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hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-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. final-model.pt +3 -0
  4. loss.tsv +11 -0
  5. runs/events.out.tfevents.1696983785.de2e83fddbee.1120.3 +3 -0
  6. test.tsv +0 -0
  7. training.log +262 -0
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final-model.pt 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 00:32:26 0.0002 1.1001 0.2150 0.4557 0.4476 0.4516 0.3238
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+ 2 00:41:51 0.0001 0.1488 0.1096 0.7408 0.7429 0.7418 0.6163
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+ 3 00:51:50 0.0001 0.0795 0.1327 0.7458 0.7946 0.7694 0.6425
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+ 4 01:01:32 0.0001 0.0565 0.1545 0.7076 0.8068 0.7540 0.6222
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+ 5 01:11:05 0.0001 0.0402 0.1752 0.7835 0.8027 0.7930 0.6727
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+ 6 01:20:44 0.0001 0.0312 0.1925 0.7462 0.8000 0.7722 0.6469
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+ 7 01:30:01 0.0001 0.0206 0.2199 0.7836 0.7932 0.7884 0.6693
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+ 8 01:39:29 0.0000 0.0169 0.2238 0.7577 0.8000 0.7783 0.6548
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+ 9 01:48:54 0.0000 0.0115 0.2389 0.7744 0.8082 0.7909 0.6712
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+ 10 01:58:26 0.0000 0.0092 0.2415 0.7694 0.7946 0.7818 0.6591
runs/events.out.tfevents.1696983785.de2e83fddbee.1120.3 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-11 00:23:05,794 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:23:05,796 Model: "SequenceTagger(
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+ (embeddings): ByT5Embeddings(
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+ (model): T5EncoderModel(
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+ (shared): Embedding(384, 1472)
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+ (encoder): T5Stack(
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+ (embed_tokens): Embedding(384, 1472)
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+ (block): ModuleList(
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+ (0): T5Block(
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+ (layer): ModuleList(
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+ (0): T5LayerSelfAttention(
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+ (SelfAttention): T5Attention(
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+ (q): Linear(in_features=1472, out_features=384, bias=False)
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+ (k): Linear(in_features=1472, out_features=384, bias=False)
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+ (v): Linear(in_features=1472, out_features=384, bias=False)
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+ (o): Linear(in_features=384, out_features=1472, bias=False)
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+ (relative_attention_bias): Embedding(32, 6)
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (1): T5LayerFF(
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+ (DenseReluDense): T5DenseGatedActDense(
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+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ (act): NewGELUActivation()
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, 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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+ (1-11): 11 x T5Block(
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+ (layer): ModuleList(
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+ (0): T5LayerSelfAttention(
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+ (SelfAttention): T5Attention(
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+ (q): Linear(in_features=1472, out_features=384, bias=False)
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+ (k): Linear(in_features=1472, out_features=384, bias=False)
