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roberta-small-ucic
marzieh-maleki/roberta-small-ucic
roberta-small-ucic is a text classification model from marzieh-maleki. 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
- Aug 3, 2026
- Updated
- Aug 3, 2026
- SHA
- 9b4af2ba
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Base models
Datasets
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-small-ucic
This model is a fine-tuned version of smallbenchnlp/roberta-small on the trec dataset. It achieves the following results on the evaluation set:
- Loss: 2.2140
- Accuracy: 0.542
- F1: 0.1365
- Precision: 0.1192
- Recall: 0.1703
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
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 3.73 | 0.6462 | 50 | 3.5638 | 0.176 | 0.0067 | 0.0039 | 0.0222 |
| 3.436 | 1.2843 | 100 | 3.3628 | 0.176 | 0.0067 | 0.0039 | 0.0222 |
| 3.2804 | 1.9305 | 150 | 3.1425 | 0.26 | 0.0321 | 0.0488 | 0.0520 |
| 3.0182 | 2.5687 | 200 | 2.9318 | 0.384 | 0.0672 | 0.0688 | 0.0930 |
| 2.8872 | 3.2068 | 250 | 2.7501 | 0.468 | 0.1066 | 0.1073 | 0.1278 |
| 2.7232 | 3.8530 | 300 | 2.6119 | 0.492 | 0.1109 | 0.1051 | 0.1370 |
| 2.5765 | 4.4911 | 350 | 2.4985 | 0.508 | 0.1228 | 0.1211 | 0.1477 |
| 2.4639 | 5.1292 | 400 | 2.4120 | 0.514 | 0.1250 | 0.1241 | 0.1502 |
| 2.4198 | 5.7754 | 450 | 2.3442 | 0.524 | 0.1307 | 0.1230 | 0.1575 |
| 2.332 | 6.4136 | 500 | 2.2935 | 0.53 | 0.1321 | 0.1202 | 0.1606 |
| 2.2779 | 7.0517 | 550 | 2.2594 | 0.536 | 0.1359 | 0.1210 | 0.1672 |
| 2.2767 | 7.6979 | 600 | 2.2363 | 0.538 | 0.1379 | 0.1243 | 0.1676 |
| 2.2479 | 8.3360 | 650 | 2.2224 | 0.542 | 0.1367 | 0.1195 | 0.1703 |
| 2.2666 | 8.9822 | 700 | 2.2161 | 0.542 | 0.1365 | 0.1192 | 0.1703 |
| 2.2366 | 9.6204 | 750 | 2.2140 | 0.542 | 0.1365 | 0.1192 | 0.1703 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.8.4
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Aug 3, 2026
- Updated
- Aug 3, 2026