---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- kanishka/babylm2-rewritten-clean-spacy_random-removal-num-adj
metrics:
- accuracy
model-index:
- name: opt-babylm2-rewritten-clean-spacy_random-removal-num-adj-earlystop-bpe_seed-1024_1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/babylm2-rewritten-clean-spacy_random-removal-num-adj
      type: kanishka/babylm2-rewritten-clean-spacy_random-removal-num-adj
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4785579769152222
---

<!-- 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-num-adj-earlystop-bpe_seed-1024_1e-3

This model was trained from scratch on the kanishka/babylm2-rewritten-clean-spacy_random-removal-num-adj dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6847
- Accuracy: 0.4786

## 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: 1024
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 4.0912        | 0.9997  | 2240  | 3.8117          | 0.3613   |
| 3.4432        | 1.9997  | 4480  | 3.2982          | 0.4102   |
| 3.1317        | 2.9997  | 6720  | 3.0935          | 0.4303   |
| 2.9687        | 3.9997  | 8960  | 2.9876          | 0.4411   |
| 2.8476        | 4.9997  | 11200 | 2.9233          | 0.4474   |
| 2.7908        | 5.9997  | 13440 | 2.8864          | 0.4513   |
| 2.7475        | 6.9997  | 15680 | 2.8587          | 0.4544   |
| 2.7106        | 7.9997  | 17920 | 2.8397          | 0.4567   |
| 2.6915        | 8.9997  | 20160 | 2.8251          | 0.4583   |
| 2.6701        | 9.9997  | 22400 | 2.8133          | 0.4597   |
| 2.6549        | 10.9997 | 24640 | 2.8065          | 0.4606   |
| 2.6374        | 11.9997 | 26880 | 2.7963          | 0.4614   |
| 2.6417        | 12.9997 | 29120 | 2.7917          | 0.4621   |
| 2.6321        | 13.9997 | 31360 | 2.7886          | 0.4626   |
| 2.6147        | 14.9997 | 33600 | 2.7674          | 0.4653   |
| 2.571         | 15.9997 | 35840 | 2.7426          | 0.4684   |
| 2.5232        | 16.9997 | 38080 | 2.7212          | 0.4715   |
| 2.4666        | 17.9997 | 40320 | 2.7036          | 0.4742   |
| 2.4024        | 18.9997 | 42560 | 2.6877          | 0.4770   |
| 2.3301        | 19.9997 | 44800 | 2.6847          | 0.4786   |


### Framework versions

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