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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: 0.1205 |
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- Precision: 0.9307 |
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- Recall: 0.9389 |
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- F1: 0.9348 |
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- Accuracy: 0.9816 |
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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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- lr_scheduler_warmup_steps: 500 |
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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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| 0.3889 | 1.0 | 477 | 0.0832 | 0.8808 | 0.8987 | 0.8897 | 0.9743 | |
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| 0.0736 | 2.0 | 954 | 0.0703 | 0.9170 | 0.9226 | 0.9198 | 0.9796 | |
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| 0.0361 | 3.0 | 1431 | 0.0784 | 0.9227 | 0.9321 | 0.9274 | 0.9801 | |
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| 0.0216 | 4.0 | 1908 | 0.0863 | 0.9235 | 0.9328 | 0.9281 | 0.9801 | |
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| 0.0116 | 5.0 | 2385 | 0.0977 | 0.9292 | 0.9371 | 0.9332 | 0.9809 | |
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| 0.007 | 6.0 | 2862 | 0.1071 | 0.9270 | 0.9356 | 0.9313 | 0.9808 | |
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| 0.0046 | 7.0 | 3339 | 0.1123 | 0.9322 | 0.9378 | 0.9350 | 0.9818 | |
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| 0.0029 | 8.0 | 3816 | 0.1179 | 0.9310 | 0.9371 | 0.9340 | 0.9814 | |
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| 0.0021 | 9.0 | 4293 | 0.1187 | 0.9293 | 0.9375 | 0.9334 | 0.9812 | |
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| 0.0013 | 10.0 | 4770 | 0.1205 | 0.9307 | 0.9389 | 0.9348 | 0.9816 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.15.2 |
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