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hmbench-newseye-fr-hmteams-bs8-wsFalse-e10-lr5e-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.1697545536.0468bd9609d6.7281.1 +3 -0
  5. test.tsv +0 -0
  6. training.log +240 -0
best-model.pt ADDED
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dev.tsv ADDED
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loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
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+ 1 12:26:49 0.0000 0.4823 0.1075 0.7022 0.7701 0.7346 0.5970
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+ 2 12:28:03 0.0000 0.1081 0.1073 0.7364 0.8095 0.7712 0.6453
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+ 3 12:29:18 0.0000 0.0712 0.1030 0.7973 0.8082 0.8027 0.6867
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+ 4 12:30:33 0.0000 0.0489 0.1495 0.7072 0.8313 0.7642 0.6345
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+ 5 12:31:47 0.0000 0.0392 0.1533 0.7862 0.8354 0.8100 0.6993
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+ 6 12:33:02 0.0000 0.0294 0.2080 0.8030 0.8095 0.8062 0.6943
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+ 7 12:34:16 0.0000 0.0208 0.2054 0.7928 0.8381 0.8148 0.7056
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+ 8 12:35:30 0.0000 0.0146 0.2075 0.8243 0.8231 0.8237 0.7220
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+ 9 12:36:45 0.0000 0.0115 0.2226 0.8182 0.8204 0.8193 0.7111
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+ 10 12:37:59 0.0000 0.0069 0.2221 0.8158 0.8313 0.8235 0.7171
runs/events.out.tfevents.1697545536.0468bd9609d6.7281.1 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ size 502461
test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 12:25:36,997 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:25:36,998 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=17, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-17 12:25:36,998 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:25:36,998 MultiCorpus: 7142 train + 698 dev + 2570 test sentences
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+ - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator
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+ 2023-10-17 12:25:36,998 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:25:36,998 Train: 7142 sentences
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+ 2023-10-17 12:25:36,998 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 12:25:36,998 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:25:36,998 Training Params:
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+ 2023-10-17 12:25:36,998 - learning_rate: "5e-05"
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+ 2023-10-17 12:25:36,998 - mini_batch_size: "8"
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+ 2023-10-17 12:25:36,998 - max_epochs: "10"
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+ 2023-10-17 12:25:36,998 - shuffle: "True"
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+ 2023-10-17 12:25:36,998 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:25:36,998 Plugins:
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+ 2023-10-17 12:25:36,998 - TensorboardLogger
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+ 2023-10-17 12:25:36,998 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 12:25:36,999 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:25:36,999 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 12:25:36,999 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 12:25:36,999 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:25:36,999 Computation:
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+ 2023-10-17 12:25:36,999 - compute on device: cuda:0
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+ 2023-10-17 12:25:36,999 - embedding storage: none
