-17 15:55:53,772 epoch 9 - iter 528/886 - loss 0.11070955 - samples/sec: 410.63 - lr: 0.000050
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- 2021-11-17 15:56:07,050 epoch 9 - iter 616/886 - loss 0.11027549 - samples/sec: 424.44 - lr: 0.000050
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- 2021-11-17 15:56:20,322 epoch 9 - iter 704/886 - loss 0.11003220 - samples/sec: 424.64 - lr: 0.000050
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- 2021-11-17 15:56:33,497 epoch 9 - iter 792/886 - loss 0.10976900 - samples/sec: 427.78 - lr: 0.000050
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- 2021-11-17 15:56:46,751 epoch 9 - iter 880/886 - loss 0.11015739 - samples/sec: 425.22 - lr: 0.000050
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- 2021-11-17 15:56:47,659 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:56:47,660 EPOCH 9 done: loss 0.1102 - lr 0.0000500
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- 2021-11-17 15:57:02,117 DEV : loss 0.0028099738992750645 - f1-score (micro avg) 0.9994
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- 2021-11-17 15:57:02,205 BAD EPOCHS (no improvement): 1
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- 2021-11-17 15:57:02,206 ----------------------------------------------------------------------------------------------------
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- 2021-11-17 15:57:15,740 epoch 10 - iter 88/886 - loss 0.11323596 - samples/sec: 416.50 - lr: 0.000050
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- 2021-11-17 15:57:28,942 epoch 10 - iter 176/886 - loss 0.11324876 - samples/sec: 426.89 - lr: 0.000050
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- 2021-11-17 15:57:42,141 epoch 10 - iter 264/886 - loss 0.11189004 - samples/sec: 426.98 - lr: 0.000050
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- 2021-11-17 15:57:55,416 epoch 10 - iter 352/886 - loss 0.11062028 - samples/sec: 424.72 - lr: 0.000050
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- 2021-11-17 15:58:08,673 epoch 10 - iter 440/886 - loss 0.10959000 - samples/sec: 425.11 - lr: 0.000050
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- 2021-11-17 15:58:21,918 epoch 10 - iter 528/886 - loss 0.10964689 - samples/sec: 425.52 - lr: 0.000050
331
- 2021-11-17 15:58:35,102 epoch 10 - iter 616/886 - loss 0.11011373 - samples/sec: 427.66 - lr: 0.000050
332
- 2021-11-17 15:58:48,156 epoch 10 - iter 704/886 - loss 0.10975773 - samples/sec: 431.74 - lr: 0.000050
333
- 2021-11-17 15:59:01,225 epoch 10 - iter 792/886 - loss 0.10955614 - samples/sec: 431.43 - lr: 0.000050
334
- 2021-11-17 15:59:14,205 epoch 10 - iter 880/886 - loss 0.10966756 - samples/sec: 434.19 - lr: 0.000050
335
- 2021-11-17 15:59:15,113 ----------------------------------------------------------------------------------------------------
336
- 2021-11-17 15:59:15,114 EPOCH 10 done: loss 0.1096 - lr 0.0000500
337
- 2021-11-17 15:59:30,962 DEV : loss 0.004609304014593363 - f1-score (micro avg) 0.9993
338
- 2021-11-17 15:59:31,047 BAD EPOCHS (no improvement): 2
339
- 2021-11-17 15:59:31,418 ----------------------------------------------------------------------------------------------------
340
- 2021-11-17 15:59:31,419 loading file training/flair_ner/17112021_152905/best-model.pt
