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hmbench-icdar-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5

Token Classification
Flair
PyTorch
French
sequence-tagger-model
Model card Files Community
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  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. loss.tsv +11 -0
  4. test.tsv +0 -0
  5. training.log +243 -0
best-model.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:dbe60d6a981482245e44650c8c54100b556a1398453008eeb73788589e0f1e38
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+ size 443311111
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 01:18:03 0.0000 0.3399 0.0973 0.6586 0.6810 0.6696 0.5244
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+ 2 01:19:05 0.0000 0.1032 0.0910 0.7357 0.7398 0.7377 0.5995
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+ 3 01:20:08 0.0000 0.0715 0.1188 0.7030 0.7817 0.7402 0.6072
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+ 4 01:21:10 0.0000 0.0559 0.1232 0.7281 0.7692 0.7481 0.6176
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+ 5 01:22:12 0.0000 0.0413 0.1672 0.7582 0.7590 0.7586 0.6324
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+ 6 01:23:15 0.0000 0.0301 0.1759 0.7269 0.7828 0.7538 0.6268
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+ 7 01:24:18 0.0000 0.0238 0.1903 0.7301 0.7771 0.7529 0.6240
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+ 8 01:25:20 0.0000 0.0155 0.2063 0.7315 0.7952 0.7621 0.6339
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+ 9 01:26:23 0.0000 0.0115 0.2141 0.7522 0.7726 0.7623 0.6365
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+ 10 01:27:26 0.0000 0.0084 0.2256 0.7536 0.7749 0.7641 0.6396
test.tsv ADDED
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training.log ADDED
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+ 2023-10-14 01:17:01,167 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:17:01,168 Model: "SequenceTagger(
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+ (embeddings): TransformerWordEmbeddings(
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+ (model): BertModel(
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+ (embeddings): BertEmbeddings(
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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): BertEncoder(
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+ (layer): ModuleList(
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+ (0-11): 12 x BertLayer(
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+ (attention): BertAttention(
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+ (self): BertSelfAttention(
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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): BertSelfOutput(
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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): BertIntermediate(
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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): BertOutput(
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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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+ (pooler): BertPooler(
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+ (dense): Linear(in_features=768, out_features=768, bias=True)
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+ (activation): Tanh()
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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=13, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-14 01:17:01,168 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:17:01,168 MultiCorpus: 7936 train + 992 dev + 992 test sentences
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+ - NER_ICDAR_EUROPEANA Corpus: 7936 train + 992 dev + 992 test sentences - /root/.flair/datasets/ner_icdar_europeana/fr
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+ 2023-10-14 01:17:01,168 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:17:01,168 Train: 7936 sentences
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+ 2023-10-14 01:17:01,168 (train_with_dev=False, train_with_test=False)
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+ 2023-10-14 01:17:01,168 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:17:01,168 Training Params:
