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hmbench-letemps-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3

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 +238 -0
best-model.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:bf58ef194970bc1e42fa6ad04ac3649c07435f79da83023d71baea27b164676d
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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 21:57:52 0.0000 0.2617 0.1234 0.5174 0.6979 0.5943 0.4351
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+ 2 22:00:43 0.0000 0.0993 0.1349 0.5389 0.8169 0.6494 0.4877
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+ 3 22:03:33 0.0000 0.0750 0.2054 0.5613 0.7540 0.6436 0.4807
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+ 4 22:06:24 0.0000 0.0537 0.2250 0.5236 0.7735 0.6245 0.4630
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+ 5 22:09:17 0.0000 0.0385 0.2994 0.5375 0.7780 0.6358 0.4752
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+ 6 22:12:20 0.0000 0.0269 0.3372 0.5549 0.7174 0.6257 0.4641
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+ 7 22:15:13 0.0000 0.0198 0.3545 0.5519 0.7666 0.6418 0.4810
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+ 8 22:18:04 0.0000 0.0144 0.3676 0.5585 0.7860 0.6530 0.4935
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+ 9 22:20:54 0.0000 0.0081 0.3925 0.5507 0.7643 0.6402 0.4799
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+ 10 22:23:42 0.0000 0.0051 0.4006 0.5466 0.7849 0.6444 0.4831
test.tsv ADDED
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training.log ADDED
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+ 2023-10-14 21:54:59,595 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:54:59,596 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 21:54:59,596 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:54:59,596 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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+ 2023-10-14 21:54:59,596 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:54:59,596 Train: 14465 sentences
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+ 2023-10-14 21:54:59,596 (train_with_dev=False, train_with_test=False)
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+ 2023-10-14 21:54:59,596 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:54:59,596 Training Params:
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+ 2023-10-14 21:54:59,596 - learning_rate: "3e-05"
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+ 2023-10-14 21:54:59,596 - mini_batch_size: "4"
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+ 2023-10-14 21:54:59,596 - max_epochs: "10"
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+ 2023-10-14 21:54:59,596 - shuffle: "True"
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+ 2023-10-14 21:54:59,596 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:54:59,596 Plugins:
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+ 2023-10-14 21:54:59,596 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-14 21:54:59,596 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:54:59,596 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-14 21:54:59,596 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-14 21:54:59,596 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:54:59,596 Computation:
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+ 2023-10-14 21:54:59,596 - compute on device: cuda:0
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+ 2023-10-14 21:54:59,597 - embedding storage: none
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+ 2023-10-14 21:54:59,597 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:54:59,597 Model training base path: "hmbench-letemps/fr-dbmdz/bert-base-historic-multilingual-cased-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3"
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+ 2023-10-14 21:54:59,597 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:54:59,597 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:55:18,748 epoch 1 - iter 361/3617 - loss 1.37470380 - time (sec): 19.15 - samples/sec: 2000.76 - lr: 0.000003 - momentum: 0.000000
