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hmbench-newseye-de-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2

Token Classification
Flair
PyTorch
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German
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.1696933045.6d4c7681f95b.1253.4 +3 -0
  6. test.tsv +0 -0
  7. training.log +263 -0
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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 10:41:45 0.0001 0.9266 0.1398 0.2139 0.3333 0.2605 0.1498
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+ 2 11:06:18 0.0001 0.1514 0.1232 0.2611 0.4110 0.3194 0.1910
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+ 3 11:30:33 0.0001 0.0914 0.1645 0.3088 0.4773 0.3750 0.2316
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+ 4 11:54:36 0.0001 0.0656 0.2648 0.2392 0.5341 0.3304 0.1990
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+ 5 12:18:32 0.0001 0.0467 0.2965 0.2771 0.5227 0.3622 0.2224
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+ 6 12:42:28 0.0001 0.0344 0.3337 0.2892 0.5455 0.3780 0.2345
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+ 7 13:06:32 0.0001 0.0256 0.3979 0.2805 0.5530 0.3722 0.2299
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+ 8 13:30:36 0.0000 0.0179 0.4046 0.2989 0.5758 0.3935 0.2470
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+ 9 13:54:42 0.0000 0.0129 0.4416 0.2836 0.5549 0.3754 0.2324
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+ 10 14:18:43 0.0000 0.0085 0.4613 0.2849 0.5720 0.3804 0.2363
runs/events.out.tfevents.1696933045.6d4c7681f95b.1253.4 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-10 10:17:25,583 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:17:25,586 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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+ 2023-10-10 10:17:25,586 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:17:25,587 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences
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+ - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator
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+ 2023-10-10 10:17:25,587 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:17:25,587 Train: 20847 sentences
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+ 2023-10-10 10:17:25,587 (train_with_dev=False, train_with_test=False)
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+ 2023-10-10 10:17:25,587 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:17:25,587 Training Params:
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+ 2023-10-10 10:17:25,587 - learning_rate: "0.00015"
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+ 2023-10-10 10:17:25,587 - mini_batch_size: "8"
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+ 2023-10-10 10:17:25,587 - max_epochs: "10"
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+ 2023-10-10 10:17:25,587 - shuffle: "True"
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+ 2023-10-10 10:17:25,587 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:17:25,587 Plugins:
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+ 2023-10-10 10:17:25,588 - TensorboardLogger
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+ 2023-10-10 10:17:25,588 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-10 10:17:25,588 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:17:25,588 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-10 10:17:25,588 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-10 10:17:25,588 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:17:25,588 Computation:
