---
library_name: transformers
license: mit
base_model: xlnet-large-cased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: CS221-xlnet-large-cased-finetuned-augmentation
  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. -->

# CS221-xlnet-large-cased-finetuned-augmentation

This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5488
- F1: 0.7778
- Roc Auc: 0.8358
- Accuracy: 0.5486

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.5737        | 1.0   | 165  | 0.5256          | 0.2747 | 0.5539  | 0.2067   |
| 0.433         | 2.0   | 330  | 0.3995          | 0.5828 | 0.7099  | 0.4027   |
| 0.3391        | 3.0   | 495  | 0.3516          | 0.7006 | 0.7727  | 0.4514   |
| 0.2481        | 4.0   | 660  | 0.3694          | 0.7110 | 0.7813  | 0.5015   |
| 0.1585        | 5.0   | 825  | 0.4033          | 0.7513 | 0.8097  | 0.4985   |
| 0.1021        | 6.0   | 990  | 0.4539          | 0.7405 | 0.7987  | 0.4878   |
| 0.0813        | 7.0   | 1155 | 0.4708          | 0.7430 | 0.7991  | 0.4985   |
| 0.0512        | 8.0   | 1320 | 0.5113          | 0.7554 | 0.8162  | 0.5426   |
| 0.0287        | 9.0   | 1485 | 0.5563          | 0.7598 | 0.8223  | 0.5289   |
| 0.0129        | 10.0  | 1650 | 0.5488          | 0.7778 | 0.8358  | 0.5486   |
| 0.0144        | 11.0  | 1815 | 0.5748          | 0.7595 | 0.8157  | 0.5471   |
| 0.0094        | 12.0  | 1980 | 0.6090          | 0.7557 | 0.8152  | 0.5532   |
| 0.0057        | 13.0  | 2145 | 0.6303          | 0.7592 | 0.8167  | 0.5395   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0