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kcbert-base-finetuned-nsmc

This model is a fine-tuned version of beomi/kcbert-base on the nsmc dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4197
  • Accuracy: 0.9020
  • F1: 0.9033
  • Recall: 0.9095
  • Precision: 0.8972

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Recall Precision
0.3028 0.32 3000 0.2994 0.8769 0.8732 0.8422 0.9066
0.2833 0.64 6000 0.2766 0.8880 0.8844 0.8512 0.9203
0.2719 0.96 9000 0.2527 0.8980 0.8981 0.8933 0.9030
0.1938 1.28 12000 0.2934 0.8969 0.8965 0.8869 0.9062
0.1907 1.6 15000 0.3141 0.8992 0.8999 0.9003 0.8996
0.1824 1.92 18000 0.3537 0.8986 0.8964 0.8711 0.9232
0.1261 2.24 21000 0.4197 0.9020 0.9033 0.9095 0.8972
0.1237 2.56 24000 0.4170 0.8995 0.9017 0.9156 0.8882
0.1182 2.88 27000 0.4165 0.9020 0.9036 0.9130 0.8945

Framework versions

  • Transformers 4.11.3
  • Pytorch 1.9.1
  • Datasets 1.14.0
  • Tokenizers 0.10.3
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Dataset used to train jaesun/kcbert-base-finetuned-nsmc

Evaluation results