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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: CodeBertaCLM |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# CodeBertaCLM |
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This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5831 |
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- Accuracy: 0.0144 |
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- F1: 0.0144 |
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- Bleu4: 0.0421 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 200 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Bleu4 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:| |
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| 3.6734 | 1.0 | 1673 | 3.6884 | 0.0159 | 0.0159 | 0.0131 | |
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| 2.8139 | 2.0 | 3346 | 3.2517 | 0.0164 | 0.0164 | 0.0192 | |
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| 2.4176 | 3.0 | 5019 | 3.0747 | 0.0178 | 0.0178 | 0.0332 | |
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| 2.2785 | 4.0 | 6692 | 2.9695 | 0.0174 | 0.0174 | 0.0347 | |
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| 2.1557 | 5.0 | 8365 | 2.8886 | 0.0171 | 0.0171 | 0.0377 | |
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| 2.0357 | 6.0 | 10038 | 2.8313 | 0.0158 | 0.0158 | 0.0394 | |
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| 1.9615 | 7.0 | 11711 | 2.7865 | 0.0158 | 0.0158 | 0.0393 | |
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| 1.8982 | 8.0 | 13384 | 2.7498 | 0.0147 | 0.0147 | 0.0399 | |
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| 1.8233 | 9.0 | 15057 | 2.7195 | 0.0149 | 0.0149 | 0.0430 | |
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| 1.7866 | 10.0 | 16730 | 2.6925 | 0.0157 | 0.0157 | 0.0485 | |
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| 1.7237 | 11.0 | 18403 | 2.6745 | 0.0146 | 0.0146 | 0.0419 | |
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| 1.6757 | 12.0 | 20076 | 2.6616 | 0.0146 | 0.0146 | 0.0403 | |
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| 1.6452 | 13.0 | 21749 | 2.6377 | 0.0147 | 0.0147 | 0.0403 | |
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| 1.6036 | 14.0 | 23422 | 2.6216 | 0.0145 | 0.0145 | 0.0397 | |
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| 1.5818 | 15.0 | 25095 | 2.6169 | 0.0150 | 0.0150 | 0.0413 | |
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| 1.5389 | 16.0 | 26768 | 2.6047 | 0.0146 | 0.0146 | 0.0420 | |
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| 1.5131 | 17.0 | 28441 | 2.5940 | 0.0153 | 0.0153 | 0.0433 | |
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| 1.4822 | 18.0 | 30114 | 2.5899 | 0.0145 | 0.0145 | 0.0404 | |
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| 1.4461 | 19.0 | 31787 | 2.5812 | 0.0150 | 0.0150 | 0.0423 | |
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| 1.4149 | 20.0 | 33460 | 2.5841 | 0.0148 | 0.0148 | 0.0418 | |
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| 1.3933 | 21.0 | 35133 | 2.5783 | 0.0139 | 0.0139 | 0.0386 | |
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| 1.3752 | 22.0 | 36806 | 2.5730 | 0.0151 | 0.0151 | 0.0444 | |
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| 1.3412 | 23.0 | 38479 | 2.5709 | 0.0149 | 0.0149 | 0.0419 | |
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| 1.3307 | 24.0 | 40152 | 2.5699 | 0.0143 | 0.0143 | 0.0424 | |
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| 1.2909 | 25.0 | 41825 | 2.5648 | 0.0144 | 0.0144 | 0.0416 | |
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| 1.2679 | 26.0 | 43498 | 2.5615 | 0.0145 | 0.0145 | 0.0420 | |
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| 1.2603 | 27.0 | 45171 | 2.5626 | 0.0148 | 0.0148 | 0.0433 | |
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| 1.2203 | 28.0 | 46844 | 2.5670 | 0.0148 | 0.0148 | 0.0410 | |
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| 1.2134 | 29.0 | 48517 | 2.5536 | 0.0147 | 0.0147 | 0.0422 | |
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| 1.1907 | 30.0 | 50190 | 2.5701 | 0.0139 | 0.0139 | 0.0404 | |
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| 1.1702 | 31.0 | 51863 | 2.5722 | 0.0143 | 0.0143 | 0.0424 | |
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| 1.1555 | 32.0 | 53536 | 2.5679 | 0.0144 | 0.0144 | 0.0434 | |
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| 1.1371 | 33.0 | 55209 | 2.5694 | 0.0146 | 0.0146 | 0.0431 | |
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| 1.1189 | 34.0 | 56882 | 2.5692 | 0.0141 | 0.0141 | 0.0422 | |
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| 1.0989 | 35.0 | 58555 | 2.5831 | 0.0144 | 0.0144 | 0.0421 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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