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flair-hipe-2022-letemps-fr

Token Classification
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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.1697561236.4aef72135bc5.1113.14 +3 -0
  5. test.tsv +0 -0
  6. training.log +239 -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 16:49:31 0.0000 0.3594 0.1047 0.5091 0.7712 0.6133 0.4511
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+ 2 16:51:50 0.0000 0.0868 0.1000 0.5147 0.7815 0.6206 0.4581
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+ 3 16:54:12 0.0000 0.0627 0.1352 0.5386 0.7654 0.6323 0.4691
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+ 4 16:56:33 0.0000 0.0458 0.1966 0.5388 0.8192 0.6500 0.4901
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+ 5 16:58:55 0.0000 0.0324 0.2978 0.5171 0.8318 0.6377 0.4773
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+ 7 17:03:52 0.0000 0.0157 0.3318 0.5580 0.8089 0.6604 0.5014
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+ 8 17:06:17 0.0000 0.0106 0.3687 0.5709 0.7780 0.6586 0.4978
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+ 9 17:08:33 0.0000 0.0070 0.3884 0.5736 0.8066 0.6705 0.5124
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+ 10 17:10:51 0.0000 0.0050 0.3977 0.5689 0.8078 0.6676 0.5101
runs/events.out.tfevents.1697561236.4aef72135bc5.1113.14 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 16:47:16,055 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:47:16,056 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): ElectraModel(
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+ (embeddings): ElectraEmbeddings(
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+ (word_embeddings): Embedding(32001, 768)
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+ (position_embeddings): Embedding(512, 768)
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+ (token_type_embeddings): Embedding(2, 768)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (encoder): ElectraEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x ElectraLayer(
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+ (attention): ElectraAttention(
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+ (self): ElectraSelfAttention(
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+ (query): Linear(in_features=768, out_features=768, bias=True)
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+ (key): Linear(in_features=768, out_features=768, bias=True)
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+ (value): Linear(in_features=768, out_features=768, bias=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (output): ElectraSelfOutput(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ (intermediate): ElectraIntermediate(
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+ (dense): Linear(in_features=768, out_features=3072, bias=True)
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+ (intermediate_act_fn): GELUActivation()
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+ )
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+ (output): ElectraOutput(
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+ (dense): Linear(in_features=3072, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ )
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+ )
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+ )
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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=768, out_features=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-17 16:47:16,057 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:47:16,057 MultiCorpus: 14465 train + 1392 dev + 2432 test sentences
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+ - NER_HIPE_2022 Corpus: 14465 train + 1392 dev + 2432 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/letemps/fr/with_doc_seperator
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+ 2023-10-17 16:47:16,057 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:47:16,057 Train: 14465 sentences
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+ 2023-10-17 16:47:16,057 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 16:47:16,057 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:47:16,057 Training Params:
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+ 2023-10-17 16:47:16,057 - learning_rate: "3e-05"
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+ 2023-10-17 16:47:16,057 - mini_batch_size: "8"
