---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- kanishka/babylm2-rewritten-clean-spacy
metrics:
- accuracy
model-index:
- name: opt-babylm2-rewritten-clean-spacy-earlystop-bpe_seed-211_1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/babylm2-rewritten-clean-spacy
      type: kanishka/babylm2-rewritten-clean-spacy
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.47886644176457666
---

<!-- 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-rewritten-clean-spacy-earlystop-bpe_seed-211_1e-3

This model was trained from scratch on the kanishka/babylm2-rewritten-clean-spacy dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6837
- Accuracy: 0.4789

## 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: 211
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 4.1           | 1.0     | 2256  | 3.8208          | 0.3601   |
| 3.4569        | 2.0     | 4512  | 3.3085          | 0.4082   |
| 3.1304        | 3.0     | 6768  | 3.0990          | 0.4293   |
| 2.9241        | 4.0     | 9024  | 2.9892          | 0.4405   |
| 2.8456        | 5.0     | 11280 | 2.9303          | 0.4467   |
| 2.7882        | 6.0     | 13536 | 2.8887          | 0.4510   |
| 2.7426        | 7.0     | 15792 | 2.8618          | 0.4543   |
| 2.7124        | 8.0     | 18048 | 2.8404          | 0.4565   |
| 2.689         | 9.0     | 20304 | 2.8293          | 0.4580   |
| 2.6674        | 10.0    | 22560 | 2.8172          | 0.4592   |
| 2.651         | 11.0    | 24816 | 2.8067          | 0.4604   |
| 2.6483        | 12.0    | 27072 | 2.8001          | 0.4613   |
| 2.639         | 13.0    | 29328 | 2.7981          | 0.4619   |
| 2.6268        | 14.0    | 31584 | 2.7924          | 0.4620   |
| 2.6116        | 15.0    | 33840 | 2.7665          | 0.4651   |
| 2.5657        | 16.0    | 36096 | 2.7436          | 0.4687   |
| 2.5199        | 17.0    | 38352 | 2.7187          | 0.4717   |
| 2.4626        | 18.0    | 40608 | 2.7018          | 0.4746   |
| 2.3982        | 19.0    | 42864 | 2.6870          | 0.4773   |
| 2.3238        | 19.9915 | 45100 | 2.6837          | 0.4789   |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0