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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-test |
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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-test |
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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: 0.1051 |
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- Precision: 0.9154 |
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- Recall: 0.9295 |
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- F1: 0.9224 |
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- Accuracy: 0.9778 |
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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: 128 |
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- eval_batch_size: 64 |
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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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| 0.4118 | 1.0 | 60 | 0.1230 | 0.7683 | 0.8344 | 0.8000 | 0.9584 | |
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| 0.1013 | 2.0 | 120 | 0.0996 | 0.8134 | 0.8677 | 0.8397 | 0.9649 | |
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| 0.0694 | 3.0 | 180 | 0.0961 | 0.8295 | 0.8783 | 0.8532 | 0.9676 | |
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| 0.0523 | 4.0 | 240 | 0.0861 | 0.9030 | 0.9198 | 0.9113 | 0.9762 | |
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| 0.0309 | 5.0 | 300 | 0.0847 | 0.9088 | 0.9239 | 0.9163 | 0.9775 | |
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| 0.0236 | 6.0 | 360 | 0.0950 | 0.9103 | 0.9253 | 0.9177 | 0.9772 | |
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| 0.019 | 7.0 | 420 | 0.0974 | 0.9158 | 0.9277 | 0.9217 | 0.9775 | |
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| 0.0153 | 8.0 | 480 | 0.0996 | 0.9139 | 0.9278 | 0.9208 | 0.9781 | |
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| 0.0122 | 9.0 | 540 | 0.1029 | 0.9143 | 0.9284 | 0.9213 | 0.9781 | |
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| 0.0104 | 10.0 | 600 | 0.1051 | 0.9154 | 0.9295 | 0.9224 | 0.9778 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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