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flair-hipe-2022-hipe2020-de

Token Classification
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sequence-tagger-model
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. loss.tsv +11 -0
  4. runs/events.out.tfevents.1697337106.d3463e005216.2433.15 +3 -0
  5. test.tsv +0 -0
  6. training.log +265 -0
best-model.pt ADDED
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dev.tsv 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 02:34:58 0.0002 1.5004 0.3781 0.0000 0.0000 0.0000 0.0000
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+ 2 02:38:12 0.0001 0.2512 0.1952 0.7005 0.6200 0.6578 0.5093
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+ 3 02:41:27 0.0001 0.1218 0.1559 0.7431 0.7170 0.7298 0.5966
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+ 4 02:44:39 0.0001 0.0690 0.1609 0.7340 0.7701 0.7516 0.6215
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+ 5 02:47:53 0.0001 0.0419 0.1873 0.7668 0.7740 0.7704 0.6408
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+ 6 02:51:13 0.0001 0.0278 0.2143 0.7695 0.7647 0.7671 0.6375
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+ 7 02:54:28 0.0001 0.0185 0.2163 0.7689 0.7701 0.7695 0.6421
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+ 8 02:57:44 0.0000 0.0118 0.2224 0.7793 0.7647 0.7719 0.6460
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+ 9 03:01:01 0.0000 0.0091 0.2321 0.7963 0.7826 0.7894 0.6647
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+ 10 03:04:17 0.0000 0.0059 0.2379 0.7885 0.7811 0.7848 0.6590
runs/events.out.tfevents.1697337106.d3463e005216.2433.15 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ size 502124
test.tsv ADDED
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training.log ADDED
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+ 2023-10-15 02:31:46,751 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:31:46,752 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)
51
+ (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=21, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-15 02:31:46,752 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:31:46,752 MultiCorpus: 3575 train + 1235 dev + 1266 test sentences
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+ - NER_HIPE_2022 Corpus: 3575 train + 1235 dev + 1266 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/de/with_doc_seperator
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+ 2023-10-15 02:31:46,752 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:31:46,752 Train: 3575 sentences
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+ 2023-10-15 02:31:46,752 (train_with_dev=False, train_with_test=False)
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+ 2023-10-15 02:31:46,752 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:31:46,752 Training Params:
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+ 2023-10-15 02:31:46,752 - learning_rate: "0.00016"
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+ 2023-10-15 02:31:46,752 - mini_batch_size: "4"
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+ 2023-10-15 02:31:46,752 - max_epochs: "10"
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+ 2023-10-15 02:31:46,752 - shuffle: "True"
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+ 2023-10-15 02:31:46,753 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:31:46,753 Plugins:
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+ 2023-10-15 02:31:46,753 - TensorboardLogger
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+ 2023-10-15 02:31:46,753 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-15 02:31:46,753 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:31:46,753 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-15 02:31:46,753 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-15 02:31:46,753 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:31:46,753 Computation:
