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hmbench-newseye-fr-hmteams-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3

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. loss.tsv +11 -0
  4. runs/events.out.tfevents.1697554374.0468bd9609d6.7281.11 +3 -0
  5. test.tsv +0 -0
  6. training.log +238 -0
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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 14:54:27 0.0000 0.4330 0.1146 0.7286 0.7524 0.7403 0.6024
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+ 2 14:56:02 0.0000 0.1265 0.1421 0.7497 0.7905 0.7695 0.6420
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+ 3 14:57:36 0.0000 0.0866 0.1605 0.7883 0.7850 0.7866 0.6709
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+ 4 14:59:09 0.0000 0.0646 0.1810 0.7631 0.7932 0.7779 0.6543
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+ 5 15:00:44 0.0000 0.0482 0.2074 0.7591 0.7633 0.7612 0.6325
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+ 6 15:02:20 0.0000 0.0345 0.2403 0.8128 0.8095 0.8112 0.6951
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+ 7 15:03:56 0.0000 0.0281 0.2226 0.7834 0.8068 0.7949 0.6746
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+ 8 15:05:30 0.0000 0.0165 0.2379 0.7880 0.8041 0.7960 0.6739
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+ 9 15:07:06 0.0000 0.0107 0.2556 0.7992 0.7959 0.7975 0.6810
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+ 10 15:08:39 0.0000 0.0073 0.2583 0.7957 0.8000 0.7978 0.6821
runs/events.out.tfevents.1697554374.0468bd9609d6.7281.11 ADDED
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+ version https://git-lfs.github.com/spec/v1
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test.tsv ADDED
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training.log ADDED
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+ 2023-10-17 14:52:54,571 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:52:54,572 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): ElectraModel(
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+ (embeddings): ElectraEmbeddings(
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+ (word_embeddings): Embedding(32001, 768)
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+ (position_embeddings): Embedding(512, 768)
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+ (token_type_embeddings): Embedding(2, 768)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (encoder): ElectraEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x ElectraLayer(
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+ (attention): ElectraAttention(
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+ (self): ElectraSelfAttention(
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+ (query): Linear(in_features=768, out_features=768, bias=True)
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+ (key): Linear(in_features=768, out_features=768, bias=True)
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+ (value): Linear(in_features=768, out_features=768, bias=True)
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+ (dropout): Dropout(p=0.1, inplace=False)
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+ )
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+ (output): ElectraSelfOutput(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, 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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+ (intermediate): ElectraIntermediate(
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+ (dense): Linear(in_features=768, out_features=3072, bias=True)
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+ (intermediate_act_fn): GELUActivation()
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+ )
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+ (output): ElectraOutput(