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+ 2023-10-14 21:55:36,278 epoch 1 - iter 722/3617 - loss 0.80586607 - time (sec): 36.68 - samples/sec: 2074.90 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-14 21:55:52,539 epoch 1 - iter 1083/3617 - loss 0.59505650 - time (sec): 52.94 - samples/sec: 2137.86 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-14 21:56:08,972 epoch 1 - iter 1444/3617 - loss 0.47633157 - time (sec): 69.37 - samples/sec: 2200.13 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-14 21:56:25,348 epoch 1 - iter 1805/3617 - loss 0.40695084 - time (sec): 85.75 - samples/sec: 2221.40 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-14 21:56:41,760 epoch 1 - iter 2166/3617 - loss 0.35885887 - time (sec): 102.16 - samples/sec: 2250.04 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-14 21:56:57,915 epoch 1 - iter 2527/3617 - loss 0.32613123 - time (sec): 118.32 - samples/sec: 2256.83 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-14 21:57:14,256 epoch 1 - iter 2888/3617 - loss 0.29928065 - time (sec): 134.66 - samples/sec: 2261.76 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-14 21:57:30,968 epoch 1 - iter 3249/3617 - loss 0.27794024 - time (sec): 151.37 - samples/sec: 2260.71 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-14 21:57:47,160 epoch 1 - iter 3610/3617 - loss 0.26205461 - time (sec): 167.56 - samples/sec: 2263.05 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-14 21:57:47,478 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:57:47,478 EPOCH 1 done: loss 0.2617 - lr: 0.000030
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+ 2023-10-14 21:57:52,218 DEV : loss 0.12341408431529999 - f1-score (micro avg) 0.5943
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+ 2023-10-14 21:57:52,255 saving best model
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+ 2023-10-14 21:57:52,708 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 21:58:10,216 epoch 2 - iter 361/3617 - loss 0.10403458 - time (sec): 17.51 - samples/sec: 2122.79 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-14 21:58:26,642 epoch 2 - iter 722/3617 - loss 0.10235270 - time (sec): 33.93 - samples/sec: 2224.39 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-14 21:58:43,240 epoch 2 - iter 1083/3617 - loss 0.10201030 - time (sec): 50.53 - samples/sec: 2254.16 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-14 21:58:59,738 epoch 2 - iter 1444/3617 - loss 0.10128214 - time (sec): 67.03 - samples/sec: 2281.33 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-14 21:59:15,971 epoch 2 - iter 1805/3617 - loss 0.09923902 - time (sec): 83.26 - samples/sec: 2304.80 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-14 21:59:32,225 epoch 2 - iter 2166/3617 - loss 0.09995662 - time (sec): 99.51 - samples/sec: 2306.90 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-14 21:59:48,412 epoch 2 - iter 2527/3617 - loss 0.10116313 - time (sec): 115.70 - samples/sec: 2306.41 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-14 22:00:04,589 epoch 2 - iter 2888/3617 - loss 0.10129391 - time (sec): 131.88 - samples/sec: 2296.43 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-14 22:00:21,042 epoch 2 - iter 3249/3617 - loss 0.09991245 - time (sec): 148.33 - samples/sec: 2303.58 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-14 22:00:37,242 epoch 2 - iter 3610/3617 - loss 0.09941004 - time (sec): 164.53 - samples/sec: 2305.17 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-14 22:00:37,554 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 22:00:37,554 EPOCH 2 done: loss 0.0993 - lr: 0.000027
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+ 2023-10-14 22:00:43,848 DEV : loss 0.1348618119955063 - f1-score (micro avg) 0.6494
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+ 2023-10-14 22:00:43,876 saving best model
