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

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: 3.0889
  • Accuracy: 0.4598

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
3.6605 1.0 527 3.6385 0.1020
3.6135 2.0 1055 3.5710 0.1200
3.4222 3.0 1582 3.3394 0.1738
3.1948 4.0 2110 3.2132 0.2052
2.8791 5.0 2637 2.9508 0.2581
2.7807 6.0 3165 2.7201 0.3109
2.4647 7.0 3692 2.6056 0.3393
2.3009 8.0 4220 2.4893 0.3816
2.0953 9.0 4747 2.4874 0.3902
1.8074 10.0 5275 2.4705 0.4035
1.8209 11.0 5802 2.4465 0.4177
1.4822 12.0 6330 2.5310 0.4228
1.426 13.0 6857 2.5097 0.4305
1.2877 14.0 7385 2.5365 0.4368
1.0833 15.0 7912 2.5874 0.4404
1.0709 16.0 8440 2.6478 0.4373
0.8176 17.0 8967 2.7096 0.4409
0.803 18.0 9495 2.7965 0.4491
0.6678 19.0 10022 2.9335 0.4470
0.7066 20.0 10550 3.0013 0.4408
0.5935 21.0 11077 2.9613 0.4544
0.5703 22.0 11605 2.9915 0.4534
0.5 23.0 12132 3.0625 0.4556
0.55 24.0 12660 3.0889 0.4598
0.3977 25.0 13187 3.1962 0.4551
0.4578 26.0 13715 3.2863 0.4574
0.3343 27.0 14242 3.3401 0.4531
0.4414 28.0 14770 3.3229 0.4557
0.2551 29.0 15297 3.4294 0.4567

Framework versions

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