---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_naans_new
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_naans_new-seed_211-1e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_naans_new
      type: kanishka/counterfactual_babylm_naans_new
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40601854249753977
---

<!-- 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_naans_new-seed_211-1e-4

This model was trained from scratch on the kanishka/counterfactual_babylm_naans_new dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4159
- Accuracy: 0.4060

## 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: 211
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0574        | 1.0   | 18595  | 4.2668          | 0.3095   |
| 3.574         | 2.0   | 37190  | 3.7340          | 0.3638   |
| 3.3998        | 3.0   | 55785  | 3.5946          | 0.3793   |
| 3.2908        | 4.0   | 74380  | 3.5169          | 0.3871   |
| 3.2244        | 5.0   | 92975  | 3.4843          | 0.3919   |
| 3.1723        | 6.0   | 111570 | 3.4423          | 0.3955   |
| 3.1287        | 7.0   | 130165 | 3.4224          | 0.3987   |
| 3.0995        | 8.0   | 148760 | 3.4119          | 0.4001   |
| 3.0666        | 9.0   | 167355 | 3.4093          | 0.4014   |
| 3.0395        | 10.0  | 185950 | 3.3993          | 0.4024   |
| 3.0097        | 11.0  | 204545 | 3.4087          | 0.4031   |
| 2.9923        | 12.0  | 223140 | 3.4030          | 0.4042   |
| 2.9703        | 13.0  | 241735 | 3.3938          | 0.4047   |
| 2.9483        | 14.0  | 260330 | 3.4000          | 0.4051   |
| 2.9286        | 15.0  | 278925 | 3.4069          | 0.4048   |
| 2.9143        | 16.0  | 297520 | 3.4020          | 0.4056   |
| 2.8935        | 17.0  | 316115 | 3.4100          | 0.4055   |
| 2.8782        | 18.0  | 334710 | 3.4071          | 0.4058   |
| 2.8613        | 19.0  | 353305 | 3.4123          | 0.4062   |
| 2.8439        | 20.0  | 371900 | 3.4159          | 0.4060   |


### Framework versions

- Transformers 4.38.0
- Pytorch 2.3.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2