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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-r4a2d0.05-0
    results: []

sentiment-lora-r4a2d0.05-0

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: 0.3486
  • Accuracy: 0.8396
  • Precision: 0.8055
  • Recall: 0.8115
  • F1: 0.8084

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
0.5619 1.0 122 0.5127 0.7168 0.6536 0.6446 0.6484
0.5059 2.0 244 0.4967 0.7343 0.6956 0.7220 0.7022
0.4822 3.0 366 0.4506 0.7469 0.7006 0.7159 0.7065
0.4402 4.0 488 0.3984 0.8195 0.7876 0.7623 0.7728
0.4068 5.0 610 0.4136 0.7870 0.7473 0.7718 0.7561
0.3791 6.0 732 0.3771 0.8321 0.7972 0.7987 0.7979
0.3635 7.0 854 0.3916 0.8195 0.7822 0.8048 0.7912
0.3433 8.0 976 0.3799 0.8296 0.7934 0.8019 0.7974
0.3379 9.0 1098 0.3714 0.8271 0.7903 0.8026 0.7959
0.3296 10.0 1220 0.3635 0.8371 0.8032 0.8047 0.8040
0.3105 11.0 1342 0.3652 0.8296 0.7933 0.8044 0.7984
0.3024 12.0 1464 0.3702 0.8346 0.7988 0.8180 0.8069
0.309 13.0 1586 0.3512 0.8371 0.8032 0.8047 0.8040
0.3021 14.0 1708 0.3505 0.8396 0.8060 0.8090 0.8075
0.2903 15.0 1830 0.3553 0.8421 0.8077 0.8208 0.8136
0.2834 16.0 1952 0.3530 0.8396 0.8046 0.8215 0.8119
0.2811 17.0 2074 0.3471 0.8446 0.8120 0.8151 0.8135
0.288 18.0 2196 0.3505 0.8446 0.8107 0.8226 0.8161
0.277 19.0 2318 0.3479 0.8396 0.8055 0.8115 0.8084
0.2775 20.0 2440 0.3486 0.8396 0.8055 0.8115 0.8084

Framework versions

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