---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_excess_adj_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-3e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_aann_excess_adj_removal
      type: kanishka/counterfactual_babylm_aann_excess_adj_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40517314741870225
---

<!-- 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-adj_num_freq_balanced-3e-4

This model was trained from scratch on the kanishka/counterfactual_babylm_aann_excess_adj_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4663
- Accuracy: 0.4052

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7291        | 1.0   | 18629  | 3.9229          | 0.3464   |
| 3.4237        | 2.0   | 37258  | 3.6542          | 0.3747   |
| 3.2842        | 3.0   | 55887  | 3.5183          | 0.3880   |
| 3.1952        | 4.0   | 74516  | 3.4748          | 0.3938   |
| 3.1351        | 5.0   | 93145  | 3.4493          | 0.3972   |
| 3.0844        | 6.0   | 111774 | 3.4156          | 0.4005   |
| 3.0442        | 7.0   | 130403 | 3.3854          | 0.4032   |
| 3.008         | 8.0   | 149032 | 3.4062          | 0.4030   |
| 2.9768        | 9.0   | 167661 | 3.3970          | 0.4047   |
| 2.9498        | 10.0  | 186290 | 3.4024          | 0.4047   |
| 2.917         | 11.0  | 204919 | 3.4242          | 0.4039   |
| 2.9005        | 12.0  | 223548 | 3.4093          | 0.4049   |
| 2.8747        | 13.0  | 242177 | 3.4192          | 0.4051   |
| 2.8542        | 14.0  | 260806 | 3.4233          | 0.4053   |
| 2.8326        | 15.0  | 279435 | 3.4314          | 0.4054   |
| 2.8125        | 16.0  | 298064 | 3.4404          | 0.4052   |
| 2.7911        | 17.0  | 316693 | 3.4450          | 0.4054   |
| 2.7682        | 18.0  | 335322 | 3.4488          | 0.4054   |
| 2.7512        | 19.0  | 353951 | 3.4581          | 0.4054   |
| 2.7331        | 20.0  | 372580 | 3.4663          | 0.4052   |


### Framework versions

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