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+ 2023-10-14 22:00:44,365 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 22:01:00,728 epoch 3 - iter 361/3617 - loss 0.06498786 - time (sec): 16.36 - samples/sec: 2373.91 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-14 22:01:16,889 epoch 3 - iter 722/3617 - loss 0.07260502 - time (sec): 32.52 - samples/sec: 2355.96 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-14 22:01:33,116 epoch 3 - iter 1083/3617 - loss 0.07621223 - time (sec): 48.75 - samples/sec: 2337.97 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-14 22:01:49,355 epoch 3 - iter 1444/3617 - loss 0.07608788 - time (sec): 64.98 - samples/sec: 2351.04 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-14 22:02:05,843 epoch 3 - iter 1805/3617 - loss 0.07598984 - time (sec): 81.47 - samples/sec: 2333.63 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-14 22:02:21,958 epoch 3 - iter 2166/3617 - loss 0.07775218 - time (sec): 97.59 - samples/sec: 2335.11 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-14 22:02:38,247 epoch 3 - iter 2527/3617 - loss 0.07602466 - time (sec): 113.88 - samples/sec: 2332.17 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-14 22:02:54,496 epoch 3 - iter 2888/3617 - loss 0.07632851 - time (sec): 130.13 - samples/sec: 2327.74 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-14 22:03:10,904 epoch 3 - iter 3249/3617 - loss 0.07723473 - time (sec): 146.53 - samples/sec: 2329.75 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-14 22:03:27,104 epoch 3 - iter 3610/3617 - loss 0.07511861 - time (sec): 162.73 - samples/sec: 2330.38 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-14 22:03:27,411 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 22:03:27,411 EPOCH 3 done: loss 0.0750 - lr: 0.000023
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+ 2023-10-14 22:03:33,762 DEV : loss 0.20539723336696625 - f1-score (micro avg) 0.6436
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+ 2023-10-14 22:03:33,797 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 22:03:50,426 epoch 4 - iter 361/3617 - loss 0.04544183 - time (sec): 16.63 - samples/sec: 2346.47 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-14 22:04:06,629 epoch 4 - iter 722/3617 - loss 0.04633691 - time (sec): 32.83 - samples/sec: 2331.87 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-14 22:04:22,959 epoch 4 - iter 1083/3617 - loss 0.04843502 - time (sec): 49.16 - samples/sec: 2334.59 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-14 22:04:39,149 epoch 4 - iter 1444/3617 - loss 0.05354134 - time (sec): 65.35 - samples/sec: 2319.23 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-14 22:04:55,305 epoch 4 - iter 1805/3617 - loss 0.05419777 - time (sec): 81.51 - samples/sec: 2319.80 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-14 22:05:11,659 epoch 4 - iter 2166/3617 - loss 0.05236216 - time (sec): 97.86 - samples/sec: 2323.42 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-14 22:05:27,796 epoch 4 - iter 2527/3617 - loss 0.05247379 - time (sec): 114.00 - samples/sec: 2328.95 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-14 22:05:44,068 epoch 4 - iter 2888/3617 - loss 0.05210563 - time (sec): 130.27 - samples/sec: 2335.22 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-14 22:06:00,162 epoch 4 - iter 3249/3617 - loss 0.05267914 - time (sec): 146.36 - samples/sec: 2334.44 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-14 22:06:16,279 epoch 4 - iter 3610/3617 - loss 0.05369010 - time (sec): 162.48 - samples/sec: 2333.65 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-14 22:06:16,593 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 22:06:16,593 EPOCH 4 done: loss 0.0537 - lr: 0.000020
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+ 2023-10-14 22:06:24,103 DEV : loss 0.22502246499061584 - f1-score (micro avg) 0.6245
133
+ 2023-10-14 22:06:24,150 ----------------------------------------------------------------------------------------------------
134
