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Bertimbau-Large-Offensive
JFrediani/Bertimbau-Large-Offensive
Bertimbau-Large-Offensive is a text classification model from JFrediani. 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
- Jul 21, 2024
- Updated
- Jul 21, 2024
- SHA
- 64142c51
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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. -->
content
This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4768
- Accuracy: 0.7739
- F1-score: 0.7823
- Recall: 0.9002
- Precision: 0.6917
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: 2.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.5016 | 0.3814 | 500 | 0.4686 | 0.7736 | 0.7865 | 0.9022 | 0.6971 |
| 0.4628 | 0.7628 | 1000 | 0.4437 | 0.7753 | 0.7769 | 0.8464 | 0.7180 |
| 0.4139 | 1.1442 | 1500 | 0.4633 | 0.7773 | 0.7573 | 0.7517 | 0.7630 |
| 0.3569 | 1.5256 | 2000 | 0.5019 | 0.7831 | 0.7930 | 0.8991 | 0.7093 |
| 0.357 | 1.9069 | 2500 | 0.4498 | 0.7839 | 0.7644 | 0.7585 | 0.7704 |
| 0.2612 | 2.2883 | 3000 | 0.6906 | 0.7665 | 0.7740 | 0.8650 | 0.7003 |
| 0.2292 | 2.6697 | 3500 | 0.6406 | 0.7624 | 0.7711 | 0.8656 | 0.6952 |
| 0.2345 | 3.0511 | 4000 | 0.8274 | 0.7687 | 0.7502 | 0.7511 | 0.7492 |
| 0.1527 | 3.4325 | 4500 | 0.8778 | 0.7602 | 0.7433 | 0.7511 | 0.7356 |
| 0.1613 | 3.8139 | 5000 | 0.8756 | 0.7564 | 0.7220 | 0.6842 | 0.7642 |
| 0.1188 | 4.1953 | 5500 | 1.2264 | 0.7567 | 0.7317 | 0.7176 | 0.7463 |
| 0.0992 | 4.5767 | 6000 | 1.2104 | 0.7636 | 0.7440 | 0.7430 | 0.7449 |
| 0.0938 | 4.9580 | 6500 | 1.1858 | 0.7616 | 0.7461 | 0.7579 | 0.7347 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Jul 21, 2024
- Updated
- Jul 21, 2024