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NADI-Region-DID-MARBERTv2
ashabrawy/NADI-Region-DID-MARBERTv2
NADI-Region-DID-MARBERTv2 is a text classification model from ashabrawy. 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
- Aug 24, 2025
- Updated
- Aug 24, 2025
- SHA
- 8938eaf8
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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. -->
NADI-Region-DID-MARBERTv2
This model is a fine-tuned version of UBC-NLP/MARBERTv2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.9010
- Accuracy: 0.5727
- F1: 0.5327
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.0875 | 1.0 | 883 | 1.2967 | 0.5970 | 0.5349 |
| 0.4608 | 2.0 | 1766 | 1.6182 | 0.5510 | 0.5175 |
| 0.2091 | 3.0 | 2649 | 2.1728 | 0.5763 | 0.5379 |
| 0.0872 | 4.0 | 3532 | 2.7735 | 0.5540 | 0.5178 |
| 0.0385 | 5.0 | 4415 | 2.9010 | 0.5727 | 0.5327 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Datasets 2.16.0
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Aug 24, 2025
- Updated
- Aug 24, 2025