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hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4

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.1697557951.0468bd9609d6.7281.15 +3 -0
  5. test.tsv +0 -0
  6. training.log +238 -0
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dev.tsv ADDED
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loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
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+ 1 15:54:03 0.0000 0.4532 0.1179 0.6950 0.7347 0.7143 0.5726
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+ 2 15:55:38 0.0000 0.1290 0.1280 0.7549 0.7878 0.7710 0.6477
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+ 4 15:58:51 0.0000 0.0662 0.1570 0.7323 0.8000 0.7646 0.6398
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+ 5 16:00:26 0.0000 0.0465 0.2014 0.8008 0.8259 0.8131 0.7009
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+ 7 16:03:39 0.0000 0.0262 0.2055 0.8068 0.8122 0.8095 0.6991
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+ 8 16:05:12 0.0000 0.0172 0.2138 0.8016 0.8136 0.8076 0.6962
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+ 9 16:06:48 0.0000 0.0111 0.2363 0.7918 0.8122 0.8019 0.6870
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+ 10 16:08:24 0.0000 0.0073 0.2332 0.8075 0.8163 0.8119 0.6993
runs/events.out.tfevents.1697557951.0468bd9609d6.7281.15 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 15:52:31,932 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:52:31,933 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 15:52:31,933 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:52:31,933 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 15:52:31,933 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:52:31,933 Train: 7142 sentences
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+ 2023-10-17 15:52:31,934 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 15:52:31,934 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:52:31,934 Training Params:
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+ 2023-10-17 15:52:31,934 - learning_rate: "5e-05"
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+ 2023-10-17 15:52:31,934 - mini_batch_size: "4"
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+ 2023-10-17 15:52:31,934 - max_epochs: "10"
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+ 2023-10-17 15:52:31,934 - shuffle: "True"
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+ 2023-10-17 15:52:31,934 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:52:31,934 Plugins:
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+ 2023-10-17 15:52:31,934 - TensorboardLogger
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+ 2023-10-17 15:52:31,934 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 15:52:31,934 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:52:31,934 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 15:52:31,934 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 15:52:31,934 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:52:31,934 Computation:
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+ 2023-10-17 15:52:31,934 - compute on device: cuda:0
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+ 2023-10-17 15:52:31,934 - embedding storage: none
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+ 2023-10-17 15:52:31,934 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:52:31,934 Model training base path: "hmbench-newseye/fr-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4"
