| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - bleu |
| | - rouge |
| | model-index: |
| | - name: t5-small-codesearchnet-multilang-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. --> |
| |
|
| | # t5-small-codesearchnet-multilang-python |
| |
|
| | 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: 0.0682 |
| | - Bleu: 0.0401 |
| | - Rouge1: 0.6333 |
| | - Rouge2: 0.6147 |
| | - Avg Length: 16.9514 |
| |
|
| | ## 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 | 0.0717 | 0.0407 | 0.6244 | 0.6063 | 17.046 | |
| | | 1.6287 | 2.0 | 750 | 0.0589 | 0.041 | 0.6321 | 0.6136 | 16.9924 | |
| | | 0.0592 | 3.0 | 1125 | 0.0551 | 0.0402 | 0.6334 | 0.6152 | 16.971 | |
| | | 0.0511 | 4.0 | 1500 | 0.0542 | 0.0402 | 0.6336 | 0.6155 | 16.9718 | |
| | | 0.0511 | 5.0 | 1875 | 0.0529 | 0.0401 | 0.6343 | 0.6161 | 16.961 | |
| | | 0.0441 | 6.0 | 2250 | 0.0531 | 0.0402 | 0.6341 | 0.6158 | 16.9626 | |
| | | 0.0399 | 7.0 | 2625 | 0.0521 | 0.0402 | 0.6337 | 0.6154 | 16.97 | |
| | | 0.0351 | 8.0 | 3000 | 0.0547 | 0.0401 | 0.6341 | 0.6159 | 16.964 | |
| | | 0.0351 | 9.0 | 3375 | 0.0545 | 0.0402 | 0.635 | 0.6167 | 16.962 | |
| | | 0.0301 | 10.0 | 3750 | 0.0557 | 0.0402 | 0.6342 | 0.6159 | 16.9646 | |
| | | 0.027 | 11.0 | 4125 | 0.0569 | 0.0402 | 0.6342 | 0.6157 | 16.9622 | |
| | | 0.0239 | 12.0 | 4500 | 0.0606 | 0.0401 | 0.6342 | 0.6158 | 16.9564 | |
| | | 0.0239 | 13.0 | 4875 | 0.0616 | 0.0401 | 0.6343 | 0.6163 | 16.963 | |
| | | 0.02 | 14.0 | 5250 | 0.0672 | 0.0401 | 0.6336 | 0.6154 | 16.9648 | |
| | | 0.0185 | 15.0 | 5625 | 0.0682 | 0.0401 | 0.6333 | 0.6147 | 16.9514 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.28.1 |
| | - Pytorch 2.0.0+cu118 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.13.3 |
| | |
