Hugging Face's logo

fydhfzh
/
hubert-classifier-aug-fold-3

Audio Classification
Transformers
TensorBoard
Safetensors
hubert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
hubert-classifier-aug-fold-3 / README.md
fydhfzh's picture
fydhfzh
End of training
229be43 verified
preview code
|
raw
history blame contribute delete
7.43 kB
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-3
    results: []

hubert-classifier-aug-fold-3

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: 0.6214
  • Accuracy: 0.8544
  • Precision: 0.8720
  • Recall: 0.8544
  • F1: 0.8540
  • Binary: 0.8989

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: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Binary
No log 0.24 50 4.4176 0.0172 0.0140 0.0172 0.0083 0.1549
No log 0.48 100 4.3072 0.0434 0.0497 0.0434 0.0197 0.2778
No log 0.72 150 3.9604 0.0906 0.0606 0.0906 0.0446 0.3581
No log 0.96 200 3.6355 0.1191 0.0507 0.1191 0.0558 0.3783
4.235 1.2 250 3.3489 0.1700 0.0800 0.1700 0.0858 0.4157
4.235 1.44 300 3.1008 0.2015 0.1270 0.2015 0.1158 0.4382
4.235 1.68 350 2.8220 0.2906 0.2075 0.2906 0.2012 0.5014
4.235 1.92 400 2.5557 0.3843 0.3160 0.3843 0.3099 0.5671
3.2055 2.16 450 2.1790 0.4801 0.4047 0.4801 0.4036 0.6344
3.2055 2.4 500 1.9034 0.5790 0.5557 0.5790 0.5261 0.7028
3.2055 2.63 550 1.6707 0.6135 0.6116 0.6135 0.5701 0.7273
3.2055 2.87 600 1.4658 0.6285 0.6047 0.6285 0.5817 0.7381
2.1878 3.11 650 1.3665 0.6457 0.6522 0.6457 0.6153 0.7534
2.1878 3.35 700 1.2309 0.6757 0.6806 0.6757 0.6446 0.7730
2.1878 3.59 750 1.1077 0.7169 0.7307 0.7169 0.6966 0.7999
2.1878 3.83 800 1.0393 0.7341 0.7548 0.7341 0.7226 0.8130
1.534 4.07 850 0.9478 0.7678 0.7794 0.7678 0.7572 0.8384
1.534 4.31 900 0.8755 0.7715 0.7789 0.7715 0.7627 0.8395
1.534 4.55 950 0.8563 0.7618 0.7737 0.7618 0.7491 0.8330
1.534 4.79 1000 0.7866 0.8007 0.8046 0.8007 0.7921 0.8616
1.2035 5.03 1050 0.7462 0.8007 0.8212 0.8007 0.7945 0.8591
1.2035 5.27 1100 0.7003 0.8157 0.8272 0.8157 0.8126 0.8717
1.2035 5.51 1150 0.7421 0.8105 0.8262 0.8105 0.8074 0.8672
1.2035 5.75 1200 0.7638 0.7993 0.8294 0.7993 0.7979 0.8595
1.2035 5.99 1250 0.6872 0.8187 0.8330 0.8187 0.8171 0.8742
0.9909 6.23 1300 0.6378 0.8345 0.8462 0.8345 0.8338 0.8840
0.9909 6.47 1350 0.6835 0.8075 0.8266 0.8075 0.8063 0.8669
0.9909 6.71 1400 0.6367 0.8345 0.8480 0.8345 0.8337 0.8874
0.9909 6.95 1450 0.5793 0.8434 0.8521 0.8434 0.8425 0.8931
0.8826 7.19 1500 0.6528 0.8307 0.8458 0.8307 0.8293 0.8824
0.8826 7.43 1550 0.6361 0.8225 0.8382 0.8225 0.8218 0.8761
0.8826 7.66 1600 0.6189 0.8360 0.8478 0.8360 0.8334 0.8855
0.8826 7.9 1650 0.6078 0.8337 0.8433 0.8337 0.8321 0.8831
0.7752 8.14 1700 0.6868 0.8315 0.8436 0.8315 0.8289 0.8835
0.7752 8.38 1750 0.6118 0.8419 0.8549 0.8419 0.8411 0.8897
0.7752 8.62 1800 0.5837 0.8532 0.8660 0.8532 0.8531 0.8974
0.7752 8.86 1850 0.5758 0.8487 0.8613 0.8487 0.8494 0.8956
0.7067 9.1 1900 0.6950 0.8307 0.8490 0.8307 0.8279 0.8827
0.7067 9.34 1950 0.5968 0.8479 0.8595 0.8479 0.8470 0.8942
0.7067 9.58 2000 0.5714 0.8614 0.8696 0.8614 0.8613 0.9035
0.7067 9.82 2050 0.6389 0.8427 0.8538 0.8427 0.8415 0.8903
0.6457 10.06 2100 0.6504 0.8502 0.8639 0.8502 0.8504 0.8948
0.6457 10.3 2150 0.5776 0.8547 0.8659 0.8547 0.8534 0.8988
0.6457 10.54 2200 0.6775 0.8434 0.8570 0.8434 0.8438 0.8912
0.6457 10.78 2250 0.5849 0.8569 0.8686 0.8569 0.8579 0.9013
0.6098 11.02 2300 0.5767 0.8622 0.8706 0.8622 0.8632 0.9037
0.6098 11.26 2350 0.6875 0.8404 0.8588 0.8404 0.8404 0.8898
0.6098 11.5 2400 0.7397 0.8352 0.8483 0.8352 0.8340 0.8865
0.6098 11.74 2450 0.5998 0.8629 0.8716 0.8629 0.8618 0.9053
0.6098 11.98 2500 0.6435 0.8449 0.8549 0.8449 0.8441 0.8918
0.5538 12.22 2550 0.6969 0.8502 0.8640 0.8502 0.8508 0.8965
0.5538 12.46 2600 0.6323 0.8577 0.8710 0.8577 0.8566 0.9006
0.5538 12.69 2650 0.6989 0.8532 0.8660 0.8532 0.8525 0.8981
0.5538 12.93 2700 0.6736 0.8554 0.8666 0.8554 0.8552 0.8994
0.5356 13.17 2750 0.6737 0.8487 0.8584 0.8487 0.8469 0.8960
0.5356 13.41 2800 0.6893 0.8457 0.8565 0.8457 0.8452 0.8921

Framework versions

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