metadata

tags:
  - generated_from_trainer
datasets:
  - kanishka/counterfactual-babylm-new_regex_aanns_removal
metrics:
  - accuracy
model-index:
  - name: >-
      smolm-autoreg-bpe-counterfactual-babylm-new_regex_aanns_removal-seed_42-1e-3
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: kanishka/counterfactual-babylm-new_regex_aanns_removal
          type: kanishka/counterfactual-babylm-new_regex_aanns_removal
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.41202925698119025

smolm-autoreg-bpe-counterfactual-babylm-new_regex_aanns_removal-seed_42-1e-3

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: 3.3818
Accuracy: 0.4120

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
3.6013	1.0	18593	3.7859	0.3595
3.3808	2.0	37186	3.5963	0.3806
3.2581	3.0	55779	3.4526	0.3931
3.174	4.0	74372	3.4158	0.3984
3.1218	5.0	92965	3.4018	0.4022
3.0764	6.0	111558	3.3647	0.4060
3.0403	7.0	130151	3.3497	0.4073
3.0123	8.0	148744	3.3577	0.4084
2.9806	9.0	167337	3.3481	0.4096
2.9559	10.0	185930	3.3229	0.4107
2.9341	11.0	204523	3.3348	0.4109
2.9141	12.0	223116	3.3268	0.4113
2.8904	13.0	241709	3.3276	0.4122
2.8682	14.0	260302	3.3432	0.4128
2.8518	15.0	278895	3.3533	0.4120
2.8294	16.0	297488	3.3578	0.4120
2.8079	17.0	316081	3.3555	0.4124
2.7936	18.0	334674	3.3698	0.4121
2.7716	19.0	353267	3.3713	0.4124
2.7573	20.0	371860	3.3818	0.4120

Framework versions

Transformers 4.38.0
Pytorch 2.3.1+cu121
Datasets 2.16.1
Tokenizers 0.15.2