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-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: 0.4118896526593414

smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-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: 3.4114
Accuracy: 0.4119

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: 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
3.5998	1.0	18600	3.7955	0.3595
3.3776	2.0	37200	3.5874	0.3805
3.245	3.0	55800	3.4956	0.3923
3.1698	4.0	74400	3.4301	0.3991
3.1095	5.0	93000	3.4080	0.4017
3.0618	6.0	111600	3.3783	0.4047
3.0262	7.0	130200	3.3656	0.4063
2.9992	8.0	148800	3.3350	0.4088
2.9653	9.0	167400	3.3531	0.4103
2.9376	10.0	186000	3.3526	0.4110
2.9136	11.0	204600	3.3538	0.4098
2.8922	12.0	223200	3.3425	0.4120
2.8698	13.0	241800	3.3346	0.4124
2.8466	14.0	260400	3.3660	0.4110
2.8253	15.0	279000	3.3566	0.4127
2.8058	16.0	297600	3.3781	0.4113
2.7908	17.0	316200	3.3851	0.4119
2.7701	18.0	334800	3.3872	0.4128
2.7511	19.0	353400	3.4038	0.4120
2.7292	20.0	372000	3.4114	0.4119

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1