---
library_name: transformers
license: mit
base_model: xlnet/xlnet-large-cased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: UIT-NO-PRExlnet-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-NO-PRExlnet-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: 0.6311
- F1: 0.7534
- Roc Auc: 0.8047
- Accuracy: 0.5018

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.6008        | 1.0   | 139  | 0.5907          | 0.0994 | 0.5061  | 0.1227   |
| 0.5594        | 2.0   | 278  | 0.5834          | 0.1435 | 0.5     | 0.1300   |
| 0.4458        | 3.0   | 417  | 0.3967          | 0.6474 | 0.7336  | 0.4007   |
| 0.3153        | 4.0   | 556  | 0.3647          | 0.7128 | 0.7775  | 0.4495   |
| 0.2474        | 5.0   | 695  | 0.3392          | 0.7382 | 0.7952  | 0.4693   |
| 0.1915        | 6.0   | 834  | 0.3702          | 0.7346 | 0.7980  | 0.5054   |
| 0.1194        | 7.0   | 973  | 0.4083          | 0.7340 | 0.7994  | 0.4982   |
| 0.0953        | 8.0   | 1112 | 0.4656          | 0.7507 | 0.8101  | 0.4910   |
| 0.0482        | 9.0   | 1251 | 0.5682          | 0.7438 | 0.7934  | 0.4838   |
| 0.0504        | 10.0  | 1390 | 0.5374          | 0.7419 | 0.8069  | 0.4729   |
| 0.0265        | 11.0  | 1529 | 0.6019          | 0.7408 | 0.8011  | 0.4838   |
| 0.0082        | 12.0  | 1668 | 0.6136          | 0.7429 | 0.8015  | 0.4874   |
| 0.0077        | 13.0  | 1807 | 0.6212          | 0.7461 | 0.8020  | 0.4982   |
| 0.0117        | 14.0  | 1946 | 0.6089          | 0.7519 | 0.8086  | 0.4928   |
| 0.0044        | 15.0  | 2085 | 0.6246          | 0.7508 | 0.8050  | 0.5      |
| 0.0041        | 16.0  | 2224 | 0.6382          | 0.7460 | 0.8005  | 0.4946   |
| 0.0024        | 17.0  | 2363 | 0.6333          | 0.7467 | 0.8011  | 0.5      |
| 0.0053        | 18.0  | 2502 | 0.6311          | 0.7534 | 0.8047  | 0.5018   |
| 0.0027        | 19.0  | 2641 | 0.6311          | 0.7508 | 0.8032  | 0.5036   |
| 0.0029        | 20.0  | 2780 | 0.6318          | 0.7520 | 0.8037  | 0.5054   |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.21.0