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---
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language:
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- mn
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license: apache-2.0
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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: bert-base-multilingual-cased-mongolian-ner
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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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# bert-base-multilingual-cased-mongolian-ner
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1423
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- Precision: 0.9057
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- Recall: 0.9188
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- F1: 0.9122
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- Accuracy: 0.9753
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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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| 0.1726 | 1.0 | 477 | 0.1052 | 0.8531 | 0.8851 | 0.8688 | 0.9664 |
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| 0.0827 | 2.0 | 954 | 0.0975 | 0.8722 | 0.8987 | 0.8852 | 0.9699 |
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| 0.0571 | 3.0 | 1431 | 0.0926 | 0.8847 | 0.9054 | 0.8950 | 0.9719 |
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| 0.0376 | 4.0 | 1908 | 0.1052 | 0.8980 | 0.9119 | 0.9049 | 0.9727 |
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| 0.0271 | 5.0 | 2385 | 0.1137 | 0.9021 | 0.9158 | 0.9089 | 0.9746 |
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| 0.0182 | 6.0 | 2862 | 0.1304 | 0.8839 | 0.9106 | 0.8970 | 0.9712 |
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| 0.0145 | 7.0 | 3339 | 0.1274 | 0.9042 | 0.9187 | 0.9114 | 0.9748 |
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| 0.0097 | 8.0 | 3816 | 0.1375 | 0.9009 | 0.9169 | 0.9088 | 0.9739 |
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| 0.0063 | 9.0 | 4293 | 0.1421 | 0.9017 | 0.9171 | 0.9093 | 0.9748 |
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| 0.0049 | 10.0 | 4770 | 0.1423 | 0.9057 | 0.9188 | 0.9122 | 0.9753 |
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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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