| --- |
| license: apache-2.0 |
| base_model: Salesforce/codet5-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: codet5-small-ft-v4 |
| 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-ft-v4 |
|
|
| 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: 0.4863 |
| - Rouge1: 63.4219 |
| - Rouge2: 52.7146 |
| - Rougel: 62.9897 |
| - Rougelsum: 62.9844 |
| - Gen Len: 17.0139 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
| | 0.6991 | 1.0 | 3063 | 0.5867 | 61.4049 | 50.9933 | 60.9107 | 60.9129 | 17.1624 | |
| | 0.6145 | 2.0 | 6126 | 0.5189 | 62.4441 | 51.6001 | 62.0291 | 62.02 | 16.9374 | |
| | 0.5615 | 3.0 | 9189 | 0.4973 | 63.3391 | 52.7352 | 62.9065 | 62.9124 | 17.1099 | |
| | 0.5491 | 4.0 | 12252 | 0.4863 | 63.4219 | 52.7146 | 62.9897 | 62.9844 | 17.0139 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.38.0.dev0 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.0 |
|
|
