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sentiment-lora-r4a2d0.05-1

This model is a fine-tuned version of indolem/indobert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3356
  • Accuracy: 0.8622
  • Precision: 0.8399
  • Recall: 0.8200
  • F1: 0.8289

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: 5e-05
  • train_batch_size: 30
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20.0

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.5657 1.0 122 0.5182 0.7243 0.6604 0.6424 0.6488
0.5109 2.0 244 0.5051 0.7243 0.6748 0.6874 0.6796
0.48 3.0 366 0.4643 0.7569 0.7047 0.6880 0.6948
0.434 4.0 488 0.4281 0.7920 0.7497 0.7378 0.7431
0.4106 5.0 610 0.4194 0.7920 0.7528 0.7778 0.7618
0.3812 6.0 732 0.3936 0.8296 0.8008 0.7744 0.7854
0.3689 7.0 854 0.3700 0.8521 0.8220 0.8204 0.8212
0.3489 8.0 976 0.3656 0.8346 0.8088 0.7780 0.7905
0.3502 9.0 1098 0.3640 0.8371 0.8101 0.7847 0.7955
0.3349 10.0 1220 0.3608 0.8346 0.8074 0.7805 0.7917
0.3189 11.0 1342 0.3574 0.8396 0.8128 0.7890 0.7992
0.3121 12.0 1464 0.3547 0.8471 0.8175 0.8093 0.8132
0.3181 13.0 1586 0.3478 0.8521 0.8332 0.7979 0.8122
0.3092 14.0 1708 0.3435 0.8596 0.8374 0.8157 0.8253
0.3018 15.0 1830 0.3466 0.8546 0.8296 0.8121 0.8200
0.2955 16.0 1952 0.3365 0.8596 0.8347 0.8207 0.8272
0.2917 17.0 2074 0.3353 0.8596 0.8374 0.8157 0.8253
0.2956 18.0 2196 0.3379 0.8596 0.8360 0.8182 0.8262
0.2899 19.0 2318 0.3353 0.8647 0.8455 0.8192 0.8306
0.2885 20.0 2440 0.3356 0.8622 0.8399 0.8200 0.8289

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.15.2
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