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hmbench-newseye-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1

Token Classification
Flair
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French
sequence-tagger-model
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+ 2023-10-25 14:22:05,423 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:22:05,424 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(64001, 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=17, bias=True)
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+ (loss_function): CrossEntropyLoss()
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+ )"
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+ 2023-10-25 14:22:05,424 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:22:05,424 MultiCorpus: 7142 train + 698 dev + 2570 test sentences
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+ - NER_HIPE_2022 Corpus: 7142 train + 698 dev + 2570 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/fr/with_doc_seperator
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+ 2023-10-25 14:22:05,424 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:22:05,424 Train: 7142 sentences
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+ 2023-10-25 14:22:05,424 (train_with_dev=False, train_with_test=False)
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+ 2023-10-25 14:22:05,424 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:22:05,424 Training Params:
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+ 2023-10-25 14:22:05,425 - learning_rate: "5e-05"
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+ 2023-10-25 14:22:05,425 - mini_batch_size: "8"
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+ 2023-10-25 14:22:05,425 - max_epochs: "10"
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+ 2023-10-25 14:22:05,425 - shuffle: "True"
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+ 2023-10-25 14:22:05,425 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:22:05,425 Plugins:
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+ 2023-10-25 14:22:05,425 - TensorboardLogger
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+ 2023-10-25 14:22:05,425 - LinearScheduler | warmup_fraction: '0.1'
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+ 2023-10-25 14:22:05,425 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:22:05,425 Final evaluation on model from best epoch (best-model.pt)
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+ 2023-10-25 14:22:05,425 - metric: "('micro avg', 'f1-score')"
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+ 2023-10-25 14:22:05,425 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:22:05,425 Computation:
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+ 2023-10-25 14:22:05,425 - compute on device: cuda:0
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+ 2023-10-25 14:22:05,425 - embedding storage: none
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+ 2023-10-25 14:22:05,425 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:22:05,425 Model training base path: "hmbench-newseye/fr-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1"
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+ 2023-10-25 14:22:05,425 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:22:05,425 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:22:05,425 Logging anything other than scalars to TensorBoard is currently not supported.
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+ 2023-10-25 14:22:11,296 epoch 1 - iter 89/893 - loss 2.22174821 - time (sec): 5.87 - samples/sec: 4255.73 - lr: 0.000005 - momentum: 0.000000
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+ 2023-10-25 14:22:17,176 epoch 1 - iter 178/893 - loss 1.34900675 - time (sec): 11.75 - samples/sec: 4297.23 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-25 14:22:23,077 epoch 1 - iter 267/893 - loss 1.01310745 - time (sec): 17.65 - samples/sec: 4246.13 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 14:22:28,941 epoch 1 - iter 356/893 - loss 0.81673971 - time (sec): 23.52 - samples/sec: 4266.25 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 14:22:34,652 epoch 1 - iter 445/893 - loss 0.69153729 - time (sec): 29.23 - samples/sec: 4280.94 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 14:22:40,228 epoch 1 - iter 534/893 - loss 0.60855944 - time (sec): 34.80 - samples/sec: 4283.75 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-25 14:22:46,157 epoch 1 - iter 623/893 - loss 0.53908676 - time (sec): 40.73 - samples/sec: 4303.65 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-25 14:22:51,797 epoch 1 - iter 712/893 - loss 0.49305012 - time (sec): 46.37 - samples/sec: 4290.84 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-25 14:22:57,456 epoch 1 - iter 801/893 - loss 0.45348166 - time (sec): 52.03 - samples/sec: 4296.11 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-25 14:23:03,344 epoch 1 - iter 890/893 - loss 0.42527446 - time (sec): 57.92 - samples/sec: 4279.37 - lr: 0.000050 - momentum: 0.000000
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+ 2023-10-25 14:23:03,553 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:23:03,554 EPOCH 1 done: loss 0.4241 - lr: 0.000050
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+ 2023-10-25 14:23:07,042 DEV : loss 0.10224700719118118 - f1-score (micro avg) 0.7363
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+ 2023-10-25 14:23:07,064 saving best model
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+ 2023-10-25 14:23:07,584 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:23:13,537 epoch 2 - iter 89/893 - loss 0.10807083 - time (sec): 5.95 - samples/sec: 4146.32 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-25 14:23:19,506 epoch 2 - iter 178/893 - loss 0.10717817 - time (sec): 11.92 - samples/sec: 4148.68 - lr: 0.000049 - momentum: 0.000000
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+ 2023-10-25 14:23:25,279 epoch 2 - iter 267/893 - loss 0.10936825 - time (sec): 17.69 - samples/sec: 4151.97 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-25 14:23:31,126 epoch 2 - iter 356/893 - loss 0.11237855 - time (sec): 23.54 - samples/sec: 4166.50 - lr: 0.000048 - momentum: 0.000000
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+ 2023-10-25 14:23:36,586 epoch 2 - iter 445/893 - loss 0.10585519 - time (sec): 29.00 - samples/sec: 4220.66 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-25 14:23:42,078 epoch 2 - iter 534/893 - loss 0.10593438 - time (sec): 34.49 - samples/sec: 4248.78 - lr: 0.000047 - momentum: 0.000000
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+ 2023-10-25 14:23:47,781 epoch 2 - iter 623/893 - loss 0.10798060 - time (sec): 40.20 - samples/sec: 4281.71 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-25 14:23:53,567 epoch 2 - iter 712/893 - loss 0.10704577 - time (sec): 45.98 - samples/sec: 4307.87 - lr: 0.000046 - momentum: 0.000000
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+ 2023-10-25 14:23:59,120 epoch 2 - iter 801/893 - loss 0.10429214 - time (sec): 51.53 - samples/sec: 4347.28 - lr: 0.000045 - momentum: 0.000000
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+ 2023-10-25 14:24:04,739 epoch 2 - iter 890/893 - loss 0.10359464 - time (sec): 57.15 - samples/sec: 4337.39 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-25 14:24:04,955 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:24:04,955 EPOCH 2 done: loss 0.1033 - lr: 0.000044
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+ 2023-10-25 14:24:10,135 DEV : loss 0.0969092845916748 - f1-score (micro avg) 0.7786
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+ 2023-10-25 14:24:10,157 saving best model
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+ 2023-10-25 14:24:10,855 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:24:16,400 epoch 3 - iter 89/893 - loss 0.05486912 - time (sec): 5.54 - samples/sec: 4434.09 - lr: 0.000044 - momentum: 0.000000
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+ 2023-10-25 14:24:22,119 epoch 3 - iter 178/893 - loss 0.06296271 - time (sec): 11.26 - samples/sec: 4548.70 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-25 14:24:27,693 epoch 3 - iter 267/893 - loss 0.06237710 - time (sec): 16.83 - samples/sec: 4480.75 - lr: 0.000043 - momentum: 0.000000
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+ 2023-10-25 14:24:33,190 epoch 3 - iter 356/893 - loss 0.07078572 - time (sec): 22.33 - samples/sec: 4456.89 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-25 14:24:38,702 epoch 3 - iter 445/893 - loss 0.06966703 - time (sec): 27.84 - samples/sec: 4421.33 - lr: 0.000042 - momentum: 0.000000
