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jn
WaiLwin/jn
jn is a machine learning model from WaiLwin. 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 transformers. 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
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Sep 27, 2025
- Updated
- Sep 27, 2025
- SHA
- 856af7a9
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. -->
jn
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3406
- Topology Accuracy: 0.9883
- Service Accuracy: 0.9746
- Combined Accuracy: 0.9814
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: 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Topology Accuracy | Service Accuracy | Combined Accuracy |
|---|---|---|---|---|---|---|
| 0.6178 | 1.0 | 96 | 0.6028 | 0.9805 | 0.7012 | 0.8408 |
| 0.5546 | 2.0 | 192 | 0.5229 | 0.9766 | 0.7109 | 0.8438 |
| 0.4255 | 3.0 | 288 | 0.4324 | 0.9805 | 0.9023 | 0.9414 |
| 0.3934 | 4.0 | 384 | 0.3546 | 0.9805 | 0.9668 | 0.9736 |
| 0.3195 | 5.0 | 480 | 0.3477 | 0.9844 | 0.9648 | 0.9746 |
| 0.3052 | 6.0 | 576 | 0.3458 | 0.9863 | 0.9746 | 0.9805 |
| 0.3139 | 7.0 | 672 | 0.3529 | 0.9863 | 0.9688 | 0.9775 |
| 0.3049 | 8.0 | 768 | 0.3533 | 0.9844 | 0.9707 | 0.9775 |
| 0.3145 | 9.0 | 864 | 0.3517 | 0.9863 | 0.9648 | 0.9756 |
| 0.3025 | 10.0 | 960 | 0.3462 | 0.9883 | 0.9668 | 0.9775 |
| 0.3031 | 11.0 | 1056 | 0.3420 | 0.9902 | 0.9707 | 0.9805 |
| 0.3008 | 12.0 | 1152 | 0.3417 | 0.9883 | 0.9727 | 0.9805 |
| 0.3184 | 13.0 | 1248 | 0.3418 | 0.9883 | 0.9746 | 0.9814 |
| 0.2996 | 14.0 | 1344 | 0.3404 | 0.9883 | 0.9746 | 0.9814 |
| 0.3023 | 15.0 | 1440 | 0.3406 | 0.9883 | 0.9746 | 0.9814 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Sep 27, 2025
- Updated
- Sep 27, 2025