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bart-large

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metadata
license: apache-2.0
tags:
  - generated_from_trainer
base_model: facebook/bart-large
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: bart-large
    results: []

bart-large

This model is a fine-tuned version of facebook/bart-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.0027
  • Accuracy: 0.7916
  • Precision: 0.7858
  • Recall: 0.7916
  • Precision Macro: 0.7201
  • Recall Macro: 0.7056
  • Macro Fpr: 0.0201
  • Weighted Fpr: 0.0195
  • Weighted Specificity: 0.9714
  • Macro Specificity: 0.9836
  • Weighted Sensitivity: 0.7823
  • Macro Sensitivity: 0.7056
  • F1 Micro: 0.7823
  • F1 Macro: 0.7080
  • F1 Weighted: 0.7801

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: 5e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall Precision Macro Recall Macro Macro Fpr Weighted Fpr Weighted Specificity Macro Specificity Weighted Sensitivity Macro Sensitivity F1 Micro F1 Macro F1 Weighted
1.1685 1.0 2569 1.2587 0.6847 0.6360 0.6847 0.4176 0.4720 0.0331 0.0318 0.9550 0.9760 0.6847 0.4720 0.6847 0.4296 0.6471
1.1965 2.0 5138 1.1623 0.6638 0.6943 0.6638 0.4564 0.4261 0.0342 0.0349 0.9654 0.9753 0.6638 0.4261 0.6638 0.3955 0.6468
1.189 3.0 7707 1.3574 0.7235 0.7220 0.7235 0.5413 0.5528 0.0271 0.0266 0.9628 0.9791 0.7235 0.5528 0.7235 0.5196 0.7031
1.0127 4.0 10276 1.4685 0.7668 0.7584 0.7668 0.6671 0.6202 0.0224 0.0213 0.9653 0.9821 0.7668 0.6202 0.7668 0.6233 0.7569
1.0205 5.0 12845 1.4232 0.7668 0.7711 0.7668 0.6765 0.6872 0.0215 0.0213 0.9737 0.9827 0.7668 0.6872 0.7668 0.6732 0.7643
0.7927 6.0 15414 1.5678 0.7428 0.7451 0.7428 0.6489 0.6333 0.0248 0.0241 0.9690 0.9808 0.7428 0.6333 0.7428 0.6108 0.7292
0.7701 7.0 17983 1.7337 0.7467 0.7600 0.7467 0.6863 0.6536 0.0240 0.0237 0.9680 0.9810 0.7467 0.6536 0.7467 0.6584 0.7399
0.584 8.0 20552 1.6188 0.7692 0.7766 0.7692 0.6979 0.7065 0.0214 0.0210 0.9706 0.9827 0.7692 0.7065 0.7692 0.6980 0.7683
0.5659 9.0 23121 1.6983 0.7599 0.7665 0.7599 0.7000 0.6804 0.0227 0.0221 0.9695 0.9820 0.7599 0.6804 0.7599 0.6728 0.7542
0.7021 10.0 25690 1.6445 0.7699 0.7656 0.7699 0.7144 0.6857 0.0223 0.0209 0.9608 0.9821 0.7699 0.6857 0.7699 0.6954 0.7634
0.6216 11.0 28259 1.6562 0.7676 0.7634 0.7676 0.6856 0.6776 0.0223 0.0212 0.9640 0.9821 0.7676 0.6776 0.7676 0.6786 0.7624
0.6408 12.0 30828 1.6682 0.7668 0.7629 0.7668 0.6706 0.6719 0.0223 0.0213 0.9666 0.9822 0.7668 0.6719 0.7668 0.6666 0.7608
0.523 13.0 33397 1.7727 0.7653 0.7674 0.7653 0.8238 0.6934 0.0226 0.0214 0.9659 0.9821 0.7653 0.6934 0.7653 0.7066 0.7534
0.3688 14.0 35966 1.8404 0.7792 0.7788 0.7792 0.7229 0.6921 0.0209 0.0198 0.9675 0.9831 0.7792 0.6921 0.7792 0.6960 0.7731
0.2394 15.0 38535 1.7885 0.7816 0.7809 0.7816 0.7441 0.7115 0.0210 0.0196 0.9628 0.9830 0.7816 0.7115 0.7816 0.7230 0.7765
0.2734 16.0 41104 1.8944 0.7777 0.7870 0.7777 0.7539 0.7265 0.0203 0.0200 0.9724 0.9833 0.7777 0.7265 0.7777 0.7295 0.7777
0.4319 17.0 43673 1.7744 0.7885 0.7847 0.7885 0.7247 0.7320 0.0195 0.0188 0.9718 0.9840 0.7885 0.7320 0.7885 0.7269 0.7855
0.2347 18.0 46242 2.0036 0.7413 0.7352 0.7413 0.6934 0.6799 0.0255 0.0243 0.9597 0.9801 0.7413 0.6799 0.7413 0.6825 0.7354
0.1882 19.0 48811 1.9298 0.7816 0.7804 0.7816 0.7243 0.7262 0.0202 0.0196 0.9708 0.9835 0.7816 0.7262 0.7816 0.7225 0.7792
0.1799 20.0 51380 1.9688 0.7792 0.7892 0.7792 0.7312 0.7343 0.0205 0.0198 0.9714 0.9834 0.7792 0.7343 0.7792 0.7242 0.7779
0.1366 21.0 53949 1.9910 0.7847 0.7846 0.7847 0.7148 0.7455 0.0198 0.0192 0.9730 0.9838 0.7847 0.7455 0.7847 0.7265 0.7833
0.1793 22.0 56518 2.2548 0.7630 0.7648 0.7630 0.7150 0.7273 0.0230 0.0217 0.9633 0.9818 0.7630 0.7273 0.7630 0.7150 0.7582
0.1749 23.0 59087 2.1109 0.7816 0.7768 0.7816 0.7466 0.7230 0.0205 0.0196 0.9690 0.9834 0.7816 0.7230 0.7816 0.7289 0.7774
0.1154 24.0 61656 2.0637 0.7878 0.7837 0.7878 0.7590 0.7269 0.0196 0.0189 0.9718 0.9840 0.7878 0.7269 0.7878 0.7331 0.7828
0.1447 25.0 64225 2.0027 0.7916 0.7858 0.7916 0.7750 0.7299 0.0194 0.0185 0.9697 0.9841 0.7916 0.7299 0.7916 0.7408 0.7861
0.0806 26.0 66794 2.0777 0.7885 0.7831 0.7885 0.7162 0.7134 0.0196 0.0188 0.9715 0.9840 0.7885 0.7134 0.7885 0.7118 0.7840
0.0407 27.0 69363 2.1754 0.7885 0.7863 0.7885 0.7192 0.7080 0.0194 0.0188 0.9725 0.9841 0.7885 0.7080 0.7885 0.7105 0.7866
0.0701 28.0 71932 2.1578 0.7823 0.7817 0.7823 0.7130 0.7097 0.0201 0.0195 0.9714 0.9836 0.7823 0.7097 0.7823 0.7066 0.7810
0.1034 29.0 74501 2.2132 0.7800 0.7789 0.7800 0.7163 0.7044 0.0203 0.0197 0.9713 0.9834 0.7800 0.7044 0.7800 0.7064 0.7785
0.0388 30.0 77070 2.1833 0.7823 0.7806 0.7823 0.7201 0.7056 0.0201 0.0195 0.9714 0.9836 0.7823 0.7056 0.7823 0.7080 0.7801

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.19.0
  • Tokenizers 0.15.1