| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - wikisql |
| model-index: |
| - name: EN_mt5-base_10_wikiSQL |
| 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. --> |
|
|
| # EN_mt5-base_10_wikiSQL |
| |
| This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wikisql dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0849 |
| - Rouge2 Precision: 0.864 |
| - Rouge2 Recall: 0.787 |
| - Rouge2 Fmeasure: 0.8178 |
| |
| ## 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: 21 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
| |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | 0.1677 | 1.0 | 3085 | 0.1224 | 0.8269 | 0.7506 | 0.7803 | |
| | 0.1287 | 2.0 | 6170 | 0.1028 | 0.8458 | 0.7673 | 0.7988 | |
| | 0.1086 | 3.0 | 9255 | 0.0959 | 0.8511 | 0.7727 | 0.8042 | |
| | 0.0965 | 4.0 | 12340 | 0.0900 | 0.8543 | 0.777 | 0.808 | |
| | 0.089 | 5.0 | 15425 | 0.0883 | 0.8575 | 0.7802 | 0.8111 | |
| | 0.0809 | 6.0 | 18510 | 0.0866 | 0.8606 | 0.7834 | 0.8143 | |
| | 0.0771 | 7.0 | 21595 | 0.0860 | 0.8625 | 0.7851 | 0.8161 | |
| | 0.0745 | 8.0 | 24680 | 0.0855 | 0.8633 | 0.7862 | 0.8171 | |
| | 0.0715 | 9.0 | 27765 | 0.0848 | 0.8641 | 0.7869 | 0.8178 | |
| | 0.0702 | 10.0 | 30850 | 0.0849 | 0.864 | 0.787 | 0.8178 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.26.1 |
| - Pytorch 2.0.1+cu117 |
| - Datasets 2.14.7.dev0 |
| - Tokenizers 0.13.3 |
| |
