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distilbert-base-uncased-finetuned-dob

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metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
model-index:
  - name: distilbert-base-uncased-finetuned-dob
    results: []

distilbert-base-uncased-finetuned-dob

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

  • Loss: 0.0001

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: 128
  • eval_batch_size: 128
  • 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
0.5564 1.0 10 0.3900
0.3348 2.0 20 0.1773
0.1584 3.0 30 0.0763
0.0758 4.0 40 0.0294
0.0322 5.0 50 0.0055
0.0075 6.0 60 0.0023
0.0035 7.0 70 0.0015
0.0021 8.0 80 0.0011
0.0013 9.0 90 0.0009
0.0011 10.0 100 0.0008
0.0009 11.0 110 0.0008
0.0009 12.0 120 0.0007
0.0008 13.0 130 0.0006
0.0008 14.0 140 0.0006
0.0007 15.0 150 0.0006
0.0007 16.0 160 0.0005
0.0006 17.0 170 0.0005
0.0006 18.0 180 0.0005
0.0005 19.0 190 0.0004
0.0005 20.0 200 0.0004
0.0005 21.0 210 0.0004
0.0005 22.0 220 0.0004
0.0005 23.0 230 0.0004
0.0004 24.0 240 0.0004
0.0004 25.0 250 0.0003
0.0004 26.0 260 0.0003
0.0004 27.0 270 0.0003
0.0004 28.0 280 0.0003
0.0003 29.0 290 0.0003
0.0003 30.0 300 0.0003
0.0003 31.0 310 0.0003
0.0004 32.0 320 0.0003
0.0003 33.0 330 0.0003
0.0003 34.0 340 0.0003
0.0003 35.0 350 0.0003
0.0003 36.0 360 0.0003
0.0003 37.0 370 0.0002
0.0003 38.0 380 0.0002
0.0003 39.0 390 0.0002
0.0003 40.0 400 0.0002
0.0003 41.0 410 0.0002
0.0003 42.0 420 0.0002
0.0002 43.0 430 0.0002
0.0002 44.0 440 0.0002
0.0002 45.0 450 0.0002
0.0002 46.0 460 0.0002
0.0002 47.0 470 0.0002
0.0002 48.0 480 0.0002
0.0002 49.0 490 0.0002
0.0002 50.0 500 0.0002
0.0002 51.0 510 0.0002
0.0002 52.0 520 0.0002
0.0002 53.0 530 0.0002
0.0002 54.0 540 0.0002
0.0002 55.0 550 0.0002
0.0002 56.0 560 0.0002
0.0002 57.0 570 0.0002
0.0002 58.0 580 0.0002
0.0002 59.0 590 0.0002
0.0002 60.0 600 0.0002
0.0002 61.0 610 0.0002
0.0002 62.0 620 0.0002
0.0002 63.0 630 0.0002
0.0002 64.0 640 0.0002
0.0002 65.0 650 0.0002
0.0002 66.0 660 0.0002
0.0002 67.0 670 0.0002
0.0002 68.0 680 0.0002
0.0002 69.0 690 0.0002
0.0002 70.0 700 0.0002
0.0002 71.0 710 0.0002
0.0002 72.0 720 0.0002
0.0002 73.0 730 0.0002
0.0002 74.0 740 0.0002
0.0001 75.0 750 0.0002
0.0002 76.0 760 0.0002
0.0002 77.0 770 0.0002
0.0001 78.0 780 0.0002
0.0002 79.0 790 0.0002
0.0002 80.0 800 0.0002
0.0001 81.0 810 0.0002
0.0002 82.0 820 0.0002
0.0001 83.0 830 0.0001
0.0001 84.0 840 0.0001
0.0001 85.0 850 0.0001
0.0001 86.0 860 0.0001
0.0001 87.0 870 0.0001
0.0001 88.0 880 0.0001
0.0001 89.0 890 0.0001
0.0001 90.0 900 0.0001
0.0001 91.0 910 0.0001
0.0001 92.0 920 0.0001
0.0001 93.0 930 0.0001
0.0001 94.0 940 0.0001
0.0001 95.0 950 0.0001
0.0001 96.0 960 0.0001
0.0001 97.0 970 0.0001
0.0001 98.0 980 0.0001
0.0001 99.0 990 0.0001
0.0001 100.0 1000 0.0001

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.0