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DIALOGUE_one

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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_one
    results: []

DIALOGUE_one

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: 0.1947
  • 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: 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: 30

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
1.1919 0.62 30 0.8161 1.0 1.0 1.0 1.0
0.6182 1.25 60 0.2981 0.9762 0.9737 0.9736 0.9737
0.2564 1.88 90 0.1427 0.9762 0.9737 0.9736 0.9737
0.0833 2.5 120 0.0918 0.9762 0.9737 0.9736 0.9737
0.0436 3.12 150 0.1185 0.9762 0.9737 0.9736 0.9737
0.0215 3.75 180 0.1243 0.9762 0.9737 0.9736 0.9737
0.0109 4.38 210 0.1179 0.9762 0.9737 0.9736 0.9737
0.0075 5.0 240 0.1240 0.9762 0.9737 0.9736 0.9737
0.0062 5.62 270 0.1362 0.9762 0.9737 0.9736 0.9737
0.0049 6.25 300 0.1385 0.9762 0.9737 0.9736 0.9737
0.0042 6.88 330 0.1572 0.9762 0.9737 0.9736 0.9737
0.0037 7.5 360 0.1569 0.9762 0.9737 0.9736 0.9737
0.0031 8.12 390 0.1501 0.9762 0.9737 0.9736 0.9737
0.0029 8.75 420 0.1563 0.9762 0.9737 0.9736 0.9737
0.0024 9.38 450 0.1617 0.9762 0.9737 0.9736 0.9737
0.0023 10.0 480 0.1625 0.9762 0.9737 0.9736 0.9737
0.0021 10.62 510 0.1658 0.9762 0.9737 0.9736 0.9737
0.002 11.25 540 0.1699 0.9762 0.9737 0.9736 0.9737
0.0017 11.88 570 0.1727 0.9762 0.9737 0.9736 0.9737
0.0017 12.5 600 0.1731 0.9762 0.9737 0.9736 0.9737
0.0015 13.12 630 0.1756 0.9762 0.9737 0.9736 0.9737
0.0015 13.75 660 0.1764 0.9762 0.9737 0.9736 0.9737
0.0014 14.38 690 0.1797 0.9762 0.9737 0.9736 0.9737
0.0013 15.0 720 0.1817 0.9762 0.9737 0.9736 0.9737
0.0012 15.62 750 0.1822 0.9762 0.9737 0.9736 0.9737
0.0011 16.25 780 0.1833 0.9762 0.9737 0.9736 0.9737
0.0011 16.88 810 0.1843 0.9762 0.9737 0.9736 0.9737
0.001 17.5 840 0.1857 0.9762 0.9737 0.9736 0.9737
0.001 18.12 870 0.1872 0.9762 0.9737 0.9736 0.9737
0.0009 18.75 900 0.1884 0.9762 0.9737 0.9736 0.9737
0.0009 19.38 930 0.1879 0.9762 0.9737 0.9736 0.9737
0.0009 20.0 960 0.1882 0.9762 0.9737 0.9736 0.9737
0.0008 20.62 990 0.1888 0.9762 0.9737 0.9736 0.9737
0.0008 21.25 1020 0.1895 0.9762 0.9737 0.9736 0.9737
0.0008 21.88 1050 0.1902 0.9762 0.9737 0.9736 0.9737
0.0007 22.5 1080 0.1904 0.9762 0.9737 0.9736 0.9737
0.0008 23.12 1110 0.1911 0.9762 0.9737 0.9736 0.9737
0.0007 23.75 1140 0.1919 0.9762 0.9737 0.9736 0.9737
0.0007 24.38 1170 0.1923 0.9762 0.9737 0.9736 0.9737
0.0007 25.0 1200 0.1928 0.9762 0.9737 0.9736 0.9737
0.0007 25.62 1230 0.1933 0.9762 0.9737 0.9736 0.9737
0.0007 26.25 1260 0.1938 0.9762 0.9737 0.9736 0.9737
0.0007 26.88 1290 0.1939 0.9762 0.9737 0.9736 0.9737
0.0007 27.5 1320 0.1943 0.9762 0.9737 0.9736 0.9737
0.0006 28.12 1350 0.1945 0.9762 0.9737 0.9736 0.9737
0.0007 28.75 1380 0.1946 0.9762 0.9737 0.9736 0.9737
0.0007 29.38 1410 0.1947 0.9762 0.9737 0.9736 0.9737
0.0007 30.0 1440 0.1947 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