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hubert-classifier-aug-80

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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-80
    results: []

hubert-classifier-aug-80

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: 0.7879
  • Accuracy: 0.8248
  • Precision: 0.8454
  • Recall: 0.8248
  • F1: 0.8211
  • Binary: 0.8778

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: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Binary
No log 0.22 50 4.4263 0.0135 0.0002 0.0135 0.0004 0.1245
No log 0.43 100 4.4241 0.0148 0.0002 0.0148 0.0004 0.1261
No log 0.65 150 4.4226 0.0135 0.0002 0.0135 0.0004 0.1333
No log 0.86 200 4.0724 0.0310 0.0015 0.0310 0.0027 0.2334
No log 1.08 250 3.9071 0.0270 0.0009 0.0270 0.0016 0.2287
No log 1.29 300 3.8704 0.0283 0.0009 0.0283 0.0017 0.2327
No log 1.51 350 3.8439 0.0256 0.0007 0.0256 0.0014 0.2306
No log 1.73 400 3.8189 0.0256 0.0015 0.0256 0.0025 0.2330
No log 1.94 450 3.7439 0.0391 0.0058 0.0391 0.0068 0.2950
4.1116 2.16 500 3.5560 0.0404 0.0033 0.0404 0.0046 0.3185
4.1116 2.37 550 3.4895 0.0404 0.0035 0.0404 0.0059 0.3150
4.1116 2.59 600 3.3966 0.0512 0.0053 0.0512 0.0094 0.3253
4.1116 2.8 650 3.3257 0.0687 0.0157 0.0687 0.0175 0.3420
4.1116 3.02 700 3.2077 0.0593 0.0106 0.0593 0.0156 0.3136
4.1116 3.24 750 3.1384 0.0957 0.0225 0.0957 0.0302 0.3609
4.1116 3.45 800 3.1351 0.0943 0.0273 0.0943 0.0364 0.3597
4.1116 3.67 850 2.9107 0.1253 0.0606 0.1253 0.0646 0.3829
4.1116 3.88 900 2.8937 0.1509 0.0677 0.1509 0.0712 0.3927
4.1116 4.1 950 2.7737 0.1806 0.1052 0.1806 0.1128 0.4205
3.2639 4.31 1000 2.6901 0.1658 0.0774 0.1658 0.0851 0.4117
3.2639 4.53 1050 2.5441 0.2183 0.1329 0.2183 0.1424 0.4482
3.2639 4.75 1100 2.4408 0.2197 0.1407 0.2197 0.1382 0.4488
3.2639 4.96 1150 2.4113 0.2278 0.1691 0.2278 0.1517 0.4562
3.2639 5.18 1200 2.2525 0.2790 0.2052 0.2790 0.1916 0.4904
3.2639 5.39 1250 2.2126 0.2817 0.2064 0.2817 0.1939 0.4962
3.2639 5.61 1300 2.1644 0.2951 0.2583 0.2951 0.2264 0.5039
3.2639 5.83 1350 2.1951 0.3275 0.2823 0.3275 0.2698 0.5199
3.2639 6.04 1400 1.9989 0.3666 0.3230 0.3666 0.3087 0.5532
3.2639 6.26 1450 1.8910 0.3962 0.3735 0.3962 0.3340 0.5749
2.4809 6.47 1500 1.8342 0.4084 0.4092 0.4084 0.3542 0.5838
2.4809 6.69 1550 1.8166 0.4272 0.4345 0.4272 0.3809 0.5954
2.4809 6.9 1600 1.6498 0.4838 0.4594 0.4838 0.4297 0.6345
2.4809 7.12 1650 1.6093 0.5040 0.5262 0.5040 0.4666 0.6515
2.4809 7.34 1700 1.5510 0.5296 0.5257 0.5296 0.4896 0.6689
2.4809 7.55 1750 1.5003 0.5175 0.5164 0.5175 0.4669 0.6621
2.4809 7.77 1800 1.4597 0.5270 0.5263 0.5270 0.4861 0.6671
2.4809 7.98 1850 1.3801 0.5916 0.6024 0.5916 0.5598 0.7115
2.4809 8.2 1900 1.3262 0.5863 0.5970 0.5863 0.5574 0.7101
2.4809 8.41 1950 1.2342 0.5943 0.5938 0.5943 0.5648 0.7163
1.8737 8.63 2000 1.2114 0.6173 0.6210 0.6173 0.5957 0.7333
1.8737 8.85 2050 1.1831 0.6321 0.6535 0.6321 0.6072 0.7414
1.8737 9.06 2100 1.1501 0.6563 0.6882 0.6563 0.6398 0.7567
1.8737 9.28 2150 1.0732 0.6941 0.7109 0.6941 0.6802 0.7841
1.8737 9.49 2200 1.1194 0.6604 0.6696 0.6604 0.6424 0.7615
1.8737 9.71 2250 0.9827 0.7035 0.7331 0.7035 0.6924 0.7926
