---
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