Hugging Face's logo

kanishka
/
smolm-autoreg-bpe-counterfactual-babylm-indef_articles_with_pl_nouns_removal-1e-3

Text Generation
Transformers
Safetensors
opt
Generated from Trainer
Eval Results
Inference Endpoints
text-generation-inference
Model card Files Community
smolm-autoreg-bpe-counterfactual-babylm-indef_articles_with_pl_nouns_removal-1e-3 / README.md
kanishka's picture
kanishka
End of training
4689630
preview code
|
raw history blame contribute delete
No virus
3.17 kB
metadata
tags:
  - generated_from_trainer
datasets:
  - kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
metrics:
  - accuracy
model-index:
  - name: >-
      smolm-autoreg-bpe-counterfactual-babylm-indef_articles_with_pl_nouns_removal-1e-3
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: >-
            kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
          type: >-
            kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.41252109443859236

smolm-autoreg-bpe-counterfactual-babylm-indef_articles_with_pl_nouns_removal-1e-3

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

  • Loss: 3.4176
  • Accuracy: 0.4125

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: 16
  • eval_batch_size: 64
  • seed: 42
  • 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
3.5148 1.0 37201 3.7270 0.3671
3.3074 2.0 74402 3.4841 0.3897
3.1988 3.0 111603 3.4300 0.3979
3.152 4.0 148804 3.3774 0.4050
3.0973 5.0 186005 3.3462 0.4090
3.0543 6.0 223206 3.3687 0.4064
3.0161 7.0 260407 3.3391 0.4114
2.9858 8.0 297608 3.3477 0.4105
2.9718 9.0 334809 3.3436 0.4112
2.9399 10.0 372010 3.3451 0.4121
2.9207 11.0 409211 3.3586 0.4130
2.8987 12.0 446412 3.3554 0.4123
2.8779 13.0 483613 3.3616 0.4130
2.8519 14.0 520814 3.3696 0.4129
2.8395 15.0 558015 3.3729 0.4128
2.8151 16.0 595216 3.3718 0.4140
2.798 17.0 632417 3.3858 0.4128
2.7738 18.0 669618 3.4080 0.4130
2.7555 19.0 706819 3.4067 0.4131
2.7434 20.0 744020 3.4176 0.4125

Framework versions

  • Transformers 4.36.0
  • Pytorch 2.1.1+cu121
  • Datasets 2.15.0
  • Tokenizers 0.15.0