transformers tensorboard safetensors opt text-generation generated_from_trainer dataset:kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-3e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal
      type: kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4089445254884035
---

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# smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-3e-4

This model was trained from scratch on the kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4202
- Accuracy: 0.4089

## 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: 0.0003
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7444        | 1.0   | 18600  | 3.9303          | 0.3452   |
| 3.4367        | 2.0   | 37200  | 3.6375          | 0.3746   |
| 3.29          | 3.0   | 55800  | 3.5155          | 0.3880   |
| 3.2081        | 4.0   | 74400  | 3.4751          | 0.3940   |
| 3.1438        | 5.0   | 93000  | 3.4190          | 0.3983   |
| 3.0947        | 6.0   | 111600 | 3.3905          | 0.4022   |
| 3.0569        | 7.0   | 130200 | 3.3832          | 0.4029   |
| 3.029         | 8.0   | 148800 | 3.3740          | 0.4051   |
| 2.9953        | 9.0   | 167400 | 3.3781          | 0.4059   |
| 2.9667        | 10.0  | 186000 | 3.3879          | 0.4069   |
| 2.9426        | 11.0  | 204600 | 3.3766          | 0.4071   |
| 2.9217        | 12.0  | 223200 | 3.3644          | 0.4085   |
| 2.8993        | 13.0  | 241800 | 3.3694          | 0.4082   |
| 2.8758        | 14.0  | 260400 | 3.3866          | 0.4088   |
| 2.8544        | 15.0  | 279000 | 3.3849          | 0.4087   |
| 2.8363        | 16.0  | 297600 | 3.4028          | 0.4086   |
| 2.8222        | 17.0  | 316200 | 3.3990          | 0.4089   |
| 2.8018        | 18.0  | 334800 | 3.3984          | 0.4096   |
| 2.7834        | 19.0  | 353400 | 3.4143          | 0.4091   |
| 2.7626        | 20.0  | 372000 | 3.4202          | 0.4089   |


### Framework versions

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