---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_high_variability_numeral
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_aann_high_variability_numeral-seed_1024-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_aann_high_variability_numeral
      type: kanishka/counterfactual_babylm_aann_high_variability_numeral
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.41021489963935376
---

<!-- 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_aann_high_variability_numeral-seed_1024-1e-3

This model was trained from scratch on the kanishka/counterfactual_babylm_aann_high_variability_numeral dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4236
- Accuracy: 0.4102

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.5932        | 1.0   | 18596  | 3.7701          | 0.3588   |
| 3.3833        | 2.0   | 37192  | 3.5597          | 0.3819   |
| 3.2597        | 3.0   | 55788  | 3.4648          | 0.3927   |
| 3.1741        | 4.0   | 74384  | 3.4191          | 0.3977   |
| 3.1213        | 5.0   | 92980  | 3.3967          | 0.4009   |
| 3.0783        | 6.0   | 111576 | 3.3773          | 0.4050   |
| 3.0456        | 7.0   | 130172 | 3.3826          | 0.4055   |
| 3.0126        | 8.0   | 148768 | 3.3547          | 0.4077   |
| 2.9843        | 9.0   | 167364 | 3.3614          | 0.4083   |
| 2.9592        | 10.0  | 185960 | 3.3779          | 0.4085   |
| 2.9367        | 11.0  | 204556 | 3.3604          | 0.4099   |
| 2.9145        | 12.0  | 223152 | 3.3759          | 0.4097   |
| 2.8924        | 13.0  | 241748 | 3.3856          | 0.4096   |
| 2.8757        | 14.0  | 260344 | 3.3844          | 0.4105   |
| 2.8545        | 15.0  | 278940 | 3.3832          | 0.4107   |
| 2.8339        | 16.0  | 297536 | 3.4079          | 0.4098   |
| 2.8157        | 17.0  | 316132 | 3.3884          | 0.4104   |
| 2.7966        | 18.0  | 334728 | 3.4081          | 0.4105   |
| 2.7807        | 19.0  | 353324 | 3.4181          | 0.4104   |
| 2.7595        | 20.0  | 371920 | 3.4236          | 0.4102   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.19.1