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+ (dense): Linear(in_features=3072, out_features=768, bias=True)
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+ (LayerNorm): LayerNorm((768,), eps=1e-12, 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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+ )
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+ )
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+ (locked_dropout): LockedDropout(p=0.5)
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+ (linear): Linear(in_features=768, out_features=17, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-17 14:52:54,572 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:52:54,572 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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+ 2023-10-17 14:52:54,572 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:52:54,572 Train: 7142 sentences
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+ 2023-10-17 14:52:54,572 (train_with_dev=False, train_with_test=False)
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+ 2023-10-17 14:52:54,572 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:52:54,572 Training Params:
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+ 2023-10-17 14:52:54,572 - learning_rate: "5e-05"
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+ 2023-10-17 14:52:54,572 - mini_batch_size: "4"
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+ 2023-10-17 14:52:54,572 - max_epochs: "10"
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+ 2023-10-17 14:52:54,572 - shuffle: "True"
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+ 2023-10-17 14:52:54,572 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:52:54,572 Plugins:
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+ 2023-10-17 14:52:54,572 - TensorboardLogger
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+ 2023-10-17 14:52:54,572 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-17 14:52:54,572 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:52:54,572 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-17 14:52:54,572 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-17 14:52:54,572 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:52:54,572 Computation:
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+ 2023-10-17 14:52:54,572 - compute on device: cuda:0
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+ 2023-10-17 14:52:54,573 - embedding storage: none
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+ 2023-10-17 14:52:54,573 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:52:54,573 Model training base path: "hmbench-newseye/fr-hmteams/teams-base-historic-multilingual-discriminator-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3"
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+ 2023-10-17 14:52:54,573 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:52:54,573 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:52:54,573 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-17 14:53:03,192 epoch 1 - iter 178/1786 - loss 2.34357014 - time (sec): 8.62 - samples/sec: 2790.75 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-17 14:53:11,959 epoch 1 - iter 356/1786 - loss 1.40781693 - time (sec): 17.39 - samples/sec: 2809.96 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-17 14:53:20,638 epoch 1 - iter 534/1786 - loss 1.06730323 - time (sec): 26.06 - samples/sec: 2742.77 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-17 14:53:29,450 epoch 1 - iter 712/1786 - loss 0.84752372 - time (sec): 34.88 - samples/sec: 2786.76 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-17 14:53:38,306 epoch 1 - iter 890/1786 - loss 0.71167479 - time (sec): 43.73 - samples/sec: 2802.24 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-17 14:53:47,145 