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hmbench-topres19th-en-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1

Token Classification
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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.1697189551.6d4c7681f95b.3224.2 +3 -0
  6. test.tsv +0 -0
  7. training.log +260 -0
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dev.tsv ADDED
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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 09:39:59 0.0001 0.9029 0.0855 0.4738 0.6878 0.5611 0.4045
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+ 2 09:47:30 0.0001 0.0930 0.0580 0.7656 0.8270 0.7951 0.6759
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+ 3 09:55:03 0.0001 0.0548 0.0595 0.7463 0.8439 0.7921 0.6780
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+ 4 10:02:57 0.0001 0.0334 0.0731 0.7444 0.8481 0.7929 0.6745
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+ 5 10:10:27 0.0001 0.0225 0.0850 0.7491 0.8565 0.7992 0.6812
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+ 6 10:18:12 0.0001 0.0141 0.0962 0.7444 0.8354 0.7873 0.6644
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+ 7 10:26:02 0.0001 0.0100 0.1002 0.7608 0.8186 0.7886 0.6690
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+ 8 10:33:51 0.0000 0.0055 0.1157 0.7293 0.8186 0.7714 0.6424
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+ 9 10:41:31 0.0000 0.0047 0.1180 0.7568 0.8270 0.7903 0.6735
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+ 10 10:49:24 0.0000 0.0028 0.1190 0.7510 0.8270 0.7871 0.6689
runs/events.out.tfevents.1697189551.6d4c7681f95b.3224.2 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-13 09:32:31,304 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:32:31,306 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=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-13 09:32:31,306 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:32:31,307 MultiCorpus: 6183 train + 680 dev + 2113 test sentences
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+ - NER_HIPE_2022 Corpus: 6183 train + 680 dev + 2113 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/topres19th/en/with_doc_seperator
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+ 2023-10-13 09:32:31,307 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:32:31,307 Train: 6183 sentences
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+ 2023-10-13 09:32:31,307 (train_with_dev=False, train_with_test=False)
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+ 2023-10-13 09:32:31,307 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:32:31,307 Training Params:
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+ 2023-10-13 09:32:31,307 - learning_rate: "0.00015"
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+ 2023-10-13 09:32:31,307 - mini_batch_size: "4"
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+ 2023-10-13 09:32:31,307 - max_epochs: "10"
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+ 2023-10-13 09:32:31,307 - shuffle: "True"
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+ 2023-10-13 09:32:31,308 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:32:31,308 Plugins:
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+ 2023-10-13 09:32:31,308 - TensorboardLogger
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+ 2023-10-13 09:32:31,308 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-13 09:32:31,308 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:32:31,308 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-13 09:32:31,308 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-13 09:32:31,308 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:32:31,308 Computation:
