sentiment-lora-r16-4
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: 0.2751
- Accuracy: 0.8872
- Precision: 0.8658
- Recall: 0.8602
- F1: 0.8629
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 |
---|---|---|---|---|---|---|---|
0.5585 | 1.0 | 122 | 0.5224 | 0.7218 | 0.6715 | 0.6832 | 0.6760 |
0.4948 | 2.0 | 244 | 0.4534 | 0.7970 | 0.7873 | 0.6914 | 0.7128 |
0.4389 | 3.0 | 366 | 0.3953 | 0.8145 | 0.7809 | 0.7563 | 0.7665 |
0.3871 | 4.0 | 488 | 0.3608 | 0.8195 | 0.7818 | 0.7998 | 0.7894 |
0.3569 | 5.0 | 610 | 0.3384 | 0.8421 | 0.8092 | 0.8108 | 0.8100 |
0.3206 | 6.0 | 732 | 0.3370 | 0.8446 | 0.8103 | 0.8276 | 0.8178 |
0.309 | 7.0 | 854 | 0.3202 | 0.8672 | 0.8449 | 0.8285 | 0.8360 |
0.2971 | 8.0 | 976 | 0.3165 | 0.8747 | 0.8625 | 0.8263 | 0.8413 |
0.2834 | 9.0 | 1098 | 0.3064 | 0.8672 | 0.8496 | 0.8210 | 0.8332 |
0.266 | 10.0 | 1220 | 0.3070 | 0.8722 | 0.8431 | 0.8546 | 0.8484 |
0.2627 | 11.0 | 1342 | 0.2936 | 0.8797 | 0.8549 | 0.8549 | 0.8549 |
0.2523 | 12.0 | 1464 | 0.2869 | 0.8797 | 0.8616 | 0.8424 | 0.8510 |
0.2503 | 13.0 | 1586 | 0.2826 | 0.8722 | 0.8445 | 0.8496 | 0.8470 |
0.2505 | 14.0 | 1708 | 0.2885 | 0.8772 | 0.8483 | 0.8631 | 0.8550 |
0.2373 | 15.0 | 1830 | 0.2788 | 0.8772 | 0.8489 | 0.8606 | 0.8544 |
0.2308 | 16.0 | 1952 | 0.2759 | 0.8897 | 0.8649 | 0.8720 | 0.8683 |
0.2354 | 17.0 | 2074 | 0.2755 | 0.8847 | 0.8609 | 0.8609 | 0.8609 |
0.2304 | 18.0 | 2196 | 0.2755 | 0.8847 | 0.8621 | 0.8584 | 0.8602 |
0.2262 | 19.0 | 2318 | 0.2754 | 0.8872 | 0.8634 | 0.8652 | 0.8643 |
0.2293 | 20.0 | 2440 | 0.2751 | 0.8872 | 0.8658 | 0.8602 | 0.8629 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
Model tree for apwic/sentiment-lora-r16-4
Base model
indolem/indobert-base-uncased