| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - bleu |
| - rouge |
| model-index: |
| - name: t5-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. --> |
|
|
| # t5-small-codesearchnet-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: 0.0785 |
| - Bleu: 0.035 |
| - Rouge1: 0.6257 |
| - Rouge2: 0.6078 |
| - Avg Length: 16.9954 |
|
|
| ## 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 | 0.0801 | 0.0358 | 0.6174 | 0.6 | 17.1074 | |
| | 1.6066 | 2.0 | 750 | 0.0674 | 0.036 | 0.6249 | 0.6068 | 17.0262 | |
| | 0.0584 | 3.0 | 1125 | 0.0632 | 0.0351 | 0.6255 | 0.6075 | 16.9962 | |
| | 0.0484 | 4.0 | 1500 | 0.0605 | 0.0351 | 0.6251 | 0.6071 | 17.003 | |
| | 0.0484 | 5.0 | 1875 | 0.0596 | 0.035 | 0.6255 | 0.6075 | 17.0012 | |
| | 0.0418 | 6.0 | 2250 | 0.0602 | 0.035 | 0.6258 | 0.608 | 16.9958 | |
| | 0.0377 | 7.0 | 2625 | 0.0593 | 0.0351 | 0.6259 | 0.6079 | 17.0004 | |
| | 0.033 | 8.0 | 3000 | 0.0618 | 0.035 | 0.6257 | 0.6078 | 17.0032 | |
| | 0.033 | 9.0 | 3375 | 0.0637 | 0.035 | 0.6257 | 0.6078 | 16.998 | |
| | 0.028 | 10.0 | 3750 | 0.0645 | 0.035 | 0.6257 | 0.6079 | 16.9984 | |
| | 0.0255 | 11.0 | 4125 | 0.0650 | 0.035 | 0.6255 | 0.6078 | 17.0008 | |
| | 0.0226 | 12.0 | 4500 | 0.0748 | 0.035 | 0.6254 | 0.6076 | 16.9976 | |
| | 0.0226 | 13.0 | 4875 | 0.0714 | 0.035 | 0.6256 | 0.6079 | 16.9954 | |
| | 0.019 | 14.0 | 5250 | 0.0747 | 0.0349 | 0.6253 | 0.6077 | 16.994 | |
| | 0.0172 | 15.0 | 5625 | 0.0785 | 0.035 | 0.6257 | 0.6078 | 16.9954 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.28.1 |
| - Pytorch 2.0.0+cu118 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
| |
