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---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_excess_adj_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-adj_num_freq_balanced-3e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_aann_excess_adj_removal
type: kanishka/counterfactual_babylm_aann_excess_adj_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.40517314741870225
---
<!-- 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-adj_num_freq_balanced-3e-4
This model was trained from scratch on the kanishka/counterfactual_babylm_aann_excess_adj_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4663
- Accuracy: 0.4052
## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7291 | 1.0 | 18629 | 3.9229 | 0.3464 |
| 3.4237 | 2.0 | 37258 | 3.6542 | 0.3747 |
| 3.2842 | 3.0 | 55887 | 3.5183 | 0.3880 |
| 3.1952 | 4.0 | 74516 | 3.4748 | 0.3938 |
| 3.1351 | 5.0 | 93145 | 3.4493 | 0.3972 |
| 3.0844 | 6.0 | 111774 | 3.4156 | 0.4005 |
| 3.0442 | 7.0 | 130403 | 3.3854 | 0.4032 |
| 3.008 | 8.0 | 149032 | 3.4062 | 0.4030 |
| 2.9768 | 9.0 | 167661 | 3.3970 | 0.4047 |
| 2.9498 | 10.0 | 186290 | 3.4024 | 0.4047 |
| 2.917 | 11.0 | 204919 | 3.4242 | 0.4039 |
| 2.9005 | 12.0 | 223548 | 3.4093 | 0.4049 |
| 2.8747 | 13.0 | 242177 | 3.4192 | 0.4051 |
| 2.8542 | 14.0 | 260806 | 3.4233 | 0.4053 |
| 2.8326 | 15.0 | 279435 | 3.4314 | 0.4054 |
| 2.8125 | 16.0 | 298064 | 3.4404 | 0.4052 |
| 2.7911 | 17.0 | 316693 | 3.4450 | 0.4054 |
| 2.7682 | 18.0 | 335322 | 3.4488 | 0.4054 |
| 2.7512 | 19.0 | 353951 | 3.4581 | 0.4054 |
| 2.7331 | 20.0 | 372580 | 3.4663 | 0.4052 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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