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hmbench-newseye-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4

Token Classification
Flair
PyTorch
TensorBoard
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.1697045789.de2e83fddbee.1120.14 +3 -0
  6. test.tsv +0 -0
  7. training.log +263 -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 17:45:34 0.0001 1.0810 0.2036 0.4954 0.5905 0.5388 0.4030
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+ 2 17:54:54 0.0001 0.1412 0.1027 0.7552 0.7850 0.7698 0.6483
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+ 3 18:04:21 0.0001 0.0743 0.1320 0.7400 0.8054 0.7713 0.6463
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+ 4 18:13:42 0.0001 0.0522 0.1518 0.7590 0.8054 0.7815 0.6541
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+ 5 18:23:14 0.0001 0.0380 0.1601 0.7940 0.8231 0.8083 0.6938
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+ 6 18:32:49 0.0001 0.0275 0.1976 0.7807 0.8041 0.7922 0.6693
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+ 7 18:42:18 0.0001 0.0207 0.2033 0.7776 0.8041 0.7906 0.6655
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+ 8 18:51:59 0.0000 0.0155 0.2236 0.7800 0.8054 0.7925 0.6689
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+ 9 19:01:39 0.0000 0.0110 0.2319 0.7768 0.8190 0.7974 0.6764
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+ 10 19:11:34 0.0000 0.0090 0.2363 0.7744 0.8082 0.7909 0.6674
runs/events.out.tfevents.1697045789.de2e83fddbee.1120.14 ADDED
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-11 17:36:29,073 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:36:29,075 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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+ 2023-10-11 17:36:29,075 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:36:29,075 MultiCorpus: 7142 train + 698 dev + 2570 test sentences
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+ - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator
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+ 2023-10-11 17:36:29,075 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:36:29,075 Train: 7142 sentences
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+ 2023-10-11 17:36:29,075 (train_with_dev=False, train_with_test=False)
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+ 2023-10-11 17:36:29,075 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:36:29,075 Training Params:
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+ 2023-10-11 17:36:29,076 - learning_rate: "0.00015"
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+ 2023-10-11 17:36:29,076 - mini_batch_size: "4"
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+ 2023-10-11 17:36:29,076 - max_epochs: "10"
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+ 2023-10-11 17:36:29,076 - shuffle: "True"
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+ 2023-10-11 17:36:29,076 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:36:29,076 Plugins:
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+ 2023-10-11 17:36:29,076 - TensorboardLogger
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+ 2023-10-11 17:36:29,076 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-11 17:36:29,076 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:36:29,076 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-11 17:36:29,076 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-11 17:36:29,076 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:36:29,076 Computation:
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+ 2023-10-11 17:36:29,076 - compute on device: cuda:0