91
+ 2023-10-15 02:31:46,753 - compute on device: cuda:0
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+ 2023-10-15 02:31:46,753 - embedding storage: none
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+ 2023-10-15 02:31:46,753 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:31:46,753 Model training base path: "hmbench-hipe2020/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4"
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+ 2023-10-15 02:31:46,753 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:31:46,753 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:31:46,753 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-15 02:32:03,162 epoch 1 - iter 89/894 - loss 3.02344183 - time (sec): 16.41 - samples/sec: 503.29 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-15 02:32:22,172 epoch 1 - iter 178/894 - loss 2.95643439 - time (sec): 35.42 - samples/sec: 517.95 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-15 02:32:39,335 epoch 1 - iter 267/894 - loss 2.78708734 - time (sec): 52.58 - samples/sec: 525.09 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-15 02:32:55,879 epoch 1 - iter 356/894 - loss 2.58160891 - time (sec): 69.13 - samples/sec: 521.14 - lr: 0.000064 - momentum: 0.000000
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+ 2023-10-15 02:33:12,285 epoch 1 - iter 445/894 - loss 2.35433260 - time (sec): 85.53 - samples/sec: 520.19 - lr: 0.000079 - momentum: 0.000000
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+ 2023-10-15 02:33:29,031 epoch 1 - iter 534/894 - loss 2.10818662 - time (sec): 102.28 - samples/sec: 520.02 - lr: 0.000095 - momentum: 0.000000
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+ 2023-10-15 02:33:45,298 epoch 1 - iter 623/894 - loss 1.90435758 - time (sec): 118.54 - samples/sec: 518.72 - lr: 0.000111 - momentum: 0.000000
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+ 2023-10-15 02:34:01,309 epoch 1 - iter 712/894 - loss 1.75522635 - time (sec): 134.55 - samples/sec: 514.33 - lr: 0.000127 - momentum: 0.000000
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+ 2023-10-15 02:34:17,945 epoch 1 - iter 801/894 - loss 1.61964852 - time (sec): 151.19 - samples/sec: 514.93 - lr: 0.000143 - momentum: 0.000000
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+ 2023-10-15 02:34:34,731 epoch 1 - iter 890/894 - loss 1.50331592 - time (sec): 167.98 - samples/sec: 513.77 - lr: 0.000159 - momentum: 0.000000
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+ 2023-10-15 02:34:35,368 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:34:35,368 EPOCH 1 done: loss 1.5004 - lr: 0.000159
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+ 2023-10-15 02:34:58,797 DEV : loss 0.3780643343925476 - f1-score (micro avg) 0.0
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+ 2023-10-15 02:34:58,823 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:35:15,413 epoch 2 - iter 89/894 - loss 0.35647784 - time (sec): 16.59 - samples/sec: 506.48 - lr: 0.000158 - momentum: 0.000000
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+ 2023-10-15 02:35:32,068 epoch 2 - iter 178/894 - loss 0.34495992 - time (sec): 33.24 - samples/sec: 488.93 - lr: 0.000156 - momentum: 0.000000
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+ 2023-10-15 02:35:48,763 epoch 2 - iter 267/894 - loss 0.32633621 - time (sec): 49.94 - samples/sec: 498.77 - lr: 0.000155 - momentum: 0.000000
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+ 2023-10-15 02:36:05,361 epoch 2 - iter 356/894 - loss 0.30999671 - time (sec): 66.54 - samples/sec: 501.31 - lr: 0.000153 - momentum: 0.000000
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+ 2023-10-15 02:36:22,230 epoch 2 - iter 445/894 - loss 0.29402029 - time (sec): 83.41 - samples/sec: 504.29 - lr: 0.000151 - momentum: 0.000000
