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bert-safety-monitor-complete
Felipe1908/bert-safety-monitor-complete
bert-safety-monitor-complete is a text classification model from Felipe1908. 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
- Nov 27, 2025
- Updated
- Nov 27, 2025
- SHA
- 09893205
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Base models
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. -->
bert-safety-monitor-complete
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5434
- Accuracy: 0.8307
- F1 Macro: 0.7834
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: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| No log | 1.0 | 353 | 0.3735 | 0.8227 | 0.7834 |
| 0.46 | 2.0 | 706 | 0.5032 | 0.7987 | 0.7740 |
| 0.2828 | 3.0 | 1059 | 0.4577 | 0.8147 | 0.7760 |
| 0.2828 | 4.0 | 1412 | 0.5434 | 0.8307 | 0.7834 |
Framework versions
- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Nov 27, 2025
- Updated
- Nov 27, 2025