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--- |
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language: |
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- mn |
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license: mit |
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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: xlm-roberta-base-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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# xlm-roberta-base-mongolian-ner |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1166 |
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- Precision: 0.9251 |
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- Recall: 0.9335 |
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- F1: 0.9293 |
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- Accuracy: 0.9787 |
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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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| 0.2013 | 1.0 | 477 | 0.0958 | 0.8951 | 0.9124 | 0.9037 | 0.9731 | |
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| 0.0846 | 2.0 | 954 | 0.0825 | 0.9155 | 0.9240 | 0.9197 | 0.9774 | |
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| 0.0622 | 3.0 | 1431 | 0.0844 | 0.9109 | 0.9235 | 0.9172 | 0.9766 | |
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| 0.0456 | 4.0 | 1908 | 0.0940 | 0.9174 | 0.9266 | 0.9220 | 0.9767 | |
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| 0.0347 | 5.0 | 2385 | 0.1015 | 0.9184 | 0.9284 | 0.9234 | 0.9770 | |
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| 0.0253 | 6.0 | 2862 | 0.1117 | 0.9174 | 0.9254 | 0.9214 | 0.9764 | |
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| 0.0203 | 7.0 | 3339 | 0.1147 | 0.9225 | 0.9310 | 0.9267 | 0.9780 | |
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| 0.0152 | 8.0 | 3816 | 0.1129 | 0.9229 | 0.9316 | 0.9272 | 0.9779 | |
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| 0.0129 | 9.0 | 4293 | 0.1150 | 0.9245 | 0.9324 | 0.9285 | 0.9784 | |
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| 0.0102 | 10.0 | 4770 | 0.1166 | 0.9251 | 0.9335 | 0.9293 | 0.9787 | |
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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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