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+ 2023-10-11 17:36:29,076 - embedding storage: none
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+ 2023-10-11 17:36:29,076 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:36:29,076 Model training base path: "hmbench-newseye/fr-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4"
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+ 2023-10-11 17:36:29,077 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:36:29,077 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:36:29,077 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-11 17:37:20,456 epoch 1 - iter 178/1786 - loss 2.81924723 - time (sec): 51.38 - samples/sec: 454.30 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-11 17:38:12,331 epoch 1 - iter 356/1786 - loss 2.64980608 - time (sec): 103.25 - samples/sec: 464.05 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-11 17:39:03,793 epoch 1 - iter 534/1786 - loss 2.37963726 - time (sec): 154.71 - samples/sec: 465.85 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-11 17:39:56,229 epoch 1 - iter 712/1786 - loss 2.08450845 - time (sec): 207.15 - samples/sec: 467.58 - lr: 0.000060 - momentum: 0.000000
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+ 2023-10-11 17:40:47,993 epoch 1 - iter 890/1786 - loss 1.81534257 - time (sec): 258.91 - samples/sec: 464.69 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-11 17:41:41,378 epoch 1 - iter 1068/1786 - loss 1.59589063 - time (sec): 312.30 - samples/sec: 468.60 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-11 17:42:35,379 epoch 1 - iter 1246/1786 - loss 1.41200758 - time (sec): 366.30 - samples/sec: 472.40 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-11 17:43:27,539 epoch 1 - iter 1424/1786 - loss 1.28012419 - time (sec): 418.46 - samples/sec: 473.51 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-11 17:44:19,457 epoch 1 - iter 1602/1786 - loss 1.17694462 - time (sec): 470.38 - samples/sec: 472.74 - lr: 0.000134 - momentum: 0.000000
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+ 2023-10-11 17:45:12,644 epoch 1 - iter 1780/1786 - loss 1.08357721 - time (sec): 523.57 - samples/sec: 473.74 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-11 17:45:14,235 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:45:14,235 EPOCH 1 done: loss 1.0810 - lr: 0.000149
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+ 2023-10-11 17:45:34,466 DEV : loss 0.20361904799938202 - f1-score (micro avg) 0.5388
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+ 2023-10-11 17:45:34,497 saving best model
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+ 2023-10-11 17:45:35,356 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:46:28,481 epoch 2 - iter 178/1786 - loss 0.21290432 - time (sec): 53.12 - samples/sec: 484.33 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-11 17:47:21,521 epoch 2 - iter 356/1786 - loss 0.20649008 - time (sec): 106.16 - samples/sec: 479.48 - lr: 0.000147 - momentum: 0.000000
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+ 2023-10-11 17:48:16,086 epoch 2 - iter 534/1786 - loss 0.19057159 - time (sec): 160.73 - samples/sec: 482.34 - lr: 0.000145 - momentum: 0.000000
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+ 2023-10-11 17:49:08,697 epoch 2 - iter 712/1786 - loss 0.17993568 - time (sec): 213.34 - samples/sec: 472.54 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-11 17:50:01,167 epoch 2 - iter 890/1786 - loss 0.17058695 - time (sec): 265.81 - samples/sec: 471.80 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-11 17:50:53,743 epoch 2 - iter 1068/1786 - loss 0.16082396 - time (sec): 318.38 - samples/sec: 470.38 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-11 17:51:45,421 epoch 2 - iter 1246/1786 - loss 0.15569068 - time (sec): 370.06 - samples/sec: 469.79 - lr: 0.000138 - momentum: 0.000000
