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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: mongolian-xlm-roberta-base-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-xlm-roberta-base-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: 0.1298 |
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- Precision: 0.9227 |
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- Recall: 0.9298 |
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- F1: 0.9262 |
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- Accuracy: 0.9770 |
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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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| 0.203 | 1.0 | 477 | 0.0961 | 0.8798 | 0.8986 | 0.8891 | 0.9708 | |
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| 0.0807 | 2.0 | 954 | 0.0912 | 0.8989 | 0.9173 | 0.9080 | 0.9734 | |
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| 0.0581 | 3.0 | 1431 | 0.0860 | 0.9087 | 0.9219 | 0.9152 | 0.9754 | |
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| 0.0433 | 4.0 | 1908 | 0.0954 | 0.9133 | 0.9255 | 0.9194 | 0.9763 | |
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| 0.0316 | 5.0 | 2385 | 0.1010 | 0.9183 | 0.9265 | 0.9224 | 0.9767 | |
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| 0.0234 | 6.0 | 2862 | 0.1077 | 0.9178 | 0.9286 | 0.9232 | 0.9770 | |
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| 0.0178 | 7.0 | 3339 | 0.1195 | 0.9223 | 0.9291 | 0.9257 | 0.9765 | |
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| 0.0142 | 8.0 | 3816 | 0.1263 | 0.9154 | 0.9280 | 0.9216 | 0.9767 | |
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| 0.0108 | 9.0 | 4293 | 0.1284 | 0.9204 | 0.9297 | 0.9250 | 0.9769 | |
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| 0.0088 | 10.0 | 4770 | 0.1298 | 0.9227 | 0.9298 | 0.9262 | 0.9770 | |
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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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