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hmbyt5-preliminary
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flair-hipe-2022-newseye-de

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.1697152410.6d4c7681f95b.1253.19 +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 23:39:46 0.0002 0.8016 0.1220 0.2984 0.2538 0.2743 0.1591
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+ 2 00:06:05 0.0001 0.1550 0.1397 0.2954 0.5057 0.3729 0.2294
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+ 3 00:32:27 0.0001 0.1057 0.2025 0.2801 0.6042 0.3827 0.2382
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+ 4 00:58:58 0.0001 0.0720 0.2412 0.3048 0.5871 0.4013 0.2522
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+ 5 01:25:04 0.0001 0.0493 0.3131 0.2813 0.5398 0.3699 0.2282
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+ 6 01:50:43 0.0001 0.0327 0.3447 0.3001 0.5871 0.3972 0.2494
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+ 7 02:16:18 0.0001 0.0234 0.4042 0.2928 0.5966 0.3928 0.2471
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+ 8 02:42:27 0.0000 0.0158 0.3817 0.3280 0.6193 0.4289 0.2750
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+ 9 03:09:09 0.0000 0.0105 0.4895 0.2855 0.6136 0.3897 0.2436
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+ 10 03:36:00 0.0000 0.0065 0.4975 0.2876 0.6155 0.3920 0.2451
runs/events.out.tfevents.1697152410.6d4c7681f95b.1253.19 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-12 23:13:30,507 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:13:30,510 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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+ 2023-10-12 23:13:30,510 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:13:30,510 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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+ 2023-10-12 23:13:30,510 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:13:30,510 Train: 20847 sentences
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+ 2023-10-12 23:13:30,510 (train_with_dev=False, train_with_test=False)
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+ 2023-10-12 23:13:30,511 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:13:30,511 Training Params:
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+ 2023-10-12 23:13:30,511 - learning_rate: "0.00016"
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+ 2023-10-12 23:13:30,511 - mini_batch_size: "4"
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+ 2023-10-12 23:13:30,511 - max_epochs: "10"
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+ 2023-10-12 23:13:30,511 - shuffle: "True"
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+ 2023-10-12 23:13:30,511 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:13:30,511 Plugins:
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+ 2023-10-12 23:13:30,511 - TensorboardLogger
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+ 2023-10-12 23:13:30,511 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-12 23:13:30,511 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:13:30,511 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-12 23:13:30,511 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-12 23:13:30,512 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:13:30,512 Computation:
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+ 2023-10-12 23:13:30,512 - compute on device: cuda:0
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+ 2023-10-12 23:13:30,512 - embedding storage: none
