---
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