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hmbench-letemps-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5

Token Classification
Flair
PyTorch
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. test.tsv +0 -0
  5. training.log +239 -0
best-model.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:8027be05df82de08719218d73db98575866d81189d16a68333dc1c8723b22245
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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 02:15:58 0.0000 0.2879 0.1317 0.5120 0.7826 0.6190 0.4554
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+ 2 02:17:59 0.0000 0.0807 0.1259 0.5314 0.8032 0.6396 0.4795
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+ 3 02:20:02 0.0000 0.0575 0.1452 0.5372 0.7689 0.6325 0.4716
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+ 4 02:22:04 0.0000 0.0408 0.2067 0.5689 0.7368 0.6421 0.4813
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+ 5 02:24:09 0.0000 0.0302 0.3111 0.5272 0.7883 0.6318 0.4693
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+ 6 02:26:11 0.0000 0.0214 0.3326 0.5542 0.7838 0.6493 0.4889
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+ 7 02:28:15 0.0000 0.0154 0.3708 0.5469 0.7471 0.6315 0.4694
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+ 8 02:30:13 0.0000 0.0097 0.4017 0.5552 0.7597 0.6415 0.4798
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+ 9 02:32:11 0.0000 0.0070 0.4087 0.5553 0.7815 0.6492 0.4893
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+ 10 02:34:15 0.0000 0.0046 0.4163 0.5544 0.7757 0.6466 0.4860
test.tsv ADDED
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training.log ADDED
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+ 2023-10-15 02:14:00,073 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:14:00,074 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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+ 2023-10-15 02:14:00,074 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:14:00,074 MultiCorpus: 14465 train + 1392 dev + 2432 test sentences
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+ - NER_HIPE_2022 Corpus: 14465 train + 1392 dev + 2432 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/letemps/fr/with_doc_seperator
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+ 2023-10-15 02:14:00,074 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:14:00,074 Train: 14465 sentences
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+ 2023-10-15 02:14:00,074 (train_with_dev=False, train_with_test=False)
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+ 2023-10-15 02:14:00,074 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:14:00,075 Training Params:
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+ 2023-10-15 02:14:00,075 - learning_rate: "3e-05"
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+ 2023-10-15 02:14:00,075 - mini_batch_size: "8"
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+ 2023-10-15 02:14:00,075 - max_epochs: "10"
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+ 2023-10-15 02:14:00,075 - shuffle: "True"
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+ 2023-10-15 02:14:00,075 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:14:00,075 Plugins:
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+ 2023-10-15 02:14:00,075 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-15 02:14:00,075 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:14:00,075 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-15 02:14:00,075 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-15 02:14:00,075 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:14:00,075 Computation:
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+ 2023-10-15 02:14:00,075 - compute on device: cuda:0
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+ 2023-10-15 02:14:00,075 - embedding storage: none
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+ 2023-10-15 02:14:00,075 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:14:00,075 Model training base path: "hmbench-letemps/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
