| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/flan-t5-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| model-index: |
| - name: flanT5_Task2 |
| 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. --> |
|
|
| # flanT5_Task2 |
| |
| This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.1812 |
| - Accuracy: 0.7706 |
| - Precision: 0.7861 |
| - Recall: 0.7435 |
| - F1 score: 0.7642 |
| |
| ## 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: 0.0001 |
| - train_batch_size: 1 |
| - eval_batch_size: 1 |
| - seed: 42 |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 score | |
| |:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
| | 1.1443 | 0.4205 | 2500 | 1.6635 | 0.6718 | 0.7829 | 0.4753 | 0.5915 | |
| | 1.0447 | 0.8410 | 5000 | 0.5585 | 0.7282 | 0.8149 | 0.5906 | 0.6849 | |
| | 0.9057 | 1.2616 | 7500 | 0.9051 | 0.7318 | 0.7275 | 0.7412 | 0.7343 | |
| | 0.8348 | 1.6821 | 10000 | 0.6307 | 0.7659 | 0.8742 | 0.6212 | 0.7263 | |
| | 0.7331 | 2.1026 | 12500 | 0.9500 | 0.7612 | 0.7489 | 0.7859 | 0.7669 | |
| | 0.6167 | 2.5231 | 15000 | 1.1524 | 0.7788 | 0.7970 | 0.7482 | 0.7718 | |
| | 0.6209 | 2.9437 | 17500 | 1.1690 | 0.7635 | 0.7872 | 0.7224 | 0.7534 | |
| | 0.4411 | 3.3642 | 20000 | 1.7563 | 0.7847 | 0.8438 | 0.6988 | 0.7645 | |
| | 0.4196 | 3.7847 | 22500 | 1.7767 | 0.7412 | 0.7204 | 0.7882 | 0.7528 | |
| | 0.292 | 4.2052 | 25000 | 2.0410 | 0.7624 | 0.7648 | 0.7576 | 0.7612 | |
| | 0.1791 | 4.6257 | 27500 | 2.1812 | 0.7706 | 0.7861 | 0.7435 | 0.7642 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.48.3 |
| - Pytorch 2.3.0+cu121 |
| - Datasets 3.2.0 |
| - Tokenizers 0.21.0 |
| |
