---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-pipps-random_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-pipps-random_removal-seed_211-1e-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: 0.4112973955375624
---

<!-- 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-seed_211-1e-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: 3.3976
- Accuracy: 0.4113

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6046        | 1.0   | 18592  | 3.7833          | 0.3594   |
| 3.3837        | 2.0   | 37184  | 3.5854          | 0.3805   |
| 3.26          | 3.0   | 55776  | 3.4488          | 0.3931   |
| 3.1824        | 4.0   | 74368  | 3.4153          | 0.3986   |
| 3.1238        | 5.0   | 92960  | 3.3853          | 0.4028   |
| 3.0837        | 6.0   | 111552 | 3.3512          | 0.4060   |
| 3.0442        | 7.0   | 130144 | 3.3564          | 0.4065   |
| 3.0168        | 8.0   | 148736 | 3.3438          | 0.4083   |
| 2.9792        | 9.0   | 167328 | 3.3495          | 0.4090   |
| 2.9607        | 10.0  | 185920 | 3.3579          | 0.4091   |
| 2.9363        | 11.0  | 204512 | 3.3420          | 0.4116   |
| 2.9148        | 12.0  | 223104 | 3.3631          | 0.4106   |
| 2.893         | 13.0  | 241696 | 3.3609          | 0.4106   |
| 2.8729        | 14.0  | 260288 | 3.3806          | 0.4101   |
| 2.8543        | 15.0  | 278880 | 3.3685          | 0.4112   |
| 2.8352        | 16.0  | 297472 | 3.3734          | 0.4119   |
| 2.8131        | 17.0  | 316064 | 3.3759          | 0.4115   |
| 2.7949        | 18.0  | 334656 | 3.3842          | 0.4111   |
| 2.7756        | 19.0  | 353248 | 3.3893          | 0.4115   |
| 2.7607        | 20.0  | 371840 | 3.3976          | 0.4113   |


### Framework versions

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