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hubert-classifier-aug-fold-2

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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-2
    results: []

hubert-classifier-aug-fold-2

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: 0.7259
  • Accuracy: 0.8720
  • Precision: 0.8869
  • Recall: 0.8720
  • F1: 0.8705
  • Binary: 0.9115

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
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Binary
No log 0.13 50 4.4197 0.0270 0.0232 0.0270 0.0117 0.1849
No log 0.27 100 4.2998 0.0432 0.0232 0.0432 0.0109 0.3126
No log 0.4 150 3.9731 0.0553 0.0154 0.0553 0.0170 0.3332
No log 0.54 200 3.6492 0.0594 0.0211 0.0594 0.0169 0.3355
No log 0.67 250 3.4482 0.0945 0.0252 0.0945 0.0317 0.3599
No log 0.81 300 3.2094 0.1215 0.0559 0.1215 0.0495 0.3815
No log 0.94 350 3.0184 0.1660 0.1000 0.1660 0.0883 0.4140
3.8426 1.08 400 2.6834 0.2807 0.2104 0.2807 0.2054 0.4961
3.8426 1.21 450 2.3799 0.3900 0.3073 0.3900 0.3055 0.5725
3.8426 1.35 500 2.0607 0.4440 0.4235 0.4440 0.3765 0.6111
3.8426 1.48 550 1.7232 0.5520 0.5014 0.5520 0.4917 0.6857
3.8426 1.62 600 1.5077 0.6019 0.5531 0.6019 0.5435 0.7232
3.8426 1.75 650 1.3166 0.6181 0.6024 0.6181 0.5743 0.7335
3.8426 1.89 700 1.2097 0.6802 0.6583 0.6802 0.6449 0.7750
2.1078 2.02 750 1.1776 0.6856 0.6934 0.6856 0.6510 0.7808
2.1078 2.16 800 1.0102 0.7382 0.7335 0.7382 0.7132 0.8152
2.1078 2.29 850 0.9295 0.7409 0.7557 0.7409 0.7242 0.8189
2.1078 2.43 900 0.8473 0.7760 0.7916 0.7760 0.7673 0.8444
2.1078 2.56 950 0.7956 0.8057 0.8147 0.8057 0.7962 0.8632
2.1078 2.7 1000 0.8091 0.7908 0.8199 0.7908 0.7817 0.8534
2.1078 2.83 1050 0.7345 0.8084 0.8208 0.8084 0.8027 0.8650
2.1078 2.96 1100 0.7217 0.7935 0.7964 0.7935 0.7835 0.8557
1.1182 3.1 1150 0.7444 0.7895 0.8096 0.7895 0.7827 0.8545
1.1182 3.23 1200 0.7182 0.8057 0.8153 0.8057 0.7971 0.8638
1.1182 3.37 1250 0.6496 0.8313 0.8414 0.8313 0.8267 0.8808
1.1182 3.5 1300 0.7163 0.8111 0.8349 0.8111 0.8101 0.8684
1.1182 3.64 1350 0.7026 0.8354 0.8543 0.8354 0.8309 0.8846
1.1182 3.77 1400 0.6504 0.8246 0.8381 0.8246 0.8194 0.8765
1.1182 3.91 1450 0.6685 0.8381 0.8525 0.8381 0.8358 0.8879
0.7752 4.04 1500 0.6600 0.8340 0.8528 0.8340 0.8314 0.8839
0.7752 4.18 1550 0.6048 0.8475 0.8594 0.8475 0.8440 0.8920
0.7752 4.31 1600 0.5907 0.8435 0.8575 0.8435 0.8397 0.8906
0.7752 4.45 1650 0.6118 0.8502 0.8673 0.8502 0.8476 0.8949
0.7752 4.58 1700 0.6096 0.8610 0.8724 0.8610 0.8581 0.9024
0.7752 4.72 1750 0.6032 0.8529 0.8694 0.8529 0.8499 0.8977
0.7752 4.85 1800 0.6705 0.8502 0.8625 0.8502 0.8447 0.8962
0.7752 4.99 1850 0.6740 0.8381 0.8538 0.8381 0.8331 0.8877
0.6054 5.12 1900 0.6444 0.8367 0.8471 0.8367 0.8302 0.8853
0.6054 5.26 1950 0.6167 0.8529 0.8648 0.8529 0.8500 0.8976
0.6054 5.39 2000 0.6535 0.8462 0.8657 0.8462 0.8429 0.8939
0.6054 5.53 2050 0.6420 0.8556 0.8688 0.8556 0.8550 0.8992
