---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_low_variability_noun
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_aann_low_variability_noun_1024-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_aann_low_variability_noun
      type: kanishka/counterfactual_babylm_aann_low_variability_noun
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40927603188483547
---

<!-- 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_low_variability_noun_1024-1e-3

This model was trained from scratch on the kanishka/counterfactual_babylm_aann_low_variability_noun dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4134
- Accuracy: 0.4093

## 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: 0.001
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6017        | 1.0   | 18595  | 3.7832          | 0.3588   |
| 3.3832        | 2.0   | 37190  | 3.5725          | 0.3809   |
| 3.2557        | 3.0   | 55785  | 3.4670          | 0.3932   |
| 3.1779        | 4.0   | 74380  | 3.4315          | 0.3979   |
| 3.1207        | 5.0   | 92975  | 3.3998          | 0.4016   |
| 3.0759        | 6.0   | 111570 | 3.3830          | 0.4037   |
| 3.0401        | 7.0   | 130165 | 3.3819          | 0.4054   |
| 3.0134        | 8.0   | 148760 | 3.3636          | 0.4073   |
| 2.9862        | 9.0   | 167355 | 3.3830          | 0.4070   |
| 2.9548        | 10.0  | 185950 | 3.3661          | 0.4078   |
| 2.9335        | 11.0  | 204545 | 3.3690          | 0.4085   |
| 2.9121        | 12.0  | 223140 | 3.3669          | 0.4088   |
| 2.8942        | 13.0  | 241735 | 3.3727          | 0.4092   |
| 2.8708        | 14.0  | 260330 | 3.3823          | 0.4091   |
| 2.8487        | 15.0  | 278925 | 3.3783          | 0.4094   |
| 2.8298        | 16.0  | 297520 | 3.3950          | 0.4091   |
| 2.8116        | 17.0  | 316115 | 3.3998          | 0.4095   |
| 2.7953        | 18.0  | 334710 | 3.4066          | 0.4092   |
| 2.775         | 19.0  | 353305 | 3.4064          | 0.4094   |
| 2.759         | 20.0  | 371900 | 3.4134          | 0.4093   |


### Framework versions

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