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hubert-classifier-aug-fold-4-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-4
    results: []

hubert-classifier-aug-fold-4

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: 0.6062
  • Accuracy: 0.8491
  • Precision: 0.8645
  • Recall: 0.8491
  • F1: 0.8497
  • Binary: 0.8941

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 4.3309 0.0297 0.0020 0.0297 0.0036 0.2159
No log 0.43 100 3.6722 0.0553 0.0262 0.0553 0.0174 0.3324
No log 0.65 150 3.3950 0.0621 0.0161 0.0621 0.0170 0.3379
No log 0.86 200 3.2145 0.1134 0.0428 0.1134 0.0506 0.3713
3.8422 1.08 250 3.0635 0.1525 0.0902 0.1525 0.0873 0.4015
3.8422 1.29 300 2.8389 0.2119 0.1084 0.2119 0.1229 0.4354
3.8422 1.51 350 2.5852 0.2348 0.1770 0.2348 0.1484 0.4611
3.8422 1.72 400 2.3922 0.2888 0.2381 0.2888 0.2034 0.4977
3.8422 1.94 450 2.2068 0.3185 0.2658 0.3185 0.2420 0.5202
2.9357 2.16 500 2.0744 0.4103 0.4153 0.4103 0.3548 0.5827
2.9357 2.37 550 1.8778 0.4588 0.3960 0.4588 0.3847 0.6197
2.9357 2.59 600 1.8338 0.4440 0.4075 0.4440 0.3748 0.6085
2.9357 2.8 650 1.6534 0.4777 0.4265 0.4777 0.4116 0.6337
2.3301 3.02 700 1.5716 0.5344 0.5094 0.5344 0.4768 0.6733
2.3301 3.23 750 1.4864 0.5655 0.5298 0.5655 0.5208 0.6926
2.3301 3.45 800 1.4863 0.5452 0.5615 0.5452 0.5023 0.6803
2.3301 3.66 850 1.3882 0.5655 0.5432 0.5655 0.5064 0.6949
2.3301 3.88 900 1.3190 0.5897 0.5922 0.5897 0.5473 0.7117
1.9679 4.09 950 1.1769 0.6559 0.6830 0.6559 0.6272 0.7580
1.9679 4.31 1000 1.2232 0.6289 0.6469 0.6289 0.6012 0.7402
1.9679 4.53 1050 1.1078 0.6694 0.6994 0.6694 0.6583 0.7675
1.9679 4.74 1100 1.0104 0.7072 0.7350 0.7072 0.6881 0.7949
1.9679 4.96 1150 0.9920 0.7152 0.7264 0.7152 0.6941 0.8016
1.7302 5.17 1200 0.9389 0.7449 0.7648 0.7449 0.7335 0.8208
1.7302 5.39 1250 0.8678 0.7611 0.7735 0.7611 0.7461 0.8344
1.7302 5.6 1300 0.8991 0.7449 0.7474 0.7449 0.7261 0.8217
1.7302 5.82 1350 0.7903 0.7665 0.7756 0.7665 0.7544 0.8368
1.531 6.03 1400 0.8221 0.7665 0.7852 0.7665 0.7528 0.8355
1.531 6.25 1450 0.7516 0.7773 0.8021 0.7773 0.7606 0.8436
1.531 6.47 1500 0.7573 0.7787 0.7870 0.7787 0.7655 0.8452
1.531 6.68 1550 0.7561 0.7719 0.7814 0.7719 0.7595 0.8401
1.531 6.9 1600 0.6898 0.8030 0.8144 0.8030 0.7957 0.8609
1.3639 7.11 1650 0.6912 0.7989 0.8155 0.7989 0.7916 0.8603
1.3639 7.33 1700 0.6771 0.7989 0.8201 0.7989 0.7915 0.8590
1.3639 7.54 1750 0.6652 0.8016 0.8207 0.8016 0.7952 0.8617
1.3639 7.76 1800 0.7130 0.8016 0.8227 0.8016 0.7972 0.8613
1.3639 7.97 1850 0.6661 0.7962 0.8173 0.7962 0.7878 0.8575
1.2774 8.19 1900 0.6532 0.8178 0.8313 0.8178 0.8130 0.8722
1.2774 8.41 1950 0.6291 0.8178 0.8307 0.8178 0.8144 0.8729
1.2774 8.62 2000 0.6585 0.8030 0.8183 0.8030 0.7958 0.8618
1.2774 8.84 2050 0.6427 0.8084 0.8216 0.8084 0.8038 0.8656
1.1807 9.05 2100 0.7025 0.8124 0.8246 0.8124 0.8081 0.8686
1.1807 9.27 2150 0.6977 0.8016 0.8211 0.8016 0.7980 0.8599
1.1807 9.48 2200 0.6439 0.8246 0.8404 0.8246 0.8223 0.8760
1.1807 9.7 2250 0.5890 0.8394 0.8503 0.8394 0.8374 0.8860
1.1807 9.91 2300 0.5406 0.8313 0.8429 0.8313 0.8281 0.8816
1.105 10.13 2350 0.6131 0.8232 0.8371 0.8232 0.8203 0.8742