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+ 2023-10-13 09:32:31,308 - compute on device: cuda:0
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+ 2023-10-13 09:32:31,308 - embedding storage: none
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+ 2023-10-13 09:32:31,308 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:32:31,308 Model training base path: "hmbench-topres19th/en-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-13 09:32:31,309 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:32:31,309 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:32:31,309 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-13 09:33:15,220 epoch 1 - iter 154/1546 - loss 2.58089173 - time (sec): 43.91 - samples/sec: 283.06 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-13 09:33:57,641 epoch 1 - iter 308/1546 - loss 2.49475826 - time (sec): 86.33 - samples/sec: 283.99 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-13 09:34:40,223 epoch 1 - iter 462/1546 - loss 2.24605833 - time (sec): 128.91 - samples/sec: 280.80 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-13 09:35:23,021 epoch 1 - iter 616/1546 - loss 1.91937604 - time (sec): 171.71 - samples/sec: 288.08 - lr: 0.000060 - momentum: 0.000000
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+ 2023-10-13 09:36:05,203 epoch 1 - iter 770/1546 - loss 1.63450044 - time (sec): 213.89 - samples/sec: 287.57 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-13 09:36:48,056 epoch 1 - iter 924/1546 - loss 1.40442989 - time (sec): 256.74 - samples/sec: 286.70 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-13 09:37:31,857 epoch 1 - iter 1078/1546 - loss 1.23099071 - time (sec): 300.55 - samples/sec: 287.02 - lr: 0.000104 - momentum: 0.000000
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+ 2023-10-13 09:38:14,433 epoch 1 - iter 1232/1546 - loss 1.10986676 - time (sec): 343.12 - samples/sec: 284.50 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-13 09:38:58,506 epoch 1 - iter 1386/1546 - loss 0.99545808 - time (sec): 387.20 - samples/sec: 287.13 - lr: 0.000134 - momentum: 0.000000
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+ 2023-10-13 09:39:41,758 epoch 1 - iter 1540/1546 - loss 0.90547295 - time (sec): 430.45 - samples/sec: 287.74 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-13 09:39:43,282 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:39:43,282 EPOCH 1 done: loss 0.9029 - lr: 0.000149
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+ 2023-10-13 09:39:59,716 DEV : loss 0.08553236722946167 - f1-score (micro avg) 0.5611
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+ 2023-10-13 09:39:59,746 saving best model
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+ 2023-10-13 09:40:00,659 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:40:42,617 epoch 2 - iter 154/1546 - loss 0.12200903 - time (sec): 41.96 - samples/sec: 278.77 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-13 09:41:25,024 epoch 2 - iter 308/1546 - loss 0.11893143 - time (sec): 84.36 - samples/sec: 279.44 - lr: 0.000147 - momentum: 0.000000
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+ 2023-10-13 09:42:09,579 epoch 2 - iter 462/1546 - loss 0.11299889 - time (sec): 128.92 - samples/sec: 283.78 - lr: 0.000145 - momentum: 0.000000
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+ 2023-10-13 09:42:52,516 epoch 2 - iter 616/1546 - loss 0.10688952 - time (sec): 171.85 - samples/sec: 287.48 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-13 09:43:35,911 epoch 2 - iter 770/1546 - loss 0.10165840 - time (sec): 215.25 - samples/sec: 288.56 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-13 09:44:18,065 epoch 2 - iter 924/1546 - loss 0.09938803 - time (sec): 257.40 - samples/sec: 285.76 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-13 09:45:01,291 epoch 2 - iter 1078/1546 - loss 0.09941699 - time (sec): 300.63 - samples/sec: 284.82 - lr: 0.000138 - momentum: 0.000000