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+ 2023-10-17 15:52:31,934 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:52:31,934 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:52:31,934 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 15:52:41,382 epoch 1 - iter 178/1786 - loss 2.34367862 - time (sec): 9.45 - samples/sec: 2678.64 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-17 15:52:50,231 epoch 1 - iter 356/1786 - loss 1.45266562 - time (sec): 18.30 - samples/sec: 2757.85 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 15:52:58,887 epoch 1 - iter 534/1786 - loss 1.09361339 - time (sec): 26.95 - samples/sec: 2753.10 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 15:53:07,495 epoch 1 - iter 712/1786 - loss 0.88980840 - time (sec): 35.56 - samples/sec: 2755.02 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 15:53:16,282 epoch 1 - iter 890/1786 - loss 0.75657457 - time (sec): 44.35 - samples/sec: 2730.28 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 15:53:24,937 epoch 1 - iter 1068/1786 - loss 0.66057703 - time (sec): 53.00 - samples/sec: 2749.27 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 15:53:33,748 epoch 1 - iter 1246/1786 - loss 0.59119045 - time (sec): 61.81 - samples/sec: 2757.19 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 15:53:42,598 epoch 1 - iter 1424/1786 - loss 0.53087097 - time (sec): 70.66 - samples/sec: 2786.60 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 15:53:51,598 epoch 1 - iter 1602/1786 - loss 0.48612856 - time (sec): 79.66 - samples/sec: 2801.21 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 15:54:00,393 epoch 1 - iter 1780/1786 - loss 0.45402159 - time (sec): 88.46 - samples/sec: 2805.78 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-17 15:54:00,645 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:54:00,645 EPOCH 1 done: loss 0.4532 - lr: 0.000050
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+ 2023-10-17 15:54:03,411 DEV : loss 0.1179049089550972 - f1-score (micro avg) 0.7143
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+ 2023-10-17 15:54:03,428 saving best model
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+ 2023-10-17 15:54:03,836 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 15:54:12,853 epoch 2 - iter 178/1786 - loss 0.14248804 - time (sec): 9.02 - samples/sec: 3071.74 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 15:54:21,514 epoch 2 - iter 356/1786 - loss 0.13559186 - time (sec): 17.68 - samples/sec: 2924.14 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 15:54:30,388 epoch 2 - iter 534/1786 - loss 0.13301377 - time (sec): 26.55 - samples/sec: 2880.33 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 15:54:39,321 epoch 2 - iter 712/1786 - loss 0.13307675 - time (sec): 35.48 - samples/sec: 2830.18 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 15:54:48,107 epoch 2 - iter 890/1786 - loss 0.13518362 - time (sec): 44.27 - samples/sec: 2795.59 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 15:54:56,864 epoch 2 - iter 1068/1786 - loss 0.13571605 - time (sec): 53.03 - samples/sec: 2785.25 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 15:55:05,785 epoch 2 - iter 1246/1786 - loss 0.13436108 - time (sec): 61.95 - samples/sec: 2775.13 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 15:55:15,560 epoch 2 - iter 1424/1786 - loss 0.13261596 - time (sec): 71.72 - samples/sec: 2761.75 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 15:55:24,336 epoch 2 - iter 1602/1786 - loss 0.13050720 - time (sec): 80.50 - samples/sec: 2762.84 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 15:55:33,886 epoch 2 - iter 1780/1786 - loss 0.12881272 - time (sec): 90.05 - samples/sec: 2757.02 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 15:55:34,158 ----------------------------------------------------------------------------------------------------
100
+ 2023-10-17 15:55:34,159 EPOCH 2 done: loss 0.1290 - lr: 0.000044
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+ 2023-10-17 15:55:38,485 DEV : loss 0.1279807686805725 - f1-score (micro avg) 0.771
102
+ 2023-10-17 15:55:38,506 saving best model
103
+ 2023-10-17 15:55:39,049 ----------------------------------------------------------------------------------------------------
104