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+ 2023-10-15 02:14:00,075 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:14:00,075 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:14:10,927 epoch 1 - iter 180/1809 - loss 1.71620325 - time (sec): 10.85 - samples/sec: 3306.55 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-15 02:14:22,349 epoch 1 - iter 360/1809 - loss 0.94066267 - time (sec): 22.27 - samples/sec: 3372.78 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-15 02:14:33,394 epoch 1 - iter 540/1809 - loss 0.69247038 - time (sec): 33.32 - samples/sec: 3362.94 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-15 02:14:44,314 epoch 1 - iter 720/1809 - loss 0.55675267 - time (sec): 44.24 - samples/sec: 3376.46 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-15 02:14:55,634 epoch 1 - iter 900/1809 - loss 0.46969736 - time (sec): 55.56 - samples/sec: 3377.19 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-15 02:15:06,901 epoch 1 - iter 1080/1809 - loss 0.41189108 - time (sec): 66.83 - samples/sec: 3355.68 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-15 02:15:18,514 epoch 1 - iter 1260/1809 - loss 0.36613576 - time (sec): 78.44 - samples/sec: 3364.39 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-15 02:15:30,173 epoch 1 - iter 1440/1809 - loss 0.33395568 - time (sec): 90.10 - samples/sec: 3354.90 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-15 02:15:41,643 epoch 1 - iter 1620/1809 - loss 0.30862678 - time (sec): 101.57 - samples/sec: 3345.11 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-15 02:15:53,267 epoch 1 - iter 1800/1809 - loss 0.28855675 - time (sec): 113.19 - samples/sec: 3342.88 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-15 02:15:53,844 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:15:53,844 EPOCH 1 done: loss 0.2879 - lr: 0.000030
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+ 2023-10-15 02:15:58,741 DEV : loss 0.13172951340675354 - f1-score (micro avg) 0.619
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+ 2023-10-15 02:15:58,782 saving best model
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+ 2023-10-15 02:15:59,172 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:16:11,817 epoch 2 - iter 180/1809 - loss 0.08947028 - time (sec): 12.64 - samples/sec: 3016.49 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-15 02:16:23,203 epoch 2 - iter 360/1809 - loss 0.08337000 - time (sec): 24.03 - samples/sec: 3124.59 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-15 02:16:34,060 epoch 2 - iter 540/1809 - loss 0.08264446 - time (sec): 34.89 - samples/sec: 3153.05 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-15 02:16:45,345 epoch 2 - iter 720/1809 - loss 0.08177670 - time (sec): 46.17 - samples/sec: 3219.93 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-15 02:16:56,562 epoch 2 - iter 900/1809 - loss 0.08349985 - time (sec): 57.39 - samples/sec: 3268.13 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-15 02:17:07,800 epoch 2 - iter 1080/1809 - loss 0.08456116 - time (sec): 68.63 - samples/sec: 3302.75 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-15 02:17:19,132 epoch 2 - iter 1260/1809 - loss 0.08295867 - time (sec): 79.96 - samples/sec: 3308.07 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-15 02:17:30,433 epoch 2 - iter 1440/1809 - loss 0.08216898 - time (sec): 91.26 - samples/sec: 3314.28 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-15 02:17:42,075 epoch 2 - iter 1620/1809 - loss 0.08149738 - time (sec): 102.90 - samples/sec: 3308.35 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-15 02:17:53,256 epoch 2 - iter 1800/1809 - loss 0.08048420 - time (sec): 114.08 - samples/sec: 3314.18 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-15 02:17:53,802 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:17:53,803 EPOCH 2 done: loss 0.0807 - lr: 0.000027
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+ 2023-10-15 02:17:59,385 DEV : loss 0.12593407928943634 - f1-score (micro avg) 0.6396
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+ 2023-10-15 02:17:59,416 saving best model
