metadata

tags:
  - generated_from_trainer
datasets:
  - kanishka/counterfactual-babylm-only_other_det_removal
metrics:
  - accuracy
model-index:
  - name: >-
      smolm-autoreg-bpe-counterfactual-babylm-only_other_det_removal-seed_1024-1e-3
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: kanishka/counterfactual-babylm-only_other_det_removal
          type: kanishka/counterfactual-babylm-only_other_det_removal
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.4105017384701812

smolm-autoreg-bpe-counterfactual-babylm-only_other_det_removal-seed_1024-1e-3

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

Loss: 3.4160
Accuracy: 0.4105

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: 1024
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
3.6007	1.0	18597	3.8014	0.3575
3.3846	2.0	37194	3.5881	0.3796
3.2609	3.0	55791	3.4855	0.3918
3.1804	4.0	74388	3.4168	0.3979
3.1278	5.0	92985	3.4013	0.4018
3.081	6.0	111582	3.3683	0.4041
3.0471	7.0	130179	3.3773	0.4055
3.0189	8.0	148776	3.3797	0.4069
2.988	9.0	167373	3.3716	0.4074
2.9624	10.0	185970	3.3675	0.4088
2.9372	11.0	204567	3.3803	0.4093
2.9153	12.0	223164	3.3654	0.4096
2.8939	13.0	241761	3.3777	0.4098
2.8704	14.0	260358	3.3811	0.4103
2.8503	15.0	278955	3.3847	0.4102
2.8343	16.0	297552	3.3952	0.4100
2.8131	17.0	316149	3.4062	0.4103
2.7975	18.0	334746	3.4120	0.4102
2.7753	19.0	353343	3.4110	0.4105
2.7567	20.0	371940	3.4160	0.4105

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1