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+ 2023-10-10 10:17:25,588 - compute on device: cuda:0
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+ 2023-10-10 10:17:25,588 - embedding storage: none
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+ 2023-10-10 10:17:25,588 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:17:25,588 Model training base path: "hmbench-newseye/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2"
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+ 2023-10-10 10:17:25,588 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:17:25,588 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:17:25,589 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-10 10:19:50,358 epoch 1 - iter 260/2606 - loss 2.82858377 - time (sec): 144.77 - samples/sec: 246.69 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-10 10:22:07,314 epoch 1 - iter 520/2606 - loss 2.59379089 - time (sec): 281.72 - samples/sec: 255.43 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-10 10:24:28,436 epoch 1 - iter 780/2606 - loss 2.18946733 - time (sec): 422.84 - samples/sec: 258.37 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-10 10:26:51,376 epoch 1 - iter 1040/2606 - loss 1.78347214 - time (sec): 565.78 - samples/sec: 262.46 - lr: 0.000060 - momentum: 0.000000
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+ 2023-10-10 10:29:09,559 epoch 1 - iter 1300/2606 - loss 1.51733285 - time (sec): 703.97 - samples/sec: 263.06 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-10 10:31:37,873 epoch 1 - iter 1560/2606 - loss 1.33965070 - time (sec): 852.28 - samples/sec: 261.90 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-10 10:33:58,911 epoch 1 - iter 1820/2606 - loss 1.20517898 - time (sec): 993.32 - samples/sec: 261.43 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-10 10:36:19,665 epoch 1 - iter 2080/2606 - loss 1.09396343 - time (sec): 1134.07 - samples/sec: 260.27 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-10 10:38:40,561 epoch 1 - iter 2340/2606 - loss 1.00927714 - time (sec): 1274.97 - samples/sec: 258.05 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-10 10:41:03,325 epoch 1 - iter 2600/2606 - loss 0.92825533 - time (sec): 1417.73 - samples/sec: 258.37 - lr: 0.000150 - momentum: 0.000000
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+ 2023-10-10 10:41:06,639 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:41:06,639 EPOCH 1 done: loss 0.9266 - lr: 0.000150
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+ 2023-10-10 10:41:45,319 DEV : loss 0.13984432816505432 - f1-score (micro avg) 0.2605
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+ 2023-10-10 10:41:45,373 saving best model
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+ 2023-10-10 10:41:46,388 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 10:44:03,782 epoch 2 - iter 260/2606 - loss 0.20048946 - time (sec): 137.39 - samples/sec: 254.87 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-10 10:46:21,256 epoch 2 - iter 520/2606 - loss 0.19474593 - time (sec): 274.86 - samples/sec: 257.90 - lr: 0.000147 - momentum: 0.000000
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+ 2023-10-10 10:48:42,738 epoch 2 - iter 780/2606 - loss 0.18435066 - time (sec): 416.35 - samples/sec: 258.36 - lr: 0.000145 - momentum: 0.000000
