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-3e-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: 0.4089445254884035
smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-3e-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: 3.4202
- Accuracy: 0.4089
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: 0.0003
- 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 |
|---|---|---|---|---|
| 3.7444 | 1.0 | 18600 | 3.9303 | 0.3452 |
| 3.4367 | 2.0 | 37200 | 3.6375 | 0.3746 |
| 3.29 | 3.0 | 55800 | 3.5155 | 0.3880 |
| 3.2081 | 4.0 | 74400 | 3.4751 | 0.3940 |
| 3.1438 | 5.0 | 93000 | 3.4190 | 0.3983 |
| 3.0947 | 6.0 | 111600 | 3.3905 | 0.4022 |
| 3.0569 | 7.0 | 130200 | 3.3832 | 0.4029 |
| 3.029 | 8.0 | 148800 | 3.3740 | 0.4051 |
| 2.9953 | 9.0 | 167400 | 3.3781 | 0.4059 |
| 2.9667 | 10.0 | 186000 | 3.3879 | 0.4069 |
| 2.9426 | 11.0 | 204600 | 3.3766 | 0.4071 |
| 2.9217 | 12.0 | 223200 | 3.3644 | 0.4085 |
| 2.8993 | 13.0 | 241800 | 3.3694 | 0.4082 |
| 2.8758 | 14.0 | 260400 | 3.3866 | 0.4088 |
| 2.8544 | 15.0 | 279000 | 3.3849 | 0.4087 |
| 2.8363 | 16.0 | 297600 | 3.4028 | 0.4086 |
| 2.8222 | 17.0 | 316200 | 3.3990 | 0.4089 |
| 2.8018 | 18.0 | 334800 | 3.3984 | 0.4096 |
| 2.7834 | 19.0 | 353400 | 3.4143 | 0.4091 |
| 2.7626 | 20.0 | 372000 | 3.4202 | 0.4089 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1