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End of training
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metadata
tags:
  - generated_from_trainer
datasets:
  - kanishka/counterfactual_babylm_naans_new
metrics:
  - accuracy
model-index:
  - name: smolm-autoreg-bpe-counterfactual_babylm_naans_new-seed_1024-1e-4
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: kanishka/counterfactual_babylm_naans_new
          type: kanishka/counterfactual_babylm_naans_new
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.40723568402608223

smolm-autoreg-bpe-counterfactual_babylm_naans_new-seed_1024-1e-4

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

  • Loss: 3.4373
  • Accuracy: 0.4072

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: 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
4.0553 1.0 18595 4.2752 0.3077
3.5587 2.0 37190 3.7501 0.3633
3.3865 3.0 55785 3.5884 0.3815
3.2906 4.0 74380 3.5054 0.3891
3.2202 5.0 92975 3.4769 0.3943
3.1699 6.0 111570 3.4426 0.3977
3.1232 7.0 130165 3.4478 0.3994
3.091 8.0 148760 3.4243 0.4014
3.0613 9.0 167355 3.4169 0.4030
3.0322 10.0 185950 3.4142 0.4050
3.0107 11.0 204545 3.4052 0.4049
2.9844 12.0 223140 3.4128 0.4053
2.9671 13.0 241735 3.4150 0.4062
2.9477 14.0 260330 3.4174 0.4062
2.9272 15.0 278925 3.4275 0.4067
2.9088 16.0 297520 3.4271 0.4068
2.8915 17.0 316115 3.4245 0.4071
2.872 18.0 334710 3.4262 0.4070
2.8514 19.0 353305 3.4322 0.4073
2.8467 20.0 371900 3.4373 0.4072

Framework versions

  • Transformers 4.38.0
  • Pytorch 2.3.1+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.2