---
license: mit
base_model: xlnet/xlnet-base-cased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xlnet-base-cased-airlines-news-multi-label
  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-base-cased-airlines-news-multi-label

This model is a fine-tuned version of [xlnet/xlnet-base-cased](https://huggingface.co./xlnet/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2822
- F1: 0.6647
- Roc Auc: 0.8080
- Accuracy: 0.6116

## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 150  | 0.2088          | 0.5979 | 0.7399  | 0.6027   |
| No log        | 2.0   | 300  | 0.1928          | 0.6596 | 0.7725  | 0.6562   |
| No log        | 3.0   | 450  | 0.2049          | 0.6327 | 0.7653  | 0.5982   |
| 0.2167        | 4.0   | 600  | 0.2226          | 0.6506 | 0.8007  | 0.6027   |
| 0.2167        | 5.0   | 750  | 0.2280          | 0.6288 | 0.7666  | 0.5893   |
| 0.2167        | 6.0   | 900  | 0.2418          | 0.6295 | 0.7709  | 0.5938   |
| 0.0812        | 7.0   | 1050 | 0.2610          | 0.6258 | 0.7722  | 0.5982   |
| 0.0812        | 8.0   | 1200 | 0.2756          | 0.6098 | 0.7606  | 0.5804   |
| 0.0812        | 9.0   | 1350 | 0.2822          | 0.6647 | 0.8080  | 0.6116   |
| 0.0325        | 10.0  | 1500 | 0.2908          | 0.6378 | 0.7873  | 0.5938   |
| 0.0325        | 11.0  | 1650 | 0.3050          | 0.6319 | 0.7860  | 0.5938   |
| 0.0325        | 12.0  | 1800 | 0.3044          | 0.6277 | 0.7830  | 0.5804   |
| 0.0325        | 13.0  | 1950 | 0.3030          | 0.6254 | 0.7804  | 0.5804   |
| 0.015         | 14.0  | 2100 | 0.3057          | 0.6319 | 0.7860  | 0.5848   |
| 0.015         | 15.0  | 2250 | 0.3013          | 0.6168 | 0.7744  | 0.5670   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1