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hmbench-newseye-de-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1

Token Classification
Flair
PyTorch
TensorBoard
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.1696886396.6d4c7681f95b.1253.1 +3 -0
  6. test.tsv +0 -0
  7. training.log +262 -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 21:44:07 0.0002 0.9198 0.1330 0.3557 0.2614 0.3013 0.1774
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+ 2 22:08:03 0.0001 0.1613 0.1153 0.2934 0.5568 0.3843 0.2382
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+ 3 22:32:18 0.0001 0.0940 0.2147 0.2381 0.6364 0.3466 0.2107
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+ 4 22:56:28 0.0001 0.0665 0.2531 0.2467 0.6042 0.3504 0.2134
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+ 5 23:20:48 0.0001 0.0505 0.2984 0.2735 0.6402 0.3832 0.2387
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+ 6 23:45:11 0.0001 0.0368 0.3561 0.2710 0.6042 0.3742 0.2313
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+ 7 00:09:54 0.0001 0.0266 0.3664 0.2890 0.6136 0.3930 0.2462
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+ 8 00:34:22 0.0000 0.0194 0.4113 0.3098 0.6080 0.4105 0.2599
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+ 9 00:58:48 0.0000 0.0140 0.4543 0.2917 0.6155 0.3959 0.2485
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+ 10 01:23:06 0.0000 0.0101 0.4743 0.2868 0.6231 0.3928 0.2461
runs/events.out.tfevents.1696886396.6d4c7681f95b.1253.1 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-09 21:19:56,874 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:19:56,877 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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+ 2023-10-09 21:19:56,877 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:19:56,877 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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+ 2023-10-09 21:19:56,877 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:19:56,877 Train: 20847 sentences
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+ 2023-10-09 21:19:56,877 (train_with_dev=False, train_with_test=False)
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+ 2023-10-09 21:19:56,878 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:19:56,878 Training Params:
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+ 2023-10-09 21:19:56,878 - learning_rate: "0.00016"
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+ 2023-10-09 21:19:56,878 - mini_batch_size: "8"
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+ 2023-10-09 21:19:56,878 - max_epochs: "10"
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+ 2023-10-09 21:19:56,878 - shuffle: "True"
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+ 2023-10-09 21:19:56,878 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:19:56,878 Plugins:
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+ 2023-10-09 21:19:56,878 - TensorboardLogger
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+ 2023-10-09 21:19:56,878 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-09 21:19:56,878 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:19:56,878 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-09 21:19:56,879 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-09 21:19:56,879 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:19:56,879 Computation:
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+ 2023-10-09 21:19:56,879 - compute on device: cuda:0
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+ 2023-10-09 21:19:56,879 - embedding storage: none
