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---
library_name: transformers
license: mit
base_model: xlnet/xlnet-large-cased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: UIT-xlnet-large-cased-finetuned
results: []
---
<!-- 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. -->
# UIT-xlnet-large-cased-finetuned
This model is a fine-tuned version of [xlnet/xlnet-large-cased](https://huggingface.co./xlnet/xlnet-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7544
- F1: 0.7191
- Roc Auc: 0.7866
- Accuracy: 0.4765
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.6047 | 1.0 | 139 | 0.5968 | 0.1435 | 0.5 | 0.1300 |
| 0.551 | 2.0 | 278 | 0.5818 | 0.1393 | 0.4981 | 0.1318 |
| 0.549 | 3.0 | 417 | 0.5342 | 0.3274 | 0.5764 | 0.1931 |
| 0.4438 | 4.0 | 556 | 0.5083 | 0.4820 | 0.6362 | 0.3105 |
| 0.3647 | 5.0 | 695 | 0.4219 | 0.6477 | 0.7325 | 0.4043 |
| 0.3083 | 6.0 | 834 | 0.4281 | 0.6663 | 0.7489 | 0.4079 |
| 0.2307 | 7.0 | 973 | 0.4171 | 0.6962 | 0.7756 | 0.4404 |
| 0.1962 | 8.0 | 1112 | 0.4786 | 0.6985 | 0.7706 | 0.4242 |
| 0.1404 | 9.0 | 1251 | 0.5594 | 0.6960 | 0.7769 | 0.4152 |
| 0.0739 | 10.0 | 1390 | 0.5989 | 0.7033 | 0.7768 | 0.4567 |
| 0.0604 | 11.0 | 1529 | 0.6251 | 0.7028 | 0.7758 | 0.4603 |
| 0.0357 | 12.0 | 1668 | 0.6687 | 0.7077 | 0.7822 | 0.4531 |
| 0.0198 | 13.0 | 1807 | 0.7097 | 0.6973 | 0.7701 | 0.4422 |
| 0.0339 | 14.0 | 1946 | 0.7104 | 0.6992 | 0.7732 | 0.4531 |
| 0.0228 | 15.0 | 2085 | 0.7339 | 0.7150 | 0.7842 | 0.4765 |
| 0.0147 | 16.0 | 2224 | 0.7418 | 0.6941 | 0.7734 | 0.4711 |
| 0.0078 | 17.0 | 2363 | 0.7514 | 0.7130 | 0.7833 | 0.4765 |
| 0.0069 | 18.0 | 2502 | 0.7544 | 0.7191 | 0.7866 | 0.4765 |
| 0.0067 | 19.0 | 2641 | 0.7570 | 0.7146 | 0.7845 | 0.4693 |
| 0.005 | 20.0 | 2780 | 0.7579 | 0.7134 | 0.7834 | 0.4729 |
### Framework versions
- Transformers 4.48.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.21.0
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