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+ 2023-10-17 12:25:36,999 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:25:36,999 Model training base path: "hmbench-newseye/fr-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-17 12:25:36,999 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:25:36,999 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:25:36,999 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 12:25:44,054 epoch 1 - iter 89/893 - loss 2.51270054 - time (sec): 7.05 - samples/sec: 3473.60 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-17 12:25:50,693 epoch 1 - iter 178/893 - loss 1.56797013 - time (sec): 13.69 - samples/sec: 3627.57 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 12:25:57,490 epoch 1 - iter 267/893 - loss 1.16082935 - time (sec): 20.49 - samples/sec: 3664.35 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 12:26:04,127 epoch 1 - iter 356/893 - loss 0.95358020 - time (sec): 27.13 - samples/sec: 3623.69 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 12:26:11,127 epoch 1 - iter 445/893 - loss 0.80942724 - time (sec): 34.13 - samples/sec: 3607.41 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 12:26:18,044 epoch 1 - iter 534/893 - loss 0.70411281 - time (sec): 41.04 - samples/sec: 3613.53 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 12:26:24,723 epoch 1 - iter 623/893 - loss 0.63046341 - time (sec): 47.72 - samples/sec: 3617.11 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 12:26:31,917 epoch 1 - iter 712/893 - loss 0.56581935 - time (sec): 54.92 - samples/sec: 3609.36 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 12:26:39,013 epoch 1 - iter 801/893 - loss 0.52137910 - time (sec): 62.01 - samples/sec: 3587.67 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 12:26:46,067 epoch 1 - iter 890/893 - loss 0.48321528 - time (sec): 69.07 - samples/sec: 3590.42 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-17 12:26:46,259 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:26:46,260 EPOCH 1 done: loss 0.4823 - lr: 0.000050
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+ 2023-10-17 12:26:49,326 DEV : loss 0.10747521370649338 - f1-score (micro avg) 0.7346
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+ 2023-10-17 12:26:49,342 saving best model
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+ 2023-10-17 12:26:49,698 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:26:55,961 epoch 2 - iter 89/893 - loss 0.13115492 - time (sec): 6.26 - samples/sec: 3759.94 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 12:27:02,936 epoch 2 - iter 178/893 - loss 0.12309626 - time (sec): 13.24 - samples/sec: 3673.41 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 12:27:10,293 epoch 2 - iter 267/893 - loss 0.11730129 - time (sec): 20.59 - samples/sec: 3585.41 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 12:27:17,302 epoch 2 - iter 356/893 - loss 0.11326307 - time (sec): 27.60 - samples/sec: 3571.89 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 12:27:24,123 epoch 2 - iter 445/893 - loss 0.11005394 - time (sec): 34.42 - samples/sec: 3578.68 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 12:27:31,020 epoch 2 - iter 534/893 - loss 0.11030052 - time (sec): 41.32 - samples/sec: 3591.15 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 12:27:38,052 epoch 2 - iter 623/893 - loss 0.11047052 - time (sec): 48.35 - samples/sec: 3563.48 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 12:27:44,943 epoch 2 - iter 712/893 - loss 0.10912533 - time (sec): 55.24 - samples/sec: 3573.03 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 12:27:52,179 epoch 2 - iter 801/893 - loss 0.10854466 - time (sec): 62.48 - samples/sec: 3599.44 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 12:27:59,024 epoch 2 - iter 890/893 - loss 0.10807744 - time (sec): 69.32 - samples/sec: 3576.74 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 12:27:59,280 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:27:59,280 EPOCH 2 done: loss 0.1081 - lr: 0.000044
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+ 2023-10-17 12:28:03,929 DEV : loss 0.10729347169399261 - f1-score (micro avg) 0.7712
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+ 2023-10-17 12:28:03,944 saving best model
