---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_anans_new
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_anans_new-seed_1024-1e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_anans_new
      type: kanishka/counterfactual_babylm_anans_new
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4072716245668577
---

<!-- 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_anans_new-seed_1024-1e-4

This model was trained from scratch on the kanishka/counterfactual_babylm_anans_new dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4358
- Accuracy: 0.4073

## 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: 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0554        | 1.0   | 18595  | 4.2729          | 0.3076   |
| 3.5589        | 2.0   | 37190  | 3.7451          | 0.3634   |
| 3.3868        | 3.0   | 55785  | 3.5863          | 0.3813   |
| 3.2904        | 4.0   | 74380  | 3.5049          | 0.3888   |
| 3.2198        | 5.0   | 92975  | 3.4777          | 0.3946   |
| 3.1698        | 6.0   | 111570 | 3.4524          | 0.3970   |
| 3.1232        | 7.0   | 130165 | 3.4485          | 0.3993   |
| 3.0906        | 8.0   | 148760 | 3.4315          | 0.4013   |
| 3.0612        | 9.0   | 167355 | 3.4062          | 0.4034   |
| 3.0318        | 10.0  | 185950 | 3.4156          | 0.4043   |
| 3.0102        | 11.0  | 204545 | 3.4183          | 0.4043   |
| 2.9841        | 12.0  | 223140 | 3.4149          | 0.4052   |
| 2.9673        | 13.0  | 241735 | 3.4120          | 0.4061   |
| 2.9473        | 14.0  | 260330 | 3.4053          | 0.4069   |
| 2.9271        | 15.0  | 278925 | 3.4140          | 0.4066   |
| 2.9087        | 16.0  | 297520 | 3.4227          | 0.4067   |
| 2.8915        | 17.0  | 316115 | 3.4177          | 0.4070   |
| 2.8719        | 18.0  | 334710 | 3.4238          | 0.4071   |
| 2.8512        | 19.0  | 353305 | 3.4332          | 0.4071   |
| 2.8466        | 20.0  | 371900 | 3.4358          | 0.4073   |


### Framework versions

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