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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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