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+ 2023-10-10 10:51:06,768 epoch 2 - iter 1040/2606 - loss 0.17821735 - time (sec): 560.38 - samples/sec: 254.46 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-10 10:53:29,090 epoch 2 - iter 1300/2606 - loss 0.17222389 - time (sec): 702.70 - samples/sec: 255.82 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-10 10:55:53,869 epoch 2 - iter 1560/2606 - loss 0.16461177 - time (sec): 847.48 - samples/sec: 256.54 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-10 10:58:21,709 epoch 2 - iter 1820/2606 - loss 0.16129296 - time (sec): 995.32 - samples/sec: 256.21 - lr: 0.000138 - momentum: 0.000000
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+ 2023-10-10 11:00:47,042 epoch 2 - iter 2080/2606 - loss 0.15850392 - time (sec): 1140.65 - samples/sec: 254.82 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-10 11:03:09,654 epoch 2 - iter 2340/2606 - loss 0.15546215 - time (sec): 1283.26 - samples/sec: 254.16 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-10 11:05:34,501 epoch 2 - iter 2600/2606 - loss 0.15151244 - time (sec): 1428.11 - samples/sec: 256.78 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-10 11:05:37,581 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 11:05:37,582 EPOCH 2 done: loss 0.1514 - lr: 0.000133
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+ 2023-10-10 11:06:18,193 DEV : loss 0.12324459105730057 - f1-score (micro avg) 0.3194
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+ 2023-10-10 11:06:18,251 saving best model
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+ 2023-10-10 11:06:20,967 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 11:08:38,954 epoch 3 - iter 260/2606 - loss 0.08453196 - time (sec): 137.98 - samples/sec: 257.85 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-10 11:11:00,136 epoch 3 - iter 520/2606 - loss 0.08884281 - time (sec): 279.16 - samples/sec: 259.92 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-10 11:13:22,749 epoch 3 - iter 780/2606 - loss 0.09171291 - time (sec): 421.78 - samples/sec: 259.93 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-10 11:15:41,613 epoch 3 - iter 1040/2606 - loss 0.09501175 - time (sec): 560.64 - samples/sec: 259.52 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-10 11:18:01,854 epoch 3 - iter 1300/2606 - loss 0.09753731 - time (sec): 700.88 - samples/sec: 264.60 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-10 11:20:24,416 epoch 3 - iter 1560/2606 - loss 0.09540397 - time (sec): 843.45 - samples/sec: 264.50 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-10 11:22:44,671 epoch 3 - iter 1820/2606 - loss 0.09431741 - time (sec): 983.70 - samples/sec: 264.06 - lr: 0.000122 - momentum: 0.000000
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+ 2023-10-10 11:25:05,740 epoch 3 - iter 2080/2606 - loss 0.09273873 - time (sec): 1124.77 - samples/sec: 263.24 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-10 11:27:19,392 epoch 3 - iter 2340/2606 - loss 0.09140459 - time (sec): 1258.42 - samples/sec: 261.51 - lr: 0.000118 - momentum: 0.000000
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+ 2023-10-10 11:29:46,096 epoch 3 - iter 2600/2606 - loss 0.09148228 - time (sec): 1405.13 - samples/sec: 260.87 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-10 11:29:49,569 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-10 11:29:49,569 EPOCH 3 done: loss 0.0914 - lr: 0.000117
140
+ 2023-10-10 11:30:33,329 DEV : loss 0.16454216837882996 - f1-score (micro avg) 0.375
141
+ 2023-10-10 11:30:33,407 saving best model
142
+ 2023-10-10 11:30:36,290 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-10 11:33:00,647 epoch 4 - iter 260/2606 - loss 0.05238673 - time (sec): 144.35 - samples/sec: 255.20 - lr: 0.000115 - momentum: 0.000000