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+ (v): Linear(in_features=1472, out_features=384, bias=False)
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+ (o): Linear(in_features=384, out_features=1472, bias=False)
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (1): T5LayerFF(
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+ (DenseReluDense): T5DenseGatedActDense(
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+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
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+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ (act): NewGELUActivation()
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+ )
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+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, 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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+ (final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, 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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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=1472, out_features=17, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-11 00:23:05,796 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:23:05,796 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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+ 2023-10-11 00:23:05,796 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:23:05,796 Train: 7142 sentences
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+ 2023-10-11 00:23:05,797 (train_with_dev=False, train_with_test=False)
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+ 2023-10-11 00:23:05,797 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:23:05,797 Training Params:
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+ 2023-10-11 00:23:05,797 - learning_rate: "0.00016"
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+ 2023-10-11 00:23:05,797 - mini_batch_size: "4"
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+ 2023-10-11 00:23:05,797 - max_epochs: "10"
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+ 2023-10-11 00:23:05,797 - shuffle: "True"
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+ 2023-10-11 00:23:05,797 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:23:05,797 Plugins:
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+ 2023-10-11 00:23:05,797 - TensorboardLogger
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+ 2023-10-11 00:23:05,797 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-11 00:23:05,797 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:23:05,797 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-11 00:23:05,797 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-11 00:23:05,797 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:23:05,798 Computation:
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+ 2023-10-11 00:23:05,798 - compute on device: cuda:0
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+ 2023-10-11 00:23:05,798 - embedding storage: none
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+ 2023-10-11 00:23:05,798 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:23:05,798 Model training base path: "hmbench-newseye/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-11 00:23:05,798 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:23:05,798 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:23:05,798 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-11 00:24:00,729 epoch 1 - iter 178/1786 - loss 2.82172262 - time (sec): 54.93 - samples/sec: 463.83 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-11 00:24:54,757 epoch 1 - iter 356/1786 - loss 2.68438929 - time (sec): 108.96 - samples/sec: 462.67 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-11 00:25:49,197 epoch 1 - iter 534/1786 - loss 2.40556334 - time (sec): 163.40 - samples/sec: 459.28 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-11 00:26:43,891 epoch 1 - iter 712/1786 - loss 2.09683094 - time (sec): 218.09 - samples/sec: 456.19 - lr: 0.000064 - momentum: 0.000000
