| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - bleu |
| - rouge |
| model-index: |
| - name: bert-small-codesearchnet-python |
| 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. --> |
|
|
| # bert-small-codesearchnet-python |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0582 |
| - Bleu: 0.0347 |
| - Rouge1: 0.6428 |
| - Rouge2: 0.6252 |
| - Avg Length: 17.891 |
|
|
| ## 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 | 1.2151 | 0.0 | 0.0928 | 0.0083 | 10.684 | |
| | 1.9359 | 2.0 | 750 | 1.0291 | 0.0032 | 0.1752 | 0.0338 | 15.0624 | |
| | 0.9422 | 3.0 | 1125 | 0.9173 | 0.0061 | 0.2506 | 0.0711 | 17.9358 | |
| | 0.776 | 4.0 | 1500 | 0.8058 | 0.0088 | 0.3321 | 0.1409 | 18.3724 | |
| | 0.776 | 5.0 | 1875 | 0.6915 | 0.0123 | 0.4044 | 0.2267 | 18.564 | |
| | 0.6218 | 6.0 | 2250 | 0.5281 | 0.0193 | 0.5382 | 0.4097 | 17.5586 | |
| | 0.4363 | 7.0 | 2625 | 0.1897 | 0.0333 | 0.6311 | 0.6002 | 17.8768 | |
| | 0.1518 | 8.0 | 3000 | 0.0834 | 0.0346 | 0.6413 | 0.621 | 17.879 | |
| | 0.1518 | 9.0 | 3375 | 0.0587 | 0.0349 | 0.6439 | 0.6268 | 17.8886 | |
| | 0.0579 | 10.0 | 3750 | 0.0547 | 0.0348 | 0.6443 | 0.6276 | 17.885 | |
| | 0.0437 | 11.0 | 4125 | 0.0525 | 0.0348 | 0.6442 | 0.6278 | 17.8766 | |
| | 0.0365 | 12.0 | 4500 | 0.0550 | 0.0347 | 0.6436 | 0.6266 | 17.8876 | |
| | 0.0365 | 13.0 | 4875 | 0.0545 | 0.0347 | 0.6439 | 0.627 | 17.876 | |
| | 0.032 | 14.0 | 5250 | 0.0539 | 0.0347 | 0.644 | 0.6268 | 17.8822 | |
| | 0.0288 | 15.0 | 5625 | 0.0582 | 0.0347 | 0.6428 | 0.6252 | 17.891 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.28.1 |
| - Pytorch 2.0.0+cu118 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
| |
