---
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_211-1e-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: 0.4109943845202858
---

<!-- 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_211-1e-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: 3.4143
- Accuracy: 0.4110

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6004        | 1.0   | 18597  | 3.8219          | 0.3575   |
| 3.3852        | 2.0   | 37194  | 3.6092          | 0.3797   |
| 3.2597        | 3.0   | 55791  | 3.4837          | 0.3910   |
| 3.1758        | 4.0   | 74388  | 3.4364          | 0.3981   |
| 3.1197        | 5.0   | 92985  | 3.4116          | 0.4017   |
| 3.08          | 6.0   | 111582 | 3.3782          | 0.4040   |
| 3.0418        | 7.0   | 130179 | 3.3885          | 0.4055   |
| 3.0088        | 8.0   | 148776 | 3.3884          | 0.4062   |
| 2.9856        | 9.0   | 167373 | 3.3548          | 0.4077   |
| 2.9598        | 10.0  | 185970 | 3.3782          | 0.4090   |
| 2.9364        | 11.0  | 204567 | 3.3851          | 0.4093   |
| 2.9156        | 12.0  | 223164 | 3.3803          | 0.4097   |
| 2.8949        | 13.0  | 241761 | 3.3869          | 0.4100   |
| 2.8719        | 14.0  | 260358 | 3.3813          | 0.4104   |
| 2.8526        | 15.0  | 278955 | 3.3859          | 0.4108   |
| 2.8289        | 16.0  | 297552 | 3.3980          | 0.4103   |
| 2.8104        | 17.0  | 316149 | 3.3981          | 0.4109   |
| 2.7958        | 18.0  | 334746 | 3.4054          | 0.4110   |
| 2.781         | 19.0  | 353343 | 3.4057          | 0.4110   |
| 2.7571        | 20.0  | 371940 | 3.4143          | 0.4110   |


### Framework versions

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