---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-indef_articles_with_pl_nouns_removal-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
      type: kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.41252109443859236
---

<!-- 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-indef_articles_with_pl_nouns_removal-1e-3

This model was trained from scratch on the kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4176
- Accuracy: 0.4125

## 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: 16
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.5148        | 1.0   | 37201  | 3.7270          | 0.3671   |
| 3.3074        | 2.0   | 74402  | 3.4841          | 0.3897   |
| 3.1988        | 3.0   | 111603 | 3.4300          | 0.3979   |
| 3.152         | 4.0   | 148804 | 3.3774          | 0.4050   |
| 3.0973        | 5.0   | 186005 | 3.3462          | 0.4090   |
| 3.0543        | 6.0   | 223206 | 3.3687          | 0.4064   |
| 3.0161        | 7.0   | 260407 | 3.3391          | 0.4114   |
| 2.9858        | 8.0   | 297608 | 3.3477          | 0.4105   |
| 2.9718        | 9.0   | 334809 | 3.3436          | 0.4112   |
| 2.9399        | 10.0  | 372010 | 3.3451          | 0.4121   |
| 2.9207        | 11.0  | 409211 | 3.3586          | 0.4130   |
| 2.8987        | 12.0  | 446412 | 3.3554          | 0.4123   |
| 2.8779        | 13.0  | 483613 | 3.3616          | 0.4130   |
| 2.8519        | 14.0  | 520814 | 3.3696          | 0.4129   |
| 2.8395        | 15.0  | 558015 | 3.3729          | 0.4128   |
| 2.8151        | 16.0  | 595216 | 3.3718          | 0.4140   |
| 2.798         | 17.0  | 632417 | 3.3858          | 0.4128   |
| 2.7738        | 18.0  | 669618 | 3.4080          | 0.4130   |
| 2.7555        | 19.0  | 706819 | 3.4067          | 0.4131   |
| 2.7434        | 20.0  | 744020 | 3.4176          | 0.4125   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0