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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: 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: 0.1263
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- Precision: 0.9352
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- Recall: 0.9416
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- F1: 0.9384
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- Accuracy: 0.9817
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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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| 0.161 | 1.0 | 477 | 0.0722 | 0.9132 | 0.9248 | 0.9190 | 0.9786 |
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| 0.052 | 2.0 | 954 | 0.0732 | 0.9211 | 0.9353 | 0.9282 | 0.9797 |
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| 0.028 | 3.0 | 1431 | 0.0802 | 0.9280 | 0.9354 | 0.9317 | 0.9804 |
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| 0.015 | 4.0 | 1908 | 0.0954 | 0.9190 | 0.9324 | 0.9257 | 0.9791 |
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| 0.0101 | 5.0 | 2385 | 0.0978 | 0.9312 | 0.9385 | 0.9348 | 0.9809 |
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| 0.0055 | 6.0 | 2862 | 0.1072 | 0.9315 | 0.9392 | 0.9353 | 0.9810 |
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| 0.0035 | 7.0 | 3339 | 0.1165 | 0.9313 | 0.9392 | 0.9352 | 0.9807 |
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| 0.0026 | 8.0 | 3816 | 0.1223 | 0.9338 | 0.9403 | 0.9371 | 0.9812 |
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| 0.002 | 9.0 | 4293 | 0.1234 | 0.9341 | 0.9398 | 0.9369 | 0.9813 |
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| 0.0009 | 10.0 | 4770 | 0.1263 | 0.9352 | 0.9416 | 0.9384 | 0.9817 |
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### Framework versions
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- Transformers 4.28.1
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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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