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+ 2023-10-15 02:36:38,633 epoch 2 - iter 534/894 - loss 0.29260383 - time (sec): 99.81 - samples/sec: 504.68 - lr: 0.000149 - momentum: 0.000000
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+ 2023-10-15 02:36:55,012 epoch 2 - iter 623/894 - loss 0.28062458 - time (sec): 116.19 - samples/sec: 505.86 - lr: 0.000148 - momentum: 0.000000
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+ 2023-10-15 02:37:11,215 epoch 2 - iter 712/894 - loss 0.27449208 - time (sec): 132.39 - samples/sec: 505.71 - lr: 0.000146 - momentum: 0.000000
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+ 2023-10-15 02:37:28,432 epoch 2 - iter 801/894 - loss 0.26359435 - time (sec): 149.61 - samples/sec: 509.57 - lr: 0.000144 - momentum: 0.000000
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+ 2023-10-15 02:37:47,059 epoch 2 - iter 890/894 - loss 0.25229683 - time (sec): 168.23 - samples/sec: 511.90 - lr: 0.000142 - momentum: 0.000000
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+ 2023-10-15 02:37:47,825 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:37:47,825 EPOCH 2 done: loss 0.2512 - lr: 0.000142
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+ 2023-10-15 02:38:12,900 DEV : loss 0.19516690075397491 - f1-score (micro avg) 0.6578
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+ 2023-10-15 02:38:12,926 saving best model
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+ 2023-10-15 02:38:13,600 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:38:30,620 epoch 3 - iter 89/894 - loss 0.19012779 - time (sec): 17.02 - samples/sec: 538.74 - lr: 0.000140 - momentum: 0.000000
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+ 2023-10-15 02:38:47,328 epoch 3 - iter 178/894 - loss 0.15803457 - time (sec): 33.73 - samples/sec: 540.12 - lr: 0.000139 - momentum: 0.000000
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+ 2023-10-15 02:39:04,061 epoch 3 - iter 267/894 - loss 0.14197851 - time (sec): 50.46 - samples/sec: 531.87 - lr: 0.000137 - momentum: 0.000000
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+ 2023-10-15 02:39:20,354 epoch 3 - iter 356/894 - loss 0.14302842 - time (sec): 66.75 - samples/sec: 525.00 - lr: 0.000135 - momentum: 0.000000
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+ 2023-10-15 02:39:36,895 epoch 3 - iter 445/894 - loss 0.13867980 - time (sec): 83.29 - samples/sec: 524.43 - lr: 0.000133 - momentum: 0.000000
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+ 2023-10-15 02:39:52,835 epoch 3 - iter 534/894 - loss 0.13292117 - time (sec): 99.23 - samples/sec: 516.98 - lr: 0.000132 - momentum: 0.000000
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+ 2023-10-15 02:40:09,133 epoch 3 - iter 623/894 - loss 0.13020535 - time (sec): 115.53 - samples/sec: 513.60 - lr: 0.000130 - momentum: 0.000000
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+ 2023-10-15 02:40:25,404 epoch 3 - iter 712/894 - loss 0.12587355 - time (sec): 131.80 - samples/sec: 512.31 - lr: 0.000128 - momentum: 0.000000
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+ 2023-10-15 02:40:43,932 epoch 3 - iter 801/894 - loss 0.12594823 - time (sec): 150.33 - samples/sec: 513.37 - lr: 0.000126 - momentum: 0.000000
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+ 2023-10-15 02:41:01,043 epoch 3 - iter 890/894 - loss 0.12151089 - time (sec): 167.44 - samples/sec: 514.04 - lr: 0.000125 - momentum: 0.000000
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+ 2023-10-15 02:41:01,800 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:41:01,800 EPOCH 3 done: loss 0.1218 - lr: 0.000125
139
+ 2023-10-15 02:41:27,066 DEV : loss 0.15591633319854736 - f1-score (micro avg) 0.7298
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+ 2023-10-15 02:41:27,092 saving best model
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+ 2023-10-15 02:41:27,983 ----------------------------------------------------------------------------------------------------