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+ 2023-10-14 01:17:01,168 - learning_rate: "5e-05"
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+ 2023-10-14 01:17:01,168 - mini_batch_size: "8"
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+ 2023-10-14 01:17:01,168 - max_epochs: "10"
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+ 2023-10-14 01:17:01,168 - shuffle: "True"
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+ 2023-10-14 01:17:01,168 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:17:01,168 Plugins:
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+ 2023-10-14 01:17:01,168 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-14 01:17:01,169 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:17:01,169 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-14 01:17:01,169 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-14 01:17:01,169 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:17:01,169 Computation:
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+ 2023-10-14 01:17:01,169 - compute on device: cuda:0
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+ 2023-10-14 01:17:01,169 - embedding storage: none
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+ 2023-10-14 01:17:01,169 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:17:01,169 Model training base path: "hmbench-icdar/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-14 01:17:01,169 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:17:01,169 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:17:06,714 epoch 1 - iter 99/992 - loss 1.86588415 - time (sec): 5.54 - samples/sec: 2785.36 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-14 01:17:12,449 epoch 1 - iter 198/992 - loss 1.09873625 - time (sec): 11.28 - samples/sec: 2794.75 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-14 01:17:18,387 epoch 1 - iter 297/992 - loss 0.80249953 - time (sec): 17.22 - samples/sec: 2794.10 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-14 01:17:23,944 epoch 1 - iter 396/992 - loss 0.64530064 - time (sec): 22.77 - samples/sec: 2826.21 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-14 01:17:29,847 epoch 1 - iter 495/992 - loss 0.54951004 - time (sec): 28.68 - samples/sec: 2818.40 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-14 01:17:35,860 epoch 1 - iter 594/992 - loss 0.47822528 - time (sec): 34.69 - samples/sec: 2821.08 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-14 01:17:41,757 epoch 1 - iter 693/992 - loss 0.43156759 - time (sec): 40.59 - samples/sec: 2802.37 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-14 01:17:47,720 epoch 1 - iter 792/992 - loss 0.39387496 - time (sec): 46.55 - samples/sec: 2794.11 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-14 01:17:53,595 epoch 1 - iter 891/992 - loss 0.36458290 - time (sec): 52.42 - samples/sec: 2790.26 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-14 01:17:59,691 epoch 1 - iter 990/992 - loss 0.34069275 - time (sec): 58.52 - samples/sec: 2792.26 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-14 01:17:59,900 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:17:59,900 EPOCH 1 done: loss 0.3399 - lr: 0.000050
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+ 2023-10-14 01:18:03,409 DEV : loss 0.09731486439704895 - f1-score (micro avg) 0.6696
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+ 2023-10-14 01:18:03,433 saving best model
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+ 2023-10-14 01:18:03,828 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:18:09,534 epoch 2 - iter 99/992 - loss 0.12915040 - time (sec): 5.70 - samples/sec: 2665.74 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-14 01:18:15,365 epoch 2 - iter 198/992 - loss 0.11559230 - time (sec): 11.54 - samples/sec: 2704.02 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-14 01:18:20,960 epoch 2 - iter 297/992 - loss 0.11414920 - time (sec): 17.13 - samples/sec: 2751.32 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-14 01:18:26,882 epoch 2 - iter 396/992 - loss 0.10894772 - time (sec): 23.05 - samples/sec: 2761.13 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-14 01:18:32,637 epoch 2 - iter 495/992 - loss 0.10862939 - time (sec): 28.81 - samples/sec: 2806.00 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-14 01:18:38,576 epoch 2 - iter 594/992 - loss 0.10732068 - time (sec): 34.75 - samples/sec: 2812.77 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-14 01:18:44,413 epoch 2 - iter 693/992 - loss 0.10605689 - time (sec): 40.58 - samples/sec: 2814.76 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-14 01:18:50,172 epoch 2 - iter 792/992 - loss 0.10337874 - time (sec): 46.34 - samples/sec: 2810.16 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-14 01:18:56,347 epoch 2 - iter 891/992 - loss 0.10259994 - time (sec): 52.52 - samples/sec: 2799.94 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-14 01:19:02,162 epoch 2 - iter 990/992 - loss 0.10325477 - time (sec): 58.33 - samples/sec: 2802.95 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-14 01:19:02,321 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:19:02,321 EPOCH 2 done: loss 0.1032 - lr: 0.000044
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+ 2023-10-14 01:19:05,742 DEV : loss 0.09102991223335266 - f1-score (micro avg) 0.7377
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+ 2023-10-14 01:19:05,763 saving best model
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+ 2023-10-14 01:19:06,277 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:19:11,924 epoch 3 - iter 99/992 - loss 0.06196304 - time (sec): 5.64 - samples/sec: 2675.30 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-14 01:19:17,975 epoch 3 - iter 198/992 - loss 0.06676977 - time (sec): 11.70 - samples/sec: 2774.34 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-14 01:19:23,503 epoch 3 - iter 297/992 - loss 0.07044537 - time (sec): 17.22 - samples/sec: 2780.26 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-14 01:19:29,407 epoch 3 - iter 396/992 - loss 0.07030135 - time (sec): 23.13 - samples/sec: 2755.93 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-14 01:19:35,437 epoch 3 - iter 495/992 - loss 0.06871568 - time (sec): 29.16 - samples/sec: 2793.45 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-14 01:19:41,277 epoch 3 - iter 594/992 - loss 0.07133824 - time (sec): 35.00 - samples/sec: 2795.31 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-14 01:19:47,157 epoch 3 - iter 693/992 - loss 0.07146656 - time (sec): 40.88 - samples/sec: 2803.67 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-14 01:19:53,718 epoch 3 - iter 792/992 - loss 0.07164041 - time (sec): 47.44 - samples/sec: 2769.56 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-14 01:19:59,430 epoch 3 - iter 891/992 - loss 0.07108609 - time (sec): 53.15 - samples/sec: 2765.26 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-14 01:20:05,167 epoch 3 - iter 990/992 - loss 0.07155895 - time (sec): 58.89 - samples/sec: 2778.37 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-14 01:20:05,297 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 01:20:05,298 EPOCH 3 done: loss 0.0715 - lr: 0.000039
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+ 2023-10-14 01:20:08,732 DEV : loss 0.11880763620138168 - f1-score (micro avg) 0.7402
119
+ 2023-10-14 01:20:08,754 saving best model
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+ 2023-10-14 01:20:09,277 ----------------------------------------------------------------------------------------------------
121
+ 2023-10-14 01:20:15,188 epoch 4 - iter 99/992 - loss 0.04511134 - time (sec): 5.91 - samples/sec: 2964.60 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-14 01:20:20,986 epoch 4 - iter 198/992 - loss 0.04991613 - time (sec): 11.71 - samples/sec: 2885.57 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-14 01:20:26,705 epoch 4 - iter 297/992 - loss 0.05461873 - time (sec): 17.43 - samples/sec: 2874.60 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-14 01:20:32,606 epoch 4 - iter 396/992 - loss 0.05366870 - time (sec): 23.33 - samples/sec: 2832.04 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-14 01:20:38,619 epoch 4 - iter 495/992 - loss 0.05304821 - time (sec): 29.34 - samples/sec: 2819.86 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-14 01:20:44,603 epoch 4 - iter 594/992 - loss 0.05335391 - time (sec): 35.32 - samples/sec: 2795.76 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-14 01:20:50,230 epoch 4 - iter 693/992 - loss 0.05328344 - time (sec): 40.95 - samples/sec: 2791.28 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-14 01:20:55,778 epoch 4 - iter 792/992 - loss 0.05344227 - time (sec): 46.50 - samples/sec: 2806.23 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-14 01:21:01,258 epoch 4 - iter 891/992 - loss 0.05342411 - time (sec): 51.98 - samples/sec: 2812.25 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-14 01:21:07,289 epoch 4 - iter 990/992 - loss 0.05595410 - time (sec): 58.01 - samples/sec: 2821.18 - lr: 0.000033 - momentum: 0.000000
