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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-facebook-xlm-v-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-facebook-xlm-v-base-ner
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This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1036
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- Precision: 0.9263
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- Recall: 0.9352
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- F1: 0.9307
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- Accuracy: 0.9792
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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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| 0.3409 | 1.0 | 477 | 0.1186 | 0.8832 | 0.9019 | 0.8924 | 0.9691 |
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| 0.0953 | 2.0 | 954 | 0.0883 | 0.9130 | 0.9235 | 0.9182 | 0.9770 |
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| 0.066 | 3.0 | 1431 | 0.0837 | 0.9166 | 0.9264 | 0.9215 | 0.9768 |
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| 0.0487 | 4.0 | 1908 | 0.0918 | 0.9244 | 0.9286 | 0.9265 | 0.9778 |
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| 0.0388 | 5.0 | 2385 | 0.0902 | 0.9218 | 0.9317 | 0.9268 | 0.9787 |
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| 0.0304 | 6.0 | 2862 | 0.0955 | 0.9202 | 0.9296 | 0.9249 | 0.9780 |
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| 0.0226 | 7.0 | 3339 | 0.0992 | 0.9226 | 0.9311 | 0.9269 | 0.9781 |
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| 0.0192 | 8.0 | 3816 | 0.0962 | 0.9256 | 0.9328 | 0.9292 | 0.9790 |
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| 0.0153 | 9.0 | 4293 | 0.1025 | 0.9243 | 0.9347 | 0.9295 | 0.9791 |
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| 0.0133 | 10.0 | 4770 | 0.1036 | 0.9263 | 0.9352 | 0.9307 | 0.9792 |
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### Framework versions
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- Transformers 4.29.2
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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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