epoch 1 - iter 1068/1786 - loss 0.62544453 - time (sec): 52.57 - samples/sec: 2794.21 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-17 14:53:56,284 epoch 1 - iter 1246/1786 - loss 0.55608152 - time (sec): 61.71 - samples/sec: 2792.42 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 14:54:05,381 epoch 1 - iter 1424/1786 - loss 0.50198941 - time (sec): 70.81 - samples/sec: 2811.79 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 14:54:14,199 epoch 1 - iter 1602/1786 - loss 0.46440329 - time (sec): 79.63 - samples/sec: 2819.64 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 14:54:22,832 epoch 1 - iter 1780/1786 - loss 0.43392835 - time (sec): 88.26 - samples/sec: 2809.33 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-17 14:54:23,104 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:54:23,104 EPOCH 1 done: loss 0.4330 - lr: 0.000050
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+ 2023-10-17 14:54:27,153 DEV : loss 0.11458766460418701 - f1-score (micro avg) 0.7403
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+ 2023-10-17 14:54:27,169 saving best model
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+ 2023-10-17 14:54:27,519 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:54:36,852 epoch 2 - iter 178/1786 - loss 0.14020957 - time (sec): 9.33 - samples/sec: 2564.60 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 14:54:46,095 epoch 2 - iter 356/1786 - loss 0.13246321 - time (sec): 18.57 - samples/sec: 2692.71 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-17 14:54:55,132 epoch 2 - iter 534/1786 - loss 0.12996578 - time (sec): 27.61 - samples/sec: 2710.66 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 14:55:04,131 epoch 2 - iter 712/1786 - loss 0.13085505 - time (sec): 36.61 - samples/sec: 2724.67 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-17 14:55:12,791 epoch 2 - iter 890/1786 - loss 0.13070280 - time (sec): 45.27 - samples/sec: 2727.42 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 14:55:21,707 epoch 2 - iter 1068/1786 - loss 0.12652829 - time (sec): 54.19 - samples/sec: 2744.97 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-17 14:55:30,604 epoch 2 - iter 1246/1786 - loss 0.12732731 - time (sec): 63.08 - samples/sec: 2736.02 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 14:55:39,573 epoch 2 - iter 1424/1786 - loss 0.12632951 - time (sec): 72.05 - samples/sec: 2752.70 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-17 14:55:48,453 epoch 2 - iter 1602/1786 - loss 0.12684493 - time (sec): 80.93 - samples/sec: 2764.66 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-17 14:55:57,231 epoch 2 - iter 1780/1786 - loss 0.12675945 - time (sec): 89.71 - samples/sec: 2764.76 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 14:55:57,511 ----------------------------------------------------------------------------------------------------
100
+ 2023-10-17 14:55:57,512 EPOCH 2 done: loss 0.1265 - lr: 0.000044
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+ 2023-10-17 14:56:02,464 DEV : loss 0.1421259194612503 - f1-score (micro avg) 0.7695
102
+ 2023-10-17 14:56:02,482 saving best model
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+ 2023-10-17 14:56:02,942 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:56:11,753 epoch 3 - iter 178/1786 - loss 0.09648196 - time (sec): 8.81 - samples/sec: 2560.72 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-17 14:56:20,746 epoch 3 - iter 356/1786 - loss 0.08669305 - time (sec): 17.80 - samples/sec: 2701.77 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 14:56:29,588 epoch 3 - iter 534/1786 - loss 0.08198309 - time (sec): 26.64 - samples/sec: 2747.94 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-17 14:56:38,450 epoch 3 - iter 712/1786 - loss 0.08374774 - time (sec): 35.51 - samples/sec: 2754.30 