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+ 2023-10-09 21:19:56,879 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:19:56,879 Model training base path: "hmbench-newseye/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-09 21:19:56,879 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:19:56,879 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:19:56,879 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-09 21:22:21,512 epoch 1 - iter 260/2606 - loss 2.80647480 - time (sec): 144.63 - samples/sec: 272.42 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-09 21:24:40,110 epoch 1 - iter 520/2606 - loss 2.57714924 - time (sec): 283.23 - samples/sec: 261.01 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-09 21:27:08,137 epoch 1 - iter 780/2606 - loss 2.16321673 - time (sec): 431.26 - samples/sec: 255.15 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-09 21:29:25,932 epoch 1 - iter 1040/2606 - loss 1.80319624 - time (sec): 569.05 - samples/sec: 253.10 - lr: 0.000064 - momentum: 0.000000
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+ 2023-10-09 21:31:44,572 epoch 1 - iter 1300/2606 - loss 1.51984788 - time (sec): 707.69 - samples/sec: 256.83 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-09 21:34:06,385 epoch 1 - iter 1560/2606 - loss 1.33539090 - time (sec): 849.50 - samples/sec: 258.86 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-09 21:36:25,234 epoch 1 - iter 1820/2606 - loss 1.20340260 - time (sec): 988.35 - samples/sec: 257.95 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-09 21:38:45,336 epoch 1 - iter 2080/2606 - loss 1.08973347 - time (sec): 1128.45 - samples/sec: 258.54 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-09 21:41:05,295 epoch 1 - iter 2340/2606 - loss 0.99333935 - time (sec): 1268.41 - samples/sec: 260.51 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-09 21:43:27,691 epoch 1 - iter 2600/2606 - loss 0.92110059 - time (sec): 1410.81 - samples/sec: 259.88 - lr: 0.000160 - momentum: 0.000000
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+ 2023-10-09 21:43:30,711 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:43:30,711 EPOCH 1 done: loss 0.9198 - lr: 0.000160
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+ 2023-10-09 21:44:07,491 DEV : loss 0.1330375373363495 - f1-score (micro avg) 0.3013
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+ 2023-10-09 21:44:07,556 saving best model
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+ 2023-10-09 21:44:08,556 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 21:46:29,548 epoch 2 - iter 260/2606 - loss 0.21276153 - time (sec): 140.99 - samples/sec: 282.02 - lr: 0.000158 - momentum: 0.000000
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+ 2023-10-09 21:48:49,379 epoch 2 - iter 520/2606 - loss 0.21284661 - time (sec): 280.82 - samples/sec: 279.79 - lr: 0.000156 - momentum: 0.000000
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+ 2023-10-09 21:51:07,729 epoch 2 - iter 780/2606 - loss 0.19909410 - time (sec): 419.17 - samples/sec: 276.40 - lr: 0.000155 - momentum: 0.000000
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+ 2023-10-09 21:53:23,117 epoch 2 - iter 1040/2606 - loss 0.19175427 - time (sec): 554.56 - samples/sec: 271.02 - lr: 0.000153 - momentum: 0.000000
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+ 2023-10-09 21:55:40,552 epoch 2 - iter 1300/2606 - loss 0.18755619 - time (sec): 691.99 - samples/sec: 269.33 - lr: 0.000151 - momentum: 0.000000
