---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_prototypical_only
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-3e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_prototypical_only
      type: kanishka/counterfactual_babylm_prototypical_only
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40804219428728983
---

<!-- 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-prototypical_only-3e-4

This model was trained from scratch on the kanishka/counterfactual_babylm_prototypical_only dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4082
- Accuracy: 0.4080

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7341        | 1.0   | 18593  | 3.8768          | 0.3468   |
| 3.4322        | 2.0   | 37186  | 3.6158          | 0.3751   |
| 3.2902        | 3.0   | 55779  | 3.4817          | 0.3883   |
| 3.21          | 4.0   | 74372  | 3.4286          | 0.3960   |
| 3.1498        | 5.0   | 92965  | 3.4151          | 0.3978   |
| 3.0981        | 6.0   | 111558 | 3.3790          | 0.4022   |
| 3.0651        | 7.0   | 130151 | 3.3750          | 0.4034   |
| 3.0292        | 8.0   | 148744 | 3.3879          | 0.4041   |
| 3.0031        | 9.0   | 167337 | 3.3773          | 0.4046   |
| 2.9713        | 10.0  | 185930 | 3.3769          | 0.4061   |
| 2.9529        | 11.0  | 204523 | 3.3778          | 0.4069   |
| 2.9286        | 12.0  | 223116 | 3.3612          | 0.4077   |
| 2.9065        | 13.0  | 241709 | 3.3686          | 0.4073   |
| 2.8837        | 14.0  | 260302 | 3.3861          | 0.4078   |
| 2.8621        | 15.0  | 278895 | 3.3851          | 0.4077   |
| 2.8487        | 16.0  | 297488 | 3.3876          | 0.4080   |
| 2.8243        | 17.0  | 316081 | 3.3908          | 0.4081   |
| 2.8078        | 18.0  | 334674 | 3.3952          | 0.4082   |
| 2.7887        | 19.0  | 353267 | 3.4020          | 0.4082   |
| 2.7716        | 20.0  | 371860 | 3.4082          | 0.4080   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0