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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: CommitPredictor |
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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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# CommitPredictor |
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This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4811 |
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- Accuracy: 0.8991 |
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- F1: 0.8991 |
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- Bleu4: 0.9479 |
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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: 50 |
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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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| 1.143 | 1.0 | 687 | 0.6993 | 0.8563 | 0.8563 | 0.8531 | |
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| 0.7772 | 2.0 | 1374 | 0.6482 | 0.8677 | 0.8677 | 0.9036 | |
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| 0.6738 | 3.0 | 2061 | 0.6211 | 0.8734 | 0.8734 | 0.8189 | |
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| 0.6544 | 4.0 | 2748 | 0.5942 | 0.8782 | 0.8782 | 0.9196 | |
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| 0.6295 | 5.0 | 3435 | 0.5805 | 0.8815 | 0.8815 | 0.8079 | |
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| 0.5966 | 6.0 | 4122 | 0.5609 | 0.8838 | 0.8838 | 0.8186 | |
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| 0.5916 | 7.0 | 4809 | 0.5514 | 0.8870 | 0.8870 | 0.9103 | |
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| 0.5732 | 8.0 | 5496 | 0.5492 | 0.8861 | 0.8861 | 0.8067 | |
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| 0.5559 | 9.0 | 6183 | 0.5389 | 0.8881 | 0.8881 | 0.9353 | |
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| 0.5511 | 10.0 | 6870 | 0.5257 | 0.8901 | 0.8901 | 0.9297 | |
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| 0.5345 | 11.0 | 7557 | 0.5319 | 0.8905 | 0.8905 | 0.9363 | |
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| 0.5287 | 12.0 | 8244 | 0.5220 | 0.8911 | 0.8911 | 0.8816 | |
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| 0.5226 | 13.0 | 8931 | 0.5139 | 0.8938 | 0.8938 | 0.9438 | |
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| 0.5147 | 14.0 | 9618 | 0.5124 | 0.8929 | 0.8929 | 0.9145 | |
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| 0.511 | 15.0 | 10305 | 0.5131 | 0.8932 | 0.8932 | 0.8570 | |
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| 0.4996 | 16.0 | 10992 | 0.4997 | 0.8964 | 0.8964 | 0.9287 | |
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| 0.4949 | 17.0 | 11679 | 0.5033 | 0.8958 | 0.8958 | 0.9460 | |
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| 0.4882 | 18.0 | 12366 | 0.5003 | 0.8971 | 0.8971 | 0.7739 | |
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| 0.4837 | 19.0 | 13053 | 0.4914 | 0.8979 | 0.8979 | 0.9014 | |
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| 0.4822 | 20.0 | 13740 | 0.4962 | 0.8963 | 0.8963 | 0.9330 | |
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| 0.4778 | 21.0 | 14427 | 0.4844 | 0.8971 | 0.8971 | 0.8454 | |
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| 0.4704 | 22.0 | 15114 | 0.4809 | 0.8988 | 0.8988 | 0.9274 | |
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| 0.4676 | 23.0 | 15801 | 0.4735 | 0.9009 | 0.9009 | 0.9445 | |
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| 0.4663 | 24.0 | 16488 | 0.4792 | 0.8990 | 0.8990 | 0.9001 | |
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| 0.4605 | 25.0 | 17175 | 0.4826 | 0.8995 | 0.8995 | 0.8313 | |
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| 0.4621 | 26.0 | 17862 | 0.4811 | 0.8991 | 0.8991 | 0.9479 | |
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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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