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+ 2023-10-17 12:28:04,550 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:28:11,804 epoch 3 - iter 89/893 - loss 0.07385466 - time (sec): 7.25 - samples/sec: 3586.68 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 12:28:18,766 epoch 3 - iter 178/893 - loss 0.06943125 - time (sec): 14.21 - samples/sec: 3555.12 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 12:28:26,515 epoch 3 - iter 267/893 - loss 0.07106101 - time (sec): 21.96 - samples/sec: 3470.07 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 12:28:33,657 epoch 3 - iter 356/893 - loss 0.07024538 - time (sec): 29.11 - samples/sec: 3536.22 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 12:28:40,826 epoch 3 - iter 445/893 - loss 0.07099337 - time (sec): 36.27 - samples/sec: 3534.14 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 12:28:47,363 epoch 3 - iter 534/893 - loss 0.07156220 - time (sec): 42.81 - samples/sec: 3553.29 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 12:28:54,140 epoch 3 - iter 623/893 - loss 0.07248512 - time (sec): 49.59 - samples/sec: 3563.39 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 12:29:00,842 epoch 3 - iter 712/893 - loss 0.07186031 - time (sec): 56.29 - samples/sec: 3564.65 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 12:29:07,960 epoch 3 - iter 801/893 - loss 0.07047222 - time (sec): 63.41 - samples/sec: 3548.23 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 12:29:14,213 epoch 3 - iter 890/893 - loss 0.07129680 - time (sec): 69.66 - samples/sec: 3560.12 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 12:29:14,438 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:29:14,438 EPOCH 3 done: loss 0.0712 - lr: 0.000039
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+ 2023-10-17 12:29:18,643 DEV : loss 0.10302536189556122 - f1-score (micro avg) 0.8027
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+ 2023-10-17 12:29:18,660 saving best model
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+ 2023-10-17 12:29:19,131 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-17 12:29:26,034 epoch 4 - iter 89/893 - loss 0.04394687 - time (sec): 6.90 - samples/sec: 3629.74 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 12:29:32,980 epoch 4 - iter 178/893 - loss 0.04673629 - time (sec): 13.85 - samples/sec: 3600.47 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 12:29:40,460 epoch 4 - iter 267/893 - loss 0.04981548 - time (sec): 21.33 - samples/sec: 3539.04 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 12:29:47,170 epoch 4 - iter 356/893 - loss 0.05110971 - time (sec): 28.04 - samples/sec: 3559.54 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 12:29:54,331 epoch 4 - iter 445/893 - loss 0.05026700 - time (sec): 35.20 - samples/sec: 3546.87 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 12:30:01,406 epoch 4 - iter 534/893 - loss 0.05089379 - time (sec): 42.27 - samples/sec: 3562.51 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 12:30:08,585 epoch 4 - iter 623/893 - loss 0.04964456 - time (sec): 49.45 - samples/sec: 3552.29 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 12:30:15,076 epoch 4 - iter 712/893 - loss 0.04857108 - time (sec): 55.94 - samples/sec: 3550.83 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 12:30:21,863 epoch 4 - iter 801/893 - loss 0.04927161 - time (sec): 62.73 - samples/sec: 3550.83 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 12:30:28,909 epoch 4 - iter 890/893 - loss 0.04890995 - time (sec): 69.77 - samples/sec: 3556.09 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 12:30:29,111 ----------------------------------------------------------------------------------------------------
130
+ 2023-10-17 12:30:29,112 EPOCH 4 done: loss 0.0489 - lr: 0.000033
131
+ 2023-10-17 12:30:33,260 DEV : loss 0.1494728922843933 - f1-score (micro avg) 0.7642
