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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: mongolian-bert-base-multilingual-cased-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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# mongolian-bert-base-multilingual-cased-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: 0.1399
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- Precision: 0.9072
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- Recall: 0.9189
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- F1: 0.9131
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- Accuracy: 0.9759
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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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| 0.1794 | 1.0 | 477 | 0.1089 | 0.8606 | 0.8871 | 0.8737 | 0.9685 |
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| 0.0859 | 2.0 | 954 | 0.0978 | 0.8734 | 0.8973 | 0.8852 | 0.9703 |
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| 0.0597 | 3.0 | 1431 | 0.0959 | 0.8970 | 0.9080 | 0.9025 | 0.9749 |
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| 0.042 | 4.0 | 1908 | 0.1032 | 0.9008 | 0.9167 | 0.9087 | 0.9751 |
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| 0.028 | 5.0 | 2385 | 0.1177 | 0.9011 | 0.9157 | 0.9083 | 0.9755 |
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| 0.02 | 6.0 | 2862 | 0.1239 | 0.9048 | 0.9150 | 0.9099 | 0.9749 |
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| 0.0143 | 7.0 | 3339 | 0.1289 | 0.9045 | 0.9168 | 0.9106 | 0.9749 |
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| 0.009 | 8.0 | 3816 | 0.1376 | 0.9037 | 0.9171 | 0.9103 | 0.9755 |
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| 0.0068 | 9.0 | 4293 | 0.1372 | 0.9067 | 0.9188 | 0.9127 | 0.9763 |
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| 0.0053 | 10.0 | 4770 | 0.1399 | 0.9072 | 0.9189 | 0.9131 | 0.9759 |
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### Framework versions
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- Transformers 4.29.2
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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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