142
+ 2023-10-15 02:41:44,324 epoch 4 - iter 89/894 - loss 0.07103724 - time (sec): 16.34 - samples/sec: 506.27 - lr: 0.000123 - momentum: 0.000000
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+ 2023-10-15 02:42:01,963 epoch 4 - iter 178/894 - loss 0.06257623 - time (sec): 33.98 - samples/sec: 517.81 - lr: 0.000121 - momentum: 0.000000
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+ 2023-10-15 02:42:18,072 epoch 4 - iter 267/894 - loss 0.06910373 - time (sec): 50.09 - samples/sec: 511.23 - lr: 0.000119 - momentum: 0.000000
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+ 2023-10-15 02:42:34,820 epoch 4 - iter 356/894 - loss 0.07107525 - time (sec): 66.83 - samples/sec: 520.12 - lr: 0.000117 - momentum: 0.000000
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+ 2023-10-15 02:42:51,325 epoch 4 - iter 445/894 - loss 0.06802427 - time (sec): 83.34 - samples/sec: 522.37 - lr: 0.000116 - momentum: 0.000000
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+ 2023-10-15 02:43:07,291 epoch 4 - iter 534/894 - loss 0.06713645 - time (sec): 99.30 - samples/sec: 519.31 - lr: 0.000114 - momentum: 0.000000
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+ 2023-10-15 02:43:23,747 epoch 4 - iter 623/894 - loss 0.06876201 - time (sec): 115.76 - samples/sec: 521.62 - lr: 0.000112 - momentum: 0.000000
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+ 2023-10-15 02:43:41,754 epoch 4 - iter 712/894 - loss 0.07019890 - time (sec): 133.77 - samples/sec: 519.61 - lr: 0.000110 - momentum: 0.000000
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+ 2023-10-15 02:43:57,924 epoch 4 - iter 801/894 - loss 0.06984123 - time (sec): 149.94 - samples/sec: 518.42 - lr: 0.000109 - momentum: 0.000000
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+ 2023-10-15 02:44:14,341 epoch 4 - iter 890/894 - loss 0.06910896 - time (sec): 166.35 - samples/sec: 518.79 - lr: 0.000107 - momentum: 0.000000
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+ 2023-10-15 02:44:14,960 ----------------------------------------------------------------------------------------------------
153
+ 2023-10-15 02:44:14,961 EPOCH 4 done: loss 0.0690 - lr: 0.000107
154
+ 2023-10-15 02:44:39,949 DEV : loss 0.1609293520450592 - f1-score (micro avg) 0.7516
155
+ 2023-10-15 02:44:39,975 saving best model
156
+ 2023-10-15 02:44:40,880 ----------------------------------------------------------------------------------------------------
157
+ 2023-10-15 02:44:57,848 epoch 5 - iter 89/894 - loss 0.03775608 - time (sec): 16.97 - samples/sec: 531.50 - lr: 0.000105 - momentum: 0.000000
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+ 2023-10-15 02:45:14,380 epoch 5 - iter 178/894 - loss 0.03532971 - time (sec): 33.50 - samples/sec: 524.28 - lr: 0.000103 - momentum: 0.000000
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+ 2023-10-15 02:45:31,264 epoch 5 - iter 267/894 - loss 0.03513730 - time (sec): 50.38 - samples/sec: 530.22 - lr: 0.000101 - momentum: 0.000000
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+ 2023-10-15 02:45:47,982 epoch 5 - iter 356/894 - loss 0.03993442 - time (sec): 67.10 - samples/sec: 528.81 - lr: 0.000100 - momentum: 0.000000
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+ 2023-10-15 02:46:06,528 epoch 5 - iter 445/894 - loss 0.04620953 - time (sec): 85.65 - samples/sec: 529.50 - lr: 0.000098 - momentum: 0.000000
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+ 2023-10-15 02:46:22,864 epoch 5 - iter 534/894 - loss 0.04713336 - time (sec): 101.98 - samples/sec: 527.63 - lr: 0.000096 - momentum: 0.000000
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+ 2023-10-15 02:46:38,857 epoch 5 - iter 623/894 - loss 0.04525939 - time (sec): 117.97 - samples/sec: 521.37 - lr: 0.000094 - momentum: 0.000000
