---
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
---

<!-- 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-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