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

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: 0.2329
- F1: 0.9001
- Roc Auc: 0.6501
- Hamming: 0.9145

## 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: 9e-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: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Hamming |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:-------:|
| No log        | 1.0   | 128  | 0.2866          | 0.8594 | 0.5     | 0.9041  |
| No log        | 2.0   | 256  | 0.2520          | 0.8955 | 0.5943  | 0.9130  |
| No log        | 3.0   | 384  | 0.2431          | 0.8984 | 0.6493  | 0.9130  |
| 0.3656        | 4.0   | 512  | 0.2384          | 0.8984 | 0.6622  | 0.9115  |
| 0.3656        | 5.0   | 640  | 0.2329          | 0.9001 | 0.6501  | 0.9145  |
| 0.3656        | 6.0   | 768  | 0.2353          | 0.9000 | 0.6699  | 0.9130  |
| 0.3656        | 7.0   | 896  | 0.2336          | 0.8959 | 0.6735  | 0.9071  |
| 0.2788        | 8.0   | 1024 | 0.2318          | 0.8957 | 0.6606  | 0.9086  |
| 0.2788        | 9.0   | 1152 | 0.2327          | 0.8961 | 0.6606  | 0.9086  |
| 0.2788        | 10.0  | 1280 | 0.2317          | 0.8975 | 0.6545  | 0.9100  |
| 0.2788        | 11.0  | 1408 | 0.2311          | 0.8975 | 0.6545  | 0.9100  |
| 0.2659        | 12.0  | 1536 | 0.2309          | 0.8982 | 0.6554  | 0.9115  |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1