341
- 2021-11-17 15:59:49,424 0.9993 0.9993 0.9993 0.9993
342
- 2021-11-17 15:59:49,425
343
  Results:
344
  - F-score (micro) 0.9993
345
- - F-score (macro) 0.9984
346
  - Accuracy 0.9993
347
 
348
  By class:
349
  precision recall f1-score support
350
 
351
- nb_rounds 0.9988 0.9991 0.9990 6882
352
- duration_br_min 0.9997 0.9979 0.9988 3303
353
- duration_wt_sd 1.0000 1.0000 1.0000 3251
354
- duration_wt_min 1.0000 1.0000 1.0000 2698
355
- duration_br_sd 0.9995 0.9995 0.9995 2003
356
- duration_wt_hr 1.0000 1.0000 1.0000 1068
357
- duration_br_hr 0.9830 1.0000 0.9914 231
358
 
359
- micro avg 0.9993 0.9993 0.9993 19436
360
- macro avg 0.9973 0.9995 0.9984 19436
361
- weighted avg 0.9993 0.9993 0.9993 19436
362
- samples avg 0.9993 0.9993 0.9993 19436
363
 
364
- 2021-11-17 15:59:49,425 ----------------------------------------------------------------------------------------------------
1
+ 2021-11-17 23:21:43,874 ----------------------------------------------------------------------------------------------------
2
+ 2021-11-17 23:21:43,875 Model: "SequenceTagger(
3
  (embeddings): TransformerWordEmbeddings(
4
  (model): RobertaModel(
5
  (embeddings): RobertaEmbeddings(
165
  (weights): None
166
  (weight_tensor) None
167
  )"
168
+ 2021-11-17 23:21:43,876 ----------------------------------------------------------------------------------------------------
169
+ 2021-11-17 23:21:43,877 Corpus: "Corpus: 56700 train + 6300 dev + 7000 test sentences"
170
+ 2021-11-17 23:21:43,877 ----------------------------------------------------------------------------------------------------
171
+ 2021-11-17 23:21:43,878 Parameters:
172
+ 2021-11-17 23:21:43,878 - learning_rate: "5e-05"
173
+ 2021-11-17 23:21:43,879 - mini_batch_size: "64"
174
+ 2021-11-17 23:21:43,879 - patience: "3"
175
+ 2021-11-17 23:21:43,879 - anneal_factor: "0.5"
176
+ 2021-11-17 23:21:43,880 - max_epochs: "8"
177
+ 2021-11-17 23:21:43,881 - shuffle: "True"
178
+ 2021-11-17 23:21:43,881 - train_with_dev: "False"
179
+ 2021-11-17 23:21:43,882 - batch_growth_annealing: "False"
180
+ 2021-11-17 23:21:43,882 ----------------------------------------------------------------------------------------------------
181
+ 2021-11-17 23:21:43,883 Model training base path: "training/flair_ner/en/17112021_231902"
182
+ 2021-11-17 23:21:43,883 ----------------------------------------------------------------------------------------------------
183
+ 2021-11-17 23:21:43,884 Device: cuda
184
+ 2021-11-17 23:21:43,885 ----------------------------------------------------------------------------------------------------
185
+ 2021-11-17 23:21:43,885 Embeddings storage mode: cpu
186
+ 2021-11-17 23:21:43,886 ----------------------------------------------------------------------------------------------------
187
+ 2021-11-17 23:21:57,350 epoch 1 - iter 88/886 - loss 0.50060718 - samples/sec: 418.55 - lr: 0.000050
188
+ 2021-11-17 23:22:10,500 epoch 1 - iter 176/886 - loss 0.32189657 - samples/sec: 428.58 - lr: 0.000050
189
+ 2021-11-17 23:22:23,215 epoch 1 - iter 264/886 - loss 0.25798771 - samples/sec: 443.41 - lr: 0.000050
190
+ 2021-11-17 23:22:35,888 epoch 1 - iter 352/886 - loss 0.22669943 - samples/sec: 444.82 - lr: 0.000050
191
+ 2021-11-17 23:22:48,672 epoch 1 - iter 440/886 - loss 0.20548598 - samples/sec: 440.79 - lr: 0.000050
