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BantuBERTa-vmw-finetuned-vmw-noaug
Kuongan/BantuBERTa-vmw-finetuned-vmw-noaug
BantuBERTa-vmw-finetuned-vmw-noaug is a text classification model from Kuongan. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as cc-by-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- cc-by-4.0
- Model type
- roberta
- Access
- Public
- Created
- Feb 27, 2025
- Updated
- Feb 27, 2025
- SHA
- 39701d62
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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. -->
BantuBERTa-vmw-finetuned-vmw-noaug
This model is a fine-tuned version of Kuongan/BantuBERTa-vmw-finetuned on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4673
- F1: 0.2169
- Roc Auc: 0.5714
- Accuracy: 0.4651
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.1056 | 1.0 | 49 | 0.3296 | 0.1466 | 0.5417 | 0.4729 |
| 0.1022 | 2.0 | 98 | 0.3384 | 0.1506 | 0.5425 | 0.4806 |
| 0.0889 | 3.0 | 147 | 0.3528 | 0.1372 | 0.5439 | 0.4651 |
| 0.071 | 4.0 | 196 | 0.3808 | 0.1874 | 0.5581 | 0.4264 |
| 0.0659 | 5.0 | 245 | 0.3800 | 0.1664 | 0.5478 | 0.4690 |
| 0.0534 | 6.0 | 294 | 0.3999 | 0.1873 | 0.5564 | 0.4651 |
| 0.0407 | 7.0 | 343 | 0.4108 | 0.1439 | 0.5378 | 0.4535 |
| 0.0368 | 8.0 | 392 | 0.4235 | 0.1970 | 0.5590 | 0.4612 |
| 0.0247 | 9.0 | 441 | 0.4248 | 0.1724 | 0.5497 | 0.4729 |
| 0.0213 | 10.0 | 490 | 0.4313 | 0.2049 | 0.5648 | 0.4690 |
| 0.0164 | 11.0 | 539 | 0.4384 | 0.1918 | 0.5592 | 0.4690 |
| 0.0144 | 12.0 | 588 | 0.4576 | 0.2116 | 0.5694 | 0.4574 |
| 0.0138 | 13.0 | 637 | 0.4575 | 0.2075 | 0.5665 | 0.4729 |
| 0.0117 | 14.0 | 686 | 0.4694 | 0.2008 | 0.5638 | 0.4574 |
| 0.0114 | 15.0 | 735 | 0.4673 | 0.2169 | 0.5714 | 0.4651 |
| 0.0098 | 16.0 | 784 | 0.4703 | 0.2034 | 0.5646 | 0.4690 |
| 0.0096 | 17.0 | 833 | 0.4729 | 0.1943 | 0.5600 | 0.4574 |
| 0.0102 | 18.0 | 882 | 0.4707 | 0.1989 | 0.5619 | 0.4690 |
| 0.0101 | 19.0 | 931 | 0.4720 | 0.1990 | 0.5622 | 0.4690 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- cc-by-4.0
- Created
- Feb 27, 2025
- Updated
- Feb 27, 2025