---
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_trainer
model-index:
- name: XLnet-cased-AS-HU-f1-score
  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-cased-AS-HU-f1-score

This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co./xlnet-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6569
- F1-score: 0.8203

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- 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-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7105        | 1.0   | 64   | 0.6670          | 0.3697   |
| 0.654         | 2.0   | 128  | 0.6053          | 0.6119   |
| 0.509         | 3.0   | 192  | 0.4952          | 0.7471   |
| 0.3514        | 4.0   | 256  | 0.5171          | 0.7849   |
| 0.2233        | 5.0   | 320  | 0.6229          | 0.8059   |
| 0.1441        | 6.0   | 384  | 0.9291          | 0.8112   |
| 0.1376        | 7.0   | 448  | 1.4415          | 0.7421   |
| 0.0624        | 8.0   | 512  | 1.3527          | 0.8128   |
| 0.0224        | 9.0   | 576  | 1.4643          | 0.8103   |
| 0.0136        | 10.0  | 640  | 1.3725          | 0.8268   |
| 0.0177        | 11.0  | 704  | 1.5826          | 0.8142   |
| 0.0101        | 12.0  | 768  | 1.6241          | 0.8150   |
| 0.0003        | 13.0  | 832  | 1.6149          | 0.8165   |
| 0.0002        | 14.0  | 896  | 1.6481          | 0.8203   |
| 0.0001        | 15.0  | 960  | 1.6569          | 0.8203   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.0.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1