| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: ALL_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. --> |
|
|
| # ALL_mt5-base_15_wikiSQL |
| |
| This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2022 |
| - Rouge2 Precision: 0.7669 |
| - Rouge2 Recall: 0.6952 |
| - Rouge2 Fmeasure: 0.7236 |
| |
| ## 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: 40 |
| - 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | 0.322 | 1.0 | 3239 | 0.2649 | 0.7077 | 0.6357 | 0.6638 | |
| | 0.2728 | 2.0 | 6478 | 0.2361 | 0.7294 | 0.657 | 0.6857 | |
| | 0.2353 | 3.0 | 9717 | 0.2220 | 0.7396 | 0.6677 | 0.6962 | |
| | 0.2192 | 4.0 | 12956 | 0.2159 | 0.7491 | 0.6752 | 0.7046 | |
| | 0.2044 | 5.0 | 16195 | 0.2106 | 0.7521 | 0.6797 | 0.7084 | |
| | 0.1916 | 6.0 | 19434 | 0.2076 | 0.7558 | 0.6841 | 0.7125 | |
| | 0.1815 | 7.0 | 22673 | 0.2059 | 0.759 | 0.6869 | 0.7155 | |
| | 0.1713 | 8.0 | 25912 | 0.2050 | 0.7612 | 0.6896 | 0.7179 | |
| | 0.1705 | 9.0 | 29151 | 0.2034 | 0.7644 | 0.6917 | 0.7206 | |
| | 0.1652 | 10.0 | 32390 | 0.2042 | 0.7649 | 0.6928 | 0.7214 | |
| | 0.16 | 11.0 | 35629 | 0.2026 | 0.7661 | 0.6938 | 0.7225 | |
| | 0.1534 | 12.0 | 38868 | 0.2022 | 0.7659 | 0.694 | 0.7225 | |
| | 0.1516 | 13.0 | 42107 | 0.2024 | 0.7671 | 0.695 | 0.7236 | |
| | 0.1517 | 14.0 | 45346 | 0.2024 | 0.7667 | 0.6951 | 0.7235 | |
| | 0.1503 | 15.0 | 48585 | 0.2022 | 0.7669 | 0.6952 | 0.7236 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.26.1 |
| - Pytorch 2.0.1+cu117 |
| - Datasets 2.14.7.dev0 |
| - Tokenizers 0.13.3 |
| |
