metadata

tags:
  - generated_from_trainer
datasets:
  - kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
metrics:
  - accuracy
model-index:
  - name: >-
      smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-seed_211-1e-3
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
          type: kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.40923404527178003

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

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

Loss: 3.4372
Accuracy: 0.4092

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
3.6018	1.0	18600	3.7779	0.3590
3.3799	2.0	37200	3.5990	0.3799
3.2535	3.0	55800	3.4629	0.3928
3.1731	4.0	74400	3.4447	0.3979
3.1186	5.0	93000	3.4295	0.4009
3.0776	6.0	111600	3.4004	0.4034
3.0407	7.0	130200	3.3850	0.4053
3.0066	8.0	148800	3.3648	0.4061
2.9851	9.0	167400	3.3985	0.4074
2.953	10.0	186000	3.3964	0.4077
2.9321	11.0	204600	3.3816	0.4088
2.9082	12.0	223200	3.3780	0.4093
2.8881	13.0	241800	3.4020	0.4090
2.8698	14.0	260400	3.4057	0.4091
2.8441	15.0	279000	3.3906	0.4094
2.8256	16.0	297600	3.4051	0.4094
2.808	17.0	316200	3.4108	0.4093
2.7945	18.0	334800	3.4283	0.4094
2.7744	19.0	353400	3.4362	0.4094
2.7567	20.0	372000	3.4372	0.4092

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1