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hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5

Token Classification
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French
sequence-tagger-model
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. final-model.pt +3 -0
  4. loss.tsv +11 -0
  5. runs/events.out.tfevents.1697198500.de2e83fddbee.1952.18 +3 -0
  6. test.tsv +0 -0
  7. training.log +261 -0
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final-model.pt ADDED
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loss.tsv ADDED
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+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
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+ 1 12:11:27 0.0001 0.8562 0.1292 0.6408 0.6821 0.6608 0.5262
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+ 2 12:21:22 0.0001 0.1168 0.0859 0.7160 0.7500 0.7326 0.5995
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+ 3 12:31:22 0.0001 0.0758 0.0977 0.7169 0.7964 0.7546 0.6286
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+ 4 12:41:43 0.0001 0.0540 0.1302 0.7557 0.7839 0.7696 0.6447
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+ 5 12:51:45 0.0001 0.0411 0.1499 0.7465 0.7862 0.7658 0.6411
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+ 6 13:01:25 0.0001 0.0314 0.1547 0.7443 0.7771 0.7604 0.6349
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+ 7 13:11:15 0.0001 0.0239 0.1833 0.7516 0.7771 0.7642 0.6385
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+ 8 13:20:48 0.0000 0.0151 0.2029 0.7605 0.7760 0.7682 0.6441
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+ 9 13:30:13 0.0000 0.0124 0.2170 0.7533 0.7704 0.7617 0.6341
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+ 10 13:39:51 0.0000 0.0080 0.2280 0.7530 0.7692 0.7611 0.6326
runs/events.out.tfevents.1697198500.de2e83fddbee.1952.18 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-13 12:01:40,581 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:01:40,583 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 12:01:40,583 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:01:40,583 MultiCorpus: 7936 train + 992 dev + 992 test sentences
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+ - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /root/.flair/datasets/ner_icdar_europeana/fr
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+ 2023-10-13 12:01:40,583 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:01:40,583 Train: 7936 sentences
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+ 2023-10-13 12:01:40,584 (train_with_dev=False, train_with_test=False)
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+ 2023-10-13 12:01:40,584 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:01:40,584 Training Params:
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+ 2023-10-13 12:01:40,584 - learning_rate: "0.00015"
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+ 2023-10-13 12:01:40,584 - mini_batch_size: "4"
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+ 2023-10-13 12:01:40,584 - max_epochs: "10"
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+ 2023-10-13 12:01:40,584 - shuffle: "True"
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+ 2023-10-13 12:01:40,584 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:01:40,584 Plugins:
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+ 2023-10-13 12:01:40,584 - TensorboardLogger
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+ 2023-10-13 12:01:40,584 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-13 12:01:40,584 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:01:40,584 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-13 12:01:40,584 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-13 12:01:40,584 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:01:40,585 Computation:
