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irplag_codebert_ep30_bs16_lr2e-05_l512_s42_ppn_loss
buelfhood/irplag_codebert_ep30_bs16_lr2e-05_l512_s42_ppn_loss
irplag_codebert_ep30_bs16_lr2e-05_l512_s42_ppn_loss is a text classification model from buelfhood. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Nov 16, 2025
- Updated
- Nov 16, 2025
- SHA
- eeac4cae
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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. -->
irplag_codebert_ep30_bs16_lr2e-05_l512_s42_ppn_loss
This model is a fine-tuned version of microsoft/codebert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0350
- Accuracy: 0.9855
- Recall: 0.9818
- Precision: 1.0
- F1: 0.9908
- F Beta Score: 0.9873
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | F Beta Score |
|---|---|---|---|---|---|---|---|---|
| 0.6899 | 1.0 | 21 | 0.6792 | 0.8116 | 1.0 | 0.8088 | 0.8943 | 0.9322 |
| 0.6685 | 2.0 | 42 | 0.6671 | 0.2464 | 0.0545 | 1.0 | 0.1034 | 0.0769 |
| 0.4233 | 3.0 | 63 | 0.2961 | 0.7971 | 0.7455 | 1.0 | 0.8542 | 0.8088 |
| 0.2026 | 4.0 | 84 | 0.1093 | 0.9565 | 0.9455 | 1.0 | 0.9720 | 0.9616 |
| 0.1918 | 5.0 | 105 | 0.0447 | 0.9855 | 0.9818 | 1.0 | 0.9908 | 0.9873 |
| 0.0525 | 6.0 | 126 | 0.0420 | 0.9855 | 0.9818 | 1.0 | 0.9908 | 0.9873 |
| 0.0308 | 7.0 | 147 | 0.0401 | 0.9855 | 0.9818 | 1.0 | 0.9908 | 0.9873 |
| 0.0256 | 8.0 | 168 | 0.0561 | 0.9710 | 0.9636 | 1.0 | 0.9815 | 0.9745 |
| 0.0475 | 9.0 | 189 | 0.0353 | 0.9855 | 0.9818 | 1.0 | 0.9908 | 0.9873 |
| 0.0472 | 10.0 | 210 | 0.0350 | 0.9855 | 0.9818 | 1.0 | 0.9908 | 0.9873 |
| 0.0215 | 11.0 | 231 | 0.0382 | 0.9855 | 0.9818 | 1.0 | 0.9908 | 0.9873 |
| 0.0026 | 12.0 | 252 | 0.0385 | 0.9855 | 0.9818 | 1.0 | 0.9908 | 0.9873 |
| 0.0192 | 13.0 | 273 | 0.0355 | 0.9855 | 0.9818 | 1.0 | 0.9908 | 0.9873 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.1.0
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Nov 16, 2025
- Updated
- Nov 16, 2025