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--- |
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language: |
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- mn |
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base_model: bayartsogt/mongolian-roberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: roberta-base-ner-demo |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# roberta-base-ner-demo |
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This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1352 |
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- Precision: 0.9297 |
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- Recall: 0.9366 |
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- F1: 0.9331 |
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- Accuracy: 0.9801 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.1678 | 1.0 | 477 | 0.0929 | 0.8136 | 0.8806 | 0.8457 | 0.9679 | |
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| 0.0635 | 2.0 | 954 | 0.0894 | 0.8477 | 0.8933 | 0.8699 | 0.9708 | |
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| 0.0291 | 3.0 | 1431 | 0.0840 | 0.9262 | 0.9357 | 0.9309 | 0.9809 | |
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| 0.0163 | 4.0 | 1908 | 0.0928 | 0.9269 | 0.9357 | 0.9313 | 0.9805 | |
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| 0.0087 | 5.0 | 2385 | 0.1048 | 0.9259 | 0.9352 | 0.9305 | 0.9802 | |
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| 0.0059 | 6.0 | 2862 | 0.1179 | 0.9271 | 0.9339 | 0.9305 | 0.9794 | |
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| 0.0032 | 7.0 | 3339 | 0.1230 | 0.9278 | 0.9353 | 0.9316 | 0.9800 | |
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| 0.002 | 8.0 | 3816 | 0.1335 | 0.9285 | 0.9337 | 0.9311 | 0.9795 | |
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| 0.0016 | 9.0 | 4293 | 0.1341 | 0.9287 | 0.9358 | 0.9322 | 0.9799 | |
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| 0.0013 | 10.0 | 4770 | 0.1352 | 0.9297 | 0.9366 | 0.9331 | 0.9801 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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