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+ 2023-10-09 21:57:56,677 epoch 2 - iter 1560/2606 - loss 0.18201966 - time (sec): 828.12 - samples/sec: 268.26 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-09 22:00:21,283 epoch 2 - iter 1820/2606 - loss 0.17717852 - time (sec): 972.72 - samples/sec: 265.45 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-09 22:02:40,967 epoch 2 - iter 2080/2606 - loss 0.17086806 - time (sec): 1112.41 - samples/sec: 265.29 - lr: 0.000146 - momentum: 0.000000
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+ 2023-10-09 22:05:00,885 epoch 2 - iter 2340/2606 - loss 0.16582540 - time (sec): 1252.33 - samples/sec: 265.78 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-09 22:07:18,380 epoch 2 - iter 2600/2606 - loss 0.16149225 - time (sec): 1389.82 - samples/sec: 263.81 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-09 22:07:21,444 ----------------------------------------------------------------------------------------------------
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+ 2023-10-09 22:07:21,445 EPOCH 2 done: loss 0.1613 - lr: 0.000142
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+ 2023-10-09 22:08:03,474 DEV : loss 0.11526025831699371 - f1-score (micro avg) 0.3843
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+ 2023-10-09 22:08:03,533 saving best model
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+ 2023-10-09 22:08:06,253 ----------------------------------------------------------------------------------------------------
128
+ 2023-10-09 22:10:30,351 epoch 3 - iter 260/2606 - loss 0.09533533 - time (sec): 144.09 - samples/sec: 252.71 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-09 22:12:48,656 epoch 3 - iter 520/2606 - loss 0.09967778 - time (sec): 282.40 - samples/sec: 252.71 - lr: 0.000139 - momentum: 0.000000
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+ 2023-10-09 22:15:13,286 epoch 3 - iter 780/2606 - loss 0.09556336 - time (sec): 427.02 - samples/sec: 259.00 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-09 22:17:29,465 epoch 3 - iter 1040/2606 - loss 0.09703779 - time (sec): 563.20 - samples/sec: 255.55 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-09 22:19:43,209 epoch 3 - iter 1300/2606 - loss 0.09641637 - time (sec): 696.95 - samples/sec: 254.29 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-09 22:22:06,685 epoch 3 - iter 1560/2606 - loss 0.09648911 - time (sec): 840.42 - samples/sec: 258.14 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-09 22:24:35,613 epoch 3 - iter 1820/2606 - loss 0.09589935 - time (sec): 989.35 - samples/sec: 258.39 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-09 22:26:56,117 epoch 3 - iter 2080/2606 - loss 0.09505206 - time (sec): 1129.86 - samples/sec: 259.55 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-09 22:29:16,749 epoch 3 - iter 2340/2606 - loss 0.09458005 - time (sec): 1270.49 - samples/sec: 260.79 - lr: 0.000126 - momentum: 0.000000
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+ 2023-10-09 22:31:34,336 epoch 3 - iter 2600/2606 - loss 0.09389335 - time (sec): 1408.08 - samples/sec: 260.50 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-09 22:31:37,238 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-09 22:31:37,238 EPOCH 3 done: loss 0.0940 - lr: 0.000125
140
+ 2023-10-09 22:32:18,211 DEV : loss 0.21473725140094757 - f1-score (micro avg) 0.3466
141
+ 2023-10-09 22:32:18,273 ----------------------------------------------------------------------------------------------------
142
+ 2023-10-09 22:34:36,848 epoch 4 - iter 260/2606 - loss 0.06749285 - time (sec): 138.57 - samples/sec: 263.49 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-09 22:36:57,878 epoch 4 - iter 520/2606 - loss 0.06262288 - time (sec): 279.60 - samples/sec: 257.39 - lr: 0.000121 - momentum: 0.000000
