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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: 0.7374
- F1: 0.7190
- Roc Auc: 0.7861
- Accuracy: 0.4368
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.6144 | 1.0 | 139 | 0.5899 | 0.0858 | 0.5045 | 0.1245 |
| 0.55 | 2.0 | 278 | 0.5547 | 0.2409 | 0.5430 | 0.1625 |
| 0.5049 | 3.0 | 417 | 0.4514 | 0.5298 | 0.6834 | 0.3574 |
| 0.4 | 4.0 | 556 | 0.4586 | 0.5030 | 0.6732 | 0.3538 |
| 0.3206 | 5.0 | 695 | 0.4345 | 0.6371 | 0.7279 | 0.3827 |
| 0.2969 | 6.0 | 834 | 0.4264 | 0.6807 | 0.7627 | 0.4170 |
| 0.2036 | 7.0 | 973 | 0.4564 | 0.6871 | 0.7633 | 0.4495 |
| 0.1986 | 8.0 | 1112 | 0.4918 | 0.6930 | 0.7632 | 0.4170 |
| 0.1437 | 9.0 | 1251 | 0.5499 | 0.7021 | 0.7861 | 0.3953 |
| 0.1066 | 10.0 | 1390 | 0.5774 | 0.7028 | 0.7725 | 0.4224 |
| 0.0674 | 11.0 | 1529 | 0.6038 | 0.7145 | 0.7824 | 0.4585 |
| 0.038 | 12.0 | 1668 | 0.6528 | 0.7020 | 0.7797 | 0.4458 |
| 0.0341 | 13.0 | 1807 | 0.6681 | 0.7092 | 0.7781 | 0.4477 |
| 0.0308 | 14.0 | 1946 | 0.6986 | 0.7085 | 0.7796 | 0.4260 |
| 0.0109 | 15.0 | 2085 | 0.7297 | 0.7087 | 0.7796 | 0.4332 |
| 0.0224 | 16.0 | 2224 | 0.7307 | 0.7156 | 0.7838 | 0.4440 |
| 0.0097 | 17.0 | 2363 | 0.7358 | 0.7155 | 0.7816 | 0.4440 |
| 0.0103 | 18.0 | 2502 | 0.7374 | 0.7190 | 0.7861 | 0.4368 |
| 0.0116 | 19.0 | 2641 | 0.7381 | 0.7150 | 0.7829 | 0.4368 |
| 0.0077 | 20.0 | 2780 | 0.7383 | 0.7130 | 0.7817 | 0.4350 |
### Framework versions
- Transformers 4.48.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.21.0
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