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v2-heladepdet-bert-finetuned-regression
chiabingxuan/v2-heladepdet-bert-finetuned-regression
v2-heladepdet-bert-finetuned-regression 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
- Apr 2, 2026
- Updated
- Apr 2, 2026
- SHA
- a0fc4e07
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>
v2-heladepdet-bert-finetuned-regression
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: 1.2472
- Mse: 0.6236
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse |
|---|---|---|---|---|
| 2.0911 | 0.5734 | 250 | 1.5180 | 0.7590 |
| 1.4813 | 1.1468 | 500 | 1.4577 | 0.7289 |
| 1.3926 | 1.7202 | 750 | 1.3690 | 0.6845 |
| 1.3587 | 2.2936 | 1000 | 1.3469 | 0.6735 |
| 1.3002 | 2.8670 | 1250 | 1.3042 | 0.6521 |
| 1.2859 | 3.4404 | 1500 | 1.2955 | 0.6478 |
| 1.2476 | 4.0138 | 1750 | 1.2676 | 0.6338 |
| 1.2422 | 4.5872 | 2000 | 1.2743 | 0.6371 |
| 1.1986 | 5.1606 | 2250 | 1.2791 | 0.6396 |
| 1.1851 | 5.7339 | 2500 | 1.2508 | 0.6254 |
| 1.1985 | 6.3073 | 2750 | 1.2740 | 0.6370 |
| 1.1668 | 6.8807 | 3000 | 1.2533 | 0.6267 |
| 1.1449 | 7.4541 | 3250 | 1.2408 | 0.6204 |
| 1.1500 | 8.0275 | 3500 | 1.2453 | 0.6227 |
| 1.1371 | 8.6009 | 3750 | 1.2391 | 0.6195 |
| 1.1325 | 9.1743 | 4000 | 1.2520 | 0.6260 |
| 1.1088 | 9.7477 | 4250 | 1.2472 | 0.6236 |
Framework versions
- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
- Library
- peft
- License
- apache-2.0
- Created
- Apr 2, 2026
- Updated
- Apr 2, 2026