smolm-autoreg-bpe-counterfactual-babylm-new_regex_aanns_removal-1e-4

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

• Loss: 3.3971
• Accuracy: 0.4081

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: 0.0001
• 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
4.0555	1.0	18593	4.2608	0.3086
3.5626	2.0	37186	3.7490	0.3634
3.3926	3.0	55779	3.5729	0.3804
3.2863	4.0	74372	3.5122	0.3883
3.2223	5.0	92965	3.4704	0.3932
3.1687	6.0	111558	3.4542	0.3960
3.1265	7.0	130151	3.4307	0.3987
3.0955	8.0	148744	3.4010	0.4014
3.0614	9.0	167337	3.3947	0.4026
3.0346	10.0	185930	3.3861	0.4037
3.0121	11.0	204523	3.3788	0.4047
2.9917	12.0	223116	3.3737	0.4050
2.968	13.0	241709	3.3828	0.4055
2.9462	14.0	260302	3.3921	0.4060
2.9308	15.0	278895	3.3842	0.4073
2.9096	16.0	297488	3.3800	0.4075
2.889	17.0	316081	3.3850	0.4077
2.8779	18.0	334674	3.3920	0.4076
2.8585	19.0	353267	3.3898	0.4084
2.8469	20.0	371860	3.3971	0.4081

Framework versions

• Transformers 4.40.1
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.19.1