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+ 2023-10-15 02:46:55,181 epoch 5 - iter 712/894 - loss 0.04267360 - time (sec): 134.30 - samples/sec: 519.69 - lr: 0.000093 - momentum: 0.000000
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+ 2023-10-15 02:47:11,200 epoch 5 - iter 801/894 - loss 0.04096140 - time (sec): 150.32 - samples/sec: 516.27 - lr: 0.000091 - momentum: 0.000000
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+ 2023-10-15 02:47:27,819 epoch 5 - iter 890/894 - loss 0.04206314 - time (sec): 166.94 - samples/sec: 516.61 - lr: 0.000089 - momentum: 0.000000
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+ 2023-10-15 02:47:28,490 ----------------------------------------------------------------------------------------------------
168
+ 2023-10-15 02:47:28,490 EPOCH 5 done: loss 0.0419 - lr: 0.000089
169
+ 2023-10-15 02:47:53,602 DEV : loss 0.1873023360967636 - f1-score (micro avg) 0.7704
170
+ 2023-10-15 02:47:53,628 saving best model
171
+ 2023-10-15 02:47:54,607 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-15 02:48:12,579 epoch 6 - iter 89/894 - loss 0.04090361 - time (sec): 17.97 - samples/sec: 505.20 - lr: 0.000087 - momentum: 0.000000
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+ 2023-10-15 02:48:30,268 epoch 6 - iter 178/894 - loss 0.03139198 - time (sec): 35.66 - samples/sec: 515.84 - lr: 0.000085 - momentum: 0.000000
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+ 2023-10-15 02:48:46,856 epoch 6 - iter 267/894 - loss 0.03064485 - time (sec): 52.25 - samples/sec: 522.23 - lr: 0.000084 - momentum: 0.000000
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+ 2023-10-15 02:49:03,120 epoch 6 - iter 356/894 - loss 0.02852187 - time (sec): 68.51 - samples/sec: 516.68 - lr: 0.000082 - momentum: 0.000000
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+ 2023-10-15 02:49:20,293 epoch 6 - iter 445/894 - loss 0.02788748 - time (sec): 85.68 - samples/sec: 508.91 - lr: 0.000080 - momentum: 0.000000
177
+ 2023-10-15 02:49:37,772 epoch 6 - iter 534/894 - loss 0.02794351 - time (sec): 103.16 - samples/sec: 506.10 - lr: 0.000078 - momentum: 0.000000
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+ 2023-10-15 02:49:55,339 epoch 6 - iter 623/894 - loss 0.02802367 - time (sec): 120.73 - samples/sec: 502.99 - lr: 0.000077 - momentum: 0.000000
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+ 2023-10-15 02:50:12,915 epoch 6 - iter 712/894 - loss 0.02792162 - time (sec): 138.31 - samples/sec: 500.00 - lr: 0.000075 - momentum: 0.000000
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+ 2023-10-15 02:50:29,545 epoch 6 - iter 801/894 - loss 0.02919230 - time (sec): 154.94 - samples/sec: 499.81 - lr: 0.000073 - momentum: 0.000000
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+ 2023-10-15 02:50:46,019 epoch 6 - iter 890/894 - loss 0.02781489 - time (sec): 171.41 - samples/sec: 503.33 - lr: 0.000071 - momentum: 0.000000
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+ 2023-10-15 02:50:46,685 ----------------------------------------------------------------------------------------------------
183
+ 2023-10-15 02:50:46,685 EPOCH 6 done: loss 0.0278 - lr: 0.000071
184
+ 2023-10-15 02:51:13,116 DEV : loss 0.21434210240840912 - f1-score (micro avg) 0.7671
185
+ 2023-10-15 02:51:13,144 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-15 02:51:32,358 epoch 7 - iter 89/894 - loss 0.03055746 - time (sec): 19.21 - samples/sec: 506.79 - lr: 0.000069 - momentum: 0.000000
187
+ 2023-10-15 02:51:49,459 epoch 7 - iter 178/894 - loss 0.03178652 - time (sec): 36.31 - samples/sec: 508.13 - lr: 0.000068 - momentum: 0.000000
188
+ 2023-10-15 02:52:05,517 epoch 7 - iter 267/894 - loss 0.02947788 - time (sec): 52.37 - samples/sec: 496.56 - lr: 0.000066 - momentum: 0.000000
189
+ 2023-10-15 02:52:21,830 epoch 7 - iter 356/894 - loss 0.02621194 - time (sec): 68.68 - samples/sec: 497.65 - lr: 0.000064 - momentum: 0.000000
190
