metadata
library_name: transformers
tags:
- text-classification
- modernbert
- generated-data
base_model: illuin/roberta-large-bne
metrics:
- name: loss
type: loss
value: 0.46496206521987915
- name: accuracy
type: accuracy
value: 0.8856666666666667
- name: f1
type: f1
value: 0.8856143611327717
- name: precision
type: precision
value: 0.8855866997834395
- name: recall
type: recall
value: 0.8856544163260209
- name: runtime
type: runtime
value: 10.4045
- name: samples_per_second
type: samples_per_second
value: 576.672
- name: steps_per_second
type: steps_per_second
value: 36.042
- name: epoch
type: epoch
value: 3
Gender Classifier (Fine-tuned illuin/roberta-large-bne)
This model was fine-tuned to classify text into: male, female, neutral
Performance Metrics
| Metric | Value |
|---|---|
| loss | 0.4650 |
| accuracy | 0.8857 |
| f1 | 0.8856 |
| precision | 0.8856 |
| recall | 0.8857 |
| runtime | 10.4045 |
| samples_per_second | 576.6720 |
| steps_per_second | 36.0420 |
| 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.'))