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+ 2023-10-11 00:27:39,073 epoch 1 - iter 890/1786 - loss 1.79374454 - time (sec): 273.27 - samples/sec: 459.99 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-11 00:28:31,677 epoch 1 - iter 1068/1786 - loss 1.59687181 - time (sec): 325.88 - samples/sec: 456.68 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-11 00:29:23,556 epoch 1 - iter 1246/1786 - loss 1.43709470 - time (sec): 377.76 - samples/sec: 456.38 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-11 00:30:17,433 epoch 1 - iter 1424/1786 - loss 1.30135011 - time (sec): 431.63 - samples/sec: 457.91 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-11 00:31:10,923 epoch 1 - iter 1602/1786 - loss 1.19051970 - time (sec): 485.12 - samples/sec: 460.52 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-11 00:32:04,890 epoch 1 - iter 1780/1786 - loss 1.10265404 - time (sec): 539.09 - samples/sec: 459.99 - lr: 0.000159 - momentum: 0.000000
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+ 2023-10-11 00:32:06,577 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:32:06,577 EPOCH 1 done: loss 1.1001 - lr: 0.000159
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+ 2023-10-11 00:32:26,381 DEV : loss 0.21496298909187317 - f1-score (micro avg) 0.4516
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+ 2023-10-11 00:32:26,413 saving best model
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+ 2023-10-11 00:32:27,258 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:33:22,790 epoch 2 - iter 178/1786 - loss 0.22116459 - time (sec): 55.53 - samples/sec: 476.30 - lr: 0.000158 - momentum: 0.000000
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+ 2023-10-11 00:34:16,303 epoch 2 - iter 356/1786 - loss 0.21373852 - time (sec): 109.04 - samples/sec: 462.61 - lr: 0.000156 - momentum: 0.000000
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+ 2023-10-11 00:35:09,232 epoch 2 - iter 534/1786 - loss 0.19894140 - time (sec): 161.97 - samples/sec: 458.65 - lr: 0.000155 - momentum: 0.000000
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+ 2023-10-11 00:36:03,022 epoch 2 - iter 712/1786 - loss 0.18566527 - time (sec): 215.76 - samples/sec: 460.41 - lr: 0.000153 - momentum: 0.000000
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+ 2023-10-11 00:36:56,171 epoch 2 - iter 890/1786 - loss 0.17473385 - time (sec): 268.91 - samples/sec: 461.71 - lr: 0.000151 - momentum: 0.000000
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+ 2023-10-11 00:37:48,589 epoch 2 - iter 1068/1786 - loss 0.16918782 - time (sec): 321.33 - samples/sec: 461.01 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-11 00:38:43,484 epoch 2 - iter 1246/1786 - loss 0.16287825 - time (sec): 376.22 - samples/sec: 461.21 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-11 00:39:39,645 epoch 2 - iter 1424/1786 - loss 0.15775704 - time (sec): 432.38 - samples/sec: 461.75 - lr: 0.000146 - momentum: 0.000000
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+ 2023-10-11 00:40:34,151 epoch 2 - iter 1602/1786 - loss 0.15320043 - time (sec): 486.89 - samples/sec: 459.43 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-11 00:41:28,520 epoch 2 - iter 1780/1786 - loss 0.14884221 - time (sec): 541.26 - samples/sec: 458.41 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-11 00:41:30,090 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 00:41:30,091 EPOCH 2 done: loss 0.1488 - lr: 0.000142
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+ 2023-10-11 00:41:51,592 DEV : loss 0.10961832106113434 - f1-score (micro avg) 0.7418
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+ 2023-10-11 00:41:51,627 saving best model
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+ 2023-10-11 00:42:03,657 ----------------------------------------------------------------------------------------------------
128