+ 2023-10-15 02:52:38,189 epoch 7 - iter 445/894 - loss 0.02435064 - time (sec): 85.04 - samples/sec: 498.38 - lr: 0.000062 - momentum: 0.000000
191
+ 2023-10-15 02:52:54,896 epoch 7 - iter 534/894 - loss 0.02215860 - time (sec): 101.75 - samples/sec: 503.67 - lr: 0.000061 - momentum: 0.000000
192
+ 2023-10-15 02:53:11,073 epoch 7 - iter 623/894 - loss 0.02099170 - time (sec): 117.93 - samples/sec: 501.57 - lr: 0.000059 - momentum: 0.000000
193
+ 2023-10-15 02:53:27,755 epoch 7 - iter 712/894 - loss 0.01995778 - time (sec): 134.61 - samples/sec: 504.04 - lr: 0.000057 - momentum: 0.000000
194
+ 2023-10-15 02:53:44,630 epoch 7 - iter 801/894 - loss 0.01902734 - time (sec): 151.48 - samples/sec: 507.27 - lr: 0.000055 - momentum: 0.000000
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+ 2023-10-15 02:54:02,012 epoch 7 - iter 890/894 - loss 0.01833316 - time (sec): 168.87 - samples/sec: 510.97 - lr: 0.000053 - momentum: 0.000000
196
+ 2023-10-15 02:54:02,650 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-15 02:54:02,651 EPOCH 7 done: loss 0.0185 - lr: 0.000053
198
+ 2023-10-15 02:54:28,844 DEV : loss 0.21627573668956757 - f1-score (micro avg) 0.7695
199
+ 2023-10-15 02:54:28,870 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-15 02:54:45,753 epoch 8 - iter 89/894 - loss 0.01084712 - time (sec): 16.88 - samples/sec: 505.28 - lr: 0.000052 - momentum: 0.000000
201
+ 2023-10-15 02:55:03,142 epoch 8 - iter 178/894 - loss 0.01079641 - time (sec): 34.27 - samples/sec: 513.12 - lr: 0.000050 - momentum: 0.000000
202
+ 2023-10-15 02:55:20,027 epoch 8 - iter 267/894 - loss 0.00894694 - time (sec): 51.16 - samples/sec: 521.76 - lr: 0.000048 - momentum: 0.000000
203
+ 2023-10-15 02:55:36,682 epoch 8 - iter 356/894 - loss 0.00974459 - time (sec): 67.81 - samples/sec: 522.44 - lr: 0.000046 - momentum: 0.000000
204
+ 2023-10-15 02:55:53,263 epoch 8 - iter 445/894 - loss 0.01110363 - time (sec): 84.39 - samples/sec: 522.29 - lr: 0.000045 - momentum: 0.000000
205
+ 2023-10-15 02:56:09,389 epoch 8 - iter 534/894 - loss 0.01181829 - time (sec): 100.52 - samples/sec: 517.26 - lr: 0.000043 - momentum: 0.000000
206
+ 2023-10-15 02:56:26,017 epoch 8 - iter 623/894 - loss 0.01176535 - time (sec): 117.15 - samples/sec: 516.61 - lr: 0.000041 - momentum: 0.000000
207
+ 2023-10-15 02:56:42,522 epoch 8 - iter 712/894 - loss 0.01085512 - time (sec): 133.65 - samples/sec: 516.06 - lr: 0.000039 - momentum: 0.000000
208
+ 2023-10-15 02:57:01,040 epoch 8 - iter 801/894 - loss 0.01135469 - time (sec): 152.17 - samples/sec: 515.39 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-15 02:57:17,154 epoch 8 - iter 890/894 - loss 0.01148836 - time (sec): 168.28 - samples/sec: 512.32 - lr: 0.000036 - momentum: 0.000000
210
+ 2023-10-15 02:57:17,838 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-15 02:57:17,839 EPOCH 8 done: loss 0.0118 - lr: 0.000036
212
+ 2023-10-15 02:57:44,212 DEV : loss 0.22242531180381775 - f1-score (micro avg) 0.7719
213
+ 2023-10-15 02:57:44,240 saving best model
214
+ 2023-10-15 02:57:46,752 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-15 02:58:02,946 epoch 9 - iter 89/894 - loss 0.00595299 - time (sec): 16.19 - samples/sec: 494.63 - lr: 0.000034 - momentum: 0.000000
216
+ 2023-10-15 02:58:19,361 epoch 9 - iter 178/894 - loss 0.00644078 - time (sec): 32.61 - samples/sec: 498.46 - lr: 0.000032 - momentum: 0.000000
217
+ 2023-10-15 02:58:35,843 epoch 9 - iter 267/894 - loss 0.01132599 - time (sec): 49.09 - samples/sec: 505.33 - lr: 0.000030 - momentum: 0.000000
218
+ 2023-10-15 02:58:52,419 epoch 9 - iter 356/894 - loss 0.00904748 - time (sec): 65.67 - samples/sec: 510.07 - lr: 0.000029 - momentum: 0.000000
219
+ 2023-10-15 02:59:10,359 epoch 9 - iter 445/894 - loss 0.01098782 - time (sec): 83.60 - samples/sec: 506.84 - lr: 0.000027 - momentum: 0.000000
220
+ 2023-10-15 02:59:27,525 epoch 9 - iter 534/894 - loss 0.01062907 - time (sec): 100.77 - samples/sec: 512.80 - lr: 0.000025 - momentum: 0.000000