131
+ 2023-10-14 01:21:07,456 ----------------------------------------------------------------------------------------------------
132
+ 2023-10-14 01:21:07,456 EPOCH 4 done: loss 0.0559 - lr: 0.000033
133
+ 2023-10-14 01:21:10,852 DEV : loss 0.1232018768787384 - f1-score (micro avg) 0.7481
134
+ 2023-10-14 01:21:10,872 saving best model
135
+ 2023-10-14 01:21:11,360 ----------------------------------------------------------------------------------------------------
136
+ 2023-10-14 01:21:17,069 epoch 5 - iter 99/992 - loss 0.03903289 - time (sec): 5.71 - samples/sec: 2900.52 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-14 01:21:22,768 epoch 5 - iter 198/992 - loss 0.03876095 - time (sec): 11.41 - samples/sec: 2924.11 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-14 01:21:28,419 epoch 5 - iter 297/992 - loss 0.04243056 - time (sec): 17.06 - samples/sec: 2890.32 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-14 01:21:34,101 epoch 5 - iter 396/992 - loss 0.03983144 - time (sec): 22.74 - samples/sec: 2898.00 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-14 01:21:39,707 epoch 5 - iter 495/992 - loss 0.03928814 - time (sec): 28.34 - samples/sec: 2904.32 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-14 01:21:45,374 epoch 5 - iter 594/992 - loss 0.03949960 - time (sec): 34.01 - samples/sec: 2907.48 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-14 01:21:50,895 epoch 5 - iter 693/992 - loss 0.04107109 - time (sec): 39.53 - samples/sec: 2888.93 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-14 01:21:57,000 epoch 5 - iter 792/992 - loss 0.04131385 - time (sec): 45.64 - samples/sec: 2875.31 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-14 01:22:02,977 epoch 5 - iter 891/992 - loss 0.04155687 - time (sec): 51.61 - samples/sec: 2853.04 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-14 01:22:08,801 epoch 5 - iter 990/992 - loss 0.04121265 - time (sec): 57.44 - samples/sec: 2850.10 - lr: 0.000028 - momentum: 0.000000
146
+ 2023-10-14 01:22:08,913 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-14 01:22:08,913 EPOCH 5 done: loss 0.0413 - lr: 0.000028
148
+ 2023-10-14 01:22:12,796 DEV : loss 0.16722512245178223 - f1-score (micro avg) 0.7586
149
+ 2023-10-14 01:22:12,817 saving best model
150
+ 2023-10-14 01:22:13,338 ----------------------------------------------------------------------------------------------------
151
+ 2023-10-14 01:22:19,723 epoch 6 - iter 99/992 - loss 0.03646699 - time (sec): 6.38 - samples/sec: 2714.64 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-14 01:22:25,276 epoch 6 - iter 198/992 - loss 0.03616235 - time (sec): 11.94 - samples/sec: 2798.79 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-14 01:22:30,923 epoch 6 - iter 297/992 - loss 0.03179580 - time (sec): 17.58 - samples/sec: 2799.10 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-14 01:22:36,766 epoch 6 - iter 396/992 - loss 0.03143445 - time (sec): 23.43 - samples/sec: 2811.42 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-14 01:22:42,630 epoch 6 - iter 495/992 - loss 0.03072024 - time (sec): 29.29 - samples/sec: 2815.34 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-14 01:22:48,259 epoch 6 - iter 594/992 - loss 0.03079986 - time (sec): 34.92 - samples/sec: 2818.20 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-14 01:22:54,316 epoch 6 - iter 693/992 - loss 0.03043770 - time (sec): 40.98 - samples/sec: 2799.34 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-14 01:23:00,319 epoch 6 - iter 792/992 - loss 0.02994022 - time (sec): 46.98 - samples/sec: 2790.61 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-14 01:23:06,471 epoch 6 - iter 891/992 - loss 0.03012561 - time (sec): 53.13 - samples/sec: 2787.05 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-14 01:23:12,180 epoch 6 - iter 990/992 - loss 0.03016026 - time (sec): 58.84 - samples/sec: 2782.11 - lr: 0.000022 - momentum: 0.000000
161
+ 2023-10-14 01:23:12,291 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-14 01:23:12,291 EPOCH 6 done: loss 0.0301 - lr: 0.000022