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 14:56:47,471 epoch 3 - iter 890/1786 - loss 0.08441770 - time (sec): 44.53 - samples/sec: 2744.81 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-17 14:56:56,230 epoch 3 - iter 1068/1786 - loss 0.08501205 - time (sec): 53.29 - samples/sec: 2735.07 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 14:57:05,041 epoch 3 - iter 1246/1786 - loss 0.08606738 - time (sec): 62.10 - samples/sec: 2772.35 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-17 14:57:13,860 epoch 3 - iter 1424/1786 - loss 0.08594360 - time (sec): 70.92 - samples/sec: 2790.52 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-17 14:57:22,820 epoch 3 - iter 1602/1786 - loss 0.08524957 - time (sec): 79.88 - samples/sec: 2805.33 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 14:57:31,499 epoch 3 - iter 1780/1786 - loss 0.08632208 - time (sec): 88.55 - samples/sec: 2799.84 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-17 14:57:31,759 ----------------------------------------------------------------------------------------------------
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+ 2023-10-17 14:57:31,760 EPOCH 3 done: loss 0.0866 - lr: 0.000039
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+ 2023-10-17 14:57:36,017 DEV : loss 0.16046550869941711 - f1-score (micro avg) 0.7866
117
+ 2023-10-17 14:57:36,034 saving best model
118
+ 2023-10-17 14:57:36,496 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-17 14:57:44,948 epoch 4 - iter 178/1786 - loss 0.05362232 - time (sec): 8.45 - samples/sec: 2870.63 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 14:57:54,046 epoch 4 - iter 356/1786 - loss 0.06672816 - time (sec): 17.55 - samples/sec: 2907.75 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-17 14:58:02,956 epoch 4 - iter 534/1786 - loss 0.06403011 - time (sec): 26.46 - samples/sec: 2887.17 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 14:58:11,815 epoch 4 - iter 712/1786 - loss 0.06474490 - time (sec): 35.32 - samples/sec: 2857.05 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-17 14:58:20,533 epoch 4 - iter 890/1786 - loss 0.06436458 - time (sec): 44.03 - samples/sec: 2846.34 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 14:58:29,239 epoch 4 - iter 1068/1786 - loss 0.06292209 - time (sec): 52.74 - samples/sec: 2841.50 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-17 14:58:37,846 epoch 4 - iter 1246/1786 - loss 0.06337824 - time (sec): 61.35 - samples/sec: 2817.24 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-17 14:58:46,836 epoch 4 - iter 1424/1786 - loss 0.06433756 - time (sec): 70.34 - samples/sec: 2813.22 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 14:58:55,515 epoch 4 - iter 1602/1786 - loss 0.06494053 - time (sec): 79.02 - samples/sec: 2820.58 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-17 14:59:04,297 epoch 4 - iter 1780/1786 - loss 0.06441300 - time (sec): 87.80 - samples/sec: 2825.51 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 14:59:04,567 ----------------------------------------------------------------------------------------------------
130
+ 2023-10-17 14:59:04,567 EPOCH 4 done: loss 0.0646 - lr: 0.000033
131
+ 2023-10-17 14:59:09,315 DEV : loss 0.18103061616420746 - f1-score (micro avg) 0.7779
132
+ 2023-10-17 14:59:09,334 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-17 14:59:18,302 epoch 5 - iter 178/1786 - loss 0.03339553 - time (sec): 8.97 - samples/sec: 2750.16 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-17 14:59:27,368 epoch 5 - iter 356/1786 - loss 0.03967613 - time (sec): 18.03 - samples/sec: 2817.11 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 14:59:36,320 epoch 5 - iter 534/1786 - loss 0.04092048 - time (sec): 26.98 - samples/sec: 2843.97 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-17 14:59:45,069 epoch 5 - iter 712/1786 - loss 0.04374407 - time (sec): 35.73 - samples/sec: 2841.71 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 14:59:53,905 epoch 5 - iter 890/1786 - loss 0.04683876 - time (sec): 44.57 - samples/sec: 2813.94 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-17 15:00:03,115 epoch 5 - iter 1068/1786 - loss 0.04672502 - time (sec): 53.78 - samples/sec: 2817.89 - lr: 0.000030 - momentum: 0.000000