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+ 2023-10-10 11:35:25,933 epoch 4 - iter 520/2606 - loss 0.06195508 - time (sec): 289.64 - samples/sec: 263.40 - lr: 0.000113 - momentum: 0.000000
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+ 2023-10-10 11:37:44,551 epoch 4 - iter 780/2606 - loss 0.06048133 - time (sec): 428.26 - samples/sec: 263.11 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-10 11:40:01,800 epoch 4 - iter 1040/2606 - loss 0.06150890 - time (sec): 565.51 - samples/sec: 262.28 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-10 11:42:24,722 epoch 4 - iter 1300/2606 - loss 0.05978791 - time (sec): 708.43 - samples/sec: 261.76 - lr: 0.000108 - momentum: 0.000000
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+ 2023-10-10 11:44:44,516 epoch 4 - iter 1560/2606 - loss 0.06398599 - time (sec): 848.22 - samples/sec: 261.95 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-10 11:47:03,146 epoch 4 - iter 1820/2606 - loss 0.06393966 - time (sec): 986.85 - samples/sec: 263.46 - lr: 0.000105 - momentum: 0.000000
150
+ 2023-10-10 11:49:18,081 epoch 4 - iter 2080/2606 - loss 0.06373753 - time (sec): 1121.79 - samples/sec: 262.88 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-10 11:51:36,877 epoch 4 - iter 2340/2606 - loss 0.06554623 - time (sec): 1260.58 - samples/sec: 262.80 - lr: 0.000102 - momentum: 0.000000
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+ 2023-10-10 11:53:53,323 epoch 4 - iter 2600/2606 - loss 0.06560053 - time (sec): 1397.03 - samples/sec: 262.60 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-10 11:53:56,216 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-10 11:53:56,216 EPOCH 4 done: loss 0.0656 - lr: 0.000100
155
+ 2023-10-10 11:54:36,789 DEV : loss 0.26484549045562744 - f1-score (micro avg) 0.3304
156
+ 2023-10-10 11:54:36,848 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-10 11:56:53,975 epoch 5 - iter 260/2606 - loss 0.04025488 - time (sec): 137.12 - samples/sec: 250.50 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-10 11:59:13,175 epoch 5 - iter 520/2606 - loss 0.04564273 - time (sec): 276.32 - samples/sec: 254.62 - lr: 0.000097 - momentum: 0.000000
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+ 2023-10-10 12:01:31,458 epoch 5 - iter 780/2606 - loss 0.04191303 - time (sec): 414.61 - samples/sec: 261.94 - lr: 0.000095 - momentum: 0.000000
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+ 2023-10-10 12:03:47,948 epoch 5 - iter 1040/2606 - loss 0.04370480 - time (sec): 551.10 - samples/sec: 263.07 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-10 12:06:09,643 epoch 5 - iter 1300/2606 - loss 0.04555479 - time (sec): 692.79 - samples/sec: 263.14 - lr: 0.000092 - momentum: 0.000000
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+ 2023-10-10 12:08:27,869 epoch 5 - iter 1560/2606 - loss 0.04638323 - time (sec): 831.02 - samples/sec: 263.60 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-10 12:10:44,931 epoch 5 - iter 1820/2606 - loss 0.04605700 - time (sec): 968.08 - samples/sec: 262.91 - lr: 0.000088 - momentum: 0.000000
164
+ 2023-10-10 12:13:06,008 epoch 5 - iter 2080/2606 - loss 0.04618736 - time (sec): 1109.16 - samples/sec: 264.32 - lr: 0.000087 - momentum: 0.000000
165
+ 2023-10-10 12:15:28,125 epoch 5 - iter 2340/2606 - loss 0.04664304 - time (sec): 1251.27 - samples/sec: 264.95 - lr: 0.000085 - momentum: 0.000000
166
+ 2023-10-10 12:17:46,232 epoch 5 - iter 2600/2606 - loss 0.04668505 - time (sec): 1389.38 - samples/sec: 263.95 - lr: 0.000083 - momentum: 0.000000
167
+ 2023-10-10 12:17:49,292 ----------------------------------------------------------------------------------------------------