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+ 2023-10-12 23:13:30,512 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:13:30,512 Model training base path: "hmbench-newseye/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-12 23:13:30,512 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:13:30,512 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:13:30,512 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-12 23:16:08,563 epoch 1 - iter 521/5212 - loss 2.75555085 - time (sec): 158.05 - samples/sec: 257.52 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-12 23:18:42,724 epoch 1 - iter 1042/5212 - loss 2.30548429 - time (sec): 312.21 - samples/sec: 253.96 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-12 23:21:17,162 epoch 1 - iter 1563/5212 - loss 1.81068959 - time (sec): 466.65 - samples/sec: 246.98 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-12 23:23:52,349 epoch 1 - iter 2084/5212 - loss 1.47926246 - time (sec): 621.83 - samples/sec: 244.78 - lr: 0.000064 - momentum: 0.000000
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+ 2023-10-12 23:26:27,234 epoch 1 - iter 2605/5212 - loss 1.28430112 - time (sec): 776.72 - samples/sec: 244.12 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-12 23:29:02,351 epoch 1 - iter 3126/5212 - loss 1.12994919 - time (sec): 931.84 - samples/sec: 244.35 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-12 23:31:34,040 epoch 1 - iter 3647/5212 - loss 1.01667901 - time (sec): 1083.53 - samples/sec: 243.26 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-12 23:34:06,711 epoch 1 - iter 4168/5212 - loss 0.92769967 - time (sec): 1236.20 - samples/sec: 242.59 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-12 23:36:37,508 epoch 1 - iter 4689/5212 - loss 0.85937678 - time (sec): 1386.99 - samples/sec: 240.83 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-12 23:39:08,101 epoch 1 - iter 5210/5212 - loss 0.80186809 - time (sec): 1537.59 - samples/sec: 238.87 - lr: 0.000160 - momentum: 0.000000
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+ 2023-10-12 23:39:08,629 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:39:08,629 EPOCH 1 done: loss 0.8016 - lr: 0.000160
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+ 2023-10-12 23:39:46,522 DEV : loss 0.12198188155889511 - f1-score (micro avg) 0.2743
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+ 2023-10-12 23:39:46,580 saving best model
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+ 2023-10-12 23:39:47,523 ----------------------------------------------------------------------------------------------------
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+ 2023-10-12 23:42:21,633 epoch 2 - iter 521/5212 - loss 0.20982971 - time (sec): 154.11 - samples/sec: 232.22 - lr: 0.000158 - momentum: 0.000000
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+ 2023-10-12 23:44:56,075 epoch 2 - iter 1042/5212 - loss 0.18609627 - time (sec): 308.55 - samples/sec: 233.20 - lr: 0.000156 - momentum: 0.000000
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+ 2023-10-12 23:47:29,762 epoch 2 - iter 1563/5212 - loss 0.17643072 - time (sec): 462.24 - samples/sec: 230.90 - lr: 0.000155 - momentum: 0.000000
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+ 2023-10-12 23:50:04,642 epoch 2 - iter 2084/5212 - loss 0.17285415 - time (sec): 617.12 - samples/sec: 232.34 - lr: 0.000153 - momentum: 0.000000