+ 2023-10-14 22:06:42,193 epoch 5 - iter 361/3617 - loss 0.03757363 - time (sec): 18.04 - samples/sec: 2095.78 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-14 22:06:58,597 epoch 5 - iter 722/3617 - loss 0.03566151 - time (sec): 34.44 - samples/sec: 2204.45 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-14 22:07:14,945 epoch 5 - iter 1083/3617 - loss 0.03610137 - time (sec): 50.79 - samples/sec: 2237.25 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-14 22:07:31,411 epoch 5 - iter 1444/3617 - loss 0.03673278 - time (sec): 67.26 - samples/sec: 2238.77 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-14 22:07:47,752 epoch 5 - iter 1805/3617 - loss 0.03672910 - time (sec): 83.60 - samples/sec: 2251.84 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-14 22:08:04,113 epoch 5 - iter 2166/3617 - loss 0.03708856 - time (sec): 99.96 - samples/sec: 2255.25 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-14 22:08:20,339 epoch 5 - iter 2527/3617 - loss 0.03713523 - time (sec): 116.19 - samples/sec: 2258.75 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-14 22:08:36,957 epoch 5 - iter 2888/3617 - loss 0.03750961 - time (sec): 132.80 - samples/sec: 2275.54 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-14 22:08:53,212 epoch 5 - iter 3249/3617 - loss 0.03760435 - time (sec): 149.06 - samples/sec: 2285.38 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-14 22:09:09,636 epoch 5 - iter 3610/3617 - loss 0.03853007 - time (sec): 165.48 - samples/sec: 2292.93 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-10-14 22:09:09,948 ----------------------------------------------------------------------------------------------------
145
+ 2023-10-14 22:09:09,948 EPOCH 5 done: loss 0.0385 - lr: 0.000017
146
+ 2023-10-14 22:09:17,131 DEV : loss 0.2994045317173004 - f1-score (micro avg) 0.6358
147
+ 2023-10-14 22:09:17,161 ----------------------------------------------------------------------------------------------------
148
+ 2023-10-14 22:09:33,755 epoch 6 - iter 361/3617 - loss 0.02535187 - time (sec): 16.59 - samples/sec: 2169.49 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-14 22:09:50,064 epoch 6 - iter 722/3617 - loss 0.02736771 - time (sec): 32.90 - samples/sec: 2260.33 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-14 22:10:06,390 epoch 6 - iter 1083/3617 - loss 0.02713759 - time (sec): 49.23 - samples/sec: 2260.87 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-14 22:10:22,740 epoch 6 - iter 1444/3617 - loss 0.02699120 - time (sec): 65.58 - samples/sec: 2275.19 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-14 22:10:39,484 epoch 6 - iter 1805/3617 - loss 0.02728068 - time (sec): 82.32 - samples/sec: 2282.65 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-14 22:10:57,852 epoch 6 - iter 2166/3617 - loss 0.02913853 - time (sec): 100.69 - samples/sec: 2246.59 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-14 22:11:17,253 epoch 6 - iter 2527/3617 - loss 0.02836692 - time (sec): 120.09 - samples/sec: 2209.10 - lr: 0.000014 - momentum: 0.000000
155
+ 2023-10-14 22:11:36,100 epoch 6 - iter 2888/3617 - loss 0.02781183 - time (sec): 138.94 - samples/sec: 2194.97 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-14 22:11:54,931 epoch 6 - iter 3249/3617 - loss 0.02758622 - time (sec): 157.77 - samples/sec: 2164.32 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-10-14 22:12:13,902 epoch 6 - iter 3610/3617 - loss 0.02692781 - time (sec): 176.74 - samples/sec: 2145.77 - lr: 0.000013 - momentum: 0.000000
158
+ 2023-10-14 22:12:14,277 ----------------------------------------------------------------------------------------------------
159
+ 2023-10-14 22:12:14,277 EPOCH 6 done: loss 0.0269 - lr: 0.000013
160
+ 2023-10-14 22:12:19,958 DEV : loss 0.337155818939209 - f1-score (micro avg) 0.6257
161
+ 2023-10-14 22:12:20,003 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-14 22:12:37,007 epoch 7 - iter 361/3617 - loss 0.01474905 - time (sec): 17.00 - samples/sec: 2190.69 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-14 22:12:53,626 epoch 7 - iter 722/3617 - loss 0.01455228 - time (sec): 33.62 - samples/sec: 2278.91 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-14 22:13:09,984 epoch 7 - iter 1083/3617 - loss 0.01703615 - time (sec): 49.98 - samples/sec: 2320.82 - lr: 0.000012 - momentum: 0.000000
