---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-indef_articles_with_pl_nouns-removal-3e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
      type: kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40992788926628976
---

<!-- 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-indef_articles_with_pl_nouns-removal-3e-4

This model was trained from scratch on the kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4054
- Accuracy: 0.4099

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7446        | 1.0   | 18601  | 3.9130          | 0.3462   |
| 3.4295        | 2.0   | 37202  | 3.6268          | 0.3752   |
| 3.2878        | 3.0   | 55803  | 3.5073          | 0.3878   |
| 3.2031        | 4.0   | 74404  | 3.4630          | 0.3948   |
| 3.1443        | 5.0   | 93005  | 3.4077          | 0.3994   |
| 3.0973        | 6.0   | 111606 | 3.3724          | 0.4028   |
| 3.0617        | 7.0   | 130207 | 3.3562          | 0.4062   |
| 3.0252        | 8.0   | 148808 | 3.3648          | 0.4059   |
| 2.994         | 9.0   | 167409 | 3.3582          | 0.4071   |
| 2.9693        | 10.0  | 186010 | 3.3688          | 0.4075   |
| 2.9383        | 11.0  | 204611 | 3.3513          | 0.4092   |
| 2.9188        | 12.0  | 223212 | 3.3659          | 0.4086   |
| 2.8978        | 13.0  | 241813 | 3.3581          | 0.4097   |
| 2.8784        | 14.0  | 260414 | 3.3657          | 0.4103   |
| 2.8592        | 15.0  | 279015 | 3.3693          | 0.4102   |
| 2.8415        | 16.0  | 297616 | 3.3867          | 0.4092   |
| 2.8198        | 17.0  | 316217 | 3.3790          | 0.4101   |
| 2.8013        | 18.0  | 334818 | 3.3924          | 0.4099   |
| 2.7836        | 19.0  | 353419 | 3.4014          | 0.4098   |
| 2.7626        | 20.0  | 372020 | 3.4054          | 0.4099   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0