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---
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language:
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- mn
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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-hrl
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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-hrl
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This model is a fine-tuned version of [Davlan/xlm-roberta-base-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-base-ner-hrl) 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: 0.9303
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- Recall: 0.9375
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- F1: 0.9339
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- Accuracy: 0.9794
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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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| 0.1449 | 1.0 | 477 | 0.0884 | 0.8968 | 0.9156 | 0.9061 | 0.9730 |
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| 0.0737 | 2.0 | 954 | 0.0840 | 0.9205 | 0.9283 | 0.9244 | 0.9771 |
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| 0.0503 | 3.0 | 1431 | 0.0843 | 0.9229 | 0.9312 | 0.9270 | 0.9788 |
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| 0.0367 | 4.0 | 1908 | 0.0959 | 0.9232 | 0.9326 | 0.9279 | 0.9781 |
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| 0.0268 | 5.0 | 2385 | 0.0991 | 0.9297 | 0.9357 | 0.9327 | 0.9797 |
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| 0.02 | 6.0 | 2862 | 0.1067 | 0.9246 | 0.9316 | 0.9281 | 0.9783 |
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| 0.0149 | 7.0 | 3339 | 0.1147 | 0.9265 | 0.9345 | 0.9305 | 0.9786 |
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| 0.0115 | 8.0 | 3816 | 0.1193 | 0.9289 | 0.9362 | 0.9325 | 0.9795 |
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| 0.0095 | 9.0 | 4293 | 0.1208 | 0.9304 | 0.9369 | 0.9336 | 0.9794 |
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| 0.008 | 10.0 | 4770 | 0.1224 | 0.9303 | 0.9375 | 0.9339 | 0.9794 |
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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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