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