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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-1e-3
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: 0.41252109443859236
---
<!-- 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-1e-3
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: 3.4176
- Accuracy: 0.4125
## 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: 16
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.5148 | 1.0 | 37201 | 3.7270 | 0.3671 |
| 3.3074 | 2.0 | 74402 | 3.4841 | 0.3897 |
| 3.1988 | 3.0 | 111603 | 3.4300 | 0.3979 |
| 3.152 | 4.0 | 148804 | 3.3774 | 0.4050 |
| 3.0973 | 5.0 | 186005 | 3.3462 | 0.4090 |
| 3.0543 | 6.0 | 223206 | 3.3687 | 0.4064 |
| 3.0161 | 7.0 | 260407 | 3.3391 | 0.4114 |
| 2.9858 | 8.0 | 297608 | 3.3477 | 0.4105 |
| 2.9718 | 9.0 | 334809 | 3.3436 | 0.4112 |
| 2.9399 | 10.0 | 372010 | 3.3451 | 0.4121 |
| 2.9207 | 11.0 | 409211 | 3.3586 | 0.4130 |
| 2.8987 | 12.0 | 446412 | 3.3554 | 0.4123 |
| 2.8779 | 13.0 | 483613 | 3.3616 | 0.4130 |
| 2.8519 | 14.0 | 520814 | 3.3696 | 0.4129 |
| 2.8395 | 15.0 | 558015 | 3.3729 | 0.4128 |
| 2.8151 | 16.0 | 595216 | 3.3718 | 0.4140 |
| 2.798 | 17.0 | 632417 | 3.3858 | 0.4128 |
| 2.7738 | 18.0 | 669618 | 3.4080 | 0.4130 |
| 2.7555 | 19.0 | 706819 | 3.4067 | 0.4131 |
| 2.7434 | 20.0 | 744020 | 3.4176 | 0.4125 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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