+ 2023-10-17 15:55:47,780 epoch 3 - iter 178/1786 - loss 0.09685860 - time (sec): 8.73 - samples/sec: 2786.19 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 15:55:56,581 epoch 3 - iter 356/1786 - loss 0.08761037 - time (sec): 17.53 - samples/sec: 2841.07 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 15:56:05,831 epoch 3 - iter 534/1786 - loss 0.08657818 - time (sec): 26.78 - samples/sec: 2886.82 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 15:56:14,768 epoch 3 - iter 712/1786 - loss 0.08368218 - time (sec): 35.72 - samples/sec: 2854.17 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 15:56:23,797 epoch 3 - iter 890/1786 - loss 0.08955065 - time (sec): 44.75 - samples/sec: 2865.47 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 15:56:32,871 epoch 3 - iter 1068/1786 - loss 0.09043215 - time (sec): 53.82 - samples/sec: 2796.37 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 15:56:42,648 epoch 3 - iter 1246/1786 - loss 0.08937320 - time (sec): 63.60 - samples/sec: 2731.12 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 15:56:52,145 epoch 3 - iter 1424/1786 - loss 0.08925155 - time (sec): 73.09 - samples/sec: 2725.98 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 15:57:01,535 epoch 3 - iter 1602/1786 - loss 0.08899510 - time (sec): 82.48 - samples/sec: 2728.03 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 15:57:10,421 epoch 3 - iter 1780/1786 - loss 0.08771303 - time (sec): 91.37 - samples/sec: 2713.68 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 15:57:10,724 ----------------------------------------------------------------------------------------------------
115
+ 2023-10-17 15:57:10,724 EPOCH 3 done: loss 0.0878 - lr: 0.000039
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+ 2023-10-17 15:57:15,642 DEV : loss 0.14156535267829895 - f1-score (micro avg) 0.8008
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+ 2023-10-17 15:57:15,659 saving best model
118
+ 2023-10-17 15:57:16,163 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-17 15:57:25,653 epoch 4 - iter 178/1786 - loss 0.07429879 - time (sec): 9.49 - samples/sec: 2701.82 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 15:57:34,755 epoch 4 - iter 356/1786 - loss 0.06430215 - time (sec): 18.59 - samples/sec: 2733.12 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 15:57:43,760 epoch 4 - iter 534/1786 - loss 0.06406374 - time (sec): 27.59 - samples/sec: 2746.44 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 15:57:52,967 epoch 4 - iter 712/1786 - loss 0.06410664 - time (sec): 36.80 - samples/sec: 2702.68 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 15:58:01,889 epoch 4 - iter 890/1786 - loss 0.06504755 - time (sec): 45.72 - samples/sec: 2702.00 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 15:58:10,570 epoch 4 - iter 1068/1786 - loss 0.06510199 - time (sec): 54.40 - samples/sec: 2713.58 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 15:58:19,502 epoch 4 - iter 1246/1786 - loss 0.06435600 - time (sec): 63.34 - samples/sec: 2736.48 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 15:58:28,372 epoch 4 - iter 1424/1786 - loss 0.06458328 - time (sec): 72.21 - samples/sec: 2746.20 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 15:58:37,436 epoch 4 - iter 1602/1786 - loss 0.06468852 - time (sec): 81.27 - samples/sec: 2751.42 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 15:58:46,379 epoch 4 - iter 1780/1786 - loss 0.06609802 - time (sec): 90.21 - samples/sec: 2747.15 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 15:58:46,710 ----------------------------------------------------------------------------------------------------
130
+ 2023-10-17 15:58:46,711 EPOCH 4 done: loss 0.0662 - lr: 0.000033
131
+ 2023-10-17 15:58:51,103 DEV : loss 0.15699328482151031 - f1-score (micro avg) 0.7646
132
