| --- |
| license: apache-2.0 |
| base_model: Salesforce/codet5-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: codet5-small-java-buggy-to-fixed-code |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # codet5-small-java-buggy-to-fixed-code |
|
|
| This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1496 |
| - Rouge1: 28.945 |
| - Rouge2: 25.498 |
| - Rougel: 28.8801 |
| - Rougelsum: 28.9201 |
| - Gen Len: 18.9938 |
|
|
| ## Model description |
|
|
| More information needed |
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|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
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|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 4 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
| | 0.26 | 1.0 | 3750 | 0.1799 | 28.745 | 25.2441 | 28.6738 | 28.7241 | 18.9872 | |
| | 0.212 | 2.0 | 7500 | 0.1589 | 28.8636 | 25.4151 | 28.7997 | 28.8427 | 18.9926 | |
| | 0.1975 | 3.0 | 11250 | 0.1510 | 28.9016 | 25.4349 | 28.8356 | 28.8809 | 18.989 | |
| | 0.1887 | 4.0 | 15000 | 0.1496 | 28.945 | 25.498 | 28.8801 | 28.9201 | 18.9938 | |
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|
| ### Framework versions |
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|
| - Transformers 4.36.0.dev0 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
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|
