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heladepdet-bert-finetuned-classification
chiabingxuan/heladepdet-bert-finetuned-classification
heladepdet-bert-finetuned-classification is a machine learning model from chiabingxuan. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Mar 19, 2026
- Updated
- Mar 19, 2026
- SHA
- c3b9a40a
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. -->
<a href="https://huggingface.co/spaces/chiabingxuan/DSA4262-FineTuning" target="_blank"><img src="https://raw.githubusercontent.com/gradio-app/trackio/refs/heads/main/trackio/assets/badge.png" alt="Visualize in Trackio" title="Visualize in Trackio" style="height: 40px;"/></a>
heladepdet-bert-finetuned-classification
This model is a fine-tuned version of google-bert/bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6113
- Accuracy: 0.6866
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3234 | 0.2867 | 250 | 1.1322 | 0.4828 |
| 1.0071 | 0.5734 | 500 | 0.8364 | 0.6016 |
| 0.8471 | 0.8601 | 750 | 0.7494 | 0.6251 |
| 0.7557 | 1.1468 | 1000 | 0.7120 | 0.6447 |
| 0.7137 | 1.4335 | 1250 | 0.6895 | 0.6567 |
| 0.6944 | 1.7202 | 1500 | 0.6622 | 0.6705 |
| 0.6728 | 2.0069 | 1750 | 0.6579 | 0.6573 |
| 0.6648 | 2.2936 | 2000 | 0.6464 | 0.6716 |
| 0.6472 | 2.5803 | 2250 | 0.6320 | 0.6716 |
| 0.6261 | 2.8670 | 2500 | 0.6269 | 0.6716 |
| 0.6482 | 3.1537 | 2750 | 0.6214 | 0.6780 |
| 0.6327 | 3.4404 | 3000 | 0.6196 | 0.6762 |
| 0.6220 | 3.7271 | 3250 | 0.6163 | 0.7009 |
| 0.5996 | 4.0138 | 3500 | 0.6171 | 0.6762 |
| 0.6033 | 4.3005 | 3750 | 0.6135 | 0.6774 |
| 0.6108 | 4.5872 | 4000 | 0.6121 | 0.6900 |
| 0.6350 | 4.8739 | 4250 | 0.6113 | 0.6866 |
Framework versions
- PEFT 0.18.1
- Transformers 5.2.0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
- Library
- peft
- License
- apache-2.0
- Created
- Mar 19, 2026
- Updated
- Mar 19, 2026