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---
base_model: bigcode/starencoder
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: classifier-llama3-sql-500k
results: []
---
<!-- 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. -->
# classifier-llama3-sql-500k
This model is a fine-tuned version of [bigcode/starencoder](https://huggingface.co/bigcode/starencoder) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4719
- Precision: 0.6074
- Recall: 0.4618
- F1 Macro: 0.4864
- Accuracy: 0.5478
- F1 Binary Minimum3: 0.8854
- F1 Binary Minimum2: 0.9418
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 256
- seed: 0
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy | F1 Binary Minimum3 | F1 Binary Minimum2 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|:------------------:|:------------------:|
| No log | 0 | 0 | 8.5378 | 0.0326 | 0.2 | 0.0561 | 0.1632 | 0 | 0 |
| 0.5231 | 1.2837 | 1000 | 0.5326 | 0.5973 | 0.4176 | 0.4388 | 0.5192 | 0.8777 | 0.9339 |
| 0.5041 | 2.5674 | 2000 | 0.5038 | 0.6039 | 0.4350 | 0.4533 | 0.5348 | 0.8824 | 0.9392 |
| 0.5062 | 3.8511 | 3000 | 0.4965 | 0.6035 | 0.4452 | 0.4652 | 0.5402 | 0.8829 | 0.9411 |
| 0.498 | 5.1348 | 4000 | 0.4916 | 0.6036 | 0.4445 | 0.4634 | 0.5398 | 0.8848 | 0.9404 |
| 0.5036 | 6.4185 | 5000 | 0.4894 | 0.5963 | 0.4531 | 0.4789 | 0.5397 | 0.8842 | 0.9396 |
| 0.4968 | 7.7022 | 6000 | 0.4880 | 0.6082 | 0.4402 | 0.4595 | 0.5347 | 0.8826 | 0.9396 |
| 0.498 | 8.9859 | 7000 | 0.4835 | 0.6032 | 0.4592 | 0.4843 | 0.5440 | 0.8837 | 0.9413 |
| 0.4849 | 10.2696 | 8000 | 0.4816 | 0.6168 | 0.4555 | 0.4799 | 0.5442 | 0.8844 | 0.9412 |
| 0.4925 | 11.5533 | 9000 | 0.4821 | 0.5868 | 0.4595 | 0.4861 | 0.5422 | 0.8843 | 0.9405 |
| 0.477 | 12.8370 | 10000 | 0.4800 | 0.6117 | 0.4472 | 0.4688 | 0.5404 | 0.8849 | 0.9403 |
| 0.4753 | 14.1207 | 11000 | 0.4790 | 0.6111 | 0.4533 | 0.4737 | 0.5444 | 0.8842 | 0.9420 |
| 0.4863 | 15.4044 | 12000 | 0.4809 | 0.5849 | 0.4593 | 0.4858 | 0.5426 | 0.8847 | 0.9402 |
| 0.4794 | 16.6881 | 13000 | 0.4761 | 0.6116 | 0.4565 | 0.4820 | 0.5442 | 0.8844 | 0.9410 |
| 0.4684 | 17.9718 | 14000 | 0.4766 | 0.6044 | 0.4533 | 0.4756 | 0.5444 | 0.8852 | 0.9412 |
| 0.4814 | 19.2555 | 15000 | 0.4748 | 0.6093 | 0.4614 | 0.4842 | 0.5496 | 0.8844 | 0.9427 |
| 0.4993 | 20.5392 | 16000 | 0.4746 | 0.5977 | 0.4620 | 0.4879 | 0.5464 | 0.8849 | 0.9415 |
| 0.4788 | 21.8228 | 17000 | 0.4739 | 0.6125 | 0.4592 | 0.4809 | 0.5482 | 0.8860 | 0.9426 |
| 0.4857 | 23.1065 | 18000 | 0.4747 | 0.6190 | 0.4546 | 0.4771 | 0.5457 | 0.8858 | 0.9414 |
| 0.4709 | 24.3902 | 19000 | 0.4728 | 0.6132 | 0.4566 | 0.4800 | 0.5462 | 0.8850 | 0.9417 |
| 0.4803 | 25.6739 | 20000 | 0.4754 | 0.5999 | 0.4585 | 0.4858 | 0.5435 | 0.8856 | 0.9397 |
| 0.4731 | 26.9576 | 21000 | 0.4725 | 0.6100 | 0.4575 | 0.4805 | 0.5470 | 0.8859 | 0.9415 |
| 0.4788 | 28.2413 | 22000 | 0.4725 | 0.6087 | 0.4609 | 0.4861 | 0.5478 | 0.8861 | 0.9415 |
| 0.4594 | 29.5250 | 23000 | 0.4719 | 0.6074 | 0.4618 | 0.4864 | 0.5478 | 0.8854 | 0.9418 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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