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disfluency-large-3

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roberta
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metadata
base_model: vinai/phobert-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: disfluency-large-3
    results: []

disfluency-large-3

This model is a fine-tuned version of vinai/phobert-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 42.3155
  • Precision: 0.9886
  • Recall: 0.9862
  • F1: 0.9874
  • Accuracy: 0.9956

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: 32
  • eval_batch_size: 32
  • 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 Precision Recall F1 Accuracy
No log 1.0 140 131.9614 0.8037 0.8438 0.8232 0.9411
No log 2.0 280 41.8031 0.9487 0.9549 0.9518 0.9855
No log 3.0 420 27.3502 0.9664 0.9681 0.9673 0.9906
178.6738 4.0 560 22.9255 0.9741 0.9730 0.9735 0.9925
178.6738 5.0 700 25.3163 0.9676 0.9688 0.9682 0.9919
178.6738 6.0 840 24.1142 0.9723 0.9718 0.9720 0.9925
178.6738 7.0 980 22.2517 0.9777 0.9766 0.9771 0.9938
25.7318 8.0 1120 24.4542 0.9760 0.9772 0.9766 0.9936
25.7318 9.0 1260 27.1333 0.9740 0.9700 0.9720 0.9929
25.7318 10.0 1400 24.5889 0.9789 0.9778 0.9784 0.9938
16.0059 11.0 1540 26.1038 0.9819 0.9808 0.9814 0.9936
16.0059 12.0 1680 23.3198 0.9790 0.9814 0.9802 0.9941
16.0059 13.0 1820 30.8831 0.9778 0.9772 0.9775 0.9930
16.0059 14.0 1960 28.1502 0.9843 0.9814 0.9828 0.9946
11.2302 15.0 2100 29.2842 0.9790 0.9808 0.9799 0.9937
11.2302 16.0 2240 28.5446 0.9819 0.9796 0.9807 0.9945
11.2302 17.0 2380 25.4603 0.9850 0.9838 0.9844 0.9953
8.9848 18.0 2520 29.3936 0.9801 0.9760 0.9780 0.9929
8.9848 19.0 2660 31.2320 0.9796 0.9796 0.9796 0.9944
8.9848 20.0 2800 34.0474 0.9849 0.9802 0.9825 0.9943
8.9848 21.0 2940 32.9968 0.9849 0.9826 0.9838 0.9948
8.2401 22.0 3080 39.6873 0.9819 0.9808 0.9814 0.9946
8.2401 23.0 3220 42.7506 0.9819 0.9802 0.9811 0.9945
8.2401 24.0 3360 33.8886 0.9856 0.9862 0.9859 0.9954
7.099 25.0 3500 36.8275 0.9819 0.9808 0.9814 0.9941
7.099 26.0 3640 36.7838 0.9831 0.9814 0.9823 0.9951
7.099 27.0 3780 39.2226 0.9813 0.9790 0.9801 0.9947
7.099 28.0 3920 39.2492 0.9843 0.9820 0.9832 0.9949
5.6646 29.0 4060 41.4139 0.9790 0.9790 0.9790 0.9944
5.6646 30.0 4200 41.4583 0.9838 0.9826 0.9832 0.9949
5.6646 31.0 4340 47.1872 0.9801 0.9778 0.9789 0.9941
5.6646 32.0 4480 41.3073 0.9862 0.9844 0.9853 0.9956
5.304 33.0 4620 44.8882 0.9796 0.9790 0.9793 0.9945
5.304 34.0 4760 52.3203 0.9783 0.9772 0.9778 0.9941
5.304 35.0 4900 43.9140 0.9825 0.9808 0.9817 0.9951
4.7574 36.0 5040 46.8215 0.9819 0.9802 0.9811 0.9947
4.7574 37.0 5180 39.5738 0.9867 0.9844 0.9856 0.9959
4.7574 38.0 5320 39.9370 0.9837 0.9814 0.9826 0.9955
4.7574 39.0 5460 40.4614 0.9856 0.9844 0.9850 0.9956
3.8125 40.0 5600 38.6418 0.9885 0.9850 0.9868 0.9959
3.8125 41.0 5740 42.7438 0.9813 0.9796 0.9805 0.9947
3.8125 42.0 5880 52.7676 0.9689 0.9730 0.9709 0.9940
3.2902 43.0 6020 38.5737 0.9825 0.9808 0.9817 0.9953
3.2902 44.0 6160 42.4615 0.9868 0.9850 0.9859 0.9952
3.2902 45.0 6300 43.5099 0.9856 0.9838 0.9847 0.9956
3.2902 46.0 6440 45.0846 0.9837 0.9820 0.9829 0.9952
4.0467 47.0 6580 41.7571 0.9862 0.9850 0.9856 0.9955
4.0467 48.0 6720 50.8592 0.9807 0.9778 0.9792 0.9945
4.0467 49.0 6860 42.3155 0.9886 0.9862 0.9874 0.9956
2.3503 50.0 7000 45.7602 0.9873 0.9850 0.9862 0.9952
2.3503 51.0 7140 43.4314 0.9856 0.9838 0.9847 0.9953
2.3503 52.0 7280 47.4167 0.9813 0.9790 0.9801 0.9949
2.3503 53.0 7420 46.8868 0.9838 0.9826 0.9832 0.9952
2.841 54.0 7560 50.8428 0.9843 0.9814 0.9828 0.9950
2.841 55.0 7700 49.0097 0.9825 0.9808 0.9817 0.9949
2.841 56.0 7840 49.0165 0.9831 0.9802 0.9816 0.9950
2.841 57.0 7980 46.3213 0.9838 0.9826 0.9832 0.9953
1.8064 58.0 8120 49.3268 0.9825 0.9790 0.9807 0.9946
1.8064 59.0 8260 48.1988 0.9849 0.9814 0.9831 0.9952
1.8064 60.0 8400 46.5527 0.9838 0.9826 0.9832 0.9955
1.5941 61.0 8540 57.5747 0.9807 0.9790 0.9798 0.9942
1.5941 62.0 8680 56.6894 0.9801 0.9790 0.9796 0.9945
1.5941 63.0 8820 58.1243 0.9808 0.9802 0.9805 0.9945
1.5941 64.0 8960 53.2165 0.9837 0.9808 0.9822 0.9951
1.5057 65.0 9100 52.2484 0.9832 0.9820 0.9826 0.9949
1.5057 66.0 9240 49.2435 0.9837 0.9814 0.9826 0.9951
1.5057 67.0 9380 51.2186 0.9796 0.9790 0.9793 0.9948
1.5084 68.0 9520 54.1799 0.9825 0.9808 0.9817 0.9947
1.5084 69.0 9660 56.3696 0.9807 0.9778 0.9792 0.9945
1.5084 70.0 9800 52.6295 0.9837 0.9802 0.9819 0.9948
1.5084 71.0 9940 51.2577 0.9825 0.9790 0.9807 0.9950
1.0448 72.0 10080 56.0093 0.9807 0.9790 0.9798 0.9945
1.0448 73.0 10220 50.7540 0.9831 0.9808 0.9819 0.9951
1.0448 74.0 10360 52.9783 0.9819 0.9790 0.9804 0.9947

Framework versions

  • Transformers 4.32.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3