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ko_fin_ner_roberta_small_model
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: 0.2873
- Precision: 0.7436
- Recall: 0.8774
- F1: 0.8050
- Accuracy: 0.9374
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 25 | 1.0272 | 0.1215 | 0.1662 | 0.1404 | 0.7237 |
| No log | 2.0 | 50 | 0.7136 | 0.2360 | 0.4033 | 0.2978 | 0.7695 |
| No log | 3.0 | 75 | 0.5289 | 0.3422 | 0.5586 | 0.4244 | 0.8285 |
| No log | 4.0 | 100 | 0.4404 | 0.4184 | 0.6076 | 0.4956 | 0.8730 |
| No log | 5.0 | 125 | 0.3768 | 0.4124 | 0.6540 | 0.5058 | 0.8866 |
| No log | 6.0 | 150 | 0.3484 | 0.4758 | 0.6975 | 0.5657 | 0.8953 |
| No log | 7.0 | 175 | 0.3236 | 0.5477 | 0.7357 | 0.6279 | 0.9039 |
| No log | 8.0 | 200 | 0.3097 | 0.5702 | 0.7520 | 0.6486 | 0.9015 |
| No log | 9.0 | 225 | 0.3168 | 0.6167 | 0.7629 | 0.6821 | 0.9096 |
| No log | 10.0 | 250 | 0.2950 | 0.6176 | 0.8011 | 0.6975 | 0.9145 |
| No log | 11.0 | 275 | 0.2806 | 0.6674 | 0.8147 | 0.7337 | 0.9195 |
| No log | 12.0 | 300 | 0.2749 | 0.6853 | 0.8365 | 0.7534 | 0.9266 |
| No log | 13.0 | 325 | 0.2743 | 0.7002 | 0.8338 | 0.7612 | 0.9292 |
| No log | 14.0 | 350 | 0.2862 | 0.6774 | 0.8011 | 0.7341 | 0.9238 |
| No log | 15.0 | 375 | 0.2703 | 0.6879 | 0.8529 | 0.7616 | 0.9276 |
| No log | 16.0 | 400 | 0.2752 | 0.7036 | 0.8474 | 0.7689 | 0.9293 |
| No log | 17.0 | 425 | 0.2721 | 0.6998 | 0.8447 | 0.7654 | 0.9305 |
| No log | 18.0 | 450 | 0.2831 | 0.6979 | 0.8311 | 0.7587 | 0.9299 |
| No log | 19.0 | 475 | 0.2857 | 0.7252 | 0.8556 | 0.7850 | 0.9319 |
| 0.2786 | 20.0 | 500 | 0.2792 | 0.7260 | 0.8665 | 0.7901 | 0.9319 |
| 0.2786 | 21.0 | 525 | 0.2604 | 0.7355 | 0.8638 | 0.7945 | 0.9349 |
| 0.2786 | 22.0 | 550 | 0.2603 | 0.7092 | 0.8638 | 0.7789 | 0.9359 |
| 0.2786 | 23.0 | 575 | 0.3026 | 0.7227 | 0.8665 | 0.7881 | 0.9342 |
| 0.2786 | 24.0 | 600 | 0.2800 | 0.7431 | 0.8747 | 0.8035 | 0.9375 |
| 0.2786 | 25.0 | 625 | 0.2838 | 0.7283 | 0.8692 | 0.7925 | 0.9361 |
| 0.2786 | 26.0 | 650 | 0.2813 | 0.7339 | 0.8719 | 0.7970 | 0.9371 |
| 0.2786 | 27.0 | 675 | 0.2881 | 0.7407 | 0.8719 | 0.8010 | 0.9358 |
| 0.2786 | 28.0 | 700 | 0.2894 | 0.7379 | 0.8747 | 0.8005 | 0.9362 |
| 0.2786 | 29.0 | 725 | 0.2889 | 0.7483 | 0.8747 | 0.8065 | 0.9368 |
| 0.2786 | 30.0 | 750 | 0.2873 | 0.7436 | 0.8774 | 0.8050 | 0.9374 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
