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hubert-classifier-aug-fold-3-old

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metadata
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: hubert-classifier-aug-fold-3
    results: []

hubert-classifier-aug-fold-3

This model is a fine-tuned version of facebook/hubert-base-ls960 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4622
  • Accuracy: 0.8962
  • Precision: 0.9090
  • Recall: 0.8962
  • F1: 0.8963
  • Binary: 0.9276

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: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • 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 F1 Binary
No log 0.22 50 3.8684 0.0648 0.0195 0.0648 0.0184 0.3348
No log 0.43 100 3.4085 0.0891 0.0169 0.0891 0.0238 0.3588
No log 0.65 150 3.1493 0.1107 0.0376 0.1107 0.0422 0.3746
No log 0.86 200 2.8408 0.1822 0.1026 0.1822 0.1043 0.4273
3.6116 1.08 250 2.6147 0.2780 0.1840 0.2780 0.1853 0.4906
3.6116 1.29 300 2.4272 0.2982 0.2217 0.2982 0.2156 0.5023
3.6116 1.51 350 2.1582 0.4103 0.3356 0.4103 0.3247 0.5852
3.6116 1.72 400 2.0102 0.4143 0.3454 0.4143 0.3447 0.5892
3.6116 1.94 450 1.8796 0.4615 0.4433 0.4615 0.4066 0.6225
2.6485 2.16 500 1.6625 0.5412 0.5251 0.5412 0.4856 0.6771
2.6485 2.37 550 1.5422 0.5843 0.5780 0.5843 0.5307 0.7078
2.6485 2.59 600 1.4263 0.6073 0.5806 0.6073 0.5573 0.7246
2.6485 2.8 650 1.2985 0.6451 0.6244 0.6451 0.6039 0.7503
2.1146 3.02 700 1.2788 0.6613 0.6564 0.6613 0.6273 0.7614
2.1146 3.23 750 1.2186 0.6802 0.6820 0.6802 0.6499 0.7742
2.1146 3.45 800 1.1269 0.7152 0.7428 0.7152 0.6978 0.7991
2.1146 3.66 850 1.1179 0.6680 0.6970 0.6680 0.6377 0.7675
2.1146 3.88 900 1.0928 0.7031 0.7264 0.7031 0.6793 0.7889
1.8074 4.09 950 0.9427 0.7638 0.7780 0.7638 0.7513 0.8350
1.8074 4.31 1000 0.8876 0.7692 0.7952 0.7692 0.7603 0.8382
1.8074 4.53 1050 0.8686 0.7773 0.7852 0.7773 0.7664 0.8425
1.8074 4.74 1100 0.8814 0.7665 0.7770 0.7665 0.7497 0.8363
1.8074 4.96 1150 0.8280 0.7706 0.7896 0.7706 0.7603 0.8401
1.5857 5.17 1200 0.8050 0.7773 0.8023 0.7773 0.7720 0.8439
1.5857 5.39 1250 0.7475 0.8016 0.8114 0.8016 0.7976 0.8609
1.5857 5.6 1300 0.7396 0.7895 0.8187 0.7895 0.7859 0.8521
1.5857 5.82 1350 0.7637 0.8030 0.8177 0.8030 0.7953 0.8598
1.4411 6.03 1400 0.7511 0.7976 0.8157 0.7976 0.7934 0.8574
1.4411 6.25 1450 0.6479 0.8232 0.8392 0.8232 0.8185 0.8756
1.4411 6.47 1500 0.6521 0.8286 0.8494 0.8286 0.8233 0.8803
1.4411 6.68 1550 0.5778 0.8529 0.8637 0.8529 0.8501 0.8962
1.4411 6.9 1600 0.5898 0.8259 0.8428 0.8259 0.8249 0.8776
1.3162 7.11 1650 0.5784 0.8421 0.8614 0.8421 0.8404 0.8892
1.3162 7.33 1700 0.6395 0.8232 0.8407 0.8232 0.8170 0.8764
1.3162 7.54 1750 0.6334 0.8340 0.8519 0.8340 0.8320 0.8834
1.3162 7.76 1800 0.6133 0.8286 0.8513 0.8286 0.8274 0.8798
1.3162 7.97 1850 0.5488 0.8502 0.8663 0.8502 0.8496 0.8949
1.2312 8.19 1900 0.6521 0.8246 0.8411 0.8246 0.8227 0.8769
1.2312 8.41 1950 0.5706 0.8529 0.8669 0.8529 0.8528 0.8962
1.2312 8.62 2000 0.5822 0.8462 0.8596 0.8462 0.8448 0.8924