168
+ 2023-10-10 12:17:49,292 EPOCH 5 done: loss 0.0467 - lr: 0.000083
169
+ 2023-10-10 12:18:32,521 DEV : loss 0.29650354385375977 - f1-score (micro avg) 0.3622
170
+ 2023-10-10 12:18:32,581 ----------------------------------------------------------------------------------------------------
171
+ 2023-10-10 12:20:49,821 epoch 6 - iter 260/2606 - loss 0.03241520 - time (sec): 137.24 - samples/sec: 254.95 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-10 12:23:05,715 epoch 6 - iter 520/2606 - loss 0.02994548 - time (sec): 273.13 - samples/sec: 256.33 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-10 12:25:25,839 epoch 6 - iter 780/2606 - loss 0.03207625 - time (sec): 413.25 - samples/sec: 261.76 - lr: 0.000078 - momentum: 0.000000
174
+ 2023-10-10 12:27:48,917 epoch 6 - iter 1040/2606 - loss 0.03228089 - time (sec): 556.33 - samples/sec: 263.64 - lr: 0.000077 - momentum: 0.000000
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+ 2023-10-10 12:30:11,973 epoch 6 - iter 1300/2606 - loss 0.03163962 - time (sec): 699.39 - samples/sec: 265.05 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-10 12:32:30,584 epoch 6 - iter 1560/2606 - loss 0.03281868 - time (sec): 838.00 - samples/sec: 263.89 - lr: 0.000073 - momentum: 0.000000
177
+ 2023-10-10 12:34:50,290 epoch 6 - iter 1820/2606 - loss 0.03179360 - time (sec): 977.71 - samples/sec: 263.53 - lr: 0.000072 - momentum: 0.000000
178
+ 2023-10-10 12:37:07,793 epoch 6 - iter 2080/2606 - loss 0.03208471 - time (sec): 1115.21 - samples/sec: 264.32 - lr: 0.000070 - momentum: 0.000000
179
+ 2023-10-10 12:39:24,116 epoch 6 - iter 2340/2606 - loss 0.03214342 - time (sec): 1251.53 - samples/sec: 263.15 - lr: 0.000068 - momentum: 0.000000
180
+ 2023-10-10 12:41:43,053 epoch 6 - iter 2600/2606 - loss 0.03446797 - time (sec): 1390.47 - samples/sec: 263.73 - lr: 0.000067 - momentum: 0.000000
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+ 2023-10-10 12:41:45,990 ----------------------------------------------------------------------------------------------------
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+ 2023-10-10 12:41:45,991 EPOCH 6 done: loss 0.0344 - lr: 0.000067
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+ 2023-10-10 12:42:28,894 DEV : loss 0.33373507857322693 - f1-score (micro avg) 0.378
184
+ 2023-10-10 12:42:28,972 saving best model
185
+ 2023-10-10 12:42:31,708 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-10 12:44:49,513 epoch 7 - iter 260/2606 - loss 0.02101641 - time (sec): 137.80 - samples/sec: 257.43 - lr: 0.000065 - momentum: 0.000000
187
+ 2023-10-10 12:47:08,074 epoch 7 - iter 520/2606 - loss 0.01944662 - time (sec): 276.36 - samples/sec: 258.39 - lr: 0.000063 - momentum: 0.000000
188
+ 2023-10-10 12:49:26,954 epoch 7 - iter 780/2606 - loss 0.02172094 - time (sec): 415.24 - samples/sec: 259.97 - lr: 0.000062 - momentum: 0.000000
189
+ 2023-10-10 12:51:45,464 epoch 7 - iter 1040/2606 - loss 0.02238459 - time (sec): 553.75 - samples/sec: 260.69 - lr: 0.000060 - momentum: 0.000000
190
+ 2023-10-10 12:54:07,890 epoch 7 - iter 1300/2606 - loss 0.02535700 - time (sec): 696.18 - samples/sec: 262.29 - lr: 0.000058 - momentum: 0.000000
191
+ 2023-10-10 12:56:26,291 epoch 7 - iter 1560/2606 - loss 0.02596897 - time (sec): 834.58 - samples/sec: 262.39 - lr: 0.000057 - momentum: 0.000000
192
+ 2023-10-10 12:58:45,212 epoch 7 - iter 1820/2606 - loss 0.02683384 - time (sec): 973.50 - samples/sec: 262.42 - lr: 0.000055 - momentum: 0.000000
