---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_measure_nps_as_singular_new
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual_babylm_measure_nps_as_singular_new-1e-4
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/counterfactual_babylm_measure_nps_as_singular_new
      type: kanishka/counterfactual_babylm_measure_nps_as_singular_new
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.40681131693060796
---

<!-- 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_measure_nps_as_singular_new-1e-4

This model was trained from scratch on the kanishka/counterfactual_babylm_measure_nps_as_singular_new dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4240
- Accuracy: 0.4068

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0517        | 1.0   | 18602  | 4.2617          | 0.3086   |
| 3.5614        | 2.0   | 37204  | 3.7325          | 0.3617   |
| 3.3871        | 3.0   | 55806  | 3.5926          | 0.3794   |
| 3.2873        | 4.0   | 74408  | 3.4903          | 0.3889   |
| 3.2166        | 5.0   | 93010  | 3.4705          | 0.3930   |
| 3.1683        | 6.0   | 111612 | 3.4386          | 0.3965   |
| 3.122         | 7.0   | 130214 | 3.4230          | 0.3987   |
| 3.0883        | 8.0   | 148816 | 3.4103          | 0.4020   |
| 3.059         | 9.0   | 167418 | 3.4161          | 0.4022   |
| 3.0294        | 10.0  | 186020 | 3.4004          | 0.4039   |
| 3.0081        | 11.0  | 204622 | 3.4048          | 0.4041   |
| 2.9849        | 12.0  | 223224 | 3.4068          | 0.4046   |
| 2.9618        | 13.0  | 241826 | 3.4127          | 0.4048   |
| 2.9398        | 14.0  | 260428 | 3.4079          | 0.4054   |
| 2.9226        | 15.0  | 279030 | 3.3963          | 0.4065   |
| 2.9009        | 16.0  | 297632 | 3.4036          | 0.4068   |
| 2.8845        | 17.0  | 316234 | 3.4090          | 0.4067   |
| 2.8685        | 18.0  | 334836 | 3.4054          | 0.4071   |
| 2.8513        | 19.0  | 353438 | 3.4187          | 0.4069   |
| 2.8368        | 20.0  | 372040 | 3.4240          | 0.4068   |


### Framework versions

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