139
+ 2023-10-17 15:00:12,273 epoch 5 - iter 1246/1786 - loss 0.04806529 - time (sec): 62.94 - samples/sec: 2794.30 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 15:00:21,497 epoch 5 - iter 1424/1786 - loss 0.04855191 - time (sec): 72.16 - samples/sec: 2774.59 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-17 15:00:30,474 epoch 5 - iter 1602/1786 - loss 0.04738804 - time (sec): 81.14 - samples/sec: 2767.55 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-17 15:00:39,349 epoch 5 - iter 1780/1786 - loss 0.04826172 - time (sec): 90.01 - samples/sec: 2756.61 - lr: 0.000028 - momentum: 0.000000
143
+ 2023-10-17 15:00:39,661 ----------------------------------------------------------------------------------------------------
144
+ 2023-10-17 15:00:39,661 EPOCH 5 done: loss 0.0482 - lr: 0.000028
145
+ 2023-10-17 15:00:44,200 DEV : loss 0.20736941695213318 - f1-score (micro avg) 0.7612
146
+ 2023-10-17 15:00:44,219 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-17 15:00:53,164 epoch 6 - iter 178/1786 - loss 0.02216489 - time (sec): 8.94 - samples/sec: 2750.82 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 15:01:02,289 epoch 6 - iter 356/1786 - loss 0.03505341 - time (sec): 18.07 - samples/sec: 2819.10 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-17 15:01:11,960 epoch 6 - iter 534/1786 - loss 0.03233599 - time (sec): 27.74 - samples/sec: 2719.90 - lr: 0.000026 - momentum: 0.000000
150
+ 2023-10-17 15:01:21,146 epoch 6 - iter 712/1786 - loss 0.03206542 - time (sec): 36.93 - samples/sec: 2693.98 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-17 15:01:30,650 epoch 6 - iter 890/1786 - loss 0.03265728 - time (sec): 46.43 - samples/sec: 2662.60 - lr: 0.000025 - momentum: 0.000000
152
+ 2023-10-17 15:01:40,031 epoch 6 - iter 1068/1786 - loss 0.03291601 - time (sec): 55.81 - samples/sec: 2653.82 - lr: 0.000024 - momentum: 0.000000
153
+ 2023-10-17 15:01:48,833 epoch 6 - iter 1246/1786 - loss 0.03440927 - time (sec): 64.61 - samples/sec: 2679.28 - lr: 0.000024 - momentum: 0.000000
154
+ 2023-10-17 15:01:57,756 epoch 6 - iter 1424/1786 - loss 0.03461216 - time (sec): 73.54 - samples/sec: 2693.26 - lr: 0.000023 - momentum: 0.000000
155
+ 2023-10-17 15:02:06,771 epoch 6 - iter 1602/1786 - loss 0.03499687 - time (sec): 82.55 - samples/sec: 2705.31 - lr: 0.000023 - momentum: 0.000000
156
+ 2023-10-17 15:02:15,500 epoch 6 - iter 1780/1786 - loss 0.03462841 - time (sec): 91.28 - samples/sec: 2717.57 - lr: 0.000022 - momentum: 0.000000
157
+ 2023-10-17 15:02:15,798 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-17 15:02:15,798 EPOCH 6 done: loss 0.0345 - lr: 0.000022
159
+ 2023-10-17 15:02:20,235 DEV : loss 0.2402777373790741 - f1-score (micro avg) 0.8112
160
+ 2023-10-17 15:02:20,253 saving best model
161
+ 2023-10-17 15:02:20,692 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-17 15:02:29,984 epoch 7 - iter 178/1786 - loss 0.02884356 - time (sec): 9.29 - samples/sec: 2773.61 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-17 15:02:39,181 epoch 7 - iter 356/1786 - loss 0.02553577 - time (sec): 18.48 - samples/sec: 2715.73 - lr: 0.000021 - momentum: 0.000000
164
+ 2023-10-17 15:02:48,182 epoch 7 - iter 534/1786 - loss 0.02422475 - time (sec): 27.48 - samples/sec: 2715.65 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-17 15:02:57,380 epoch 7 - iter 712/1786 - loss 0.02794156 - time (sec): 36.68 - samples/sec: 2752.78 - lr: 0.000020 - momentum: 0.000000
