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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-davlan-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-davlan-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: 0.1166
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- Precision: 0.9265
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- Recall: 0.9340
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- F1: 0.9303
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- Accuracy: 0.9798
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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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| 0.159 | 1.0 | 477 | 0.0792 | 0.9061 | 0.9167 | 0.9114 | 0.9764 |
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| 0.0754 | 2.0 | 954 | 0.0721 | 0.9142 | 0.9257 | 0.9199 | 0.9783 |
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| 0.0515 | 3.0 | 1431 | 0.0803 | 0.9189 | 0.9317 | 0.9253 | 0.9787 |
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| 0.0374 | 4.0 | 1908 | 0.0758 | 0.9295 | 0.9353 | 0.9324 | 0.9804 |
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| 0.0279 | 5.0 | 2385 | 0.0912 | 0.9267 | 0.9340 | 0.9304 | 0.9796 |
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| 0.0214 | 6.0 | 2862 | 0.0993 | 0.9256 | 0.9325 | 0.9290 | 0.9796 |
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| 0.0159 | 7.0 | 3339 | 0.1078 | 0.9237 | 0.9327 | 0.9282 | 0.9789 |
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| 0.0122 | 8.0 | 3816 | 0.1108 | 0.9259 | 0.9347 | 0.9303 | 0.9797 |
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| 0.0102 | 9.0 | 4293 | 0.1147 | 0.9263 | 0.9348 | 0.9305 | 0.9797 |
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| 0.0077 | 10.0 | 4770 | 0.1166 | 0.9265 | 0.9340 | 0.9303 | 0.9798 |
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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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