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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-r4a1d0.1-0
    results: []

sentiment-lora-r4a1d0.1-0

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: 0.3483
  • Accuracy: 0.8446
  • Precision: 0.8111
  • Recall: 0.8201
  • F1: 0.8153

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
0.5617 1.0 122 0.5117 0.7193 0.6580 0.6514 0.6543
0.5046 2.0 244 0.4917 0.7419 0.7042 0.7324 0.7112
0.4798 3.0 366 0.4466 0.7594 0.7129 0.7248 0.7179
0.4374 4.0 488 0.3994 0.8195 0.7866 0.7648 0.7741
0.4037 5.0 610 0.4150 0.7845 0.7480 0.7800 0.7575
0.3741 6.0 732 0.3737 0.8371 0.8028 0.8072 0.8049
0.3574 7.0 854 0.3776 0.8221 0.7845 0.7991 0.7909
0.3387 8.0 976 0.3654 0.8446 0.8120 0.8151 0.8135
0.3293 9.0 1098 0.3627 0.8371 0.8021 0.8122 0.8068
0.3209 10.0 1220 0.3553 0.8371 0.8032 0.8047 0.8040
0.2967 11.0 1342 0.3674 0.8346 0.7989 0.8130 0.8052
0.2928 12.0 1464 0.3707 0.8321 0.7960 0.8112 0.8027
0.2967 13.0 1586 0.3514 0.8471 0.8153 0.8168 0.8160
0.2934 14.0 1708 0.3507 0.8421 0.8083 0.8158 0.8119
0.2811 15.0 1830 0.3553 0.8346 0.7991 0.8105 0.8043
0.2738 16.0 1952 0.3555 0.8421 0.8077 0.8208 0.8136
0.2717 17.0 2074 0.3468 0.8496 0.8174 0.8236 0.8204
0.278 18.0 2196 0.3510 0.8421 0.8080 0.8183 0.8127
0.2701 19.0 2318 0.3471 0.8471 0.8142 0.8218 0.8178
0.2722 20.0 2440 0.3483 0.8446 0.8111 0.8201 0.8153

Framework versions

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