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smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-seed_1024-1e-3

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smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-seed_1024-1e-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_1024-1e-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: 0.41096838506284816

smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-seed_1024-1e-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: 3.4006
  • Accuracy: 0.4110

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
3.6013 1.0 18600 3.7573 0.3598
3.3813 2.0 37200 3.5688 0.3805
3.2541 3.0 55800 3.4489 0.3922
3.174 4.0 74400 3.4158 0.3980
3.1166 5.0 93000 3.3767 0.4028
3.0777 6.0 111600 3.3729 0.4036
3.0372 7.0 130200 3.3464 0.4071
3.0083 8.0 148800 3.3503 0.4081
2.9762 9.0 167400 3.3317 0.4098
2.9515 10.0 186000 3.3434 0.4088
2.9338 11.0 204600 3.3526 0.4102
2.9063 12.0 223200 3.3577 0.4095
2.8871 13.0 241800 3.3493 0.4101
2.8654 14.0 260400 3.3641 0.4106
2.8465 15.0 279000 3.3597 0.4115
2.8233 16.0 297600 3.3748 0.4106
2.8071 17.0 316200 3.3754 0.4113
2.7899 18.0 334800 3.3833 0.4111
2.7669 19.0 353400 3.3913 0.4112
2.7513 20.0 372000 3.4006 0.4110

Framework versions

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