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+ 2023-10-17 16:47:16,057 - max_epochs: "10"
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+ 2023-10-17 16:47:16,057 - shuffle: "True"
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+ 2023-10-17 16:47:16,057 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:47:16,057 Plugins:
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+ 2023-10-17 16:47:16,057 - TensorboardLogger
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+ 2023-10-17 16:47:16,057 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 16:47:16,057 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:47:16,057 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 16:47:16,057 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 16:47:16,057 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:47:16,057 Computation:
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+ 2023-10-17 16:47:16,057 - compute on device: cuda:0
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+ 2023-10-17 16:47:16,058 - embedding storage: none
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+ 2023-10-17 16:47:16,058 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:47:16,058 Model training base path: "hmbench-letemps/fr-hmteams/teams-base-historic-multilingual-discriminator-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4"
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+ 2023-10-17 16:47:16,058 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:47:16,058 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:47:16,058 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 16:47:28,647 epoch 1 - iter 180/1809 - loss 2.37829275 - time (sec): 12.59 - samples/sec: 2902.52 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-17 16:47:41,603 epoch 1 - iter 360/1809 - loss 1.29142331 - time (sec): 25.54 - samples/sec: 2959.16 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-17 16:47:54,374 epoch 1 - iter 540/1809 - loss 0.91673625 - time (sec): 38.31 - samples/sec: 2962.10 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 16:48:07,489 epoch 1 - iter 720/1809 - loss 0.72239703 - time (sec): 51.43 - samples/sec: 2963.13 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 16:48:20,496 epoch 1 - iter 900/1809 - loss 0.60634230 - time (sec): 64.44 - samples/sec: 2936.89 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 16:48:33,381 epoch 1 - iter 1080/1809 - loss 0.52686295 - time (sec): 77.32 - samples/sec: 2940.72 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-17 16:48:46,313 epoch 1 - iter 1260/1809 - loss 0.46693740 - time (sec): 90.25 - samples/sec: 2944.10 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 16:48:59,460 epoch 1 - iter 1440/1809 - loss 0.42178664 - time (sec): 103.40 - samples/sec: 2949.31 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 16:49:12,569 epoch 1 - iter 1620/1809 - loss 0.38783448 - time (sec): 116.51 - samples/sec: 2935.62 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 16:49:25,738 epoch 1 - iter 1800/1809 - loss 0.36040896 - time (sec): 129.68 - samples/sec: 2918.82 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 16:49:26,324 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:49:26,324 EPOCH 1 done: loss 0.3594 - lr: 0.000030
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+ 2023-10-17 16:49:31,785 DEV : loss 0.10473097860813141 - f1-score (micro avg) 0.6133
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+ 2023-10-17 16:49:31,826 saving best model
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+ 2023-10-17 16:49:32,320 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:49:45,341 epoch 2 - iter 180/1809 - loss 0.09588522 - time (sec): 13.02 - samples/sec: 2975.94 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 16:49:58,243 epoch 2 - iter 360/1809 - loss 0.08882249 - time (sec): 25.92 - samples/sec: 2950.13 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 16:50:11,235 epoch 2 - iter 540/1809 - loss 0.08363106 - time (sec): 38.91 - samples/sec: 2948.32 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 16:50:23,803 epoch 2 - iter 720/1809 - loss 0.08455393 - time (sec): 51.48 - samples/sec: 2937.41 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 16:50:37,115 epoch 2 - iter 900/1809 - loss 0.08508426 - time (sec): 64.79 - samples/sec: 2906.26 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 