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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: 0.4812 |
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- Accuracy: 0.8993 |
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- F1: 0.8993 |
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- Bleu4: 0.9483 |
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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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| 1.1319 | 1.0 | 687 | 0.6982 | 0.8562 | 0.8562 | 0.8551 | |
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| 0.7784 | 2.0 | 1374 | 0.6501 | 0.8665 | 0.8665 | 0.8977 | |
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| 0.6779 | 3.0 | 2061 | 0.6229 | 0.8733 | 0.8733 | 0.8535 | |
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| 0.6579 | 4.0 | 2748 | 0.5978 | 0.8769 | 0.8769 | 0.9176 | |
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| 0.6319 | 5.0 | 3435 | 0.5833 | 0.8808 | 0.8808 | 0.8073 | |
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| 0.5988 | 6.0 | 4122 | 0.5627 | 0.8834 | 0.8834 | 0.9241 | |
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| 0.5939 | 7.0 | 4809 | 0.5533 | 0.8864 | 0.8864 | 0.9212 | |
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| 0.575 | 8.0 | 5496 | 0.5512 | 0.8860 | 0.8860 | 0.7943 | |
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| 0.5574 | 9.0 | 6183 | 0.5412 | 0.8879 | 0.8879 | 0.9396 | |
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| 0.553 | 10.0 | 6870 | 0.5276 | 0.8899 | 0.8899 | 0.8301 | |
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| 0.5371 | 11.0 | 7557 | 0.5341 | 0.8893 | 0.8893 | 0.9350 | |
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| 0.5302 | 12.0 | 8244 | 0.5236 | 0.8909 | 0.8909 | 0.8813 | |
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| 0.5245 | 13.0 | 8931 | 0.5153 | 0.8933 | 0.8933 | 0.8817 | |
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| 0.5165 | 14.0 | 9618 | 0.5138 | 0.8926 | 0.8926 | 0.9174 | |
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| 0.5122 | 15.0 | 10305 | 0.5144 | 0.8930 | 0.8930 | 0.8318 | |
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| 0.5007 | 16.0 | 10992 | 0.5007 | 0.8957 | 0.8957 | 0.9350 | |
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| 0.4954 | 17.0 | 11679 | 0.5041 | 0.8960 | 0.8960 | 0.9355 | |
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| 0.4894 | 18.0 | 12366 | 0.5000 | 0.8967 | 0.8967 | 0.7818 | |
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| 0.4851 | 19.0 | 13053 | 0.4915 | 0.8982 | 0.8982 | 0.9190 | |
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| 0.483 | 20.0 | 13740 | 0.4970 | 0.8962 | 0.8962 | 0.9359 | |
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| 0.4792 | 21.0 | 14427 | 0.4849 | 0.8971 | 0.8971 | 0.8458 | |
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| 0.4716 | 22.0 | 15114 | 0.4809 | 0.8990 | 0.8990 | 0.9367 | |
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| 0.4691 | 23.0 | 15801 | 0.4732 | 0.9006 | 0.9006 | 0.9478 | |
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| 0.4675 | 24.0 | 16488 | 0.4805 | 0.8989 | 0.8989 | 0.9412 | |
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| 0.4618 | 25.0 | 17175 | 0.4837 | 0.8997 | 0.8997 | 0.8373 | |
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| 0.4633 | 26.0 | 17862 | 0.4812 | 0.8993 | 0.8993 | 0.9483 | |
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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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