---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_naans_new
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_naans_new-seed_1024-1e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_naans_new
      type: kanishka/counterfactual_babylm_naans_new
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40723568402608223
---

<!-- 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_naans_new-seed_1024-1e-4

This model was trained from scratch on the kanishka/counterfactual_babylm_naans_new dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4373
- Accuracy: 0.4072

## 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: 1024
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0553        | 1.0   | 18595  | 4.2752          | 0.3077   |
| 3.5587        | 2.0   | 37190  | 3.7501          | 0.3633   |
| 3.3865        | 3.0   | 55785  | 3.5884          | 0.3815   |
| 3.2906        | 4.0   | 74380  | 3.5054          | 0.3891   |
| 3.2202        | 5.0   | 92975  | 3.4769          | 0.3943   |
| 3.1699        | 6.0   | 111570 | 3.4426          | 0.3977   |
| 3.1232        | 7.0   | 130165 | 3.4478          | 0.3994   |
| 3.091         | 8.0   | 148760 | 3.4243          | 0.4014   |
| 3.0613        | 9.0   | 167355 | 3.4169          | 0.4030   |
| 3.0322        | 10.0  | 185950 | 3.4142          | 0.4050   |
| 3.0107        | 11.0  | 204545 | 3.4052          | 0.4049   |
| 2.9844        | 12.0  | 223140 | 3.4128          | 0.4053   |
| 2.9671        | 13.0  | 241735 | 3.4150          | 0.4062   |
| 2.9477        | 14.0  | 260330 | 3.4174          | 0.4062   |
| 2.9272        | 15.0  | 278925 | 3.4275          | 0.4067   |
| 2.9088        | 16.0  | 297520 | 3.4271          | 0.4068   |
| 2.8915        | 17.0  | 316115 | 3.4245          | 0.4071   |
| 2.872         | 18.0  | 334710 | 3.4262          | 0.4070   |
| 2.8514        | 19.0  | 353305 | 3.4322          | 0.4073   |
| 2.8467        | 20.0  | 371900 | 3.4373          | 0.4072   |


### Framework versions

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