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+ 2023-10-13 12:01:40,585 - compute on device: cuda:0
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+ 2023-10-13 12:01:40,585 - embedding storage: none
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+ 2023-10-13 12:01:40,585 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:01:40,585 Model training base path: "hmbench-icdar/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-13 12:01:40,585 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:01:40,585 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:01:40,585 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-13 12:02:36,986 epoch 1 - iter 198/1984 - loss 2.53587256 - time (sec): 56.40 - samples/sec: 313.46 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-13 12:03:30,634 epoch 1 - iter 396/1984 - loss 2.35663481 - time (sec): 110.05 - samples/sec: 303.24 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-13 12:04:25,848 epoch 1 - iter 594/1984 - loss 2.03833143 - time (sec): 165.26 - samples/sec: 306.16 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-13 12:05:19,291 epoch 1 - iter 792/1984 - loss 1.75219619 - time (sec): 218.70 - samples/sec: 299.99 - lr: 0.000060 - momentum: 0.000000
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+ 2023-10-13 12:06:17,514 epoch 1 - iter 990/1984 - loss 1.50365684 - time (sec): 276.93 - samples/sec: 294.62 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-13 12:07:11,217 epoch 1 - iter 1188/1984 - loss 1.30904393 - time (sec): 330.63 - samples/sec: 295.02 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-13 12:08:05,806 epoch 1 - iter 1386/1984 - loss 1.15357563 - time (sec): 385.22 - samples/sec: 296.78 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-13 12:09:02,915 epoch 1 - iter 1584/1984 - loss 1.03648294 - time (sec): 442.33 - samples/sec: 294.58 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-13 12:10:00,529 epoch 1 - iter 1782/1984 - loss 0.93126337 - time (sec): 499.94 - samples/sec: 296.04 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-13 12:11:00,429 epoch 1 - iter 1980/1984 - loss 0.85719194 - time (sec): 559.84 - samples/sec: 292.50 - lr: 0.000150 - momentum: 0.000000
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+ 2023-10-13 12:11:01,586 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:11:01,587 EPOCH 1 done: loss 0.8562 - lr: 0.000150
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+ 2023-10-13 12:11:27,268 DEV : loss 0.12921574711799622 - f1-score (micro avg) 0.6608
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+ 2023-10-13 12:11:27,311 saving best model
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+ 2023-10-13 12:11:28,286 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:12:25,572 epoch 2 - iter 198/1984 - loss 0.15246598 - time (sec): 57.28 - samples/sec: 288.91 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-13 12:13:23,204 epoch 2 - iter 396/1984 - loss 0.13947723 - time (sec): 114.92 - samples/sec: 289.19 - lr: 0.000147 - momentum: 0.000000
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+ 2023-10-13 12:14:19,334 epoch 2 - iter 594/1984 - loss 0.13236118 - time (sec): 171.05 - samples/sec: 293.49 - lr: 0.000145 - momentum: 0.000000
