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progpedia19_codebert_ep30_bs16_lr5e-05_l512_s42_ppn_loss
buelfhood/progpedia19_codebert_ep30_bs16_lr5e-05_l512_s42_ppn_loss
progpedia19_codebert_ep30_bs16_lr5e-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 17, 2025
- Updated
- Nov 17, 2025
- SHA
- 02740fb4
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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. -->
progpedia19_codebert_ep30_bs16_lr5e-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: 0.1603
- Accuracy: 0.9938
- Recall: 0.8333
- Precision: 1.0
- F1: 0.9091
- F Beta Score: 0.8784
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: 5e-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 |
|---|---|---|---|---|---|---|---|---|
| 0.6528 | 1.0 | 94 | 0.8573 | 0.9658 | 0.5 | 0.5455 | 0.5217 | 0.5132 |
| 0.7033 | 2.0 | 188 | 0.8802 | 0.9658 | 0.5 | 0.5455 | 0.5217 | 0.5132 |
| 1.1734 | 3.0 | 282 | 0.7792 | 0.9814 | 0.5 | 1.0 | 0.6667 | 0.5909 |
| 0.2532 | 4.0 | 376 | 0.7538 | 0.9814 | 0.5 | 1.0 | 0.6667 | 0.5909 |
| 0.3261 | 5.0 | 470 | 0.7569 | 0.9814 | 0.5 | 1.0 | 0.6667 | 0.5909 |
| 0.1588 | 6.0 | 564 | 0.7095 | 0.9845 | 0.5833 | 1.0 | 0.7368 | 0.6691 |
| 0.0738 | 7.0 | 658 | 0.2285 | 0.9938 | 0.9167 | 0.9167 | 0.9167 | 0.9167 |
| 0.2398 | 8.0 | 752 | 0.2046 | 0.9969 | 0.9167 | 1.0 | 0.9565 | 0.9408 |
| 0.3371 | 9.0 | 846 | 0.2934 | 0.9938 | 0.8333 | 1.0 | 0.9091 | 0.8784 |
| 0.1299 | 10.0 | 940 | 0.1603 | 0.9938 | 0.8333 | 1.0 | 0.9091 | 0.8784 |
| 0.001 | 11.0 | 1034 | 0.4494 | 0.9938 | 0.8333 | 1.0 | 0.9091 | 0.8784 |
| 0.0011 | 12.0 | 1128 | 0.4120 | 0.9938 | 0.8333 | 1.0 | 0.9091 | 0.8784 |
| 0.0005 | 13.0 | 1222 | 0.4311 | 0.9938 | 0.8333 | 1.0 | 0.9091 | 0.8784 |
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