roberta-base-ner-demo
This model is a fine-tuned version of bayartsogt/mongolian-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1227
- Precision: 0.9299
- Recall: 0.9375
- F1: 0.9337
- Accuracy: 0.9818
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.1679 | 1.0 | 477 | 0.0860 | 0.8327 | 0.8878 | 0.8594 | 0.9716 |
0.0622 | 2.0 | 954 | 0.0739 | 0.9227 | 0.9300 | 0.9264 | 0.9807 |
0.029 | 3.0 | 1431 | 0.0770 | 0.9241 | 0.9347 | 0.9294 | 0.9820 |
0.0159 | 4.0 | 1908 | 0.0877 | 0.9327 | 0.9397 | 0.9362 | 0.9824 |
0.0091 | 5.0 | 2385 | 0.1074 | 0.9253 | 0.9324 | 0.9288 | 0.9805 |
0.0054 | 6.0 | 2862 | 0.1114 | 0.9277 | 0.9363 | 0.9320 | 0.9812 |
0.0042 | 7.0 | 3339 | 0.1137 | 0.9289 | 0.9359 | 0.9324 | 0.9815 |
0.0026 | 8.0 | 3816 | 0.1203 | 0.9301 | 0.9368 | 0.9334 | 0.9819 |
0.0021 | 9.0 | 4293 | 0.1222 | 0.9291 | 0.9373 | 0.9332 | 0.9818 |
0.0014 | 10.0 | 4770 | 0.1227 | 0.9299 | 0.9375 | 0.9337 | 0.9818 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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