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+ 2023-10-13 12:15:15,682 epoch 2 - iter 792/1984 - loss 0.13139937 - time (sec): 227.39 - samples/sec: 289.49 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-13 12:16:13,148 epoch 2 - iter 990/1984 - loss 0.12747303 - time (sec): 284.86 - samples/sec: 288.91 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-13 12:17:07,128 epoch 2 - iter 1188/1984 - loss 0.12581150 - time (sec): 338.84 - samples/sec: 290.91 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-13 12:18:01,605 epoch 2 - iter 1386/1984 - loss 0.12386868 - time (sec): 393.32 - samples/sec: 292.02 - lr: 0.000138 - momentum: 0.000000
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+ 2023-10-13 12:18:57,699 epoch 2 - iter 1584/1984 - loss 0.12091584 - time (sec): 449.41 - samples/sec: 291.07 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-13 12:19:53,333 epoch 2 - iter 1782/1984 - loss 0.11952771 - time (sec): 505.04 - samples/sec: 289.53 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-13 12:20:51,124 epoch 2 - iter 1980/1984 - loss 0.11686668 - time (sec): 562.84 - samples/sec: 290.88 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-13 12:20:52,222 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:20:52,223 EPOCH 2 done: loss 0.1168 - lr: 0.000133
125
+ 2023-10-13 12:21:22,532 DEV : loss 0.08594389259815216 - f1-score (micro avg) 0.7326
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+ 2023-10-13 12:21:22,586 saving best model
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+ 2023-10-13 12:21:25,326 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:22:22,376 epoch 3 - iter 198/1984 - loss 0.06835268 - time (sec): 57.05 - samples/sec: 282.30 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-13 12:23:21,479 epoch 3 - iter 396/1984 - loss 0.07762823 - time (sec): 116.15 - samples/sec: 279.06 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-13 12:24:16,710 epoch 3 - iter 594/1984 - loss 0.07827809 - time (sec): 171.38 - samples/sec: 283.05 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-13 12:25:12,056 epoch 3 - iter 792/1984 - loss 0.07869207 - time (sec): 226.73 - samples/sec: 286.28 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-13 12:26:07,407 epoch 3 - iter 990/1984 - loss 0.07826522 - time (sec): 282.08 - samples/sec: 287.01 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-13 12:27:04,007 epoch 3 - iter 1188/1984 - loss 0.07782816 - time (sec): 338.68 - samples/sec: 288.60 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-13 12:28:00,792 epoch 3 - iter 1386/1984 - loss 0.07814075 - time (sec): 395.46 - samples/sec: 288.89 - lr: 0.000122 - momentum: 0.000000
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+ 2023-10-13 12:28:58,604 epoch 3 - iter 1584/1984 - loss 0.07681156 - time (sec): 453.27 - samples/sec: 288.09 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-13 12:29:54,823 epoch 3 - iter 1782/1984 - loss 0.07566942 - time (sec): 509.49 - samples/sec: 288.85 - lr: 0.000118 - momentum: 0.000000
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+ 2023-10-13 12:30:54,387 epoch 3 - iter 1980/1984 - loss 0.07589565 - time (sec): 569.06 - samples/sec: 287.58 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-13 12:30:55,447 ----------------------------------------------------------------------------------------------------
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+ 2023-10-13 12:30:55,448 EPOCH 3 done: loss 0.0758 - lr: 0.000117
140
+ 2023-10-13 12:31:22,850 DEV : loss 0.09770967811346054 - f1-score (micro avg) 0.7546
141
+ 2023-10-13 12:31:22,896 saving best model
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+ 2023-10-13 12:31:25,680 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-13 12:32:22,939 epoch 4 - iter 198/1984 - loss 0.05842859 - time (sec): 57.25 - samples/sec: 287.82 - lr: 0.000115 - momentum: 0.000000