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+ 2023-10-12 23:52:37,758 epoch 2 - iter 2605/5212 - loss 0.17102477 - time (sec): 770.23 - samples/sec: 233.35 - lr: 0.000151 - momentum: 0.000000
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+ 2023-10-12 23:55:11,394 epoch 2 - iter 3126/5212 - loss 0.16423488 - time (sec): 923.87 - samples/sec: 236.27 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-12 23:57:41,803 epoch 2 - iter 3647/5212 - loss 0.16019015 - time (sec): 1074.28 - samples/sec: 236.44 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-13 00:00:16,286 epoch 2 - iter 4168/5212 - loss 0.15881036 - time (sec): 1228.76 - samples/sec: 237.64 - lr: 0.000146 - momentum: 0.000000
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+ 2023-10-13 00:02:49,463 epoch 2 - iter 4689/5212 - loss 0.15679286 - time (sec): 1381.94 - samples/sec: 238.64 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-13 00:05:22,498 epoch 2 - iter 5210/5212 - loss 0.15495624 - time (sec): 1534.97 - samples/sec: 239.24 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-13 00:05:23,131 ----------------------------------------------------------------------------------------------------
124
+ 2023-10-13 00:05:23,132 EPOCH 2 done: loss 0.1550 - lr: 0.000142
125
+ 2023-10-13 00:06:05,180 DEV : loss 0.1396581381559372 - f1-score (micro avg) 0.3729
126
+ 2023-10-13 00:06:05,233 saving best model
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+ 2023-10-13 00:06:07,867 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 00:08:37,736 epoch 3 - iter 521/5212 - loss 0.11118070 - time (sec): 149.87 - samples/sec: 237.18 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-13 00:11:08,565 epoch 3 - iter 1042/5212 - loss 0.11386111 - time (sec): 300.69 - samples/sec: 229.34 - lr: 0.000139 - momentum: 0.000000
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+ 2023-10-13 00:13:43,196 epoch 3 - iter 1563/5212 - loss 0.11114589 - time (sec): 455.32 - samples/sec: 234.58 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-13 00:16:15,918 epoch 3 - iter 2084/5212 - loss 0.10856248 - time (sec): 608.05 - samples/sec: 234.81 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-13 00:18:50,169 epoch 3 - iter 2605/5212 - loss 0.10632374 - time (sec): 762.30 - samples/sec: 235.60 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-13 00:21:23,791 epoch 3 - iter 3126/5212 - loss 0.10650590 - time (sec): 915.92 - samples/sec: 236.45 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-13 00:23:56,304 epoch 3 - iter 3647/5212 - loss 0.10745965 - time (sec): 1068.43 - samples/sec: 235.83 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-13 00:26:30,815 epoch 3 - iter 4168/5212 - loss 0.10776351 - time (sec): 1222.94 - samples/sec: 237.02 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-13 00:29:07,508 epoch 3 - iter 4689/5212 - loss 0.10598550 - time (sec): 1379.64 - samples/sec: 238.76 - lr: 0.000126 - momentum: 0.000000
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+ 2023-10-13 00:31:42,918 epoch 3 - iter 5210/5212 - loss 0.10574885 - time (sec): 1535.05 - samples/sec: 239.29 - lr: 0.000124 - momentum: 0.000000
138
+ 2023-10-13 00:31:43,399 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-13 00:31:43,400 EPOCH 3 done: loss 0.1057 - lr: 0.000124
140
+ 2023-10-13 00:32:27,379 DEV : loss 0.20252744853496552 - f1-score (micro avg) 0.3827
141
+ 2023-10-13 00:32:27,450 saving best model
142
