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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 | |