| --- |
| base_model: bigcode/starencoder |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - accuracy |
| model-index: |
| - name: stack-edu-classifier-c |
| 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. --> |
|
|
| # stack-edu-classifier-c |
|
|
| 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: 0.4660 |
| - Precision: 0.4766 |
| - Recall: 0.3489 |
| - F1 Macro: 0.3689 |
| - Accuracy: 0.5471 |
| - F1 Binary Minimum3: 0.7028 |
|
|
| ## 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: 64 |
| - eval_batch_size: 256 |
| - seed: 0 |
| - distributed_type: multi-GPU |
| - num_devices: 2 |
| - total_train_batch_size: 128 |
| - total_eval_batch_size: 512 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 200 |
| - num_epochs: 20 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy | F1 Binary Minimum3 | |
| |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|:------------------:| |
| | No log | 0 | 0 | 5.6344 | 0.0030 | 0.1667 | 0.0058 | 0.0178 | 0 | |
| | 0.5079 | 1.4493 | 1000 | 0.5017 | 0.4119 | 0.3155 | 0.3188 | 0.5307 | 0.6809 | |
| | 0.5001 | 2.8986 | 2000 | 0.4960 | 0.4423 | 0.3275 | 0.3419 | 0.5208 | 0.7030 | |
| | 0.4739 | 4.3478 | 3000 | 0.4846 | 0.4462 | 0.3255 | 0.3353 | 0.5374 | 0.6863 | |
| | 0.4739 | 5.7971 | 4000 | 0.4784 | 0.4522 | 0.3223 | 0.3328 | 0.5354 | 0.6934 | |
| | 0.4508 | 7.2464 | 5000 | 0.4890 | 0.4492 | 0.3423 | 0.3540 | 0.5409 | 0.6770 | |
| | 0.4798 | 8.6957 | 6000 | 0.4746 | 0.4663 | 0.3353 | 0.3520 | 0.5308 | 0.7028 | |
| | 0.4613 | 10.1449 | 7000 | 0.4775 | 0.4707 | 0.3254 | 0.3405 | 0.5385 | 0.6900 | |
| | 0.4668 | 11.5942 | 8000 | 0.4934 | 0.4711 | 0.3347 | 0.3526 | 0.5149 | 0.7036 | |
| | 0.4657 | 13.0435 | 9000 | 0.4690 | 0.4797 | 0.3330 | 0.3496 | 0.5390 | 0.7002 | |
| | 0.4561 | 14.4928 | 10000 | 0.4676 | 0.4807 | 0.3375 | 0.3561 | 0.5398 | 0.7040 | |
| | 0.4597 | 15.9420 | 11000 | 0.4668 | 0.4788 | 0.3336 | 0.3497 | 0.5413 | 0.7037 | |
| | 0.4524 | 17.3913 | 12000 | 0.4680 | 0.4759 | 0.3353 | 0.3541 | 0.5370 | 0.7038 | |
| | 0.4674 | 18.8406 | 13000 | 0.4660 | 0.4766 | 0.3489 | 0.3689 | 0.5471 | 0.7028 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.43.4 |
| - Pytorch 2.4.0+cu121 |
| - Datasets 2.21.0 |
| - Tokenizers 0.19.1 |
| |
