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klue-roberta-small-ner-finetuned
This model is a fine-tuned version of klue/roberta-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0354
- Precision: 0.8583
- Recall: 0.9039
- F1: 0.8805
- Accuracy: 0.9918
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 437 | 0.0663 | 0.6610 | 0.7288 | 0.6933 | 0.9808 |
0.182 | 2.0 | 874 | 0.0484 | 0.7019 | 0.7769 | 0.7375 | 0.9831 |
0.0488 | 3.0 | 1311 | 0.0336 | 0.8109 | 0.8429 | 0.8266 | 0.9907 |
0.0289 | 4.0 | 1748 | 0.0329 | 0.8103 | 0.8824 | 0.8448 | 0.9904 |
0.0219 | 5.0 | 2185 | 0.0363 | 0.8270 | 0.8845 | 0.8548 | 0.9907 |
0.0161 | 6.0 | 2622 | 0.0354 | 0.8583 | 0.9039 | 0.8805 | 0.9918 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3