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roberta-large-finetuned-augmentation-LUNAR-TAPT
sercetexam9/roberta-large-finetuned-augmentation-LUNAR-TAPT
roberta-large-finetuned-augmentation-LUNAR-TAPT is a text classification model from sercetexam9. 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
- Jan 28, 2025
- Updated
- Jan 28, 2025
- SHA
- f37ef524
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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-large-finetuned-augmentation-LUNAR-TAPT
This model is a fine-tuned version of FacebookAI/roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4897
- F1: 0.8302
- Roc Auc: 0.8696
- Accuracy: 0.6338
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.3371 | 1.0 | 317 | 0.3025 | 0.7356 | 0.8000 | 0.5233 |
| 0.2571 | 2.0 | 634 | 0.3055 | 0.7376 | 0.7942 | 0.5572 |
| 0.1848 | 3.0 | 951 | 0.2850 | 0.7964 | 0.8431 | 0.5912 |
| 0.124 | 4.0 | 1268 | 0.3223 | 0.7738 | 0.8164 | 0.5635 |
| 0.0701 | 5.0 | 1585 | 0.3219 | 0.8091 | 0.8597 | 0.5951 |
| 0.0491 | 6.0 | 1902 | 0.3576 | 0.8148 | 0.8547 | 0.6014 |
| 0.0432 | 7.0 | 2219 | 0.3808 | 0.8216 | 0.8665 | 0.6196 |
| 0.0352 | 8.0 | 2536 | 0.3945 | 0.8278 | 0.8721 | 0.6259 |
| 0.0282 | 9.0 | 2853 | 0.4357 | 0.8173 | 0.8580 | 0.6054 |
| 0.012 | 10.0 | 3170 | 0.4670 | 0.8208 | 0.8679 | 0.5951 |
| 0.0054 | 11.0 | 3487 | 0.4864 | 0.8177 | 0.8599 | 0.6038 |
| 0.0029 | 12.0 | 3804 | 0.4882 | 0.8289 | 0.8687 | 0.6259 |
| 0.0011 | 13.0 | 4121 | 0.4897 | 0.8302 | 0.8696 | 0.6338 |
| 0.0012 | 14.0 | 4438 | 0.5079 | 0.8273 | 0.8680 | 0.6251 |
| 0.0008 | 15.0 | 4755 | 0.5146 | 0.8285 | 0.8688 | 0.6227 |
| 0.0007 | 16.0 | 5072 | 0.5100 | 0.8282 | 0.8693 | 0.6338 |
| 0.0008 | 17.0 | 5389 | 0.5158 | 0.8282 | 0.8673 | 0.6330 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jan 28, 2025
- Updated
- Jan 28, 2025