| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - bleu |
| - rouge |
| model-index: |
| - name: t5-small-codesearchnet-multilang-python-java |
| 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. --> |
|
|
| # t5-small-codesearchnet-multilang-python-java |
|
|
| This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7015 |
| - Bleu: 0.0045 |
| - Rouge1: 0.2194 |
| - Rouge2: 0.0741 |
| - Avg Length: 15.9976 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 10 |
| - total_train_batch_size: 80 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge1 | Rouge2 | Avg Length | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:----------:| |
| | No log | 1.0 | 375 | 0.9005 | 0.0013 | 0.1397 | 0.0334 | 16.3976 | |
| | 2.3568 | 2.0 | 750 | 0.8036 | 0.0023 | 0.1737 | 0.0526 | 15.8896 | |
| | 0.7576 | 3.0 | 1125 | 0.7584 | 0.0021 | 0.1856 | 0.0558 | 15.3102 | |
| | 0.6778 | 4.0 | 1500 | 0.7298 | 0.0024 | 0.1922 | 0.0597 | 15.3544 | |
| | 0.6778 | 5.0 | 1875 | 0.7114 | 0.0037 | 0.2114 | 0.0704 | 15.7588 | |
| | 0.6206 | 6.0 | 2250 | 0.6949 | 0.0039 | 0.2093 | 0.0729 | 15.8088 | |
| | 0.5856 | 7.0 | 2625 | 0.6927 | 0.0042 | 0.2143 | 0.0711 | 16.5838 | |
| | 0.5447 | 8.0 | 3000 | 0.6867 | 0.005 | 0.2151 | 0.0717 | 17.2174 | |
| | 0.5447 | 9.0 | 3375 | 0.6895 | 0.0043 | 0.2179 | 0.0736 | 16.1068 | |
| | 0.5117 | 10.0 | 3750 | 0.6876 | 0.0038 | 0.2229 | 0.0777 | 15.5094 | |
| | 0.4892 | 11.0 | 4125 | 0.6800 | 0.0047 | 0.2201 | 0.0783 | 16.6902 | |
| | 0.4629 | 12.0 | 4500 | 0.6903 | 0.0047 | 0.2203 | 0.0771 | 16.7658 | |
| | 0.4629 | 13.0 | 4875 | 0.6947 | 0.0056 | 0.227 | 0.0777 | 16.8108 | |
| | 0.4355 | 14.0 | 5250 | 0.6999 | 0.0027 | 0.2028 | 0.0715 | 15.6776 | |
| | 0.418 | 15.0 | 5625 | 0.7015 | 0.0045 | 0.2194 | 0.0741 | 15.9976 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.28.1 |
| - Pytorch 2.0.0+cu118 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
| |
