File size: 1,487 Bytes
b0c3d71 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
library_name: transformers
tags:
- text-classification
- modernbert
- generated-data
base_model: PeterPanecillo/PlanTL-GOB-ES-roberta-base-bne-copy
metrics:
- name: loss
type: loss
value: 0.42353931069374084
- name: accuracy
type: accuracy
value: 0.905
- name: f1
type: f1
value: 0.9049896921601716
- name: precision
type: precision
value: 0.9049971109583139
- name: recall
type: recall
value: 0.9049918871427097
- name: runtime
type: runtime
value: 2.153
- name: samples_per_second
type: samples_per_second
value: 2786.842
- name: steps_per_second
type: steps_per_second
value: 43.661
- name: epoch
type: epoch
value: 3.0
---
# Gender Classifier (Fine-tuned PeterPanecillo/PlanTL-GOB-ES-roberta-base-bne-copy)
This model was fine-tuned to classify text into: male, female, neutral
## Performance Metrics
| Metric | Value |
| :--- | :--- |
| **loss** | 0.4235 |
| **accuracy** | 0.9050 |
| **f1** | 0.9050 |
| **precision** | 0.9050 |
| **recall** | 0.9050 |
| **runtime** | 2.1530 |
| **samples_per_second** | 2786.8420 |
| **steps_per_second** | 43.6610 |
| **epoch** | 3.0000 |
## Hyperparameters
- **Batch Size**: 64
- **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.'))
``` |