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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-xlm-roberta-large-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-xlm-roberta-large-ner
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1256
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- Precision: 0.9361
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- Recall: 0.9423
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- F1: 0.9392
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- Accuracy: 0.9824
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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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| 0.1837 | 1.0 | 477 | 0.0939 | 0.8524 | 0.8895 | 0.8705 | 0.9745 |
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| 0.0736 | 2.0 | 954 | 0.0731 | 0.9318 | 0.9370 | 0.9344 | 0.9809 |
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| 0.0525 | 3.0 | 1431 | 0.0724 | 0.9244 | 0.9311 | 0.9278 | 0.9795 |
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| 0.036 | 4.0 | 1908 | 0.0807 | 0.9312 | 0.9409 | 0.9361 | 0.9819 |
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| 0.0248 | 5.0 | 2385 | 0.0855 | 0.9314 | 0.9407 | 0.9360 | 0.9814 |
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| 0.0163 | 6.0 | 2862 | 0.1014 | 0.9327 | 0.9397 | 0.9362 | 0.9815 |
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| 0.0112 | 7.0 | 3339 | 0.0997 | 0.9354 | 0.9433 | 0.9393 | 0.9822 |
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| 0.0064 | 8.0 | 3816 | 0.1171 | 0.9384 | 0.9432 | 0.9408 | 0.9824 |
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| 0.0049 | 9.0 | 4293 | 0.1237 | 0.9355 | 0.9418 | 0.9387 | 0.9822 |
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| 0.0024 | 10.0 | 4770 | 0.1256 | 0.9361 | 0.9423 | 0.9392 | 0.9824 |
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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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