metadata
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal
metrics:
- accuracy
model-index:
- name: >-
smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-1e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: >-
kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal
type: >-
kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.4080045140970133
smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-1e-4
This model was trained from scratch on the kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal dataset. It achieves the following results on the evaluation set:
- Loss: 3.4138
- Accuracy: 0.4080
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 |
|---|---|---|---|---|
| 4.0521 | 1.0 | 18600 | 4.2759 | 0.3096 |
| 3.567 | 2.0 | 37200 | 3.7516 | 0.3623 |
| 3.3864 | 3.0 | 55800 | 3.5931 | 0.3802 |
| 3.2901 | 4.0 | 74400 | 3.5232 | 0.3883 |
| 3.2176 | 5.0 | 93000 | 3.4594 | 0.3939 |
| 3.1641 | 6.0 | 111600 | 3.4612 | 0.3961 |
| 3.1229 | 7.0 | 130200 | 3.4155 | 0.4000 |
| 3.0932 | 8.0 | 148800 | 3.4064 | 0.4015 |
| 3.0577 | 9.0 | 167400 | 3.4074 | 0.4036 |
| 3.0285 | 10.0 | 186000 | 3.3945 | 0.4058 |
| 3.0042 | 11.0 | 204600 | 3.3962 | 0.4052 |
| 2.9833 | 12.0 | 223200 | 3.3878 | 0.4060 |
| 2.9614 | 13.0 | 241800 | 3.3943 | 0.4065 |
| 2.9382 | 14.0 | 260400 | 3.3899 | 0.4072 |
| 2.9179 | 15.0 | 279000 | 3.3926 | 0.4075 |
| 2.9009 | 16.0 | 297600 | 3.4043 | 0.4072 |
| 2.8878 | 17.0 | 316200 | 3.3955 | 0.4079 |
| 2.8705 | 18.0 | 334800 | 3.4079 | 0.4078 |
| 2.8533 | 19.0 | 353400 | 3.4119 | 0.4077 |
| 2.8352 | 20.0 | 372000 | 3.4138 | 0.4080 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1