Edit model card

hubert-base-ls960

This model is a fine-tuned version of facebook/hubert-base-ls960 on the galsenai/waxal_dataset dataset. It achieves the following results on the evaluation set:

  • Loss: 2.1857
  • Accuracy: 0.6442
  • Precision: 0.8369
  • F1: 0.7121

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: 3e-05
  • train_batch_size: 30
  • eval_batch_size: 30
  • seed: 0
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 120
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 32.0

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision F1
4.523 2.53 500 5.1547 0.0205 0.0047 0.0037
3.4187 5.05 1000 4.6287 0.0337 0.0256 0.0163
2.3533 7.58 1500 4.2550 0.0944 0.1033 0.0641
1.7145 10.1 2000 3.9540 0.1095 0.2091 0.0964
1.3245 12.63 2500 3.8557 0.1758 0.3609 0.1859
1.0729 15.15 3000 3.7411 0.2247 0.4918 0.2537
0.8955 17.68 3500 3.2683 0.3789 0.6162 0.4256
0.7697 20.2 4000 2.8749 0.4612 0.7106 0.5171
0.6864 22.73 4500 2.7251 0.5169 0.7437 0.5779
0.6061 25.25 5000 2.5061 0.5631 0.8043 0.6335
0.5777 27.78 5500 2.2830 0.6177 0.8183 0.6837
0.5304 30.3 6000 2.1857 0.6442 0.8369 0.7121

Framework versions

  • Transformers 4.27.0.dev0
  • Pytorch 1.11.0+cu113
  • Datasets 2.9.1.dev0
  • Tokenizers 0.13.2
Downloads last month
13
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.