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+ 2023-10-13 12:33:20,230 epoch 4 - iter 396/1984 - loss 0.05467250 - time (sec): 114.55 - samples/sec: 285.40 - lr: 0.000113 - momentum: 0.000000
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+ 2023-10-13 12:34:20,580 epoch 4 - iter 594/1984 - loss 0.05230249 - time (sec): 174.90 - samples/sec: 279.68 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-13 12:35:21,055 epoch 4 - iter 792/1984 - loss 0.05292552 - time (sec): 235.37 - samples/sec: 277.36 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-13 12:36:20,546 epoch 4 - iter 990/1984 - loss 0.05358516 - time (sec): 294.86 - samples/sec: 279.46 - lr: 0.000108 - momentum: 0.000000
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+ 2023-10-13 12:37:21,108 epoch 4 - iter 1188/1984 - loss 0.05228562 - time (sec): 355.42 - samples/sec: 279.04 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-13 12:38:21,766 epoch 4 - iter 1386/1984 - loss 0.05219360 - time (sec): 416.08 - samples/sec: 276.24 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-13 12:39:19,041 epoch 4 - iter 1584/1984 - loss 0.05282172 - time (sec): 473.36 - samples/sec: 277.03 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-13 12:40:16,271 epoch 4 - iter 1782/1984 - loss 0.05363652 - time (sec): 530.59 - samples/sec: 277.86 - lr: 0.000102 - momentum: 0.000000
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+ 2023-10-13 12:41:14,283 epoch 4 - iter 1980/1984 - loss 0.05409466 - time (sec): 588.60 - samples/sec: 278.11 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-13 12:41:15,420 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-13 12:41:15,421 EPOCH 4 done: loss 0.0540 - lr: 0.000100
155
+ 2023-10-13 12:41:43,934 DEV : loss 0.13024039566516876 - f1-score (micro avg) 0.7696
156
+ 2023-10-13 12:41:43,978 saving best model
157
+ 2023-10-13 12:41:49,138 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-13 12:42:46,154 epoch 5 - iter 198/1984 - loss 0.03679399 - time (sec): 57.01 - samples/sec: 294.51 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-13 12:43:43,295 epoch 5 - iter 396/1984 - loss 0.03905502 - time (sec): 114.15 - samples/sec: 292.02 - lr: 0.000097 - momentum: 0.000000
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+ 2023-10-13 12:44:40,686 epoch 5 - iter 594/1984 - loss 0.04296223 - time (sec): 171.54 - samples/sec: 291.74 - lr: 0.000095 - momentum: 0.000000
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+ 2023-10-13 12:45:35,672 epoch 5 - iter 792/1984 - loss 0.04216427 - time (sec): 226.53 - samples/sec: 293.31 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-13 12:46:31,117 epoch 5 - iter 990/1984 - loss 0.04149098 - time (sec): 281.97 - samples/sec: 292.56 - lr: 0.000092 - momentum: 0.000000
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+ 2023-10-13 12:47:30,500 epoch 5 - iter 1188/1984 - loss 0.04224225 - time (sec): 341.36 - samples/sec: 287.90 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-13 12:48:25,231 epoch 5 - iter 1386/1984 - loss 0.04138722 - time (sec): 396.09 - samples/sec: 288.37 - lr: 0.000088 - momentum: 0.000000
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+ 2023-10-13 12:49:21,590 epoch 5 - iter 1584/1984 - loss 0.04151736 - time (sec): 452.45 - samples/sec: 290.10 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-13 12:50:20,385 epoch 5 - iter 1782/1984 - loss 0.04213374 - time (sec): 511.24 - samples/sec: 288.80 - lr: 0.000085 - momentum: 0.000000
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+ 2023-10-13 12:51:16,349 epoch 5 - iter 1980/1984 - loss 0.04118791 - time (sec): 567.21 - samples/sec: 288.75 - lr: 0.000083 - momentum: 0.000000
