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hubert-classifier-aug-fold-7-best

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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-7
    results: []

hubert-classifier-aug-fold-7

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: 0.4362
  • Accuracy: 0.8976
  • Precision: 0.9089
  • Recall: 0.8976
  • F1: 0.8953
  • Binary: 0.9294

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 0.19 50 4.0533 0.0324 0.0014 0.0324 0.0027 0.2927
No log 0.38 100 3.5885 0.0514 0.0169 0.0514 0.0106 0.3262
No log 0.58 150 3.3501 0.0541 0.0206 0.0541 0.0140 0.3330
No log 0.77 200 3.2675 0.0649 0.0154 0.0649 0.0203 0.3405
No log 0.96 250 3.0965 0.1162 0.0330 0.1162 0.0456 0.3797
3.7373 1.15 300 2.9467 0.1514 0.0616 0.1514 0.0770 0.4035
3.7373 1.34 350 2.7676 0.2 0.1022 0.2 0.1111 0.4384
3.7373 1.53 400 2.5435 0.2649 0.1888 0.2649 0.1783 0.4805
3.7373 1.73 450 2.3812 0.2973 0.1993 0.2973 0.1965 0.5068
3.7373 1.92 500 2.1573 0.3946 0.3075 0.3946 0.3120 0.5749
2.9139 2.11 550 1.9561 0.4486 0.4077 0.4486 0.3798 0.6132
2.9139 2.3 600 1.7966 0.4784 0.4652 0.4784 0.4273 0.6324
2.9139 2.49 650 1.7610 0.5270 0.5083 0.5270 0.4675 0.6632
2.9139 2.68 700 1.5796 0.5351 0.4840 0.5351 0.4750 0.6741
2.9139 2.88 750 1.4707 0.5676 0.5624 0.5676 0.5239 0.6935
2.2164 3.07 800 1.3680 0.6162 0.6049 0.6162 0.5829 0.7286
2.2164 3.26 850 1.2484 0.6162 0.6078 0.6162 0.5800 0.7319
2.2164 3.45 900 1.1271 0.6649 0.6659 0.6649 0.6400 0.7646
2.2164 3.64 950 1.0343 0.7108 0.7139 0.7108 0.6851 0.7959
2.2164 3.84 1000 1.0379 0.7027 0.7076 0.7027 0.6733 0.7922
1.8319 4.03 1050 1.0744 0.7 0.7494 0.7 0.6818 0.7935
1.8319 4.22 1100 0.9615 0.7324 0.7692 0.7324 0.7194 0.8141
1.8319 4.41 1150 0.8683 0.7514 0.7827 0.7514 0.7341 0.8251
1.8319 4.6 1200 0.8870 0.7432 0.7827 0.7432 0.7307 0.8195
1.8319 4.79 1250 0.8191 0.7676 0.7874 0.7676 0.7516 0.8357
1.8319 4.99 1300 0.7923 0.7784 0.8235 0.7784 0.7701 0.8441
1.5844 5.18 1350 0.7525 0.8 0.8203 0.8 0.7905 0.8605
1.5844 5.37 1400 0.7352 0.8 0.8401 0.8 0.7994 0.8603
1.5844 5.56 1450 0.6931 0.8081 0.8423 0.8081 0.8017 0.8649
1.5844 5.75 1500 0.6872 0.8081 0.8367 0.8081 0.8005 0.8670
1.5844 5.94 1550 0.6630 0.8189 0.8507 0.8189 0.8133 0.8735
1.4042 6.14 1600 0.6284 0.8216 0.8424 0.8216 0.8153 0.8757
1.4042 6.33 1650 0.7190 0.7865 0.8274 0.7865 0.7788 0.8508
1.4042 6.52 1700 0.6470 0.8216 0.8428 0.8216 0.8163 0.8754
1.4042 6.71 1750 0.6415 0.8324 0.8655 0.8324 0.8277 0.8822
1.4042 6.9 1800 0.6644 0.8216 0.8554 0.8216 0.8133 0.8735
1.2826 7.09 1850 0.6328 0.8243 0.8607 0.8243 0.8217 0.8781
1.2826 7.29 1900 0.6106 0.8351 0.8673 0.8351 0.8284 0.8857
1.2826 7.48 1950 0.6186 0.8297 0.8686 0.8297 0.8248 0.8803
1.2826 7.67 2000 0.6167 0.8351 0.8709 0.8351 0.8321 0.8838
1.2826 7.86 2050 0.5680 0.8378 0.8691 0.8378 0.8352 0.8857
1.1959 8.05 2100 0.5415 0.8541 0.8849 0.8541 0.8512 0.8978
1.1959 8.25 2150 0.5322 0.8568 0.8910 0.8568 0.8552 0.8997
1.1959 8.44 2200 0.5865 0.8432 0.8675 0.8432 0.8373 0.8914
1.1959 8.63 2250 0.5779 0.8541 0.8865 0.8541 0.8512 0.9000
1.1959 8.82 2300 0.5011 0.8757 0.9080 0.8757 0.8752 0.9154
1.1236 9.01 2350 0.5108 0.8514 0.8804 0.8514 0.8498 0.8981
1.1236 9.2 2400 0.5375 0.8486 0.8772 0.8486 0.8459 0.8962
1.1236 9.4 2450 0.5775 0.8459 0.8746 0.8459 0.8473 0.8943
1.1236 9.59 2500 0.5318 0.8514 0.8862 0.8514 0.8497 0.9003
1.1236 9.78 2550 0.5484 0.8459 0.8761 0.8459 0.8439 0.8976
