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