---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-only_random_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-only_random_removal-seed_1024-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual-babylm-only_random_removal
      type: kanishka/counterfactual-babylm-only_random_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.41051635038682427
---

<!-- 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_random_removal-seed_1024-1e-3

This model was trained from scratch on the kanishka/counterfactual-babylm-only_random_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4109
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6055        | 1.0   | 18588  | 3.7771          | 0.3590   |
| 3.3861        | 2.0   | 37176  | 3.5828          | 0.3803   |
| 3.2583        | 3.0   | 55764  | 3.5065          | 0.3913   |
| 3.176         | 4.0   | 74352  | 3.4318          | 0.3973   |
| 3.1227        | 5.0   | 92940  | 3.4132          | 0.4013   |
| 3.0828        | 6.0   | 111528 | 3.3847          | 0.4036   |
| 3.0461        | 7.0   | 130116 | 3.3778          | 0.4051   |
| 3.0138        | 8.0   | 148704 | 3.3612          | 0.4069   |
| 2.9878        | 9.0   | 167292 | 3.3629          | 0.4078   |
| 2.9634        | 10.0  | 185880 | 3.3489          | 0.4093   |
| 2.9347        | 11.0  | 204468 | 3.3616          | 0.4096   |
| 2.9136        | 12.0  | 223056 | 3.3726          | 0.4097   |
| 2.8947        | 13.0  | 241644 | 3.3682          | 0.4099   |
| 2.8789        | 14.0  | 260232 | 3.3817          | 0.4099   |
| 2.8559        | 15.0  | 278820 | 3.3847          | 0.4099   |
| 2.8374        | 16.0  | 297408 | 3.3835          | 0.4102   |
| 2.8117        | 17.0  | 315996 | 3.3940          | 0.4100   |
| 2.7969        | 18.0  | 334584 | 3.4024          | 0.4102   |
| 2.7772        | 19.0  | 353172 | 3.4032          | 0.4105   |
| 2.76          | 20.0  | 371760 | 3.4109          | 0.4105   |


### Framework versions

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