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results_indobert-base-p2_preprocessing_without_stopword_tuning
Alfanatasya/results_indobert-base-p2_preprocessing_without_stopword_tuning
results_indobert-base-p2_preprocessing_without_stopword_tuning is a text classification model from Alfanatasya. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Aug 8, 2025
- Updated
- Aug 8, 2025
- SHA
- 6b9f124f
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
results_indobert-base-p2_preprocessing_without_stopword_tuning
This model is a fine-tuned version of indobenchmark/indobert-base-p2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9807
- Accuracy: 0.725
- Precision: 0.7489
- Recall: 0.7253
- F1: 0.7304
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: 1.782889017449145e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adafactor and the args are: No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.1561 | 1.0 | 111 | 0.9020 | 0.6523 | 0.6678 | 0.6605 | 0.6332 |
| 0.7527 | 2.0 | 222 | 0.8551 | 0.7114 | 0.7081 | 0.7289 | 0.7072 |
| 0.6042 | 3.0 | 333 | 0.7805 | 0.7227 | 0.7336 | 0.7323 | 0.7241 |
| 0.48 | 4.0 | 444 | 1.0114 | 0.6886 | 0.7113 | 0.7043 | 0.6832 |
| 0.3828 | 5.0 | 555 | 0.9657 | 0.7023 | 0.7031 | 0.7135 | 0.7041 |
| 0.3044 | 6.0 | 666 | 0.9807 | 0.725 | 0.7489 | 0.7253 | 0.7304 |
| 0.2357 | 7.0 | 777 | 1.1037 | 0.7023 | 0.7056 | 0.7150 | 0.7088 |
| 0.1791 | 8.0 | 888 | 1.1875 | 0.7136 | 0.7258 | 0.7245 | 0.7233 |
| 0.1396 | 9.0 | 999 | 1.3068 | 0.7 | 0.7011 | 0.7123 | 0.7036 |
| 0.1205 | 10.0 | 1110 | 1.4030 | 0.7136 | 0.7228 | 0.7267 | 0.7212 |
| 0.0856 | 11.0 | 1221 | 1.5024 | 0.7 | 0.7128 | 0.7133 | 0.7104 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 4.0.0
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Aug 8, 2025
- Updated
- Aug 8, 2025