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+ 2023-10-13 09:45:44,201 epoch 2 - iter 1232/1546 - loss 0.09981071 - time (sec): 343.54 - samples/sec: 284.64 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-13 09:46:28,346 epoch 2 - iter 1386/1546 - loss 0.09642655 - time (sec): 387.69 - samples/sec: 287.09 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-13 09:47:11,211 epoch 2 - iter 1540/1546 - loss 0.09319186 - time (sec): 430.55 - samples/sec: 287.59 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-13 09:47:12,875 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:47:12,876 EPOCH 2 done: loss 0.0930 - lr: 0.000133
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+ 2023-10-13 09:47:30,832 DEV : loss 0.05797132849693298 - f1-score (micro avg) 0.7951
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+ 2023-10-13 09:47:30,866 saving best model
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+ 2023-10-13 09:47:33,588 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 09:48:17,153 epoch 3 - iter 154/1546 - loss 0.07139060 - time (sec): 43.56 - samples/sec: 279.60 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-13 09:49:00,659 epoch 3 - iter 308/1546 - loss 0.06425129 - time (sec): 87.06 - samples/sec: 279.79 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-13 09:49:43,640 epoch 3 - iter 462/1546 - loss 0.05602698 - time (sec): 130.05 - samples/sec: 287.03 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-13 09:50:25,253 epoch 3 - iter 616/1546 - loss 0.05576436 - time (sec): 171.66 - samples/sec: 281.80 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-13 09:51:08,706 epoch 3 - iter 770/1546 - loss 0.05520624 - time (sec): 215.11 - samples/sec: 282.93 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-13 09:51:51,867 epoch 3 - iter 924/1546 - loss 0.05690583 - time (sec): 258.27 - samples/sec: 284.51 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-13 09:52:34,906 epoch 3 - iter 1078/1546 - loss 0.05578666 - time (sec): 301.31 - samples/sec: 286.31 - lr: 0.000122 - momentum: 0.000000
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+ 2023-10-13 09:53:18,728 epoch 3 - iter 1232/1546 - loss 0.05491332 - time (sec): 345.13 - samples/sec: 286.89 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-13 09:54:02,189 epoch 3 - iter 1386/1546 - loss 0.05522112 - time (sec): 388.59 - samples/sec: 288.15 - lr: 0.000118 - momentum: 0.000000
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+ 2023-10-13 09:54:44,026 epoch 3 - iter 1540/1546 - loss 0.05495390 - time (sec): 430.43 - samples/sec: 287.77 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-13 09:54:45,676 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-13 09:54:45,677 EPOCH 3 done: loss 0.0548 - lr: 0.000117
140
+ 2023-10-13 09:55:03,127 DEV : loss 0.059545643627643585 - f1-score (micro avg) 0.7921
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+ 2023-10-13 09:55:03,161 ----------------------------------------------------------------------------------------------------
142
+ 2023-10-13 09:55:47,609 epoch 4 - iter 154/1546 - loss 0.03703647 - time (sec): 44.45 - samples/sec: 284.50 - lr: 0.000115 - momentum: 0.000000
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+ 2023-10-13 09:56:30,722 epoch 4 - iter 308/1546 - loss 0.03106991 - time (sec): 87.56 - samples/sec: 283.25 - lr: 0.000113 - momentum: 0.000000
