---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- kanishka/babylm2-rewritten-clean-spacy-random_removal_numadj
metrics:
- accuracy
model-index:
- name: opt-babylm2-rewritten-clean-spacy-random_removal_numadj-earlystop-bpe_seed-42_1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/babylm2-rewritten-clean-spacy-random_removal_numadj
      type: kanishka/babylm2-rewritten-clean-spacy-random_removal_numadj
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.47811193958124093
---

<!-- 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-random_removal_numadj-earlystop-bpe_seed-42_1e-3

This model was trained from scratch on the kanishka/babylm2-rewritten-clean-spacy-random_removal_numadj dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6927
- Accuracy: 0.4781

## 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: 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 32.5094       | 0.9997  | 2243  | 3.8102          | 0.3615   |
| 27.4934       | 1.9997  | 4486  | 3.2932          | 0.4103   |
| 24.9364       | 2.9997  | 6729  | 3.0832          | 0.4316   |
| 23.6407       | 3.9997  | 8972  | 2.9806          | 0.4416   |
| 22.7053       | 4.9997  | 11215 | 2.9227          | 0.4477   |
| 22.2601       | 5.9997  | 13458 | 2.8859          | 0.4513   |
| 21.9123       | 6.9997  | 15701 | 2.8600          | 0.4546   |
| 21.6403       | 7.9997  | 17944 | 2.8425          | 0.4570   |
| 21.5087       | 8.9997  | 20187 | 2.8276          | 0.4585   |
| 21.3483       | 9.9997  | 22430 | 2.8189          | 0.4596   |
| 21.2068       | 10.9997 | 24673 | 2.8091          | 0.4604   |
| 21.0757       | 11.9997 | 26916 | 2.8028          | 0.4610   |
| 21.12         | 12.9997 | 29159 | 2.7997          | 0.4619   |
| 21.0442       | 13.9997 | 31402 | 2.7952          | 0.4622   |
| 20.9217       | 14.9997 | 33645 | 2.7750          | 0.4649   |
| 20.5419       | 15.9997 | 35888 | 2.7506          | 0.4683   |
| 20.1666       | 16.9997 | 38131 | 2.7245          | 0.4714   |
| 19.7172       | 17.9997 | 40374 | 2.7101          | 0.4740   |
| 19.1888       | 18.9997 | 42617 | 2.6955          | 0.4768   |
| 18.63         | 19.9997 | 44860 | 2.6927          | 0.4781   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0