16:50:49,757 epoch 2 - iter 1080/1809 - loss 0.08610959 - time (sec): 77.44 - samples/sec: 2898.77 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 16:51:03,116 epoch 2 - iter 1260/1809 - loss 0.08766651 - time (sec): 90.80 - samples/sec: 2888.85 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 16:51:16,240 epoch 2 - iter 1440/1809 - loss 0.08705399 - time (sec): 103.92 - samples/sec: 2899.26 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 16:51:29,593 epoch 2 - iter 1620/1809 - loss 0.08720869 - time (sec): 117.27 - samples/sec: 2886.58 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 16:51:42,832 epoch 2 - iter 1800/1809 - loss 0.08686349 - time (sec): 130.51 - samples/sec: 2895.75 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 16:51:43,549 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:51:43,550 EPOCH 2 done: loss 0.0868 - lr: 0.000027
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+ 2023-10-17 16:51:50,679 DEV : loss 0.09997577220201492 - f1-score (micro avg) 0.6206
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+ 2023-10-17 16:51:50,720 saving best model
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+ 2023-10-17 16:51:51,301 ----------------------------------------------------------------------------------------------------
104
+ 2023-10-17 16:52:04,562 epoch 3 - iter 180/1809 - loss 0.06057686 - time (sec): 13.26 - samples/sec: 2882.78 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 16:52:17,739 epoch 3 - iter 360/1809 - loss 0.05898876 - time (sec): 26.44 - samples/sec: 2894.23 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 16:52:30,634 epoch 3 - iter 540/1809 - loss 0.05667154 - time (sec): 39.33 - samples/sec: 2893.24 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 16:52:43,874 epoch 3 - iter 720/1809 - loss 0.05829348 - time (sec): 52.57 - samples/sec: 2883.78 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 16:52:56,734 epoch 3 - iter 900/1809 - loss 0.05931267 - time (sec): 65.43 - samples/sec: 2887.88 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 16:53:09,638 epoch 3 - iter 1080/1809 - loss 0.05999951 - time (sec): 78.34 - samples/sec: 2893.92 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 16:53:23,322 epoch 3 - iter 1260/1809 - loss 0.05998945 - time (sec): 92.02 - samples/sec: 2880.58 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 16:53:37,421 epoch 3 - iter 1440/1809 - loss 0.06121733 - time (sec): 106.12 - samples/sec: 2846.19 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 16:53:51,481 epoch 3 - iter 1620/1809 - loss 0.06250987 - time (sec): 120.18 - samples/sec: 2822.20 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-17 16:54:05,576 epoch 3 - iter 1800/1809 - loss 0.06260649 - time (sec): 134.27 - samples/sec: 2818.01 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 16:54:06,170 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 16:54:06,171 EPOCH 3 done: loss 0.0627 - lr: 0.000023
116
+ 2023-10-17 16:54:12,478 DEV : loss 0.13520988821983337 - f1-score (micro avg) 0.6323
117
+ 2023-10-17 16:54:12,520 saving best model
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+ 2023-10-17 16:54:13,120 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-17 16:54:26,854 epoch 4 - iter 180/1809 - loss 0.03589391 - time (sec): 13.73 - samples/sec: 2725.59 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 16:54:41,062 epoch 4 - iter 360/1809 - loss 0.04243949 - time (sec): 27.94 - samples/sec: 2704.29 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-17 16:54:55,361 epoch 4 - iter 540/1809 - loss 0.04312213 - time (sec): 42.24 - samples/sec: 2711.73 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 16:55:08,899 epoch 4 - iter 720/1809 - loss 0.04407922 - time (sec): 55.78 - samples/sec: 2715.57 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 16:55:21,317 epoch 4 - iter 900/1809 - loss 0.04309184 - time (sec): 68.20 - samples/sec: 2751.30 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 16:55:35,293 epoch 4 - iter 1080/1809 - loss 0.04461675 - time (sec): 82.17 - samples/sec: 2765.77 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 16:55:47,893 epoch 4 - iter 1260/1809 - loss 0.04482022 - time (sec): 94.77 - samples/sec: 2791.18 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 16:56:00,644 epoch 4 - iter 1440/1809 - loss 0.04402527 - time (sec): 107.52 - samples/sec: 2805.31 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 16:56:13,843 epoch 4 - iter 1620/1809 - loss 0.04508043 - time (sec): 120.72 - samples/sec: 2821.11 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 16:56:26,940 epoch 4 - iter 1800/1809 - loss 0.04577307 - time (sec): 133.82 - samples/sec: 2826.69 - lr: 0.000020 - momentum: 0.000000