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+ 2023-10-13 09:57:14,974 epoch 4 - iter 462/1546 - loss 0.02898605 - time (sec): 131.81 - samples/sec: 285.23 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-13 09:57:58,608 epoch 4 - iter 616/1546 - loss 0.03074237 - time (sec): 175.44 - samples/sec: 276.89 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-13 09:58:43,273 epoch 4 - iter 770/1546 - loss 0.03301947 - time (sec): 220.11 - samples/sec: 277.51 - lr: 0.000108 - momentum: 0.000000
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+ 2023-10-13 09:59:30,913 epoch 4 - iter 924/1546 - loss 0.03399197 - time (sec): 267.75 - samples/sec: 275.87 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-13 10:00:17,635 epoch 4 - iter 1078/1546 - loss 0.03359479 - time (sec): 314.47 - samples/sec: 275.40 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-13 10:01:04,322 epoch 4 - iter 1232/1546 - loss 0.03330922 - time (sec): 361.16 - samples/sec: 274.56 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-13 10:01:50,860 epoch 4 - iter 1386/1546 - loss 0.03362633 - time (sec): 407.70 - samples/sec: 274.21 - lr: 0.000102 - momentum: 0.000000
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+ 2023-10-13 10:02:37,520 epoch 4 - iter 1540/1546 - loss 0.03347558 - time (sec): 454.36 - samples/sec: 272.59 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-13 10:02:39,239 ----------------------------------------------------------------------------------------------------
153
+ 2023-10-13 10:02:39,239 EPOCH 4 done: loss 0.0334 - lr: 0.000100
154
+ 2023-10-13 10:02:57,010 DEV : loss 0.07305894047021866 - f1-score (micro avg) 0.7929
155
+ 2023-10-13 10:02:57,040 ----------------------------------------------------------------------------------------------------
156
+ 2023-10-13 10:03:44,621 epoch 5 - iter 154/1546 - loss 0.01538529 - time (sec): 47.58 - samples/sec: 265.85 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-13 10:04:30,440 epoch 5 - iter 308/1546 - loss 0.02039276 - time (sec): 93.40 - samples/sec: 275.59 - lr: 0.000097 - momentum: 0.000000
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+ 2023-10-13 10:05:13,779 epoch 5 - iter 462/1546 - loss 0.02114600 - time (sec): 136.74 - samples/sec: 273.31 - lr: 0.000095 - momentum: 0.000000
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+ 2023-10-13 10:05:55,130 epoch 5 - iter 616/1546 - loss 0.02172052 - time (sec): 178.09 - samples/sec: 280.56 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-13 10:06:35,667 epoch 5 - iter 770/1546 - loss 0.02047965 - time (sec): 218.62 - samples/sec: 283.38 - lr: 0.000092 - momentum: 0.000000
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+ 2023-10-13 10:07:16,245 epoch 5 - iter 924/1546 - loss 0.02169642 - time (sec): 259.20 - samples/sec: 286.55 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-13 10:07:59,279 epoch 5 - iter 1078/1546 - loss 0.02168417 - time (sec): 302.24 - samples/sec: 285.58 - lr: 0.000088 - momentum: 0.000000
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+ 2023-10-13 10:08:42,699 epoch 5 - iter 1232/1546 - loss 0.02271991 - time (sec): 345.66 - samples/sec: 284.75 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-13 10:09:25,956 epoch 5 - iter 1386/1546 - loss 0.02322415 - time (sec): 388.91 - samples/sec: 285.85 - lr: 0.000085 - momentum: 0.000000
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+ 2023-10-13 10:10:09,101 epoch 5 - iter 1540/1546 - loss 0.02247912 - time (sec): 432.06 - samples/sec: 286.30 - lr: 0.000083 - momentum: 0.000000
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+ 2023-10-13 10:10:10,819 ----------------------------------------------------------------------------------------------------
