| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: ja_classification |
| 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. --> |
|
|
| # ja_classification |
| |
| This model was trained from scratch on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0798 |
| - Precision: 0.9847 |
| - Recall: 0.9847 |
| - F1: 0.9847 |
| - Accuracy: 0.9847 |
| |
| ## 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: 2e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0502 | 1.0 | 1125 | 0.0527 | 0.9814 | 0.9814 | 0.9814 | 0.9814 | |
| | 0.0419 | 2.0 | 2250 | 0.0495 | 0.9829 | 0.9829 | 0.9829 | 0.9829 | |
| | 0.0373 | 3.0 | 3375 | 0.0482 | 0.9837 | 0.9837 | 0.9837 | 0.9837 | |
| | 0.0257 | 4.0 | 4500 | 0.0517 | 0.9840 | 0.9840 | 0.9840 | 0.9840 | |
| | 0.0188 | 5.0 | 5625 | 0.0567 | 0.9844 | 0.9844 | 0.9844 | 0.9844 | |
| | 0.0219 | 6.0 | 6750 | 0.0621 | 0.9844 | 0.9844 | 0.9844 | 0.9844 | |
| | 0.0092 | 7.0 | 7875 | 0.0702 | 0.9845 | 0.9845 | 0.9845 | 0.9845 | |
| | 0.0059 | 8.0 | 9000 | 0.0739 | 0.9846 | 0.9846 | 0.9846 | 0.9846 | |
| | 0.0064 | 9.0 | 10125 | 0.0754 | 0.9845 | 0.9845 | 0.9845 | 0.9845 | |
| | 0.0059 | 10.0 | 11250 | 0.0798 | 0.9847 | 0.9847 | 0.9847 | 0.9847 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.28.1 |
| - Pytorch 1.13.0+cu117 |
| - Datasets 2.11.0 |
| - Tokenizers 0.11.6 |
| |
