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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: canine-s-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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# canine-s-mongolian-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: 0.3588
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- Precision: 0.5262
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- Recall: 0.5383
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- F1: 0.5322
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- Accuracy: 0.9073
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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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| 0.6287 | 1.0 | 477 | 0.5233 | 0.2908 | 0.1814 | 0.2234 | 0.8471 |
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| 0.4898 | 2.0 | 954 | 0.4404 | 0.3727 | 0.2993 | 0.3320 | 0.8677 |
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| 0.403 | 3.0 | 1431 | 0.3939 | 0.4498 | 0.3922 | 0.4191 | 0.8832 |
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| 0.3416 | 4.0 | 1908 | 0.3720 | 0.4734 | 0.4606 | 0.4669 | 0.8934 |
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| 0.2986 | 5.0 | 2385 | 0.3640 | 0.5093 | 0.4745 | 0.4913 | 0.8987 |
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| 0.2693 | 6.0 | 2862 | 0.3599 | 0.5126 | 0.5039 | 0.5082 | 0.9012 |
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| 0.243 | 7.0 | 3339 | 0.3534 | 0.5009 | 0.5241 | 0.5123 | 0.9026 |
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| 0.2262 | 8.0 | 3816 | 0.3582 | 0.5103 | 0.5315 | 0.5207 | 0.9048 |
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| 0.213 | 9.0 | 4293 | 0.3534 | 0.5195 | 0.5327 | 0.5260 | 0.9059 |
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| 0.2064 | 10.0 | 4770 | 0.3588 | 0.5262 | 0.5383 | 0.5322 | 0.9073 |
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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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