166
+ 2023-10-17 15:03:06,635 epoch 7 - iter 890/1786 - loss 0.02949847 - time (sec): 45.94 - samples/sec: 2733.04 - lr: 0.000019 - momentum: 0.000000
167
+ 2023-10-17 15:03:16,160 epoch 7 - iter 1068/1786 - loss 0.02887539 - time (sec): 55.46 - samples/sec: 2720.71 - lr: 0.000019 - momentum: 0.000000
168
+ 2023-10-17 15:03:25,064 epoch 7 - iter 1246/1786 - loss 0.02971168 - time (sec): 64.37 - samples/sec: 2725.25 - lr: 0.000018 - momentum: 0.000000
169
+ 2023-10-17 15:03:33,838 epoch 7 - iter 1424/1786 - loss 0.02979510 - time (sec): 73.14 - samples/sec: 2715.19 - lr: 0.000018 - momentum: 0.000000
170
+ 2023-10-17 15:03:42,556 epoch 7 - iter 1602/1786 - loss 0.02897065 - time (sec): 81.86 - samples/sec: 2725.86 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 15:03:51,419 epoch 7 - iter 1780/1786 - loss 0.02808217 - time (sec): 90.72 - samples/sec: 2736.21 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-17 15:03:51,685 ----------------------------------------------------------------------------------------------------
173
+ 2023-10-17 15:03:51,686 EPOCH 7 done: loss 0.0281 - lr: 0.000017
174
+ 2023-10-17 15:03:56,572 DEV : loss 0.2226112335920334 - f1-score (micro avg) 0.7949
175
+ 2023-10-17 15:03:56,590 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-17 15:04:05,424 epoch 8 - iter 178/1786 - loss 0.01609297 - time (sec): 8.83 - samples/sec: 2697.83 - lr: 0.000016 - momentum: 0.000000
177
+ 2023-10-17 15:04:14,488 epoch 8 - iter 356/1786 - loss 0.01460834 - time (sec): 17.90 - samples/sec: 2783.84 - lr: 0.000016 - momentum: 0.000000
178
+ 2023-10-17 15:04:23,260 epoch 8 - iter 534/1786 - loss 0.01586187 - time (sec): 26.67 - samples/sec: 2714.18 - lr: 0.000015 - momentum: 0.000000
179
+ 2023-10-17 15:04:32,238 epoch 8 - iter 712/1786 - loss 0.01654564 - time (sec): 35.65 - samples/sec: 2737.96 - lr: 0.000014 - momentum: 0.000000
180
+ 2023-10-17 15:04:41,330 epoch 8 - iter 890/1786 - loss 0.01727812 - time (sec): 44.74 - samples/sec: 2757.92 - lr: 0.000014 - momentum: 0.000000
181
+ 2023-10-17 15:04:50,003 epoch 8 - iter 1068/1786 - loss 0.01761990 - time (sec): 53.41 - samples/sec: 2777.98 - lr: 0.000013 - momentum: 0.000000
182
+ 2023-10-17 15:04:58,640 epoch 8 - iter 1246/1786 - loss 0.01726152 - time (sec): 62.05 - samples/sec: 2782.42 - lr: 0.000013 - momentum: 0.000000
183
+ 2023-10-17 15:05:07,456 epoch 8 - iter 1424/1786 - loss 0.01689803 - time (sec): 70.86 - samples/sec: 2771.40 - lr: 0.000012 - momentum: 0.000000
184
+ 2023-10-17 15:05:16,193 epoch 8 - iter 1602/1786 - loss 0.01640200 - time (sec): 79.60 - samples/sec: 2769.89 - lr: 0.000012 - momentum: 0.000000
185
+ 2023-10-17 15:05:25,538 epoch 8 - iter 1780/1786 - loss 0.01650120 - time (sec): 88.95 - samples/sec: 2786.95 - lr: 0.000011 - momentum: 0.000000
186
+ 2023-10-17 15:05:25,849 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-17 15:05:25,850 EPOCH 8 done: loss 0.0165 - lr: 0.000011
188
+ 2023-10-17 15:05:30,212 DEV : loss 0.23785527050495148 - f1-score (micro avg) 0.796
189
+ 2023-10-17 15:05:30,230 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-17 15:05:39,268 epoch 9 - iter 178/1786 - loss 0.01549311 - time (sec): 9.04 - samples/sec: 2757.67 - lr: 0.000011 - momentum: 0.000000
191
+ 2023-10-17 15:05:48,446 epoch 9 - iter 356/1786 - loss 0.01455785 - time (sec): 18.21 - samples/sec: 2698.56 - lr: 0.000010 - momentum: 0.000000
192
+ 2023-10-17 15:05:57,751 epoch 9 - iter 534/1786 - loss 0.01158104 - time (sec): 27.52 - samples/sec: 2723.46 - lr: 0.000009 - momentum: 0.000000
193
+ 2023-10-17 15:06:06,429 epoch 9 - iter 712/1786 - loss 0.01064053 - time (sec): 36.20 - samples/sec: 2738.43 - lr: 0.000009 - momentum: 0.000000
194
+ 2023-10-17 15:06:15,245 epoch 9 - iter 890/1786 - loss 0.01068597 - time (sec): 45.01 - samples/sec: 2731.24 - lr: 0.000008 - momentum: 0.000000
