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CS221-xlm-roberta-base-orm-finetuned-finetuned-orm-tapt
Kuongan/CS221-xlm-roberta-base-orm-finetuned-finetuned-orm-tapt
CS221-xlm-roberta-base-orm-finetuned-finetuned-orm-tapt is a text classification model from Kuongan. 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
- xlm-roberta
- Access
- Public
- Created
- Jan 28, 2025
- Updated
- Jan 28, 2025
- SHA
- df6274b3
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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. -->
CS221-xlm-roberta-base-orm-finetuned-finetuned-orm-tapt
This model is a fine-tuned version of Kuongan/xlm-roberta-base-orm-finetuned on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1585
- F1: 0.7761
- Roc Auc: 0.8594
- Accuracy: 0.8023
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.1357 | 1.0 | 226 | 0.1244 | 0.6851 | 0.8156 | 0.7894 |
| 0.1306 | 2.0 | 452 | 0.1244 | 0.6946 | 0.8366 | 0.8001 |
| 0.1151 | 3.0 | 678 | 0.1231 | 0.7179 | 0.8454 | 0.8087 |
| 0.1002 | 4.0 | 904 | 0.1351 | 0.7313 | 0.8424 | 0.7930 |
| 0.0801 | 5.0 | 1130 | 0.1574 | 0.7465 | 0.8632 | 0.7701 |
| 0.0784 | 6.0 | 1356 | 0.1402 | 0.7424 | 0.8525 | 0.7822 |
| 0.0731 | 7.0 | 1582 | 0.1521 | 0.7605 | 0.8529 | 0.7808 |
| 0.0547 | 8.0 | 1808 | 0.1619 | 0.7545 | 0.8614 | 0.7758 |
| 0.0375 | 9.0 | 2034 | 0.1509 | 0.7680 | 0.8729 | 0.7872 |
| 0.0314 | 10.0 | 2260 | 0.1711 | 0.7521 | 0.8667 | 0.7815 |
| 0.0313 | 11.0 | 2486 | 0.1585 | 0.7761 | 0.8594 | 0.8023 |
| 0.0242 | 12.0 | 2712 | 0.1686 | 0.7635 | 0.8732 | 0.7915 |
| 0.0194 | 13.0 | 2938 | 0.1706 | 0.7644 | 0.8690 | 0.7973 |
| 0.0167 | 14.0 | 3164 | 0.1669 | 0.7623 | 0.8659 | 0.7915 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Jan 28, 2025
- Updated
- Jan 28, 2025