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---
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-3
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: 0.4118896526593414
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-1e-3
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: 3.4114
- Accuracy: 0.4119
## 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: 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.5998 | 1.0 | 18600 | 3.7955 | 0.3595 |
| 3.3776 | 2.0 | 37200 | 3.5874 | 0.3805 |
| 3.245 | 3.0 | 55800 | 3.4956 | 0.3923 |
| 3.1698 | 4.0 | 74400 | 3.4301 | 0.3991 |
| 3.1095 | 5.0 | 93000 | 3.4080 | 0.4017 |
| 3.0618 | 6.0 | 111600 | 3.3783 | 0.4047 |
| 3.0262 | 7.0 | 130200 | 3.3656 | 0.4063 |
| 2.9992 | 8.0 | 148800 | 3.3350 | 0.4088 |
| 2.9653 | 9.0 | 167400 | 3.3531 | 0.4103 |
| 2.9376 | 10.0 | 186000 | 3.3526 | 0.4110 |
| 2.9136 | 11.0 | 204600 | 3.3538 | 0.4098 |
| 2.8922 | 12.0 | 223200 | 3.3425 | 0.4120 |
| 2.8698 | 13.0 | 241800 | 3.3346 | 0.4124 |
| 2.8466 | 14.0 | 260400 | 3.3660 | 0.4110 |
| 2.8253 | 15.0 | 279000 | 3.3566 | 0.4127 |
| 2.8058 | 16.0 | 297600 | 3.3781 | 0.4113 |
| 2.7908 | 17.0 | 316200 | 3.3851 | 0.4119 |
| 2.7701 | 18.0 | 334800 | 3.3872 | 0.4128 |
| 2.7511 | 19.0 | 353400 | 3.4038 | 0.4120 |
| 2.7292 | 20.0 | 372000 | 3.4114 | 0.4119 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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