167
+ 2023-10-13 10:10:10,820 EPOCH 5 done: loss 0.0225 - lr: 0.000083
168
+ 2023-10-13 10:10:27,483 DEV : loss 0.08502887934446335 - f1-score (micro avg) 0.7992
169
+ 2023-10-13 10:10:27,511 saving best model
170
+ 2023-10-13 10:10:30,134 ----------------------------------------------------------------------------------------------------
171
+ 2023-10-13 10:11:15,341 epoch 6 - iter 154/1546 - loss 0.01540326 - time (sec): 45.20 - samples/sec: 291.20 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-13 10:11:59,889 epoch 6 - iter 308/1546 - loss 0.01726855 - time (sec): 89.75 - samples/sec: 287.52 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-13 10:12:45,995 epoch 6 - iter 462/1546 - loss 0.01478350 - time (sec): 135.86 - samples/sec: 285.80 - lr: 0.000078 - momentum: 0.000000
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+ 2023-10-13 10:13:28,634 epoch 6 - iter 616/1546 - loss 0.01486102 - time (sec): 178.50 - samples/sec: 284.86 - lr: 0.000077 - momentum: 0.000000
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+ 2023-10-13 10:14:13,366 epoch 6 - iter 770/1546 - loss 0.01271067 - time (sec): 223.23 - samples/sec: 283.34 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-13 10:14:56,982 epoch 6 - iter 924/1546 - loss 0.01374191 - time (sec): 266.84 - samples/sec: 277.93 - lr: 0.000073 - momentum: 0.000000
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+ 2023-10-13 10:15:40,790 epoch 6 - iter 1078/1546 - loss 0.01309584 - time (sec): 310.65 - samples/sec: 279.56 - lr: 0.000072 - momentum: 0.000000
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+ 2023-10-13 10:16:24,499 epoch 6 - iter 1232/1546 - loss 0.01254562 - time (sec): 354.36 - samples/sec: 278.96 - lr: 0.000070 - momentum: 0.000000
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+ 2023-10-13 10:17:08,743 epoch 6 - iter 1386/1546 - loss 0.01378174 - time (sec): 398.60 - samples/sec: 277.93 - lr: 0.000068 - momentum: 0.000000
180
+ 2023-10-13 10:17:53,163 epoch 6 - iter 1540/1546 - loss 0.01418016 - time (sec): 443.02 - samples/sec: 279.19 - lr: 0.000067 - momentum: 0.000000
181
+ 2023-10-13 10:17:54,953 ----------------------------------------------------------------------------------------------------
182
+ 2023-10-13 10:17:54,953 EPOCH 6 done: loss 0.0141 - lr: 0.000067
183
+ 2023-10-13 10:18:12,876 DEV : loss 0.09621559828519821 - f1-score (micro avg) 0.7873
184
+ 2023-10-13 10:18:12,919 ----------------------------------------------------------------------------------------------------
185
+ 2023-10-13 10:18:57,224 epoch 7 - iter 154/1546 - loss 0.00910352 - time (sec): 44.30 - samples/sec: 265.40 - lr: 0.000065 - momentum: 0.000000
186
+ 2023-10-13 10:19:42,915 epoch 7 - iter 308/1546 - loss 0.01161357 - time (sec): 89.99 - samples/sec: 277.88 - lr: 0.000063 - momentum: 0.000000
187
+ 2023-10-13 10:20:28,122 epoch 7 - iter 462/1546 - loss 0.01227135 - time (sec): 135.20 - samples/sec: 272.24 - lr: 0.000062 - momentum: 0.000000
188
+ 2023-10-13 10:21:12,787 epoch 7 - iter 616/1546 - loss 0.01136743 - time (sec): 179.87 - samples/sec: 270.08 - lr: 0.000060 - momentum: 0.000000
189
+ 2023-10-13 10:21:56,983 epoch 7 - iter 770/1546 - loss 0.01071208 - time (sec): 224.06 - samples/sec: 266.68 - lr: 0.000058 - momentum: 0.000000
190
+ 2023-10-13 10:22:42,738 epoch 7 - iter 924/1546 - loss 0.01094035 - time (sec): 269.82 - samples/sec: 269.52 - lr: 0.000057 - momentum: 0.000000
191
+ 2023-10-13 10:23:28,286 epoch 7 - iter 1078/1546 - loss 0.01073439 - time (sec): 315.36 - samples/sec: 273.01 - lr: 0.000055 - momentum: 0.000000
192
+ 2023-10-13 10:24:12,816 epoch 7 - iter 1232/1546 - loss 0.01035952 - time (sec): 359.89 - samples/sec: 274.29 - lr: 0.000053 - momentum: 0.000000