1.105 10.34 2400 0.6241 0.8232 0.8421 0.8232 0.8206 0.8738
1.105 10.56 2450 0.6349 0.8354 0.8498 0.8354 0.8328 0.8837
1.105 10.78 2500 0.7053 0.8165 0.8290 0.8165 0.8118 0.8713
1.105 10.99 2550 0.5652 0.8381 0.8504 0.8381 0.8353 0.8866
1.0741 11.21 2600 0.5764 0.8408 0.8533 0.8408 0.8378 0.8887
1.0741 11.42 2650 0.5663 0.8448 0.8612 0.8448 0.8441 0.8915
1.0741 11.64 2700 0.6290 0.8219 0.8361 0.8219 0.8194 0.8741
1.0741 11.85 2750 0.5994 0.8381 0.8546 0.8381 0.8355 0.8873
1.0208 12.07 2800 0.5851 0.8327 0.8434 0.8327 0.8289 0.8826
1.0208 12.28 2850 0.6522 0.8219 0.8411 0.8219 0.8204 0.8746
1.0208 12.5 2900 0.6401 0.8273 0.8392 0.8273 0.8241 0.8779
1.0208 12.72 2950 0.5764 0.8475 0.8607 0.8475 0.8448 0.8930
1.0208 12.93 3000 0.5834 0.8354 0.8432 0.8354 0.8315 0.8839
0.9784 13.15 3050 0.6171 0.8394 0.8562 0.8394 0.8367 0.8872
0.9784 13.36 3100 0.6362 0.8300 0.8428 0.8300 0.8258 0.8798
0.9784 13.58 3150 0.6154 0.8313 0.8466 0.8313 0.8301 0.8807
0.9784 13.79 3200 0.5939 0.8421 0.8561 0.8421 0.8395 0.8887
0.9237 14.01 3250 0.6167 0.8435 0.8516 0.8435 0.8412 0.8900
0.9237 14.22 3300 0.6338 0.8408 0.8490 0.8408 0.8388 0.8887
0.9237 14.44 3350 0.6051 0.8421 0.8558 0.8421 0.8408 0.8900
0.9237 14.66 3400 0.5816 0.8367 0.8493 0.8367 0.8343 0.8854
0.9237 14.87 3450 0.6617 0.8327 0.8512 0.8327 0.8309 0.8825
0.8932 15.09 3500 0.6038 0.8448 0.8590 0.8448 0.8439 0.8915
0.8932 15.3 3550 0.6460 0.8408 0.8543 0.8408 0.8389 0.8883
0.8932 15.52 3600 0.5571 0.8489 0.8586 0.8489 0.8474 0.8943
0.8932 15.73 3650 0.6321 0.8273 0.8420 0.8273 0.8251 0.8792
0.8932 15.95 3700 0.6127 0.8448 0.8598 0.8448 0.8422 0.8919
0.856 16.16 3750 0.5622 0.8502 0.8620 0.8502 0.8492 0.8957
0.856 16.38 3800 0.5919 0.8529 0.8648 0.8529 0.8508 0.8981
0.856 16.59 3850 0.5345 0.8489 0.8582 0.8489 0.8468 0.8947
0.856 16.81 3900 0.6384 0.8408 0.8585 0.8408 0.8399 0.8883
0.8435 17.03 3950 0.5643 0.8596 0.8724 0.8596 0.8582 0.9024
0.8435 17.24 4000 0.5582 0.8448 0.8573 0.8448 0.8441 0.8911
0.8435 17.46 4050 0.5755 0.8489 0.8590 0.8489 0.8467 0.8943
0.8435 17.67 4100 0.6104 0.8340 0.8478 0.8340 0.8309 0.8839
0.8435 17.89 4150 0.6327 0.8435 0.8568 0.8435 0.8413 0.8906
0.7849 18.1 4200 0.5686 0.8583 0.8732 0.8583 0.8566 0.9009
0.7849 18.32 4250 0.6191 0.8462 0.8622 0.8462 0.8430 0.8924
0.7849 18.53 4300 0.5812 0.8489 0.8641 0.8489 0.8491 0.8934
0.7849 18.75 4350 0.5785 0.8529 0.8678 0.8529 0.8519 0.8966
0.7849 18.97 4400 0.5474 0.8502 0.8638 0.8502 0.8484 0.8957
0.779 19.18 4450 0.5515 0.8637 0.8753 0.8637 0.8620 0.9047
0.779 19.4 4500 0.5591 0.8623 0.8736 0.8623 0.8610 0.9047
0.779 19.61 4550 0.5287 0.8718 0.8806 0.8718 0.8700 0.9108
0.779 19.83 4600 0.5561 0.8610 0.8704 0.8610 0.8598 0.9032
0.7593 20.04 4650 0.5487 0.8583 0.8686 0.8583 0.8566 0.9013
0.7593 20.26 4700 0.5873 0.8502 0.8649 0.8502 0.8470 0.8957
0.7593 20.47 4750 0.5470 0.8475 0.8603 0.8475 0.8454 0.8934
0.7593 20.69 4800 0.5921 0.8543 0.8680 0.8543 0.8521 0.8977
0.7593 20.91 4850 0.5692 0.8543 0.8658 0.8543 0.8523 0.8981
0.7392 21.12 4900 0.5863 0.8394 0.8527 0.8394 0.8370 0.8881

Framework versions

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