---
library_name: transformers
tags:
- text-classification
- modernbert
- generated-data
base_model: xlm-roberta-large
metrics:
- name: loss
  type: loss
  value: 0.4027690887451172
- name: accuracy
  type: accuracy
  value: 0.883
- name: f1
  type: f1
  value: 0.8826794072627964
- name: precision
  type: precision
  value: 0.8852544531362957
- name: recall
  type: recall
  value: 0.8831381197007152
- name: runtime
  type: runtime
  value: 43.8849
- name: samples_per_second
  type: samples_per_second
  value: 273.443
- name: steps_per_second
  type: steps_per_second
  value: 34.18
- name: epoch
  type: epoch
  value: 3.0
---

# Gender Classifier (Fine-tuned xlm-roberta-large)

This model was fine-tuned to classify text into: male, female, neutral

## Performance Metrics
| Metric | Value |
| :--- | :--- |
| **loss** | 0.4028 |
| **accuracy** | 0.8830 |
| **f1** | 0.8827 |
| **precision** | 0.8853 |
| **recall** | 0.8831 |
| **runtime** | 43.8849 |
| **samples_per_second** | 273.4430 |
| **steps_per_second** | 34.1800 |
| **epoch** | 3.0000 |

## Hyperparameters
- **Batch Size**: 8
- **Learning Rate**: 5e-05
- **Epochs**: 3
- **Weight Decay**: 0.01
- **Mixed Precision (FP16)**: True

## Quick Usage
```python
from transformers import pipeline

# Load the model directly from this folder or HF Hub
classifier = pipeline('text-classification', model='.')
print(classifier('She is a great engineer.'))
```