165
+ 2023-10-14 22:13:26,219 epoch 7 - iter 1444/3617 - loss 0.01832267 - time (sec): 66.21 - samples/sec: 2305.64 - lr: 0.000012 - momentum: 0.000000
166
+ 2023-10-14 22:13:42,728 epoch 7 - iter 1805/3617 - loss 0.02031330 - time (sec): 82.72 - samples/sec: 2302.63 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-14 22:13:58,984 epoch 7 - iter 2166/3617 - loss 0.02071575 - time (sec): 98.98 - samples/sec: 2307.64 - lr: 0.000011 - momentum: 0.000000
168
+ 2023-10-14 22:14:15,313 epoch 7 - iter 2527/3617 - loss 0.02056007 - time (sec): 115.31 - samples/sec: 2319.44 - lr: 0.000011 - momentum: 0.000000
169
+ 2023-10-14 22:14:31,743 epoch 7 - iter 2888/3617 - loss 0.01989770 - time (sec): 131.74 - samples/sec: 2301.33 - lr: 0.000011 - momentum: 0.000000
170
+ 2023-10-14 22:14:48,890 epoch 7 - iter 3249/3617 - loss 0.01977656 - time (sec): 148.88 - samples/sec: 2293.85 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-14 22:15:06,207 epoch 7 - iter 3610/3617 - loss 0.01987886 - time (sec): 166.20 - samples/sec: 2281.30 - lr: 0.000010 - momentum: 0.000000
172
+ 2023-10-14 22:15:06,529 ----------------------------------------------------------------------------------------------------
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+ 2023-10-14 22:15:06,530 EPOCH 7 done: loss 0.0198 - lr: 0.000010
174
+ 2023-10-14 22:15:13,093 DEV : loss 0.3545047342777252 - f1-score (micro avg) 0.6418
175
+ 2023-10-14 22:15:13,126 ----------------------------------------------------------------------------------------------------
176
+ 2023-10-14 22:15:30,580 epoch 8 - iter 361/3617 - loss 0.01017045 - time (sec): 17.45 - samples/sec: 2112.81 - lr: 0.000010 - momentum: 0.000000
177
+ 2023-10-14 22:15:47,501 epoch 8 - iter 722/3617 - loss 0.01126589 - time (sec): 34.37 - samples/sec: 2200.48 - lr: 0.000009 - momentum: 0.000000
178
+ 2023-10-14 22:16:04,038 epoch 8 - iter 1083/3617 - loss 0.01307129 - time (sec): 50.91 - samples/sec: 2233.78 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-14 22:16:20,600 epoch 8 - iter 1444/3617 - loss 0.01282998 - time (sec): 67.47 - samples/sec: 2244.92 - lr: 0.000009 - momentum: 0.000000
180
+ 2023-10-14 22:16:37,101 epoch 8 - iter 1805/3617 - loss 0.01232805 - time (sec): 83.97 - samples/sec: 2244.87 - lr: 0.000008 - momentum: 0.000000
181
+ 2023-10-14 22:16:53,501 epoch 8 - iter 2166/3617 - loss 0.01310305 - time (sec): 100.37 - samples/sec: 2262.49 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-14 22:17:09,729 epoch 8 - iter 2527/3617 - loss 0.01331872 - time (sec): 116.60 - samples/sec: 2279.94 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-14 22:17:25,984 epoch 8 - iter 2888/3617 - loss 0.01411043 - time (sec): 132.86 - samples/sec: 2281.65 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-14 22:17:41,747 epoch 8 - iter 3249/3617 - loss 0.01398398 - time (sec): 148.62 - samples/sec: 2291.33 - lr: 0.000007 - momentum: 0.000000
185
+ 2023-10-14 22:17:57,730 epoch 8 - iter 3610/3617 - loss 0.01442377 - time (sec): 164.60 - samples/sec: 2305.18 - lr: 0.000007 - momentum: 0.000000
186
+ 2023-10-14 22:17:58,039 ----------------------------------------------------------------------------------------------------
187
+ 2023-10-14 22:17:58,039 EPOCH 8 done: loss 0.0144 - lr: 0.000007
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+ 2023-10-14 22:18:04,449 DEV : loss 0.367567777633667 - f1-score (micro avg) 0.653
189
+ 2023-10-14 22:18:04,480 saving best model
190
+ 2023-10-14 22:18:04,985 ----------------------------------------------------------------------------------------------------
191
+ 2023-10-14 22:18:21,415 epoch 9 - iter 361/3617 - loss 0.00607067 - time (sec): 16.43 - samples/sec: 2327.48 - lr: 0.000006 - momentum: 0.000000
192
+ 2023-10-14 22:18:37,591 epoch 9 - iter 722/3617 - loss 0.00553435 - time (sec): 32.60 - samples/sec: 2324.37 - lr: 0.000006 - momentum: 0.000000
193
+ 2023-10-14 22:18:53,803 epoch 9 - iter 1083/3617 - loss 0.00695481 - time (sec): 48.81 - samples/sec: 2326.73 - lr: 0.000006 - momentum: 0.000000
194