1.2312 8.84 2050 0.5332 0.8502 0.8646 0.8502 0.8498 0.8953
1.1409 9.05 2100 0.5226 0.8650 0.8743 0.8650 0.8631 0.9053
1.1409 9.27 2150 0.5451 0.8623 0.8750 0.8623 0.8617 0.9032
1.1409 9.48 2200 0.5940 0.8381 0.8510 0.8381 0.8365 0.8860
1.1409 9.7 2250 0.5303 0.8570 0.8686 0.8570 0.8568 0.8988
1.1409 9.91 2300 0.5706 0.8448 0.8622 0.8448 0.8429 0.8912
1.0865 10.13 2350 0.5140 0.8623 0.8780 0.8623 0.8635 0.9026
1.0865 10.34 2400 0.5106 0.8704 0.8811 0.8704 0.8692 0.9092
1.0865 10.56 2450 0.5478 0.8583 0.8753 0.8583 0.8570 0.9005
1.0865 10.78 2500 0.6036 0.8583 0.8694 0.8583 0.8548 0.9003
1.0865 10.99 2550 0.5360 0.8543 0.8712 0.8543 0.8498 0.8984
1.0383 11.21 2600 0.5426 0.8570 0.8691 0.8570 0.8558 0.8982
1.0383 11.42 2650 0.5124 0.8691 0.8777 0.8691 0.8673 0.9067
1.0383 11.64 2700 0.5676 0.8435 0.8554 0.8435 0.8422 0.8892
1.0383 11.85 2750 0.5387 0.8596 0.8700 0.8596 0.8590 0.9022
0.9938 12.07 2800 0.5402 0.8691 0.8778 0.8691 0.8675 0.9089
0.9938 12.28 2850 0.5814 0.8529 0.8603 0.8529 0.8496 0.8969
0.9938 12.5 2900 0.5124 0.8623 0.8705 0.8623 0.8594 0.9034
0.9938 12.72 2950 0.5077 0.8623 0.8739 0.8623 0.8604 0.9032
0.9938 12.93 3000 0.5305 0.8704 0.8785 0.8704 0.8675 0.9101
0.9526 13.15 3050 0.5455 0.8718 0.8849 0.8718 0.8707 0.9100
0.9526 13.36 3100 0.5153 0.8826 0.8939 0.8826 0.8822 0.9175
0.9526 13.58 3150 0.5218 0.8826 0.8902 0.8826 0.8813 0.9167
0.9526 13.79 3200 0.5361 0.8637 0.8756 0.8637 0.8634 0.9030
0.91 14.01 3250 0.5174 0.8785 0.8873 0.8785 0.8780 0.9139
0.91 14.22 3300 0.5346 0.8799 0.8892 0.8799 0.8787 0.9158
0.91 14.44 3350 0.5586 0.8650 0.8747 0.8650 0.8634 0.9050
0.91 14.66 3400 0.5504 0.8704 0.8816 0.8704 0.8698 0.9097
0.91 14.87 3450 0.5643 0.8718 0.8814 0.8718 0.8700 0.9101
0.8689 15.09 3500 0.5425 0.8650 0.8766 0.8650 0.8642 0.9043
0.8689 15.3 3550 0.5609 0.8623 0.8775 0.8623 0.8616 0.9038
0.8689 15.52 3600 0.5440 0.8745 0.8847 0.8745 0.8739 0.9116
0.8689 15.73 3650 0.5020 0.8718 0.8814 0.8718 0.8714 0.9103
0.8689 15.95 3700 0.5650 0.8718 0.8810 0.8718 0.8704 0.9099
0.8437 16.16 3750 0.5115 0.8785 0.8874 0.8785 0.8774 0.9146
0.8437 16.38 3800 0.5651 0.8596 0.8735 0.8596 0.8592 0.9022
0.8437 16.59 3850 0.4996 0.8920 0.9025 0.8920 0.8921 0.9242
0.8437 16.81 3900 0.5528 0.8772 0.8887 0.8772 0.8765 0.9134
0.8213 17.03 3950 0.5568 0.8677 0.8816 0.8677 0.8666 0.9074
0.8213 17.24 4000 0.5270 0.8812 0.8906 0.8812 0.8804 0.9167
0.8213 17.46 4050 0.5239 0.8812 0.8922 0.8812 0.8800 0.9162
0.8213 17.67 4100 0.4915 0.8839 0.8921 0.8839 0.8834 0.9181
0.8213 17.89 4150 0.5282 0.8812 0.8914 0.8812 0.8807 0.9152
0.7835 18.1 4200 0.5031 0.8866 0.8959 0.8866 0.8865 0.9194
0.7835 18.32 4250 0.4997 0.8812 0.8898 0.8812 0.8803 0.9158
0.7835 18.53 4300 0.5080 0.8826 0.8904 0.8826 0.8809 0.9167
0.7835 18.75 4350 0.5264 0.8812 0.8898 0.8812 0.8800 0.9158
0.7835 18.97 4400 0.5487 0.8718 0.8808 0.8718 0.8707 0.9105
0.7606 19.18 4450 0.5266 0.8772 0.8877 0.8772 0.8759 0.9139
0.7606 19.4 4500 0.5257 0.8772 0.8875 0.8772 0.8770 0.9139
