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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: 0.1809
  • 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
1.1724 0.62 30 0.8000 0.9224 0.9079 0.9071 0.9079
0.6097 1.25 60 0.2953 0.9762 0.9737 0.9736 0.9737
0.2608 1.88 90 0.1342 0.9762 0.9737 0.9736 0.9737
0.0958 2.5 120 0.0711 0.9762 0.9737 0.9736 0.9737
0.0424 3.12 150 0.1116 0.9762 0.9737 0.9736 0.9737
0.0205 3.75 180 0.1195 0.9762 0.9737 0.9736 0.9737
0.0119 4.38 210 0.0987 0.9762 0.9737 0.9736 0.9737
0.0087 5.0 240 0.1092 0.9762 0.9737 0.9736 0.9737
0.0068 5.62 270 0.1103 0.9762 0.9737 0.9736 0.9737
0.0057 6.25 300 0.1116 0.9762 0.9737 0.9736 0.9737
0.0048 6.88 330 0.1350 0.9762 0.9737 0.9736 0.9737
0.0041 7.5 360 0.1359 0.9762 0.9737 0.9736 0.9737
0.0037 8.12 390 0.1339 0.9762 0.9737 0.9736 0.9737
0.0031 8.75 420 0.1655 0.9762 0.9737 0.9736 0.9737
0.0028 9.38 450 0.1608 0.9762 0.9737 0.9736 0.9737
0.0026 10.0 480 0.1545 0.9762 0.9737 0.9736 0.9737
0.0024 10.62 510 0.1554 0.9762 0.9737 0.9736 0.9737
0.0022 11.25 540 0.1602 0.9762 0.9737 0.9736 0.9737
0.002 11.88 570 0.1619 0.9762 0.9737 0.9736 0.9737
0.0019 12.5 600 0.1632 0.9762 0.9737 0.9736 0.9737
0.0017 13.12 630 0.1643 0.9762 0.9737 0.9736 0.9737
0.0016 13.75 660 0.1647 0.9762 0.9737 0.9736 0.9737
0.0016 14.38 690 0.1667 0.9762 0.9737 0.9736 0.9737
0.0015 15.0 720 0.1683 0.9762 0.9737 0.9736 0.9737
0.0013 15.62 750 0.1688 0.9762 0.9737 0.9736 0.9737
0.0013 16.25 780 0.1702 0.9762 0.9737 0.9736 0.9737
0.0012 16.88 810 0.1708 0.9762 0.9737 0.9736 0.9737
0.0011 17.5 840 0.1715 0.9762 0.9737 0.9736 0.9737
0.0011 18.12 870 0.1742 0.9762 0.9737 0.9736 0.9737
0.001 18.75 900 0.1754 0.9762 0.9737 0.9736 0.9737
0.001 19.38 930 0.1755 0.9762 0.9737 0.9736 0.9737
0.001 20.0 960 0.1759 0.9762 0.9737 0.9736 0.9737
0.0009 20.62 990 0.1765 0.9762 0.9737 0.9736 0.9737
0.0009 21.25 1020 0.1776 0.9762 0.9737 0.9736 0.9737
0.0009 21.88 1050 0.1779 0.9762 0.9737 0.9736 0.9737
0.0008 22.5 1080 0.1776 0.9762 0.9737 0.9736 0.9737
0.0009 23.12 1110 0.1782 0.9762 0.9737 0.9736 0.9737
0.0008 23.75 1140 0.1789 0.9762 0.9737 0.9736 0.9737
0.0008 24.38 1170 0.1792 0.9762 0.9737 0.9736 0.9737
0.0008 25.0 1200 0.1796 0.9762 0.9737 0.9736 0.9737
0.0008 25.62 1230 0.1799 0.9762 0.9737 0.9736 0.9737
0.0008 26.25 1260 0.1803 0.9762 0.9737 0.9736 0.9737
0.0007 26.88 1290 0.1804 0.9762 0.9737 0.9736 0.9737
0.0008 27.5 1320 0.1808 0.9762 0.9737 0.9736 0.9737
0.0007 28.12 1350 0.1809 0.9762 0.9737 0.9736 0.9737
0.0007 28.75 1380 0.1810 0.9762 0.9737 0.9736 0.9737
0.0008 29.38 1410 0.1809 0.9762 0.9737 0.9736 0.9737
0.0008 30.0 1440 0.1809 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