+ 2023-10-17 15:58:51,123 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-17 15:59:00,412 epoch 5 - iter 178/1786 - loss 0.04586062 - time (sec): 9.29 - samples/sec: 2686.32 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 15:59:09,251 epoch 5 - iter 356/1786 - loss 0.04291853 - time (sec): 18.13 - samples/sec: 2704.13 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 15:59:18,399 epoch 5 - iter 534/1786 - loss 0.04747939 - time (sec): 27.27 - samples/sec: 2695.65 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 15:59:27,224 epoch 5 - iter 712/1786 - loss 0.04918069 - time (sec): 36.10 - samples/sec: 2684.48 - lr: 0.000031 - momentum: 0.000000
137
+ 2023-10-17 15:59:36,569 epoch 5 - iter 890/1786 - loss 0.04784106 - time (sec): 45.44 - samples/sec: 2669.77 - lr: 0.000031 - momentum: 0.000000
138
+ 2023-10-17 15:59:45,755 epoch 5 - iter 1068/1786 - loss 0.04772355 - time (sec): 54.63 - samples/sec: 2682.24 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 15:59:54,755 epoch 5 - iter 1246/1786 - loss 0.04737999 - time (sec): 63.63 - samples/sec: 2703.26 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 16:00:03,645 epoch 5 - iter 1424/1786 - loss 0.04771482 - time (sec): 72.52 - samples/sec: 2718.78 - lr: 0.000029 - momentum: 0.000000
141
+ 2023-10-17 16:00:12,703 epoch 5 - iter 1602/1786 - loss 0.04810478 - time (sec): 81.58 - samples/sec: 2736.47 - lr: 0.000028 - momentum: 0.000000
142
+ 2023-10-17 16:00:21,665 epoch 5 - iter 1780/1786 - loss 0.04647691 - time (sec): 90.54 - samples/sec: 2741.01 - lr: 0.000028 - momentum: 0.000000
143
+ 2023-10-17 16:00:21,939 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 16:00:21,939 EPOCH 5 done: loss 0.0465 - lr: 0.000028
145
+ 2023-10-17 16:00:26,974 DEV : loss 0.20135752856731415 - f1-score (micro avg) 0.8131
146
+ 2023-10-17 16:00:26,998 saving best model
147
+ 2023-10-17 16:00:27,492 ----------------------------------------------------------------------------------------------------
148
+ 2023-10-17 16:00:36,901 epoch 6 - iter 178/1786 - loss 0.02371619 - time (sec): 9.41 - samples/sec: 2639.52 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 16:00:45,622 epoch 6 - iter 356/1786 - loss 0.02704307 - time (sec): 18.13 - samples/sec: 2637.58 - lr: 0.000027 - momentum: 0.000000
150
+ 2023-10-17 16:00:54,994 epoch 6 - iter 534/1786 - loss 0.02894294 - time (sec): 27.50 - samples/sec: 2658.13 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 16:01:04,286 epoch 6 - iter 712/1786 - loss 0.03110961 - time (sec): 36.79 - samples/sec: 2677.96 - lr: 0.000026 - momentum: 0.000000
152
+ 2023-10-17 16:01:13,832 epoch 6 - iter 890/1786 - loss 0.03368810 - time (sec): 46.34 - samples/sec: 2660.27 - lr: 0.000025 - momentum: 0.000000
153
+ 2023-10-17 16:01:23,709 epoch 6 - iter 1068/1786 - loss 0.03458587 - time (sec): 56.21 - samples/sec: 2659.74 - lr: 0.000024 - momentum: 0.000000
154
+ 2023-10-17 16:01:32,950 epoch 6 - iter 1246/1786 - loss 0.03418798 - time (sec): 65.46 - samples/sec: 2669.61 - lr: 0.000024 - momentum: 0.000000
155
+ 2023-10-17 16:01:41,737 epoch 6 - iter 1424/1786 - loss 0.03451417 - time (sec): 74.24 - samples/sec: 2687.45 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 16:01:50,597 epoch 6 - iter 1602/1786 - loss 0.03509028 - time (sec): 83.10 - samples/sec: 2698.62 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 16:01:59,482 epoch 6 - iter 1780/1786 - loss 0.03614456 - time (sec): 91.99 - samples/sec: 2696.20 - lr: 0.000022 - momentum: 0.000000
158
+ 2023-10-17 16:01:59,765 ----------------------------------------------------------------------------------------------------
159
+ 2023-10-17 16:01:59,766 EPOCH 6 done: loss 0.0362 - lr: 0.000022
160
+ 2023-10-17 16:02:04,017 DEV : loss 0.1925005316734314 - f1-score (micro avg) 0.8075
161
+ 2023-10-17 16:02:04,044 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-17 16:02:13,664 epoch 7 - iter 178/1786 - loss 0.02284920 - time (sec): 9.62 - samples/sec: 2717.79 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 16:02:22,697 epoch 7 - iter 356/1786 - loss 0.02740520 - time (sec): 18.65 - samples/sec: 2714.50 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 16:02:31,859 epoch 7 - iter 534/1786 - loss 0.02590493 - time (sec): 27.81 - samples/sec: 2665.99 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 16:02:41,484 epoch 7 - iter 712/1786 - loss 0.02755967 - time (sec): 37.44 - samples/sec: 2657.64 - lr: 0.000020 - momentum: 0.000000
