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metadata
language:
  - id
license: mit
base_model: indolem/indobert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: sentiment-lora-r2a0d0.05-0
    results: []

sentiment-lora-r2a0d0.05-0

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: 0.3642
  • Accuracy: 0.8346
  • Precision: 0.7993
  • Recall: 0.8080
  • F1: 0.8034

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
0.5633 1.0 122 0.5100 0.7168 0.6536 0.6446 0.6484
0.5083 2.0 244 0.4999 0.7243 0.6825 0.7049 0.6887
0.4904 3.0 366 0.4595 0.7619 0.7120 0.7065 0.7091
0.4644 4.0 488 0.4287 0.7920 0.7520 0.7253 0.7358
0.4439 5.0 610 0.4399 0.7519 0.7127 0.7395 0.7203
0.4241 6.0 732 0.4027 0.8221 0.7860 0.7816 0.7837
0.4092 7.0 854 0.4019 0.8070 0.7674 0.7835 0.7743
0.3891 8.0 976 0.3805 0.8271 0.7912 0.7926 0.7919
0.3777 9.0 1098 0.3789 0.8271 0.7912 0.7926 0.7919
0.369 10.0 1220 0.3758 0.8396 0.8071 0.8040 0.8055
0.3531 11.0 1342 0.3805 0.8296 0.7933 0.8044 0.7984
0.3486 12.0 1464 0.3801 0.8321 0.7960 0.8112 0.8027
0.3472 13.0 1586 0.3675 0.8421 0.8098 0.8083 0.8091
0.3379 14.0 1708 0.3654 0.8371 0.8032 0.8047 0.8040
0.3353 15.0 1830 0.3703 0.8421 0.8080 0.8183 0.8127
0.3213 16.0 1952 0.3709 0.8371 0.8019 0.8147 0.8077
0.3214 17.0 2074 0.3641 0.8371 0.8024 0.8097 0.8059
0.3225 18.0 2196 0.3640 0.8371 0.8024 0.8097 0.8059
0.3159 19.0 2318 0.3649 0.8346 0.7993 0.8080 0.8034
0.3195 20.0 2440 0.3642 0.8346 0.7993 0.8080 0.8034

Framework versions

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