---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-new_regex_aanns_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-new_regex_aanns_removal-seed_211-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual-babylm-new_regex_aanns_removal
      type: kanishka/counterfactual-babylm-new_regex_aanns_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.41341864917568283
---

<!-- 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-new_regex_aanns_removal-seed_211-1e-3

This model was trained from scratch on the kanishka/counterfactual-babylm-new_regex_aanns_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3885
- Accuracy: 0.4134

## 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: 211
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6058        | 1.0   | 18593  | 3.7591          | 0.3583   |
| 3.3838        | 2.0   | 37186  | 3.5626          | 0.3824   |
| 3.2578        | 3.0   | 55779  | 3.4700          | 0.3923   |
| 3.1777        | 4.0   | 74372  | 3.4090          | 0.3992   |
| 3.1262        | 5.0   | 92965  | 3.4092          | 0.4021   |
| 3.0786        | 6.0   | 111558 | 3.3686          | 0.4073   |
| 3.0425        | 7.0   | 130151 | 3.3363          | 0.4099   |
| 3.0098        | 8.0   | 148744 | 3.3507          | 0.4092   |
| 2.9845        | 9.0   | 167337 | 3.3483          | 0.4113   |
| 2.9554        | 10.0  | 185930 | 3.3369          | 0.4122   |
| 2.9372        | 11.0  | 204523 | 3.3210          | 0.4144   |
| 2.9131        | 12.0  | 223116 | 3.3488          | 0.4121   |
| 2.8914        | 13.0  | 241709 | 3.3448          | 0.4139   |
| 2.8744        | 14.0  | 260302 | 3.3473          | 0.4130   |
| 2.8505        | 15.0  | 278895 | 3.3552          | 0.4135   |
| 2.8346        | 16.0  | 297488 | 3.3626          | 0.4135   |
| 2.8113        | 17.0  | 316081 | 3.3734          | 0.4128   |
| 2.7967        | 18.0  | 334674 | 3.3720          | 0.4132   |
| 2.7775        | 19.0  | 353267 | 3.3848          | 0.4132   |
| 2.7551        | 20.0  | 371860 | 3.3885          | 0.4134   |


### Framework versions

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