129
+ 2023-10-17 16:56:27,553 ----------------------------------------------------------------------------------------------------
130
+ 2023-10-17 16:56:27,554 EPOCH 4 done: loss 0.0458 - lr: 0.000020
131
+ 2023-10-17 16:56:33,892 DEV : loss 0.19658434391021729 - f1-score (micro avg) 0.65
132
+ 2023-10-17 16:56:33,932 saving best model
133
+ 2023-10-17 16:56:34,506 ----------------------------------------------------------------------------------------------------
134
+ 2023-10-17 16:56:47,272 epoch 5 - iter 180/1809 - loss 0.02885208 - time (sec): 12.76 - samples/sec: 2976.47 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 16:57:00,161 epoch 5 - iter 360/1809 - loss 0.03100421 - time (sec): 25.65 - samples/sec: 2946.51 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-17 16:57:13,078 epoch 5 - iter 540/1809 - loss 0.03368269 - time (sec): 38.57 - samples/sec: 2921.39 - lr: 0.000019 - momentum: 0.000000
137
+ 2023-10-17 16:57:25,741 epoch 5 - iter 720/1809 - loss 0.03096940 - time (sec): 51.23 - samples/sec: 2936.84 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-10-17 16:57:38,316 epoch 5 - iter 900/1809 - loss 0.03114871 - time (sec): 63.81 - samples/sec: 2937.46 - lr: 0.000018 - momentum: 0.000000
139
+ 2023-10-17 16:57:50,872 epoch 5 - iter 1080/1809 - loss 0.03098647 - time (sec): 76.36 - samples/sec: 2935.27 - lr: 0.000018 - momentum: 0.000000
140
+ 2023-10-17 16:58:04,501 epoch 5 - iter 1260/1809 - loss 0.03174442 - time (sec): 89.99 - samples/sec: 2923.36 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-10-17 16:58:18,452 epoch 5 - iter 1440/1809 - loss 0.03137972 - time (sec): 103.94 - samples/sec: 2896.31 - lr: 0.000017 - momentum: 0.000000
142
+ 2023-10-17 16:58:32,933 epoch 5 - iter 1620/1809 - loss 0.03319045 - time (sec): 118.42 - samples/sec: 2866.30 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 16:58:47,625 epoch 5 - iter 1800/1809 - loss 0.03251336 - time (sec): 133.12 - samples/sec: 2841.97 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-10-17 16:58:48,292 ----------------------------------------------------------------------------------------------------
145
+ 2023-10-17 16:58:48,292 EPOCH 5 done: loss 0.0324 - lr: 0.000017
146
+ 2023-10-17 16:58:55,306 DEV : loss 0.2977985441684723 - f1-score (micro avg) 0.6377
147
+ 2023-10-17 16:58:55,351 ----------------------------------------------------------------------------------------------------
148
+ 2023-10-17 16:59:09,632 epoch 6 - iter 180/1809 - loss 0.02528166 - time (sec): 14.28 - samples/sec: 2659.02 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-17 16:59:22,461 epoch 6 - iter 360/1809 - loss 0.02336089 - time (sec): 27.11 - samples/sec: 2746.08 - lr: 0.000016 - momentum: 0.000000
150
+ 2023-10-17 16:59:36,882 epoch 6 - iter 540/1809 - loss 0.02596319 - time (sec): 41.53 - samples/sec: 2699.54 - lr: 0.000016 - momentum: 0.000000
151
+ 2023-10-17 16:59:51,458 epoch 6 - iter 720/1809 - loss 0.02376158 - time (sec): 56.11 - samples/sec: 2706.80 - lr: 0.000015 - momentum: 0.000000
152
+ 2023-10-17 17:00:04,585 epoch 6 - iter 900/1809 - loss 0.02282026 - time (sec): 69.23 - samples/sec: 2735.97 - lr: 0.000015 - momentum: 0.000000
153
+ 2023-10-17 17:00:18,532 epoch 6 - iter 1080/1809 - loss 0.02378981 - time (sec): 83.18 - samples/sec: 2736.20 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-10-17 17:00:32,532 epoch 6 - iter 1260/1809 - loss 0.02488103 - time (sec): 97.18 - samples/sec: 2705.92 - lr: 0.000014 - momentum: 0.000000
155
+ 2023-10-17 17:00:47,158 epoch 6 - iter 1440/1809 - loss 0.02406285 - time (sec): 111.81 - samples/sec: 2693.01 - lr: 0.000014 - momentum: 0.000000
156
+ 2023-10-17 17:01:00,454 epoch 6 - iter 1620/1809 - loss 0.02458081 - time (sec): 125.10 - samples/sec: 2712.95 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-10-17 17:01:14,994 epoch 6 - iter 1800/1809 - loss 0.02416930 - time (sec): 139.64 - samples/sec: 2709.21 - lr: 0.000013 - momentum: 0.000000
158
+ 2023-10-17 17:01:15,660 ----------------------------------------------------------------------------------------------------
159