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+ 2023-10-11 17:52:38,828 epoch 2 - iter 1424/1786 - loss 0.14996741 - time (sec): 423.47 - samples/sec: 468.27 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-11 17:53:34,073 epoch 2 - iter 1602/1786 - loss 0.14514985 - time (sec): 478.71 - samples/sec: 463.26 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-11 17:54:30,018 epoch 2 - iter 1780/1786 - loss 0.14157379 - time (sec): 534.66 - samples/sec: 463.16 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-11 17:54:32,031 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:54:32,032 EPOCH 2 done: loss 0.1412 - lr: 0.000133
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+ 2023-10-11 17:54:54,099 DEV : loss 0.10268282890319824 - f1-score (micro avg) 0.7698
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+ 2023-10-11 17:54:54,135 saving best model
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+ 2023-10-11 17:54:56,735 ----------------------------------------------------------------------------------------------------
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+ 2023-10-11 17:55:52,053 epoch 3 - iter 178/1786 - loss 0.07581422 - time (sec): 55.31 - samples/sec: 448.39 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-11 17:56:46,482 epoch 3 - iter 356/1786 - loss 0.08043397 - time (sec): 109.74 - samples/sec: 455.00 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-11 17:57:40,496 epoch 3 - iter 534/1786 - loss 0.07520227 - time (sec): 163.76 - samples/sec: 452.76 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-11 17:58:34,143 epoch 3 - iter 712/1786 - loss 0.07247742 - time (sec): 217.40 - samples/sec: 451.46 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-11 17:59:27,936 epoch 3 - iter 890/1786 - loss 0.07466025 - time (sec): 271.20 - samples/sec: 452.66 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-11 18:00:21,462 epoch 3 - iter 1068/1786 - loss 0.07516997 - time (sec): 324.72 - samples/sec: 454.15 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-11 18:01:15,676 epoch 3 - iter 1246/1786 - loss 0.07379608 - time (sec): 378.94 - samples/sec: 453.76 - lr: 0.000122 - momentum: 0.000000
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+ 2023-10-11 18:02:09,837 epoch 3 - iter 1424/1786 - loss 0.07445074 - time (sec): 433.10 - samples/sec: 455.26 - lr: 0.000120 - momentum: 0.000000
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+ 2023-10-11 18:03:03,839 epoch 3 - iter 1602/1786 - loss 0.07358598 - time (sec): 487.10 - samples/sec: 456.93 - lr: 0.000118 - momentum: 0.000000
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+ 2023-10-11 18:03:58,389 epoch 3 - iter 1780/1786 - loss 0.07423850 - time (sec): 541.65 - samples/sec: 457.42 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-11 18:04:00,209 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-11 18:04:00,209 EPOCH 3 done: loss 0.0743 - lr: 0.000117
140
+ 2023-10-11 18:04:21,642 DEV : loss 0.13196961581707 - f1-score (micro avg) 0.7713
141
+ 2023-10-11 18:04:21,673 saving best model
142
+ 2023-10-11 18:04:24,252 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-11 18:05:16,538 epoch 4 - iter 178/1786 - loss 0.05605407 - time (sec): 52.28 - samples/sec: 471.55 - lr: 0.000115 - momentum: 0.000000
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+ 2023-10-11 18:06:08,745 epoch 4 - iter 356/1786 - loss 0.05000837 - time (sec): 104.49 - samples/sec: 475.50 - lr: 0.000113 - momentum: 0.000000
