---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-only_other_det_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-only_other_det_removal-seed_1024-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual-babylm-only_other_det_removal
      type: kanishka/counterfactual-babylm-only_other_det_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4105017384701812
---

<!-- 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-only_other_det_removal-seed_1024-1e-3

This model was trained from scratch on the kanishka/counterfactual-babylm-only_other_det_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4160
- Accuracy: 0.4105

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6007        | 1.0   | 18597  | 3.8014          | 0.3575   |
| 3.3846        | 2.0   | 37194  | 3.5881          | 0.3796   |
| 3.2609        | 3.0   | 55791  | 3.4855          | 0.3918   |
| 3.1804        | 4.0   | 74388  | 3.4168          | 0.3979   |
| 3.1278        | 5.0   | 92985  | 3.4013          | 0.4018   |
| 3.081         | 6.0   | 111582 | 3.3683          | 0.4041   |
| 3.0471        | 7.0   | 130179 | 3.3773          | 0.4055   |
| 3.0189        | 8.0   | 148776 | 3.3797          | 0.4069   |
| 2.988         | 9.0   | 167373 | 3.3716          | 0.4074   |
| 2.9624        | 10.0  | 185970 | 3.3675          | 0.4088   |
| 2.9372        | 11.0  | 204567 | 3.3803          | 0.4093   |
| 2.9153        | 12.0  | 223164 | 3.3654          | 0.4096   |
| 2.8939        | 13.0  | 241761 | 3.3777          | 0.4098   |
| 2.8704        | 14.0  | 260358 | 3.3811          | 0.4103   |
| 2.8503        | 15.0  | 278955 | 3.3847          | 0.4102   |
| 2.8343        | 16.0  | 297552 | 3.3952          | 0.4100   |
| 2.8131        | 17.0  | 316149 | 3.4062          | 0.4103   |
| 2.7975        | 18.0  | 334746 | 3.4120          | 0.4102   |
| 2.7753        | 19.0  | 353343 | 3.4110          | 0.4105   |
| 2.7567        | 20.0  | 371940 | 3.4160          | 0.4105   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1