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--- |
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language: |
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- mn |
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base_model: bayartsogt/mongolian-roberta-base |
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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: 0.1231 |
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- Precision: 0.9199 |
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- Recall: 0.9222 |
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- F1: 0.9210 |
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- Accuracy: 0.9811 |
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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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| 0.1804 | 1.0 | 477 | 0.0823 | 0.8069 | 0.8646 | 0.8347 | 0.9712 | |
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| 0.0667 | 2.0 | 954 | 0.0779 | 0.8221 | 0.8807 | 0.8504 | 0.9738 | |
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| 0.0392 | 3.0 | 1431 | 0.0953 | 0.8444 | 0.8853 | 0.8644 | 0.9744 | |
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| 0.0205 | 4.0 | 1908 | 0.0942 | 0.9201 | 0.9148 | 0.9175 | 0.9802 | |
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| 0.0113 | 5.0 | 2385 | 0.1011 | 0.9168 | 0.9213 | 0.9191 | 0.9812 | |
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| 0.0093 | 6.0 | 2862 | 0.1053 | 0.9087 | 0.9183 | 0.9135 | 0.9805 | |
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| 0.007 | 7.0 | 3339 | 0.1162 | 0.9211 | 0.9213 | 0.9212 | 0.9815 | |
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| 0.0037 | 8.0 | 3816 | 0.1230 | 0.9167 | 0.9202 | 0.9185 | 0.9806 | |
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| 0.0025 | 9.0 | 4293 | 0.1215 | 0.9198 | 0.9229 | 0.9213 | 0.9813 | |
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| 0.0022 | 10.0 | 4770 | 0.1231 | 0.9199 | 0.9222 | 0.9210 | 0.9811 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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