---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-new_regex_aanns_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-new_regex_aanns_removal-1e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual-babylm-new_regex_aanns_removal
      type: kanishka/counterfactual-babylm-new_regex_aanns_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4080815719204785
---

<!-- 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-new_regex_aanns_removal-1e-4

This model was trained from scratch on the kanishka/counterfactual-babylm-new_regex_aanns_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3971
- Accuracy: 0.4081

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0555        | 1.0   | 18593  | 4.2608          | 0.3086   |
| 3.5626        | 2.0   | 37186  | 3.7490          | 0.3634   |
| 3.3926        | 3.0   | 55779  | 3.5729          | 0.3804   |
| 3.2863        | 4.0   | 74372  | 3.5122          | 0.3883   |
| 3.2223        | 5.0   | 92965  | 3.4704          | 0.3932   |
| 3.1687        | 6.0   | 111558 | 3.4542          | 0.3960   |
| 3.1265        | 7.0   | 130151 | 3.4307          | 0.3987   |
| 3.0955        | 8.0   | 148744 | 3.4010          | 0.4014   |
| 3.0614        | 9.0   | 167337 | 3.3947          | 0.4026   |
| 3.0346        | 10.0  | 185930 | 3.3861          | 0.4037   |
| 3.0121        | 11.0  | 204523 | 3.3788          | 0.4047   |
| 2.9917        | 12.0  | 223116 | 3.3737          | 0.4050   |
| 2.968         | 13.0  | 241709 | 3.3828          | 0.4055   |
| 2.9462        | 14.0  | 260302 | 3.3921          | 0.4060   |
| 2.9308        | 15.0  | 278895 | 3.3842          | 0.4073   |
| 2.9096        | 16.0  | 297488 | 3.3800          | 0.4075   |
| 2.889         | 17.0  | 316081 | 3.3850          | 0.4077   |
| 2.8779        | 18.0  | 334674 | 3.3920          | 0.4076   |
| 2.8585        | 19.0  | 353267 | 3.3898          | 0.4084   |
| 2.8469        | 20.0  | 371860 | 3.3971          | 0.4081   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.19.1