Downloads · 30 days
9
4% of all-time downloads
v2-heladepdet-bert-finetuned-classification
chiabingxuan/v2-heladepdet-bert-finetuned-classification
v2-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. --
All-time downloads
215
Public
Repo size
42.8 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors2.4 MB · 78%
At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Apr 2, 2026
- Updated
- Apr 2, 2026
- SHA
- 16c10243
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-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: 1.0636
- Accuracy: 0.7790
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 | Accuracy |
|---|---|---|---|---|
| 2.3444 | 0.5734 | 250 | 1.8315 | 0.6005 |
| 1.6084 | 1.1468 | 500 | 1.3722 | 0.6912 |
| 1.3134 | 1.7202 | 750 | 1.2174 | 0.7279 |
| 1.2049 | 2.2936 | 1000 | 1.1384 | 0.7589 |
| 1.1106 | 2.8670 | 1250 | 1.1195 | 0.7612 |
| 1.0872 | 3.4404 | 1500 | 1.1067 | 0.7623 |
| 1.0584 | 4.0138 | 1750 | 1.0888 | 0.7732 |
| 1.0103 | 4.5872 | 2000 | 1.0923 | 0.7715 |
| 1.0291 | 5.1606 | 2250 | 1.0798 | 0.7664 |
| 0.9996 | 5.7339 | 2500 | 1.0695 | 0.7750 |
| 0.9968 | 6.3073 | 2750 | 1.0799 | 0.7664 |
| 0.9800 | 6.8807 | 3000 | 1.0611 | 0.7750 |
| 0.9489 | 7.4541 | 3250 | 1.0626 | 0.7750 |
| 0.9542 | 8.0275 | 3500 | 1.0615 | 0.7790 |
| 0.9469 | 8.6009 | 3750 | 1.0603 | 0.7744 |
| 0.9380 | 9.1743 | 4000 | 1.0683 | 0.7767 |
| 0.9338 | 9.7477 | 4250 | 1.0636 | 0.7790 |
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