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DIALOGUE_overfit_check

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metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: DIALOGUE_overfit_check
    results: []

DIALOGUE_overfit_check

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: 0.1840
  • Precision: 0.9762
  • Recall: 0.9737
  • F1: 0.9736
  • Accuracy: 0.9737

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: 3e-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: 30

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
1.0266 0.62 30 0.5087 1.0 1.0 1.0 1.0
0.4009 1.25 60 0.1389 0.9762 0.9737 0.9736 0.9737
0.1301 1.88 90 0.1436 0.9637 0.9605 0.9604 0.9605
0.0342 2.5 120 0.1055 0.9762 0.9737 0.9736 0.9737
0.0288 3.12 150 0.1395 0.9762 0.9737 0.9736 0.9737
0.0099 3.75 180 0.1259 0.9762 0.9737 0.9736 0.9737
0.0057 4.38 210 0.1315 0.9762 0.9737 0.9736 0.9737
0.0042 5.0 240 0.1338 0.9762 0.9737 0.9736 0.9737
0.0033 5.62 270 0.1373 0.9762 0.9737 0.9736 0.9737
0.0027 6.25 300 0.1403 0.9762 0.9737 0.9736 0.9737
0.0024 6.88 330 0.1457 0.9762 0.9737 0.9736 0.9737
0.002 7.5 360 0.1483 0.9762 0.9737 0.9736 0.9737
0.0017 8.12 390 0.1483 0.9762 0.9737 0.9736 0.9737
0.0016 8.75 420 0.1503 0.9762 0.9737 0.9736 0.9737
0.0014 9.38 450 0.1535 0.9762 0.9737 0.9736 0.9737
0.0013 10.0 480 0.1546 0.9762 0.9737 0.9736 0.9737
0.0012 10.62 510 0.1576 0.9762 0.9737 0.9736 0.9737
0.0011 11.25 540 0.1593 0.9762 0.9737 0.9736 0.9737
0.001 11.88 570 0.1672 0.9762 0.9737 0.9736 0.9737
0.0009 12.5 600 0.1686 0.9762 0.9737 0.9736 0.9737
0.0008 13.12 630 0.1696 0.9762 0.9737 0.9736 0.9737
0.0008 13.75 660 0.1696 0.9762 0.9737 0.9736 0.9737
0.0007 14.38 690 0.1702 0.9762 0.9737 0.9736 0.9737
0.0007 15.0 720 0.1711 0.9762 0.9737 0.9736 0.9737
0.0006 15.62 750 0.1716 0.9762 0.9737 0.9736 0.9737
0.0006 16.25 780 0.1726 0.9762 0.9737 0.9736 0.9737
0.0006 16.88 810 0.1731 0.9762 0.9737 0.9736 0.9737
0.0006 17.5 840 0.1744 0.9762 0.9737 0.9736 0.9737
0.0006 18.12 870 0.1762 0.9762 0.9737 0.9736 0.9737
0.0005 18.75 900 0.1773 0.9762 0.9737 0.9736 0.9737
0.0005 19.38 930 0.1777 0.9762 0.9737 0.9736 0.9737
0.0005 20.0 960 0.1781 0.9762 0.9737 0.9736 0.9737
0.0005 20.62 990 0.1785 0.9762 0.9737 0.9736 0.9737
0.0004 21.25 1020 0.1795 0.9762 0.9737 0.9736 0.9737
0.0004 21.88 1050 0.1801 0.9762 0.9737 0.9736 0.9737
0.0004 22.5 1080 0.1805 0.9762 0.9737 0.9736 0.9737
0.0004 23.12 1110 0.1812 0.9762 0.9737 0.9736 0.9737
0.0004 23.75 1140 0.1818 0.9762 0.9737 0.9736 0.9737
0.0004 24.38 1170 0.1821 0.9762 0.9737 0.9736 0.9737
0.0004 25.0 1200 0.1824 0.9762 0.9737 0.9736 0.9737
0.0004 25.62 1230 0.1827 0.9762 0.9737 0.9736 0.9737
0.0004 26.25 1260 0.1831 0.9762 0.9737 0.9736 0.9737
0.0004 26.88 1290 0.1833 0.9762 0.9737 0.9736 0.9737
0.0004 27.5 1320 0.1836 0.9762 0.9737 0.9736 0.9737
0.0004 28.12 1350 0.1838 0.9762 0.9737 0.9736 0.9737
0.0004 28.75 1380 0.1839 0.9762 0.9737 0.9736 0.9737
0.0004 29.38 1410 0.1840 0.9762 0.9737 0.9736 0.9737
0.0004 30.0 1440 0.1840 0.9762 0.9737 0.9736 0.9737

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0