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+ 2023-10-25 14:24:44,432 epoch 3 - iter 534/893 - loss 0.06853292 - time (sec): 33.57 - samples/sec: 4407.07 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-25 14:24:50,191 epoch 3 - iter 623/893 - loss 0.06693004 - time (sec): 39.33 - samples/sec: 4415.91 - lr: 0.000041 - momentum: 0.000000
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+ 2023-10-25 14:24:55,886 epoch 3 - iter 712/893 - loss 0.06730109 - time (sec): 45.03 - samples/sec: 4413.01 - lr: 0.000040 - momentum: 0.000000
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+ 2023-10-25 14:25:01,340 epoch 3 - iter 801/893 - loss 0.06610240 - time (sec): 50.48 - samples/sec: 4414.05 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-25 14:25:06,891 epoch 3 - iter 890/893 - loss 0.06743181 - time (sec): 56.03 - samples/sec: 4428.50 - lr: 0.000039 - momentum: 0.000000
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+ 2023-10-25 14:25:07,061 ----------------------------------------------------------------------------------------------------
119
+ 2023-10-25 14:25:07,061 EPOCH 3 done: loss 0.0674 - lr: 0.000039
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+ 2023-10-25 14:25:10,865 DEV : loss 0.11728406697511673 - f1-score (micro avg) 0.7507
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+ 2023-10-25 14:25:10,887 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:25:16,941 epoch 4 - iter 89/893 - loss 0.05172042 - time (sec): 6.05 - samples/sec: 4265.61 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-25 14:25:22,824 epoch 4 - iter 178/893 - loss 0.04890968 - time (sec): 11.93 - samples/sec: 4188.03 - lr: 0.000038 - momentum: 0.000000
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+ 2023-10-25 14:25:28,627 epoch 4 - iter 267/893 - loss 0.04835057 - time (sec): 17.74 - samples/sec: 4153.56 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-25 14:25:34,625 epoch 4 - iter 356/893 - loss 0.04648503 - time (sec): 23.74 - samples/sec: 4206.05 - lr: 0.000037 - momentum: 0.000000
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+ 2023-10-25 14:25:41,203 epoch 4 - iter 445/893 - loss 0.04728395 - time (sec): 30.31 - samples/sec: 4135.62 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-25 14:25:46,943 epoch 4 - iter 534/893 - loss 0.04862880 - time (sec): 36.05 - samples/sec: 4139.07 - lr: 0.000036 - momentum: 0.000000
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+ 2023-10-25 14:25:52,737 epoch 4 - iter 623/893 - loss 0.04803766 - time (sec): 41.85 - samples/sec: 4149.38 - lr: 0.000035 - momentum: 0.000000
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+ 2023-10-25 14:25:58,381 epoch 4 - iter 712/893 - loss 0.04864147 - time (sec): 47.49 - samples/sec: 4175.34 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-25 14:26:04,318 epoch 4 - iter 801/893 - loss 0.04871377 - time (sec): 53.43 - samples/sec: 4193.31 - lr: 0.000034 - momentum: 0.000000
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+ 2023-10-25 14:26:09,795 epoch 4 - iter 890/893 - loss 0.04850206 - time (sec): 58.91 - samples/sec: 4210.00 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-25 14:26:09,973 ----------------------------------------------------------------------------------------------------
133
+ 2023-10-25 14:26:09,973 EPOCH 4 done: loss 0.0486 - lr: 0.000033
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+ 2023-10-25 14:26:14,041 DEV : loss 0.15373246371746063 - f1-score (micro avg) 0.7653
135
+ 2023-10-25 14:26:14,065 ----------------------------------------------------------------------------------------------------
136
+ 2023-10-25 14:26:20,047 epoch 5 - iter 89/893 - loss 0.03855375 - time (sec): 5.98 - samples/sec: 4417.22 - lr: 0.000033 - momentum: 0.000000
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+ 2023-10-25 14:26:25,827 epoch 5 - iter 178/893 - loss 0.04024206 - time (sec): 11.76 - samples/sec: 4380.57 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-25 14:26:31,615 epoch 5 - iter 267/893 - loss 0.03624782 - time (sec): 17.55 - samples/sec: 4351.23 - lr: 0.000032 - momentum: 0.000000
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+ 2023-10-25 14:26:37,230 epoch 5 - iter 356/893 - loss 0.03803371 - time (sec): 23.16 - samples/sec: 4321.77 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-25 14:26:42,808 epoch 5 - iter 445/893 - loss 0.03757448 - time (sec): 28.74 - samples/sec: 4285.27 - lr: 0.000031 - momentum: 0.000000