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+ 2023-10-11 18:07:01,886 epoch 4 - iter 534/1786 - loss 0.05222854 - time (sec): 157.63 - samples/sec: 481.86 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-11 18:07:54,105 epoch 4 - iter 712/1786 - loss 0.05172677 - time (sec): 209.85 - samples/sec: 479.63 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-11 18:08:45,957 epoch 4 - iter 890/1786 - loss 0.05101322 - time (sec): 261.70 - samples/sec: 477.03 - lr: 0.000108 - momentum: 0.000000
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+ 2023-10-11 18:09:40,097 epoch 4 - iter 1068/1786 - loss 0.05070108 - time (sec): 315.84 - samples/sec: 474.53 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-11 18:10:36,928 epoch 4 - iter 1246/1786 - loss 0.05208864 - time (sec): 372.67 - samples/sec: 473.16 - lr: 0.000105 - momentum: 0.000000
150
+ 2023-10-11 18:11:31,138 epoch 4 - iter 1424/1786 - loss 0.05315052 - time (sec): 426.88 - samples/sec: 467.69 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-11 18:12:24,160 epoch 4 - iter 1602/1786 - loss 0.05314471 - time (sec): 479.91 - samples/sec: 466.16 - lr: 0.000102 - momentum: 0.000000
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+ 2023-10-11 18:13:18,131 epoch 4 - iter 1780/1786 - loss 0.05220709 - time (sec): 533.88 - samples/sec: 464.57 - lr: 0.000100 - momentum: 0.000000
153
+ 2023-10-11 18:13:19,774 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-11 18:13:19,775 EPOCH 4 done: loss 0.0522 - lr: 0.000100
155
+ 2023-10-11 18:13:42,131 DEV : loss 0.15180714428424835 - f1-score (micro avg) 0.7815
156
+ 2023-10-11 18:13:42,166 saving best model
157
+ 2023-10-11 18:13:46,961 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-11 18:14:40,307 epoch 5 - iter 178/1786 - loss 0.04155092 - time (sec): 53.34 - samples/sec: 445.72 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-11 18:15:33,137 epoch 5 - iter 356/1786 - loss 0.03973759 - time (sec): 106.17 - samples/sec: 441.09 - lr: 0.000097 - momentum: 0.000000
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+ 2023-10-11 18:16:28,231 epoch 5 - iter 534/1786 - loss 0.03869343 - time (sec): 161.27 - samples/sec: 455.20 - lr: 0.000095 - momentum: 0.000000
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+ 2023-10-11 18:17:22,718 epoch 5 - iter 712/1786 - loss 0.03913412 - time (sec): 215.75 - samples/sec: 457.97 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-11 18:18:15,097 epoch 5 - iter 890/1786 - loss 0.03875654 - time (sec): 268.13 - samples/sec: 458.77 - lr: 0.000092 - momentum: 0.000000
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+ 2023-10-11 18:19:07,851 epoch 5 - iter 1068/1786 - loss 0.03759774 - time (sec): 320.89 - samples/sec: 456.30 - lr: 0.000090 - momentum: 0.000000
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+ 2023-10-11 18:20:03,982 epoch 5 - iter 1246/1786 - loss 0.03756197 - time (sec): 377.02 - samples/sec: 458.06 - lr: 0.000088 - momentum: 0.000000
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+ 2023-10-11 18:20:58,516 epoch 5 - iter 1424/1786 - loss 0.03737673 - time (sec): 431.55 - samples/sec: 455.81 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-11 18:21:52,617 epoch 5 - iter 1602/1786 - loss 0.03776158 - time (sec): 485.65 - samples/sec: 457.43 - lr: 0.000085 - momentum: 0.000000
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+ 2023-10-11 18:22:49,426 epoch 5 - iter 1780/1786 - loss 0.03807107 - time (sec): 542.46 - samples/sec: 456.73 - lr: 0.000083 - momentum: 0.000000
168