193
+ 2023-10-13 10:24:58,624 epoch 7 - iter 1386/1546 - loss 0.00984261 - time (sec): 405.70 - samples/sec: 277.29 - lr: 0.000052 - momentum: 0.000000
194
+ 2023-10-13 10:25:42,975 epoch 7 - iter 1540/1546 - loss 0.00997254 - time (sec): 450.05 - samples/sec: 275.15 - lr: 0.000050 - momentum: 0.000000
195
+ 2023-10-13 10:25:44,642 ----------------------------------------------------------------------------------------------------
196
+ 2023-10-13 10:25:44,642 EPOCH 7 done: loss 0.0100 - lr: 0.000050
197
+ 2023-10-13 10:26:02,375 DEV : loss 0.10020222514867783 - f1-score (micro avg) 0.7886
198
+ 2023-10-13 10:26:02,405 ----------------------------------------------------------------------------------------------------
199
+ 2023-10-13 10:26:47,796 epoch 8 - iter 154/1546 - loss 0.01244001 - time (sec): 45.39 - samples/sec: 258.30 - lr: 0.000048 - momentum: 0.000000
200
+ 2023-10-13 10:27:31,481 epoch 8 - iter 308/1546 - loss 0.00842998 - time (sec): 89.07 - samples/sec: 271.57 - lr: 0.000047 - momentum: 0.000000
201
+ 2023-10-13 10:28:17,362 epoch 8 - iter 462/1546 - loss 0.00627451 - time (sec): 134.95 - samples/sec: 270.72 - lr: 0.000045 - momentum: 0.000000
202
+ 2023-10-13 10:29:01,594 epoch 8 - iter 616/1546 - loss 0.00697601 - time (sec): 179.19 - samples/sec: 271.98 - lr: 0.000043 - momentum: 0.000000
203
+ 2023-10-13 10:29:46,681 epoch 8 - iter 770/1546 - loss 0.00681597 - time (sec): 224.27 - samples/sec: 274.03 - lr: 0.000042 - momentum: 0.000000
204
+ 2023-10-13 10:30:31,390 epoch 8 - iter 924/1546 - loss 0.00609977 - time (sec): 268.98 - samples/sec: 273.00 - lr: 0.000040 - momentum: 0.000000
205
+ 2023-10-13 10:31:17,039 epoch 8 - iter 1078/1546 - loss 0.00559074 - time (sec): 314.63 - samples/sec: 273.38 - lr: 0.000038 - momentum: 0.000000
206
+ 2023-10-13 10:32:01,870 epoch 8 - iter 1232/1546 - loss 0.00526804 - time (sec): 359.46 - samples/sec: 274.25 - lr: 0.000037 - momentum: 0.000000
207
+ 2023-10-13 10:32:47,288 epoch 8 - iter 1386/1546 - loss 0.00531994 - time (sec): 404.88 - samples/sec: 275.89 - lr: 0.000035 - momentum: 0.000000
208
+ 2023-10-13 10:33:31,162 epoch 8 - iter 1540/1546 - loss 0.00552273 - time (sec): 448.75 - samples/sec: 275.71 - lr: 0.000033 - momentum: 0.000000
209
+ 2023-10-13 10:33:32,970 ----------------------------------------------------------------------------------------------------
210
+ 2023-10-13 10:33:32,970 EPOCH 8 done: loss 0.0055 - lr: 0.000033
211
+ 2023-10-13 10:33:50,993 DEV : loss 0.11565513908863068 - f1-score (micro avg) 0.7714
212
+ 2023-10-13 10:33:51,025 ----------------------------------------------------------------------------------------------------
213
+ 2023-10-13 10:34:35,062 epoch 9 - iter 154/1546 - loss 0.00353921 - time (sec): 44.03 - samples/sec: 288.53 - lr: 0.000032 - momentum: 0.000000
214
+ 2023-10-13 10:35:19,747 epoch 9 - iter 308/1546 - loss 0.00481950 - time (sec): 88.72 - samples/sec: 293.87 - lr: 0.000030 - momentum: 0.000000
215
+ 2023-10-13 10:36:02,894 epoch 9 - iter 462/1546 - loss 0.00429851 - time (sec): 131.87 - samples/sec: 287.58 - lr: 0.000028 - momentum: 0.000000
216
+ 2023-10-13 10:36:47,612 epoch 9 - iter 616/1546 - loss 0.00413043 - time (sec): 176.58 - samples/sec: 287.83 - lr: 0.000027 - momentum: 0.000000
217
+ 2023-10-13 10:37:30,752 epoch 9 - iter 770/1546 - loss 0.00537386 - time (sec): 219.72 - samples/sec: 282.70 - lr: 0.000025 - momentum: 0.000000
218
+ 2023-10-13 10:38:15,023 epoch 9 - iter 924/1546 - loss 0.00487878 - time (sec): 264.00 - samples/sec: 282.14 - lr: 0.000023 - momentum: 0.000000
219
+ 2023-10-13 10:38:59,330 epoch 9 - iter 1078/1546 - loss 0.00467215 - time (sec): 308.30 - samples/sec: 281.03 - lr: 0.000022 - momentum: 0.000000
