metadata
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
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
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.'))