| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - wikisql |
| | model-index: |
| | - name: t5-base-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. --> |
| |
|
| | # t5-base-wikisql |
| |
|
| | This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the wikisql dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0822 |
| | - Rouge2 Precision: 0.8552 |
| | - Rouge2 Recall: 0.7632 |
| | - Rouge2 Fmeasure: 0.7994 |
| |
|
| | ## 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: 100 |
| | - eval_batch_size: 100 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 15 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
| | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | | 0.3772 | 1.0 | 648 | 0.1194 | 0.8164 | 0.7266 | 0.7619 | |
| | | 0.1367 | 2.0 | 1296 | 0.1029 | 0.8322 | 0.7413 | 0.777 | |
| | | 0.1184 | 3.0 | 1944 | 0.0960 | 0.839 | 0.7477 | 0.7837 | |
| | | 0.0999 | 4.0 | 2592 | 0.0920 | 0.8447 | 0.7527 | 0.789 | |
| | | 0.0943 | 5.0 | 3240 | 0.0884 | 0.8473 | 0.7549 | 0.7913 | |
| | | 0.0886 | 6.0 | 3888 | 0.0868 | 0.8494 | 0.7568 | 0.7933 | |
| | | 0.0821 | 7.0 | 4536 | 0.0852 | 0.8516 | 0.7588 | 0.7954 | |
| | | 0.0792 | 8.0 | 5184 | 0.0845 | 0.8534 | 0.7605 | 0.7971 | |
| | | 0.0765 | 9.0 | 5832 | 0.0836 | 0.8539 | 0.7622 | 0.7983 | |
| | | 0.0741 | 10.0 | 6480 | 0.0825 | 0.8536 | 0.7616 | 0.7978 | |
| | | 0.0708 | 11.0 | 7128 | 0.0827 | 0.8548 | 0.7625 | 0.7989 | |
| | | 0.0693 | 12.0 | 7776 | 0.0822 | 0.8547 | 0.7629 | 0.799 | |
| | | 0.0686 | 13.0 | 8424 | 0.0822 | 0.855 | 0.7631 | 0.7993 | |
| | | 0.0672 | 14.0 | 9072 | 0.0823 | 0.8553 | 0.7633 | 0.7995 | |
| | | 0.0664 | 15.0 | 9720 | 0.0822 | 0.8552 | 0.7632 | 0.7994 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.26.1 |
| | - Pytorch 2.0.1 |
| | - Datasets 2.14.7 |
| | - Tokenizers 0.13.3 |
| |
|