221
+ 2023-10-15 02:59:44,394 epoch 9 - iter 623/894 - loss 0.01018680 - time (sec): 117.64 - samples/sec: 512.48 - lr: 0.000023 - momentum: 0.000000
222
+ 2023-10-15 03:00:01,280 epoch 9 - iter 712/894 - loss 0.00968917 - time (sec): 134.53 - samples/sec: 513.69 - lr: 0.000022 - momentum: 0.000000
223
+ 2023-10-15 03:00:17,808 epoch 9 - iter 801/894 - loss 0.00910352 - time (sec): 151.05 - samples/sec: 515.92 - lr: 0.000020 - momentum: 0.000000
224
+ 2023-10-15 03:00:34,257 epoch 9 - iter 890/894 - loss 0.00910813 - time (sec): 167.50 - samples/sec: 514.73 - lr: 0.000018 - momentum: 0.000000
225
+ 2023-10-15 03:00:34,964 ----------------------------------------------------------------------------------------------------
226
+ 2023-10-15 03:00:34,964 EPOCH 9 done: loss 0.0091 - lr: 0.000018
227
+ 2023-10-15 03:01:01,017 DEV : loss 0.23208962380886078 - f1-score (micro avg) 0.7894
228
+ 2023-10-15 03:01:01,043 saving best model
229
+ 2023-10-15 03:01:03,945 ----------------------------------------------------------------------------------------------------
230
+ 2023-10-15 03:01:22,920 epoch 10 - iter 89/894 - loss 0.00741155 - time (sec): 18.97 - samples/sec: 530.99 - lr: 0.000016 - momentum: 0.000000
231
+ 2023-10-15 03:01:39,312 epoch 10 - iter 178/894 - loss 0.00663561 - time (sec): 35.36 - samples/sec: 518.24 - lr: 0.000014 - momentum: 0.000000
232
+ 2023-10-15 03:01:55,571 epoch 10 - iter 267/894 - loss 0.00512243 - time (sec): 51.62 - samples/sec: 515.38 - lr: 0.000013 - momentum: 0.000000
233
+ 2023-10-15 03:02:11,926 epoch 10 - iter 356/894 - loss 0.00494199 - time (sec): 67.98 - samples/sec: 513.59 - lr: 0.000011 - momentum: 0.000000
234
+ 2023-10-15 03:02:29,205 epoch 10 - iter 445/894 - loss 0.00519207 - time (sec): 85.26 - samples/sec: 519.56 - lr: 0.000009 - momentum: 0.000000
235
+ 2023-10-15 03:02:45,598 epoch 10 - iter 534/894 - loss 0.00528129 - time (sec): 101.65 - samples/sec: 517.28 - lr: 0.000007 - momentum: 0.000000
236
+ 2023-10-15 03:03:02,594 epoch 10 - iter 623/894 - loss 0.00652465 - time (sec): 118.65 - samples/sec: 515.94 - lr: 0.000006 - momentum: 0.000000
237
+ 2023-10-15 03:03:18,989 epoch 10 - iter 712/894 - loss 0.00616289 - time (sec): 135.04 - samples/sec: 517.96 - lr: 0.000004 - momentum: 0.000000
238
+ 2023-10-15 03:03:35,161 epoch 10 - iter 801/894 - loss 0.00585123 - time (sec): 151.21 - samples/sec: 516.29 - lr: 0.000002 - momentum: 0.000000
239
+ 2023-10-15 03:03:51,457 epoch 10 - iter 890/894 - loss 0.00587903 - time (sec): 167.51 - samples/sec: 515.29 - lr: 0.000000 - momentum: 0.000000
240
+ 2023-10-15 03:03:52,093 ----------------------------------------------------------------------------------------------------
241
+ 2023-10-15 03:03:52,093 EPOCH 10 done: loss 0.0059 - lr: 0.000000
242
+ 2023-10-15 03:04:17,600 DEV : loss 0.2378893792629242 - f1-score (micro avg) 0.7848
243
+ 2023-10-15 03:04:18,259 ----------------------------------------------------------------------------------------------------
244
+ 2023-10-15 03:04:18,261 Loading model from best epoch ...
245
+ 2023-10-15 03:04:26,176 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-prod, B-prod, E-prod, I-prod, S-time, B-time, E-time, I-time
246
+ 2023-10-15 03:04:49,929
247
+ Results:
248
+ - F-score (micro) 0.7741
249
+ - F-score (macro) 0.6991
250
+ - Accuracy 0.6448
251
+
252
+ By class:
253
+ precision recall f1-score support
254
+
255
+ loc 0.8588 0.8674 0.8631 596
256
+ pers 0.6898 0.7748 0.7298 333
257
+ org 0.5923 0.5833 0.5878 132
258
+ prod 0.6731 0.5303 0.5932 66
259
+ time 0.7292 0.7143 0.7216 49
260
+
261
+ micro avg 0.7645 0.7840 0.7741 1176
262
+ macro avg 0.7086 0.6940 0.6991 1176
263
+ weighted avg 0.7652 0.7840 0.7734 1176
264
+
265
+ 2023-10-15 03:04:49,929 ----------------------------------------------------------------------------------------------------