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hubert-classifier-aug-fold-1-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-1
    results: []

hubert-classifier-aug-fold-1

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: 0.5129
  • Accuracy: 0.8706
  • Precision: 0.8830
  • Recall: 0.8706
  • F1: 0.8687
  • Binary: 0.9084

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 3.8853 0.0499 0.0125 0.0499 0.0132 0.3132
No log 0.43 100 3.4193 0.0702 0.0103 0.0702 0.0167 0.3417
No log 0.65 150 3.1606 0.1161 0.0528 0.1161 0.0512 0.3783
No log 0.86 200 2.9650 0.1714 0.1093 0.1714 0.1008 0.4148
3.6314 1.08 250 2.7976 0.1714 0.0947 0.1714 0.0927 0.4148
3.6314 1.29 300 2.4874 0.2740 0.1723 0.2740 0.1770 0.4870
3.6314 1.51 350 2.3192 0.3414 0.2741 0.3414 0.2588 0.5379
3.6314 1.72 400 2.1116 0.4278 0.3772 0.4278 0.3514 0.5988
3.6314 1.94 450 1.9495 0.4683 0.4712 0.4683 0.4075 0.6273
2.6994 2.16 500 1.8632 0.4764 0.4337 0.4764 0.4098 0.6313
2.6994 2.37 550 1.6822 0.5250 0.4922 0.5250 0.4621 0.6664
2.6994 2.59 600 1.5299 0.5938 0.5590 0.5938 0.5296 0.7165
2.6994 2.8 650 1.4177 0.6140 0.5885 0.6140 0.5664 0.7298
2.1575 3.02 700 1.3614 0.6100 0.5558 0.6100 0.5498 0.7283
2.1575 3.23 750 1.2209 0.6491 0.6650 0.6491 0.6086 0.7528
2.1575 3.45 800 1.2059 0.6829 0.7281 0.6829 0.6553 0.7748
2.1575 3.66 850 1.1207 0.7031 0.7301 0.7031 0.6741 0.7908
2.1575 3.88 900 1.0553 0.7139 0.7368 0.7139 0.6967 0.7993
1.8321 4.09 950 0.9864 0.7490 0.7412 0.7490 0.7291 0.8250
1.8321 4.31 1000 0.9365 0.7544 0.7653 0.7544 0.7360 0.8278
1.8321 4.53 1050 0.9050 0.7584 0.7617 0.7584 0.7438 0.8323
1.8321 4.74 1100 0.8241 0.7760 0.7896 0.7760 0.7638 0.8424
1.8321 4.96 1150 0.8260 0.7611 0.7712 0.7611 0.7455 0.8335
1.6136 5.17 1200 0.8008 0.7814 0.8013 0.7814 0.7738 0.8474
1.6136 5.39 1250 0.7588 0.7787 0.7981 0.7787 0.7694 0.8453
1.6136 5.6 1300 0.7619 0.7868 0.7963 0.7868 0.7755 0.8520
1.6136 5.82 1350 0.7611 0.7868 0.7972 0.7868 0.7755 0.8514
1.4406 6.03 1400 0.7094 0.7976 0.8090 0.7976 0.7923 0.8591
1.4406 6.25 1450 0.7133 0.7976 0.7990 0.7976 0.7873 0.8595
1.4406 6.47 1500 0.7126 0.8016 0.8151 0.8016 0.7921 0.8618
1.4406 6.68 1550 0.6381 0.8043 0.8242 0.8043 0.7979 0.8642
1.4406 6.9 1600 0.6248 0.8111 0.8253 0.8111 0.8079 0.8683
1.3033 7.11 1650 0.6333 0.8138 0.8391 0.8138 0.8125 0.8699
1.3033 7.33 1700 0.6087 0.8232 0.8450 0.8232 0.8188 0.8775
1.3033 7.54 1750 0.5919 0.8273 0.8347 0.8273 0.8199 0.8796
1.3033 7.76 1800 0.5835 0.8232 0.8398 0.8232 0.8198 0.8779
1.3033 7.97 1850 0.5697 0.8232 0.8397 0.8232 0.8192 0.8780
1.2408 8.19 1900 0.6186 0.8111 0.8354 0.8111 0.8050 0.8694
