---
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-1e-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: 0.40654968657553286
---

<!-- 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-1e-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: 3.4247
- Accuracy: 0.4065

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0532        | 1.0   | 18597  | 4.2579          | 0.3085   |
| 3.566         | 2.0   | 37194  | 3.7605          | 0.3620   |
| 3.3886        | 3.0   | 55791  | 3.5962          | 0.3806   |
| 3.2899        | 4.0   | 74388  | 3.5175          | 0.3894   |
| 3.2214        | 5.0   | 92985  | 3.4618          | 0.3939   |
| 3.1702        | 6.0   | 111582 | 3.4252          | 0.3979   |
| 3.1294        | 7.0   | 130179 | 3.4255          | 0.3995   |
| 3.0899        | 8.0   | 148776 | 3.4190          | 0.4010   |
| 3.0639        | 9.0   | 167373 | 3.4041          | 0.4027   |
| 3.0329        | 10.0  | 185970 | 3.4231          | 0.4029   |
| 3.0093        | 11.0  | 204567 | 3.4100          | 0.4045   |
| 2.9859        | 12.0  | 223164 | 3.4097          | 0.4049   |
| 2.9662        | 13.0  | 241761 | 3.4043          | 0.4053   |
| 2.9424        | 14.0  | 260358 | 3.4046          | 0.4057   |
| 2.928         | 15.0  | 278955 | 3.4079          | 0.4059   |
| 2.908         | 16.0  | 297552 | 3.4119          | 0.4061   |
| 2.8912        | 17.0  | 316149 | 3.4119          | 0.4062   |
| 2.8716        | 18.0  | 334746 | 3.4159          | 0.4064   |
| 2.8589        | 19.0  | 353343 | 3.4223          | 0.4065   |
| 2.8424        | 20.0  | 371940 | 3.4247          | 0.4065   |


### Framework versions

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