+ 2023-10-11 18:22:51,427 ----------------------------------------------------------------------------------------------------
169
+ 2023-10-11 18:22:51,427 EPOCH 5 done: loss 0.0380 - lr: 0.000083
170
+ 2023-10-11 18:23:14,760 DEV : loss 0.16010259091854095 - f1-score (micro avg) 0.8083
171
+ 2023-10-11 18:23:14,792 saving best model
172
+ 2023-10-11 18:23:17,472 ----------------------------------------------------------------------------------------------------
173
+ 2023-10-11 18:24:12,145 epoch 6 - iter 178/1786 - loss 0.03238792 - time (sec): 54.67 - samples/sec: 459.55 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-11 18:25:05,682 epoch 6 - iter 356/1786 - loss 0.02941863 - time (sec): 108.20 - samples/sec: 458.47 - lr: 0.000080 - momentum: 0.000000
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+ 2023-10-11 18:25:58,332 epoch 6 - iter 534/1786 - loss 0.02729745 - time (sec): 160.85 - samples/sec: 457.92 - lr: 0.000078 - momentum: 0.000000
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+ 2023-10-11 18:26:52,267 epoch 6 - iter 712/1786 - loss 0.02776539 - time (sec): 214.79 - samples/sec: 459.33 - lr: 0.000077 - momentum: 0.000000
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+ 2023-10-11 18:27:47,349 epoch 6 - iter 890/1786 - loss 0.02725920 - time (sec): 269.87 - samples/sec: 455.75 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-11 18:28:44,263 epoch 6 - iter 1068/1786 - loss 0.02841412 - time (sec): 326.79 - samples/sec: 454.06 - lr: 0.000073 - momentum: 0.000000
179
+ 2023-10-11 18:29:41,292 epoch 6 - iter 1246/1786 - loss 0.02811007 - time (sec): 383.82 - samples/sec: 451.55 - lr: 0.000072 - momentum: 0.000000
180
+ 2023-10-11 18:30:36,642 epoch 6 - iter 1424/1786 - loss 0.02811630 - time (sec): 439.16 - samples/sec: 451.16 - lr: 0.000070 - momentum: 0.000000
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+ 2023-10-11 18:31:31,328 epoch 6 - iter 1602/1786 - loss 0.02770252 - time (sec): 493.85 - samples/sec: 451.21 - lr: 0.000068 - momentum: 0.000000
182
+ 2023-10-11 18:32:25,480 epoch 6 - iter 1780/1786 - loss 0.02744603 - time (sec): 548.00 - samples/sec: 452.48 - lr: 0.000067 - momentum: 0.000000
183
+ 2023-10-11 18:32:27,134 ----------------------------------------------------------------------------------------------------
184
+ 2023-10-11 18:32:27,135 EPOCH 6 done: loss 0.0275 - lr: 0.000067
185
+ 2023-10-11 18:32:49,419 DEV : loss 0.1975564956665039 - f1-score (micro avg) 0.7922
186
+ 2023-10-11 18:32:49,452 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-11 18:33:42,384 epoch 7 - iter 178/1786 - loss 0.02364687 - time (sec): 52.93 - samples/sec: 456.89 - lr: 0.000065 - momentum: 0.000000
188
+ 2023-10-11 18:34:36,361 epoch 7 - iter 356/1786 - loss 0.02566367 - time (sec): 106.91 - samples/sec: 456.18 - lr: 0.000063 - momentum: 0.000000
189
+ 2023-10-11 18:35:30,916 epoch 7 - iter 534/1786 - loss 0.02607659 - time (sec): 161.46 - samples/sec: 451.34 - lr: 0.000062 - momentum: 0.000000
190
+ 2023-10-11 18:36:25,221 epoch 7 - iter 712/1786 - loss 0.02345986 - time (sec): 215.77 - samples/sec: 456.83 - lr: 0.000060 - momentum: 0.000000
191
+ 2023-10-11 18:37:20,581 epoch 7 - iter 890/1786 - loss 0.02360187 - time (sec): 271.13 - samples/sec: 456.96 - lr: 0.000058 - momentum: 0.000000
192
+ 2023-10-11 18:38:15,580 epoch 7 - iter 1068/1786 - loss 0.02238154 - time (sec): 326.13 - samples/sec: 455.70 - lr: 0.000057 - momentum: 0.000000
193
+ 2023-10-11 18:39:10,005 epoch 7 - iter 1246/1786 - loss 0.02133457 - time (sec): 380.55 - samples/sec: 454.89 - lr: 0.000055 - momentum: 0.000000
194
+ 2023-10-11 18:40:05,662 epoch 7 - iter 1424/1786 - loss 0.02172766 - time (sec): 436.21 - samples/sec: 455.44 - lr: 0.000053 - momentum: 0.000000
