---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-pipps-random_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-pipps-random_removal-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual-babylm-pipps-random_removal
      type: kanishka/counterfactual-babylm-pipps-random_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4119714215135951
---

<!-- 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-pipps-random_removal-1e-3

This model was trained from scratch on the kanishka/counterfactual-babylm-pipps-random_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3829
- Accuracy: 0.4120

## 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: 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6058        | 1.0   | 18592  | 3.8079          | 0.3582   |
| 3.3918        | 2.0   | 37184  | 3.5864          | 0.3803   |
| 3.264         | 3.0   | 55776  | 3.4837          | 0.3930   |
| 3.1794        | 4.0   | 74368  | 3.4301          | 0.3984   |
| 3.1239        | 5.0   | 92960  | 3.3843          | 0.4023   |
| 3.0814        | 6.0   | 111552 | 3.3626          | 0.4045   |
| 3.0416        | 7.0   | 130144 | 3.3471          | 0.4076   |
| 3.0128        | 8.0   | 148736 | 3.3522          | 0.4079   |
| 2.9879        | 9.0   | 167328 | 3.3497          | 0.4087   |
| 2.9616        | 10.0  | 185920 | 3.3193          | 0.4123   |
| 2.941         | 11.0  | 204512 | 3.3381          | 0.4113   |
| 2.9156        | 12.0  | 223104 | 3.3479          | 0.4114   |
| 2.8946        | 13.0  | 241696 | 3.3280          | 0.4130   |
| 2.8744        | 14.0  | 260288 | 3.3445          | 0.4123   |
| 2.8532        | 15.0  | 278880 | 3.3571          | 0.4119   |
| 2.831         | 16.0  | 297472 | 3.3629          | 0.4122   |
| 2.8168        | 17.0  | 316064 | 3.3629          | 0.4121   |
| 2.7943        | 18.0  | 334656 | 3.3743          | 0.4119   |
| 2.7777        | 19.0  | 353248 | 3.3781          | 0.4121   |
| 2.7631        | 20.0  | 371840 | 3.3829          | 0.4120   |


### Framework versions

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