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scenescope_roberta_v3
RedMinder56/scenescope_roberta_v3
scenescope_roberta_v3 is a text classification model from RedMinder56. 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
- Apr 6, 2026
- Updated
- Apr 6, 2026
- SHA
- efe77a07
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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. -->
scenescope_roberta_v3
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1025
- Accuracy: 0.5155
- F1 Macro: 0.5164
- F1 Weighted: 0.5162
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: 16
- eval_batch_size: 16
- 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
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|
| 1.3835 | 1.0 | 76 | 1.3524 | 0.4109 | 0.3679 | 0.3682 |
| 1.2281 | 2.0 | 152 | 1.1090 | 0.5155 | 0.5188 | 0.5185 |
| 1.0200 | 3.0 | 228 | 1.0982 | 0.5504 | 0.5019 | 0.5008 |
| 0.8586 | 4.0 | 304 | 1.0808 | 0.5233 | 0.5123 | 0.5115 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Apr 6, 2026
- Updated
- Apr 6, 2026