---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_anans_new
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_anans_new-3e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_anans_new
      type: kanishka/counterfactual_babylm_anans_new
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4096272415057219
---

<!-- 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_anans_new-3e-4

This model was trained from scratch on the kanishka/counterfactual_babylm_anans_new dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4257
- Accuracy: 0.4096

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7355        | 1.0   | 18595  | 3.8995          | 0.3461   |
| 3.4292        | 2.0   | 37190  | 3.6087          | 0.3768   |
| 3.2927        | 3.0   | 55785  | 3.5001          | 0.3895   |
| 3.1999        | 4.0   | 74380  | 3.4743          | 0.3952   |
| 3.1448        | 5.0   | 92975  | 3.4089          | 0.3996   |
| 3.0986        | 6.0   | 111570 | 3.4166          | 0.4022   |
| 3.0592        | 7.0   | 130165 | 3.4032          | 0.4036   |
| 3.0279        | 8.0   | 148760 | 3.3746          | 0.4062   |
| 2.9977        | 9.0   | 167355 | 3.3709          | 0.4068   |
| 2.9761        | 10.0  | 185950 | 3.3795          | 0.4071   |
| 2.9464        | 11.0  | 204545 | 3.3783          | 0.4080   |
| 2.9234        | 12.0  | 223140 | 3.3832          | 0.4084   |
| 2.9068        | 13.0  | 241735 | 3.3838          | 0.4087   |
| 2.88          | 14.0  | 260330 | 3.3881          | 0.4091   |
| 2.8614        | 15.0  | 278925 | 3.3863          | 0.4097   |
| 2.841         | 16.0  | 297520 | 3.4092          | 0.4094   |
| 2.8225        | 17.0  | 316115 | 3.3966          | 0.4098   |
| 2.8062        | 18.0  | 334710 | 3.4095          | 0.4096   |
| 2.7904        | 19.0  | 353305 | 3.4169          | 0.4098   |
| 2.775         | 20.0  | 371900 | 3.4257          | 0.4096   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.19.1