+ 2023-10-14 22:19:10,128 epoch 9 - iter 1444/3617 - loss 0.00740150 - time (sec): 65.14 - samples/sec: 2339.99 - lr: 0.000005 - momentum: 0.000000
195
+ 2023-10-14 22:19:26,337 epoch 9 - iter 1805/3617 - loss 0.00819984 - time (sec): 81.35 - samples/sec: 2342.15 - lr: 0.000005 - momentum: 0.000000
196
+ 2023-10-14 22:19:42,694 epoch 9 - iter 2166/3617 - loss 0.00848387 - time (sec): 97.70 - samples/sec: 2352.83 - lr: 0.000005 - momentum: 0.000000
197
+ 2023-10-14 22:19:58,978 epoch 9 - iter 2527/3617 - loss 0.00854654 - time (sec): 113.99 - samples/sec: 2347.77 - lr: 0.000004 - momentum: 0.000000
198
+ 2023-10-14 22:20:15,184 epoch 9 - iter 2888/3617 - loss 0.00817439 - time (sec): 130.19 - samples/sec: 2338.55 - lr: 0.000004 - momentum: 0.000000
199
+ 2023-10-14 22:20:31,446 epoch 9 - iter 3249/3617 - loss 0.00797782 - time (sec): 146.46 - samples/sec: 2339.00 - lr: 0.000004 - momentum: 0.000000
200
+ 2023-10-14 22:20:47,549 epoch 9 - iter 3610/3617 - loss 0.00806657 - time (sec): 162.56 - samples/sec: 2333.16 - lr: 0.000003 - momentum: 0.000000
201
+ 2023-10-14 22:20:47,856 ----------------------------------------------------------------------------------------------------
202
+ 2023-10-14 22:20:47,856 EPOCH 9 done: loss 0.0081 - lr: 0.000003
203
+ 2023-10-14 22:20:54,174 DEV : loss 0.39251258969306946 - f1-score (micro avg) 0.6402
204
+ 2023-10-14 22:20:54,203 ----------------------------------------------------------------------------------------------------
205
+ 2023-10-14 22:21:10,540 epoch 10 - iter 361/3617 - loss 0.00300548 - time (sec): 16.34 - samples/sec: 2337.28 - lr: 0.000003 - momentum: 0.000000
206
+ 2023-10-14 22:21:26,713 epoch 10 - iter 722/3617 - loss 0.00462564 - time (sec): 32.51 - samples/sec: 2349.14 - lr: 0.000003 - momentum: 0.000000
207
+ 2023-10-14 22:21:42,957 epoch 10 - iter 1083/3617 - loss 0.00462801 - time (sec): 48.75 - samples/sec: 2330.43 - lr: 0.000002 - momentum: 0.000000
208
+ 2023-10-14 22:21:59,261 epoch 10 - iter 1444/3617 - loss 0.00469015 - time (sec): 65.06 - samples/sec: 2336.89 - lr: 0.000002 - momentum: 0.000000
209
+ 2023-10-14 22:22:15,731 epoch 10 - iter 1805/3617 - loss 0.00462676 - time (sec): 81.53 - samples/sec: 2334.17 - lr: 0.000002 - momentum: 0.000000
210
+ 2023-10-14 22:22:32,003 epoch 10 - iter 2166/3617 - loss 0.00461601 - time (sec): 97.80 - samples/sec: 2329.77 - lr: 0.000001 - momentum: 0.000000
211
+ 2023-10-14 22:22:48,206 epoch 10 - iter 2527/3617 - loss 0.00485498 - time (sec): 114.00 - samples/sec: 2329.35 - lr: 0.000001 - momentum: 0.000000
212
+ 2023-10-14 22:23:04,501 epoch 10 - iter 2888/3617 - loss 0.00441201 - time (sec): 130.30 - samples/sec: 2334.21 - lr: 0.000001 - momentum: 0.000000
213
+ 2023-10-14 22:23:20,612 epoch 10 - iter 3249/3617 - loss 0.00517285 - time (sec): 146.41 - samples/sec: 2321.40 - lr: 0.000000 - momentum: 0.000000
214
+ 2023-10-14 22:23:36,898 epoch 10 - iter 3610/3617 - loss 0.00509801 - time (sec): 162.69 - samples/sec: 2332.40 - lr: 0.000000 - momentum: 0.000000
215
+ 2023-10-14 22:23:37,189 ----------------------------------------------------------------------------------------------------
216
+ 2023-10-14 22:23:37,189 EPOCH 10 done: loss 0.0051 - lr: 0.000000
217
+ 2023-10-14 22:23:42,818 DEV : loss 0.400580495595932 - f1-score (micro avg) 0.6444
218
+ 2023-10-14 22:23:43,200 ----------------------------------------------------------------------------------------------------
219
+ 2023-10-14 22:23:43,201 Loading model from best epoch ...
220
+ 2023-10-14 22:23:45,437 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
221
+ 2023-10-14 22:23:52,349
222
+ Results:
223
+ - F-score (micro) 0.6435
224
+ - F-score (macro) 0.5103
225
+ - Accuracy 0.4894
226
+
227
+ By class:
228
+ precision recall f1-score support
229
+
230
+ loc 0.6322 0.7851 0.7004 591
231
+ pers 0.5625 0.7311 0.6358 357
232
+ org 0.2000 0.1899 0.1948 79
233
+
234
+ micro avg 0.5813 0.7205 0.6435 1027
235
+ macro avg 0.4649 0.5687 0.5103 1027
236
+ weighted avg 0.5747 0.7205 0.6390 1027
237
+
238
+ 2023-10-14 22:23:52,349 ----------------------------------------------------------------------------------------------------