0.7606 19.61 4550 0.5321 0.8880 0.8977 0.8880 0.8882 0.9215
0.7606 19.83 4600 0.5349 0.8772 0.8880 0.8772 0.8765 0.9139
0.7342 20.04 4650 0.5250 0.8880 0.8962 0.8880 0.8877 0.9219
0.7342 20.26 4700 0.5081 0.8907 0.8990 0.8907 0.8904 0.9232
0.7342 20.47 4750 0.4958 0.8839 0.8941 0.8839 0.8842 0.9171
0.7342 20.69 4800 0.5293 0.8826 0.8928 0.8826 0.8819 0.9181
0.7342 20.91 4850 0.5094 0.8812 0.8924 0.8812 0.8805 0.9167
0.7129 21.12 4900 0.4922 0.8920 0.8997 0.8920 0.8908 0.9242
0.7129 21.34 4950 0.5078 0.8907 0.9000 0.8907 0.8901 0.9238
0.7129 21.55 5000 0.5303 0.8799 0.8892 0.8799 0.8781 0.9167
0.7129 21.77 5050 0.5531 0.8731 0.8842 0.8731 0.8711 0.9115
0.7129 21.98 5100 0.5572 0.8799 0.8920 0.8799 0.8784 0.9158
0.7032 22.2 5150 0.5151 0.8799 0.8903 0.8799 0.8793 0.9167
0.7032 22.41 5200 0.5090 0.8812 0.8921 0.8812 0.8808 0.9177
0.7032 22.63 5250 0.5318 0.8799 0.8891 0.8799 0.8785 0.9158
0.7032 22.84 5300 0.5114 0.8826 0.8897 0.8826 0.8812 0.9171
0.6809 23.06 5350 0.5049 0.8866 0.8946 0.8866 0.8858 0.9209
0.6809 23.28 5400 0.5378 0.8799 0.8901 0.8799 0.8786 0.9152
0.6809 23.49 5450 0.5088 0.8812 0.8905 0.8812 0.8806 0.9158
0.6809 23.71 5500 0.4883 0.8920 0.9033 0.8920 0.8925 0.9252
0.6809 23.92 5550 0.5168 0.8799 0.8911 0.8799 0.8800 0.9152
0.6604 24.14 5600 0.5167 0.8799 0.8907 0.8799 0.8795 0.9148
0.6604 24.35 5650 0.5092 0.8866 0.9011 0.8866 0.8878 0.9200
0.6604 24.57 5700 0.5048 0.8961 0.9069 0.8961 0.8965 0.9270
0.6604 24.78 5750 0.5303 0.8839 0.8973 0.8839 0.8835 0.9186
0.6604 25.0 5800 0.4996 0.8934 0.9041 0.8934 0.8939 0.9242
0.6595 25.22 5850 0.5095 0.8934 0.9033 0.8934 0.8927 0.9242
0.6595 25.43 5900 0.5109 0.8920 0.9024 0.8920 0.8921 0.9232
0.6595 25.65 5950 0.4993 0.8893 0.8973 0.8893 0.8890 0.9219
0.6595 25.86 6000 0.4954 0.8934 0.9022 0.8934 0.8928 0.9247
0.6347 26.08 6050 0.4939 0.8988 0.9076 0.8988 0.8986 0.9279
0.6347 26.29 6100 0.4820 0.8974 0.9049 0.8974 0.8970 0.9270
0.6347 26.51 6150 0.5168 0.8880 0.8952 0.8880 0.8869 0.9205
0.6347 26.72 6200 0.5275 0.8839 0.8916 0.8839 0.8827 0.9181
0.6347 26.94 6250 0.5026 0.8907 0.8991 0.8907 0.8898 0.9219
0.6361 27.16 6300 0.5003 0.8988 0.9076 0.8988 0.8984 0.9275
0.6361 27.37 6350 0.4777 0.8988 0.9069 0.8988 0.8984 0.9275
0.6361 27.59 6400 0.4904 0.8988 0.9079 0.8988 0.8986 0.9275
0.6361 27.8 6450 0.4885 0.9001 0.9084 0.9001 0.8998 0.9285
0.631 28.02 6500 0.5134 0.8893 0.8973 0.8893 0.8882 0.9209
0.631 28.23 6550 0.5128 0.8920 0.9011 0.8920 0.8916 0.9232
0.631 28.45 6600 0.5136 0.8947 0.9032 0.8947 0.8942 0.9251
0.631 28.66 6650 0.5148 0.8907 0.8998 0.8907 0.8900 0.9219
0.631 28.88 6700 0.5143 0.8893 0.8971 0.8893 0.8883 0.9215
0.6104 29.09 6750 0.5237 0.8853 0.8952 0.8853 0.8844 0.9181
0.6104 29.31 6800 0.5187 0.8880 0.8976 0.8880 0.8873 0.9200
0.6104 29.53 6850 0.5183 0.8866 0.8964 0.8866 0.8860 0.9190
0.6104 29.74 6900 0.5172 0.8907 0.9006 0.8907 0.8899 0.9219
0.6104 29.96 6950 0.5141 0.8907 0.9001 0.8907 0.8902 0.9219

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.3.0
  • Datasets 2.19.1
  • Tokenizers 0.15.1