---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- kanishka/babylm2-rewritten-clean-spacy_no-num-adj
metrics:
- accuracy
model-index:
- name: opt-babylm2-rewritten-clean-spacy_no-num-adj-earlystop-bpe_seed-211_1e-3
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: kanishka/babylm2-rewritten-clean-spacy_no-num-adj
      type: kanishka/babylm2-rewritten-clean-spacy_no-num-adj
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4782639238952008
---

<!-- 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_no-num-adj-earlystop-bpe_seed-211_1e-3

This model was trained from scratch on the kanishka/babylm2-rewritten-clean-spacy_no-num-adj dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6884
- Accuracy: 0.4783

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 4.0839        | 1.0     | 2225  | 3.8319          | 0.3594   |
| 3.4456        | 2.0     | 4450  | 3.3209          | 0.4073   |
| 3.1244        | 3.0     | 6675  | 3.1047          | 0.4285   |
| 2.9574        | 4.0     | 8900  | 3.0003          | 0.4397   |
| 2.8363        | 5.0     | 11125 | 2.9326          | 0.4464   |
| 2.7798        | 6.0     | 13350 | 2.8933          | 0.4502   |
| 2.7383        | 7.0     | 15575 | 2.8661          | 0.4534   |
| 2.706         | 8.0     | 17800 | 2.8462          | 0.4563   |
| 2.6843        | 9.0     | 20025 | 2.8286          | 0.4581   |
| 2.6625        | 10.0    | 22250 | 2.8218          | 0.4589   |
| 2.6482        | 11.0    | 24475 | 2.8130          | 0.4597   |
| 2.6327        | 12.0    | 26700 | 2.8075          | 0.4604   |
| 2.6196        | 13.0    | 28925 | 2.7995          | 0.4610   |
| 2.6254        | 14.0    | 31150 | 2.7951          | 0.4620   |
| 2.6119        | 15.0    | 33375 | 2.7756          | 0.4640   |
| 2.5659        | 16.0    | 35600 | 2.7508          | 0.4678   |
| 2.5146        | 17.0    | 37825 | 2.7269          | 0.4709   |
| 2.4598        | 18.0    | 40050 | 2.7063          | 0.4740   |
| 2.394         | 19.0    | 42275 | 2.6902          | 0.4770   |
| 2.3215        | 19.9913 | 44480 | 2.6884          | 0.4783   |


### Framework versions

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