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roberta_results
dinnifrisya/roberta_results
roberta_results is a text classification model from dinnifrisya. 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
- roberta
- Access
- Public
- Created
- Jun 10, 2026
- Updated
- Jun 10, 2026
- SHA
- 12038cf9
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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_results
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7168
- Accuracy: 0.8489
- F1: 0.8490
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
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.4862 | 1.0 | 6396 | 0.4655 | 0.8131 | 0.8158 |
| 0.3555 | 2.0 | 12792 | 0.4431 | 0.8435 | 0.8435 |
| 0.2856 | 3.0 | 19188 | 0.4953 | 0.8481 | 0.8482 |
| 0.2035 | 4.0 | 25584 | 0.6295 | 0.8499 | 0.8500 |
| 0.1557 | 5.0 | 31980 | 0.7168 | 0.8489 | 0.8490 |
Framework versions
- Transformers 5.10.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Jun 10, 2026
- Updated
- Jun 10, 2026