---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- kanishka/babylm2-clean
metrics:
- accuracy
model-index:
- name: opt-babylm2-clean-20-epochs-earlystop_seed-42_1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/babylm2-clean
      type: kanishka/babylm2-clean
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.46725332517112805
---

<!-- 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. -->

# opt-babylm2-clean-20-epochs-earlystop_seed-42_1e-3

This model was trained from scratch on the kanishka/babylm2-clean dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7611
- Accuracy: 0.4673

## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 4.24          | 0.9998  | 2198  | 3.9528          | 0.3447   |
| 3.5581        | 2.0     | 4397  | 3.4120          | 0.3948   |
| 3.2191        | 2.9998  | 6595  | 3.1806          | 0.4180   |
| 3.0495        | 4.0     | 8794  | 3.0720          | 0.4287   |
| 2.9458        | 4.9998  | 10992 | 3.0054          | 0.4354   |
| 2.8669        | 6.0     | 13191 | 2.9663          | 0.4399   |
| 2.8256        | 6.9998  | 15389 | 2.9382          | 0.4430   |
| 2.79          | 8.0     | 17588 | 2.9199          | 0.4452   |
| 2.7624        | 8.9998  | 19786 | 2.9052          | 0.4468   |
| 2.7361        | 10.0    | 21985 | 2.8915          | 0.4482   |
| 2.7354        | 10.9998 | 24183 | 2.8843          | 0.4491   |
| 2.7225        | 12.0    | 26382 | 2.8777          | 0.4500   |
| 2.7092        | 12.9998 | 28580 | 2.8708          | 0.4505   |
| 2.6987        | 14.0    | 30779 | 2.8688          | 0.4509   |
| 2.6894        | 14.9998 | 32977 | 2.8542          | 0.4527   |
| 2.6561        | 16.0    | 35176 | 2.8258          | 0.4564   |
| 2.6055        | 16.9998 | 37374 | 2.8005          | 0.4595   |
| 2.5464        | 18.0    | 39573 | 2.7814          | 0.4627   |
| 2.4778        | 18.9998 | 41771 | 2.7630          | 0.4658   |
| 2.4036        | 19.9955 | 43960 | 2.7611          | 0.4673   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0