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roberta-base-finetuned-mrpc
nicolassaublet/roberta-base-finetuned-mrpc
roberta-base-finetuned-mrpc is a text classification model from nicolassaublet. 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 30, 2025
- Updated
- Sep 30, 2025
- SHA
- b03a3c94
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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-finetuned-mrpc
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5657
- Accuracy: 0.8922
- F1: 0.9209
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 230 | 0.3243 | 0.8603 | 0.8935 |
| No log | 2.0 | 460 | 0.3411 | 0.8775 | 0.9129 |
| 0.3879 | 3.0 | 690 | 0.4662 | 0.8554 | 0.8876 |
| 0.3879 | 4.0 | 920 | 0.5847 | 0.8873 | 0.9187 |
| 0.1569 | 5.0 | 1150 | 0.5657 | 0.8922 | 0.9209 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Sep 30, 2025
- Updated
- Sep 30, 2025