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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: mn-xlm-roberta-base-named-entity
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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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# mn-xlm-roberta-base-named-entity
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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: 0.1224
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- Precision: 0.9275
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- Recall: 0.9364
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- F1: 0.9319
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- Accuracy: 0.9783
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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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| 0.2015 | 1.0 | 477 | 0.0915 | 0.8830 | 0.9076 | 0.8951 | 0.9724 |
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| 0.0837 | 2.0 | 954 | 0.0872 | 0.9089 | 0.9202 | 0.9145 | 0.9757 |
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| 0.0605 | 3.0 | 1431 | 0.0814 | 0.9134 | 0.9275 | 0.9204 | 0.9768 |
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| 0.0447 | 4.0 | 1908 | 0.0906 | 0.9219 | 0.9316 | 0.9267 | 0.9774 |
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| 0.0317 | 5.0 | 2385 | 0.0969 | 0.9229 | 0.9330 | 0.9280 | 0.9782 |
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| 0.0254 | 6.0 | 2862 | 0.1121 | 0.9216 | 0.9343 | 0.9279 | 0.9777 |
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| 0.0195 | 7.0 | 3339 | 0.1143 | 0.9298 | 0.9364 | 0.9331 | 0.9790 |
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| 0.0145 | 8.0 | 3816 | 0.1175 | 0.9229 | 0.9337 | 0.9283 | 0.9773 |
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| 0.0114 | 9.0 | 4293 | 0.1205 | 0.9233 | 0.9332 | 0.9282 | 0.9774 |
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| 0.0091 | 10.0 | 4770 | 0.1224 | 0.9275 | 0.9364 | 0.9319 | 0.9783 |
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### Framework versions
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- Transformers 4.28.0
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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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