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results
Karyl-Maxine/results
results is a text classification model from Karyl-Maxine. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- roberta
- Access
- Public
- Created
- Feb 21, 2026
- Updated
- Feb 21, 2026
- SHA
- d473146c
Try a prompt
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. -->
results
This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2961
- Accuracy: 0.9011
- F1: 0.7469
- Precision: 0.6734
- Recall: 0.8384
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
- 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 | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2932 | 1.0 | 1137 | 0.2963 | 0.9011 | 0.7463 | 0.6741 | 0.8359 |
| 0.2329 | 2.0 | 2274 | 0.2637 | 0.9235 | 0.7723 | 0.8016 | 0.7449 |
| 0.1552 | 3.0 | 3411 | 0.3774 | 0.9094 | 0.7559 | 0.7121 | 0.8056 |
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
- apache-2.0
- Created
- Feb 21, 2026
- Updated
- Feb 21, 2026