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+ 2023-10-13 12:51:17,450 ----------------------------------------------------------------------------------------------------
169
+ 2023-10-13 12:51:17,451 EPOCH 5 done: loss 0.0411 - lr: 0.000083
170
+ 2023-10-13 12:51:45,096 DEV : loss 0.1498626172542572 - f1-score (micro avg) 0.7658
171
+ 2023-10-13 12:51:45,140 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-13 12:52:42,428 epoch 6 - iter 198/1984 - loss 0.02535367 - time (sec): 57.29 - samples/sec: 298.24 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-13 12:53:43,797 epoch 6 - iter 396/1984 - loss 0.02460873 - time (sec): 118.65 - samples/sec: 282.58 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-13 12:54:39,904 epoch 6 - iter 594/1984 - loss 0.02590999 - time (sec): 174.76 - samples/sec: 282.27 - lr: 0.000078 - momentum: 0.000000
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+ 2023-10-13 12:55:35,769 epoch 6 - iter 792/1984 - loss 0.02897049 - time (sec): 230.63 - samples/sec: 285.87 - lr: 0.000077 - momentum: 0.000000
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+ 2023-10-13 12:56:29,696 epoch 6 - iter 990/1984 - loss 0.02985477 - time (sec): 284.55 - samples/sec: 289.08 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-13 12:57:23,689 epoch 6 - iter 1188/1984 - loss 0.02976773 - time (sec): 338.55 - samples/sec: 291.14 - lr: 0.000073 - momentum: 0.000000
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+ 2023-10-13 12:58:17,578 epoch 6 - iter 1386/1984 - loss 0.02995438 - time (sec): 392.44 - samples/sec: 292.99 - lr: 0.000072 - momentum: 0.000000
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+ 2023-10-13 12:59:11,837 epoch 6 - iter 1584/1984 - loss 0.03040644 - time (sec): 446.70 - samples/sec: 293.10 - lr: 0.000070 - momentum: 0.000000
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+ 2023-10-13 13:00:05,389 epoch 6 - iter 1782/1984 - loss 0.03000902 - time (sec): 500.25 - samples/sec: 294.19 - lr: 0.000068 - momentum: 0.000000
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+ 2023-10-13 13:00:58,136 epoch 6 - iter 1980/1984 - loss 0.03131165 - time (sec): 552.99 - samples/sec: 296.04 - lr: 0.000067 - momentum: 0.000000
182
+ 2023-10-13 13:00:59,167 ----------------------------------------------------------------------------------------------------
183
+ 2023-10-13 13:00:59,167 EPOCH 6 done: loss 0.0314 - lr: 0.000067
184
+ 2023-10-13 13:01:25,953 DEV : loss 0.1546768993139267 - f1-score (micro avg) 0.7604
185
+ 2023-10-13 13:01:25,994 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-13 13:02:20,487 epoch 7 - iter 198/1984 - loss 0.02164230 - time (sec): 54.49 - samples/sec: 302.56 - lr: 0.000065 - momentum: 0.000000
187
+ 2023-10-13 13:03:15,597 epoch 7 - iter 396/1984 - loss 0.02187396 - time (sec): 109.60 - samples/sec: 294.20 - lr: 0.000063 - momentum: 0.000000
188
+ 2023-10-13 13:04:11,813 epoch 7 - iter 594/1984 - loss 0.02064500 - time (sec): 165.82 - samples/sec: 296.65 - lr: 0.000062 - momentum: 0.000000
189
+ 2023-10-13 13:05:08,654 epoch 7 - iter 792/1984 - loss 0.02239191 - time (sec): 222.66 - samples/sec: 292.47 - lr: 0.000060 - momentum: 0.000000
190
+ 2023-10-13 13:06:06,207 epoch 7 - iter 990/1984 - loss 0.02117540 - time (sec): 280.21 - samples/sec: 290.85 - lr: 0.000058 - momentum: 0.000000
191
+ 2023-10-13 13:07:00,612 epoch 7 - iter 1188/1984 - loss 0.02163675 - time (sec): 334.62 - samples/sec: 292.24 - lr: 0.000057 - momentum: 0.000000
192
+ 2023-10-13 13:07:58,537 epoch 7 - iter 1386/1984 - loss 0.02215893 - time (sec): 392.54 - samples/sec: 290.62 - lr: 0.000055 - momentum: 0.000000
