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rf_news_clf_fine_tuned_marbert
najla45/rf_news_clf_fine_tuned_marbert
rf_news_clf_fine_tuned_marbert is a text classification model from najla45. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Nov 11, 2025
- Updated
- Nov 12, 2025
- SHA
- 1e9ad523
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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. -->
rf_news_clf_fine_tuned_marbert
This model is a fine-tuned version of UBC-NLP/MARBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0541
- Accuracy: 0.9890
- F1: 0.9890
- Precision: 0.9890
- Recall: 0.9890
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.1714 | 1.0 | 3911 | 0.1103 | 0.9787 | 0.9787 | 0.9790 | 0.9787 |
| 0.0015 | 2.0 | 7822 | 0.0734 | 0.9877 | 0.9877 | 0.9877 | 0.9877 |
| 0.0011 | 3.0 | 11733 | 0.0541 | 0.9890 | 0.9890 | 0.9890 | 0.9890 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Nov 11, 2025
- Updated
- Nov 12, 2025