bert-finetuned-ner / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: bert-finetuned-ner
    results: []

bert-finetuned-ner

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.9992

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: 100

Training results

Training Loss Epoch Step Validation Loss
No log 1.0 67 8.8964
No log 2.0 134 8.8550
No log 3.0 201 8.7423
No log 4.0 268 8.5343
No log 5.0 335 8.3243
No log 6.0 402 8.1392
No log 7.0 469 7.9616
8.6114 8.0 536 7.7924
8.6114 9.0 603 7.6305
8.6114 10.0 670 7.4707
8.6114 11.0 737 7.3065
8.6114 12.0 804 7.1600
8.6114 13.0 871 7.0228
8.6114 14.0 938 6.8804
7.5409 15.0 1005 6.7334
7.5409 16.0 1072 6.6021
7.5409 17.0 1139 6.4789
7.5409 18.0 1206 6.3473
7.5409 19.0 1273 6.2252
7.5409 20.0 1340 6.1058
7.5409 21.0 1407 5.9892
7.5409 22.0 1474 5.8674
6.6051 23.0 1541 5.7496
6.6051 24.0 1608 5.6393
6.6051 25.0 1675 5.5244
6.6051 26.0 1742 5.4279
6.6051 27.0 1809 5.3221
6.6051 28.0 1876 5.2126
6.6051 29.0 1943 5.1221
5.8003 30.0 2010 5.0178
5.8003 31.0 2077 4.9183
5.8003 32.0 2144 4.8303
5.8003 33.0 2211 4.7328
5.8003 34.0 2278 4.6467
5.8003 35.0 2345 4.5548
5.8003 36.0 2412 4.4697
5.8003 37.0 2479 4.3860
5.0905 38.0 2546 4.2980
5.0905 39.0 2613 4.2172
5.0905 40.0 2680 4.1351
5.0905 41.0 2747 4.0635
5.0905 42.0 2814 3.9834
5.0905 43.0 2881 3.9091
5.0905 44.0 2948 3.8376
4.481 45.0 3015 3.7662
4.481 46.0 3082 3.7011
4.481 47.0 3149 3.6335
4.481 48.0 3216 3.5671
4.481 49.0 3283 3.5011
4.481 50.0 3350 3.4388
4.481 51.0 3417 3.3746
4.481 52.0 3484 3.3151
3.9521 53.0 3551 3.2551
3.9521 54.0 3618 3.1943
3.9521 55.0 3685 3.1410
3.9521 56.0 3752 3.0885
3.9521 57.0 3819 3.0384
3.9521 58.0 3886 2.9890
3.9521 59.0 3953 2.9376
3.5177 60.0 4020 2.8906
3.5177 61.0 4087 2.8406
3.5177 62.0 4154 2.7951
3.5177 63.0 4221 2.7590
3.5177 64.0 4288 2.7136
3.5177 65.0 4355 2.6725
3.5177 66.0 4422 2.6343
3.5177 67.0 4489 2.5941
3.1601 68.0 4556 2.5563
3.1601 69.0 4623 2.5241
3.1601 70.0 4690 2.4894
3.1601 71.0 4757 2.4552
3.1601 72.0 4824 2.4227
3.1601 73.0 4891 2.3942
3.1601 74.0 4958 2.3678
2.8815 75.0 5025 2.3362
2.8815 76.0 5092 2.3100
2.8815 77.0 5159 2.2851
2.8815 78.0 5226 2.2570
2.8815 79.0 5293 2.2346
2.8815 80.0 5360 2.2155
2.8815 81.0 5427 2.1933
2.8815 82.0 5494 2.1714
2.6556 83.0 5561 2.1551
2.6556 84.0 5628 2.1381
2.6556 85.0 5695 2.1203
2.6556 86.0 5762 2.1049
2.6556 87.0 5829 2.0899
2.6556 88.0 5896 2.0796
2.6556 89.0 5963 2.0649
2.5131 90.0 6030 2.0534
2.5131 91.0 6097 2.0443
2.5131 92.0 6164 2.0360
2.5131 93.0 6231 2.0258
2.5131 94.0 6298 2.0190
2.5131 95.0 6365 2.0111
2.5131 96.0 6432 2.0100
2.5131 97.0 6499 2.0040
2.4077 98.0 6566 2.0005
2.4077 99.0 6633 1.9997
2.4077 100.0 6700 1.9992

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu116
  • Datasets 2.10.1
  • Tokenizers 0.13.2