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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: 0.5616
  • Accuracy: 0.8814
  • Precision: 0.8965
  • Recall: 0.8814
  • F1: 0.8795
  • Binary: 0.9182

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 4.4205 0.0310 0.0078 0.0310 0.0104 0.2054
No log 0.27 100 4.3291 0.0445 0.0108 0.0445 0.0126 0.2987
No log 0.4 150 3.9426 0.0688 0.0295 0.0688 0.0221 0.3405
No log 0.54 200 3.5950 0.0823 0.0420 0.0823 0.0336 0.3548
No log 0.67 250 3.3440 0.1323 0.0736 0.1323 0.0675 0.3893
No log 0.81 300 3.0808 0.2105 0.1599 0.2105 0.1353 0.4457
No log 0.94 350 2.8041 0.3212 0.2060 0.3212 0.2180 0.5235
3.8037 1.08 400 2.4494 0.3603 0.2731 0.3603 0.2746 0.5495
3.8037 1.21 450 2.1029 0.4588 0.3806 0.4588 0.3860 0.6201
3.8037 1.35 500 1.8222 0.5317 0.5092 0.5317 0.4811 0.6730
3.8037 1.48 550 1.6391 0.5533 0.5401 0.5533 0.5040 0.6862
3.8037 1.62 600 1.3955 0.6464 0.6432 0.6464 0.6129 0.7538
3.8037 1.75 650 1.3006 0.6653 0.6734 0.6653 0.6399 0.7645
3.8037 1.89 700 1.2028 0.6721 0.6946 0.6721 0.6492 0.7707
1.9419 2.02 750 1.0903 0.6910 0.6900 0.6910 0.6661 0.7846
1.9419 2.16 800 1.1952 0.7099 0.7198 0.7099 0.6923 0.7962
1.9419 2.29 850 0.9361 0.7341 0.7547 0.7341 0.7222 0.8146
1.9419 2.43 900 0.8789 0.7490 0.7725 0.7490 0.7348 0.8252
1.9419 2.56 950 0.8519 0.7787 0.8015 0.7787 0.7714 0.8453
1.9419 2.7 1000 0.8211 0.7692 0.7978 0.7692 0.7610 0.8394
1.9419 2.83 1050 0.7343 0.7922 0.8128 0.7922 0.7849 0.8563
1.9419 2.96 1100 0.8199 0.7760 0.8004 0.7760 0.7703 0.8451
1.0676 3.1 1150 0.6783 0.8016 0.8161 0.8016 0.7956 0.8610
1.0676 3.23 1200 0.7483 0.8043 0.8285 0.8043 0.7991 0.8638
1.0676 3.37 1250 0.8469 0.7881 0.8090 0.7881 0.7795 0.8514
1.0676 3.5 1300 0.7466 0.8003 0.8228 0.8003 0.7963 0.8603
1.0676 3.64 1350 0.7441 0.8084 0.8375 0.8084 0.8059 0.8656
1.0676 3.77 1400 0.6885 0.8124 0.8380 0.8124 0.8082 0.8695
1.0676 3.91 1450 0.8319 0.7787 0.8045 0.7787 0.7729 0.8459
0.75 4.04 1500 0.8044 0.8057 0.8320 0.8057 0.8017 0.8648
0.75 4.18 1550 0.8120 0.8016 0.8230 0.8016 0.7964 0.8618
0.75 4.31 1600 0.7503 0.8016 0.8166 0.8016 0.7965 0.8629
0.75 4.45 1650 0.7646 0.8097 0.8269 0.8097 0.8025 0.8682
0.75 4.58 1700 0.7328 0.8246 0.8442 0.8246 0.8189 0.8784
0.75 4.72 1750 0.7019 0.8300 0.8479 0.8300 0.8270 0.8831
0.75 4.85 1800 0.6364 0.8408 0.8558 0.8408 0.8379 0.8885
