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Berimbau-Base-Offensive
JFrediani/Berimbau-Base-Offensive
Berimbau-Base-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
- 5f6a6fb5
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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-base-portuguese-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4451
- Accuracy: 0.7772
- F1-score: 0.7788
- Recall: 0.8551
- Precision: 0.7150
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 |
|---|---|---|---|---|---|---|---|
| 0.5156 | 0.3814 | 500 | 0.4764 | 0.7687 | 0.7744 | 0.8972 | 0.6812 |
| 0.4498 | 0.7628 | 1000 | 0.4483 | 0.7790 | 0.7755 | 0.8622 | 0.7045 |
| 0.4198 | 1.1442 | 1500 | 0.4574 | 0.7745 | 0.7723 | 0.8642 | 0.6980 |
| 0.3687 | 1.5256 | 2000 | 0.4933 | 0.7696 | 0.7479 | 0.7723 | 0.7250 |
| 0.3591 | 1.9069 | 2500 | 0.4475 | 0.7902 | 0.7828 | 0.8545 | 0.7223 |
| 0.2809 | 2.2883 | 3000 | 0.5172 | 0.7696 | 0.7397 | 0.7400 | 0.7395 |
| 0.2712 | 2.6697 | 3500 | 0.5308 | 0.7799 | 0.7749 | 0.8564 | 0.7076 |
| 0.2482 | 3.0511 | 4000 | 0.6287 | 0.7622 | 0.7224 | 0.6992 | 0.7471 |
| 0.172 | 3.4325 | 4500 | 0.6831 | 0.7725 | 0.7491 | 0.7678 | 0.7314 |
| 0.1802 | 3.8139 | 5000 | 0.7141 | 0.7762 | 0.7570 | 0.7878 | 0.7285 |
| 0.1477 | 4.1953 | 5500 | 0.8481 | 0.7653 | 0.7444 | 0.7723 | 0.7184 |
| 0.121 | 4.5767 | 6000 | 0.9831 | 0.7639 | 0.7461 | 0.7840 | 0.7117 |
| 0.1377 | 4.9580 | 6500 | 0.9748 | 0.7662 | 0.7435 | 0.7658 | 0.7224 |
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