+ 2023-10-13 00:32:28,501 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-13 00:35:00,575 epoch 4 - iter 521/5212 - loss 0.08118201 - time (sec): 152.07 - samples/sec: 235.09 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-13 00:37:34,272 epoch 4 - iter 1042/5212 - loss 0.07254490 - time (sec): 305.77 - samples/sec: 239.09 - lr: 0.000121 - momentum: 0.000000
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+ 2023-10-13 00:40:09,337 epoch 4 - iter 1563/5212 - loss 0.07458337 - time (sec): 460.83 - samples/sec: 237.47 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-13 00:42:46,638 epoch 4 - iter 2084/5212 - loss 0.07549473 - time (sec): 618.13 - samples/sec: 233.58 - lr: 0.000117 - momentum: 0.000000
147
+ 2023-10-13 00:45:24,792 epoch 4 - iter 2605/5212 - loss 0.07444442 - time (sec): 776.29 - samples/sec: 235.28 - lr: 0.000116 - momentum: 0.000000
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+ 2023-10-13 00:48:00,846 epoch 4 - iter 3126/5212 - loss 0.07631596 - time (sec): 932.34 - samples/sec: 237.22 - lr: 0.000114 - momentum: 0.000000
149
+ 2023-10-13 00:50:35,261 epoch 4 - iter 3647/5212 - loss 0.07273579 - time (sec): 1086.76 - samples/sec: 237.66 - lr: 0.000112 - momentum: 0.000000
150
+ 2023-10-13 00:53:10,990 epoch 4 - iter 4168/5212 - loss 0.07211108 - time (sec): 1242.49 - samples/sec: 237.20 - lr: 0.000110 - momentum: 0.000000
151
+ 2023-10-13 00:55:43,694 epoch 4 - iter 4689/5212 - loss 0.07080145 - time (sec): 1395.19 - samples/sec: 237.14 - lr: 0.000108 - momentum: 0.000000
152
+ 2023-10-13 00:58:15,862 epoch 4 - iter 5210/5212 - loss 0.07202352 - time (sec): 1547.36 - samples/sec: 237.35 - lr: 0.000107 - momentum: 0.000000
153
+ 2023-10-13 00:58:16,436 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-13 00:58:16,437 EPOCH 4 done: loss 0.0720 - lr: 0.000107
155
+ 2023-10-13 00:58:58,321 DEV : loss 0.2412302941083908 - f1-score (micro avg) 0.4013
156
+ 2023-10-13 00:58:58,375 saving best model
157
+ 2023-10-13 00:59:00,992 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-13 01:01:32,332 epoch 5 - iter 521/5212 - loss 0.03749208 - time (sec): 151.33 - samples/sec: 238.31 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-13 01:04:10,857 epoch 5 - iter 1042/5212 - loss 0.04298893 - time (sec): 309.86 - samples/sec: 226.57 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-13 01:06:53,769 epoch 5 - iter 1563/5212 - loss 0.04481796 - time (sec): 472.77 - samples/sec: 227.66 - lr: 0.000101 - momentum: 0.000000
161
+ 2023-10-13 01:09:29,964 epoch 5 - iter 2084/5212 - loss 0.04877373 - time (sec): 628.97 - samples/sec: 228.57 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-13 01:12:04,424 epoch 5 - iter 2605/5212 - loss 0.04819527 - time (sec): 783.43 - samples/sec: 231.32 - lr: 0.000098 - momentum: 0.000000
163
+ 2023-10-13 01:14:31,113 epoch 5 - iter 3126/5212 - loss 0.04778786 - time (sec): 930.12 - samples/sec: 238.07 - lr: 0.000096 - momentum: 0.000000
164
+ 2023-10-13 01:17:00,477 epoch 5 - iter 3647/5212 - loss 0.04812251 - time (sec): 1079.48 - samples/sec: 240.73 - lr: 0.000094 - momentum: 0.000000
165
+ 2023-10-13 01:19:24,217 epoch 5 - iter 4168/5212 - loss 0.04864547 - time (sec): 1223.22 - samples/sec: 239.50 - lr: 0.000092 - momentum: 0.000000
166
+ 2023-10-13 01:21:53,900 epoch 5 - iter 4689/5212 - loss 0.04769308 - time (sec): 1372.90 - samples/sec: 240.77 - lr: 0.000091 - momentum: 0.000000
167
+ 2023-10-13 01:24:22,597 epoch 5 - iter 5210/5212 - loss 0.04927007 - time (sec): 1521.60 - samples/sec: 241.37 - lr: 0.000089 - momentum: 0.000000
168