0.75 4.99 1850 0.6562 0.8259 0.8461 0.8259 0.8214 0.8787
0.5856 5.12 1900 0.6412 0.8340 0.8500 0.8340 0.8304 0.8843
0.5856 5.26 1950 0.6739 0.8340 0.8548 0.8340 0.8333 0.8843
0.5856 5.39 2000 0.7564 0.8097 0.8367 0.8097 0.8090 0.8673
0.5856 5.53 2050 0.6495 0.8286 0.8473 0.8286 0.8264 0.8807
0.5856 5.66 2100 0.7090 0.8165 0.8328 0.8165 0.8159 0.8717
0.5856 5.8 2150 0.7712 0.8435 0.8654 0.8435 0.8419 0.8897
0.5856 5.93 2200 0.7484 0.8273 0.8467 0.8273 0.8230 0.8802
0.4896 6.06 2250 0.7999 0.8178 0.8344 0.8178 0.8163 0.8725
0.4896 6.2 2300 0.7098 0.8300 0.8501 0.8300 0.8280 0.8821
0.4896 6.33 2350 0.8158 0.8259 0.8434 0.8259 0.8226 0.8783
0.4896 6.47 2400 0.6873 0.8381 0.8592 0.8381 0.8367 0.8868
0.4896 6.6 2450 0.7824 0.8178 0.8394 0.8178 0.8156 0.8730
0.4896 6.74 2500 0.6733 0.8448 0.8631 0.8448 0.8444 0.8915
0.4896 6.87 2550 0.7658 0.8381 0.8577 0.8381 0.8347 0.8862
0.4288 7.01 2600 0.7045 0.8367 0.8536 0.8367 0.8343 0.8853
0.4288 7.14 2650 0.6885 0.8570 0.8748 0.8570 0.8545 0.8995
0.4288 7.28 2700 0.7015 0.8475 0.8632 0.8475 0.8462 0.8934
0.4288 7.41 2750 0.7385 0.8381 0.8539 0.8381 0.8363 0.8870
0.4288 7.55 2800 0.7196 0.8475 0.8597 0.8475 0.8473 0.8930
0.4288 7.68 2850 0.7285 0.8421 0.8558 0.8421 0.8399 0.8887
0.4288 7.82 2900 0.7507 0.8367 0.8513 0.8367 0.8346 0.8849
0.4288 7.95 2950 0.8049 0.8259 0.8522 0.8259 0.8225 0.8779
0.3797 8.09 3000 0.7337 0.8529 0.8652 0.8529 0.8494 0.8978
0.3797 8.22 3050 0.7414 0.8475 0.8628 0.8475 0.8451 0.8924
0.3797 8.36 3100 0.8024 0.8394 0.8592 0.8394 0.8364 0.8877
0.3797 8.49 3150 0.7642 0.8435 0.8635 0.8435 0.8405 0.8904
0.3797 8.63 3200 0.7560 0.8448 0.8641 0.8448 0.8415 0.8906
0.3797 8.76 3250 0.7889 0.8408 0.8640 0.8408 0.8373 0.8887
0.3797 8.89 3300 0.7479 0.8448 0.8624 0.8448 0.8436 0.8915
0.3486 9.03 3350 0.7430 0.8543 0.8727 0.8543 0.8529 0.8977
0.3486 9.16 3400 0.7325 0.8394 0.8549 0.8394 0.8367 0.8877
0.3486 9.3 3450 0.7623 0.8489 0.8641 0.8489 0.8441 0.8949
0.3486 9.43 3500 0.7893 0.8462 0.8645 0.8462 0.8425 0.8924
0.3486 9.57 3550 0.8721 0.8273 0.8504 0.8273 0.8239 0.8808
0.3486 9.7 3600 0.7886 0.8489 0.8699 0.8489 0.8454 0.8939
0.3486 9.84 3650 0.7844 0.8502 0.8692 0.8502 0.8469 0.8953
0.3486 9.97 3700 0.9039 0.8232 0.8471 0.8232 0.8179 0.8769

Framework versions

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