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+ 2023-10-25 14:26:48,363 epoch 5 - iter 534/893 - loss 0.03668820 - time (sec): 34.30 - samples/sec: 4293.91 - lr: 0.000030 - momentum: 0.000000
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+ 2023-10-25 14:26:53,894 epoch 5 - iter 623/893 - loss 0.03705404 - time (sec): 39.83 - samples/sec: 4274.67 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 14:26:59,729 epoch 5 - iter 712/893 - loss 0.03634319 - time (sec): 45.66 - samples/sec: 4325.05 - lr: 0.000029 - momentum: 0.000000
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+ 2023-10-25 14:27:05,488 epoch 5 - iter 801/893 - loss 0.03547607 - time (sec): 51.42 - samples/sec: 4340.40 - lr: 0.000028 - momentum: 0.000000
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+ 2023-10-25 14:27:10,939 epoch 5 - iter 890/893 - loss 0.03563294 - time (sec): 56.87 - samples/sec: 4360.64 - lr: 0.000028 - momentum: 0.000000
146
+ 2023-10-25 14:27:11,109 ----------------------------------------------------------------------------------------------------
147
+ 2023-10-25 14:27:11,109 EPOCH 5 done: loss 0.0355 - lr: 0.000028
148
+ 2023-10-25 14:27:15,806 DEV : loss 0.16320814192295074 - f1-score (micro avg) 0.7874
149
+ 2023-10-25 14:27:15,826 saving best model
150
+ 2023-10-25 14:27:16,529 ----------------------------------------------------------------------------------------------------
151
+ 2023-10-25 14:27:22,218 epoch 6 - iter 89/893 - loss 0.02617113 - time (sec): 5.69 - samples/sec: 4700.94 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 14:27:28,467 epoch 6 - iter 178/893 - loss 0.02520431 - time (sec): 11.94 - samples/sec: 4332.41 - lr: 0.000027 - momentum: 0.000000
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+ 2023-10-25 14:27:33,984 epoch 6 - iter 267/893 - loss 0.02638010 - time (sec): 17.45 - samples/sec: 4320.59 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 14:27:39,878 epoch 6 - iter 356/893 - loss 0.02643239 - time (sec): 23.35 - samples/sec: 4349.87 - lr: 0.000026 - momentum: 0.000000
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+ 2023-10-25 14:27:45,656 epoch 6 - iter 445/893 - loss 0.02697284 - time (sec): 29.13 - samples/sec: 4332.53 - lr: 0.000025 - momentum: 0.000000
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+ 2023-10-25 14:27:51,208 epoch 6 - iter 534/893 - loss 0.02717458 - time (sec): 34.68 - samples/sec: 4355.89 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 14:27:56,893 epoch 6 - iter 623/893 - loss 0.02638170 - time (sec): 40.36 - samples/sec: 4340.97 - lr: 0.000024 - momentum: 0.000000
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+ 2023-10-25 14:28:02,464 epoch 6 - iter 712/893 - loss 0.02646998 - time (sec): 45.93 - samples/sec: 4345.70 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 14:28:08,304 epoch 6 - iter 801/893 - loss 0.02574741 - time (sec): 51.77 - samples/sec: 4344.49 - lr: 0.000023 - momentum: 0.000000
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+ 2023-10-25 14:28:13,959 epoch 6 - iter 890/893 - loss 0.02590536 - time (sec): 57.43 - samples/sec: 4318.36 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 14:28:14,125 ----------------------------------------------------------------------------------------------------
162
+ 2023-10-25 14:28:14,126 EPOCH 6 done: loss 0.0259 - lr: 0.000022
163
+ 2023-10-25 14:28:17,933 DEV : loss 0.18096403777599335 - f1-score (micro avg) 0.7885
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+ 2023-10-25 14:28:17,953 saving best model
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+ 2023-10-25 14:28:18,694 ----------------------------------------------------------------------------------------------------
166
+ 2023-10-25 14:28:25,439 epoch 7 - iter 89/893 - loss 0.01678159 - time (sec): 6.74 - samples/sec: 3409.22 - lr: 0.000022 - momentum: 0.000000
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+ 2023-10-25 14:28:31,190 epoch 7 - iter 178/893 - loss 0.02086138 - time (sec): 12.49 - samples/sec: 3832.17 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 14:28:37,291 epoch 7 - iter 267/893 - loss 0.02080217 - time (sec): 18.59 - samples/sec: 4067.89 - lr: 0.000021 - momentum: 0.000000
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+ 2023-10-25 14:28:43,102 epoch 7 - iter 356/893 - loss 0.02143983 - time (sec): 24.40 - samples/sec: 4179.14 - lr: 0.000020 - momentum: 0.000000