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+ 2023-10-15 02:17:59,915 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:18:11,666 epoch 3 - iter 180/1809 - loss 0.05780327 - time (sec): 11.75 - samples/sec: 3344.57 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-15 02:18:23,153 epoch 3 - iter 360/1809 - loss 0.05701266 - time (sec): 23.24 - samples/sec: 3305.08 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-15 02:18:34,765 epoch 3 - iter 540/1809 - loss 0.05674387 - time (sec): 34.85 - samples/sec: 3283.83 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-15 02:18:46,421 epoch 3 - iter 720/1809 - loss 0.05547901 - time (sec): 46.50 - samples/sec: 3296.20 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-15 02:18:59,135 epoch 3 - iter 900/1809 - loss 0.05680081 - time (sec): 59.22 - samples/sec: 3220.71 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-15 02:19:10,559 epoch 3 - iter 1080/1809 - loss 0.05816395 - time (sec): 70.64 - samples/sec: 3227.10 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-15 02:19:22,063 epoch 3 - iter 1260/1809 - loss 0.05871572 - time (sec): 82.15 - samples/sec: 3224.99 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-15 02:19:33,639 epoch 3 - iter 1440/1809 - loss 0.05780555 - time (sec): 93.72 - samples/sec: 3223.82 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-15 02:19:45,305 epoch 3 - iter 1620/1809 - loss 0.05757005 - time (sec): 105.39 - samples/sec: 3225.36 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-15 02:19:56,661 epoch 3 - iter 1800/1809 - loss 0.05749567 - time (sec): 116.74 - samples/sec: 3239.30 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-15 02:19:57,152 ----------------------------------------------------------------------------------------------------
117
+ 2023-10-15 02:19:57,152 EPOCH 3 done: loss 0.0575 - lr: 0.000023
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+ 2023-10-15 02:20:02,874 DEV : loss 0.1452324539422989 - f1-score (micro avg) 0.6325
119
+ 2023-10-15 02:20:02,916 ----------------------------------------------------------------------------------------------------
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+ 2023-10-15 02:20:14,468 epoch 4 - iter 180/1809 - loss 0.03496327 - time (sec): 11.55 - samples/sec: 3129.42 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-15 02:20:26,389 epoch 4 - iter 360/1809 - loss 0.04079733 - time (sec): 23.47 - samples/sec: 3147.05 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-15 02:20:38,078 epoch 4 - iter 540/1809 - loss 0.03823883 - time (sec): 35.16 - samples/sec: 3217.16 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-15 02:20:49,395 epoch 4 - iter 720/1809 - loss 0.04151284 - time (sec): 46.48 - samples/sec: 3230.29 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-15 02:21:01,210 epoch 4 - iter 900/1809 - loss 0.04052903 - time (sec): 58.29 - samples/sec: 3250.78 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-15 02:21:12,762 epoch 4 - iter 1080/1809 - loss 0.03971197 - time (sec): 69.84 - samples/sec: 3252.29 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-15 02:21:23,785 epoch 4 - iter 1260/1809 - loss 0.04066587 - time (sec): 80.87 - samples/sec: 3260.03 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-15 02:21:34,819 epoch 4 - iter 1440/1809 - loss 0.04096194 - time (sec): 91.90 - samples/sec: 3285.20 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-15 02:21:46,289 epoch 4 - iter 1620/1809 - loss 0.04138910 - time (sec): 103.37 - samples/sec: 3289.06 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-15 02:21:57,449 epoch 4 - iter 1800/1809 - loss 0.04070492 - time (sec): 114.53 - samples/sec: 3299.55 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-10-15 02:21:58,939 ----------------------------------------------------------------------------------------------------
131
+ 2023-10-15 02:21:58,939 EPOCH 4 done: loss 0.0408 - lr: 0.000020
132
+ 2023-10-15 02:22:04,577 DEV : loss 0.20669673383235931 - f1-score (micro avg) 0.6421
133
+ 2023-10-15 02:22:04,608 saving best model
134
+ 2023-10-15 02:22:05,114 ----------------------------------------------------------------------------------------------------
135
