---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- kanishka/babylm2-rewritten-clean-spacy
metrics:
- accuracy
model-index:
- name: opt-babylm2-rewritten-clean-spacy-32k-earlystop-40epochs_seed-42_3e-4
  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: 0.4202126468521879
---

<!-- 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-32k-earlystop-40epochs_seed-42_3e-4

This model was trained from scratch on the kanishka/babylm2-rewritten-clean-spacy dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9892
- Accuracy: 0.4202

## 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: 0.0003
- 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: 40.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 6.8783        | 0.9996  | 1931  | 4.4654          | 0.2891   |
| 4.2263        | 1.9997  | 3863  | 3.9179          | 0.3337   |
| 3.7724        | 2.9999  | 5795  | 3.6397          | 0.3562   |
| 3.5091        | 4.0     | 7727  | 3.4569          | 0.3724   |
| 3.3306        | 4.9996  | 9658  | 3.3310          | 0.3838   |
| 3.2012        | 5.9997  | 11590 | 3.2469          | 0.3918   |
| 3.1088        | 6.9999  | 13522 | 3.1828          | 0.3982   |
| 3.0364        | 8.0     | 15454 | 3.1404          | 0.4023   |
| 2.9837        | 8.9996  | 17385 | 3.1080          | 0.4057   |
| 2.9377        | 9.9997  | 19317 | 3.0840          | 0.4077   |
| 2.9019        | 10.9999 | 21249 | 3.0633          | 0.4101   |
| 2.8713        | 12.0    | 23181 | 3.0505          | 0.4117   |
| 2.8449        | 12.9996 | 25112 | 3.0376          | 0.4130   |
| 2.8231        | 13.9997 | 27044 | 3.0270          | 0.4143   |
| 2.7828        | 14.9999 | 28976 | 3.0222          | 0.4150   |
| 2.7644        | 16.0    | 30908 | 3.0160          | 0.4156   |
| 2.7508        | 16.9996 | 32839 | 3.0100          | 0.4167   |
| 2.7296        | 17.9997 | 34771 | 3.0023          | 0.4178   |
| 2.7053        | 18.9999 | 36703 | 2.9967          | 0.4188   |
| 2.6821        | 20.0    | 38635 | 2.9926          | 0.4195   |
| 2.6601        | 20.9996 | 40566 | 2.9892          | 0.4202   |
| 2.6406        | 21.9997 | 42498 | 2.9898          | 0.4204   |
| 2.621         | 22.9999 | 44430 | 2.9921          | 0.4205   |
| 2.6048        | 24.0    | 46362 | 2.9928          | 0.4207   |


### Framework versions

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