---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-new_regex_aanns_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-new_regex_aanns_removal-seed_42-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual-babylm-new_regex_aanns_removal
      type: kanishka/counterfactual-babylm-new_regex_aanns_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.41202925698119025
---

<!-- 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-new_regex_aanns_removal-seed_42-1e-3

This model was trained from scratch on the kanishka/counterfactual-babylm-new_regex_aanns_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3818
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6013        | 1.0   | 18593  | 3.7859          | 0.3595   |
| 3.3808        | 2.0   | 37186  | 3.5963          | 0.3806   |
| 3.2581        | 3.0   | 55779  | 3.4526          | 0.3931   |
| 3.174         | 4.0   | 74372  | 3.4158          | 0.3984   |
| 3.1218        | 5.0   | 92965  | 3.4018          | 0.4022   |
| 3.0764        | 6.0   | 111558 | 3.3647          | 0.4060   |
| 3.0403        | 7.0   | 130151 | 3.3497          | 0.4073   |
| 3.0123        | 8.0   | 148744 | 3.3577          | 0.4084   |
| 2.9806        | 9.0   | 167337 | 3.3481          | 0.4096   |
| 2.9559        | 10.0  | 185930 | 3.3229          | 0.4107   |
| 2.9341        | 11.0  | 204523 | 3.3348          | 0.4109   |
| 2.9141        | 12.0  | 223116 | 3.3268          | 0.4113   |
| 2.8904        | 13.0  | 241709 | 3.3276          | 0.4122   |
| 2.8682        | 14.0  | 260302 | 3.3432          | 0.4128   |
| 2.8518        | 15.0  | 278895 | 3.3533          | 0.4120   |
| 2.8294        | 16.0  | 297488 | 3.3578          | 0.4120   |
| 2.8079        | 17.0  | 316081 | 3.3555          | 0.4124   |
| 2.7936        | 18.0  | 334674 | 3.3698          | 0.4121   |
| 2.7716        | 19.0  | 353267 | 3.3713          | 0.4124   |
| 2.7573        | 20.0  | 371860 | 3.3818          | 0.4120   |


### Framework versions

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