192
+ 2021-11-17 23:23:01,458 epoch 1 - iter 528/886 - loss 0.19096343 - samples/sec: 440.79 - lr: 0.000050
193
+ 2021-11-17 23:23:14,258 epoch 1 - iter 616/886 - loss 0.18023473 - samples/sec: 440.24 - lr: 0.000050
194
+ 2021-11-17 23:23:27,118 epoch 1 - iter 704/886 - loss 0.17198943 - samples/sec: 438.19 - lr: 0.000050
195
+ 2021-11-17 23:23:39,791 epoch 1 - iter 792/886 - loss 0.16499517 - samples/sec: 444.63 - lr: 0.000050
196
+ 2021-11-17 23:23:52,506 epoch 1 - iter 880/886 - loss 0.15942326 - samples/sec: 443.19 - lr: 0.000050
197
+ 2021-11-17 23:23:53,362 ----------------------------------------------------------------------------------------------------
198
+ 2021-11-17 23:23:53,363 EPOCH 1 done: loss 0.1591 - lr 0.0000500
199
+ 2021-11-17 23:24:06,817 DEV : loss 0.002542673610150814 - f1-score (micro avg) 0.9992
200
+ 2021-11-17 23:24:06,902 BAD EPOCHS (no improvement): 0
201
+ 2021-11-17 23:24:06,903 saving best model
202
+ 2021-11-17 23:24:07,239 ----------------------------------------------------------------------------------------------------
203
+ 2021-11-17 23:24:20,356 epoch 2 - iter 88/886 - loss 0.11000766 - samples/sec: 429.70 - lr: 0.000050
204
+ 2021-11-17 23:24:33,380 epoch 2 - iter 176/886 - loss 0.10909856 - samples/sec: 432.73 - lr: 0.000050
205
+ 2021-11-17 23:24:46,404 epoch 2 - iter 264/886 - loss 0.10926820 - samples/sec: 432.72 - lr: 0.000050
206
+ 2021-11-17 23:24:59,233 epoch 2 - iter 352/886 - loss 0.10950969 - samples/sec: 439.32 - lr: 0.000050
207
+ 2021-11-17 23:25:12,123 epoch 2 - iter 440/886 - loss 0.11018886 - samples/sec: 437.23 - lr: 0.000050
208
+ 2021-11-17 23:25:25,126 epoch 2 - iter 528/886 - loss 0.10995752 - samples/sec: 433.43 - lr: 0.000050
209
+ 2021-11-17 23:25:38,072 epoch 2 - iter 616/886 - loss 0.10983300 - samples/sec: 435.34 - lr: 0.000050
210
+ 2021-11-17 23:25:51,102 epoch 2 - iter 704/886 - loss 0.10978674 - samples/sec: 432.51 - lr: 0.000050
211
+ 2021-11-17 23:26:05,660 epoch 2 - iter 792/886 - loss 0.10974621 - samples/sec: 387.25 - lr: 0.000050
212
+ 2021-11-17 23:26:19,108 epoch 2 - iter 880/886 - loss 0.10964924 - samples/sec: 419.09 - lr: 0.000050
213
+ 2021-11-17 23:26:20,019 ----------------------------------------------------------------------------------------------------
214
+ 2021-11-17 23:26:20,020 EPOCH 2 done: loss 0.1098 - lr 0.0000500
215
+ 2021-11-17 23:26:34,470 DEV : loss 0.0029088123701512814 - f1-score (micro avg) 0.9988
216
+ 2021-11-17 23:26:34,553 BAD EPOCHS (no improvement): 1
217
+ 2021-11-17 23:26:34,553 ----------------------------------------------------------------------------------------------------
218
+ 2021-11-17 23:26:47,966 epoch 3 - iter 88/886 - loss 0.11118611 - samples/sec: 420.23 - lr: 0.000050
219
+ 2021-11-17 23:27:01,224 epoch 3 - iter 176/886 - loss 0.11113361 - samples/sec: 425.09 - lr: 0.000050
220
+ 2021-11-17 23:27:14,454 epoch 3 - iter 264/886 - loss 0.11038604 - samples/sec: 426.17 - lr: 0.000050
221
+ 2021-11-17 23:27:27,741 epoch 3 - iter 352/886 - loss 0.11138497 - samples/sec: 424.34 - lr: 0.000050
222
+ 2021-11-17 23:27:40,811 epoch 3 - iter 440/886 - loss 0.11143778 - samples/sec: 431.20 - lr: 0.000050
223
+ 2021-11-17 23:27:54,062 epoch 3 - iter 528/886 - loss 0.11093105 - samples/sec: 425.34 - lr: 0.000050