+ 2023-10-15 02:22:16,531 epoch 5 - iter 180/1809 - loss 0.02389214 - time (sec): 11.41 - samples/sec: 3214.97 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-15 02:22:28,115 epoch 5 - iter 360/1809 - loss 0.02800805 - time (sec): 23.00 - samples/sec: 3206.97 - lr: 0.000019 - momentum: 0.000000
137
+ 2023-10-15 02:22:39,847 epoch 5 - iter 540/1809 - loss 0.02827358 - time (sec): 34.73 - samples/sec: 3185.08 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-15 02:22:51,849 epoch 5 - iter 720/1809 - loss 0.02901182 - time (sec): 46.73 - samples/sec: 3194.78 - lr: 0.000019 - momentum: 0.000000
139
+ 2023-10-15 02:23:03,331 epoch 5 - iter 900/1809 - loss 0.02864829 - time (sec): 58.22 - samples/sec: 3204.14 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-15 02:23:14,829 epoch 5 - iter 1080/1809 - loss 0.02941320 - time (sec): 69.71 - samples/sec: 3227.23 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-10-15 02:23:26,496 epoch 5 - iter 1260/1809 - loss 0.02925040 - time (sec): 81.38 - samples/sec: 3223.63 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-15 02:23:38,853 epoch 5 - iter 1440/1809 - loss 0.03024850 - time (sec): 93.74 - samples/sec: 3224.36 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-15 02:23:50,326 epoch 5 - iter 1620/1809 - loss 0.03013904 - time (sec): 105.21 - samples/sec: 3242.00 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-10-15 02:24:01,655 epoch 5 - iter 1800/1809 - loss 0.03031792 - time (sec): 116.54 - samples/sec: 3245.16 - lr: 0.000017 - momentum: 0.000000
145
+ 2023-10-15 02:24:02,192 ----------------------------------------------------------------------------------------------------
146
+ 2023-10-15 02:24:02,192 EPOCH 5 done: loss 0.0302 - lr: 0.000017
147
+ 2023-10-15 02:24:09,030 DEV : loss 0.3110675513744354 - f1-score (micro avg) 0.6318
148
+ 2023-10-15 02:24:09,071 ----------------------------------------------------------------------------------------------------
149
+ 2023-10-15 02:24:20,379 epoch 6 - iter 180/1809 - loss 0.01816067 - time (sec): 11.31 - samples/sec: 3292.12 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-15 02:24:31,904 epoch 6 - iter 360/1809 - loss 0.02260225 - time (sec): 22.83 - samples/sec: 3258.83 - lr: 0.000016 - momentum: 0.000000
151
+ 2023-10-15 02:24:43,547 epoch 6 - iter 540/1809 - loss 0.02107577 - time (sec): 34.47 - samples/sec: 3226.15 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-10-15 02:24:55,447 epoch 6 - iter 720/1809 - loss 0.02218085 - time (sec): 46.37 - samples/sec: 3219.97 - lr: 0.000015 - momentum: 0.000000
153
+ 2023-10-15 02:25:07,299 epoch 6 - iter 900/1809 - loss 0.02197810 - time (sec): 58.23 - samples/sec: 3230.90 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-10-15 02:25:18,759 epoch 6 - iter 1080/1809 - loss 0.02231769 - time (sec): 69.69 - samples/sec: 3242.11 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-10-15 02:25:30,458 epoch 6 - iter 1260/1809 - loss 0.02186519 - time (sec): 81.39 - samples/sec: 3259.09 - lr: 0.000014 - momentum: 0.000000
156
+ 2023-10-15 02:25:41,915 epoch 6 - iter 1440/1809 - loss 0.02158008 - time (sec): 92.84 - samples/sec: 3260.83 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-10-15 02:25:53,268 epoch 6 - iter 1620/1809 - loss 0.02185088 - time (sec): 104.20 - samples/sec: 3259.37 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-10-15 02:26:04,662 epoch 6 - iter 1800/1809 - loss 0.02134928 - time (sec): 115.59 - samples/sec: 3269.94 - lr: 0.000013 - momentum: 0.000000
159
+ 2023-10-15 02:26:05,244 ----------------------------------------------------------------------------------------------------
160
+ 2023-10-15 02:26:05,244 EPOCH 6 done: loss 0.0214 - lr: 0.000013
161
+ 2023-10-15 02:26:11,783 DEV : loss 0.3325505256652832 - f1-score (micro avg) 0.6493
162
+ 2023-10-15 02:26:11,813 saving best model
163
+ 2023-10-15 02:26:12,331 ----------------------------------------------------------------------------------------------------
164
+ 2023-10-15 02:26:23,804 epoch 7 - iter 180/1809 - loss 0.01455509 - time (sec): 11.47 - samples/sec: 3329.38 - lr: 0.000013 - momentum: 0.000000
165
+ 2023-10-15 02:26:35,078 epoch 7 - iter 360/1809 - loss 0.01121576 - time (sec): 22.75 - samples/sec: 3272.00 - lr: 0.000013 - momentum: 0.000000