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+ 2023-10-17 12:30:33,277 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-17 12:30:39,784 epoch 5 - iter 89/893 - loss 0.03866759 - time (sec): 6.51 - samples/sec: 3762.35 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 12:30:46,083 epoch 5 - iter 178/893 - loss 0.03849278 - time (sec): 12.81 - samples/sec: 3724.90 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 12:30:53,079 epoch 5 - iter 267/893 - loss 0.04490770 - time (sec): 19.80 - samples/sec: 3670.89 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 12:31:00,158 epoch 5 - iter 356/893 - loss 0.04415524 - time (sec): 26.88 - samples/sec: 3637.83 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 12:31:07,534 epoch 5 - iter 445/893 - loss 0.04553621 - time (sec): 34.26 - samples/sec: 3633.96 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 12:31:14,613 epoch 5 - iter 534/893 - loss 0.04328694 - time (sec): 41.34 - samples/sec: 3609.67 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 12:31:22,041 epoch 5 - iter 623/893 - loss 0.04161932 - time (sec): 48.76 - samples/sec: 3587.47 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 12:31:28,696 epoch 5 - iter 712/893 - loss 0.04050119 - time (sec): 55.42 - samples/sec: 3603.61 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 12:31:35,963 epoch 5 - iter 801/893 - loss 0.04014660 - time (sec): 62.69 - samples/sec: 3587.44 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 12:31:42,520 epoch 5 - iter 890/893 - loss 0.03909834 - time (sec): 69.24 - samples/sec: 3583.50 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 12:31:42,686 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 12:31:42,686 EPOCH 5 done: loss 0.0392 - lr: 0.000028
145
+ 2023-10-17 12:31:47,303 DEV : loss 0.1533377468585968 - f1-score (micro avg) 0.81
146
+ 2023-10-17 12:31:47,319 saving best model
147
+ 2023-10-17 12:31:47,795 ----------------------------------------------------------------------------------------------------
148
+ 2023-10-17 12:31:54,920 epoch 6 - iter 89/893 - loss 0.02756819 - time (sec): 7.12 - samples/sec: 3532.05 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 12:32:01,948 epoch 6 - iter 178/893 - loss 0.02664455 - time (sec): 14.15 - samples/sec: 3607.81 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 12:32:08,603 epoch 6 - iter 267/893 - loss 0.02870236 - time (sec): 20.80 - samples/sec: 3631.55 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 12:32:15,508 epoch 6 - iter 356/893 - loss 0.02957670 - time (sec): 27.71 - samples/sec: 3612.67 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 12:32:22,842 epoch 6 - iter 445/893 - loss 0.02928950 - time (sec): 35.04 - samples/sec: 3582.35 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 12:32:30,106 epoch 6 - iter 534/893 - loss 0.03036133 - time (sec): 42.31 - samples/sec: 3591.43 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 12:32:36,998 epoch 6 - iter 623/893 - loss 0.03000592 - time (sec): 49.20 - samples/sec: 3584.54 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 12:32:43,597 epoch 6 - iter 712/893 - loss 0.02940656 - time (sec): 55.80 - samples/sec: 3586.57 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 12:32:50,304 epoch 6 - iter 801/893 - loss 0.02915799 - time (sec): 62.50 - samples/sec: 3577.00 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 12:32:57,362 epoch 6 - iter 890/893 - loss 0.02938837 - time (sec): 69.56 - samples/sec: 3565.24 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 12:32:57,557 ----------------------------------------------------------------------------------------------------
159
+ 2023-10-17 12:32:57,558 EPOCH 6 done: loss 0.0294 - lr: 0.000022
160
+ 2023-10-17 12:33:02,212 DEV : loss 0.20796504616737366 - f1-score (micro avg) 0.8062
161