193
+ 2023-10-13 13:08:52,925 epoch 7 - iter 1584/1984 - loss 0.02198376 - time (sec): 446.93 - samples/sec: 290.89 - lr: 0.000053 - momentum: 0.000000
194
+ 2023-10-13 13:09:48,604 epoch 7 - iter 1782/1984 - loss 0.02278283 - time (sec): 502.61 - samples/sec: 292.61 - lr: 0.000052 - momentum: 0.000000
195
+ 2023-10-13 13:10:47,760 epoch 7 - iter 1980/1984 - loss 0.02389101 - time (sec): 561.76 - samples/sec: 291.52 - lr: 0.000050 - momentum: 0.000000
196
+ 2023-10-13 13:10:48,845 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-13 13:10:48,845 EPOCH 7 done: loss 0.0239 - lr: 0.000050
198
+ 2023-10-13 13:11:15,612 DEV : loss 0.183299720287323 - f1-score (micro avg) 0.7642
199
+ 2023-10-13 13:11:15,659 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-13 13:12:11,816 epoch 8 - iter 198/1984 - loss 0.01535571 - time (sec): 56.16 - samples/sec: 293.44 - lr: 0.000048 - momentum: 0.000000
201
+ 2023-10-13 13:13:07,794 epoch 8 - iter 396/1984 - loss 0.01489584 - time (sec): 112.13 - samples/sec: 296.02 - lr: 0.000047 - momentum: 0.000000
202
+ 2023-10-13 13:14:01,252 epoch 8 - iter 594/1984 - loss 0.01439117 - time (sec): 165.59 - samples/sec: 297.78 - lr: 0.000045 - momentum: 0.000000
203
+ 2023-10-13 13:14:54,994 epoch 8 - iter 792/1984 - loss 0.01550966 - time (sec): 219.33 - samples/sec: 300.88 - lr: 0.000043 - momentum: 0.000000
204
+ 2023-10-13 13:15:53,380 epoch 8 - iter 990/1984 - loss 0.01519291 - time (sec): 277.72 - samples/sec: 296.15 - lr: 0.000042 - momentum: 0.000000
205
+ 2023-10-13 13:16:45,061 epoch 8 - iter 1188/1984 - loss 0.01508252 - time (sec): 329.40 - samples/sec: 299.29 - lr: 0.000040 - momentum: 0.000000
206
+ 2023-10-13 13:17:41,489 epoch 8 - iter 1386/1984 - loss 0.01547066 - time (sec): 385.83 - samples/sec: 297.82 - lr: 0.000038 - momentum: 0.000000
207
+ 2023-10-13 13:18:34,335 epoch 8 - iter 1584/1984 - loss 0.01553468 - time (sec): 438.67 - samples/sec: 297.60 - lr: 0.000037 - momentum: 0.000000
208
+ 2023-10-13 13:19:27,559 epoch 8 - iter 1782/1984 - loss 0.01564675 - time (sec): 491.90 - samples/sec: 298.83 - lr: 0.000035 - momentum: 0.000000
209
+ 2023-10-13 13:20:20,410 epoch 8 - iter 1980/1984 - loss 0.01495060 - time (sec): 544.75 - samples/sec: 300.60 - lr: 0.000033 - momentum: 0.000000
210
+ 2023-10-13 13:20:21,482 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-13 13:20:21,483 EPOCH 8 done: loss 0.0151 - lr: 0.000033
212
+ 2023-10-13 13:20:48,505 DEV : loss 0.20292401313781738 - f1-score (micro avg) 0.7682
213
+ 2023-10-13 13:20:48,555 ----------------------------------------------------------------------------------------------------
214
+ 2023-10-13 13:21:39,886 epoch 9 - iter 198/1984 - loss 0.01309539 - time (sec): 51.33 - samples/sec: 307.26 - lr: 0.000032 - momentum: 0.000000
215
+ 2023-10-13 13:22:31,621 epoch 9 - iter 396/1984 - loss 0.01271783 - time (sec): 103.06 - samples/sec: 308.62 - lr: 0.000030 - momentum: 0.000000
216
+ 2023-10-13 13:23:25,218 epoch 9 - iter 594/1984 - loss 0.01154190 - time (sec): 156.66 - samples/sec: 308.95 - lr: 0.000028 - momentum: 0.000000
217
+ 2023-10-13 13:24:19,703 epoch 9 - iter 792/1984 - loss 0.01142730 - time (sec): 211.15 - samples/sec: 308.12 - lr: 0.000027 - momentum: 0.000000
218
+ 2023-10-13 13:25:13,668 epoch 9 - iter 990/1984 - loss 0.01281801 - time (sec): 265.11 - samples/sec: 306.46 - lr: 0.000025 - momentum: 0.000000
219
+ 2023-10-13 13:26:07,047 epoch 9 - iter 1188/1984 - loss 0.01251343 - time (sec): 318.49 - samples/sec: 301.02 - lr: 0.000023 - momentum: 0.000000
220
