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---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-seed_1024-1e-3
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
type: kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.4096600918317765
---
<!-- 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-only_measure_nps_as_singular_removal-seed_1024-1e-3
This model was trained from scratch on the kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4259
- Accuracy: 0.4097
## 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: 1024
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6017 | 1.0 | 18600 | 3.7683 | 0.3593 |
| 3.3799 | 2.0 | 37200 | 3.5935 | 0.3790 |
| 3.2546 | 3.0 | 55800 | 3.4823 | 0.3915 |
| 3.1737 | 4.0 | 74400 | 3.4548 | 0.3978 |
| 3.1178 | 5.0 | 93000 | 3.4163 | 0.4014 |
| 3.0736 | 6.0 | 111600 | 3.4017 | 0.4038 |
| 3.0385 | 7.0 | 130200 | 3.3798 | 0.4057 |
| 3.0068 | 8.0 | 148800 | 3.3988 | 0.4060 |
| 2.9774 | 9.0 | 167400 | 3.3728 | 0.4074 |
| 2.9558 | 10.0 | 186000 | 3.3695 | 0.4087 |
| 2.9289 | 11.0 | 204600 | 3.3649 | 0.4094 |
| 2.9058 | 12.0 | 223200 | 3.3604 | 0.4095 |
| 2.8805 | 13.0 | 241800 | 3.3801 | 0.4098 |
| 2.8621 | 14.0 | 260400 | 3.3871 | 0.4095 |
| 2.8423 | 15.0 | 279000 | 3.3872 | 0.4096 |
| 2.8216 | 16.0 | 297600 | 3.3996 | 0.4097 |
| 2.8042 | 17.0 | 316200 | 3.3987 | 0.4101 |
| 2.7834 | 18.0 | 334800 | 3.4020 | 0.4101 |
| 2.7643 | 19.0 | 353400 | 3.4199 | 0.4097 |
| 2.7463 | 20.0 | 372000 | 3.4259 | 0.4097 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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