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conplag2_codebert_ep30_bs16_lr1e-05_l512_s42_ppy_loss
buelfhood/conplag2_codebert_ep30_bs16_lr1e-05_l512_s42_ppy_loss
conplag2_codebert_ep30_bs16_lr1e-05_l512_s42_ppy_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 17, 2025
- Updated
- Nov 17, 2025
- SHA
- 52169c59
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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. -->
conplag2_codebert_ep30_bs16_lr1e-05_l512_s42_ppy_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: 0.5223
- Accuracy: 0.8248
- Recall: 0.6316
- Precision: 0.7059
- F1: 0.6667
- F Beta Score: 0.6527
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: 1e-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 |
|---|---|---|---|---|---|---|---|---|
| 0.7049 | 1.0 | 40 | 0.6937 | 0.7299 | 0.0263 | 1.0 | 0.0513 | 0.0376 |
| 0.7141 | 2.0 | 80 | 0.6921 | 0.6861 | 0.0526 | 0.2222 | 0.0851 | 0.0688 |
| 0.6615 | 3.0 | 120 | 0.6791 | 0.6788 | 0.2105 | 0.3636 | 0.2667 | 0.2419 |
| 0.5884 | 4.0 | 160 | 0.6392 | 0.7007 | 0.3684 | 0.4516 | 0.4058 | 0.3906 |
| 0.5151 | 5.0 | 200 | 0.6232 | 0.8248 | 0.3684 | 1.0 | 0.5385 | 0.4573 |
| 0.4835 | 6.0 | 240 | 0.5693 | 0.8394 | 0.4737 | 0.9 | 0.6207 | 0.5545 |
| 0.3719 | 7.0 | 280 | 0.5648 | 0.8321 | 0.4737 | 0.8571 | 0.6102 | 0.5493 |
| 0.3126 | 8.0 | 320 | 0.6909 | 0.8321 | 0.4211 | 0.9412 | 0.5818 | 0.5073 |
| 0.4279 | 9.0 | 360 | 0.5223 | 0.8248 | 0.6316 | 0.7059 | 0.6667 | 0.6527 |
| 0.1961 | 10.0 | 400 | 0.7799 | 0.8467 | 0.4737 | 0.9474 | 0.6316 | 0.5598 |
| 0.2536 | 11.0 | 440 | 0.6269 | 0.8321 | 0.6316 | 0.7273 | 0.6761 | 0.6582 |
| 0.124 | 12.0 | 480 | 0.7231 | 0.8467 | 0.6316 | 0.7742 | 0.6957 | 0.6695 |
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 17, 2025
- Updated
- Nov 17, 2025