---
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: xlnet-base-cased-HU-comple
  results: []
pipeline_tag: text-classification
---

<!-- 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-HU-comple

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: 0.9240
- Accuracy: 0.8050
- F1: 0.7619
- Precision: 0.7568
- Recall: 0.7671

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6952        | 1.0   | 90   | 0.6760          | 0.5933   | 0.0    | 0.0       | 0.0    |
| 0.694         | 2.0   | 180  | 0.6782          | 0.5933   | 0.0    | 0.0       | 0.0    |
| 0.6856        | 3.0   | 270  | 0.6781          | 0.5933   | 0.0    | 0.0       | 0.0    |
| 0.6868        | 4.0   | 360  | 0.7132          | 0.5933   | 0.0    | 0.0       | 0.0    |
| 0.6353        | 5.0   | 450  | 0.6486          | 0.6657   | 0.6685 | 0.5602    | 0.8288 |
| 0.5003        | 6.0   | 540  | 0.5151          | 0.7799   | 0.7189 | 0.7481    | 0.6918 |
| 0.3585        | 7.0   | 630  | 0.5237          | 0.7660   | 0.7290 | 0.6890    | 0.7740 |
| 0.2167        | 8.0   | 720  | 0.7817          | 0.7883   | 0.7226 | 0.7734    | 0.6781 |
| 0.1643        | 9.0   | 810  | 0.8763          | 0.7939   | 0.7597 | 0.7222    | 0.8014 |
| 0.135         | 10.0  | 900  | 0.9240          | 0.8050   | 0.7619 | 0.7568    | 0.7671 |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1