| --- |
| language: |
| - mn |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: testing_mongolian-roberta_base |
| 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. --> |
|
|
| # testing_mongolian-roberta_base |
|
|
| This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1244 |
| - Precision: 0.9311 |
| - Recall: 0.9399 |
| - F1: 0.9355 |
| - Accuracy: 0.9821 |
|
|
| ## 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: 16 |
| - 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.1683 | 1.0 | 477 | 0.0805 | 0.8377 | 0.8921 | 0.8640 | 0.9730 | |
| | 0.0545 | 2.0 | 954 | 0.0739 | 0.9205 | 0.9334 | 0.9269 | 0.9806 | |
| | 0.0292 | 3.0 | 1431 | 0.0778 | 0.9270 | 0.9354 | 0.9312 | 0.9817 | |
| | 0.0164 | 4.0 | 1908 | 0.0884 | 0.9290 | 0.9360 | 0.9325 | 0.9820 | |
| | 0.008 | 5.0 | 2385 | 0.1025 | 0.9247 | 0.9365 | 0.9306 | 0.9811 | |
| | 0.0057 | 6.0 | 2862 | 0.1093 | 0.9294 | 0.9369 | 0.9331 | 0.9815 | |
| | 0.0037 | 7.0 | 3339 | 0.1173 | 0.9336 | 0.9412 | 0.9374 | 0.9822 | |
| | 0.0026 | 8.0 | 3816 | 0.1217 | 0.9281 | 0.9374 | 0.9327 | 0.9817 | |
| | 0.0016 | 9.0 | 4293 | 0.1225 | 0.9334 | 0.9399 | 0.9366 | 0.9821 | |
| | 0.0012 | 10.0 | 4770 | 0.1244 | 0.9311 | 0.9399 | 0.9355 | 0.9821 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.28.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
|
|
