| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - bleu |
| - rouge |
| model-index: |
| - name: t5-small-codesearchnet-multilang-python-java-javascript-go |
| 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-javascript-go |
|
|
| 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: 0.5955 |
| - Bleu: 0.009 |
| - Rouge1: 0.2321 |
| - Rouge2: 0.0831 |
| - Avg Length: 16.6192 |
|
|
| ## 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 | 0.7349 | 0.0028 | 0.1562 | 0.0364 | 16.436 | |
| | 2.3117 | 2.0 | 750 | 0.6613 | 0.0066 | 0.1818 | 0.0531 | 16.824 | |
| | 0.6755 | 3.0 | 1125 | 0.6233 | 0.007 | 0.1957 | 0.0594 | 16.931 | |
| | 0.5998 | 4.0 | 1500 | 0.6023 | 0.0082 | 0.202 | 0.063 | 16.7154 | |
| | 0.5998 | 5.0 | 1875 | 0.5925 | 0.0096 | 0.2154 | 0.0703 | 16.5468 | |
| | 0.5511 | 6.0 | 2250 | 0.5728 | 0.0091 | 0.2213 | 0.0774 | 15.7216 | |
| | 0.5147 | 7.0 | 2625 | 0.5670 | 0.0111 | 0.2311 | 0.0815 | 16.6658 | |
| | 0.4861 | 8.0 | 3000 | 0.5628 | 0.0089 | 0.2217 | 0.077 | 17.038 | |
| | 0.4861 | 9.0 | 3375 | 0.5598 | 0.0103 | 0.2311 | 0.0825 | 16.362 | |
| | 0.4526 | 10.0 | 3750 | 0.5589 | 0.0083 | 0.232 | 0.086 | 15.4298 | |
| | 0.4329 | 11.0 | 4125 | 0.5649 | 0.0098 | 0.2349 | 0.0839 | 16.5468 | |
| | 0.4102 | 12.0 | 4500 | 0.5633 | 0.0098 | 0.2366 | 0.0867 | 16.4136 | |
| | 0.4102 | 13.0 | 4875 | 0.5841 | 0.01 | 0.2385 | 0.0869 | 15.9864 | |
| | 0.3841 | 14.0 | 5250 | 0.5777 | 0.0128 | 0.2437 | 0.0894 | 16.842 | |
| | 0.3673 | 15.0 | 5625 | 0.5955 | 0.009 | 0.2321 | 0.0831 | 16.6192 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.28.1 |
| - Pytorch 2.0.0+cu118 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
| |