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+ 2023-10-09 22:39:15,206 epoch 4 - iter 780/2606 - loss 0.06093080 - time (sec): 416.93 - samples/sec: 258.17 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-09 22:41:33,785 epoch 4 - iter 1040/2606 - loss 0.06300101 - time (sec): 555.51 - samples/sec: 258.27 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-09 22:43:52,308 epoch 4 - iter 1300/2606 - loss 0.06730143 - time (sec): 694.03 - samples/sec: 260.72 - lr: 0.000116 - momentum: 0.000000
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+ 2023-10-09 22:46:21,314 epoch 4 - iter 1560/2606 - loss 0.06445156 - time (sec): 843.04 - samples/sec: 262.43 - lr: 0.000114 - momentum: 0.000000
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+ 2023-10-09 22:48:37,254 epoch 4 - iter 1820/2606 - loss 0.06404726 - time (sec): 978.98 - samples/sec: 261.77 - lr: 0.000112 - momentum: 0.000000
149
+ 2023-10-09 22:50:58,245 epoch 4 - iter 2080/2606 - loss 0.06441531 - time (sec): 1119.97 - samples/sec: 260.79 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-09 22:53:19,391 epoch 4 - iter 2340/2606 - loss 0.06601434 - time (sec): 1261.12 - samples/sec: 261.63 - lr: 0.000109 - momentum: 0.000000
151
+ 2023-10-09 22:55:43,080 epoch 4 - iter 2600/2606 - loss 0.06647076 - time (sec): 1404.80 - samples/sec: 261.04 - lr: 0.000107 - momentum: 0.000000
152
+ 2023-10-09 22:55:46,216 ----------------------------------------------------------------------------------------------------
153
+ 2023-10-09 22:55:46,217 EPOCH 4 done: loss 0.0665 - lr: 0.000107
154
+ 2023-10-09 22:56:28,195 DEV : loss 0.25312381982803345 - f1-score (micro avg) 0.3504
155
+ 2023-10-09 22:56:28,256 ----------------------------------------------------------------------------------------------------
156
+ 2023-10-09 22:58:52,649 epoch 5 - iter 260/2606 - loss 0.04687563 - time (sec): 144.39 - samples/sec: 238.37 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-09 23:01:12,773 epoch 5 - iter 520/2606 - loss 0.05344267 - time (sec): 284.51 - samples/sec: 250.63 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-09 23:03:33,085 epoch 5 - iter 780/2606 - loss 0.05092848 - time (sec): 424.83 - samples/sec: 258.55 - lr: 0.000101 - momentum: 0.000000
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+ 2023-10-09 23:05:57,687 epoch 5 - iter 1040/2606 - loss 0.04913735 - time (sec): 569.43 - samples/sec: 260.71 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-09 23:08:12,298 epoch 5 - iter 1300/2606 - loss 0.04916492 - time (sec): 704.04 - samples/sec: 260.14 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-09 23:10:31,281 epoch 5 - iter 1560/2606 - loss 0.05150383 - time (sec): 843.02 - samples/sec: 261.58 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-09 23:12:55,333 epoch 5 - iter 1820/2606 - loss 0.05223482 - time (sec): 987.07 - samples/sec: 262.40 - lr: 0.000094 - momentum: 0.000000
163
+ 2023-10-09 23:15:20,555 epoch 5 - iter 2080/2606 - loss 0.05153119 - time (sec): 1132.30 - samples/sec: 260.99 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-09 23:17:39,058 epoch 5 - iter 2340/2606 - loss 0.05043738 - time (sec): 1270.80 - samples/sec: 259.67 - lr: 0.000091 - momentum: 0.000000
165
+ 2023-10-09 23:20:03,897 epoch 5 - iter 2600/2606 - loss 0.05060692 - time (sec): 1415.64 - samples/sec: 258.66 - lr: 0.000089 - momentum: 0.000000
166
+ 2023-10-09 23:20:07,768 ----------------------------------------------------------------------------------------------------
167
