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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: 0.1263 |
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- Precision: 0.9352 |
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- Recall: 0.9416 |
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- F1: 0.9384 |
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- Accuracy: 0.9817 |
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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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| 0.161 | 1.0 | 477 | 0.0722 | 0.9132 | 0.9248 | 0.9190 | 0.9786 | |
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| 0.052 | 2.0 | 954 | 0.0732 | 0.9211 | 0.9353 | 0.9282 | 0.9797 | |
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| 0.028 | 3.0 | 1431 | 0.0802 | 0.9280 | 0.9354 | 0.9317 | 0.9804 | |
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| 0.015 | 4.0 | 1908 | 0.0954 | 0.9190 | 0.9324 | 0.9257 | 0.9791 | |
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| 0.0101 | 5.0 | 2385 | 0.0978 | 0.9312 | 0.9385 | 0.9348 | 0.9809 | |
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| 0.0055 | 6.0 | 2862 | 0.1072 | 0.9315 | 0.9392 | 0.9353 | 0.9810 | |
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| 0.0035 | 7.0 | 3339 | 0.1165 | 0.9313 | 0.9392 | 0.9352 | 0.9807 | |
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| 0.0026 | 8.0 | 3816 | 0.1223 | 0.9338 | 0.9403 | 0.9371 | 0.9812 | |
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| 0.002 | 9.0 | 4293 | 0.1234 | 0.9341 | 0.9398 | 0.9369 | 0.9813 | |
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| 0.0009 | 10.0 | 4770 | 0.1263 | 0.9352 | 0.9416 | 0.9384 | 0.9817 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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