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+ 2023-10-10 13:01:01,249 epoch 7 - iter 2080/2606 - loss 0.02612235 - time (sec): 1109.54 - samples/sec: 260.72 - lr: 0.000053 - momentum: 0.000000
194
+ 2023-10-10 13:03:21,969 epoch 7 - iter 2340/2606 - loss 0.02627648 - time (sec): 1250.26 - samples/sec: 261.53 - lr: 0.000052 - momentum: 0.000000
195
+ 2023-10-10 13:05:45,810 epoch 7 - iter 2600/2606 - loss 0.02563448 - time (sec): 1394.10 - samples/sec: 262.94 - lr: 0.000050 - momentum: 0.000000
196
+ 2023-10-10 13:05:48,990 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-10 13:05:48,990 EPOCH 7 done: loss 0.0256 - lr: 0.000050
198
+ 2023-10-10 13:06:31,953 DEV : loss 0.3978530466556549 - f1-score (micro avg) 0.3722
199
+ 2023-10-10 13:06:32,018 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-10 13:08:55,328 epoch 8 - iter 260/2606 - loss 0.01412281 - time (sec): 143.31 - samples/sec: 277.93 - lr: 0.000048 - momentum: 0.000000
201
+ 2023-10-10 13:11:15,605 epoch 8 - iter 520/2606 - loss 0.01664019 - time (sec): 283.58 - samples/sec: 270.88 - lr: 0.000047 - momentum: 0.000000
202
+ 2023-10-10 13:13:32,923 epoch 8 - iter 780/2606 - loss 0.01697467 - time (sec): 420.90 - samples/sec: 266.21 - lr: 0.000045 - momentum: 0.000000
203
+ 2023-10-10 13:15:54,238 epoch 8 - iter 1040/2606 - loss 0.01697407 - time (sec): 562.22 - samples/sec: 265.97 - lr: 0.000043 - momentum: 0.000000
204
+ 2023-10-10 13:18:13,799 epoch 8 - iter 1300/2606 - loss 0.01628044 - time (sec): 701.78 - samples/sec: 263.85 - lr: 0.000042 - momentum: 0.000000
205
+ 2023-10-10 13:20:35,746 epoch 8 - iter 1560/2606 - loss 0.01692923 - time (sec): 843.73 - samples/sec: 263.69 - lr: 0.000040 - momentum: 0.000000
206
+ 2023-10-10 13:22:55,645 epoch 8 - iter 1820/2606 - loss 0.01698632 - time (sec): 983.62 - samples/sec: 263.30 - lr: 0.000038 - momentum: 0.000000
207
+ 2023-10-10 13:25:13,795 epoch 8 - iter 2080/2606 - loss 0.01667188 - time (sec): 1121.77 - samples/sec: 261.61 - lr: 0.000037 - momentum: 0.000000
208
+ 2023-10-10 13:27:29,647 epoch 8 - iter 2340/2606 - loss 0.01736529 - time (sec): 1257.63 - samples/sec: 260.13 - lr: 0.000035 - momentum: 0.000000
209
+ 2023-10-10 13:29:50,730 epoch 8 - iter 2600/2606 - loss 0.01789555 - time (sec): 1398.71 - samples/sec: 262.23 - lr: 0.000033 - momentum: 0.000000
210
+ 2023-10-10 13:29:53,639 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-10 13:29:53,640 EPOCH 8 done: loss 0.0179 - lr: 0.000033
212
+ 2023-10-10 13:30:36,471 DEV : loss 0.40461236238479614 - f1-score (micro avg) 0.3935
213
+ 2023-10-10 13:30:36,533 saving best model
214
+ 2023-10-10 13:30:39,231 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-10 13:32:59,878 epoch 9 - iter 260/2606 - loss 0.01216931 - time (sec): 140.64 - samples/sec: 263.28 - lr: 0.000032 - momentum: 0.000000
216
+ 2023-10-10 13:35:25,145 epoch 9 - iter 520/2606 - loss 0.01085860 - time (sec): 285.91 - samples/sec: 270.57 - lr: 0.000030 - momentum: 0.000000
217
+ 2023-10-10 13:37:44,303 epoch 9 - iter 780/2606 - loss 0.01126911 - time (sec): 425.07 - samples/sec: 266.36 - lr: 0.000028 - momentum: 0.000000
218
+ 2023-10-10 13:39:59,037 epoch 9 - iter 1040/2606 - loss 0.01140504 - time (sec): 559.80 - samples/sec: 261.97 - lr: 0.000027 - momentum: 0.000000
219
+ 2023-10-10 13:42:20,737 epoch 9 - iter 1300/2606 - loss 0.01208397 - time (sec): 701.50 - samples/sec: 264.42 - lr: 0.000025 - momentum: 0.000000
220
+ 2023-10-10 13:44:42,081 epoch 9 - iter 1560/2606 - loss 0.01245932 - time (sec): 842.85 - samples/sec: 261.13 - lr: 0.000023 - momentum: 0.000000
221
+ 2023-10-10 13:46:58,383 epoch 9 - iter 1820/2606 - loss 0.01254583 - time (sec): 979.15 - samples/sec: 260.67 - lr: 0.000022 - momentum: 0.000000