+ 2023-10-09 23:20:07,768 EPOCH 5 done: loss 0.0505 - lr: 0.000089
168
+ 2023-10-09 23:20:48,701 DEV : loss 0.2983781099319458 - f1-score (micro avg) 0.3832
169
+ 2023-10-09 23:20:48,772 ----------------------------------------------------------------------------------------------------
170
+ 2023-10-09 23:23:07,560 epoch 6 - iter 260/2606 - loss 0.02937703 - time (sec): 138.79 - samples/sec: 264.82 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-09 23:25:30,695 epoch 6 - iter 520/2606 - loss 0.03318626 - time (sec): 281.92 - samples/sec: 251.56 - lr: 0.000085 - momentum: 0.000000
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+ 2023-10-09 23:27:52,282 epoch 6 - iter 780/2606 - loss 0.03155811 - time (sec): 423.51 - samples/sec: 259.36 - lr: 0.000084 - momentum: 0.000000
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+ 2023-10-09 23:30:13,592 epoch 6 - iter 1040/2606 - loss 0.03188441 - time (sec): 564.82 - samples/sec: 257.25 - lr: 0.000082 - momentum: 0.000000
174
+ 2023-10-09 23:32:34,084 epoch 6 - iter 1300/2606 - loss 0.03353433 - time (sec): 705.31 - samples/sec: 257.77 - lr: 0.000080 - momentum: 0.000000
175
+ 2023-10-09 23:34:58,642 epoch 6 - iter 1560/2606 - loss 0.03464167 - time (sec): 849.87 - samples/sec: 254.06 - lr: 0.000078 - momentum: 0.000000
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+ 2023-10-09 23:37:22,052 epoch 6 - iter 1820/2606 - loss 0.03470450 - time (sec): 993.28 - samples/sec: 254.45 - lr: 0.000077 - momentum: 0.000000
177
+ 2023-10-09 23:39:41,751 epoch 6 - iter 2080/2606 - loss 0.03483777 - time (sec): 1132.98 - samples/sec: 256.43 - lr: 0.000075 - momentum: 0.000000
178
+ 2023-10-09 23:42:05,986 epoch 6 - iter 2340/2606 - loss 0.03565070 - time (sec): 1277.21 - samples/sec: 257.37 - lr: 0.000073 - momentum: 0.000000
179
+ 2023-10-09 23:44:26,648 epoch 6 - iter 2600/2606 - loss 0.03682143 - time (sec): 1417.87 - samples/sec: 258.81 - lr: 0.000071 - momentum: 0.000000
180
+ 2023-10-09 23:44:29,467 ----------------------------------------------------------------------------------------------------
181
+ 2023-10-09 23:44:29,468 EPOCH 6 done: loss 0.0368 - lr: 0.000071
182
+ 2023-10-09 23:45:10,942 DEV : loss 0.35610052943229675 - f1-score (micro avg) 0.3742
183
+ 2023-10-09 23:45:11,003 ----------------------------------------------------------------------------------------------------
184
+ 2023-10-09 23:47:39,689 epoch 7 - iter 260/2606 - loss 0.02267644 - time (sec): 148.68 - samples/sec: 258.00 - lr: 0.000069 - momentum: 0.000000
185
+ 2023-10-09 23:50:07,336 epoch 7 - iter 520/2606 - loss 0.02417424 - time (sec): 296.33 - samples/sec: 258.54 - lr: 0.000068 - momentum: 0.000000
186
+ 2023-10-09 23:52:24,084 epoch 7 - iter 780/2606 - loss 0.02606608 - time (sec): 433.08 - samples/sec: 257.55 - lr: 0.000066 - momentum: 0.000000
187
+ 2023-10-09 23:54:53,690 epoch 7 - iter 1040/2606 - loss 0.02526796 - time (sec): 582.68 - samples/sec: 255.54 - lr: 0.000064 - momentum: 0.000000
188
+ 2023-10-09 23:57:14,548 epoch 7 - iter 1300/2606 - loss 0.02470524 - time (sec): 723.54 - samples/sec: 258.11 - lr: 0.000062 - momentum: 0.000000
189
+ 2023-10-09 23:59:40,240 epoch 7 - iter 1560/2606 - loss 0.02608201 - time (sec): 869.23 - samples/sec: 257.31 - lr: 0.000061 - momentum: 0.000000
190
+ 2023-10-10 00:02:10,450 epoch 7 - iter 1820/2606 - loss 0.02541811 - time (sec): 1019.44 - samples/sec: 254.49 - lr: 0.000059 - momentum: 0.000000
191
+ 2023-10-10 00:04:28,537 epoch 7 - iter 2080/2606 - loss 0.02616950 - time (sec): 1157.53 - samples/sec: 255.27 - lr: 0.000057 - momentum: 0.000000
192
