indobert-fin_news-mlm-3
This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.1666
Model description
This model is an experiment based on the methods in the paper arXiv:2310.09736 [cs.CL] by post-training the model indobert-base-p1 on the Financial News Articles dataset. The dataset was pre-processed as follows:
- Cleaning the dataset (mainly removing article header/location info, "Baca Juga" occurrences, and standard footers about Google News).
- Segmenting the dataset using the model sat-3l-sm from wtpsplit.
The post-training arguments follow that of the paper. Post-training takes a little over 8 hours (2x T4 GPU).
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.3212 | 1.0 | 740 | 4.1454 |
| 3.7192 | 2.0 | 1480 | 3.5316 |
| 3.3165 | 3.0 | 2220 | 3.2571 |
| 3.0983 | 4.0 | 2960 | 3.0149 |
| 2.8676 | 5.0 | 3700 | 2.9255 |
| 2.811 | 6.0 | 4440 | 2.8177 |
| 2.7158 | 7.0 | 5180 | 2.7367 |
| 2.5976 | 8.0 | 5920 | 2.7042 |
| 2.4428 | 9.0 | 6660 | 2.5891 |
| 2.3634 | 10.0 | 7400 | 2.5273 |
| 2.3411 | 11.0 | 8140 | 2.4821 |
| 2.2963 | 12.0 | 8880 | 2.4501 |
| 2.2155 | 13.0 | 9620 | 2.3981 |
| 2.0997 | 14.0 | 10360 | 2.4257 |
| 2.1241 | 15.0 | 11100 | 2.3665 |
| 2.0828 | 16.0 | 11840 | 2.3150 |
| 1.9577 | 17.0 | 12580 | 2.3608 |
| 1.9807 | 18.0 | 13320 | 2.2961 |
| 1.9717 | 19.0 | 14060 | 2.2521 |
| 1.8938 | 20.0 | 14800 | 2.2366 |
| 1.8353 | 21.0 | 15540 | 2.2804 |
| 1.905 | 22.0 | 16280 | 2.2110 |
| 1.8405 | 23.0 | 17020 | 2.1378 |
| 1.8043 | 24.0 | 17760 | 2.2252 |
| 1.7745 | 25.0 | 18500 | 2.1675 |
| 1.6882 | 26.0 | 19240 | 2.1594 |
| 1.6651 | 27.0 | 19980 | 2.1635 |
| 1.672 | 28.0 | 20720 | 2.1666 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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