224
+ 2021-11-17 23:28:07,198 epoch 3 - iter 616/886 - loss 0.11050488 - samples/sec: 429.21 - lr: 0.000050
225
+ 2021-11-17 23:28:20,418 epoch 3 - iter 704/886 - loss 0.11064153 - samples/sec: 426.32 - lr: 0.000050
226
+ 2021-11-17 23:28:33,690 epoch 3 - iter 792/886 - loss 0.11022304 - samples/sec: 424.79 - lr: 0.000050
227
+ 2021-11-17 23:28:47,015 epoch 3 - iter 880/886 - loss 0.11054611 - samples/sec: 422.95 - lr: 0.000050
228
+ 2021-11-17 23:28:47,991 ----------------------------------------------------------------------------------------------------
229
+ 2021-11-17 23:28:47,992 EPOCH 3 done: loss 0.1105 - lr 0.0000500
230
+ 2021-11-17 23:29:04,469 DEV : loss 0.0013118594652041793 - f1-score (micro avg) 0.9994
231
+ 2021-11-17 23:29:04,549 BAD EPOCHS (no improvement): 0
232
+ 2021-11-17 23:29:04,550 saving best model
233
+ 2021-11-17 23:29:05,206 ----------------------------------------------------------------------------------------------------
234
+ 2021-11-17 23:29:19,255 epoch 4 - iter 88/886 - loss 0.11101590 - samples/sec: 401.22 - lr: 0.000050
235
+ 2021-11-17 23:29:33,081 epoch 4 - iter 176/886 - loss 0.10997834 - samples/sec: 407.62 - lr: 0.000050
236
+ 2021-11-17 23:29:46,787 epoch 4 - iter 264/886 - loss 0.11031061 - samples/sec: 411.18 - lr: 0.000050
237
+ 2021-11-17 23:30:00,054 epoch 4 - iter 352/886 - loss 0.10969025 - samples/sec: 424.81 - lr: 0.000050
238
+ 2021-11-17 23:30:13,298 epoch 4 - iter 440/886 - loss 0.11001565 - samples/sec: 425.52 - lr: 0.000050
239
+ 2021-11-17 23:30:26,545 epoch 4 - iter 528/886 - loss 0.11013209 - samples/sec: 425.45 - lr: 0.000050
240
+ 2021-11-17 23:30:39,776 epoch 4 - iter 616/886 - loss 0.10980630 - samples/sec: 425.95 - lr: 0.000050
241
+ 2021-11-17 23:30:52,924 epoch 4 - iter 704/886 - loss 0.10947482 - samples/sec: 428.65 - lr: 0.000050
242
+ 2021-11-17 23:31:06,186 epoch 4 - iter 792/886 - loss 0.10976788 - samples/sec: 424.94 - lr: 0.000050
243
+ 2021-11-17 23:31:19,571 epoch 4 - iter 880/886 - loss 0.10976014 - samples/sec: 421.06 - lr: 0.000050
244
+ 2021-11-17 23:31:20,467 ----------------------------------------------------------------------------------------------------
245
+ 2021-11-17 23:31:20,468 EPOCH 4 done: loss 0.1098 - lr 0.0000500
246
+ 2021-11-17 23:31:36,227 DEV : loss 0.0019321050494909286 - f1-score (micro avg) 0.999
247
+ 2021-11-17 23:31:36,311 BAD EPOCHS (no improvement): 1
248
+ 2021-11-17 23:31:36,312 ----------------------------------------------------------------------------------------------------
249
+ 2021-11-17 23:31:49,776 epoch 5 - iter 88/886 - loss 0.11196203 - samples/sec: 418.62 - lr: 0.000050
250
+ 2021-11-17 23:32:03,347 epoch 5 - iter 176/886 - loss 0.11146165 - samples/sec: 415.27 - lr: 0.000050
251
+ 2021-11-17 23:32:16,869 epoch 5 - iter 264/886 - loss 0.11038997 - samples/sec: 416.80 - lr: 0.000050
252
+ 2021-11-17 23:32:30,210 epoch 5 - iter 352/886 - loss 0.10969957 - samples/sec: 422.45 - lr: 0.000050
253
+ 2021-11-17 23:32:43,385 epoch 5 - iter 440/886 - loss 0.10883622 - samples/sec: 427.75 - lr: 0.000050
254
+ 2021-11-17 23:32:57,014 epoch 5 - iter 528/886 - loss 0.10885199 - samples/sec: 413.52 - lr: 0.000050
255
+ 2021-11-17 23:33:11,225 epoch 5 - iter 616/886 - loss 0.10919470 - samples/sec: 396.74 - lr: 0.000050