1.1236 9.97 2600 0.5733 0.8486 0.8849 0.8486 0.8474 0.8951
1.0544 10.16 2650 0.5349 0.8541 0.8818 0.8541 0.8496 0.9000
1.0544 10.35 2700 0.5435 0.8459 0.8777 0.8459 0.8388 0.8932
1.0544 10.55 2750 0.4787 0.8595 0.8822 0.8595 0.8563 0.9027
1.0544 10.74 2800 0.4678 0.8595 0.8880 0.8595 0.8562 0.9027
1.0544 10.93 2850 0.4572 0.8730 0.9001 0.8730 0.8707 0.9103
1.0171 11.12 2900 0.5138 0.8568 0.8876 0.8568 0.8529 0.8997
1.0171 11.31 2950 0.5102 0.8757 0.8980 0.8757 0.8750 0.9130
1.0171 11.51 3000 0.5265 0.8676 0.8921 0.8676 0.8649 0.9076
1.0171 11.7 3050 0.4659 0.8730 0.8961 0.8730 0.8733 0.9132
1.0171 11.89 3100 0.4995 0.8676 0.8917 0.8676 0.8621 0.9084
0.9541 12.08 3150 0.4533 0.8811 0.8996 0.8811 0.8788 0.9168
0.9541 12.27 3200 0.4571 0.8865 0.9085 0.8865 0.8866 0.9205
0.9541 12.46 3250 0.4846 0.8622 0.8908 0.8622 0.8596 0.9035
0.9541 12.66 3300 0.4850 0.8730 0.8989 0.8730 0.8710 0.9111
0.9541 12.85 3350 0.4826 0.8568 0.8834 0.8568 0.8522 0.8997
0.9149 13.04 3400 0.4680 0.8730 0.8938 0.8730 0.8717 0.9103
0.9149 13.23 3450 0.5733 0.8486 0.8769 0.8486 0.8468 0.8941
0.9149 13.42 3500 0.5068 0.8730 0.8975 0.8730 0.8718 0.9111
0.9149 13.61 3550 0.4816 0.8730 0.8991 0.8730 0.8721 0.9122
0.9149 13.81 3600 0.5007 0.8676 0.8944 0.8676 0.8677 0.9095
0.9149 14.0 3650 0.4674 0.8811 0.9061 0.8811 0.8796 0.9168
0.8802 14.19 3700 0.4997 0.8622 0.8860 0.8622 0.8608 0.9035
0.8802 14.38 3750 0.4425 0.8784 0.9036 0.8784 0.8768 0.9149
0.8802 14.57 3800 0.5111 0.8811 0.9088 0.8811 0.8808 0.9170
0.8802 14.77 3850 0.4408 0.8811 0.9036 0.8811 0.8794 0.9168
0.8802 14.96 3900 0.5053 0.8622 0.8855 0.8622 0.8570 0.9035
0.8475 15.15 3950 0.5046 0.8622 0.8897 0.8622 0.8599 0.9038
0.8475 15.34 4000 0.4560 0.8649 0.8849 0.8649 0.8635 0.9068
0.8475 15.53 4050 0.4562 0.8730 0.8944 0.8730 0.8722 0.9124
0.8475 15.72 4100 0.4827 0.8622 0.8932 0.8622 0.8611 0.9027
0.8475 15.92 4150 0.4750 0.8784 0.9039 0.8784 0.8775 0.9159
0.8235 16.11 4200 0.4789 0.8703 0.8998 0.8703 0.8689 0.9092
0.8235 16.3 4250 0.4445 0.8892 0.9136 0.8892 0.8875 0.9227
0.8235 16.49 4300 0.4804 0.8703 0.8950 0.8703 0.8690 0.9086
0.8235 16.68 4350 0.4556 0.8676 0.8878 0.8676 0.8639 0.9076
0.8235 16.87 4400 0.5254 0.8622 0.8844 0.8622 0.8571 0.9030
0.7913 17.07 4450 0.4432 0.8946 0.9105 0.8946 0.8916 0.9273
0.7913 17.26 4500 0.4991 0.8622 0.8906 0.8622 0.8603 0.9035
0.7913 17.45 4550 0.4480 0.8865 0.9067 0.8865 0.8836 0.9205
0.7913 17.64 4600 0.4408 0.8757 0.8954 0.8757 0.8748 0.9130
0.7913 17.83 4650 0.4559 0.8811 0.9033 0.8811 0.8804 0.9189
0.7769 18.02 4700 0.4716 0.8919 0.9136 0.8919 0.8914 0.9254
0.7769 18.22 4750 0.4492 0.8811 0.9059 0.8811 0.8785 0.9170
0.7769 18.41 4800 0.4714 0.8811 0.9062 0.8811 0.8798 0.9170
0.7769 18.6 4850 0.4849 0.8757 0.9015 0.8757 0.8745 0.9122
0.7769 18.79 4900 0.4156 0.8946 0.9140 0.8946 0.8918 0.9262
0.7769 18.98 4950 0.4333 0.8892 0.9066 0.8892 0.8862 0.9227
0.7461 19.18 5000 0.4054 0.9054 0.9220 0.9054 0.9033 0.9341
0.7461 19.37 5050 0.4613 0.8757 0.8999 0.8757 0.8699 0.9132
0.7461 19.56 5100 0.4379 0.8865 0.9112 0.8865 0.8854 0.9219
0.7461 19.75 5150 0.4349 0.8946 0.9120 0.8946 0.8934 0.9262
0.7461 19.94 5200 0.4647 0.8811 0.9009 0.8811 0.8794 0.9181
0.7216 20.13 5250 0.4346 0.9027 0.9189 0.9027 0.9017 0.9322
0.7216 20.33 5300 0.4577 0.9 0.9156 0.9 0.8984 0.9322
0.7216 20.52 5350 0.4712 0.8946 0.9152 0.8946 0.8944 0.9276

Framework versions

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