---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_dtanns
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_aanns_dtanns-seed_1024-1e-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: 0.4055823320854937
---

<!-- 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_1024-1e-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: 3.4264
- Accuracy: 0.4056

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0514        | 1.0   | 18595  | 4.2435          | 0.3101   |
| 3.5672        | 2.0   | 37190  | 3.7652          | 0.3622   |
| 3.3933        | 3.0   | 55785  | 3.5859          | 0.3792   |
| 3.2939        | 4.0   | 74380  | 3.5397          | 0.3863   |
| 3.2248        | 5.0   | 92975  | 3.4728          | 0.3919   |
| 3.173         | 6.0   | 111570 | 3.4672          | 0.3950   |
| 3.1332        | 7.0   | 130165 | 3.4249          | 0.3987   |
| 3.0958        | 8.0   | 148760 | 3.4232          | 0.3998   |
| 3.0709        | 9.0   | 167355 | 3.4138          | 0.4012   |
| 3.0426        | 10.0  | 185950 | 3.4269          | 0.4014   |
| 3.0138        | 11.0  | 204545 | 3.4023          | 0.4037   |
| 2.995         | 12.0  | 223140 | 3.4037          | 0.4035   |
| 2.9702        | 13.0  | 241735 | 3.3991          | 0.4043   |
| 2.954         | 14.0  | 260330 | 3.4180          | 0.4042   |
| 2.9299        | 15.0  | 278925 | 3.4060          | 0.4049   |
| 2.9106        | 16.0  | 297520 | 3.4084          | 0.4049   |
| 2.8923        | 17.0  | 316115 | 3.4154          | 0.4055   |
| 2.8795        | 18.0  | 334710 | 3.4195          | 0.4057   |
| 2.8628        | 19.0  | 353305 | 3.4225          | 0.4057   |
| 2.8497        | 20.0  | 371900 | 3.4264          | 0.4056   |


### Framework versions

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