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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: 0.1372 |
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- Precision: 0.9235 |
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- Recall: 0.9342 |
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- F1: 0.9288 |
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- Accuracy: 0.9800 |
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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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| 0.1652 | 1.0 | 477 | 0.0832 | 0.8915 | 0.9136 | 0.9024 | 0.9762 | |
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| 0.0512 | 2.0 | 954 | 0.0828 | 0.9071 | 0.9244 | 0.9156 | 0.9778 | |
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| 0.0268 | 3.0 | 1431 | 0.0909 | 0.9179 | 0.9274 | 0.9226 | 0.9787 | |
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| 0.0146 | 4.0 | 1908 | 0.0975 | 0.9217 | 0.9322 | 0.9269 | 0.9798 | |
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| 0.008 | 5.0 | 2385 | 0.1127 | 0.9178 | 0.9313 | 0.9245 | 0.9793 | |
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| 0.0053 | 6.0 | 2862 | 0.1255 | 0.9207 | 0.9295 | 0.9251 | 0.9790 | |
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| 0.0034 | 7.0 | 3339 | 0.1292 | 0.9235 | 0.9335 | 0.9285 | 0.9797 | |
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| 0.0024 | 8.0 | 3816 | 0.1339 | 0.9186 | 0.9332 | 0.9258 | 0.9795 | |
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| 0.0015 | 9.0 | 4293 | 0.1359 | 0.9239 | 0.9343 | 0.9291 | 0.9800 | |
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| 0.0011 | 10.0 | 4770 | 0.1372 | 0.9235 | 0.9342 | 0.9288 | 0.9800 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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