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xlnet-large-cased-finetuned-augmentation-LUNAR-TAPT
sercetexam9/xlnet-large-cased-finetuned-augmentation-LUNAR-TAPT
xlnet-large-cased-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
- xlnet
- Access
- Public
- Created
- Jan 28, 2025
- Updated
- Jan 28, 2025
- SHA
- ee4f3f94
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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. -->
xlnet-large-cased-finetuned-augmentation-LUNAR-TAPT
This model is a fine-tuned version of xlnet/xlnet-large-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4904
- F1: 0.8291
- Roc Auc: 0.8646
- Accuracy: 0.6215
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 |
|---|---|---|---|---|---|---|
| 0.3732 | 1.0 | 318 | 0.3677 | 0.6417 | 0.7249 | 0.4227 |
| 0.291 | 2.0 | 636 | 0.2986 | 0.7666 | 0.8238 | 0.5426 |
| 0.2296 | 3.0 | 954 | 0.2937 | 0.7774 | 0.8291 | 0.5552 |
| 0.1332 | 4.0 | 1272 | 0.3269 | 0.7980 | 0.8559 | 0.5797 |
| 0.0964 | 5.0 | 1590 | 0.3768 | 0.7977 | 0.8473 | 0.5505 |
| 0.0618 | 6.0 | 1908 | 0.4196 | 0.7833 | 0.8416 | 0.5552 |
| 0.0356 | 7.0 | 2226 | 0.4305 | 0.8041 | 0.8509 | 0.5726 |
| 0.0214 | 8.0 | 2544 | 0.4510 | 0.8112 | 0.8482 | 0.5883 |
| 0.0196 | 9.0 | 2862 | 0.4708 | 0.8118 | 0.8582 | 0.5970 |
| 0.0111 | 10.0 | 3180 | 0.4950 | 0.8174 | 0.8590 | 0.5994 |
| 0.0124 | 11.0 | 3498 | 0.5083 | 0.8094 | 0.8572 | 0.5852 |
| 0.0079 | 12.0 | 3816 | 0.4904 | 0.8291 | 0.8646 | 0.6215 |
| 0.0062 | 13.0 | 4134 | 0.5218 | 0.8155 | 0.8578 | 0.5954 |
| 0.001 | 14.0 | 4452 | 0.5225 | 0.8194 | 0.8636 | 0.6073 |
| 0.0024 | 15.0 | 4770 | 0.5248 | 0.8244 | 0.8646 | 0.6088 |
| 0.0012 | 16.0 | 5088 | 0.5259 | 0.8235 | 0.8652 | 0.6073 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
- Task
- Text Classification
- Library
- transformers
- Type
- xlnet
- License
- mit
- Created
- Jan 28, 2025
- Updated
- Jan 28, 2025