---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-pipps_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-pipps_removal-seed_211-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual-babylm-pipps_removal
      type: kanishka/counterfactual-babylm-pipps_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40999993080367236
---

<!-- 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_removal-seed_211-1e-3

This model was trained from scratch on the kanishka/counterfactual-babylm-pipps_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4007
- Accuracy: 0.4100

## 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: 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6079        | 1.0   | 18593  | 3.8149          | 0.3576   |
| 3.3841        | 2.0   | 37186  | 3.5902          | 0.3792   |
| 3.2548        | 3.0   | 55779  | 3.4807          | 0.3918   |
| 3.1845        | 4.0   | 74372  | 3.4469          | 0.3968   |
| 3.1246        | 5.0   | 92965  | 3.4169          | 0.4014   |
| 3.0823        | 6.0   | 111558 | 3.3873          | 0.4035   |
| 3.0457        | 7.0   | 130151 | 3.3857          | 0.4053   |
| 3.0112        | 8.0   | 148744 | 3.3520          | 0.4070   |
| 2.9878        | 9.0   | 167337 | 3.3733          | 0.4072   |
| 2.96          | 10.0  | 185930 | 3.3503          | 0.4083   |
| 2.938         | 11.0  | 204523 | 3.3664          | 0.4084   |
| 2.9158        | 12.0  | 223116 | 3.3660          | 0.4093   |
| 2.8919        | 13.0  | 241709 | 3.3564          | 0.4101   |
| 2.8735        | 14.0  | 260302 | 3.3567          | 0.4107   |
| 2.8562        | 15.0  | 278895 | 3.3675          | 0.4100   |
| 2.8344        | 16.0  | 297488 | 3.3702          | 0.4103   |
| 2.814         | 17.0  | 316081 | 3.3808          | 0.4101   |
| 2.7973        | 18.0  | 334674 | 3.3935          | 0.4098   |
| 2.7732        | 19.0  | 353267 | 3.3887          | 0.4104   |
| 2.7585        | 20.0  | 371860 | 3.4007          | 0.4100   |


### Framework versions

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