163
+ 2023-10-14 01:23:15,730 DEV : loss 0.17587369680404663 - f1-score (micro avg) 0.7538
164
+ 2023-10-14 01:23:15,751 ----------------------------------------------------------------------------------------------------
165
+ 2023-10-14 01:23:21,560 epoch 7 - iter 99/992 - loss 0.02680858 - time (sec): 5.81 - samples/sec: 2783.87 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-14 01:23:27,386 epoch 7 - iter 198/992 - loss 0.03036132 - time (sec): 11.63 - samples/sec: 2760.05 - lr: 0.000021 - momentum: 0.000000
167
+ 2023-10-14 01:23:33,314 epoch 7 - iter 297/992 - loss 0.02504973 - time (sec): 17.56 - samples/sec: 2794.69 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-14 01:23:39,233 epoch 7 - iter 396/992 - loss 0.02602930 - time (sec): 23.48 - samples/sec: 2795.69 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-14 01:23:44,944 epoch 7 - iter 495/992 - loss 0.02459364 - time (sec): 29.19 - samples/sec: 2796.12 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-14 01:23:50,850 epoch 7 - iter 594/992 - loss 0.02499387 - time (sec): 35.10 - samples/sec: 2800.96 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-14 01:23:56,991 epoch 7 - iter 693/992 - loss 0.02457250 - time (sec): 41.24 - samples/sec: 2789.41 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-14 01:24:02,722 epoch 7 - iter 792/992 - loss 0.02502738 - time (sec): 46.97 - samples/sec: 2791.68 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-14 01:24:08,621 epoch 7 - iter 891/992 - loss 0.02451035 - time (sec): 52.87 - samples/sec: 2789.21 - lr: 0.000017 - momentum: 0.000000
174
+ 2023-10-14 01:24:14,382 epoch 7 - iter 990/992 - loss 0.02379099 - time (sec): 58.63 - samples/sec: 2791.31 - lr: 0.000017 - momentum: 0.000000
175
+ 2023-10-14 01:24:14,488 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-14 01:24:14,489 EPOCH 7 done: loss 0.0238 - lr: 0.000017
177
+ 2023-10-14 01:24:18,294 DEV : loss 0.1903119683265686 - f1-score (micro avg) 0.7529
178
+ 2023-10-14 01:24:18,315 ----------------------------------------------------------------------------------------------------
179
+ 2023-10-14 01:24:24,188 epoch 8 - iter 99/992 - loss 0.01483288 - time (sec): 5.87 - samples/sec: 2925.75 - lr: 0.000016 - momentum: 0.000000
180
+ 2023-10-14 01:24:29,964 epoch 8 - iter 198/992 - loss 0.01246495 - time (sec): 11.65 - samples/sec: 2854.69 - lr: 0.000016 - momentum: 0.000000
181
+ 2023-10-14 01:24:35,622 epoch 8 - iter 297/992 - loss 0.01429814 - time (sec): 17.31 - samples/sec: 2821.25 - lr: 0.000015 - momentum: 0.000000
182
+ 2023-10-14 01:24:41,814 epoch 8 - iter 396/992 - loss 0.01483730 - time (sec): 23.50 - samples/sec: 2813.76 - lr: 0.000014 - momentum: 0.000000
183
+ 2023-10-14 01:24:47,786 epoch 8 - iter 495/992 - loss 0.01516838 - time (sec): 29.47 - samples/sec: 2816.87 - lr: 0.000014 - momentum: 0.000000
184
+ 2023-10-14 01:24:53,786 epoch 8 - iter 594/992 - loss 0.01536659 - time (sec): 35.47 - samples/sec: 2816.83 - lr: 0.000013 - momentum: 0.000000
185
+ 2023-10-14 01:24:59,378 epoch 8 - iter 693/992 - loss 0.01490078 - time (sec): 41.06 - samples/sec: 2828.30 - lr: 0.000013 - momentum: 0.000000
186
+ 2023-10-14 01:25:05,215 epoch 8 - iter 792/992 - loss 0.01534412 - time (sec): 46.90 - samples/sec: 2812.41 - lr: 0.000012 - momentum: 0.000000
187
+ 2023-10-14 01:25:11,007 epoch 8 - iter 891/992 - loss 0.01570233 - time (sec): 52.69 - samples/sec: 2806.90 - lr: 0.000012 - momentum: 0.000000
188
+ 2023-10-14 01:25:16,688 epoch 8 - iter 990/992 - loss 0.01551159 - time (sec): 58.37 - samples/sec: 2805.59 - lr: 0.000011 - momentum: 0.000000
189
+ 2023-10-14 01:25:16,785 ----------------------------------------------------------------------------------------------------
190
+ 2023-10-14 01:25:16,785 EPOCH 8 done: loss 0.0155 - lr: 0.000011
191
+ 2023-10-14 01:25:20,520 DEV : loss 0.20634520053863525 - f1-score (micro avg) 0.7621
192
+ 2023-10-14 01:25:20,553 saving best model
193
+ 2023-10-14 01:25:21,058 ----------------------------------------------------------------------------------------------------
194
+ 2023-10-14 01:25:26,647 epoch 9 - iter 99/992 - loss 0.01027848 - time (sec): 5.59 - samples/sec: 2895.54 - lr: 0.000011 - momentum: 0.000000
195
+ 2023-10-14 01:25:32,581 epoch 9 - iter 198/992 - loss 0.01067700 - time (sec): 11.52 - samples/sec: 2883.19 - lr: 0.000010 - momentum: 0.000000
