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metadata
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: hubert-base-ls960-finetuned-ic-slurp-wt_init-frz-v1
    results: []

hubert-base-ls960-finetuned-ic-slurp-wt_init-frz-v1

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: 3.8557
  • Accuracy: 0.4665

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: 5e-05
  • train_batch_size: 24
  • eval_batch_size: 24
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 96
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
3.6161 1.0 527 3.6149 0.1639
3.4319 2.0 1055 3.4045 0.1837
3.2109 3.0 1582 3.1534 0.2204
3.0227 4.0 2110 3.0869 0.2425
2.7612 5.0 2637 2.8947 0.2796
2.6536 6.0 3165 2.7741 0.3162
2.2984 7.0 3692 2.5992 0.3517
2.2411 8.0 4220 2.5695 0.3678
2.0698 9.0 4747 2.5301 0.3828
1.781 10.0 5275 2.4942 0.4076
1.7756 11.0 5802 2.4456 0.4145
1.429 12.0 6330 2.4907 0.4214
1.4662 13.0 6857 2.5513 0.4287
1.2868 14.0 7385 2.6220 0.4254
1.0628 15.0 7912 2.6932 0.4294
1.0636 16.0 8440 2.7047 0.4348
0.861 17.0 8967 2.7132 0.4405
0.8748 18.0 9495 2.8117 0.4414
0.7779 19.0 10022 2.8338 0.4454
0.7247 20.0 10550 2.9349 0.4407
0.6041 21.0 11077 2.9980 0.4396
0.6234 22.0 11605 3.0899 0.4418
0.4641 23.0 12132 3.1206 0.4470
0.5321 24.0 12660 3.2098 0.4427
0.4293 25.0 13187 3.2953 0.4414
0.5322 26.0 13715 3.2976 0.4458
0.3345 27.0 14242 3.3888 0.4441
0.4868 28.0 14770 3.3955 0.4472
0.29 29.0 15297 3.4445 0.4451
0.2429 30.0 15825 3.4317 0.4537
0.3375 31.0 16352 3.4972 0.4534
0.26 32.0 16880 3.6675 0.4434
0.2337 33.0 17407 3.5817 0.4491
0.2984 34.0 17935 3.5766 0.4485
0.2249 35.0 18462 3.5912 0.4538
0.1962 36.0 18990 3.6414 0.4556
0.2243 37.0 19517 3.7025 0.4563
0.2169 38.0 20045 3.7524 0.4557
0.1509 39.0 20572 3.6993 0.4583
0.2106 40.0 21100 3.8040 0.4550
0.224 41.0 21627 3.7628 0.4628
0.1154 42.0 22155 3.7545 0.4652
0.1453 43.0 22682 3.7632 0.4651
0.1221 44.0 23210 3.8144 0.4596
0.1419 45.0 23737 3.8580 0.4627
0.1178 46.0 24265 3.8238 0.4656
0.1517 47.0 24792 3.8614 0.4635
0.1207 48.0 25320 3.8786 0.4644
0.1223 49.0 25847 3.8557 0.4665
0.067 49.95 26350 3.8611 0.4651

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.2.1+cu118
  • Datasets 2.17.0
  • Tokenizers 0.15.0