+ 2023-10-11 00:42:59,110 epoch 3 - iter 178/1786 - loss 0.07855565 - time (sec): 55.45 - samples/sec: 430.85 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-11 00:43:55,091 epoch 3 - iter 356/1786 - loss 0.07506546 - time (sec): 111.43 - samples/sec: 443.18 - lr: 0.000139 - momentum: 0.000000
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+ 2023-10-11 00:44:52,205 epoch 3 - iter 534/1786 - loss 0.07999800 - time (sec): 168.54 - samples/sec: 439.31 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-11 00:45:47,516 epoch 3 - iter 712/1786 - loss 0.08377628 - time (sec): 223.86 - samples/sec: 435.06 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-11 00:46:45,191 epoch 3 - iter 890/1786 - loss 0.08368157 - time (sec): 281.53 - samples/sec: 437.88 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-11 00:47:41,048 epoch 3 - iter 1068/1786 - loss 0.08196451 - time (sec): 337.39 - samples/sec: 439.01 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-11 00:48:37,387 epoch 3 - iter 1246/1786 - loss 0.07913681 - time (sec): 393.73 - samples/sec: 439.17 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-11 00:49:34,302 epoch 3 - iter 1424/1786 - loss 0.07927759 - time (sec): 450.64 - samples/sec: 440.23 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-11 00:50:31,342 epoch 3 - iter 1602/1786 - loss 0.07914526 - time (sec): 507.68 - samples/sec: 443.41 - lr: 0.000126 - momentum: 0.000000
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+ 2023-10-11 00:51:25,788 epoch 3 - iter 1780/1786 - loss 0.07955825 - time (sec): 562.13 - samples/sec: 441.21 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-11 00:51:27,478 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-11 00:51:27,479 EPOCH 3 done: loss 0.0795 - lr: 0.000125
140
+ 2023-10-11 00:51:50,488 DEV : loss 0.13271120190620422 - f1-score (micro avg) 0.7694
141
+ 2023-10-11 00:51:50,522 saving best model
142
+ 2023-10-11 00:51:59,738 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-11 00:52:53,740 epoch 4 - iter 178/1786 - loss 0.05116751 - time (sec): 54.00 - samples/sec: 461.44 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-11 00:53:48,364 epoch 4 - iter 356/1786 - loss 0.05373327 - time (sec): 108.62 - samples/sec: 453.07 - lr: 0.000121 - momentum: 0.000000
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+ 2023-10-11 00:54:42,706 epoch 4 - iter 534/1786 - loss 0.05528519 - time (sec): 162.96 - samples/sec: 452.82 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-11 00:55:39,034 epoch 4 - iter 712/1786 - loss 0.05948955 - time (sec): 219.29 - samples/sec: 452.44 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-11 00:56:35,623 epoch 4 - iter 890/1786 - loss 0.05774224 - time (sec): 275.88 - samples/sec: 455.07 - lr: 0.000116 - momentum: 0.000000
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+ 2023-10-11 00:57:31,802 epoch 4 - iter 1068/1786 - loss 0.05684100 - time (sec): 332.06 - samples/sec: 453.93 - lr: 0.000114 - momentum: 0.000000
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+ 2023-10-11 00:58:28,188 epoch 4 - iter 1246/1786 - loss 0.05589756 - time (sec): 388.45 - samples/sec: 455.82 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-11 00:59:21,599 epoch 4 - iter 1424/1786 - loss 0.05592335 - time (sec): 441.86 - samples/sec: 456.10 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-11 01:00:15,044 epoch 4 - iter 1602/1786 - loss 0.05630542 - time (sec): 495.30 - samples/sec: 454.18 - lr: 0.000109 - momentum: 0.000000
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+ 2023-10-11 01:01:08,426 epoch 4 - iter 1780/1786 - loss 0.05643132 - time (sec): 548.68 - samples/sec: 452.21 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-11 01:01:09,965 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-11 01:01:09,966 EPOCH 4 done: loss 0.0565 - lr: 0.000107
155
+ 2023-10-11 01:01:32,933 DEV : loss 0.1545310765504837 - f1-score (micro avg) 0.754
156