0.6054 5.66 2100 0.6535 0.8543 0.8696 0.8543 0.8523 0.8977
0.6054 5.8 2150 0.5879 0.8516 0.8646 0.8516 0.8483 0.8958
0.6054 5.93 2200 0.5808 0.8570 0.8701 0.8570 0.8551 0.9000
0.5025 6.06 2250 0.6637 0.8516 0.8711 0.8516 0.8482 0.8962
0.5025 6.2 2300 0.6450 0.8583 0.8734 0.8583 0.8540 0.9009
0.5025 6.33 2350 0.6152 0.8664 0.8768 0.8664 0.8644 0.9082
0.5025 6.47 2400 0.6640 0.8475 0.8620 0.8475 0.8432 0.8934
0.5025 6.6 2450 0.5817 0.8664 0.8795 0.8664 0.8645 0.9062
0.5025 6.74 2500 0.6881 0.8529 0.8673 0.8529 0.8487 0.8977
0.5025 6.87 2550 0.6868 0.8421 0.8563 0.8421 0.8388 0.8907
0.4381 7.01 2600 0.6270 0.8677 0.8823 0.8677 0.8664 0.9086
0.4381 7.14 2650 0.7011 0.8583 0.8703 0.8583 0.8537 0.9001
0.4381 7.28 2700 0.6665 0.8570 0.8757 0.8570 0.8548 0.8992
0.4381 7.41 2750 0.6948 0.8421 0.8586 0.8421 0.8425 0.8911
0.4381 7.55 2800 0.6832 0.8570 0.8710 0.8570 0.8542 0.9005
0.4381 7.68 2850 0.6391 0.8623 0.8782 0.8623 0.8620 0.9038
0.4381 7.82 2900 0.8113 0.8448 0.8609 0.8448 0.8411 0.8946
0.4381 7.95 2950 0.6688 0.8623 0.8724 0.8623 0.8603 0.9049
0.381 8.09 3000 0.6731 0.8529 0.8652 0.8529 0.8508 0.8972
0.381 8.22 3050 0.8063 0.8340 0.8507 0.8340 0.8300 0.8839
0.381 8.36 3100 0.6534 0.8596 0.8719 0.8596 0.8567 0.9009
0.381 8.49 3150 0.6772 0.8596 0.8730 0.8596 0.8574 0.9024
0.381 8.63 3200 0.6293 0.8637 0.8754 0.8637 0.8630 0.9042
0.381 8.76 3250 0.6644 0.8570 0.8705 0.8570 0.8543 0.9001
0.381 8.89 3300 0.6469 0.8623 0.8758 0.8623 0.8614 0.9043
0.3473 9.03 3350 0.6055 0.8731 0.8833 0.8731 0.8722 0.9104
0.3473 9.16 3400 0.6828 0.8650 0.8767 0.8650 0.8636 0.9047
0.3473 9.3 3450 0.6625 0.8826 0.8967 0.8826 0.8817 0.9179
0.3473 9.43 3500 0.7111 0.8583 0.8692 0.8583 0.8559 0.9011
0.3473 9.57 3550 0.7215 0.8475 0.8608 0.8475 0.8449 0.8950
0.3473 9.7 3600 0.7040 0.8556 0.8654 0.8556 0.8527 0.9001
0.3473 9.84 3650 0.6809 0.8556 0.8674 0.8556 0.8531 0.8996
0.3473 9.97 3700 0.7191 0.8610 0.8754 0.8610 0.8609 0.9034
0.3245 10.11 3750 0.7053 0.8610 0.8682 0.8610 0.8586 0.9045
0.3245 10.24 3800 0.6594 0.8745 0.8869 0.8745 0.8739 0.9132
0.3245 10.38 3850 0.6882 0.8745 0.8872 0.8745 0.8735 0.9113
0.3245 10.51 3900 0.7113 0.8596 0.8732 0.8596 0.8584 0.9035
0.3245 10.65 3950 0.7299 0.8677 0.8836 0.8677 0.8675 0.9070
0.3245 10.78 4000 0.6812 0.8758 0.8861 0.8758 0.8745 0.9132
0.3245 10.92 4050 0.6459 0.8812 0.8927 0.8812 0.8788 0.9170
0.2964 11.05 4100 0.7044 0.8677 0.8805 0.8677 0.8665 0.9072
0.2964 11.19 4150 0.6455 0.8677 0.8758 0.8677 0.8663 0.9086
0.2964 11.32 4200 0.7581 0.8704 0.8790 0.8704 0.8692 0.9093
0.2964 11.46 4250 0.7489 0.8623 0.8781 0.8623 0.8588 0.9038
0.2964 11.59 4300 0.7293 0.8556 0.8677 0.8556 0.8541 0.8991
0.2964 11.73 4350 0.7996 0.8570 0.8662 0.8570 0.8550 0.8991
0.2964 11.86 4400 0.7340 0.8556 0.8670 0.8556 0.8531 0.8985

Framework versions

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