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+ 2023-10-25 14:28:48,654 epoch 7 - iter 445/893 - loss 0.02119625 - time (sec): 29.96 - samples/sec: 4154.44 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 14:28:54,735 epoch 7 - iter 534/893 - loss 0.02007361 - time (sec): 36.04 - samples/sec: 4119.27 - lr: 0.000019 - momentum: 0.000000
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+ 2023-10-25 14:29:00,403 epoch 7 - iter 623/893 - loss 0.02020608 - time (sec): 41.71 - samples/sec: 4157.17 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 14:29:06,389 epoch 7 - iter 712/893 - loss 0.02032914 - time (sec): 47.69 - samples/sec: 4157.75 - lr: 0.000018 - momentum: 0.000000
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+ 2023-10-25 14:29:12,245 epoch 7 - iter 801/893 - loss 0.02130873 - time (sec): 53.55 - samples/sec: 4154.56 - lr: 0.000017 - momentum: 0.000000
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+ 2023-10-25 14:29:18,268 epoch 7 - iter 890/893 - loss 0.02140375 - time (sec): 59.57 - samples/sec: 4164.21 - lr: 0.000017 - momentum: 0.000000
176
+ 2023-10-25 14:29:18,452 ----------------------------------------------------------------------------------------------------
177
+ 2023-10-25 14:29:18,452 EPOCH 7 done: loss 0.0214 - lr: 0.000017
178
+ 2023-10-25 14:29:22,302 DEV : loss 0.19584135711193085 - f1-score (micro avg) 0.7874
179
+ 2023-10-25 14:29:22,325 ----------------------------------------------------------------------------------------------------
180
+ 2023-10-25 14:29:28,254 epoch 8 - iter 89/893 - loss 0.01269647 - time (sec): 5.93 - samples/sec: 4175.01 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 14:29:34,170 epoch 8 - iter 178/893 - loss 0.01103982 - time (sec): 11.84 - samples/sec: 4330.76 - lr: 0.000016 - momentum: 0.000000
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+ 2023-10-25 14:29:40,172 epoch 8 - iter 267/893 - loss 0.01056955 - time (sec): 17.84 - samples/sec: 4320.95 - lr: 0.000015 - momentum: 0.000000
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+ 2023-10-25 14:29:46,086 epoch 8 - iter 356/893 - loss 0.01165606 - time (sec): 23.76 - samples/sec: 4333.99 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 14:29:51,817 epoch 8 - iter 445/893 - loss 0.01217482 - time (sec): 29.49 - samples/sec: 4280.96 - lr: 0.000014 - momentum: 0.000000
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+ 2023-10-25 14:29:57,619 epoch 8 - iter 534/893 - loss 0.01360158 - time (sec): 35.29 - samples/sec: 4255.30 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 14:30:03,211 epoch 8 - iter 623/893 - loss 0.01358451 - time (sec): 40.88 - samples/sec: 4280.98 - lr: 0.000013 - momentum: 0.000000
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+ 2023-10-25 14:30:09,010 epoch 8 - iter 712/893 - loss 0.01326304 - time (sec): 46.68 - samples/sec: 4279.43 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 14:30:14,815 epoch 8 - iter 801/893 - loss 0.01363656 - time (sec): 52.49 - samples/sec: 4280.60 - lr: 0.000012 - momentum: 0.000000
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+ 2023-10-25 14:30:20,749 epoch 8 - iter 890/893 - loss 0.01344285 - time (sec): 58.42 - samples/sec: 4244.81 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 14:30:20,929 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:30:20,929 EPOCH 8 done: loss 0.0134 - lr: 0.000011
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+ 2023-10-25 14:30:26,071 DEV : loss 0.21516965329647064 - f1-score (micro avg) 0.796
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+ 2023-10-25 14:30:26,096 saving best model
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+ 2023-10-25 14:30:26,883 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:30:32,597 epoch 9 - iter 89/893 - loss 0.01268252 - time (sec): 5.71 - samples/sec: 4284.31 - lr: 0.000011 - momentum: 0.000000
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+ 2023-10-25 14:30:38,584 epoch 9 - iter 178/893 - loss 0.01397325 - time (sec): 11.70 - samples/sec: 4298.37 - lr: 0.000010 - momentum: 0.000000
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+ 2023-10-25 14:30:44,380 epoch 9 - iter 267/893 - loss 0.01243272 - time (sec): 17.49 - samples/sec: 4354.73 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 14:30:49,932 epoch 9 - iter 356/893 - loss 0.01305437 - time (sec): 23.05 - samples/sec: 4343.10 - lr: 0.000009 - momentum: 0.000000
