| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| - bleu |
| model-index: |
| - name: Salesforce-codet5-small-CodeXGLUE-CONCODE-test |
| 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. --> |
|
|
| # Salesforce-codet5-small-CodeXGLUE-CONCODE-test |
|
|
| 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: 0.8508 |
| - Exact Match: 0.156 |
| - Rouge1: 0.5559 |
| - Rouge2: 0.3857 |
| - Rougel: 0.5378 |
| - Rougelsum: 0.5465 |
| - Bleu: 0.1246 |
|
|
| ## 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: 0.001 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.05 |
| - num_epochs: 1 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Exact Match | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | |
| |:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------:|:------:|:---------:|:------:| |
| | 1.3563 | 0.16 | 500 | 1.1652 | 0.1115 | 0.5098 | 0.3191 | 0.4915 | 0.4982 | 0.1088 | |
| | 0.9656 | 0.32 | 1000 | 1.0435 | 0.1245 | 0.5246 | 0.3444 | 0.5075 | 0.5145 | 0.1164 | |
| | 0.8627 | 0.48 | 1500 | 0.9851 | 0.121 | 0.5275 | 0.3420 | 0.5074 | 0.5154 | 0.1132 | |
| | 0.7718 | 0.64 | 2000 | 0.9288 | 0.1385 | 0.5334 | 0.3589 | 0.5174 | 0.5242 | 0.1206 | |
| | 0.7237 | 0.8 | 2500 | 0.8867 | 0.1495 | 0.5505 | 0.3762 | 0.5328 | 0.5406 | 0.1208 | |
| | 0.6812 | 0.96 | 3000 | 0.8508 | 0.156 | 0.5559 | 0.3857 | 0.5378 | 0.5465 | 0.1246 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.27.1 |
| - Pytorch 1.12.1+cu113 |
| - Datasets 2.10.1 |
| - Tokenizers 0.13.2 |
| |