+ 2023-10-11 01:01:32,964 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-11 01:02:28,970 epoch 5 - iter 178/1786 - loss 0.03780263 - time (sec): 56.00 - samples/sec: 454.04 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-11 01:03:22,450 epoch 5 - iter 356/1786 - loss 0.04158210 - time (sec): 109.48 - samples/sec: 444.41 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-11 01:04:19,870 epoch 5 - iter 534/1786 - loss 0.03924275 - time (sec): 166.90 - samples/sec: 446.20 - lr: 0.000101 - momentum: 0.000000
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+ 2023-10-11 01:05:16,274 epoch 5 - iter 712/1786 - loss 0.04250127 - time (sec): 223.31 - samples/sec: 450.83 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-11 01:06:10,674 epoch 5 - iter 890/1786 - loss 0.04170514 - time (sec): 277.71 - samples/sec: 444.62 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-11 01:07:04,253 epoch 5 - iter 1068/1786 - loss 0.04064150 - time (sec): 331.29 - samples/sec: 444.86 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-11 01:07:58,515 epoch 5 - iter 1246/1786 - loss 0.04058142 - time (sec): 385.55 - samples/sec: 447.34 - lr: 0.000094 - momentum: 0.000000
164
+ 2023-10-11 01:08:52,190 epoch 5 - iter 1424/1786 - loss 0.04122103 - time (sec): 439.22 - samples/sec: 451.22 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-11 01:09:46,923 epoch 5 - iter 1602/1786 - loss 0.04009365 - time (sec): 493.96 - samples/sec: 451.63 - lr: 0.000091 - momentum: 0.000000
166
+ 2023-10-11 01:10:40,618 epoch 5 - iter 1780/1786 - loss 0.04017012 - time (sec): 547.65 - samples/sec: 452.91 - lr: 0.000089 - momentum: 0.000000
167
+ 2023-10-11 01:10:42,299 ----------------------------------------------------------------------------------------------------
168
+ 2023-10-11 01:10:42,299 EPOCH 5 done: loss 0.0402 - lr: 0.000089
169
+ 2023-10-11 01:11:05,576 DEV : loss 0.17520776391029358 - f1-score (micro avg) 0.793
170
+ 2023-10-11 01:11:05,607 saving best model
171
+ 2023-10-11 01:11:16,215 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-11 01:12:11,344 epoch 6 - iter 178/1786 - loss 0.03032200 - time (sec): 55.12 - samples/sec: 452.24 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-11 01:13:06,283 epoch 6 - iter 356/1786 - loss 0.03095283 - time (sec): 110.06 - samples/sec: 450.08 - lr: 0.000085 - momentum: 0.000000
174
+ 2023-10-11 01:14:01,870 epoch 6 - iter 534/1786 - loss 0.03211693 - time (sec): 165.65 - samples/sec: 453.72 - lr: 0.000084 - momentum: 0.000000
175
+ 2023-10-11 01:14:56,756 epoch 6 - iter 712/1786 - loss 0.03217743 - time (sec): 220.54 - samples/sec: 449.82 - lr: 0.000082 - momentum: 0.000000
176
+ 2023-10-11 01:15:49,861 epoch 6 - iter 890/1786 - loss 0.03143026 - time (sec): 273.64 - samples/sec: 449.60 - lr: 0.000080 - momentum: 0.000000
177
+ 2023-10-11 01:16:43,783 epoch 6 - iter 1068/1786 - loss 0.03104840 - time (sec): 327.56 - samples/sec: 451.43 - lr: 0.000078 - momentum: 0.000000
178
+ 2023-10-11 01:17:38,716 epoch 6 - iter 1246/1786 - loss 0.03042104 - time (sec): 382.50 - samples/sec: 455.49 - lr: 0.000077 - momentum: 0.000000
179
+ 2023-10-11 01:18:33,149 epoch 6 - iter 1424/1786 - loss 0.03050314 - time (sec): 436.93 - samples/sec: 455.70 - lr: 0.000075 - momentum: 0.000000
180
+ 2023-10-11 01:19:26,347 epoch 6 - iter 1602/1786 - loss 0.03080150 - time (sec): 490.13 - samples/sec: 458.40 - lr: 0.000073 - momentum: 0.000000
181
+ 2023-10-11 01:20:20,368 epoch 6 - iter 1780/1786 - loss 0.03128837 - time (sec): 544.15 - samples/sec: 455.81 - lr: 0.000071 - momentum: 0.000000
182
+ 2023-10-11 01:20:22,030 ----------------------------------------------------------------------------------------------------
183
+ 2023-10-11 01:20:22,030 EPOCH 6 done: loss 0.0312 - lr: 0.000071
184
+ 2023-10-11 01:20:43,987 DEV : loss 0.1924738883972168 - f1-score (micro avg) 0.7722
185
+ 2023-10-11 01:20:44,018 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-11 01:21:37,787 epoch 7 - iter 178/1786 - loss 0.01587708 - time (sec): 53.77 - samples/sec: 471.93 - lr: 0.000069 - momentum: 0.000000
187
