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indobert-base-p2-finetuned-indohug-standard
sissiflo/indobert-base-p2-finetuned-indohug-standard
indobert-base-p2-finetuned-indohug-standard is a fill-mask model from sissiflo. Use it when you need the model to fill a missing word. 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
- Fill-Mask
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- May 18, 2025
- Updated
- May 19, 2025
- SHA
- d55fa0cc
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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. -->
indobert-base-p2-finetuned-indohug-standard
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: 2.3066
- Model Preparation Time: 0.0033
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: 64
- eval_batch_size: 64
- 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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time |
|---|---|---|---|---|
| 4.2974 | 1.0 | 322 | 3.1340 | 0.0033 |
| 3.0043 | 2.0 | 644 | 2.7229 | 0.0033 |
| 2.6316 | 3.0 | 966 | 2.4190 | 0.0033 |
| 2.4423 | 4.0 | 1288 | 2.2960 | 0.0033 |
| 2.408 | 5.0 | 1610 | 2.3215 | 0.0033 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
- Task
- Fill-Mask
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- May 18, 2025
- Updated
- May 19, 2025