<!-- 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. -->
CodeNetClassifier
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7251
- Accuracy: 0.5581
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 106 | 0.7429 | 0.6078 |
| No log | 2.0 | 212 | 0.6718 | 0.6078 |
| No log | 3.0 | 318 | 0.6943 | 0.6078 |
| No log | 4.0 | 424 | 0.7566 | 0.5940 |
| 0.6377 | 5.0 | 530 | 0.7145 | 0.5596 |
| 0.6377 | 6.0 | 636 | 0.7177 | 0.5952 |
| 0.6377 | 7.0 | 742 | 0.6939 | 0.5458 |
| 0.6377 | 8.0 | 848 | 0.7903 | 0.4716 |
| 0.6377 | 9.0 | 954 | 0.7567 | 0.4727 |
| 0.5954 | 10.0 | 1060 | 0.7251 | 0.5581 |
Framework versions
- Transformers 4.11.3
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.10.3
