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---
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-42_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: 0.4781093360218181
---
<!-- 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-42_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: 2.6900
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 4.0896 | 1.0 | 2225 | 3.8307 | 0.3593 |
| 3.4325 | 2.0 | 4450 | 3.3145 | 0.4081 |
| 3.1208 | 3.0 | 6675 | 3.1044 | 0.4295 |
| 2.957 | 4.0 | 8900 | 2.9973 | 0.4396 |
| 2.8381 | 5.0 | 11125 | 2.9338 | 0.4464 |
| 2.7819 | 6.0 | 13350 | 2.8904 | 0.4508 |
| 2.7385 | 7.0 | 15575 | 2.8666 | 0.4531 |
| 2.7061 | 8.0 | 17800 | 2.8456 | 0.4559 |
| 2.6855 | 9.0 | 20025 | 2.8332 | 0.4575 |
| 2.6669 | 10.0 | 22250 | 2.8198 | 0.4586 |
| 2.6499 | 11.0 | 24475 | 2.8118 | 0.4597 |
| 2.6351 | 12.0 | 26700 | 2.8072 | 0.4601 |
| 2.6204 | 13.0 | 28925 | 2.8026 | 0.4612 |
| 2.6277 | 14.0 | 31150 | 2.8013 | 0.4613 |
| 2.6136 | 15.0 | 33375 | 2.7791 | 0.4638 |
| 2.5687 | 16.0 | 35600 | 2.7514 | 0.4676 |
| 2.5184 | 17.0 | 37825 | 2.7283 | 0.4708 |
| 2.4613 | 18.0 | 40050 | 2.7060 | 0.4740 |
| 2.3966 | 19.0 | 42275 | 2.6947 | 0.4766 |
| 2.3227 | 19.9913 | 44480 | 2.6900 | 0.4781 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0
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