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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-r4a1d0.1-1
    results: []

sentiment-lora-r4a1d0.1-1

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: 0.3239
  • Accuracy: 0.8622
  • Precision: 0.8373
  • Recall: 0.8250
  • F1: 0.8307

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
0.5658 1.0 122 0.5195 0.7268 0.6646 0.6492 0.6550
0.5125 2.0 244 0.5060 0.7293 0.6805 0.6935 0.6855
0.4809 3.0 366 0.4686 0.7669 0.7184 0.7151 0.7167
0.4353 4.0 488 0.4295 0.7920 0.7500 0.7353 0.7417
0.4116 5.0 610 0.4171 0.8020 0.7628 0.7849 0.7714
0.3809 6.0 732 0.3865 0.8446 0.8148 0.8051 0.8096
0.3681 7.0 854 0.3697 0.8496 0.8193 0.8161 0.8177
0.3469 8.0 976 0.3554 0.8471 0.8206 0.8018 0.8102
0.3455 9.0 1098 0.3494 0.8496 0.8211 0.8111 0.8158
0.3284 10.0 1220 0.3437 0.8496 0.8289 0.7961 0.8096
0.3132 11.0 1342 0.3371 0.8596 0.8389 0.8132 0.8243
0.3042 12.0 1464 0.3371 0.8546 0.8254 0.8221 0.8238
0.3063 13.0 1586 0.3317 0.8596 0.8406 0.8107 0.8233
0.3013 14.0 1708 0.3304 0.8622 0.8373 0.8250 0.8307
0.2928 15.0 1830 0.3295 0.8596 0.8325 0.8257 0.8290
0.2864 16.0 1952 0.3284 0.8622 0.8351 0.8300 0.8325
0.2819 17.0 2074 0.3254 0.8596 0.8347 0.8207 0.8272
0.2877 18.0 2196 0.3249 0.8596 0.8336 0.8232 0.8281
0.2819 19.0 2318 0.3241 0.8647 0.8410 0.8267 0.8333
0.2803 20.0 2440 0.3239 0.8622 0.8373 0.8250 0.8307

Framework versions

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