---
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-seed_1024-1e-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: 0.41096838506284816
---

<!-- 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-seed_1024-1e-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: 3.4006
- Accuracy: 0.4110

## 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: 1024
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6013        | 1.0   | 18600  | 3.7573          | 0.3598   |
| 3.3813        | 2.0   | 37200  | 3.5688          | 0.3805   |
| 3.2541        | 3.0   | 55800  | 3.4489          | 0.3922   |
| 3.174         | 4.0   | 74400  | 3.4158          | 0.3980   |
| 3.1166        | 5.0   | 93000  | 3.3767          | 0.4028   |
| 3.0777        | 6.0   | 111600 | 3.3729          | 0.4036   |
| 3.0372        | 7.0   | 130200 | 3.3464          | 0.4071   |
| 3.0083        | 8.0   | 148800 | 3.3503          | 0.4081   |
| 2.9762        | 9.0   | 167400 | 3.3317          | 0.4098   |
| 2.9515        | 10.0  | 186000 | 3.3434          | 0.4088   |
| 2.9338        | 11.0  | 204600 | 3.3526          | 0.4102   |
| 2.9063        | 12.0  | 223200 | 3.3577          | 0.4095   |
| 2.8871        | 13.0  | 241800 | 3.3493          | 0.4101   |
| 2.8654        | 14.0  | 260400 | 3.3641          | 0.4106   |
| 2.8465        | 15.0  | 279000 | 3.3597          | 0.4115   |
| 2.8233        | 16.0  | 297600 | 3.3748          | 0.4106   |
| 2.8071        | 17.0  | 316200 | 3.3754          | 0.4113   |
| 2.7899        | 18.0  | 334800 | 3.3833          | 0.4111   |
| 2.7669        | 19.0  | 353400 | 3.3913          | 0.4112   |
| 2.7513        | 20.0  | 372000 | 3.4006          | 0.4110   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1