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

# xlnet-large-cased-finetuned-augmentation-LUNAR-TAPT-MICRO

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: 0.5171
- F1: 0.8326
- Roc Auc: 0.8759
- Accuracy: 0.6052

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4028        | 1.0   | 318  | 0.3883          | 0.7185 | 0.7928  | 0.4082   |
| 0.3124        | 2.0   | 636  | 0.3370          | 0.7842 | 0.8459  | 0.5051   |
| 0.2128        | 3.0   | 954  | 0.3211          | 0.7902 | 0.8422  | 0.5493   |
| 0.1418        | 4.0   | 1272 | 0.3509          | 0.8048 | 0.8562  | 0.5437   |
| 0.0971        | 5.0   | 1590 | 0.3839          | 0.8036 | 0.8524  | 0.5642   |
| 0.0538        | 6.0   | 1908 | 0.4280          | 0.8232 | 0.8744  | 0.5800   |
| 0.038         | 7.0   | 2226 | 0.4600          | 0.8251 | 0.8766  | 0.5831   |
| 0.0251        | 8.0   | 2544 | 0.4612          | 0.8184 | 0.8670  | 0.5808   |
| 0.0251        | 9.0   | 2862 | 0.5169          | 0.8228 | 0.8746  | 0.5729   |
| 0.016         | 10.0  | 3180 | 0.5178          | 0.8258 | 0.8731  | 0.5902   |
| 0.0119        | 11.0  | 3498 | 0.5177          | 0.8305 | 0.8757  | 0.5997   |
| 0.0063        | 12.0  | 3816 | 0.5171          | 0.8326 | 0.8759  | 0.6052   |
| 0.0021        | 13.0  | 4134 | 0.5385          | 0.8325 | 0.8792  | 0.5934   |
| 0.0012        | 14.0  | 4452 | 0.5370          | 0.8284 | 0.8736  | 0.5950   |
| 0.0039        | 15.0  | 4770 | 0.5462          | 0.8324 | 0.8786  | 0.5997   |
| 0.0027        | 16.0  | 5088 | 0.5458          | 0.8320 | 0.8767  | 0.6044   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0