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miroberta-rmi
ayatsuri/miroberta-rmi
miroberta-rmi is a text classification model from ayatsuri. 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
- Jun 22, 2026
- Updated
- Jun 22, 2026
- SHA
- 9653454b
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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. -->
miroberta-rmi
This model is a fine-tuned version of mavinsao/mi-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3950
- Accuracy: 0.8858
- Recall: 0.8771
- Precision: 0.8799
- F1: 0.8780
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: 1e-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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|---|---|---|---|---|---|---|---|
| 0.4448 | 1.0 | 2633 | 0.3843 | 0.8773 | 0.8692 | 0.8719 | 0.8693 |
| 0.3336 | 2.0 | 5266 | 0.3761 | 0.8819 | 0.8740 | 0.8737 | 0.8732 |
| 0.2710 | 3.0 | 7899 | 0.3950 | 0.8858 | 0.8771 | 0.8799 | 0.8780 |
Framework versions
- Transformers 5.12.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jun 22, 2026
- Updated
- Jun 22, 2026