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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: 0.5888 |
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- Accuracy: 0.8783 |
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- F1: 0.8783 |
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- Bleu4: 0.8598 |
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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: 42 |
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- eval_batch_size: 42 |
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- seed: 42 |
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- gradient_accumulation_steps: 3 |
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- total_train_batch_size: 126 |
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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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- mixed_precision_training: Native AMP |
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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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| No log | 1.0 | 236 | 0.8706 | 0.8253 | 0.8253 | 0.7764 | |
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| No log | 2.0 | 472 | 0.7296 | 0.8503 | 0.8503 | 0.8287 | |
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| 1.0825 | 3.0 | 708 | 0.6826 | 0.8594 | 0.8594 | 0.8123 | |
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| 1.0825 | 4.0 | 944 | 0.6655 | 0.8645 | 0.8645 | 0.8480 | |
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| 0.755 | 5.0 | 1180 | 0.6317 | 0.8696 | 0.8696 | 0.9028 | |
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| 0.755 | 6.0 | 1416 | 0.6333 | 0.8699 | 0.8699 | 0.8870 | |
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| 0.6948 | 7.0 | 1652 | 0.6147 | 0.8738 | 0.8738 | 0.9187 | |
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| 0.6948 | 8.0 | 1888 | 0.6110 | 0.8738 | 0.8738 | 0.8080 | |
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| 0.6633 | 9.0 | 2124 | 0.5987 | 0.8770 | 0.8770 | 0.8903 | |
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| 0.6633 | 10.0 | 2360 | 0.5888 | 0.8783 | 0.8783 | 0.8598 | |
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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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