195
+ 2023-10-11 18:41:01,549 epoch 7 - iter 1602/1786 - loss 0.02138396 - time (sec): 492.10 - samples/sec: 454.50 - lr: 0.000052 - momentum: 0.000000
196
+ 2023-10-11 18:41:54,273 epoch 7 - iter 1780/1786 - loss 0.02081679 - time (sec): 544.82 - samples/sec: 454.51 - lr: 0.000050 - momentum: 0.000000
197
+ 2023-10-11 18:41:56,288 ----------------------------------------------------------------------------------------------------
198
+ 2023-10-11 18:41:56,288 EPOCH 7 done: loss 0.0207 - lr: 0.000050
199
+ 2023-10-11 18:42:18,651 DEV : loss 0.2033122181892395 - f1-score (micro avg) 0.7906
200
+ 2023-10-11 18:42:18,684 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-11 18:43:14,073 epoch 8 - iter 178/1786 - loss 0.01534023 - time (sec): 55.39 - samples/sec: 446.17 - lr: 0.000048 - momentum: 0.000000
202
+ 2023-10-11 18:44:09,481 epoch 8 - iter 356/1786 - loss 0.01688005 - time (sec): 110.79 - samples/sec: 455.36 - lr: 0.000047 - momentum: 0.000000
203
+ 2023-10-11 18:45:04,364 epoch 8 - iter 534/1786 - loss 0.01455467 - time (sec): 165.68 - samples/sec: 454.79 - lr: 0.000045 - momentum: 0.000000
204
+ 2023-10-11 18:46:00,082 epoch 8 - iter 712/1786 - loss 0.01641305 - time (sec): 221.40 - samples/sec: 453.36 - lr: 0.000043 - momentum: 0.000000
205
+ 2023-10-11 18:46:56,812 epoch 8 - iter 890/1786 - loss 0.01612635 - time (sec): 278.13 - samples/sec: 446.87 - lr: 0.000042 - momentum: 0.000000
206
+ 2023-10-11 18:47:53,561 epoch 8 - iter 1068/1786 - loss 0.01499776 - time (sec): 334.87 - samples/sec: 443.86 - lr: 0.000040 - momentum: 0.000000
207
+ 2023-10-11 18:48:50,595 epoch 8 - iter 1246/1786 - loss 0.01544156 - time (sec): 391.91 - samples/sec: 444.25 - lr: 0.000038 - momentum: 0.000000
208
+ 2023-10-11 18:49:45,028 epoch 8 - iter 1424/1786 - loss 0.01554454 - time (sec): 446.34 - samples/sec: 440.67 - lr: 0.000037 - momentum: 0.000000
209
+ 2023-10-11 18:50:39,377 epoch 8 - iter 1602/1786 - loss 0.01519250 - time (sec): 500.69 - samples/sec: 444.59 - lr: 0.000035 - momentum: 0.000000
210
+ 2023-10-11 18:51:34,428 epoch 8 - iter 1780/1786 - loss 0.01551751 - time (sec): 555.74 - samples/sec: 446.37 - lr: 0.000033 - momentum: 0.000000
211
+ 2023-10-11 18:51:36,048 ----------------------------------------------------------------------------------------------------
212
+ 2023-10-11 18:51:36,048 EPOCH 8 done: loss 0.0155 - lr: 0.000033
213
+ 2023-10-11 18:51:59,372 DEV : loss 0.2236306071281433 - f1-score (micro avg) 0.7925
214
+ 2023-10-11 18:51:59,405 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-11 18:52:53,303 epoch 9 - iter 178/1786 - loss 0.00534514 - time (sec): 53.90 - samples/sec: 438.90 - lr: 0.000032 - momentum: 0.000000
216
+ 2023-10-11 18:53:48,375 epoch 9 - iter 356/1786 - loss 0.01015032 - time (sec): 108.97 - samples/sec: 453.68 - lr: 0.000030 - momentum: 0.000000
217
+ 2023-10-11 18:54:44,141 epoch 9 - iter 534/1786 - loss 0.01306851 - time (sec): 164.73 - samples/sec: 459.92 - lr: 0.000028 - momentum: 0.000000
218
+ 2023-10-11 18:55:39,984 epoch 9 - iter 712/1786 - loss 0.01202527 - time (sec): 220.58 - samples/sec: 456.11 - lr: 0.000027 - momentum: 0.000000
219
+ 2023-10-11 18:56:34,979 epoch 9 - iter 890/1786 - loss 0.01142279 - time (sec): 275.57 - samples/sec: 452.74 - lr: 0.000025 - momentum: 0.000000
220
+ 2023-10-11 18:57:31,235 epoch 9 - iter 1068/1786 - loss 0.01115001 - time (sec): 331.83 - samples/sec: 451.22 - lr: 0.000023 - momentum: 0.000000
221
+ 2023-10-11 18:58:26,472 epoch 9 - iter 1246/1786 - loss 0.01140760 - time (sec): 387.06 - samples/sec: 449.42 - lr: 0.000022 - momentum: 0.000000
222
