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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-r8a2d0.05-1
    results: []

sentiment-lora-r8a2d0.05-1

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: 0.3148
  • Accuracy: 0.8697
  • Precision: 0.8474
  • Recall: 0.8328
  • F1: 0.8395

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 0.5161 0.7243 0.6616 0.6474 0.6529
0.5088 2.0 244 0.4913 0.7393 0.6917 0.7056 0.6971
0.4682 3.0 366 0.4424 0.7845 0.7401 0.7425 0.7413
0.4114 4.0 488 0.3980 0.8095 0.7702 0.7702 0.7702
0.3862 5.0 610 0.3890 0.8145 0.7783 0.8088 0.7889
0.3512 6.0 732 0.3583 0.8496 0.8245 0.8036 0.8128
0.3428 7.0 854 0.3496 0.8521 0.8207 0.8254 0.8229
0.3254 8.0 976 0.3425 0.8496 0.8245 0.8036 0.8128
0.3226 9.0 1098 0.3388 0.8571 0.8310 0.8189 0.8245
0.3063 10.0 1220 0.3376 0.8647 0.8439 0.8217 0.8315
0.2939 11.0 1342 0.3319 0.8672 0.8463 0.8260 0.8351
0.2838 12.0 1464 0.3323 0.8546 0.8263 0.8196 0.8229
0.2916 13.0 1586 0.3283 0.8647 0.8472 0.8167 0.8296
0.2826 14.0 1708 0.3244 0.8672 0.8463 0.8260 0.8351
0.2739 15.0 1830 0.3231 0.8697 0.8449 0.8378 0.8412
0.2674 16.0 1952 0.3221 0.8697 0.8449 0.8378 0.8412
0.2648 17.0 2074 0.3193 0.8722 0.8528 0.8321 0.8413
0.2687 18.0 2196 0.3172 0.8697 0.8460 0.8353 0.8404
0.264 19.0 2318 0.3170 0.8747 0.8552 0.8363 0.8448
0.2637 20.0 2440 0.3148 0.8697 0.8474 0.8328 0.8395

Framework versions

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