---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- kanishka/babylm2-clean-spacy
metrics:
- accuracy
model-index:
- name: opt-babylm2-clean-spacy-32k-earlystop_seed-42_1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/babylm2-clean-spacy
      type: kanishka/babylm2-clean-spacy
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.43054403912594785
---

<!-- 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-spacy-32k-earlystop_seed-42_1e-3

This model was trained from scratch on the kanishka/babylm2-clean-spacy dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9103
- Accuracy: 0.4305

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 5.9107        | 0.9995  | 1942  | 3.9887          | 0.3269   |
| 3.7896        | 1.9996  | 3885  | 3.5236          | 0.3657   |
| 3.3813        | 2.9997  | 5828  | 3.3040          | 0.3859   |
| 3.174         | 3.9997  | 7771  | 3.1921          | 0.3962   |
| 3.0533        | 4.9998  | 9714  | 3.1266          | 0.4026   |
| 2.9768        | 5.9999  | 11657 | 3.0838          | 0.4071   |
| 2.9232        | 6.9999  | 13600 | 3.0550          | 0.4101   |
| 2.8863        | 8.0     | 15543 | 3.0363          | 0.4122   |
| 2.8563        | 8.9995  | 17485 | 3.0208          | 0.4139   |
| 2.8356        | 9.9996  | 19428 | 3.0117          | 0.4151   |
| 2.816         | 10.9997 | 21371 | 3.0030          | 0.4162   |
| 2.8069        | 11.9997 | 23314 | 2.9951          | 0.4170   |
| 2.7941        | 12.9998 | 25257 | 2.9923          | 0.4175   |
| 2.7889        | 13.9999 | 27200 | 2.9888          | 0.4182   |
| 2.7802        | 14.9999 | 29143 | 2.9839          | 0.4186   |
| 2.7802        | 16.0    | 31086 | 2.9821          | 0.4190   |
| 2.7665        | 16.9995 | 33028 | 2.9626          | 0.4212   |
| 2.6908        | 17.9996 | 34971 | 2.9378          | 0.4247   |
| 2.6058        | 18.9997 | 36914 | 2.9145          | 0.4284   |
| 2.505         | 19.9910 | 38840 | 2.9103          | 0.4305   |


### Framework versions

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