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_211-1e-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: 0.41341864917568283

smolm-autoreg-bpe-counterfactual-babylm-new_regex_aanns_removal-seed_211-1e-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: 3.3885
Accuracy: 0.4134

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: 211
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
3.6058	1.0	18593	3.7591	0.3583
3.3838	2.0	37186	3.5626	0.3824
3.2578	3.0	55779	3.4700	0.3923
3.1777	4.0	74372	3.4090	0.3992
3.1262	5.0	92965	3.4092	0.4021
3.0786	6.0	111558	3.3686	0.4073
3.0425	7.0	130151	3.3363	0.4099
3.0098	8.0	148744	3.3507	0.4092
2.9845	9.0	167337	3.3483	0.4113
2.9554	10.0	185930	3.3369	0.4122
2.9372	11.0	204523	3.3210	0.4144
2.9131	12.0	223116	3.3488	0.4121
2.8914	13.0	241709	3.3448	0.4139
2.8744	14.0	260302	3.3473	0.4130
2.8505	15.0	278895	3.3552	0.4135
2.8346	16.0	297488	3.3626	0.4135
2.8113	17.0	316081	3.3734	0.4128
2.7967	18.0	334674	3.3720	0.4132
2.7775	19.0	353267	3.3848	0.4132
2.7551	20.0	371860	3.3885	0.4134

Framework versions

Transformers 4.38.0
Pytorch 2.3.1+cu121
Datasets 2.16.1
Tokenizers 0.15.2