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update model card README.md
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README.md
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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: roberta-base-ner-demo
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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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# roberta-base-ner-demo
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This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1444
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- Precision: 0.9066
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- Recall: 0.9148
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- F1: 0.9107
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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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| 0.1657 | 1.0 | 477 | 0.0976 | 0.8844 | 0.8947 | 0.8895 | 0.9692 |
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| 0.0631 | 2.0 | 954 | 0.0917 | 0.8871 | 0.9084 | 0.8976 | 0.9709 |
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| 0.0387 | 3.0 | 1431 | 0.1079 | 0.8978 | 0.9099 | 0.9038 | 0.9714 |
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| 0.0272 | 4.0 | 1908 | 0.1198 | 0.8993 | 0.9119 | 0.9056 | 0.9716 |
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| 0.017 | 5.0 | 2385 | 0.1235 | 0.9038 | 0.9108 | 0.9073 | 0.9783 |
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| 0.007 | 6.0 | 2862 | 0.1272 | 0.9085 | 0.9151 | 0.9118 | 0.9795 |
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| 0.0038 | 7.0 | 3339 | 0.1295 | 0.9064 | 0.9172 | 0.9118 | 0.9796 |
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| 0.0029 | 8.0 | 3816 | 0.1368 | 0.9045 | 0.9167 | 0.9106 | 0.9795 |
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| 0.0019 | 9.0 | 4293 | 0.1425 | 0.9076 | 0.9173 | 0.9124 | 0.9796 |
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| 0.0015 | 10.0 | 4770 | 0.1444 | 0.9066 | 0.9148 | 0.9107 | 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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