1.8737 9.92 2300 0.9956 0.7156 0.7381 0.7156 0.7047 0.8007
1.8737 10.14 2350 1.0312 0.6698 0.7095 0.6698 0.6552 0.7685
1.8737 10.36 2400 0.9753 0.7197 0.7465 0.7197 0.7070 0.8043
1.8737 10.57 2450 0.9825 0.7237 0.7322 0.7237 0.7118 0.8074
1.4378 10.79 2500 0.9829 0.6927 0.7234 0.6927 0.6821 0.7853
1.4378 11.0 2550 0.8897 0.7251 0.7515 0.7251 0.7152 0.8066
1.4378 11.22 2600 0.8627 0.7345 0.7624 0.7345 0.7277 0.8123
1.4378 11.43 2650 0.8772 0.7264 0.7602 0.7264 0.7228 0.8074
1.4378 11.65 2700 0.9209 0.7399 0.7622 0.7399 0.7321 0.8164
1.4378 11.87 2750 0.8737 0.7412 0.7623 0.7412 0.7345 0.8181
1.4378 12.08 2800 0.8638 0.7439 0.7632 0.7439 0.7370 0.8189
1.4378 12.3 2850 0.8525 0.7547 0.7763 0.7547 0.7492 0.8290
1.4378 12.51 2900 0.8238 0.7466 0.7598 0.7466 0.7382 0.8209
1.4378 12.73 2950 0.8192 0.7507 0.7771 0.7507 0.7446 0.8241
1.1771 12.94 3000 0.7660 0.7642 0.7801 0.7642 0.7589 0.8338
1.1771 13.16 3050 0.8528 0.7453 0.7676 0.7453 0.7369 0.8213
1.1771 13.38 3100 0.7580 0.7776 0.7881 0.7776 0.7707 0.8425
1.1771 13.59 3150 0.8186 0.7615 0.7849 0.7615 0.7536 0.8345
1.1771 13.81 3200 0.7512 0.7871 0.8057 0.7871 0.7808 0.8519
1.1771 14.02 3250 0.7426 0.7763 0.7965 0.7763 0.7710 0.8439
1.1771 14.24 3300 0.8203 0.7695 0.7827 0.7695 0.7619 0.8407
1.1771 14.46 3350 0.7871 0.7682 0.7878 0.7682 0.7590 0.8377
1.1771 14.67 3400 0.7761 0.7830 0.8044 0.7830 0.7733 0.8470
1.1771 14.89 3450 0.8547 0.7763 0.7965 0.7763 0.7731 0.8451
0.9984 15.1 3500 0.7879 0.7709 0.7922 0.7709 0.7633 0.8400
0.9984 15.32 3550 0.7582 0.8086 0.8235 0.8086 0.8037 0.8655
0.9984 15.53 3600 0.7084 0.7938 0.8074 0.7938 0.7872 0.8555
0.9984 15.75 3650 0.7424 0.7911 0.8099 0.7911 0.7864 0.8553
0.9984 15.97 3700 0.7255 0.8127 0.8274 0.8127 0.8090 0.8706
0.9984 16.18 3750 0.6903 0.8059 0.8216 0.8059 0.8011 0.8646
0.9984 16.4 3800 0.7078 0.8100 0.8324 0.8100 0.8043 0.8689
0.9984 16.61 3850 0.7843 0.7992 0.8218 0.7992 0.7940 0.8604
0.9984 16.83 3900 0.7239 0.7965 0.8226 0.7965 0.7936 0.8581
0.9984 17.04 3950 0.7097 0.8127 0.8283 0.8127 0.8092 0.8679
0.8969 17.26 4000 0.8020 0.7951 0.8135 0.7951 0.7914 0.8566
0.8969 17.48 4050 0.6915 0.8275 0.8477 0.8275 0.8242 0.8792
0.8969 17.69 4100 0.7548 0.8113 0.8321 0.8113 0.8071 0.8685
0.8969 17.91 4150 0.7284 0.8073 0.8293 0.8073 0.8036 0.8673
0.8969 18.12 4200 0.7304 0.8127 0.8276 0.8127 0.8092 0.8687
0.8969 18.34 4250 0.7169 0.8154 0.8319 0.8154 0.8109 0.8706
0.8969 18.55 4300 0.7189 0.8194 0.8375 0.8194 0.8173 0.8736
0.8969 18.77 4350 0.8506 0.7790 0.8073 0.7790 0.7718 0.8457
0.8969 18.99 4400 0.7322 0.8248 0.8436 0.8248 0.8211 0.8788
0.8969 19.2 4450 0.7497 0.8032 0.8212 0.8032 0.7997 0.8627
0.8076 19.42 4500 0.7879 0.8248 0.8454 0.8248 0.8211 0.8778
0.8076 19.63 4550 0.8195 0.8073 0.8314 0.8073 0.8032 0.8660
0.8076 19.85 4600 0.8176 0.8059 0.8249 0.8059 0.8032 0.8651
0.8076 20.06 4650 0.7699 0.8221 0.8375 0.8221 0.8180 0.8753
0.8076 20.28 4700 0.7316 0.8181 0.8407 0.8181 0.8162 0.8722
0.8076 20.5 4750 0.7205 0.8086 0.8272 0.8086 0.8060 0.8670
0.8076 20.71 4800 0.7689 0.7965 0.8150 0.7965 0.7930 0.8574
0.8076 20.93 4850 0.7828 0.8127 0.8297 0.8127 0.8101 0.8689

Framework versions

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