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malayalam_combined_

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metadata
base_model: facebook/w2v-bert-2.0
license: mit
metrics:
  - wer
tags:
  - generated_from_trainer
model-index:
  - name: malayalam_combined_
    results: []

Visualize in Weights & Biases

malayalam_combined_

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5025
  • Wer: 0.4256

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: 16
  • eval_batch_size: 16
  • 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: linear
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 25
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.8238 0.2031 500 0.8281 0.6745
0.7415 0.4063 1000 0.7477 0.6446
0.6913 0.6094 1500 0.6962 0.6072
0.6401 0.8125 2000 0.6981 0.5929
0.5864 1.0156 2500 0.6809 0.5712
0.5843 1.2188 3000 0.6125 0.5691
0.5547 1.4219 3500 0.6110 0.5616
0.5657 1.6250 4000 0.5882 0.5464
0.5809 1.8282 4500 0.5776 0.5481
0.5464 2.0313 5000 0.5689 0.5278
0.4974 2.2344 5500 0.5926 0.5428
0.5012 2.4375 6000 0.5622 0.5384
0.5162 2.6407 6500 0.5697 0.5179
0.5006 2.8438 7000 0.5357 0.5375
0.4661 3.0469 7500 0.5255 0.5255
0.4658 3.2501 8000 0.5182 0.5002
0.4716 3.4532 8500 0.5176 0.5044
0.4658 3.6563 9000 0.5139 0.5061
0.5031 3.8594 9500 0.5114 0.5068
0.4482 4.0626 10000 0.5331 0.5101
0.4678 4.2657 10500 0.5165 0.5126
0.4353 4.4688 11000 0.5292 0.5112
0.4711 4.6719 11500 0.5178 0.4979
0.4574 4.8751 12000 0.5215 0.5100
0.4246 5.0782 12500 0.5190 0.4938
0.4164 5.2813 13000 0.5504 0.4898
0.4181 5.4845 13500 0.5045 0.4979
0.4279 5.6876 14000 0.5118 0.4932
0.4244 5.8907 14500 0.4970 0.4842
0.4038 6.0938 15000 0.5013 0.4776
0.4179 6.2970 15500 0.5061 0.4762
0.3812 6.5001 16000 0.4987 0.4689
0.4217 6.7032 16500 0.4986 0.4807
0.3989 6.9064 17000 0.4905 0.4709
0.3741 7.1095 17500 0.4842 0.4700
0.3743 7.3126 18000 0.4869 0.4734
0.3785 7.5157 18500 0.4692 0.4690
0.3759 7.7189 19000 0.4691 0.4646
0.3809 7.9220 19500 0.4736 0.4720
0.3499 8.1251 20000 0.4787 0.4691
0.3523 8.3283 20500 0.4689 0.4680
0.3551 8.5314 21000 0.4792 0.4567
0.3672 8.7345 21500 0.4760 0.4652
0.3554 8.9376 22000 0.4649 0.4648
0.3182 9.1408 22500 0.4853 0.4565
0.3412 9.3439 23000 0.4958 0.4616
0.3494 9.5470 23500 0.4971 0.4527
0.3426 9.7502 24000 0.4959 0.4554
0.3365 9.9533 24500 0.4659 0.4582
0.3179 10.1564 25000 0.4807 0.4445
0.3361 10.3595 25500 0.4700 0.4535
0.3234 10.5627 26000 0.4562 0.4542
0.3296 10.7658 26500 0.4682 0.4452
0.3148 10.9689 27000 0.4716 0.4521
0.3112 11.1720 27500 0.4537 0.4473
0.3246 11.3752 28000 0.4594 0.4444
0.3062 11.5783 28500 0.4544 0.4445
0.2979 11.7814 29000 0.4531 0.4516
0.3108 11.9846 29500 0.4514 0.4428
0.2876 12.1877 30000 0.4598 0.4402
0.2911 12.3908 30500 0.4554 0.4426
0.2963 12.5939 31000 0.4641 0.4483
0.296 12.7971 31500 0.4575 0.4394
0.2777 13.0002 32000 0.4586 0.4444
0.2782 13.2033 32500 0.4498 0.4461
0.2695 13.4065 33000 0.4696 0.4450
0.286 13.6096 33500 0.4630 0.4383
0.279 13.8127 34000 0.4618 0.4401
0.2584 14.0158 34500 0.4526 0.4356
0.267 14.2190 35000 0.4726 0.4297
0.2667 14.4221 35500 0.4572 0.4308
0.2592 14.6252 36000 0.4795 0.4325
0.2592 14.8284 36500 0.4528 0.4303
0.2644 15.0315 37000 0.4604 0.4306
0.2312 15.2346 37500 0.4632 0.4367
0.2408 15.4377 38000 0.4670 0.4324
0.2489 15.6409 38500 0.4580 0.4253
0.2652 15.8440 39000 0.4581 0.4375
0.2367 16.0471 39500 0.4770 0.4213
0.2366 16.2503 40000 0.4751 0.4243
0.2267 16.4534 40500 0.4622 0.4282
0.2461 16.6565 41000 0.4671 0.4249
0.2326 16.8596 41500 0.4736 0.4293
0.2121 17.0628 42000 0.4905 0.4300
0.222 17.2659 42500 0.4782 0.4261
0.2202 17.4690 43000 0.4670 0.4250
0.2141 17.6722 43500 0.4688 0.4259
0.2231 17.8753 44000 0.4718 0.4254
0.2144 18.0784 44500 0.5025 0.4256

Framework versions

  • Transformers 4.43.0.dev0
  • Pytorch 1.14.0a0+44dac51
  • Datasets 2.16.1
  • Tokenizers 0.19.1