metadata

tags:
  - generated_from_trainer
datasets:
  - kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
metrics:
  - accuracy
model-index:
  - name: >-
      smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-seed_1024-1e-3
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
          type: kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.4096600918317765

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

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

Loss: 3.4259
Accuracy: 0.4097

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
3.6017	1.0	18600	3.7683	0.3593
3.3799	2.0	37200	3.5935	0.3790
3.2546	3.0	55800	3.4823	0.3915
3.1737	4.0	74400	3.4548	0.3978
3.1178	5.0	93000	3.4163	0.4014
3.0736	6.0	111600	3.4017	0.4038
3.0385	7.0	130200	3.3798	0.4057
3.0068	8.0	148800	3.3988	0.4060
2.9774	9.0	167400	3.3728	0.4074
2.9558	10.0	186000	3.3695	0.4087
2.9289	11.0	204600	3.3649	0.4094
2.9058	12.0	223200	3.3604	0.4095
2.8805	13.0	241800	3.3801	0.4098
2.8621	14.0	260400	3.3871	0.4095
2.8423	15.0	279000	3.3872	0.4096
2.8216	16.0	297600	3.3996	0.4097
2.8042	17.0	316200	3.3987	0.4101
2.7834	18.0	334800	3.4020	0.4101
2.7643	19.0	353400	3.4199	0.4097
2.7463	20.0	372000	3.4259	0.4097

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1