166
+ 2023-10-15 02:26:46,891 epoch 7 - iter 540/1809 - loss 0.01362276 - time (sec): 34.56 - samples/sec: 3260.25 - lr: 0.000012 - momentum: 0.000000
167
+ 2023-10-15 02:26:58,682 epoch 7 - iter 720/1809 - loss 0.01404265 - time (sec): 46.35 - samples/sec: 3245.06 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-10-15 02:27:09,857 epoch 7 - iter 900/1809 - loss 0.01380675 - time (sec): 57.52 - samples/sec: 3266.88 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-10-15 02:27:21,560 epoch 7 - iter 1080/1809 - loss 0.01462766 - time (sec): 69.23 - samples/sec: 3276.78 - lr: 0.000011 - momentum: 0.000000
170
+ 2023-10-15 02:27:32,867 epoch 7 - iter 1260/1809 - loss 0.01549142 - time (sec): 80.53 - samples/sec: 3283.47 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-10-15 02:27:44,136 epoch 7 - iter 1440/1809 - loss 0.01554337 - time (sec): 91.80 - samples/sec: 3284.27 - lr: 0.000011 - momentum: 0.000000
172
+ 2023-10-15 02:27:56,136 epoch 7 - iter 1620/1809 - loss 0.01548596 - time (sec): 103.80 - samples/sec: 3271.47 - lr: 0.000010 - momentum: 0.000000
173
+ 2023-10-15 02:28:07,652 epoch 7 - iter 1800/1809 - loss 0.01536064 - time (sec): 115.32 - samples/sec: 3279.88 - lr: 0.000010 - momentum: 0.000000
174
+ 2023-10-15 02:28:08,163 ----------------------------------------------------------------------------------------------------
175
+ 2023-10-15 02:28:08,164 EPOCH 7 done: loss 0.0154 - lr: 0.000010
176
+ 2023-10-15 02:28:15,762 DEV : loss 0.37081876397132874 - f1-score (micro avg) 0.6315
177
+ 2023-10-15 02:28:15,800 ----------------------------------------------------------------------------------------------------
178
+ 2023-10-15 02:28:27,190 epoch 8 - iter 180/1809 - loss 0.00878142 - time (sec): 11.39 - samples/sec: 3326.19 - lr: 0.000010 - momentum: 0.000000
179
+ 2023-10-15 02:28:38,168 epoch 8 - iter 360/1809 - loss 0.00883600 - time (sec): 22.37 - samples/sec: 3309.93 - lr: 0.000009 - momentum: 0.000000
180
+ 2023-10-15 02:28:49,182 epoch 8 - iter 540/1809 - loss 0.00912758 - time (sec): 33.38 - samples/sec: 3313.93 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-10-15 02:29:00,302 epoch 8 - iter 720/1809 - loss 0.00987622 - time (sec): 44.50 - samples/sec: 3351.66 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-10-15 02:29:11,250 epoch 8 - iter 900/1809 - loss 0.01048489 - time (sec): 55.45 - samples/sec: 3345.57 - lr: 0.000008 - momentum: 0.000000
183
+ 2023-10-15 02:29:22,244 epoch 8 - iter 1080/1809 - loss 0.01059545 - time (sec): 66.44 - samples/sec: 3354.63 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-10-15 02:29:33,312 epoch 8 - iter 1260/1809 - loss 0.01035550 - time (sec): 77.51 - samples/sec: 3362.51 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-10-15 02:29:44,498 epoch 8 - iter 1440/1809 - loss 0.01013690 - time (sec): 88.70 - samples/sec: 3378.06 - lr: 0.000007 - momentum: 0.000000
186
+ 2023-10-15 02:29:56,057 epoch 8 - iter 1620/1809 - loss 0.00979165 - time (sec): 100.26 - samples/sec: 3380.91 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-10-15 02:30:07,595 epoch 8 - iter 1800/1809 - loss 0.00963702 - time (sec): 111.79 - samples/sec: 3384.29 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-10-15 02:30:08,146 ----------------------------------------------------------------------------------------------------
189
+ 2023-10-15 02:30:08,146 EPOCH 8 done: loss 0.0097 - lr: 0.000007
190
+ 2023-10-15 02:30:13,762 DEV : loss 0.4017082154750824 - f1-score (micro avg) 0.6415
191
+ 2023-10-15 02:30:13,803 ----------------------------------------------------------------------------------------------------
192
+ 2023-10-15 02:30:25,977 epoch 9 - iter 180/1809 - loss 0.00765845 - time (sec): 12.17 - samples/sec: 3256.59 - lr: 0.000006 - momentum: 0.000000
193
+ 2023-10-15 02:30:36,972 epoch 9 - iter 360/1809 - loss 0.00675346 - time (sec): 23.17 - samples/sec: 3322.71 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-10-15 02:30:47,836 epoch 9 - iter 540/1809 - loss 0.00672088 - time (sec): 34.03 - samples/sec: 3343.95 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-10-15 02:30:58,928 epoch 9 - iter 720/1809 - loss 0.00646790 - time (sec): 45.12 - samples/sec: 3399.01 - lr: 0.000005 - momentum: 0.000000
