apwic / nerugm-lora-r8a2d0.15

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Model's Last Updated: May 25 2024

Introduction of nerugm-lora-r8a2d0.15

Model Details of nerugm-lora-r8a2d0.15

nerugm-lora-r8a2d0.15

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: 0.1281
  • Precision: 0.7470
  • Recall: 0.8629
  • F1: 0.8008
  • Accuracy: 0.9579
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: 16
  • eval_batch_size: 64
  • 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 Precision Recall F1 Accuracy
0.7018 1.0 528 0.3353 0.5529 0.4800 0.5138 0.9115
0.2639 2.0 1056 0.1912 0.6494 0.8210 0.7252 0.9412
0.1862 3.0 1584 0.1672 0.6739 0.8536 0.7531 0.9466
0.1612 4.0 2112 0.1446 0.7238 0.8512 0.7824 0.9539
0.1439 5.0 2640 0.1390 0.7254 0.8582 0.7863 0.9545
0.1358 6.0 3168 0.1392 0.7256 0.8652 0.7893 0.9551
0.129 7.0 3696 0.1384 0.7267 0.8698 0.7919 0.9561
0.1228 8.0 4224 0.1339 0.7353 0.8698 0.7969 0.9575
0.1168 9.0 4752 0.1321 0.7439 0.8559 0.7960 0.9577
0.1146 10.0 5280 0.1300 0.7445 0.8582 0.7973 0.9581
0.1105 11.0 5808 0.1327 0.7333 0.8675 0.7948 0.9571
0.1083 12.0 6336 0.1333 0.7342 0.8652 0.7943 0.9569
0.106 13.0 6864 0.1265 0.7490 0.8582 0.7999 0.9591
0.1032 14.0 7392 0.1269 0.7445 0.8582 0.7973 0.9589
0.1023 15.0 7920 0.1291 0.7455 0.8629 0.7999 0.9585
0.1014 16.0 8448 0.1271 0.7400 0.8582 0.7947 0.9575
0.1002 17.0 8976 0.1281 0.7460 0.8722 0.8042 0.9589
0.0986 18.0 9504 0.1304 0.7416 0.8722 0.8016 0.9573
0.0978 19.0 10032 0.1271 0.7520 0.8652 0.8046 0.9589
0.0984 20.0 10560 0.1281 0.7470 0.8629 0.8008 0.9579
Framework versions
  • Transformers 4.39.3
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.15.2

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