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---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-indef_articles_with_pl_nouns-removal-3e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
type: kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.40992788926628976
---
<!-- 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. -->
# smolm-autoreg-bpe-counterfactual-babylm-indef_articles_with_pl_nouns-removal-3e-4
This model was trained from scratch on the kanishka/counterfactual_babylm_aann_indef_articles_with_pl_nouns_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4054
- Accuracy: 0.4099
## 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
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7446 | 1.0 | 18601 | 3.9130 | 0.3462 |
| 3.4295 | 2.0 | 37202 | 3.6268 | 0.3752 |
| 3.2878 | 3.0 | 55803 | 3.5073 | 0.3878 |
| 3.2031 | 4.0 | 74404 | 3.4630 | 0.3948 |
| 3.1443 | 5.0 | 93005 | 3.4077 | 0.3994 |
| 3.0973 | 6.0 | 111606 | 3.3724 | 0.4028 |
| 3.0617 | 7.0 | 130207 | 3.3562 | 0.4062 |
| 3.0252 | 8.0 | 148808 | 3.3648 | 0.4059 |
| 2.994 | 9.0 | 167409 | 3.3582 | 0.4071 |
| 2.9693 | 10.0 | 186010 | 3.3688 | 0.4075 |
| 2.9383 | 11.0 | 204611 | 3.3513 | 0.4092 |
| 2.9188 | 12.0 | 223212 | 3.3659 | 0.4086 |
| 2.8978 | 13.0 | 241813 | 3.3581 | 0.4097 |
| 2.8784 | 14.0 | 260414 | 3.3657 | 0.4103 |
| 2.8592 | 15.0 | 279015 | 3.3693 | 0.4102 |
| 2.8415 | 16.0 | 297616 | 3.3867 | 0.4092 |
| 2.8198 | 17.0 | 316217 | 3.3790 | 0.4101 |
| 2.8013 | 18.0 | 334818 | 3.3924 | 0.4099 |
| 2.7836 | 19.0 | 353419 | 3.4014 | 0.4098 |
| 2.7626 | 20.0 | 372020 | 3.4054 | 0.4099 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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