+ 2023-10-17 17:01:15,661 EPOCH 6 done: loss 0.0241 - lr: 0.000013
160
+ 2023-10-17 17:01:22,099 DEV : loss 0.2875325679779053 - f1-score (micro avg) 0.6463
161
+ 2023-10-17 17:01:22,141 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-17 17:01:36,174 epoch 7 - iter 180/1809 - loss 0.01469512 - time (sec): 14.03 - samples/sec: 2577.49 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-17 17:01:50,551 epoch 7 - iter 360/1809 - loss 0.01466126 - time (sec): 28.41 - samples/sec: 2562.51 - lr: 0.000013 - momentum: 0.000000
164
+ 2023-10-17 17:02:04,459 epoch 7 - iter 540/1809 - loss 0.01507780 - time (sec): 42.32 - samples/sec: 2582.29 - lr: 0.000012 - momentum: 0.000000
165
+ 2023-10-17 17:02:18,493 epoch 7 - iter 720/1809 - loss 0.01484288 - time (sec): 56.35 - samples/sec: 2631.16 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-17 17:02:32,995 epoch 7 - iter 900/1809 - loss 0.01551643 - time (sec): 70.85 - samples/sec: 2653.49 - lr: 0.000012 - momentum: 0.000000
167
+ 2023-10-17 17:02:47,523 epoch 7 - iter 1080/1809 - loss 0.01579238 - time (sec): 85.38 - samples/sec: 2661.61 - lr: 0.000011 - momentum: 0.000000
168
+ 2023-10-17 17:03:01,558 epoch 7 - iter 1260/1809 - loss 0.01541974 - time (sec): 99.42 - samples/sec: 2659.01 - lr: 0.000011 - momentum: 0.000000
169
+ 2023-10-17 17:03:15,932 epoch 7 - iter 1440/1809 - loss 0.01565492 - time (sec): 113.79 - samples/sec: 2650.76 - lr: 0.000011 - momentum: 0.000000
170
+ 2023-10-17 17:03:30,407 epoch 7 - iter 1620/1809 - loss 0.01578131 - time (sec): 128.26 - samples/sec: 2646.94 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 17:03:45,544 epoch 7 - iter 1800/1809 - loss 0.01577763 - time (sec): 143.40 - samples/sec: 2635.32 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 17:03:46,233 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 17:03:46,234 EPOCH 7 done: loss 0.0157 - lr: 0.000010
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+ 2023-10-17 17:03:52,481 DEV : loss 0.3317669928073883 - f1-score (micro avg) 0.6604
175
+ 2023-10-17 17:03:52,524 saving best model
176
+ 2023-10-17 17:03:53,119 ----------------------------------------------------------------------------------------------------
177
+ 2023-10-17 17:04:06,717 epoch 8 - iter 180/1809 - loss 0.01083201 - time (sec): 13.60 - samples/sec: 2733.81 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 17:04:20,695 epoch 8 - iter 360/1809 - loss 0.01085551 - time (sec): 27.57 - samples/sec: 2672.29 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 17:04:34,811 epoch 8 - iter 540/1809 - loss 0.01160109 - time (sec): 41.69 - samples/sec: 2662.51 - lr: 0.000009 - momentum: 0.000000
180
+ 2023-10-17 17:04:48,963 epoch 8 - iter 720/1809 - loss 0.01150271 - time (sec): 55.84 - samples/sec: 2688.28 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-17 17:05:02,029 epoch 8 - iter 900/1809 - loss 0.01158055 - time (sec): 68.91 - samples/sec: 2729.09 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 17:05:16,268 epoch 8 - iter 1080/1809 - loss 0.01114901 - time (sec): 83.15 - samples/sec: 2709.66 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 17:05:29,595 epoch 8 - iter 1260/1809 - loss 0.01121147 - time (sec): 96.47 - samples/sec: 2733.10 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-17 17:05:43,752 epoch 8 - iter 1440/1809 - loss 0.01133313 - time (sec): 110.63 - samples/sec: 2731.90 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 17:05:56,559 epoch 8 - iter 1620/1809 - loss 0.01079204 - time (sec): 123.44 - samples/sec: 2744.91 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-17 17:06:09,770 epoch 8 - iter 1800/1809 - loss 0.01055259 - time (sec): 136.65 - samples/sec: 2764.47 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-10-17 17:06:10,429 ----------------------------------------------------------------------------------------------------
188
+ 2023-10-17 17:06:10,429 EPOCH 8 done: loss 0.0106 - lr: 0.000007
189
+ 2023-10-17 17:06:17,407 DEV : loss 0.36872246861457825 - f1-score (micro avg) 0.6586
190
+ 2023-10-17 17:06:17,455 ----------------------------------------------------------------------------------------------------
191
+ 2023-10-17 17:06:30,029 epoch 9 - iter 180/1809 - loss 0.00589952 - time (sec): 12.57 - samples/sec: 2889.81 - lr: 0.000006 - momentum: 0.000000
192
+ 2023-10-17 17:06:42,937 epoch 9 - iter 360/1809 - loss 0.00549166 - time (sec): 25.48 - samples/sec: 2899.28 - lr: 0.000006 - momentum: 0.000000
193