195
+ 2023-10-17 15:06:24,206 epoch 9 - iter 1068/1786 - loss 0.01073553 - time (sec): 53.97 - samples/sec: 2750.68 - lr: 0.000008 - momentum: 0.000000
196
+ 2023-10-17 15:06:33,658 epoch 9 - iter 1246/1786 - loss 0.01060456 - time (sec): 63.43 - samples/sec: 2747.29 - lr: 0.000007 - momentum: 0.000000
197
+ 2023-10-17 15:06:42,635 epoch 9 - iter 1424/1786 - loss 0.01063471 - time (sec): 72.40 - samples/sec: 2754.79 - lr: 0.000007 - momentum: 0.000000
198
+ 2023-10-17 15:06:51,504 epoch 9 - iter 1602/1786 - loss 0.01083306 - time (sec): 81.27 - samples/sec: 2743.16 - lr: 0.000006 - momentum: 0.000000
199
+ 2023-10-17 15:07:00,815 epoch 9 - iter 1780/1786 - loss 0.01063372 - time (sec): 90.58 - samples/sec: 2737.25 - lr: 0.000006 - momentum: 0.000000
200
+ 2023-10-17 15:07:01,104 ----------------------------------------------------------------------------------------------------
201
+ 2023-10-17 15:07:01,104 EPOCH 9 done: loss 0.0107 - lr: 0.000006
202
+ 2023-10-17 15:07:06,033 DEV : loss 0.25563544034957886 - f1-score (micro avg) 0.7975
203
+ 2023-10-17 15:07:06,052 ----------------------------------------------------------------------------------------------------
204
+ 2023-10-17 15:07:14,958 epoch 10 - iter 178/1786 - loss 0.00707783 - time (sec): 8.91 - samples/sec: 2888.08 - lr: 0.000005 - momentum: 0.000000
205
+ 2023-10-17 15:07:23,959 epoch 10 - iter 356/1786 - loss 0.00883515 - time (sec): 17.91 - samples/sec: 2733.41 - lr: 0.000004 - momentum: 0.000000
206
+ 2023-10-17 15:07:32,883 epoch 10 - iter 534/1786 - loss 0.00797908 - time (sec): 26.83 - samples/sec: 2774.80 - lr: 0.000004 - momentum: 0.000000
207
+ 2023-10-17 15:07:41,548 epoch 10 - iter 712/1786 - loss 0.00720083 - time (sec): 35.50 - samples/sec: 2745.13 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-17 15:07:50,574 epoch 10 - iter 890/1786 - loss 0.00714750 - time (sec): 44.52 - samples/sec: 2750.51 - lr: 0.000003 - momentum: 0.000000
209
+ 2023-10-17 15:07:59,491 epoch 10 - iter 1068/1786 - loss 0.00664834 - time (sec): 53.44 - samples/sec: 2731.24 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-17 15:08:08,680 epoch 10 - iter 1246/1786 - loss 0.00708464 - time (sec): 62.63 - samples/sec: 2764.16 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-17 15:08:17,473 epoch 10 - iter 1424/1786 - loss 0.00733589 - time (sec): 71.42 - samples/sec: 2762.64 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-17 15:08:26,511 epoch 10 - iter 1602/1786 - loss 0.00779833 - time (sec): 80.46 - samples/sec: 2780.02 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-17 15:08:35,379 epoch 10 - iter 1780/1786 - loss 0.00728986 - time (sec): 89.33 - samples/sec: 2776.90 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-17 15:08:35,656 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-17 15:08:35,657 EPOCH 10 done: loss 0.0073 - lr: 0.000000
216
+ 2023-10-17 15:08:39,959 DEV : loss 0.2582792043685913 - f1-score (micro avg) 0.7978
217
+ 2023-10-17 15:08:40,317 ----------------------------------------------------------------------------------------------------
218
+ 2023-10-17 15:08:40,319 Loading model from best epoch ...
219
+ 2023-10-17 15:08:41,660 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
220
+ 2023-10-17 15:08:51,322
221
+ Results:
222
+ - F-score (micro) 0.6992
223
+ - F-score (macro) 0.6347
224
+ - Accuracy 0.5516
225
+
226
+ By class:
227
+ precision recall f1-score support
228
+
229
+ LOC 0.6819 0.7087 0.6950 1095
230
+ PER 0.7733 0.7549 0.7640 1012
231
+ ORG 0.5791 0.5126 0.5438 357
232
+ HumanProd 0.4062 0.7879 0.5361 33
233
+
234
+ micro avg 0.6979 0.7004 0.6992 2497
235
+ macro avg 0.6101 0.6910 0.6347 2497
236
+ weighted avg 0.7006 0.7004 0.6993 2497
237
+
238
+ 2023-10-17 15:08:51,323 ----------------------------------------------------------------------------------------------------