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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: 0.1834 |
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- Precision: 0.6839 |
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- Recall: 0.7644 |
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- F1: 0.7219 |
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- Accuracy: 0.9459 |
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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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| 0.7672 | 1.0 | 20 | 0.5162 | 0.0825 | 0.0401 | 0.0540 | 0.8256 | |
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| 0.3886 | 2.0 | 40 | 0.3017 | 0.4778 | 0.5113 | 0.4939 | 0.9061 | |
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| 0.2163 | 3.0 | 60 | 0.2214 | 0.5543 | 0.6266 | 0.5882 | 0.9225 | |
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| 0.1199 | 4.0 | 80 | 0.1942 | 0.6346 | 0.7268 | 0.6776 | 0.9359 | |
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| 0.0742 | 5.0 | 100 | 0.1852 | 0.6396 | 0.7293 | 0.6815 | 0.9409 | |
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| 0.0555 | 6.0 | 120 | 0.1811 | 0.6943 | 0.7569 | 0.7242 | 0.9449 | |
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| 0.0407 | 7.0 | 140 | 0.1860 | 0.6804 | 0.7469 | 0.7121 | 0.9439 | |
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| 0.0346 | 8.0 | 160 | 0.1876 | 0.6952 | 0.7544 | 0.7236 | 0.9463 | |
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| 0.0302 | 9.0 | 180 | 0.1820 | 0.6868 | 0.7694 | 0.7258 | 0.9459 | |
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| 0.0289 | 10.0 | 200 | 0.1834 | 0.6839 | 0.7644 | 0.7219 | 0.9459 | |
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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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