---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_anans_new
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_anans_new-seed_211-1e-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: 0.4062615946031953
---

<!-- 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-seed_211-1e-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: 3.4227
- Accuracy: 0.4063

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0574        | 1.0   | 18595  | 4.2623          | 0.3095   |
| 3.5744        | 2.0   | 37190  | 3.7410          | 0.3630   |
| 3.3998        | 3.0   | 55785  | 3.5874          | 0.3791   |
| 3.2911        | 4.0   | 74380  | 3.5155          | 0.3873   |
| 3.2246        | 5.0   | 92975  | 3.4782          | 0.3919   |
| 3.1723        | 6.0   | 111570 | 3.4440          | 0.3962   |
| 3.1287        | 7.0   | 130165 | 3.4271          | 0.3987   |
| 3.0994        | 8.0   | 148760 | 3.3990          | 0.4007   |
| 3.0668        | 9.0   | 167355 | 3.4112          | 0.4018   |
| 3.0398        | 10.0  | 185950 | 3.3915          | 0.4033   |
| 3.0097        | 11.0  | 204545 | 3.4067          | 0.4037   |
| 2.9924        | 12.0  | 223140 | 3.4117          | 0.4039   |
| 2.9702        | 13.0  | 241735 | 3.3926          | 0.4054   |
| 2.9486        | 14.0  | 260330 | 3.4035          | 0.4053   |
| 2.9284        | 15.0  | 278925 | 3.4107          | 0.4056   |
| 2.9143        | 16.0  | 297520 | 3.4057          | 0.4061   |
| 2.8931        | 17.0  | 316115 | 3.4160          | 0.4058   |
| 2.8785        | 18.0  | 334710 | 3.4139          | 0.4063   |
| 2.8611        | 19.0  | 353305 | 3.4191          | 0.4062   |
| 2.8443        | 20.0  | 371900 | 3.4227          | 0.4063   |


### Framework versions

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