166
+ 2023-10-17 16:02:50,972 epoch 7 - iter 890/1786 - loss 0.02563833 - time (sec): 46.93 - samples/sec: 2650.69 - lr: 0.000019 - momentum: 0.000000
167
+ 2023-10-17 16:02:59,513 epoch 7 - iter 1068/1786 - loss 0.02699974 - time (sec): 55.47 - samples/sec: 2687.52 - lr: 0.000019 - momentum: 0.000000
168
+ 2023-10-17 16:03:07,993 epoch 7 - iter 1246/1786 - loss 0.02737097 - time (sec): 63.95 - samples/sec: 2710.35 - lr: 0.000018 - momentum: 0.000000
169
+ 2023-10-17 16:03:16,653 epoch 7 - iter 1424/1786 - loss 0.02693964 - time (sec): 72.61 - samples/sec: 2716.12 - lr: 0.000018 - momentum: 0.000000
170
+ 2023-10-17 16:03:26,354 epoch 7 - iter 1602/1786 - loss 0.02616714 - time (sec): 82.31 - samples/sec: 2710.40 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 16:03:35,289 epoch 7 - iter 1780/1786 - loss 0.02619563 - time (sec): 91.24 - samples/sec: 2720.76 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 16:03:35,580 ----------------------------------------------------------------------------------------------------
173
+ 2023-10-17 16:03:35,581 EPOCH 7 done: loss 0.0262 - lr: 0.000017
174
+ 2023-10-17 16:03:39,757 DEV : loss 0.20547646284103394 - f1-score (micro avg) 0.8095
175
+ 2023-10-17 16:03:39,775 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-17 16:03:48,334 epoch 8 - iter 178/1786 - loss 0.01592414 - time (sec): 8.56 - samples/sec: 2809.60 - lr: 0.000016 - momentum: 0.000000
177
+ 2023-10-17 16:03:56,788 epoch 8 - iter 356/1786 - loss 0.01794367 - time (sec): 17.01 - samples/sec: 2820.07 - lr: 0.000016 - momentum: 0.000000
178
+ 2023-10-17 16:04:05,481 epoch 8 - iter 534/1786 - loss 0.01674718 - time (sec): 25.71 - samples/sec: 2849.79 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 16:04:14,287 epoch 8 - iter 712/1786 - loss 0.01669581 - time (sec): 34.51 - samples/sec: 2877.25 - lr: 0.000014 - momentum: 0.000000
180
+ 2023-10-17 16:04:23,559 epoch 8 - iter 890/1786 - loss 0.01792091 - time (sec): 43.78 - samples/sec: 2892.21 - lr: 0.000014 - momentum: 0.000000
181
+ 2023-10-17 16:04:32,216 epoch 8 - iter 1068/1786 - loss 0.01671891 - time (sec): 52.44 - samples/sec: 2902.58 - lr: 0.000013 - momentum: 0.000000
182
+ 2023-10-17 16:04:41,111 epoch 8 - iter 1246/1786 - loss 0.01747172 - time (sec): 61.34 - samples/sec: 2884.28 - lr: 0.000013 - momentum: 0.000000
183
+ 2023-10-17 16:04:49,915 epoch 8 - iter 1424/1786 - loss 0.01727856 - time (sec): 70.14 - samples/sec: 2859.80 - lr: 0.000012 - momentum: 0.000000
184
+ 2023-10-17 16:04:58,460 epoch 8 - iter 1602/1786 - loss 0.01739919 - time (sec): 78.68 - samples/sec: 2854.52 - lr: 0.000012 - momentum: 0.000000
185
+ 2023-10-17 16:05:07,380 epoch 8 - iter 1780/1786 - loss 0.01717924 - time (sec): 87.60 - samples/sec: 2828.17 - lr: 0.000011 - momentum: 0.000000
186
+ 2023-10-17 16:05:07,703 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-17 16:05:07,703 EPOCH 8 done: loss 0.0172 - lr: 0.000011
188
+ 2023-10-17 16:05:12,475 DEV : loss 0.21377256512641907 - f1-score (micro avg) 0.8076
189
+ 2023-10-17 16:05:12,491 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-17 16:05:21,378 epoch 9 - iter 178/1786 - loss 0.00826584 - time (sec): 8.89 - samples/sec: 2711.80 - lr: 0.000011 - momentum: 0.000000
191
+ 2023-10-17 16:05:30,318 epoch 9 - iter 356/1786 - loss 0.01226686 - time (sec): 17.83 - samples/sec: 2697.41 - lr: 0.000010 - momentum: 0.000000
192
+ 2023-10-17 16:05:39,675 epoch 9 - iter 534/1786 - loss 0.01157243 - time (sec): 27.18 - samples/sec: 2673.41 - lr: 0.000009 - momentum: 0.000000
193
+ 2023-10-17 16:05:49,071 epoch 9 - iter 712/1786 - loss 0.01175115 - time (sec): 36.58 - samples/sec: 2647.63 - lr: 0.000009 - momentum: 0.000000
