---
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