1.2408 8.41 1950 0.5622 0.8327 0.8445 0.8327 0.8306 0.8830
1.2408 8.62 2000 0.5740 0.8205 0.8407 0.8205 0.8184 0.8756
1.2408 8.84 2050 0.5627 0.8300 0.8460 0.8300 0.8286 0.8816
1.1272 9.05 2100 0.5340 0.8381 0.8538 0.8381 0.8354 0.8873
1.1272 9.27 2150 0.5927 0.8246 0.8475 0.8246 0.8205 0.8795
1.1272 9.48 2200 0.5429 0.8300 0.8511 0.8300 0.8292 0.8812
1.1272 9.7 2250 0.4781 0.8529 0.8659 0.8529 0.8518 0.8977
1.1272 9.91 2300 0.5081 0.8448 0.8621 0.8448 0.8442 0.8930
1.0783 10.13 2350 0.5015 0.8489 0.8680 0.8489 0.8473 0.8947
1.0783 10.34 2400 0.5225 0.8421 0.8627 0.8421 0.8389 0.8906
1.0783 10.56 2450 0.5113 0.8435 0.8602 0.8435 0.8426 0.8911
1.0783 10.78 2500 0.5009 0.8556 0.8712 0.8556 0.8526 0.9005
1.0783 10.99 2550 0.5112 0.8421 0.8571 0.8421 0.8416 0.8922
1.0384 11.21 2600 0.4812 0.8543 0.8663 0.8543 0.8516 0.8996
1.0384 11.42 2650 0.5017 0.8583 0.8731 0.8583 0.8561 0.9020
1.0384 11.64 2700 0.4837 0.8489 0.8650 0.8489 0.8450 0.8958
1.0384 11.85 2750 0.5084 0.8435 0.8643 0.8435 0.8389 0.8920
1.0115 12.07 2800 0.4843 0.8583 0.8767 0.8583 0.8562 0.9024
1.0115 12.28 2850 0.5396 0.8502 0.8703 0.8502 0.8465 0.8973
1.0115 12.5 2900 0.5440 0.8435 0.8563 0.8435 0.8375 0.8916
1.0115 12.72 2950 0.5076 0.8489 0.8600 0.8489 0.8460 0.8958
1.0115 12.93 3000 0.5253 0.8435 0.8571 0.8435 0.8422 0.8915
0.9461 13.15 3050 0.4841 0.8664 0.8828 0.8664 0.8669 0.9081
0.9461 13.36 3100 0.4890 0.8637 0.8759 0.8637 0.8626 0.9062
0.9461 13.58 3150 0.4948 0.8664 0.8819 0.8664 0.8656 0.9076
0.9461 13.79 3200 0.4851 0.8596 0.8763 0.8596 0.8590 0.9028
0.9001 14.01 3250 0.4966 0.8583 0.8735 0.8583 0.8587 0.9020
0.9001 14.22 3300 0.5335 0.8543 0.8688 0.8543 0.8531 0.8985
0.9001 14.44 3350 0.5072 0.8596 0.8732 0.8596 0.8587 0.9024
0.9001 14.66 3400 0.4968 0.8664 0.8793 0.8664 0.8654 0.9076
0.9001 14.87 3450 0.4763 0.8637 0.8779 0.8637 0.8629 0.9057
0.8663 15.09 3500 0.5134 0.8704 0.8781 0.8704 0.8685 0.9104
0.8663 15.3 3550 0.5010 0.8664 0.8812 0.8664 0.8668 0.9081
0.8663 15.52 3600 0.5221 0.8556 0.8694 0.8556 0.8547 0.9000
0.8663 15.73 3650 0.4990 0.8704 0.8813 0.8704 0.8691 0.9104
0.8663 15.95 3700 0.5046 0.8610 0.8729 0.8610 0.8591 0.9032
0.8499 16.16 3750 0.5099 0.8583 0.8730 0.8583 0.8562 0.9019
0.8499 16.38 3800 0.4859 0.8677 0.8786 0.8677 0.8668 0.9085
0.8499 16.59 3850 0.5005 0.8637 0.8731 0.8637 0.8619 0.9067
0.8499 16.81 3900 0.5169 0.8596 0.8735 0.8596 0.8590 0.9023
0.8149 17.03 3950 0.5035 0.8583 0.8710 0.8583 0.8579 0.9019
0.8149 17.24 4000 0.5211 0.8570 0.8748 0.8570 0.8573 0.9009

Framework versions

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