|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: smolm-autoreg-bpe-counterfactual-babylm-only_indef_articles_with_pl_nouns_removal-1e-3 |
|
results: |
|
- task: |
|
name: Causal Language Modeling |
|
type: text-generation |
|
dataset: |
|
name: kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal |
|
type: kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.4118896526593414 |
|
--- |
|
|
|
<!-- 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_indef_articles_with_pl_nouns_removal-1e-3 |
|
|
|
This model was trained from scratch on the kanishka/counterfactual-babylm-only_indef_articles_with_pl_nouns_removal dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 3.4114 |
|
- Accuracy: 0.4119 |
|
|
|
## 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 |
|
- 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 | |
|
|:-------------:|:-----:|:------:|:---------------:|:--------:| |
|
| 3.5998 | 1.0 | 18600 | 3.7955 | 0.3595 | |
|
| 3.3776 | 2.0 | 37200 | 3.5874 | 0.3805 | |
|
| 3.245 | 3.0 | 55800 | 3.4956 | 0.3923 | |
|
| 3.1698 | 4.0 | 74400 | 3.4301 | 0.3991 | |
|
| 3.1095 | 5.0 | 93000 | 3.4080 | 0.4017 | |
|
| 3.0618 | 6.0 | 111600 | 3.3783 | 0.4047 | |
|
| 3.0262 | 7.0 | 130200 | 3.3656 | 0.4063 | |
|
| 2.9992 | 8.0 | 148800 | 3.3350 | 0.4088 | |
|
| 2.9653 | 9.0 | 167400 | 3.3531 | 0.4103 | |
|
| 2.9376 | 10.0 | 186000 | 3.3526 | 0.4110 | |
|
| 2.9136 | 11.0 | 204600 | 3.3538 | 0.4098 | |
|
| 2.8922 | 12.0 | 223200 | 3.3425 | 0.4120 | |
|
| 2.8698 | 13.0 | 241800 | 3.3346 | 0.4124 | |
|
| 2.8466 | 14.0 | 260400 | 3.3660 | 0.4110 | |
|
| 2.8253 | 15.0 | 279000 | 3.3566 | 0.4127 | |
|
| 2.8058 | 16.0 | 297600 | 3.3781 | 0.4113 | |
|
| 2.7908 | 17.0 | 316200 | 3.3851 | 0.4119 | |
|
| 2.7701 | 18.0 | 334800 | 3.3872 | 0.4128 | |
|
| 2.7511 | 19.0 | 353400 | 3.4038 | 0.4120 | |
|
| 2.7292 | 20.0 | 372000 | 3.4114 | 0.4119 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.2 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.1 |
|
|