+ 2023-10-13 01:24:23,115 ----------------------------------------------------------------------------------------------------
169
+ 2023-10-13 01:24:23,116 EPOCH 5 done: loss 0.0493 - lr: 0.000089
170
+ 2023-10-13 01:25:04,195 DEV : loss 0.3131482005119324 - f1-score (micro avg) 0.3699
171
+ 2023-10-13 01:25:04,249 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-13 01:27:33,202 epoch 6 - iter 521/5212 - loss 0.02676268 - time (sec): 148.95 - samples/sec: 237.45 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-13 01:30:06,274 epoch 6 - iter 1042/5212 - loss 0.02907373 - time (sec): 302.02 - samples/sec: 246.54 - lr: 0.000085 - momentum: 0.000000
174
+ 2023-10-13 01:32:37,064 epoch 6 - iter 1563/5212 - loss 0.02955452 - time (sec): 452.81 - samples/sec: 247.39 - lr: 0.000084 - momentum: 0.000000
175
+ 2023-10-13 01:35:05,717 epoch 6 - iter 2084/5212 - loss 0.03048557 - time (sec): 601.47 - samples/sec: 244.93 - lr: 0.000082 - momentum: 0.000000
176
+ 2023-10-13 01:37:31,785 epoch 6 - iter 2605/5212 - loss 0.03142527 - time (sec): 747.53 - samples/sec: 243.10 - lr: 0.000080 - momentum: 0.000000
177
+ 2023-10-13 01:40:05,706 epoch 6 - iter 3126/5212 - loss 0.03190970 - time (sec): 901.46 - samples/sec: 245.25 - lr: 0.000078 - momentum: 0.000000
178
+ 2023-10-13 01:42:35,905 epoch 6 - iter 3647/5212 - loss 0.03307510 - time (sec): 1051.65 - samples/sec: 246.50 - lr: 0.000076 - momentum: 0.000000
179
+ 2023-10-13 01:45:03,240 epoch 6 - iter 4168/5212 - loss 0.03314185 - time (sec): 1198.99 - samples/sec: 244.33 - lr: 0.000075 - momentum: 0.000000
180
+ 2023-10-13 01:47:31,293 epoch 6 - iter 4689/5212 - loss 0.03297464 - time (sec): 1347.04 - samples/sec: 244.12 - lr: 0.000073 - momentum: 0.000000
181
+ 2023-10-13 01:49:59,777 epoch 6 - iter 5210/5212 - loss 0.03275868 - time (sec): 1495.53 - samples/sec: 245.41 - lr: 0.000071 - momentum: 0.000000
182
+ 2023-10-13 01:50:00,631 ----------------------------------------------------------------------------------------------------
183
+ 2023-10-13 01:50:00,632 EPOCH 6 done: loss 0.0327 - lr: 0.000071
184
+ 2023-10-13 01:50:43,711 DEV : loss 0.34465113282203674 - f1-score (micro avg) 0.3972
185
+ 2023-10-13 01:50:43,765 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-13 01:53:11,195 epoch 7 - iter 521/5212 - loss 0.02092661 - time (sec): 147.43 - samples/sec: 250.56 - lr: 0.000069 - momentum: 0.000000
187
+ 2023-10-13 01:55:39,696 epoch 7 - iter 1042/5212 - loss 0.02158009 - time (sec): 295.93 - samples/sec: 247.29 - lr: 0.000068 - momentum: 0.000000
188
+ 2023-10-13 01:58:08,942 epoch 7 - iter 1563/5212 - loss 0.02258803 - time (sec): 445.17 - samples/sec: 246.14 - lr: 0.000066 - momentum: 0.000000
189
+ 2023-10-13 02:00:38,348 epoch 7 - iter 2084/5212 - loss 0.02191469 - time (sec): 594.58 - samples/sec: 246.67 - lr: 0.000064 - momentum: 0.000000
190
+ 2023-10-13 02:03:08,286 epoch 7 - iter 2605/5212 - loss 0.02349063 - time (sec): 744.52 - samples/sec: 247.11 - lr: 0.000062 - momentum: 0.000000
191
+ 2023-10-13 02:05:43,405 epoch 7 - iter 3126/5212 - loss 0.02309326 - time (sec): 899.64 - samples/sec: 250.85 - lr: 0.000060 - momentum: 0.000000
192
+ 2023-10-13 02:08:12,692 epoch 7 - iter 3647/5212 - loss 0.02247120 - time (sec): 1048.92 - samples/sec: 249.77 - lr: 0.000059 - momentum: 0.000000
193
+ 2023-10-13 02:10:39,254 epoch 7 - iter 4168/5212 - loss 0.02308498 - time (sec): 1195.49 - samples/sec: 247.04 - lr: 0.000057 - momentum: 0.000000
194
+ 2023-10-13 02:13:07,955 epoch 7 - iter 4689/5212 - loss 0.02310097 - time (sec): 1344.19 - samples/sec: 246.18 - lr: 0.000055 - momentum: 0.000000