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+ 2023-10-25 14:30:55,731 epoch 9 - iter 445/893 - loss 0.01171325 - time (sec): 28.85 - samples/sec: 4364.70 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 14:31:01,450 epoch 9 - iter 534/893 - loss 0.01082927 - time (sec): 34.56 - samples/sec: 4348.58 - lr: 0.000008 - momentum: 0.000000
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+ 2023-10-25 14:31:07,263 epoch 9 - iter 623/893 - loss 0.01010485 - time (sec): 40.38 - samples/sec: 4345.62 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 14:31:12,771 epoch 9 - iter 712/893 - loss 0.01004408 - time (sec): 45.88 - samples/sec: 4345.42 - lr: 0.000007 - momentum: 0.000000
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+ 2023-10-25 14:31:18,573 epoch 9 - iter 801/893 - loss 0.01006474 - time (sec): 51.69 - samples/sec: 4340.94 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 14:31:24,256 epoch 9 - iter 890/893 - loss 0.01007528 - time (sec): 57.37 - samples/sec: 4323.30 - lr: 0.000006 - momentum: 0.000000
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+ 2023-10-25 14:31:24,441 ----------------------------------------------------------------------------------------------------
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+ 2023-10-25 14:31:24,441 EPOCH 9 done: loss 0.0101 - lr: 0.000006
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+ 2023-10-25 14:31:29,063 DEV : loss 0.23022107779979706 - f1-score (micro avg) 0.7853
208
+ 2023-10-25 14:31:29,084 ----------------------------------------------------------------------------------------------------
209
+ 2023-10-25 14:31:34,994 epoch 10 - iter 89/893 - loss 0.00710690 - time (sec): 5.91 - samples/sec: 4371.85 - lr: 0.000005 - momentum: 0.000000
210
+ 2023-10-25 14:31:40,814 epoch 10 - iter 178/893 - loss 0.00583517 - time (sec): 11.73 - samples/sec: 4378.00 - lr: 0.000004 - momentum: 0.000000
211
+ 2023-10-25 14:31:46,500 epoch 10 - iter 267/893 - loss 0.00596312 - time (sec): 17.41 - samples/sec: 4319.07 - lr: 0.000004 - momentum: 0.000000
212
+ 2023-10-25 14:31:52,284 epoch 10 - iter 356/893 - loss 0.00716220 - time (sec): 23.20 - samples/sec: 4331.80 - lr: 0.000003 - momentum: 0.000000
213
+ 2023-10-25 14:31:57,828 epoch 10 - iter 445/893 - loss 0.00782994 - time (sec): 28.74 - samples/sec: 4317.08 - lr: 0.000003 - momentum: 0.000000
214
+ 2023-10-25 14:32:03,404 epoch 10 - iter 534/893 - loss 0.00818274 - time (sec): 34.32 - samples/sec: 4315.55 - lr: 0.000002 - momentum: 0.000000
215
+ 2023-10-25 14:32:09,212 epoch 10 - iter 623/893 - loss 0.00842830 - time (sec): 40.13 - samples/sec: 4322.39 - lr: 0.000002 - momentum: 0.000000
216
+ 2023-10-25 14:32:15,164 epoch 10 - iter 712/893 - loss 0.00783075 - time (sec): 46.08 - samples/sec: 4317.21 - lr: 0.000001 - momentum: 0.000000
217
+ 2023-10-25 14:32:20,786 epoch 10 - iter 801/893 - loss 0.00742591 - time (sec): 51.70 - samples/sec: 4318.44 - lr: 0.000001 - momentum: 0.000000
218
+ 2023-10-25 14:32:26,431 epoch 10 - iter 890/893 - loss 0.00733109 - time (sec): 57.35 - samples/sec: 4320.96 - lr: 0.000000 - momentum: 0.000000
219
+ 2023-10-25 14:32:26,641 ----------------------------------------------------------------------------------------------------
220
+ 2023-10-25 14:32:26,641 EPOCH 10 done: loss 0.0073 - lr: 0.000000
221
+ 2023-10-25 14:32:30,523 DEV : loss 0.2292526513338089 - f1-score (micro avg) 0.7891
222
+ 2023-10-25 14:32:31,069 ----------------------------------------------------------------------------------------------------
223
+ 2023-10-25 14:32:31,071 Loading model from best epoch ...
224
+ 2023-10-25 14:32:33,012 SequenceTagger predicts: Dictionary with 17 tags: O, S-PER, B-PER, E-PER, I-PER, S-LOC, B-LOC, E-LOC, I-LOC, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
225
+ 2023-10-25 14:32:45,073
226
+ Results:
227
+ - F-score (micro) 0.6915
228
+ - F-score (macro) 0.6191
229
+ - Accuracy 0.5445
230
+
231
+ By class:
232
+ precision recall f1-score support
233
+
234
+ LOC 0.7139 0.6767 0.6948 1095
235
+ PER 0.7722 0.7737 0.7730 1012
236
+ ORG 0.4444 0.5602 0.4957 357
237
+ HumanProd 0.4444 0.6061 0.5128 33
238
+
239
+ micro avg 0.6847 0.6984 0.6915 2497
240
+ macro avg 0.5937 0.6542 0.6191 2497
241
+ weighted avg 0.6954 0.6984 0.6956 2497
242
+
243
+ 2023-10-25 14:32:45,073 ----------------------------------------------------------------------------------------------------