| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - wikisql |
| | model-index: |
| | - name: EN_mt5-base_15_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_15_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: 0.8692 |
| | - Rouge2 Recall: 0.7928 |
| | - Rouge2 Fmeasure: 0.8234 |
| | |
| | ## 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: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - 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 | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
| | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | | 0.1534 | 1.0 | 4049 | 0.1157 | 0.8319 | 0.756 | 0.7858 | |
| | | 0.1204 | 2.0 | 8098 | 0.0980 | 0.8469 | 0.7706 | 0.8011 | |
| | | 0.1006 | 3.0 | 12147 | 0.0926 | 0.855 | 0.7775 | 0.8086 | |
| | | 0.0892 | 4.0 | 16196 | 0.0881 | 0.8579 | 0.7811 | 0.8119 | |
| | | 0.0809 | 5.0 | 20245 | 0.0857 | 0.8605 | 0.7839 | 0.8145 | |
| | | 0.0725 | 6.0 | 24294 | 0.0849 | 0.8643 | 0.787 | 0.8181 | |
| | | 0.0672 | 7.0 | 28343 | 0.0841 | 0.8662 | 0.7889 | 0.8199 | |
| | | 0.0628 | 8.0 | 32392 | 0.0847 | 0.8657 | 0.7895 | 0.82 | |
| | | 0.0589 | 9.0 | 36441 | 0.0835 | 0.8676 | 0.7909 | 0.8216 | |
| | | 0.0565 | 10.0 | 40490 | 0.0839 | 0.8685 | 0.7914 | 0.8223 | |
| | | 0.0532 | 11.0 | 44539 | 0.0837 | 0.8689 | 0.7925 | 0.8231 | |
| | | 0.051 | 12.0 | 48588 | 0.0844 | 0.8692 | 0.7927 | 0.8233 | |
| | | 0.0504 | 13.0 | 52637 | 0.0848 | 0.869 | 0.7924 | 0.8231 | |
| | | 0.0485 | 14.0 | 56686 | 0.0848 | 0.869 | 0.7928 | 0.8233 | |
| | | 0.0479 | 15.0 | 60735 | 0.0849 | 0.8692 | 0.7928 | 0.8234 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.26.1 |
| | - Pytorch 2.0.1+cu117 |
| | - Datasets 2.14.7.dev0 |
| | - Tokenizers 0.13.3 |
| | |
