---
library_name: transformers
tags:
- text-classification
- modernbert
- generated-data
base_model: answerdotai/ModernBERT-large
metrics:
- name: loss
  type: loss
  value: 0.32754603028297424
- name: accuracy
  type: accuracy
  value: 0.9385
- name: f1
  type: f1
  value: 0.9381586289173399
- name: precision
  type: precision
  value: 0.9386946502118235
- name: recall
  type: recall
  value: 0.9386318740931247
- name: runtime
  type: runtime
  value: 16.8968
- name: samples_per_second
  type: samples_per_second
  value: 355.097
- name: steps_per_second
  type: steps_per_second
  value: 22.194
- name: epoch
  type: epoch
  value: 3.0
---

# Gender Classifier (Fine-tuned answerdotai/ModernBERT-large)

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

## Performance Metrics
| Metric | Value |
| :--- | :--- |
| **loss** | 0.3275 |
| **accuracy** | 0.9385 |
| **f1** | 0.9382 |
| **precision** | 0.9387 |
| **recall** | 0.9386 |
| **runtime** | 16.8968 |
| **samples_per_second** | 355.0970 |
| **steps_per_second** | 22.1940 |
| **epoch** | 3.0000 |

## Hyperparameters
- **Batch Size**: 16
- **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.'))
```