+ 2023-10-10 00:06:52,824 epoch 7 - iter 2340/2606 - loss 0.02604706 - time (sec): 1301.82 - samples/sec: 255.00 - lr: 0.000055 - momentum: 0.000000
193
+ 2023-10-10 00:09:10,093 epoch 7 - iter 2600/2606 - loss 0.02658662 - time (sec): 1439.09 - samples/sec: 254.78 - lr: 0.000053 - momentum: 0.000000
194
+ 2023-10-10 00:09:13,227 ----------------------------------------------------------------------------------------------------
195
+ 2023-10-10 00:09:13,228 EPOCH 7 done: loss 0.0266 - lr: 0.000053
196
+ 2023-10-10 00:09:54,499 DEV : loss 0.36638563871383667 - f1-score (micro avg) 0.393
197
+ 2023-10-10 00:09:54,560 saving best model
198
+ 2023-10-10 00:09:57,284 ----------------------------------------------------------------------------------------------------
199
+ 2023-10-10 00:12:19,774 epoch 8 - iter 260/2606 - loss 0.01822913 - time (sec): 142.49 - samples/sec: 253.32 - lr: 0.000052 - momentum: 0.000000
200
+ 2023-10-10 00:14:43,139 epoch 8 - iter 520/2606 - loss 0.01834896 - time (sec): 285.85 - samples/sec: 254.92 - lr: 0.000050 - momentum: 0.000000
201
+ 2023-10-10 00:17:03,348 epoch 8 - iter 780/2606 - loss 0.01923891 - time (sec): 426.06 - samples/sec: 259.99 - lr: 0.000048 - momentum: 0.000000
202
+ 2023-10-10 00:19:26,521 epoch 8 - iter 1040/2606 - loss 0.01918401 - time (sec): 569.23 - samples/sec: 257.56 - lr: 0.000046 - momentum: 0.000000
203
+ 2023-10-10 00:21:46,854 epoch 8 - iter 1300/2606 - loss 0.02003374 - time (sec): 709.57 - samples/sec: 257.12 - lr: 0.000045 - momentum: 0.000000
204
+ 2023-10-10 00:24:08,172 epoch 8 - iter 1560/2606 - loss 0.02027599 - time (sec): 850.88 - samples/sec: 258.03 - lr: 0.000043 - momentum: 0.000000
205
+ 2023-10-10 00:26:31,945 epoch 8 - iter 1820/2606 - loss 0.02016303 - time (sec): 994.66 - samples/sec: 256.05 - lr: 0.000041 - momentum: 0.000000
206
+ 2023-10-10 00:28:52,525 epoch 8 - iter 2080/2606 - loss 0.01973949 - time (sec): 1135.24 - samples/sec: 258.50 - lr: 0.000039 - momentum: 0.000000
207
+ 2023-10-10 00:31:16,007 epoch 8 - iter 2340/2606 - loss 0.01926607 - time (sec): 1278.72 - samples/sec: 258.44 - lr: 0.000037 - momentum: 0.000000
208
+ 2023-10-10 00:33:36,034 epoch 8 - iter 2600/2606 - loss 0.01941452 - time (sec): 1418.75 - samples/sec: 258.42 - lr: 0.000036 - momentum: 0.000000
209
+ 2023-10-10 00:33:39,247 ----------------------------------------------------------------------------------------------------
210
+ 2023-10-10 00:33:39,248 EPOCH 8 done: loss 0.0194 - lr: 0.000036
211
+ 2023-10-10 00:34:22,217 DEV : loss 0.4113345742225647 - f1-score (micro avg) 0.4105
212
+ 2023-10-10 00:34:22,275 saving best model
213
+ 2023-10-10 00:34:25,003 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-10 00:36:50,095 epoch 9 - iter 260/2606 - loss 0.01806776 - time (sec): 145.09 - samples/sec: 260.15 - lr: 0.000034 - momentum: 0.000000
215
+ 2023-10-10 00:39:15,157 epoch 9 - iter 520/2606 - loss 0.01665407 - time (sec): 290.15 - samples/sec: 260.23 - lr: 0.000032 - momentum: 0.000000
216
+ 2023-10-10 00:41:35,122 epoch 9 - iter 780/2606 - loss 0.01567942 - time (sec): 430.11 - samples/sec: 255.60 - lr: 0.000030 - momentum: 0.000000
217
+ 2023-10-10 00:44:04,493 epoch 9 - iter 1040/2606 - loss 0.01509980 - time (sec): 579.49 - samples/sec: 253.38 - lr: 0.000029 - momentum: 0.000000
218
+ 2023-10-10 00:46:23,680 epoch 9 - iter 1300/2606 - loss 0.01591559 - time (sec): 718.67 - samples/sec: 255.04 - lr: 0.000027 - momentum: 0.000000
219
+ 2023-10-10 00:48:42,310 epoch 9 - iter 1560/2606 - loss 0.01573140 - time (sec): 857.30 - samples/sec: 256.72 - lr: 0.000025 - momentum: 0.000000
220
+ 2023-10-10 00:51:00,451 epoch 9 - iter 1820/2606 - loss 0.01521895 - time (sec): 995.44 - samples/sec: 256.73 - lr: 0.000023 - momentum: 0.000000
