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bert-base-cased-finetuned-ner

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bert-base-cased-finetuned-ner

This model is a fine-tuned version of google-bert/bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2379
  • Precision: 0.7741
  • Recall: 0.8457
  • F1: 0.8083
  • Accuracy: 0.9688

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: 40

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 77 0.3109 0.4607 0.3926 0.4239 0.9271
No log 2.0 154 0.2183 0.5270 0.6330 0.5752 0.9425
No log 3.0 231 0.1719 0.6398 0.7106 0.6734 0.9557
No log 4.0 308 0.1702 0.6518 0.7447 0.6951 0.9544
No log 5.0 385 0.1636 0.7006 0.7766 0.7366 0.9597
No log 6.0 462 0.1784 0.6795 0.8053 0.7371 0.9597
0.1868 7.0 539 0.1870 0.7821 0.7713 0.7766 0.9671
0.1868 8.0 616 0.1796 0.7259 0.8340 0.7762 0.9645
0.1868 9.0 693 0.1771 0.7896 0.8106 0.8000 0.9682
0.1868 10.0 770 0.1892 0.7364 0.8351 0.7827 0.9647
0.1868 11.0 847 0.1949 0.7627 0.8277 0.7939 0.9668
0.1868 12.0 924 0.1951 0.7734 0.8277 0.7996 0.9673
0.0131 13.0 1001 0.2009 0.7592 0.8319 0.7939 0.9666
0.0131 14.0 1078 0.2057 0.7623 0.8426 0.8004 0.9671
0.0131 15.0 1155 0.2067 0.7920 0.8266 0.8090 0.9687
0.0131 16.0 1232 0.2090 0.7301 0.8489 0.7850 0.9638
0.0131 17.0 1309 0.2103 0.7730 0.8330 0.8018 0.9673
0.0131 18.0 1386 0.2118 0.7823 0.8255 0.8033 0.9673
0.0131 19.0 1463 0.2202 0.7436 0.8606 0.7978 0.9654
0.0038 20.0 1540 0.2104 0.7796 0.8394 0.8084 0.9683
0.0038 21.0 1617 0.2168 0.7767 0.8362 0.8053 0.9682
0.0038 22.0 1694 0.2198 0.7713 0.8394 0.8039 0.9686
0.0038 23.0 1771 0.2210 0.7637 0.8457 0.8026 0.9678
0.0038 24.0 1848 0.2215 0.7700 0.8479 0.8071 0.9688
0.0038 25.0 1925 0.2222 0.7656 0.8479 0.8046 0.9684
0.0021 26.0 2002 0.2203 0.7756 0.8457 0.8092 0.9693
0.0021 27.0 2079 0.2249 0.7780 0.85 0.8124 0.9689
0.0021 28.0 2156 0.2315 0.7660 0.8532 0.8072 0.9678
0.0021 29.0 2233 0.2335 0.7740 0.8415 0.8063 0.9685
0.0021 30.0 2310 0.2340 0.7803 0.8351 0.8068 0.9687
0.0021 31.0 2387 0.2340 0.7747 0.8415 0.8067 0.9683
0.0021 32.0 2464 0.2364 0.7728 0.8468 0.8081 0.9683
0.001 33.0 2541 0.2419 0.7645 0.8532 0.8064 0.9678
0.001 34.0 2618 0.2378 0.7750 0.8426 0.8073 0.9683
0.001 35.0 2695 0.2398 0.7692 0.8543 0.8095 0.9683
0.001 36.0 2772 0.2371 0.7743 0.8468 0.8089 0.9688
0.001 37.0 2849 0.2374 0.7711 0.8457 0.8067 0.9686
0.001 38.0 2926 0.2380 0.7715 0.8479 0.8079 0.9686
0.0008 39.0 3003 0.2379 0.7733 0.8457 0.8079 0.9687
0.0008 40.0 3080 0.2379 0.7741 0.8457 0.8083 0.9688

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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