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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: mn-roberta-base-demo-named-entity
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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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# mn-roberta-base-demo-named-entity
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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: 0.1354
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- Precision: 0.9239
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- Recall: 0.9322
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- F1: 0.9280
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- Accuracy: 0.9797
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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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| 0.1651 | 1.0 | 477 | 0.0835 | 0.8900 | 0.9145 | 0.9021 | 0.9745 |
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| 0.0535 | 2.0 | 954 | 0.0780 | 0.9047 | 0.9243 | 0.9144 | 0.9775 |
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| 0.0267 | 3.0 | 1431 | 0.0836 | 0.9184 | 0.9307 | 0.9245 | 0.9790 |
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| 0.0159 | 4.0 | 1908 | 0.0936 | 0.9224 | 0.9329 | 0.9276 | 0.9803 |
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| 0.0083 | 5.0 | 2385 | 0.1155 | 0.9224 | 0.9307 | 0.9265 | 0.9790 |
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| 0.0055 | 6.0 | 2862 | 0.1211 | 0.9222 | 0.9316 | 0.9268 | 0.9793 |
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| 0.0034 | 7.0 | 3339 | 0.1258 | 0.9199 | 0.9329 | 0.9263 | 0.9789 |
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| 0.0025 | 8.0 | 3816 | 0.1300 | 0.9249 | 0.9339 | 0.9294 | 0.9799 |
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| 0.002 | 9.0 | 4293 | 0.1352 | 0.9231 | 0.9313 | 0.9272 | 0.9795 |
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| 0.0018 | 10.0 | 4770 | 0.1354 | 0.9239 | 0.9322 | 0.9280 | 0.9797 |
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### Framework versions
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- Transformers 4.28.0
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- Pytorch 2.0.0+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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