+ 2023-10-17 17:06:55,861 epoch 9 - iter 540/1809 - loss 0.00605800 - time (sec): 38.40 - samples/sec: 2916.24 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-10-17 17:07:09,026 epoch 9 - iter 720/1809 - loss 0.00638723 - time (sec): 51.57 - samples/sec: 2919.17 - lr: 0.000005 - momentum: 0.000000
195
+ 2023-10-17 17:07:21,620 epoch 9 - iter 900/1809 - loss 0.00618261 - time (sec): 64.16 - samples/sec: 2935.84 - lr: 0.000005 - momentum: 0.000000
196
+ 2023-10-17 17:07:34,600 epoch 9 - iter 1080/1809 - loss 0.00676500 - time (sec): 77.14 - samples/sec: 2934.03 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-10-17 17:07:48,046 epoch 9 - iter 1260/1809 - loss 0.00688842 - time (sec): 90.59 - samples/sec: 2939.28 - lr: 0.000004 - momentum: 0.000000
198
+ 2023-10-17 17:08:00,908 epoch 9 - iter 1440/1809 - loss 0.00706836 - time (sec): 103.45 - samples/sec: 2943.34 - lr: 0.000004 - momentum: 0.000000
199
+ 2023-10-17 17:08:13,738 epoch 9 - iter 1620/1809 - loss 0.00696957 - time (sec): 116.28 - samples/sec: 2937.59 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-10-17 17:08:26,475 epoch 9 - iter 1800/1809 - loss 0.00698359 - time (sec): 129.02 - samples/sec: 2933.27 - lr: 0.000003 - momentum: 0.000000
201
+ 2023-10-17 17:08:27,089 ----------------------------------------------------------------------------------------------------
202
+ 2023-10-17 17:08:27,090 EPOCH 9 done: loss 0.0070 - lr: 0.000003
203
+ 2023-10-17 17:08:33,338 DEV : loss 0.38837048411369324 - f1-score (micro avg) 0.6705
204
+ 2023-10-17 17:08:33,381 saving best model
205
+ 2023-10-17 17:08:34,006 ----------------------------------------------------------------------------------------------------
206
+ 2023-10-17 17:08:47,073 epoch 10 - iter 180/1809 - loss 0.00224797 - time (sec): 13.07 - samples/sec: 2881.57 - lr: 0.000003 - momentum: 0.000000
207
+ 2023-10-17 17:09:01,239 epoch 10 - iter 360/1809 - loss 0.00362755 - time (sec): 27.23 - samples/sec: 2854.41 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-17 17:09:14,204 epoch 10 - iter 540/1809 - loss 0.00370153 - time (sec): 40.20 - samples/sec: 2863.08 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-17 17:09:26,807 epoch 10 - iter 720/1809 - loss 0.00450308 - time (sec): 52.80 - samples/sec: 2898.14 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 17:09:39,229 epoch 10 - iter 900/1809 - loss 0.00476154 - time (sec): 65.22 - samples/sec: 2896.81 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-17 17:09:52,036 epoch 10 - iter 1080/1809 - loss 0.00513412 - time (sec): 78.03 - samples/sec: 2917.16 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 17:10:04,882 epoch 10 - iter 1260/1809 - loss 0.00519172 - time (sec): 90.87 - samples/sec: 2926.77 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-17 17:10:17,583 epoch 10 - iter 1440/1809 - loss 0.00512814 - time (sec): 103.58 - samples/sec: 2929.95 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-17 17:10:30,625 epoch 10 - iter 1620/1809 - loss 0.00520500 - time (sec): 116.62 - samples/sec: 2936.59 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-10-17 17:10:43,601 epoch 10 - iter 1800/1809 - loss 0.00503373 - time (sec): 129.59 - samples/sec: 2921.20 - lr: 0.000000 - momentum: 0.000000
216
+ 2023-10-17 17:10:44,249 ----------------------------------------------------------------------------------------------------
217
+ 2023-10-17 17:10:44,249 EPOCH 10 done: loss 0.0050 - lr: 0.000000
218
+ 2023-10-17 17:10:51,342 DEV : loss 0.397739440202713 - f1-score (micro avg) 0.6676
219
+ 2023-10-17 17:10:51,878 ----------------------------------------------------------------------------------------------------
220
+ 2023-10-17 17:10:51,880 Loading model from best epoch ...
221
+ 2023-10-17 17:10:53,630 SequenceTagger predicts: Dictionary with 13 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org
222
+ 2023-10-17 17:11:01,735
223
+ Results:
224
+ - F-score (micro) 0.6596
225
+ - F-score (macro) 0.5192
226
+ - Accuracy 0.5023
227
+
228
+ By class:
229
+ precision recall f1-score support
230
+
231
+ loc 0.6533 0.7970 0.7180 591
232
+ pers 0.5792 0.7479 0.6528 357
233
+ org 0.1972 0.1772 0.1867 79
234
+
235
+ micro avg 0.6002 0.7322 0.6596 1027
236
+ macro avg 0.4765 0.5740 0.5192 1027
237
+ weighted avg 0.5924 0.7322 0.6545 1027
238
+
239
+ 2023-10-17 17:11:01,735 ----------------------------------------------------------------------------------------------------