+ 2023-10-11 01:22:29,472 epoch 7 - iter 356/1786 - loss 0.01825781 - time (sec): 105.45 - samples/sec: 461.97 - lr: 0.000068 - momentum: 0.000000
188
+ 2023-10-11 01:23:22,449 epoch 7 - iter 534/1786 - loss 0.01732356 - time (sec): 158.43 - samples/sec: 468.28 - lr: 0.000066 - momentum: 0.000000
189
+ 2023-10-11 01:24:16,983 epoch 7 - iter 712/1786 - loss 0.01837650 - time (sec): 212.96 - samples/sec: 464.65 - lr: 0.000064 - momentum: 0.000000
190
+ 2023-10-11 01:25:10,070 epoch 7 - iter 890/1786 - loss 0.01927023 - time (sec): 266.05 - samples/sec: 462.18 - lr: 0.000062 - momentum: 0.000000
191
+ 2023-10-11 01:26:04,003 epoch 7 - iter 1068/1786 - loss 0.01865808 - time (sec): 319.98 - samples/sec: 463.41 - lr: 0.000061 - momentum: 0.000000
192
+ 2023-10-11 01:26:59,688 epoch 7 - iter 1246/1786 - loss 0.01941833 - time (sec): 375.67 - samples/sec: 460.91 - lr: 0.000059 - momentum: 0.000000
193
+ 2023-10-11 01:27:51,703 epoch 7 - iter 1424/1786 - loss 0.01972491 - time (sec): 427.68 - samples/sec: 459.36 - lr: 0.000057 - momentum: 0.000000
194
+ 2023-10-11 01:28:45,857 epoch 7 - iter 1602/1786 - loss 0.02012094 - time (sec): 481.84 - samples/sec: 462.62 - lr: 0.000055 - momentum: 0.000000
195
+ 2023-10-11 01:29:38,522 epoch 7 - iter 1780/1786 - loss 0.02068942 - time (sec): 534.50 - samples/sec: 464.17 - lr: 0.000053 - momentum: 0.000000
196
+ 2023-10-11 01:29:40,057 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-11 01:29:40,057 EPOCH 7 done: loss 0.0206 - lr: 0.000053
198
+ 2023-10-11 01:30:01,229 DEV : loss 0.21992838382720947 - f1-score (micro avg) 0.7884
199
+ 2023-10-11 01:30:01,263 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-11 01:30:53,920 epoch 8 - iter 178/1786 - loss 0.01299467 - time (sec): 52.65 - samples/sec: 466.34 - lr: 0.000052 - momentum: 0.000000
201
+ 2023-10-11 01:31:45,976 epoch 8 - iter 356/1786 - loss 0.01441319 - time (sec): 104.71 - samples/sec: 463.25 - lr: 0.000050 - momentum: 0.000000
202
+ 2023-10-11 01:32:38,715 epoch 8 - iter 534/1786 - loss 0.01543810 - time (sec): 157.45 - samples/sec: 457.64 - lr: 0.000048 - momentum: 0.000000
203
+ 2023-10-11 01:33:34,520 epoch 8 - iter 712/1786 - loss 0.01558449 - time (sec): 213.26 - samples/sec: 460.35 - lr: 0.000046 - momentum: 0.000000
204
+ 2023-10-11 01:34:29,192 epoch 8 - iter 890/1786 - loss 0.01606204 - time (sec): 267.93 - samples/sec: 457.95 - lr: 0.000044 - momentum: 0.000000
205
+ 2023-10-11 01:35:23,050 epoch 8 - iter 1068/1786 - loss 0.01737134 - time (sec): 321.78 - samples/sec: 453.47 - lr: 0.000043 - momentum: 0.000000
206
+ 2023-10-11 01:36:18,022 epoch 8 - iter 1246/1786 - loss 0.01717115 - time (sec): 376.76 - samples/sec: 453.38 - lr: 0.000041 - momentum: 0.000000
207
+ 2023-10-11 01:37:12,230 epoch 8 - iter 1424/1786 - loss 0.01719750 - time (sec): 430.96 - samples/sec: 452.83 - lr: 0.000039 - momentum: 0.000000
208
+ 2023-10-11 01:38:07,659 epoch 8 - iter 1602/1786 - loss 0.01716691 - time (sec): 486.39 - samples/sec: 454.73 - lr: 0.000037 - momentum: 0.000000
209
+ 2023-10-11 01:39:04,440 epoch 8 - iter 1780/1786 - loss 0.01671130 - time (sec): 543.18 - samples/sec: 456.11 - lr: 0.000036 - momentum: 0.000000
210
+ 2023-10-11 01:39:06,365 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-11 01:39:06,365 EPOCH 8 done: loss 0.0169 - lr: 0.000036
212
+ 2023-10-11 01:39:29,230 DEV : loss 0.22382444143295288 - f1-score (micro avg) 0.7783
213
+ 2023-10-11 01:39:29,261 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-11 01:40:24,277 epoch 9 - iter 178/1786 - loss 0.01495584 - time (sec): 55.01 - samples/sec: 452.58 - lr: 0.000034 - momentum: 0.000000
215
+ 2023-10-11 01:41:18,298 epoch 9 - iter 356/1786 - loss 0.01430322 - time (sec): 109.03 - samples/sec: 447.87 - lr: 0.000032 - momentum: 0.000000
216
+ 2023-10-11 01:42:13,747 epoch 9 - iter 534/1786 - loss 0.01462408 - time (sec): 164.48 - samples/sec: 454.99 - lr: 0.000030 - momentum: 0.000000
217
+ 2023-10-11 01:43:06,569 epoch 9 - iter 712/1786 - loss 0.01383529 - time (sec): 217.31 - samples/sec: 449.99 - lr: 0.000028 - momentum: 0.000000
218
