---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-pipps_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-pipps_removal-seed_1024-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual-babylm-pipps_removal
      type: kanishka/counterfactual-babylm-pipps_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4102895476662934
---

<!-- 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-pipps_removal-seed_1024-1e-3

This model was trained from scratch on the kanishka/counterfactual-babylm-pipps_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4120
- Accuracy: 0.4103

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.5992        | 1.0   | 18593  | 3.7873          | 0.3590   |
| 3.3841        | 2.0   | 37186  | 3.5957          | 0.3798   |
| 3.2517        | 3.0   | 55779  | 3.4368          | 0.3935   |
| 3.1729        | 4.0   | 74372  | 3.4158          | 0.3977   |
| 3.1228        | 5.0   | 92965  | 3.4132          | 0.4017   |
| 3.074         | 6.0   | 111558 | 3.3903          | 0.4035   |
| 3.0396        | 7.0   | 130151 | 3.3731          | 0.4067   |
| 3.0136        | 8.0   | 148744 | 3.3697          | 0.4065   |
| 2.9841        | 9.0   | 167337 | 3.3754          | 0.4067   |
| 2.9561        | 10.0  | 185930 | 3.3766          | 0.4088   |
| 2.9356        | 11.0  | 204523 | 3.3834          | 0.4089   |
| 2.9099        | 12.0  | 223116 | 3.3625          | 0.4105   |
| 2.8924        | 13.0  | 241709 | 3.3680          | 0.4097   |
| 2.8738        | 14.0  | 260302 | 3.3766          | 0.4103   |
| 2.8485        | 15.0  | 278895 | 3.3746          | 0.4108   |
| 2.834         | 16.0  | 297488 | 3.3823          | 0.4107   |
| 2.8108        | 17.0  | 316081 | 3.3894          | 0.4108   |
| 2.7936        | 18.0  | 334674 | 3.4001          | 0.4101   |
| 2.7783        | 19.0  | 353267 | 3.4030          | 0.4107   |
| 2.755         | 20.0  | 371860 | 3.4120          | 0.4103   |


### Framework versions

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