195
+ 2023-10-13 02:15:34,751 epoch 7 - iter 5210/5212 - loss 0.02339839 - time (sec): 1490.98 - samples/sec: 246.36 - lr: 0.000053 - momentum: 0.000000
196
+ 2023-10-13 02:15:35,257 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-13 02:15:35,257 EPOCH 7 done: loss 0.0234 - lr: 0.000053
198
+ 2023-10-13 02:16:18,509 DEV : loss 0.4042404890060425 - f1-score (micro avg) 0.3928
199
+ 2023-10-13 02:16:18,576 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-13 02:18:49,551 epoch 8 - iter 521/5212 - loss 0.01647227 - time (sec): 150.97 - samples/sec: 248.96 - lr: 0.000052 - momentum: 0.000000
201
+ 2023-10-13 02:21:19,167 epoch 8 - iter 1042/5212 - loss 0.01566138 - time (sec): 300.59 - samples/sec: 243.90 - lr: 0.000050 - momentum: 0.000000
202
+ 2023-10-13 02:23:50,869 epoch 8 - iter 1563/5212 - loss 0.01443419 - time (sec): 452.29 - samples/sec: 244.66 - lr: 0.000048 - momentum: 0.000000
203
+ 2023-10-13 02:26:22,959 epoch 8 - iter 2084/5212 - loss 0.01528289 - time (sec): 604.38 - samples/sec: 245.53 - lr: 0.000046 - momentum: 0.000000
204
+ 2023-10-13 02:28:51,081 epoch 8 - iter 2605/5212 - loss 0.01595605 - time (sec): 752.50 - samples/sec: 242.48 - lr: 0.000044 - momentum: 0.000000
205
+ 2023-10-13 02:31:21,596 epoch 8 - iter 3126/5212 - loss 0.01540844 - time (sec): 903.02 - samples/sec: 242.94 - lr: 0.000043 - momentum: 0.000000
206
+ 2023-10-13 02:33:52,731 epoch 8 - iter 3647/5212 - loss 0.01578988 - time (sec): 1054.15 - samples/sec: 240.51 - lr: 0.000041 - momentum: 0.000000
207
+ 2023-10-13 02:36:27,735 epoch 8 - iter 4168/5212 - loss 0.01607841 - time (sec): 1209.16 - samples/sec: 239.39 - lr: 0.000039 - momentum: 0.000000
208
+ 2023-10-13 02:39:07,916 epoch 8 - iter 4689/5212 - loss 0.01567215 - time (sec): 1369.34 - samples/sec: 240.95 - lr: 0.000037 - momentum: 0.000000
209
+ 2023-10-13 02:41:43,466 epoch 8 - iter 5210/5212 - loss 0.01581146 - time (sec): 1524.89 - samples/sec: 240.91 - lr: 0.000036 - momentum: 0.000000
210
+ 2023-10-13 02:41:43,935 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-13 02:41:43,935 EPOCH 8 done: loss 0.0158 - lr: 0.000036
212
+ 2023-10-13 02:42:27,273 DEV : loss 0.3817499279975891 - f1-score (micro avg) 0.4289
213
+ 2023-10-13 02:42:27,335 saving best model
214
+ 2023-10-13 02:42:29,986 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-13 02:45:06,407 epoch 9 - iter 521/5212 - loss 0.01315958 - time (sec): 156.42 - samples/sec: 239.10 - lr: 0.000034 - momentum: 0.000000
216
+ 2023-10-13 02:47:37,548 epoch 9 - iter 1042/5212 - loss 0.01366808 - time (sec): 307.56 - samples/sec: 228.85 - lr: 0.000032 - momentum: 0.000000
217
+ 2023-10-13 02:50:12,090 epoch 9 - iter 1563/5212 - loss 0.01146850 - time (sec): 462.10 - samples/sec: 232.76 - lr: 0.000030 - momentum: 0.000000
218
+ 2023-10-13 02:52:48,671 epoch 9 - iter 2084/5212 - loss 0.01157916 - time (sec): 618.68 - samples/sec: 233.94 - lr: 0.000028 - momentum: 0.000000
219
+ 2023-10-13 02:55:26,055 epoch 9 - iter 2605/5212 - loss 0.01149897 - time (sec): 776.06 - samples/sec: 236.50 - lr: 0.000027 - momentum: 0.000000
220
+ 2023-10-13 02:58:00,927 epoch 9 - iter 3126/5212 - loss 0.01120108 - time (sec): 930.94 - samples/sec: 235.44 - lr: 0.000025 - momentum: 0.000000
221
+ 2023-10-13 03:00:36,555 epoch 9 - iter 3647/5212 - loss 0.01111557 - time (sec): 1086.56 - samples/sec: 236.89 - lr: 0.000023 - momentum: 0.000000
222