+ 2023-10-13 13:27:02,940 epoch 9 - iter 1386/1984 - loss 0.01204291 - time (sec): 374.38 - samples/sec: 302.67 - lr: 0.000022 - momentum: 0.000000
221
+ 2023-10-13 13:27:56,110 epoch 9 - iter 1584/1984 - loss 0.01200449 - time (sec): 427.55 - samples/sec: 303.59 - lr: 0.000020 - momentum: 0.000000
222
+ 2023-10-13 13:28:51,579 epoch 9 - iter 1782/1984 - loss 0.01219217 - time (sec): 483.02 - samples/sec: 304.57 - lr: 0.000018 - momentum: 0.000000
223
+ 2023-10-13 13:29:45,819 epoch 9 - iter 1980/1984 - loss 0.01242827 - time (sec): 537.26 - samples/sec: 304.48 - lr: 0.000017 - momentum: 0.000000
224
+ 2023-10-13 13:29:47,047 ----------------------------------------------------------------------------------------------------
225
+ 2023-10-13 13:29:47,048 EPOCH 9 done: loss 0.0124 - lr: 0.000017
226
+ 2023-10-13 13:30:13,711 DEV : loss 0.21697697043418884 - f1-score (micro avg) 0.7617
227
+ 2023-10-13 13:30:13,764 ----------------------------------------------------------------------------------------------------
228
+ 2023-10-13 13:31:08,460 epoch 10 - iter 198/1984 - loss 0.00658872 - time (sec): 54.69 - samples/sec: 309.49 - lr: 0.000015 - momentum: 0.000000
229
+ 2023-10-13 13:32:01,825 epoch 10 - iter 396/1984 - loss 0.00947681 - time (sec): 108.06 - samples/sec: 301.78 - lr: 0.000013 - momentum: 0.000000
230
+ 2023-10-13 13:32:55,149 epoch 10 - iter 594/1984 - loss 0.00879204 - time (sec): 161.38 - samples/sec: 300.65 - lr: 0.000012 - momentum: 0.000000
231
+ 2023-10-13 13:33:51,084 epoch 10 - iter 792/1984 - loss 0.00808015 - time (sec): 217.32 - samples/sec: 296.34 - lr: 0.000010 - momentum: 0.000000
232
+ 2023-10-13 13:34:48,261 epoch 10 - iter 990/1984 - loss 0.00746892 - time (sec): 274.49 - samples/sec: 295.76 - lr: 0.000008 - momentum: 0.000000
233
+ 2023-10-13 13:35:43,142 epoch 10 - iter 1188/1984 - loss 0.00746131 - time (sec): 329.38 - samples/sec: 297.49 - lr: 0.000007 - momentum: 0.000000
234
+ 2023-10-13 13:36:37,996 epoch 10 - iter 1386/1984 - loss 0.00742811 - time (sec): 384.23 - samples/sec: 298.45 - lr: 0.000005 - momentum: 0.000000
235
+ 2023-10-13 13:37:32,826 epoch 10 - iter 1584/1984 - loss 0.00777138 - time (sec): 439.06 - samples/sec: 299.37 - lr: 0.000003 - momentum: 0.000000
236
+ 2023-10-13 13:38:29,578 epoch 10 - iter 1782/1984 - loss 0.00774151 - time (sec): 495.81 - samples/sec: 298.21 - lr: 0.000002 - momentum: 0.000000
237
+ 2023-10-13 13:39:24,305 epoch 10 - iter 1980/1984 - loss 0.00806446 - time (sec): 550.54 - samples/sec: 297.17 - lr: 0.000000 - momentum: 0.000000
238
+ 2023-10-13 13:39:25,573 ----------------------------------------------------------------------------------------------------
239
+ 2023-10-13 13:39:25,574 EPOCH 10 done: loss 0.0080 - lr: 0.000000
240
+ 2023-10-13 13:39:51,477 DEV : loss 0.22803443670272827 - f1-score (micro avg) 0.7611
241
+ 2023-10-13 13:39:52,471 ----------------------------------------------------------------------------------------------------
242
+ 2023-10-13 13:39:52,473 Loading model from best epoch ...
243
+ 2023-10-13 13:39:57,370 SequenceTagger predicts: Dictionary with 13 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG
244
+ 2023-10-13 13:40:22,958
245
+ Results:
246
+ - F-score (micro) 0.7832
247
+ - F-score (macro) 0.6902
248
+ - Accuracy 0.6628
249
+
250
+ By class:
251
+ precision recall f1-score support
252
+
253
+ LOC 0.8351 0.8656 0.8501 655
254
+ PER 0.7284 0.7937 0.7597 223
255
+ ORG 0.4828 0.4409 0.4609 127
256
+
257
+ micro avg 0.7707 0.7960 0.7832 1005
258
+ macro avg 0.6821 0.7001 0.6902 1005
259
+ weighted avg 0.7669 0.7960 0.7808 1005
260
+
261
+ 2023-10-13 13:40:22,958 ----------------------------------------------------------------------------------------------------