| | --- |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: ALL_mt5-base_15_spider_15_wikiSQL_sch |
| | 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_spider_15_wikiSQL_sch |
| |
|
| | This model was trained from scratch on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.3607 |
| | - Rouge2 Precision: 0.6413 |
| | - Rouge2 Recall: 0.441 |
| | - Rouge2 Fmeasure: 0.4931 |
| |
|
| | ## 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: 19 |
| | - 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 | |
| | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | | 0.4669 | 1.0 | 912 | 0.7241 | 0.5334 | 0.3544 | 0.3979 | |
| | | 0.1563 | 2.0 | 1824 | 0.5172 | 0.585 | 0.3984 | 0.4457 | |
| | | 0.1078 | 3.0 | 2736 | 0.3991 | 0.5991 | 0.409 | 0.4573 | |
| | | 0.084 | 4.0 | 3648 | 0.3342 | 0.6145 | 0.4193 | 0.4694 | |
| | | 0.0683 | 5.0 | 4560 | 0.3480 | 0.6179 | 0.4245 | 0.4746 | |
| | | 0.0615 | 6.0 | 5472 | 0.3146 | 0.6236 | 0.4279 | 0.4785 | |
| | | 0.0527 | 7.0 | 6384 | 0.3342 | 0.6236 | 0.4266 | 0.4776 | |
| | | 0.0469 | 8.0 | 7296 | 0.3249 | 0.6313 | 0.4325 | 0.4844 | |
| | | 0.0411 | 9.0 | 8208 | 0.3386 | 0.6306 | 0.4305 | 0.4826 | |
| | | 0.0383 | 10.0 | 9120 | 0.3410 | 0.6356 | 0.4375 | 0.4889 | |
| | | 0.0346 | 11.0 | 10032 | 0.3445 | 0.6323 | 0.4353 | 0.4867 | |
| | | 0.0332 | 12.0 | 10944 | 0.3507 | 0.6391 | 0.4397 | 0.4915 | |
| | | 0.0316 | 13.0 | 11856 | 0.3574 | 0.6403 | 0.4407 | 0.4926 | |
| | | 0.0303 | 14.0 | 12768 | 0.3589 | 0.6394 | 0.4398 | 0.4917 | |
| | | 0.0301 | 15.0 | 13680 | 0.3607 | 0.6413 | 0.441 | 0.4931 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.38.2 |
| | - Pytorch 2.2.0 |
| | - Datasets 2.16.1 |
| | - Tokenizers 0.15.1 |
| |
|
