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smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-seed_211-1e-3

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smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-seed_211-1e-3 / README.md
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metadata
tags:
  - generated_from_trainer
datasets:
  - kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal
metrics:
  - accuracy
model-index:
  - name: >-
      smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-seed_211-1e-3
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: >-
            kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal
          type: >-
            kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.412745149018544

smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-seed_211-1e-3

This model was trained from scratch on the kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal dataset. It achieves the following results on the evaluation set:

  • Loss: 3.3862
  • Accuracy: 0.4127

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: 211
  • 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
3.5992 1.0 18600 3.8124 0.3592
3.3826 2.0 37200 3.5570 0.3817
3.255 3.0 55800 3.4820 0.3917
3.1751 4.0 74400 3.4194 0.3988
3.1181 5.0 93000 3.3839 0.4022
3.074 6.0 111600 3.3598 0.4055
3.0387 7.0 130200 3.3320 0.4090
3.0113 8.0 148800 3.3243 0.4117
2.9786 9.0 167400 3.3343 0.4103
2.9522 10.0 186000 3.3475 0.4107
2.9315 11.0 204600 3.3211 0.4132
2.9096 12.0 223200 3.3419 0.4125
2.8879 13.0 241800 3.3351 0.4137
2.8675 14.0 260400 3.3329 0.4132
2.8497 15.0 279000 3.3544 0.4124
2.8277 16.0 297600 3.3686 0.4117
2.8093 17.0 316200 3.3650 0.4130
2.7915 18.0 334800 3.3731 0.4126
2.7731 19.0 353400 3.3832 0.4128
2.7504 20.0 372000 3.3862 0.4127

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1