| 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.')) | |
| ``` |