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--- |
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language: |
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- mn |
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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: mongolian-roberta-base |
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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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# mongolian-roberta-base |
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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: 0.1308 |
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- Precision: 0.9243 |
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- Recall: 0.9322 |
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- F1: 0.9283 |
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- Accuracy: 0.9799 |
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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: 9 |
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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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| 0.1632 | 1.0 | 477 | 0.0908 | 0.8293 | 0.8817 | 0.8547 | 0.9682 | |
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| 0.0607 | 2.0 | 954 | 0.0920 | 0.8506 | 0.8898 | 0.8698 | 0.9712 | |
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| 0.0331 | 3.0 | 1431 | 0.0975 | 0.9192 | 0.9267 | 0.9229 | 0.9779 | |
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| 0.0148 | 4.0 | 1908 | 0.1024 | 0.9179 | 0.9294 | 0.9236 | 0.9786 | |
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| 0.0087 | 5.0 | 2385 | 0.1091 | 0.9196 | 0.9296 | 0.9246 | 0.9796 | |
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| 0.0052 | 6.0 | 2862 | 0.1222 | 0.9240 | 0.9323 | 0.9281 | 0.9794 | |
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| 0.0033 | 7.0 | 3339 | 0.1233 | 0.9214 | 0.9317 | 0.9265 | 0.9796 | |
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| 0.0024 | 8.0 | 3816 | 0.1310 | 0.9250 | 0.9315 | 0.9282 | 0.9797 | |
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| 0.0016 | 9.0 | 4293 | 0.1308 | 0.9243 | 0.9322 | 0.9283 | 0.9799 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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