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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
    results: []

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

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: 3.1377
  • Accuracy: 0.4604

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
3.9613 1.0 527 3.8944 0.0803
3.7817 2.0 1055 3.7275 0.0910
3.6357 3.0 1582 3.5410 0.1308
3.4527 4.0 2110 3.3426 0.1676
3.0715 5.0 2637 3.0751 0.2331
2.9153 6.0 3165 2.8168 0.2969
2.5333 7.0 3692 2.6229 0.3375
2.3807 8.0 4220 2.5673 0.3620
2.181 9.0 4747 2.4933 0.3835
1.9118 10.0 5275 2.4411 0.4046
1.9015 11.0 5802 2.4254 0.4126
1.5811 12.0 6330 2.4216 0.4275
1.491 13.0 6857 2.4833 0.4284
1.3697 14.0 7385 2.5243 0.4368
1.1232 15.0 7912 2.5944 0.4309
1.1071 16.0 8440 2.6475 0.4317
0.9439 17.0 8967 2.6379 0.4449
0.917 18.0 9495 2.7438 0.4468
0.7628 19.0 10022 2.7671 0.4513
0.7642 20.0 10550 2.8993 0.4418
0.6716 21.0 11077 2.9354 0.4472
0.6166 22.0 11605 2.9961 0.4510
0.4819 23.0 12132 3.0959 0.4451
0.5903 24.0 12660 3.0542 0.4557
0.515 25.0 13187 3.0723 0.4589
0.518 26.0 13715 3.1377 0.4604
0.3902 27.0 14242 3.2230 0.4524
0.4825 28.0 14770 3.2925 0.4583
0.29 29.0 15297 3.4027 0.4498
0.2789 30.0 15825 3.3573 0.4598
0.3202 31.0 16352 3.4381 0.4542

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0+cu121
  • Datasets 2.17.1
  • Tokenizers 0.15.2