final-project / README.md
sudheer997
Merge feature-branch into main
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metadata
tags:
  - generated_from_trainer
model-index:
  - name: uaspeech-foundation-fintuned
    results: []

uaspeech-foundation-fintuned

  • Loss: 2.5324
  • Wer: 1.2855

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: 0.0001
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Wer
41.2984 0.7 500 2.8954 1.0
3.0227 1.4 1000 2.8232 1.0042
2.8283 2.11 1500 2.6291 1.0309
2.5552 2.81 2000 2.2593 1.9170
2.1714 3.51 2500 1.9586 1.9142
1.8537 4.21 3000 1.5725 1.8579
1.6087 4.92 3500 1.2772 1.7426
1.3108 5.62 4000 1.2792 1.6751
1.1652 6.32 4500 1.4565 1.6174
1.0113 7.02 5000 1.1906 1.5626
0.925 7.72 5500 1.4491 1.5260
0.8183 8.43 6000 1.3712 1.5387
0.7118 9.13 6500 1.4713 1.4866
0.6959 9.83 7000 1.3336 1.4318
0.6146 10.53 7500 1.3690 1.4177
0.5655 11.24 8000 1.3789 1.4135
0.4969 11.94 8500 1.5476 1.3966
0.4705 12.64 9000 1.9062 1.3797
0.4387 13.34 9500 1.2711 1.3924
0.4115 14.04 10000 1.6318 1.3769
0.3695 14.75 10500 1.5119 1.3755
0.377 15.45 11000 1.6637 1.3812
0.3788 16.15 11500 1.6636 1.3699
0.3396 16.85 12000 1.6572 1.3418
0.3047 17.56 12500 1.4740 1.3361
0.2804 18.26 13000 2.0885 1.3249
0.2995 18.96 13500 1.9536 1.3235
0.2628 19.66 14000 1.7736 1.3179
0.2703 20.37 14500 2.0018 1.3291
0.2335 21.07 15000 1.7962 1.3221
0.2068 21.77 15500 2.3187 1.3136
0.2311 22.47 16000 2.4853 1.3291
0.2491 23.17 16500 2.1901 1.3024
0.1836 23.88 17000 2.4344 1.2911
0.1823 24.58 17500 2.3705 1.3066
0.1575 25.28 18000 2.1864 1.2897
0.1451 25.98 18500 2.4216 1.2883
0.1502 26.69 19000 2.1780 1.2855
0.1392 27.39 19500 2.4009 1.2925
0.1609 28.09 20000 2.4250 1.2982
0.1066 28.79 20500 2.4433 1.2897
0.1514 29.49 21000 2.5063 1.2855

Framework versions

  • Transformers 4.23.1
  • Pytorch 1.12.1+cu113
  • Datasets 1.18.3
  • Tokenizers 0.13.2