196
+ 2023-10-14 01:25:38,714 epoch 9 - iter 297/992 - loss 0.01091812 - time (sec): 17.65 - samples/sec: 2834.44 - lr: 0.000009 - momentum: 0.000000
197
+ 2023-10-14 01:25:44,463 epoch 9 - iter 396/992 - loss 0.01073457 - time (sec): 23.40 - samples/sec: 2805.81 - lr: 0.000009 - momentum: 0.000000
198
+ 2023-10-14 01:25:50,371 epoch 9 - iter 495/992 - loss 0.01029950 - time (sec): 29.31 - samples/sec: 2807.06 - lr: 0.000008 - momentum: 0.000000
199
+ 2023-10-14 01:25:56,117 epoch 9 - iter 594/992 - loss 0.01110924 - time (sec): 35.06 - samples/sec: 2815.10 - lr: 0.000008 - momentum: 0.000000
200
+ 2023-10-14 01:26:02,160 epoch 9 - iter 693/992 - loss 0.01176843 - time (sec): 41.10 - samples/sec: 2792.15 - lr: 0.000007 - momentum: 0.000000
201
+ 2023-10-14 01:26:08,166 epoch 9 - iter 792/992 - loss 0.01153996 - time (sec): 47.11 - samples/sec: 2796.98 - lr: 0.000007 - momentum: 0.000000
202
+ 2023-10-14 01:26:13,818 epoch 9 - iter 891/992 - loss 0.01138962 - time (sec): 52.76 - samples/sec: 2798.37 - lr: 0.000006 - momentum: 0.000000
203
+ 2023-10-14 01:26:19,543 epoch 9 - iter 990/992 - loss 0.01156023 - time (sec): 58.48 - samples/sec: 2796.42 - lr: 0.000006 - momentum: 0.000000
204
+ 2023-10-14 01:26:19,696 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-14 01:26:19,696 EPOCH 9 done: loss 0.0115 - lr: 0.000006
206
+ 2023-10-14 01:26:23,692 DEV : loss 0.2140767127275467 - f1-score (micro avg) 0.7623
207
+ 2023-10-14 01:26:23,714 saving best model
208
+ 2023-10-14 01:26:24,211 ----------------------------------------------------------------------------------------------------
209
+ 2023-10-14 01:26:30,251 epoch 10 - iter 99/992 - loss 0.00607883 - time (sec): 6.04 - samples/sec: 2911.03 - lr: 0.000005 - momentum: 0.000000
210
+ 2023-10-14 01:26:36,244 epoch 10 - iter 198/992 - loss 0.00672977 - time (sec): 12.03 - samples/sec: 2833.64 - lr: 0.000004 - momentum: 0.000000
211
+ 2023-10-14 01:26:41,831 epoch 10 - iter 297/992 - loss 0.00683740 - time (sec): 17.62 - samples/sec: 2791.38 - lr: 0.000004 - momentum: 0.000000
212
+ 2023-10-14 01:26:47,746 epoch 10 - iter 396/992 - loss 0.00754100 - time (sec): 23.53 - samples/sec: 2795.11 - lr: 0.000003 - momentum: 0.000000
213
+ 2023-10-14 01:26:53,622 epoch 10 - iter 495/992 - loss 0.00737902 - time (sec): 29.41 - samples/sec: 2800.18 - lr: 0.000003 - momentum: 0.000000
214
+ 2023-10-14 01:26:59,458 epoch 10 - iter 594/992 - loss 0.00713022 - time (sec): 35.24 - samples/sec: 2791.65 - lr: 0.000002 - momentum: 0.000000
215
+ 2023-10-14 01:27:05,247 epoch 10 - iter 693/992 - loss 0.00806298 - time (sec): 41.03 - samples/sec: 2793.59 - lr: 0.000002 - momentum: 0.000000
216
+ 2023-10-14 01:27:11,236 epoch 10 - iter 792/992 - loss 0.00809314 - time (sec): 47.02 - samples/sec: 2792.23 - lr: 0.000001 - momentum: 0.000000
217
+ 2023-10-14 01:27:16,908 epoch 10 - iter 891/992 - loss 0.00803018 - time (sec): 52.69 - samples/sec: 2806.21 - lr: 0.000001 - momentum: 0.000000
218
+ 2023-10-14 01:27:22,660 epoch 10 - iter 990/992 - loss 0.00838705 - time (sec): 58.45 - samples/sec: 2800.71 - lr: 0.000000 - momentum: 0.000000
219
+ 2023-10-14 01:27:22,767 ----------------------------------------------------------------------------------------------------
220
+ 2023-10-14 01:27:22,767 EPOCH 10 done: loss 0.0084 - lr: 0.000000
221
+ 2023-10-14 01:27:26,208 DEV : loss 0.22556838393211365 - f1-score (micro avg) 0.7641
222
+ 2023-10-14 01:27:26,232 saving best model
223
+ 2023-10-14 01:27:27,131 ----------------------------------------------------------------------------------------------------
224
+ 2023-10-14 01:27:27,132 Loading model from best epoch ...
225
+ 2023-10-14 01:27:28,425 SequenceTagger predicts: Dictionary with 13 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG
226
+ 2023-10-14 01:27:31,682
227
+ Results:
228
+ - F-score (micro) 0.7925
229
+ - F-score (macro) 0.712
230
+ - Accuracy 0.6784
231
+
232
+ By class:
233
+ precision recall f1-score support
234
+
235
+ LOC 0.8363 0.8656 0.8507 655
236
+ PER 0.7336 0.8027 0.7666 223
237
+ ORG 0.5536 0.4882 0.5188 127
238
+
239
+ micro avg 0.7814 0.8040 0.7925 1005
240
+ macro avg 0.7078 0.7188 0.7120 1005
241
+ weighted avg 0.7778 0.8040 0.7901 1005
242
+
243
+ 2023-10-14 01:27:31,682 ----------------------------------------------------------------------------------------------------