transformers safetensors opt text-generation generated_from_trainer dataset:kanishka/counterfactual_babylm_prototypical_only model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_prototypical_only
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_prototypical_only
      type: kanishka/counterfactual_babylm_prototypical_only
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4126066894735785
---

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# smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-1e-3

This model was trained from scratch on the kanishka/counterfactual_babylm_prototypical_only dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3998
- Accuracy: 0.4126

## 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: 42
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6032        | 1.0   | 18593  | 3.7810          | 0.3584   |
| 3.3845        | 2.0   | 37186  | 3.5963          | 0.3798   |
| 3.2554        | 3.0   | 55779  | 3.4491          | 0.3922   |
| 3.1823        | 4.0   | 74372  | 3.4299          | 0.3975   |
| 3.126         | 5.0   | 92965  | 3.3983          | 0.4004   |
| 3.0782        | 6.0   | 111558 | 3.4061          | 0.4035   |
| 3.0476        | 7.0   | 130151 | 3.3689          | 0.4055   |
| 3.0126        | 8.0   | 148744 | 3.3660          | 0.4087   |
| 2.9875        | 9.0   | 167337 | 3.3452          | 0.4100   |
| 2.9573        | 10.0  | 185930 | 3.3508          | 0.4099   |
| 2.9399        | 11.0  | 204523 | 3.3627          | 0.4095   |
| 2.9164        | 12.0  | 223116 | 3.3622          | 0.4103   |
| 2.8939        | 13.0  | 241709 | 3.3502          | 0.4119   |
| 2.872         | 14.0  | 260302 | 3.3635          | 0.4119   |
| 2.8506        | 15.0  | 278895 | 3.3691          | 0.4118   |
| 2.8368        | 16.0  | 297488 | 3.3674          | 0.4131   |
| 2.8123        | 17.0  | 316081 | 3.3751          | 0.4130   |
| 2.7955        | 18.0  | 334674 | 3.3827          | 0.4134   |
| 2.7767        | 19.0  | 353267 | 3.3969          | 0.4124   |
| 2.7592        | 20.0  | 371860 | 3.3998          | 0.4126   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
