transformers tensorboard safetensors opt text-generation generated_from_trainer dataset:kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-seed_1024-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
      type: kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4096600918317765
---

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# smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-seed_1024-1e-3

This model was trained from scratch on the kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4259
- Accuracy: 0.4097

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6017        | 1.0   | 18600  | 3.7683          | 0.3593   |
| 3.3799        | 2.0   | 37200  | 3.5935          | 0.3790   |
| 3.2546        | 3.0   | 55800  | 3.4823          | 0.3915   |
| 3.1737        | 4.0   | 74400  | 3.4548          | 0.3978   |
| 3.1178        | 5.0   | 93000  | 3.4163          | 0.4014   |
| 3.0736        | 6.0   | 111600 | 3.4017          | 0.4038   |
| 3.0385        | 7.0   | 130200 | 3.3798          | 0.4057   |
| 3.0068        | 8.0   | 148800 | 3.3988          | 0.4060   |
| 2.9774        | 9.0   | 167400 | 3.3728          | 0.4074   |
| 2.9558        | 10.0  | 186000 | 3.3695          | 0.4087   |
| 2.9289        | 11.0  | 204600 | 3.3649          | 0.4094   |
| 2.9058        | 12.0  | 223200 | 3.3604          | 0.4095   |
| 2.8805        | 13.0  | 241800 | 3.3801          | 0.4098   |
| 2.8621        | 14.0  | 260400 | 3.3871          | 0.4095   |
| 2.8423        | 15.0  | 279000 | 3.3872          | 0.4096   |
| 2.8216        | 16.0  | 297600 | 3.3996          | 0.4097   |
| 2.8042        | 17.0  | 316200 | 3.3987          | 0.4101   |
| 2.7834        | 18.0  | 334800 | 3.4020          | 0.4101   |
| 2.7643        | 19.0  | 353400 | 3.4199          | 0.4097   |
| 2.7463        | 20.0  | 372000 | 3.4259          | 0.4097   |


### Framework versions

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