256
+ 2021-11-17 23:33:25,329 epoch 5 - iter 704/886 - loss 0.10968561 - samples/sec: 399.65 - lr: 0.000050
257
+ 2021-11-17 23:33:38,569 epoch 5 - iter 792/886 - loss 0.10952831 - samples/sec: 425.68 - lr: 0.000050
258
+ 2021-11-17 23:33:51,869 epoch 5 - iter 880/886 - loss 0.10925988 - samples/sec: 423.91 - lr: 0.000050
259
+ 2021-11-17 23:33:52,767 ----------------------------------------------------------------------------------------------------
260
+ 2021-11-17 23:33:52,768 EPOCH 5 done: loss 0.1092 - lr 0.0000500
261
+ 2021-11-17 23:34:08,633 DEV : loss 0.001400615437887609 - f1-score (micro avg) 0.9994
262
+ 2021-11-17 23:34:08,713 BAD EPOCHS (no improvement): 2
263
+ 2021-11-17 23:34:08,716 ----------------------------------------------------------------------------------------------------
264
+ 2021-11-17 23:34:22,104 epoch 6 - iter 88/886 - loss 0.10971184 - samples/sec: 421.02 - lr: 0.000050
265
+ 2021-11-17 23:34:35,452 epoch 6 - iter 176/886 - loss 0.10810577 - samples/sec: 422.40 - lr: 0.000050
266
+ 2021-11-17 23:34:48,789 epoch 6 - iter 264/886 - loss 0.10923295 - samples/sec: 422.58 - lr: 0.000050
267
+ 2021-11-17 23:35:02,187 epoch 6 - iter 352/886 - loss 0.10832324 - samples/sec: 420.62 - lr: 0.000050
268
+ 2021-11-17 23:35:15,501 epoch 6 - iter 440/886 - loss 0.10890621 - samples/sec: 423.47 - lr: 0.000050
269
+ 2021-11-17 23:35:28,932 epoch 6 - iter 528/886 - loss 0.10836666 - samples/sec: 419.60 - lr: 0.000050
270
+ 2021-11-17 23:35:42,421 epoch 6 - iter 616/886 - loss 0.10866986 - samples/sec: 417.83 - lr: 0.000050
271
+ 2021-11-17 23:35:56,321 epoch 6 - iter 704/886 - loss 0.10845591 - samples/sec: 405.45 - lr: 0.000050
272
+ 2021-11-17 23:36:10,189 epoch 6 - iter 792/886 - loss 0.10875052 - samples/sec: 406.44 - lr: 0.000050
273
+ 2021-11-17 23:36:23,804 epoch 6 - iter 880/886 - loss 0.10904969 - samples/sec: 413.93 - lr: 0.000050
274
+ 2021-11-17 23:36:24,703 ----------------------------------------------------------------------------------------------------
275
+ 2021-11-17 23:36:24,704 EPOCH 6 done: loss 0.1092 - lr 0.0000500
276
+ 2021-11-17 23:36:40,380 DEV : loss 0.0009049061918631196 - f1-score (micro avg) 0.9992
277
+ 2021-11-17 23:36:40,463 BAD EPOCHS (no improvement): 3
278
+ 2021-11-17 23:36:40,463 ----------------------------------------------------------------------------------------------------
279
+ 2021-11-17 23:36:54,014 epoch 7 - iter 88/886 - loss 0.11094486 - samples/sec: 415.95 - lr: 0.000050
280
+ 2021-11-17 23:37:07,422 epoch 7 - iter 176/886 - loss 0.10949810 - samples/sec: 420.52 - lr: 0.000050
281
+ 2021-11-17 23:37:21,230 epoch 7 - iter 264/886 - loss 0.10970254 - samples/sec: 408.14 - lr: 0.000050
282
+ 2021-11-17 23:37:34,444 epoch 7 - iter 352/886 - loss 0.11019445 - samples/sec: 426.59 - lr: 0.000050
283
+ 2021-11-17 23:37:47,833 epoch 7 - iter 440/886 - loss 0.11044571 - samples/sec: 420.94 - lr: 0.000050
284
+ 2021-11-17 23:38:01,118 epoch 7 - iter 528/886 - loss 0.11022272 - samples/sec: 424.19 - lr: 0.000050
285
+ 2021-11-17 23:38:14,537 epoch 7 - iter 616/886 - loss 0.10975761 - samples/sec: 420.00 - lr: 0.000050
286
+ 2021-11-17 23:38:27,909 epoch 7 - iter 704/886 - loss 0.10944174 - samples/sec: 421.63 - lr: 0.000050
287
+ 2021-11-17 23:38:41,133 epoch 7 - iter 792/886 - loss 0.10960931 - samples/sec: 426.17 - lr: 0.000050