194
+ 2023-10-17 16:05:58,453 epoch 9 - iter 890/1786 - loss 0.01181223 - time (sec): 45.96 - samples/sec: 2676.32 - lr: 0.000008 - momentum: 0.000000
195
+ 2023-10-17 16:06:07,562 epoch 9 - iter 1068/1786 - loss 0.01203776 - time (sec): 55.07 - samples/sec: 2701.84 - lr: 0.000008 - momentum: 0.000000
196
+ 2023-10-17 16:06:16,564 epoch 9 - iter 1246/1786 - loss 0.01231963 - time (sec): 64.07 - samples/sec: 2694.92 - lr: 0.000007 - momentum: 0.000000
197
+ 2023-10-17 16:06:25,422 epoch 9 - iter 1424/1786 - loss 0.01181941 - time (sec): 72.93 - samples/sec: 2710.53 - lr: 0.000007 - momentum: 0.000000
198
+ 2023-10-17 16:06:34,321 epoch 9 - iter 1602/1786 - loss 0.01147011 - time (sec): 81.83 - samples/sec: 2717.80 - lr: 0.000006 - momentum: 0.000000
199
+ 2023-10-17 16:06:43,578 epoch 9 - iter 1780/1786 - loss 0.01110474 - time (sec): 91.09 - samples/sec: 2720.88 - lr: 0.000006 - momentum: 0.000000
200
+ 2023-10-17 16:06:43,868 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-17 16:06:43,868 EPOCH 9 done: loss 0.0111 - lr: 0.000006
202
+ 2023-10-17 16:06:48,061 DEV : loss 0.23628994822502136 - f1-score (micro avg) 0.8019
203
+ 2023-10-17 16:06:48,079 ----------------------------------------------------------------------------------------------------
204
+ 2023-10-17 16:06:57,034 epoch 10 - iter 178/1786 - loss 0.01008703 - time (sec): 8.95 - samples/sec: 2703.96 - lr: 0.000005 - momentum: 0.000000
205
+ 2023-10-17 16:07:05,834 epoch 10 - iter 356/1786 - loss 0.00789102 - time (sec): 17.75 - samples/sec: 2724.66 - lr: 0.000004 - momentum: 0.000000
206
+ 2023-10-17 16:07:15,085 epoch 10 - iter 534/1786 - loss 0.00797454 - time (sec): 27.00 - samples/sec: 2742.49 - lr: 0.000004 - momentum: 0.000000
207
+ 2023-10-17 16:07:24,045 epoch 10 - iter 712/1786 - loss 0.00890035 - time (sec): 35.96 - samples/sec: 2694.39 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-17 16:07:32,988 epoch 10 - iter 890/1786 - loss 0.00798521 - time (sec): 44.91 - samples/sec: 2676.59 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-17 16:07:42,125 epoch 10 - iter 1068/1786 - loss 0.00826627 - time (sec): 54.04 - samples/sec: 2689.69 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 16:07:51,256 epoch 10 - iter 1246/1786 - loss 0.00780579 - time (sec): 63.18 - samples/sec: 2701.71 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-17 16:08:00,277 epoch 10 - iter 1424/1786 - loss 0.00770846 - time (sec): 72.20 - samples/sec: 2703.76 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 16:08:09,290 epoch 10 - iter 1602/1786 - loss 0.00704214 - time (sec): 81.21 - samples/sec: 2715.74 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-17 16:08:18,427 epoch 10 - iter 1780/1786 - loss 0.00728962 - time (sec): 90.35 - samples/sec: 2745.08 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-17 16:08:18,757 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-17 16:08:18,758 EPOCH 10 done: loss 0.0073 - lr: 0.000000
216
+ 2023-10-17 16:08:24,207 DEV : loss 0.23320543766021729 - f1-score (micro avg) 0.8119
217
+ 2023-10-17 16:08:24,758 ----------------------------------------------------------------------------------------------------
218
+ 2023-10-17 16:08:24,760 Loading model from best epoch ...
219
+ 2023-10-17 16:08:26,405 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
220
+ 2023-10-17 16:08:38,013
221
+ Results:
222
+ - F-score (micro) 0.7026
223
+ - F-score (macro) 0.6436
224
+ - Accuracy 0.557
225
+
226
+ By class:
227
+ precision recall f1-score support
228
+
229
+ LOC 0.6675 0.7534 0.7079 1095
230
+ PER 0.7524 0.7628 0.7576 1012
231
+ ORG 0.5516 0.5238 0.5374 357
232
+ HumanProd 0.5405 0.6061 0.5714 33
233
+
234
+ micro avg 0.6839 0.7225 0.7026 2497
235
+ macro avg 0.6280 0.6615 0.6436 2497
236
+ weighted avg 0.6837 0.7225 0.7018 2497
237
+
238
+ 2023-10-17 16:08:38,013 ----------------------------------------------------------------------------------------------------