+ 2023-10-17 12:33:02,229 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-17 12:33:08,958 epoch 7 - iter 89/893 - loss 0.01719836 - time (sec): 6.73 - samples/sec: 3465.65 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 12:33:16,469 epoch 7 - iter 178/893 - loss 0.01793337 - time (sec): 14.24 - samples/sec: 3498.55 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 12:33:23,220 epoch 7 - iter 267/893 - loss 0.01837316 - time (sec): 20.99 - samples/sec: 3516.43 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 12:33:30,566 epoch 7 - iter 356/893 - loss 0.01931535 - time (sec): 28.34 - samples/sec: 3552.60 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 12:33:37,346 epoch 7 - iter 445/893 - loss 0.01987539 - time (sec): 35.12 - samples/sec: 3592.41 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 12:33:43,813 epoch 7 - iter 534/893 - loss 0.02129779 - time (sec): 41.58 - samples/sec: 3572.74 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 12:33:50,667 epoch 7 - iter 623/893 - loss 0.02255359 - time (sec): 48.44 - samples/sec: 3551.20 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 12:33:57,376 epoch 7 - iter 712/893 - loss 0.02186109 - time (sec): 55.15 - samples/sec: 3557.59 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 12:34:04,807 epoch 7 - iter 801/893 - loss 0.02137142 - time (sec): 62.58 - samples/sec: 3550.20 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 12:34:11,865 epoch 7 - iter 890/893 - loss 0.02088132 - time (sec): 69.63 - samples/sec: 3563.55 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 12:34:12,047 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 12:34:12,047 EPOCH 7 done: loss 0.0208 - lr: 0.000017
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+ 2023-10-17 12:34:16,135 DEV : loss 0.20536133646965027 - f1-score (micro avg) 0.8148
175
+ 2023-10-17 12:34:16,151 saving best model
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+ 2023-10-17 12:34:16,630 ----------------------------------------------------------------------------------------------------
177
+ 2023-10-17 12:34:23,808 epoch 8 - iter 89/893 - loss 0.01542112 - time (sec): 7.18 - samples/sec: 3650.48 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 12:34:30,937 epoch 8 - iter 178/893 - loss 0.01339288 - time (sec): 14.30 - samples/sec: 3587.50 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 12:34:37,882 epoch 8 - iter 267/893 - loss 0.01315703 - time (sec): 21.25 - samples/sec: 3583.44 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 12:34:44,510 epoch 8 - iter 356/893 - loss 0.01462132 - time (sec): 27.88 - samples/sec: 3579.37 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 12:34:51,613 epoch 8 - iter 445/893 - loss 0.01468807 - time (sec): 34.98 - samples/sec: 3567.31 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-17 12:34:58,772 epoch 8 - iter 534/893 - loss 0.01541694 - time (sec): 42.14 - samples/sec: 3563.31 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 12:35:05,922 epoch 8 - iter 623/893 - loss 0.01464883 - time (sec): 49.29 - samples/sec: 3564.81 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 12:35:13,086 epoch 8 - iter 712/893 - loss 0.01436159 - time (sec): 56.45 - samples/sec: 3586.05 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 12:35:19,554 epoch 8 - iter 801/893 - loss 0.01472488 - time (sec): 62.92 - samples/sec: 3591.16 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 12:35:25,816 epoch 8 - iter 890/893 - loss 0.01463998 - time (sec): 69.18 - samples/sec: 3585.66 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 12:35:26,039 ----------------------------------------------------------------------------------------------------
188
+ 2023-10-17 12:35:26,040 EPOCH 8 done: loss 0.0146 - lr: 0.000011
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+ 2023-10-17 12:35:30,693 DEV : loss 0.2075384557247162 - f1-score (micro avg) 0.8237
190
+ 2023-10-17 12:35:30,710 saving best model
191
+ 2023-10-17 12:35:31,198 ----------------------------------------------------------------------------------------------------
192
+ 2023-10-17 12:35:38,366 epoch 9 - iter 89/893 - loss 0.01253730 - time (sec): 7.17 - samples/sec: 3569.12 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-17 12:35:45,712 epoch 9 - iter 178/893 - loss 0.01262600 - time (sec): 14.51 - samples/sec: 3506.66 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 12:35:52,657 epoch 9 - iter 267/893 - loss 0.01162607 - time (sec): 21.46 - samples/sec: 3567.08 - lr: 0.000009 - momentum: 0.000000
195
+ 2023-10-17 12:35:59,395 epoch 9 - iter 356/893 - loss 0.01094062 - time (sec): 28.20 - samples/sec: 3564.61 - lr: 0.000009 - momentum: 0.000000
196