+ 2023-10-11 18:59:22,703 epoch 9 - iter 1424/1786 - loss 0.01133329 - time (sec): 443.30 - samples/sec: 447.16 - lr: 0.000020 - momentum: 0.000000
223
+ 2023-10-11 19:00:18,219 epoch 9 - iter 1602/1786 - loss 0.01110225 - time (sec): 498.81 - samples/sec: 446.00 - lr: 0.000018 - momentum: 0.000000
224
+ 2023-10-11 19:01:15,055 epoch 9 - iter 1780/1786 - loss 0.01103208 - time (sec): 555.65 - samples/sec: 446.12 - lr: 0.000017 - momentum: 0.000000
225
+ 2023-10-11 19:01:16,897 ----------------------------------------------------------------------------------------------------
226
+ 2023-10-11 19:01:16,897 EPOCH 9 done: loss 0.0110 - lr: 0.000017
227
+ 2023-10-11 19:01:39,717 DEV : loss 0.23193296790122986 - f1-score (micro avg) 0.7974
228
+ 2023-10-11 19:01:39,750 ----------------------------------------------------------------------------------------------------
229
+ 2023-10-11 19:02:37,006 epoch 10 - iter 178/1786 - loss 0.00698458 - time (sec): 57.25 - samples/sec: 439.62 - lr: 0.000015 - momentum: 0.000000
230
+ 2023-10-11 19:03:34,910 epoch 10 - iter 356/1786 - loss 0.00817366 - time (sec): 115.16 - samples/sec: 445.19 - lr: 0.000013 - momentum: 0.000000
231
+ 2023-10-11 19:04:32,347 epoch 10 - iter 534/1786 - loss 0.00869250 - time (sec): 172.59 - samples/sec: 432.02 - lr: 0.000012 - momentum: 0.000000
232
+ 2023-10-11 19:05:30,527 epoch 10 - iter 712/1786 - loss 0.00881744 - time (sec): 230.77 - samples/sec: 430.16 - lr: 0.000010 - momentum: 0.000000
233
+ 2023-10-11 19:06:24,815 epoch 10 - iter 890/1786 - loss 0.00835789 - time (sec): 285.06 - samples/sec: 429.36 - lr: 0.000008 - momentum: 0.000000
234
+ 2023-10-11 19:07:21,724 epoch 10 - iter 1068/1786 - loss 0.00867803 - time (sec): 341.97 - samples/sec: 436.06 - lr: 0.000007 - momentum: 0.000000
235
+ 2023-10-11 19:08:17,370 epoch 10 - iter 1246/1786 - loss 0.00883856 - time (sec): 397.62 - samples/sec: 434.74 - lr: 0.000005 - momentum: 0.000000
236
+ 2023-10-11 19:09:14,246 epoch 10 - iter 1424/1786 - loss 0.00953875 - time (sec): 454.49 - samples/sec: 435.60 - lr: 0.000003 - momentum: 0.000000
237
+ 2023-10-11 19:10:12,234 epoch 10 - iter 1602/1786 - loss 0.00908507 - time (sec): 512.48 - samples/sec: 435.95 - lr: 0.000002 - momentum: 0.000000
238
+ 2023-10-11 19:11:08,898 epoch 10 - iter 1780/1786 - loss 0.00899664 - time (sec): 569.15 - samples/sec: 436.09 - lr: 0.000000 - momentum: 0.000000
239
+ 2023-10-11 19:11:10,476 ----------------------------------------------------------------------------------------------------
240
+ 2023-10-11 19:11:10,477 EPOCH 10 done: loss 0.0090 - lr: 0.000000
241
+ 2023-10-11 19:11:34,051 DEV : loss 0.2363290935754776 - f1-score (micro avg) 0.7909
242
+ 2023-10-11 19:11:35,015 ----------------------------------------------------------------------------------------------------
243
+ 2023-10-11 19:11:35,017 Loading model from best epoch ...
244
+ 2023-10-11 19:11:41,051 SequenceTagger predicts: Dictionary with 17 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, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
245
+ 2023-10-11 19:12:53,145
246
+ Results:
247
+ - F-score (micro) 0.6861
248
+ - F-score (macro) 0.6058
249
+ - Accuracy 0.5407
250
+
251
+ By class:
252
+ precision recall f1-score support
253
+
254
+ LOC 0.7063 0.7050 0.7057 1095
255
+ PER 0.7809 0.7500 0.7651 1012
256
+ ORG 0.4030 0.5994 0.4820 357
257
+ HumanProd 0.3846 0.6061 0.4706 33
258
+
259
+ micro avg 0.6665 0.7068 0.6861 2497
260
+ macro avg 0.5687 0.6651 0.6058 2497
261
+ weighted avg 0.6889 0.7068 0.6947 2497
262
+
263
+ 2023-10-11 19:12:53,145 ----------------------------------------------------------------------------------------------------