Bingsu / distilhubert-ko-zeroth

huggingface.co
Total runs: 12
24-hour runs: -2
7-day runs: -3
30-day runs: -71
Model's Last Updated: August 15 2022
automatic-speech-recognition

Introduction of distilhubert-ko-zeroth

Model Details of distilhubert-ko-zeroth

distilhubert-ko-zeroth

This model is a fine-tuned version of ntu-spml/distilhubert on the BINGSU/ZEROTH-KOREAN - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9934
  • Cer: 0.2066
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: 0.001
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 10.0
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss Cer
No log 0.57 400 3.2681 0.6761
7.285 1.15 800 1.5312 0.4170
1.259 1.72 1200 1.3459 0.3846
0.9108 2.3 1600 1.1357 0.3239
0.7227 2.87 2000 1.0571 0.3056
0.7227 3.45 2400 1.0002 0.2829
0.5689 4.02 2800 0.8773 0.2553
0.4676 4.6 3200 0.8634 0.2462
0.3805 5.17 3600 0.8504 0.2323
0.2548 5.75 4000 0.8480 0.2260
0.2548 6.32 4400 0.8550 0.2231
0.189 6.9 4800 0.8587 0.2159
0.1336 7.47 5200 0.9012 0.2101
0.0827 8.05 5600 0.9302 0.2100
0.0506 8.62 6000 0.9622 0.2063
0.0506 9.2 6400 0.9826 0.2062
0.0389 9.77 6800 0.9933 0.2067
Framework versions
  • Transformers 4.21.1
  • Pytorch 1.12.1
  • Datasets 2.4.0
  • Tokenizers 0.12.1

Runs of Bingsu distilhubert-ko-zeroth on huggingface.co

12
Total runs
-2
24-hour runs
-3
3-day runs
-3
7-day runs
-71
30-day runs

More Information About distilhubert-ko-zeroth huggingface.co Model

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distilhubert-ko-zeroth install

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