---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_dtanns
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_aann_dtanns-1e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_aann_dtanns
      type: kanishka/counterfactual_babylm_aann_dtanns
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40580987772461885
---

<!-- 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_aann_dtanns-1e-4

This model was trained from scratch on the kanishka/counterfactual_babylm_aann_dtanns dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4122
- Accuracy: 0.4058

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0526        | 1.0   | 18595  | 4.2653          | 0.3097   |
| 3.5753        | 2.0   | 37190  | 3.7488          | 0.3616   |
| 3.3983        | 3.0   | 55785  | 3.5949          | 0.3788   |
| 3.2911        | 4.0   | 74380  | 3.5416          | 0.3863   |
| 3.2282        | 5.0   | 92975  | 3.4673          | 0.3924   |
| 3.1707        | 6.0   | 111570 | 3.4627          | 0.3948   |
| 3.1335        | 7.0   | 130165 | 3.4265          | 0.3989   |
| 3.1003        | 8.0   | 148760 | 3.4135          | 0.4001   |
| 3.0686        | 9.0   | 167355 | 3.4013          | 0.4009   |
| 3.0407        | 10.0  | 185950 | 3.3952          | 0.4028   |
| 3.0138        | 11.0  | 204545 | 3.3950          | 0.4027   |
| 2.9937        | 12.0  | 223140 | 3.3930          | 0.4036   |
| 2.9758        | 13.0  | 241735 | 3.4018          | 0.4041   |
| 2.9478        | 14.0  | 260330 | 3.4024          | 0.4045   |
| 2.9311        | 15.0  | 278925 | 3.4006          | 0.4050   |
| 2.9098        | 16.0  | 297520 | 3.4037          | 0.4050   |
| 2.8959        | 17.0  | 316115 | 3.3980          | 0.4054   |
| 2.8768        | 18.0  | 334710 | 3.4074          | 0.4058   |
| 2.8627        | 19.0  | 353305 | 3.4091          | 0.4058   |
| 2.8505        | 20.0  | 371900 | 3.4122          | 0.4058   |


### Framework versions

- Transformers 4.38.0
- Pytorch 2.3.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2