---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_dtanns
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_aanns_dtanns-seed_211-1e-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: 0.40547080986496004
---

<!-- 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_aanns_dtanns-seed_211-1e-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: 3.4323
- Accuracy: 0.4055

## 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: 211
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0524        | 1.0   | 18595  | 4.2811          | 0.3084   |
| 3.5716        | 2.0   | 37190  | 3.7750          | 0.3615   |
| 3.3913        | 3.0   | 55785  | 3.6008          | 0.3779   |
| 3.2921        | 4.0   | 74380  | 3.5102          | 0.3883   |
| 3.2283        | 5.0   | 92975  | 3.4789          | 0.3930   |
| 3.1673        | 6.0   | 111570 | 3.4379          | 0.3962   |
| 3.127         | 7.0   | 130165 | 3.4175          | 0.3988   |
| 3.0935        | 8.0   | 148760 | 3.4272          | 0.3998   |
| 3.0679        | 9.0   | 167355 | 3.4150          | 0.4011   |
| 3.0402        | 10.0  | 185950 | 3.4148          | 0.4023   |
| 3.0082        | 11.0  | 204545 | 3.4169          | 0.4029   |
| 2.988         | 12.0  | 223140 | 3.4130          | 0.4035   |
| 2.967         | 13.0  | 241735 | 3.3969          | 0.4045   |
| 2.9457        | 14.0  | 260330 | 3.4080          | 0.4049   |
| 2.9268        | 15.0  | 278925 | 3.4129          | 0.4045   |
| 2.911         | 16.0  | 297520 | 3.4159          | 0.4047   |
| 2.8964        | 17.0  | 316115 | 3.4221          | 0.4051   |
| 2.8758        | 18.0  | 334710 | 3.4286          | 0.4054   |
| 2.858         | 19.0  | 353305 | 3.4265          | 0.4054   |
| 2.8493        | 20.0  | 371900 | 3.4323          | 0.4055   |


### Framework versions

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