196
+ 2023-10-15 02:31:09,758 epoch 9 - iter 900/1809 - loss 0.00676701 - time (sec): 55.95 - samples/sec: 3406.90 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-10-15 02:31:20,723 epoch 9 - iter 1080/1809 - loss 0.00669838 - time (sec): 66.92 - samples/sec: 3400.98 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-10-15 02:31:32,136 epoch 9 - iter 1260/1809 - loss 0.00698570 - time (sec): 78.33 - samples/sec: 3395.23 - lr: 0.000004 - momentum: 0.000000
199
+ 2023-10-15 02:31:43,046 epoch 9 - iter 1440/1809 - loss 0.00706185 - time (sec): 89.24 - samples/sec: 3404.64 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-10-15 02:31:54,348 epoch 9 - iter 1620/1809 - loss 0.00720346 - time (sec): 100.54 - samples/sec: 3398.13 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-10-15 02:32:05,269 epoch 9 - iter 1800/1809 - loss 0.00700774 - time (sec): 111.46 - samples/sec: 3392.28 - lr: 0.000003 - momentum: 0.000000
202
+ 2023-10-15 02:32:05,812 ----------------------------------------------------------------------------------------------------
203
+ 2023-10-15 02:32:05,812 EPOCH 9 done: loss 0.0070 - lr: 0.000003
204
+ 2023-10-15 02:32:11,497 DEV : loss 0.4086902439594269 - f1-score (micro avg) 0.6492
205
+ 2023-10-15 02:32:11,544 ----------------------------------------------------------------------------------------------------
206
+ 2023-10-15 02:32:23,632 epoch 10 - iter 180/1809 - loss 0.00472486 - time (sec): 12.09 - samples/sec: 3177.79 - lr: 0.000003 - momentum: 0.000000
207
+ 2023-10-15 02:32:35,272 epoch 10 - iter 360/1809 - loss 0.00533860 - time (sec): 23.73 - samples/sec: 3171.01 - lr: 0.000003 - momentum: 0.000000
208
+ 2023-10-15 02:32:46,964 epoch 10 - iter 540/1809 - loss 0.00499615 - time (sec): 35.42 - samples/sec: 3218.25 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-15 02:32:59,939 epoch 10 - iter 720/1809 - loss 0.00489132 - time (sec): 48.39 - samples/sec: 3122.81 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-15 02:33:11,964 epoch 10 - iter 900/1809 - loss 0.00443154 - time (sec): 60.42 - samples/sec: 3144.19 - lr: 0.000002 - momentum: 0.000000
211
+ 2023-10-15 02:33:23,384 epoch 10 - iter 1080/1809 - loss 0.00446488 - time (sec): 71.84 - samples/sec: 3161.77 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-15 02:33:34,855 epoch 10 - iter 1260/1809 - loss 0.00421573 - time (sec): 83.31 - samples/sec: 3181.83 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-15 02:33:46,612 epoch 10 - iter 1440/1809 - loss 0.00414519 - time (sec): 95.07 - samples/sec: 3175.58 - lr: 0.000001 - momentum: 0.000000
214
+ 2023-10-15 02:33:58,058 epoch 10 - iter 1620/1809 - loss 0.00475133 - time (sec): 106.51 - samples/sec: 3196.13 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-10-15 02:34:09,631 epoch 10 - iter 1800/1809 - loss 0.00456537 - time (sec): 118.08 - samples/sec: 3203.33 - lr: 0.000000 - momentum: 0.000000
216
+ 2023-10-15 02:34:10,184 ----------------------------------------------------------------------------------------------------
217
+ 2023-10-15 02:34:10,184 EPOCH 10 done: loss 0.0046 - lr: 0.000000
218
+ 2023-10-15 02:34:15,872 DEV : loss 0.4162753224372864 - f1-score (micro avg) 0.6466
219
+ 2023-10-15 02:34:16,294 ----------------------------------------------------------------------------------------------------
220
+ 2023-10-15 02:34:16,295 Loading model from best epoch ...
221
+ 2023-10-15 02:34:17,937 SequenceTagger predicts: Dictionary with 13 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
222
+ 2023-10-15 02:34:25,749
223
+ Results:
224
+ - F-score (micro) 0.6541
225
+ - F-score (macro) 0.51
226
+ - Accuracy 0.5007
227
+
228
+ By class:
229
+ precision recall f1-score support
230
+
231
+ loc 0.6394 0.7800 0.7027 591
232
+ pers 0.5851 0.7703 0.6651 357
233
+ org 0.1739 0.1519 0.1622 79
234
+
235
+ micro avg 0.5937 0.7283 0.6541 1027
236
+ macro avg 0.4661 0.5674 0.5100 1027
237
+ weighted avg 0.5847 0.7283 0.6481 1027
238
+
239
+ 2023-10-15 02:34:25,749 ----------------------------------------------------------------------------------------------------