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ko_fin_ner_roberta_small_model

Token Classification
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ko_fin_ner_roberta_small_model / README.md
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metadata
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ko_fin_ner_roberta_small_model
    results: []

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