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results_flausch_classification_gbert-large_spelling_corrected
Wiebke/results_flausch_classification_gbert-large_spelling_corrected
results_flausch_classification_gbert-large_spelling_corrected is a text classification model from Wiebke. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Jun 4, 2025
- Updated
- Jun 4, 2025
- SHA
- c7f4266a
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
results_flausch_classification_gbert-large_spelling_corrected
This model is a fine-tuned version of deepset/gbert-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2283
- Model Preparation Time: 0.0053
- Accuracy: 0.9418
- F1: 0.9417
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Accuracy | F1 |
|---|---|---|---|---|---|---|
| 0.2841 | 0.2822 | 500 | 0.2601 | 0.0053 | 0.9137 | 0.9147 |
| 0.2514 | 0.5643 | 1000 | 0.2228 | 0.0053 | 0.9218 | 0.9227 |
| 0.2371 | 0.8465 | 1500 | 0.2030 | 0.0053 | 0.9356 | 0.9352 |
| 0.2079 | 1.1287 | 2000 | 0.2800 | 0.0053 | 0.9266 | 0.9247 |
| 0.1878 | 1.4108 | 2500 | 0.2289 | 0.0053 | 0.9370 | 0.9366 |
| 0.1726 | 1.6930 | 3000 | 0.2245 | 0.0053 | 0.9376 | 0.9372 |
| 0.1727 | 1.9752 | 3500 | 0.1822 | 0.0053 | 0.9388 | 0.9388 |
| 0.1182 | 2.2573 | 4000 | 0.2783 | 0.0053 | 0.9388 | 0.9383 |
| 0.1241 | 2.5395 | 4500 | 0.2417 | 0.0053 | 0.9416 | 0.9414 |
| 0.1254 | 2.8217 | 5000 | 0.2283 | 0.0053 | 0.9418 | 0.9417 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Jun 4, 2025
- Updated
- Jun 4, 2025