222
+ 2023-10-10 13:49:17,990 epoch 9 - iter 2080/2606 - loss 0.01291335 - time (sec): 1118.75 - samples/sec: 260.52 - lr: 0.000020 - momentum: 0.000000
223
+ 2023-10-10 13:51:38,722 epoch 9 - iter 2340/2606 - loss 0.01272261 - time (sec): 1259.49 - samples/sec: 260.05 - lr: 0.000018 - momentum: 0.000000
224
+ 2023-10-10 13:53:58,619 epoch 9 - iter 2600/2606 - loss 0.01295044 - time (sec): 1399.38 - samples/sec: 262.18 - lr: 0.000017 - momentum: 0.000000
225
+ 2023-10-10 13:54:01,480 ----------------------------------------------------------------------------------------------------
226
+ 2023-10-10 13:54:01,480 EPOCH 9 done: loss 0.0129 - lr: 0.000017
227
+ 2023-10-10 13:54:42,752 DEV : loss 0.4415739178657532 - f1-score (micro avg) 0.3754
228
+ 2023-10-10 13:54:42,814 ----------------------------------------------------------------------------------------------------
229
+ 2023-10-10 13:57:04,272 epoch 10 - iter 260/2606 - loss 0.00790204 - time (sec): 141.46 - samples/sec: 270.22 - lr: 0.000015 - momentum: 0.000000
230
+ 2023-10-10 13:59:26,749 epoch 10 - iter 520/2606 - loss 0.00758597 - time (sec): 283.93 - samples/sec: 270.20 - lr: 0.000013 - momentum: 0.000000
231
+ 2023-10-10 14:01:45,507 epoch 10 - iter 780/2606 - loss 0.00856811 - time (sec): 422.69 - samples/sec: 266.23 - lr: 0.000012 - momentum: 0.000000
232
+ 2023-10-10 14:04:03,408 epoch 10 - iter 1040/2606 - loss 0.00841758 - time (sec): 560.59 - samples/sec: 259.01 - lr: 0.000010 - momentum: 0.000000
233
+ 2023-10-10 14:06:23,118 epoch 10 - iter 1300/2606 - loss 0.00819062 - time (sec): 700.30 - samples/sec: 260.67 - lr: 0.000008 - momentum: 0.000000
234
+ 2023-10-10 14:08:41,954 epoch 10 - iter 1560/2606 - loss 0.00817874 - time (sec): 839.14 - samples/sec: 260.44 - lr: 0.000007 - momentum: 0.000000
235
+ 2023-10-10 14:11:00,012 epoch 10 - iter 1820/2606 - loss 0.00826103 - time (sec): 977.20 - samples/sec: 261.61 - lr: 0.000005 - momentum: 0.000000
236
+ 2023-10-10 14:13:22,211 epoch 10 - iter 2080/2606 - loss 0.00858032 - time (sec): 1119.40 - samples/sec: 263.35 - lr: 0.000003 - momentum: 0.000000
237
+ 2023-10-10 14:15:40,101 epoch 10 - iter 2340/2606 - loss 0.00841120 - time (sec): 1257.28 - samples/sec: 262.99 - lr: 0.000002 - momentum: 0.000000
238
+ 2023-10-10 14:17:58,169 epoch 10 - iter 2600/2606 - loss 0.00852789 - time (sec): 1395.35 - samples/sec: 262.65 - lr: 0.000000 - momentum: 0.000000
239
+ 2023-10-10 14:18:01,398 ----------------------------------------------------------------------------------------------------
240
+ 2023-10-10 14:18:01,398 EPOCH 10 done: loss 0.0085 - lr: 0.000000
241
+ 2023-10-10 14:18:43,420 DEV : loss 0.46130311489105225 - f1-score (micro avg) 0.3804
242
+ 2023-10-10 14:18:44,392 ----------------------------------------------------------------------------------------------------
243
+ 2023-10-10 14:18:44,394 Loading model from best epoch ...
244
+ 2023-10-10 14:18:48,948 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
245
+ 2023-10-10 14:20:38,998
246
+ Results:
247
+ - F-score (micro) 0.4462
248
+ - F-score (macro) 0.3145
249
+ - Accuracy 0.2916
250
+
251
+ By class:
252
+ precision recall f1-score support
253
+
254
+ LOC 0.4555 0.5231 0.4870 1214
255
+ PER 0.4229 0.4517 0.4369 808
256
+ ORG 0.3343 0.3343 0.3343 353
257
+ HumanProd 0.0000 0.0000 0.0000 15
258
+
259
+ micro avg 0.4266 0.4678 0.4462 2390
260
+ macro avg 0.3032 0.3273 0.3145 2390
261
+ weighted avg 0.4237 0.4678 0.4444 2390
262
+
263
+ 2023-10-10 14:20:38,999 ----------------------------------------------------------------------------------------------------