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CodeBertaCLM
This model is a fine-tuned version of microsoft/codebert-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.5831
- Accuracy: 0.0144
- F1: 0.0144
- Bleu4: 0.0421
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Bleu4 |
|---|---|---|---|---|---|---|
| 3.6734 | 1.0 | 1673 | 3.6884 | 0.0159 | 0.0159 | 0.0131 |
| 2.8139 | 2.0 | 3346 | 3.2517 | 0.0164 | 0.0164 | 0.0192 |
| 2.4176 | 3.0 | 5019 | 3.0747 | 0.0178 | 0.0178 | 0.0332 |
| 2.2785 | 4.0 | 6692 | 2.9695 | 0.0174 | 0.0174 | 0.0347 |
| 2.1557 | 5.0 | 8365 | 2.8886 | 0.0171 | 0.0171 | 0.0377 |
| 2.0357 | 6.0 | 10038 | 2.8313 | 0.0158 | 0.0158 | 0.0394 |
| 1.9615 | 7.0 | 11711 | 2.7865 | 0.0158 | 0.0158 | 0.0393 |
| 1.8982 | 8.0 | 13384 | 2.7498 | 0.0147 | 0.0147 | 0.0399 |
| 1.8233 | 9.0 | 15057 | 2.7195 | 0.0149 | 0.0149 | 0.0430 |
| 1.7866 | 10.0 | 16730 | 2.6925 | 0.0157 | 0.0157 | 0.0485 |
| 1.7237 | 11.0 | 18403 | 2.6745 | 0.0146 | 0.0146 | 0.0419 |
| 1.6757 | 12.0 | 20076 | 2.6616 | 0.0146 | 0.0146 | 0.0403 |
| 1.6452 | 13.0 | 21749 | 2.6377 | 0.0147 | 0.0147 | 0.0403 |
| 1.6036 | 14.0 | 23422 | 2.6216 | 0.0145 | 0.0145 | 0.0397 |
| 1.5818 | 15.0 | 25095 | 2.6169 | 0.0150 | 0.0150 | 0.0413 |
| 1.5389 | 16.0 | 26768 | 2.6047 | 0.0146 | 0.0146 | 0.0420 |
| 1.5131 | 17.0 | 28441 | 2.5940 | 0.0153 | 0.0153 | 0.0433 |
| 1.4822 | 18.0 | 30114 | 2.5899 | 0.0145 | 0.0145 | 0.0404 |
| 1.4461 | 19.0 | 31787 | 2.5812 | 0.0150 | 0.0150 | 0.0423 |
| 1.4149 | 20.0 | 33460 | 2.5841 | 0.0148 | 0.0148 | 0.0418 |
| 1.3933 | 21.0 | 35133 | 2.5783 | 0.0139 | 0.0139 | 0.0386 |
| 1.3752 | 22.0 | 36806 | 2.5730 | 0.0151 | 0.0151 | 0.0444 |
| 1.3412 | 23.0 | 38479 | 2.5709 | 0.0149 | 0.0149 | 0.0419 |
| 1.3307 | 24.0 | 40152 | 2.5699 | 0.0143 | 0.0143 | 0.0424 |
| 1.2909 | 25.0 | 41825 | 2.5648 | 0.0144 | 0.0144 | 0.0416 |
| 1.2679 | 26.0 | 43498 | 2.5615 | 0.0145 | 0.0145 | 0.0420 |
| 1.2603 | 27.0 | 45171 | 2.5626 | 0.0148 | 0.0148 | 0.0433 |
| 1.2203 | 28.0 | 46844 | 2.5670 | 0.0148 | 0.0148 | 0.0410 |
| 1.2134 | 29.0 | 48517 | 2.5536 | 0.0147 | 0.0147 | 0.0422 |
| 1.1907 | 30.0 | 50190 | 2.5701 | 0.0139 | 0.0139 | 0.0404 |
| 1.1702 | 31.0 | 51863 | 2.5722 | 0.0143 | 0.0143 | 0.0424 |
| 1.1555 | 32.0 | 53536 | 2.5679 | 0.0144 | 0.0144 | 0.0434 |
| 1.1371 | 33.0 | 55209 | 2.5694 | 0.0146 | 0.0146 | 0.0431 |
| 1.1189 | 34.0 | 56882 | 2.5692 | 0.0141 | 0.0141 | 0.0422 |
| 1.0989 | 35.0 | 58555 | 2.5831 | 0.0144 | 0.0144 | 0.0421 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
