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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: 0.1428 |
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- Precision: 0.9085 |
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- Recall: 0.9203 |
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- F1: 0.9143 |
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- Accuracy: 0.9762 |
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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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| 0.1768 | 1.0 | 477 | 0.0930 | 0.8660 | 0.8939 | 0.8797 | 0.9704 | |
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| 0.0856 | 2.0 | 954 | 0.0879 | 0.8849 | 0.9082 | 0.8964 | 0.9736 | |
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| 0.0583 | 3.0 | 1431 | 0.0879 | 0.8905 | 0.9111 | 0.9007 | 0.9749 | |
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| 0.0404 | 4.0 | 1908 | 0.1053 | 0.8945 | 0.9136 | 0.9040 | 0.9731 | |
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| 0.0288 | 5.0 | 2385 | 0.1096 | 0.9044 | 0.9144 | 0.9094 | 0.9755 | |
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| 0.0196 | 6.0 | 2862 | 0.1237 | 0.9045 | 0.9176 | 0.9110 | 0.9754 | |
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| 0.014 | 7.0 | 3339 | 0.1289 | 0.9066 | 0.9187 | 0.9126 | 0.9757 | |
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| 0.0099 | 8.0 | 3816 | 0.1342 | 0.9057 | 0.9196 | 0.9126 | 0.9760 | |
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| 0.0065 | 9.0 | 4293 | 0.1396 | 0.9095 | 0.9195 | 0.9145 | 0.9761 | |
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| 0.005 | 10.0 | 4770 | 0.1428 | 0.9085 | 0.9203 | 0.9143 | 0.9762 | |
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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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