---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-only_other_det_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-only_other_det_removal-3e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual-babylm-only_other_det_removal
      type: kanishka/counterfactual-babylm-only_other_det_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40813531756892846
---

<!-- 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-only_other_det_removal-3e-4

This model was trained from scratch on the kanishka/counterfactual-babylm-only_other_det_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4419
- Accuracy: 0.4081

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7368        | 1.0   | 18597  | 3.9162          | 0.3452   |
| 3.4348        | 2.0   | 37194  | 3.6327          | 0.3748   |
| 3.2919        | 3.0   | 55791  | 3.5084          | 0.3900   |
| 3.2086        | 4.0   | 74388  | 3.4502          | 0.3956   |
| 3.1474        | 5.0   | 92985  | 3.4235          | 0.3995   |
| 3.1012        | 6.0   | 111582 | 3.4031          | 0.4020   |
| 3.0638        | 7.0   | 130179 | 3.4128          | 0.4030   |
| 3.0262        | 8.0   | 148776 | 3.3998          | 0.4046   |
| 3.0016        | 9.0   | 167373 | 3.3731          | 0.4070   |
| 2.9715        | 10.0  | 185970 | 3.4058          | 0.4062   |
| 2.9481        | 11.0  | 204567 | 3.3875          | 0.4069   |
| 2.9243        | 12.0  | 223164 | 3.4070          | 0.4070   |
| 2.9047        | 13.0  | 241761 | 3.4015          | 0.4079   |
| 2.8797        | 14.0  | 260358 | 3.4114          | 0.4077   |
| 2.8651        | 15.0  | 278955 | 3.4072          | 0.4083   |
| 2.8434        | 16.0  | 297552 | 3.4240          | 0.4075   |
| 2.8255        | 17.0  | 316149 | 3.4179          | 0.4083   |
| 2.8036        | 18.0  | 334746 | 3.4256          | 0.4082   |
| 2.7888        | 19.0  | 353343 | 3.4363          | 0.4083   |
| 2.7701        | 20.0  | 371940 | 3.4419          | 0.4081   |


### Framework versions

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