+ 2023-10-17 12:36:06,132 epoch 9 - iter 445/893 - loss 0.01073676 - time (sec): 34.93 - samples/sec: 3590.56 - lr: 0.000008 - momentum: 0.000000
197
+ 2023-10-17 12:36:12,723 epoch 9 - iter 534/893 - loss 0.01115983 - time (sec): 41.52 - samples/sec: 3613.46 - lr: 0.000008 - momentum: 0.000000
198
+ 2023-10-17 12:36:19,548 epoch 9 - iter 623/893 - loss 0.01126760 - time (sec): 48.35 - samples/sec: 3607.99 - lr: 0.000007 - momentum: 0.000000
199
+ 2023-10-17 12:36:26,185 epoch 9 - iter 712/893 - loss 0.01160709 - time (sec): 54.99 - samples/sec: 3597.68 - lr: 0.000007 - momentum: 0.000000
200
+ 2023-10-17 12:36:33,180 epoch 9 - iter 801/893 - loss 0.01166356 - time (sec): 61.98 - samples/sec: 3599.54 - lr: 0.000006 - momentum: 0.000000
201
+ 2023-10-17 12:36:40,480 epoch 9 - iter 890/893 - loss 0.01145458 - time (sec): 69.28 - samples/sec: 3581.17 - lr: 0.000006 - momentum: 0.000000
202
+ 2023-10-17 12:36:40,683 ----------------------------------------------------------------------------------------------------
203
+ 2023-10-17 12:36:40,683 EPOCH 9 done: loss 0.0115 - lr: 0.000006
204
+ 2023-10-17 12:36:45,383 DEV : loss 0.22258678078651428 - f1-score (micro avg) 0.8193
205
+ 2023-10-17 12:36:45,400 ----------------------------------------------------------------------------------------------------
206
+ 2023-10-17 12:36:52,507 epoch 10 - iter 89/893 - loss 0.00776903 - time (sec): 7.11 - samples/sec: 3560.36 - lr: 0.000005 - momentum: 0.000000
207
+ 2023-10-17 12:36:59,560 epoch 10 - iter 178/893 - loss 0.00915230 - time (sec): 14.16 - samples/sec: 3535.40 - lr: 0.000004 - momentum: 0.000000
208
+ 2023-10-17 12:37:06,350 epoch 10 - iter 267/893 - loss 0.00784800 - time (sec): 20.95 - samples/sec: 3548.45 - lr: 0.000004 - momentum: 0.000000
209
+ 2023-10-17 12:37:13,531 epoch 10 - iter 356/893 - loss 0.00767143 - time (sec): 28.13 - samples/sec: 3559.37 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-10-17 12:37:20,242 epoch 10 - iter 445/893 - loss 0.00765126 - time (sec): 34.84 - samples/sec: 3582.01 - lr: 0.000003 - momentum: 0.000000
211
+ 2023-10-17 12:37:27,267 epoch 10 - iter 534/893 - loss 0.00790874 - time (sec): 41.87 - samples/sec: 3545.89 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-10-17 12:37:33,931 epoch 10 - iter 623/893 - loss 0.00716107 - time (sec): 48.53 - samples/sec: 3552.59 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-10-17 12:37:40,932 epoch 10 - iter 712/893 - loss 0.00685343 - time (sec): 55.53 - samples/sec: 3536.99 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-17 12:37:47,813 epoch 10 - iter 801/893 - loss 0.00683376 - time (sec): 62.41 - samples/sec: 3547.70 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-10-17 12:37:55,138 epoch 10 - iter 890/893 - loss 0.00691368 - time (sec): 69.74 - samples/sec: 3557.99 - lr: 0.000000 - momentum: 0.000000
216
+ 2023-10-17 12:37:55,342 ----------------------------------------------------------------------------------------------------
217
+ 2023-10-17 12:37:55,342 EPOCH 10 done: loss 0.0069 - lr: 0.000000
218
+ 2023-10-17 12:37:59,528 DEV : loss 0.22205151617527008 - f1-score (micro avg) 0.8235
219
+ 2023-10-17 12:37:59,904 ----------------------------------------------------------------------------------------------------
220
+ 2023-10-17 12:37:59,906 Loading model from best epoch ...
221
+ 2023-10-17 12:38:01,386 SequenceTagger predicts: Dictionary with 17 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, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
222
+ 2023-10-17 12:38:10,955
223
+ Results:
224
+ - F-score (micro) 0.703
225
+ - F-score (macro) 0.6203
226
+ - Accuracy 0.56
227
+
228
+ By class:
229
+ precision recall f1-score support
230
+
231
+ LOC 0.7466 0.6995 0.7223 1095
232
+ PER 0.7818 0.7648 0.7732 1012
233
+ ORG 0.4431 0.5994 0.5095 357
234
+ HumanProd 0.3922 0.6061 0.4762 33
235
+
236
+ micro avg 0.6957 0.7105 0.7030 2497
237
+ macro avg 0.5909 0.6675 0.6203 2497
238
+ weighted avg 0.7128 0.7105 0.7093 2497
239
+
240
+ 2023-10-17 12:38:10,955 ----------------------------------------------------------------------------------------------------