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smolm-autoreg-bpe-counterfactual_babylm_aann_low_variability_numeral_1024-1e-3

This model was trained from scratch on the kanishka/counterfactual_babylm_aann_low_variability_numeral dataset. It achieves the following results on the evaluation set:

  • Loss: 3.3975
  • Accuracy: 0.4113

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: 32
  • eval_batch_size: 64
  • seed: 1024
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 32000
  • num_epochs: 20.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
3.598 1.0 18593 3.8055 0.3586
3.3819 2.0 37186 3.5620 0.3807
3.2622 3.0 55779 3.4625 0.3929
3.1788 4.0 74372 3.4131 0.3984
3.1251 5.0 92965 3.3859 0.4022
3.0765 6.0 111558 3.3707 0.4050
3.0458 7.0 130151 3.3522 0.4077
3.0116 8.0 148744 3.3560 0.4080
2.9832 9.0 167337 3.3773 0.4080
2.9565 10.0 185930 3.3513 0.4098
2.9381 11.0 204523 3.3482 0.4096
2.9171 12.0 223116 3.3354 0.4118
2.893 13.0 241709 3.3519 0.4111
2.876 14.0 260302 3.3632 0.4109
2.8472 15.0 278895 3.3530 0.4120
2.8335 16.0 297488 3.3727 0.4113
2.812 17.0 316081 3.3814 0.4110
2.7944 18.0 334674 3.3788 0.4119
2.7761 19.0 353267 3.3925 0.4114
2.7606 20.0 371860 3.3975 0.4113

Framework versions

  • Transformers 4.38.0
  • Pytorch 2.3.1+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.2
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Dataset used to train kanishka/smolm-autoreg-bpe-counterfactual_babylm_aann_low_variability_numeral-seed_1024-1e-3

Evaluation results

  • Accuracy on kanishka/counterfactual_babylm_aann_low_variability_numeral
    self-reported
    0.411