metadata

tags:
  - generated_from_trainer
datasets:
  - kanishka/counterfactual_babylm_aann_high_variability_adj
metrics:
  - accuracy
model-index:
  - name: smolm-autoreg-bpe-counterfactual_babylm_aann_high_variability_adj-1e-3
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: kanishka/counterfactual_babylm_aann_high_variability_adj
          type: kanishka/counterfactual_babylm_aann_high_variability_adj
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.40953671849382034

smolm-autoreg-bpe-counterfactual_babylm_aann_high_variability_adj-1e-3

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

Loss: 3.4138
Accuracy: 0.4095

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
3.5997	1.0	18594	3.7835	0.3590
3.3824	2.0	37188	3.5906	0.3796
3.2596	3.0	55782	3.4868	0.3927
3.1824	4.0	74376	3.4542	0.3968
3.1206	5.0	92970	3.3991	0.4011
3.0864	6.0	111564	3.3910	0.4044
3.0439	7.0	130158	3.3760	0.4060
3.0083	8.0	148752	3.3728	0.4063
2.9832	9.0	167346	3.3599	0.4079
2.9563	10.0	185940	3.3528	0.4086
2.9333	11.0	204534	3.3603	0.4092
2.9142	12.0	223128	3.3724	0.4091
2.8926	13.0	241722	3.3841	0.4086
2.8681	14.0	260316	3.3805	0.4093
2.8499	15.0	278910	3.3840	0.4094
2.8344	16.0	297504	3.4004	0.4092
2.8144	17.0	316098	3.3943	0.4096
2.7909	18.0	334692	3.4081	0.4094
2.7754	19.0	353286	3.4054	0.4096
2.7621	20.0	371880	3.4138	0.4095

Framework versions

Transformers 4.38.0
Pytorch 2.3.1+cu121
Datasets 2.16.1
Tokenizers 0.15.2