220
+ 2023-10-13 10:39:43,130 epoch 9 - iter 1232/1546 - loss 0.00481155 - time (sec): 352.10 - samples/sec: 281.30 - lr: 0.000020 - momentum: 0.000000
221
+ 2023-10-13 10:40:27,690 epoch 9 - iter 1386/1546 - loss 0.00475832 - time (sec): 396.66 - samples/sec: 280.66 - lr: 0.000018 - momentum: 0.000000
222
+ 2023-10-13 10:41:12,637 epoch 9 - iter 1540/1546 - loss 0.00468018 - time (sec): 441.61 - samples/sec: 280.11 - lr: 0.000017 - momentum: 0.000000
223
+ 2023-10-13 10:41:14,448 ----------------------------------------------------------------------------------------------------
224
+ 2023-10-13 10:41:14,449 EPOCH 9 done: loss 0.0047 - lr: 0.000017
225
+ 2023-10-13 10:41:31,579 DEV : loss 0.11798277497291565 - f1-score (micro avg) 0.7903
226
+ 2023-10-13 10:41:31,609 ----------------------------------------------------------------------------------------------------
227
+ 2023-10-13 10:42:16,010 epoch 10 - iter 154/1546 - loss 0.00481621 - time (sec): 44.40 - samples/sec: 269.92 - lr: 0.000015 - momentum: 0.000000
228
+ 2023-10-13 10:43:02,187 epoch 10 - iter 308/1546 - loss 0.00357679 - time (sec): 90.58 - samples/sec: 265.04 - lr: 0.000013 - momentum: 0.000000
229
+ 2023-10-13 10:43:51,217 epoch 10 - iter 462/1546 - loss 0.00374515 - time (sec): 139.61 - samples/sec: 269.97 - lr: 0.000012 - momentum: 0.000000
230
+ 2023-10-13 10:44:37,368 epoch 10 - iter 616/1546 - loss 0.00379115 - time (sec): 185.76 - samples/sec: 267.93 - lr: 0.000010 - momentum: 0.000000
231
+ 2023-10-13 10:45:22,324 epoch 10 - iter 770/1546 - loss 0.00346057 - time (sec): 230.71 - samples/sec: 274.58 - lr: 0.000008 - momentum: 0.000000
232
+ 2023-10-13 10:46:06,661 epoch 10 - iter 924/1546 - loss 0.00318152 - time (sec): 275.05 - samples/sec: 272.36 - lr: 0.000007 - momentum: 0.000000
233
+ 2023-10-13 10:46:51,187 epoch 10 - iter 1078/1546 - loss 0.00287153 - time (sec): 319.58 - samples/sec: 270.85 - lr: 0.000005 - momentum: 0.000000
234
+ 2023-10-13 10:47:35,782 epoch 10 - iter 1232/1546 - loss 0.00283244 - time (sec): 364.17 - samples/sec: 271.84 - lr: 0.000003 - momentum: 0.000000
235
+ 2023-10-13 10:48:20,263 epoch 10 - iter 1386/1546 - loss 0.00268580 - time (sec): 408.65 - samples/sec: 273.03 - lr: 0.000002 - momentum: 0.000000
236
+ 2023-10-13 10:49:04,820 epoch 10 - iter 1540/1546 - loss 0.00279265 - time (sec): 453.21 - samples/sec: 273.24 - lr: 0.000000 - momentum: 0.000000
237
+ 2023-10-13 10:49:06,424 ----------------------------------------------------------------------------------------------------
238
+ 2023-10-13 10:49:06,424 EPOCH 10 done: loss 0.0028 - lr: 0.000000
239
+ 2023-10-13 10:49:24,834 DEV : loss 0.11898898333311081 - f1-score (micro avg) 0.7871
240
+ 2023-10-13 10:49:25,906 ----------------------------------------------------------------------------------------------------
241
+ 2023-10-13 10:49:25,908 Loading model from best epoch ...
242
+ 2023-10-13 10:49:30,245 SequenceTagger predicts: Dictionary with 13 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-BUILDING, B-BUILDING, E-BUILDING, I-BUILDING, S-STREET, B-STREET, E-STREET, I-STREET
243
+ 2023-10-13 10:50:25,764
244
+ Results:
245
+ - F-score (micro) 0.8119
246
+ - F-score (macro) 0.7384
247
+ - Accuracy 0.7023
248
+
249
+ By class:
250
+ precision recall f1-score support
251
+
252
+ LOC 0.8253 0.8742 0.8491 946
253
+ BUILDING 0.6571 0.6216 0.6389 185
254
+ STREET 0.6769 0.7857 0.7273 56
255
+
256
+ micro avg 0.7939 0.8307 0.8119 1187
257
+ macro avg 0.7198 0.7605 0.7384 1187
258
+ weighted avg 0.7921 0.8307 0.8106 1187
259
+
260
+ 2023-10-13 10:50:25,764 ----------------------------------------------------------------------------------------------------