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csikasote/mms-1b-all-bemgen-combined-train-drodat-gdro-1.65e-4-dat-0.019-no-sd-0.00-52

sourceHugging Facecc-by-nc-4.0updated 6d agoView on Hugging Face
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Model Card

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mms-1b-all-bemgen-combined-train-drodat-gdro-1.65e-4-dat-0.019-no-sd-0.00-52

This model is a fine-tuned version of facebook/mms-1b-all on the BEMGEN - BEM dataset. It achieves the following results on the evaluation set:

  • —Loss: 12.0449
  • —Cer: 0.0516

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: 0.0003
  • —trainbatchsize: 4
  • —evalbatchsize: 4
  • —seed: 42
  • —gradientaccumulationsteps: 2
  • —totaltrainbatch_size: 8
  • —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
  • —lrschedulertype: linear
  • —lrschedulerwarmup_steps: 500
  • —num_epochs: 30.0
  • —mixedprecisiontraining: Native AMP

Training results

Training LossEpochStepValidation LossCer
266.40820.6349500264.23150.9285
28.7111.2692100014.17730.0644
30.07521.9041150013.96500.0626
27.84232.5384200013.54640.0604
24.71953.1727250013.40000.0582
25.41053.8076300013.03210.0580
25.02744.4419350013.16460.0567
25.48655.0762400012.82030.0571
22.82745.7111450012.93460.0562
22.14376.3454500012.71880.0560
22.66166.9803550012.59190.0544
20.61037.6146600012.54770.0548
21.73788.2489650012.84380.0550
19.86448.8838700012.34750.0534
20.21979.5181750012.28330.0537
18.326910.1524800012.44000.0534
19.747710.7873850012.24900.0525
18.204111.4216900012.51880.0529
19.673912.0559950012.24620.0531
19.457212.69081000012.67030.0537
17.948513.32511050012.30240.0530
17.98613.961100012.18190.0522
17.199814.59431150012.28450.0529
17.542115.22861200012.16610.0526
17.623215.86351250012.04490.0516
16.722616.49781300012.24360.0519
17.357417.13211350012.21160.0524
16.442117.76701400012.26050.0521

Framework versions

  • —Transformers 4.53.0
  • —Pytorch 2.10.0+cu128
  • —Datasets 4.5.0
  • —Tokenizers 0.21.4