221
+ 2023-10-10 00:53:21,383 epoch 9 - iter 2080/2606 - loss 0.01475674 - time (sec): 1136.38 - samples/sec: 256.09 - lr: 0.000021 - momentum: 0.000000
222
+ 2023-10-10 00:55:45,112 epoch 9 - iter 2340/2606 - loss 0.01445053 - time (sec): 1280.10 - samples/sec: 256.35 - lr: 0.000020 - momentum: 0.000000
223
+ 2023-10-10 00:58:03,910 epoch 9 - iter 2600/2606 - loss 0.01402838 - time (sec): 1418.90 - samples/sec: 258.18 - lr: 0.000018 - momentum: 0.000000
224
+ 2023-10-10 00:58:07,250 ----------------------------------------------------------------------------------------------------
225
+ 2023-10-10 00:58:07,251 EPOCH 9 done: loss 0.0140 - lr: 0.000018
226
+ 2023-10-10 00:58:48,323 DEV : loss 0.45426633954048157 - f1-score (micro avg) 0.3959
227
+ 2023-10-10 00:58:48,375 ----------------------------------------------------------------------------------------------------
228
+ 2023-10-10 01:01:08,212 epoch 10 - iter 260/2606 - loss 0.01259898 - time (sec): 139.83 - samples/sec: 262.55 - lr: 0.000016 - momentum: 0.000000
229
+ 2023-10-10 01:03:30,710 epoch 10 - iter 520/2606 - loss 0.01112512 - time (sec): 282.33 - samples/sec: 254.43 - lr: 0.000014 - momentum: 0.000000
230
+ 2023-10-10 01:05:50,144 epoch 10 - iter 780/2606 - loss 0.01157811 - time (sec): 421.77 - samples/sec: 248.87 - lr: 0.000013 - momentum: 0.000000
231
+ 2023-10-10 01:08:09,984 epoch 10 - iter 1040/2606 - loss 0.01040706 - time (sec): 561.61 - samples/sec: 256.04 - lr: 0.000011 - momentum: 0.000000
232
+ 2023-10-10 01:10:35,004 epoch 10 - iter 1300/2606 - loss 0.01105992 - time (sec): 706.63 - samples/sec: 261.18 - lr: 0.000009 - momentum: 0.000000
233
+ 2023-10-10 01:12:55,356 epoch 10 - iter 1560/2606 - loss 0.01090713 - time (sec): 846.98 - samples/sec: 259.41 - lr: 0.000007 - momentum: 0.000000
234
+ 2023-10-10 01:15:24,167 epoch 10 - iter 1820/2606 - loss 0.01106558 - time (sec): 995.79 - samples/sec: 258.10 - lr: 0.000005 - momentum: 0.000000
235
+ 2023-10-10 01:17:45,318 epoch 10 - iter 2080/2606 - loss 0.01060413 - time (sec): 1136.94 - samples/sec: 259.10 - lr: 0.000004 - momentum: 0.000000
236
+ 2023-10-10 01:20:04,288 epoch 10 - iter 2340/2606 - loss 0.01019989 - time (sec): 1275.91 - samples/sec: 260.29 - lr: 0.000002 - momentum: 0.000000
237
+ 2023-10-10 01:22:23,524 epoch 10 - iter 2600/2606 - loss 0.01013457 - time (sec): 1415.15 - samples/sec: 258.97 - lr: 0.000000 - momentum: 0.000000
238
+ 2023-10-10 01:22:26,690 ----------------------------------------------------------------------------------------------------
239
+ 2023-10-10 01:22:26,690 EPOCH 10 done: loss 0.0101 - lr: 0.000000
240
+ 2023-10-10 01:23:06,687 DEV : loss 0.4742611050605774 - f1-score (micro avg) 0.3928
241
+ 2023-10-10 01:23:07,733 ----------------------------------------------------------------------------------------------------
242
+ 2023-10-10 01:23:07,735 Loading model from best epoch ...
243
+ 2023-10-10 01:23:11,754 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
244
+ 2023-10-10 01:24:55,577
245
+ Results:
246
+ - F-score (micro) 0.4682
247
+ - F-score (macro) 0.3262
248
+ - Accuracy 0.3104
249
+
250
+ By class:
251
+ precision recall f1-score support
252
+
253
+ LOC 0.5077 0.5700 0.5371 1214
254
+ PER 0.3953 0.4554 0.4232 808
255
+ ORG 0.3407 0.3484 0.3445 353
256
+ HumanProd 0.0000 0.0000 0.0000 15
257
+
258
+ micro avg 0.4442 0.4950 0.4682 2390
259
+ macro avg 0.3109 0.3435 0.3262 2390
260
+ weighted avg 0.4418 0.4950 0.4668 2390
261
+
262
+ 2023-10-10 01:24:55,577 ----------------------------------------------------------------------------------------------------