transformers safetensors opt text-generation generated_from_trainer dataset:kanishka/counterfactual_babylm_aann_indef_naan model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_indef_naan
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-indef-naan-rerun-3e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_aann_indef_naan
      type: kanishka/counterfactual_babylm_aann_indef_naan
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.41066454597322516
---

<!-- 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-indef-naan-rerun-3e-4

This model was trained from scratch on the kanishka/counterfactual_babylm_aann_indef_naan dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3944
- Accuracy: 0.4107

## 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: 0.0003
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7328        | 1.0   | 18595  | 3.8886          | 0.3463   |
| 3.4284        | 2.0   | 37190  | 3.5889          | 0.3765   |
| 3.2877        | 3.0   | 55785  | 3.5068          | 0.3882   |
| 3.2043        | 4.0   | 74380  | 3.4467          | 0.3957   |
| 3.1467        | 5.0   | 92975  | 3.3793          | 0.4010   |
| 3.0972        | 6.0   | 111570 | 3.3796          | 0.4030   |
| 3.058         | 7.0   | 130165 | 3.3637          | 0.4048   |
| 3.0303        | 8.0   | 148760 | 3.3449          | 0.4073   |
| 2.9995        | 9.0   | 167355 | 3.3597          | 0.4074   |
| 2.9726        | 10.0  | 185950 | 3.3591          | 0.4085   |
| 2.9449        | 11.0  | 204545 | 3.3621          | 0.4086   |
| 2.9249        | 12.0  | 223140 | 3.3672          | 0.4090   |
| 2.906         | 13.0  | 241735 | 3.3656          | 0.4094   |
| 2.8857        | 14.0  | 260330 | 3.3786          | 0.4093   |
| 2.8597        | 15.0  | 278925 | 3.3611          | 0.4110   |
| 2.8438        | 16.0  | 297520 | 3.3741          | 0.4103   |
| 2.8229        | 17.0  | 316115 | 3.3816          | 0.4099   |
| 2.8013        | 18.0  | 334710 | 3.3826          | 0.4106   |
| 2.7882        | 19.0  | 353305 | 3.3887          | 0.4108   |
| 2.7683        | 20.0  | 371900 | 3.3944          | 0.4107   |


### Framework versions

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