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roberta-base-nvidia-aegis-v2
leomaurodesenv/roberta-base-nvidia-aegis-v2
roberta-base-nvidia-aegis-v2 is a text classification model from leomaurodesenv. 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
- roberta
- Access
- Public
- Created
- Sep 16, 2026
- Updated
- Sep 18, 2026
- SHA
- 464b3dd2
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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. -->
roberta-base-nvidia-aegis-v2
This model is a fine-tuned version of FacebookAI/roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3329
- Accuracy: 0.8540
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: 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7877 | 1.0 | 1203 | 0.3328 | 0.8546 |
| 0.5995 | 2.0 | 2406 | 0.3507 | 0.8551 |
| 0.5950 | 3.0 | 3609 | 0.4369 | 0.8677 |
| 0.2171 | 4.0 | 4812 | 0.5583 | 0.8709 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Sep 16, 2026
- Updated
- Sep 18, 2026