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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: 0.1225
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- Precision: 0.9338
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- Recall: 0.9396
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- F1: 0.9367
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- Accuracy: 0.9818
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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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| 0.169 | 1.0 | 477 | 0.0846 | 0.8408 | 0.8852 | 0.8625 | 0.9713 |
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| 0.0586 | 2.0 | 954 | 0.0753 | 0.9263 | 0.9347 | 0.9305 | 0.9801 |
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| 0.0288 | 3.0 | 1431 | 0.0813 | 0.9262 | 0.9355 | 0.9308 | 0.9808 |
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| 0.0158 | 4.0 | 1908 | 0.0937 | 0.9318 | 0.9384 | 0.9351 | 0.9814 |
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| 0.0102 | 5.0 | 2385 | 0.0967 | 0.9331 | 0.9386 | 0.9358 | 0.9820 |
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| 0.006 | 6.0 | 2862 | 0.1072 | 0.9318 | 0.9382 | 0.9350 | 0.9817 |
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| 0.0046 | 7.0 | 3339 | 0.1139 | 0.9354 | 0.9408 | 0.9381 | 0.9821 |
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| 0.0025 | 8.0 | 3816 | 0.1185 | 0.9341 | 0.9402 | 0.9371 | 0.9820 |
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| 0.0021 | 9.0 | 4293 | 0.1217 | 0.9347 | 0.9397 | 0.9372 | 0.9819 |
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| 0.0011 | 10.0 | 4770 | 0.1225 | 0.9338 | 0.9396 | 0.9367 | 0.9818 |
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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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