288
+ 2021-11-17 23:38:54,481 epoch 7 - iter 880/886 - loss 0.10960868 - samples/sec: 422.22 - lr: 0.000050
289
+ 2021-11-17 23:38:55,367 ----------------------------------------------------------------------------------------------------
290
+ 2021-11-17 23:38:55,368 EPOCH 7 done: loss 0.1096 - lr 0.0000500
291
+ 2021-11-17 23:39:11,689 DEV : loss 0.0013050935231149197 - f1-score (micro avg) 0.9995
292
+ 2021-11-17 23:39:11,770 BAD EPOCHS (no improvement): 0
293
+ 2021-11-17 23:39:11,773 saving best model
294
+ 2021-11-17 23:39:12,423 ----------------------------------------------------------------------------------------------------
295
+ 2021-11-17 23:39:26,468 epoch 8 - iter 88/886 - loss 0.11104233 - samples/sec: 401.32 - lr: 0.000050
296
+ 2021-11-17 23:39:40,269 epoch 8 - iter 176/886 - loss 0.11088406 - samples/sec: 408.36 - lr: 0.000050
297
+ 2021-11-17 23:39:53,968 epoch 8 - iter 264/886 - loss 0.11062941 - samples/sec: 411.41 - lr: 0.000050
298
+ 2021-11-17 23:40:07,630 epoch 8 - iter 352/886 - loss 0.11052519 - samples/sec: 412.67 - lr: 0.000050
299
+ 2021-11-17 23:40:21,700 epoch 8 - iter 440/886 - loss 0.10981883 - samples/sec: 400.57 - lr: 0.000050
300
+ 2021-11-17 23:40:35,699 epoch 8 - iter 528/886 - loss 0.10959840 - samples/sec: 402.57 - lr: 0.000050
301
+ 2021-11-17 23:40:49,510 epoch 8 - iter 616/886 - loss 0.10968087 - samples/sec: 408.23 - lr: 0.000050
302
+ 2021-11-17 23:41:03,430 epoch 8 - iter 704/886 - loss 0.10975513 - samples/sec: 404.86 - lr: 0.000050
303
+ 2021-11-17 23:41:17,719 epoch 8 - iter 792/886 - loss 0.10979006 - samples/sec: 394.41 - lr: 0.000050
304
+ 2021-11-17 23:41:32,411 epoch 8 - iter 880/886 - loss 0.10979431 - samples/sec: 383.61 - lr: 0.000050
305
+ 2021-11-17 23:41:33,357 ----------------------------------------------------------------------------------------------------
306
+ 2021-11-17 23:41:33,358 EPOCH 8 done: loss 0.1098 - lr 0.0000500
307
+ 2021-11-17 23:41:50,962 DEV : loss 0.0015213226433843374 - f1-score (micro avg) 0.9993
308
+ 2021-11-17 23:41:51,053 BAD EPOCHS (no improvement): 1
309
+ 2021-11-17 23:41:51,466 ----------------------------------------------------------------------------------------------------
310
+ 2021-11-17 23:41:51,467 loading file training/flair_ner/en/17112021_231902/best-model.pt
311
+ 2021-11-17 23:42:09,058 0.9993 0.9993 0.9993 0.9993
312
+ 2021-11-17 23:42:09,064
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
313
  Results:
314
  - F-score (micro) 0.9993
315
+ - F-score (macro) 0.9992
316
  - Accuracy 0.9993
317
 
318
  By class:
319
  precision recall f1-score support
320
 
321
+ nb_rounds 0.9999 0.9981 0.9990 6889
322
+ duration_wt_sd 1.0000 1.0000 1.0000 3292
323
+ duration_br_min 0.9975 1.0000 0.9988 3239
324
+ duration_wt_min 1.0000 1.0000 1.0000 2685
325
+ duration_br_sd 0.9981 0.9995 0.9988 2068
326
+ duration_wt_hr 1.0000 1.0000 1.0000 1023
327
+ duration_br_hr 0.9957 1.0000 0.9978 230
328
 
329
+ micro avg 0.9993 0.9993 0.9993 19426
330
+ macro avg 0.9987 0.9997 0.9992 19426
331
+ weighted avg 0.9993 0.9993 0.9993 19426
332
+ samples avg 0.9993 0.9993 0.9993 19426
333
 
334
+ 2021-11-17 23:42:09,065 ----------------------------------------------------------------------------------------------------