+ 2023-10-13 03:03:11,248 epoch 9 - iter 4168/5212 - loss 0.01081071 - time (sec): 1241.26 - samples/sec: 236.81 - lr: 0.000021 - momentum: 0.000000
223
+ 2023-10-13 03:05:48,626 epoch 9 - iter 4689/5212 - loss 0.01063389 - time (sec): 1398.63 - samples/sec: 236.23 - lr: 0.000020 - momentum: 0.000000
224
+ 2023-10-13 03:08:25,451 epoch 9 - iter 5210/5212 - loss 0.01045969 - time (sec): 1555.46 - samples/sec: 236.17 - lr: 0.000018 - momentum: 0.000000
225
+ 2023-10-13 03:08:25,935 ----------------------------------------------------------------------------------------------------
226
+ 2023-10-13 03:08:25,935 EPOCH 9 done: loss 0.0105 - lr: 0.000018
227
+ 2023-10-13 03:09:09,234 DEV : loss 0.48951616883277893 - f1-score (micro avg) 0.3897
228
+ 2023-10-13 03:09:09,294 ----------------------------------------------------------------------------------------------------
229
+ 2023-10-13 03:11:46,910 epoch 10 - iter 521/5212 - loss 0.00827833 - time (sec): 157.61 - samples/sec: 232.24 - lr: 0.000016 - momentum: 0.000000
230
+ 2023-10-13 03:14:23,722 epoch 10 - iter 1042/5212 - loss 0.00812183 - time (sec): 314.43 - samples/sec: 230.92 - lr: 0.000014 - momentum: 0.000000
231
+ 2023-10-13 03:16:55,973 epoch 10 - iter 1563/5212 - loss 0.00802548 - time (sec): 466.68 - samples/sec: 235.55 - lr: 0.000012 - momentum: 0.000000
232
+ 2023-10-13 03:19:34,134 epoch 10 - iter 2084/5212 - loss 0.00748456 - time (sec): 624.84 - samples/sec: 232.56 - lr: 0.000011 - momentum: 0.000000
233
+ 2023-10-13 03:22:19,244 epoch 10 - iter 2605/5212 - loss 0.00703452 - time (sec): 789.95 - samples/sec: 230.49 - lr: 0.000009 - momentum: 0.000000
234
+ 2023-10-13 03:25:02,871 epoch 10 - iter 3126/5212 - loss 0.00680004 - time (sec): 953.57 - samples/sec: 230.00 - lr: 0.000007 - momentum: 0.000000
235
+ 2023-10-13 03:27:34,900 epoch 10 - iter 3647/5212 - loss 0.00656432 - time (sec): 1105.60 - samples/sec: 229.70 - lr: 0.000005 - momentum: 0.000000
236
+ 2023-10-13 03:30:05,199 epoch 10 - iter 4168/5212 - loss 0.00684065 - time (sec): 1255.90 - samples/sec: 231.89 - lr: 0.000004 - momentum: 0.000000
237
+ 2023-10-13 03:32:40,991 epoch 10 - iter 4689/5212 - loss 0.00663298 - time (sec): 1411.69 - samples/sec: 232.75 - lr: 0.000002 - momentum: 0.000000
238
+ 2023-10-13 03:35:17,592 epoch 10 - iter 5210/5212 - loss 0.00646053 - time (sec): 1568.30 - samples/sec: 234.21 - lr: 0.000000 - momentum: 0.000000
239
+ 2023-10-13 03:35:18,093 ----------------------------------------------------------------------------------------------------
240
+ 2023-10-13 03:35:18,094 EPOCH 10 done: loss 0.0065 - lr: 0.000000
241
+ 2023-10-13 03:36:00,153 DEV : loss 0.4974716603755951 - f1-score (micro avg) 0.392
242
+ 2023-10-13 03:36:01,225 ----------------------------------------------------------------------------------------------------
243
+ 2023-10-13 03:36:01,227 Loading model from best epoch ...
244
+ 2023-10-13 03:36:05,239 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
+ 2023-10-13 03:37:50,682
246
+ Results:
247
+ - F-score (micro) 0.4388
248
+ - F-score (macro) 0.3278
249
+ - Accuracy 0.2862
250
+
251
+ By class:
252
+ precision recall f1-score support
253
+
254
+ LOC 0.4679 0.4868 0.4772 1214
255
+ PER 0.4373 0.4530 0.4450 808
256
+ ORG 0.2928 0.3343 0.3122 353
257
+ HumanProd 0.0909 0.0667 0.0769 15
258
+
259
+ micro avg 0.4280 0.4502 0.4388 2390
260
+ macro avg 0.3222 0.3352 0.3278 2390
261
+ weighted avg 0.4293 0.4502 0.4394 2390
262
+
263
+ 2023-10-13 03:37:50,684 ----------------------------------------------------------------------------------------------------