+ 2023-10-11 01:44:00,263 epoch 9 - iter 890/1786 - loss 0.01279666 - time (sec): 271.00 - samples/sec: 449.54 - lr: 0.000027 - momentum: 0.000000
219
+ 2023-10-11 01:44:55,264 epoch 9 - iter 1068/1786 - loss 0.01221011 - time (sec): 326.00 - samples/sec: 448.49 - lr: 0.000025 - momentum: 0.000000
220
+ 2023-10-11 01:45:49,115 epoch 9 - iter 1246/1786 - loss 0.01201829 - time (sec): 379.85 - samples/sec: 448.44 - lr: 0.000023 - momentum: 0.000000
221
+ 2023-10-11 01:46:43,117 epoch 9 - iter 1424/1786 - loss 0.01119229 - time (sec): 433.85 - samples/sec: 451.22 - lr: 0.000021 - momentum: 0.000000
222
+ 2023-10-11 01:47:37,095 epoch 9 - iter 1602/1786 - loss 0.01109461 - time (sec): 487.83 - samples/sec: 453.37 - lr: 0.000020 - momentum: 0.000000
223
+ 2023-10-11 01:48:31,386 epoch 9 - iter 1780/1786 - loss 0.01142929 - time (sec): 542.12 - samples/sec: 457.32 - lr: 0.000018 - momentum: 0.000000
224
+ 2023-10-11 01:48:33,130 ----------------------------------------------------------------------------------------------------
225
+ 2023-10-11 01:48:33,131 EPOCH 9 done: loss 0.0115 - lr: 0.000018
226
+ 2023-10-11 01:48:54,395 DEV : loss 0.23885728418827057 - f1-score (micro avg) 0.7909
227
+ 2023-10-11 01:48:54,425 ----------------------------------------------------------------------------------------------------
228
+ 2023-10-11 01:49:47,365 epoch 10 - iter 178/1786 - loss 0.00824939 - time (sec): 52.94 - samples/sec: 476.73 - lr: 0.000016 - momentum: 0.000000
229
+ 2023-10-11 01:50:40,024 epoch 10 - iter 356/1786 - loss 0.00754479 - time (sec): 105.60 - samples/sec: 466.45 - lr: 0.000014 - momentum: 0.000000
230
+ 2023-10-11 01:51:31,760 epoch 10 - iter 534/1786 - loss 0.00886912 - time (sec): 157.33 - samples/sec: 459.24 - lr: 0.000012 - momentum: 0.000000
231
+ 2023-10-11 01:52:26,703 epoch 10 - iter 712/1786 - loss 0.00857059 - time (sec): 212.28 - samples/sec: 464.60 - lr: 0.000011 - momentum: 0.000000
232
+ 2023-10-11 01:53:21,373 epoch 10 - iter 890/1786 - loss 0.00828606 - time (sec): 266.95 - samples/sec: 467.58 - lr: 0.000009 - momentum: 0.000000
233
+ 2023-10-11 01:54:16,053 epoch 10 - iter 1068/1786 - loss 0.00847447 - time (sec): 321.63 - samples/sec: 462.32 - lr: 0.000007 - momentum: 0.000000
234
+ 2023-10-11 01:55:13,521 epoch 10 - iter 1246/1786 - loss 0.00884080 - time (sec): 379.09 - samples/sec: 461.95 - lr: 0.000005 - momentum: 0.000000
235
+ 2023-10-11 01:56:09,216 epoch 10 - iter 1424/1786 - loss 0.00980399 - time (sec): 434.79 - samples/sec: 456.91 - lr: 0.000004 - momentum: 0.000000
236
+ 2023-10-11 01:57:05,107 epoch 10 - iter 1602/1786 - loss 0.00968986 - time (sec): 490.68 - samples/sec: 453.59 - lr: 0.000002 - momentum: 0.000000
237
+ 2023-10-11 01:58:03,236 epoch 10 - iter 1780/1786 - loss 0.00921449 - time (sec): 548.81 - samples/sec: 451.98 - lr: 0.000000 - momentum: 0.000000
238
+ 2023-10-11 01:58:05,045 ----------------------------------------------------------------------------------------------------
239
+ 2023-10-11 01:58:05,046 EPOCH 10 done: loss 0.0092 - lr: 0.000000
240
+ 2023-10-11 01:58:26,928 DEV : loss 0.24146509170532227 - f1-score (micro avg) 0.7818
241
+ 2023-10-11 01:58:27,995 ----------------------------------------------------------------------------------------------------
242
+ 2023-10-11 01:58:27,997 Loading model from best epoch ...
243
+ 2023-10-11 01:58:32,001 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
244
+ 2023-10-11 01:59:39,506
245
+ Results:
246
+ - F-score (micro) 0.6929
247
+ - F-score (macro) 0.6294
248
+ - Accuracy 0.5446
249
+
250
+ By class:
251
+ precision recall f1-score support
252
+
253
+ LOC 0.7365 0.6740 0.7039 1095
254
+ PER 0.7930 0.7569 0.7745 1012
255
+ ORG 0.4201 0.5742 0.4852 357
256
+ HumanProd 0.5625 0.5455 0.5538 33
257
+
258
+ micro avg 0.6941 0.6916 0.6929 2497
259
+ macro avg 0.6280 0.6376 0.6294 2497
260
+ weighted avg 0.7119 0.6916 0.6993 2497
261
+
262
+ 2023-10-11 01:59:39,506 ----------------------------------------------------------------------------------------------------