| --- |
| datasets: |
| - breadlicker45/muti-label-gender-test2 |
| base_model: |
| - ibm-granite/granite-embedding-107m-multilingual |
| pipeline_tag: text-classification |
| --- |
| |
| ### Model Description |
|
|
| This is a model for classifying male, female, and non-binary genders from one paragraph. |
|
|
| ## Training Details |
|
|
| * batch-size: 32 |
| * epochs: 3 |
| * GPU used: An Nvidia P40 gpu |
|
|
| # Evaluation |
| - eval_loss: 0.652 |
| - eval_f1: 0.593 |
| - eval_roc_auc: 0.616 |
| - eval_accuracy: 0.30039 |
| - eval_runtime: 39.7364 |
| - eval_samples_per_second: 308.961 |
| - eval_steps_per_second: 9.664 |
| - epoch: 3.0 |
|
|
| # training Metrics |
| | Step | Training Loss | Validation Loss | F1 | ROC AUC | Accuracy | |
| | ---- | ------------- | --------------- | ------ | -------- | -------- | |
| | 500 | 0.675000 | 0.666651 | 0.5384 | 0.593959 | 0.303494 | |
| | 1000 | 0.667200 | 0.665188 | 0.5496 | 0.597031 | 0.302924 | |
| | 1500 | 0.662900 | 0.661620 | 0.5836 | 0.602752 | 0.287204 | |
| | 2000 | 0.659800 | 0.662705 | 0.5710 | 0.602509 | 0.289240 | |
| | 2500 | 0.660200 | 0.664614 | 0.5511 | 0.600784 | 0.303902 | |
| | 3000 | 0.659100 | 0.658421 | 0.5650 | 0.604483 | 0.304716 | |
| | 3500 | 0.650300 | 0.657569 | 0.5821 | 0.609502 | 0.300236 | |
| | 4000 | 0.648100 | 0.654424 | 0.5830 | 0.609785 | 0.293720 | |
| | 4500 | 0.640700 | 0.654051 | 0.5743 | 0.612857 | 0.308544 | |
| | 5000 | 0.645500 | 0.651678 | 0.5806 | 0.613973 | 0.305531 | |
| | 5500 | 0.642400 | 0.651911 | 0.5808 | 0.614797 | 0.307893 | |
| | 6000 | 0.642100 | 0.651853 | 0.5795 | 0.616014 | 0.312861 | |
| | 6500 | 0.628700 | 0.653005 | 0.5909 | 0.616887 | 0.304472 | |
| | 7000 | 0.624900 | 0.653188 | 0.5849 | 0.616239 | 0.306264 | |
| | 7500 | 0.623600 | 0.652131 | 0.5938 | 0.616488 | 0.300399 | |
| | 8000 | 0.622500 | 0.652739 | 0.5855 | 0.617415 | 0.310418 | |
| | 8500 | 0.622300 | 0.651849 | 0.5916 | 0.617908 | 0.308056 | |
| | 9000 | 0.622400 | 0.651472 | 0.5910 | 0.618263 | 0.308137 | |
