KIPBERT
This model is a fine-tuned version of indolem/indobert-base-uncased on the id_nergrit_corpus dataset. It achieves the following results on the evaluation set:
- Loss: 0.1731
- Precision: 0.8058
- Recall: 0.8325
- F1: 0.8189
- Accuracy: 0.9503
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.4926 | 1.0 | 784 | 0.1810 | 0.7860 | 0.8172 | 0.8013 | 0.9450 |
| 0.1627 | 2.0 | 1568 | 0.1731 | 0.8058 | 0.8325 | 0.8189 | 0.9503 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for kiipliwooke/KIPBERT
Base model
indolem/indobert-base-uncasedDataset used to train kiipliwooke/KIPBERT
Evaluation results
- Precision on id_nergrit_corpustest set self-reported0.806
- Recall on id_nergrit_corpustest set self-reported0.833
- F1 on id_nergrit_corpustest set self-reported0.819
- Accuracy on id_nergrit_corpustest set self-reported0.950