| --- |
| language: |
| - mn |
| base_model: bayartsogt/mongolian-roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: roberta-base-ner-test |
| 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. --> |
|
|
| # roberta-base-ner-test |
|
|
| 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: 0.1051 |
| - Precision: 0.9154 |
| - Recall: 0.9295 |
| - F1: 0.9224 |
| - Accuracy: 0.9778 |
|
|
| ## 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: 128 |
| - eval_batch_size: 64 |
| - 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.4118 | 1.0 | 60 | 0.1230 | 0.7683 | 0.8344 | 0.8000 | 0.9584 | |
| | 0.1013 | 2.0 | 120 | 0.0996 | 0.8134 | 0.8677 | 0.8397 | 0.9649 | |
| | 0.0694 | 3.0 | 180 | 0.0961 | 0.8295 | 0.8783 | 0.8532 | 0.9676 | |
| | 0.0523 | 4.0 | 240 | 0.0861 | 0.9030 | 0.9198 | 0.9113 | 0.9762 | |
| | 0.0309 | 5.0 | 300 | 0.0847 | 0.9088 | 0.9239 | 0.9163 | 0.9775 | |
| | 0.0236 | 6.0 | 360 | 0.0950 | 0.9103 | 0.9253 | 0.9177 | 0.9772 | |
| | 0.019 | 7.0 | 420 | 0.0974 | 0.9158 | 0.9277 | 0.9217 | 0.9775 | |
| | 0.0153 | 8.0 | 480 | 0.0996 | 0.9139 | 0.9278 | 0.9208 | 0.9781 | |
| | 0.0122 | 9.0 | 540 | 0.1029 | 0.9143 | 0.9284 | 0.9213 | 0.9781 | |
| | 0.0104 | 10.0 | 600 | 0.1051 | 0.9154 | 0.9295 | 0.9224 | 0.9778 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.39.3 |
| - Pytorch 2.1.2 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
|
|
