---
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: trueparagraph.ai-xlnet
  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. -->

# trueparagraph.ai-xlnet

This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co./xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.8951
- F1: 0.8984
- Precision: 0.8674
- Recall: 0.9316
- Mcc: 0.7924
- Roc Auc: 0.8952
- Pr Auc: 0.8421
- Log Loss: 1.8813
- Loss: 0.2913

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Accuracy | F1     | Precision | Recall | Mcc    | Roc Auc | Pr Auc | Log Loss | Validation Loss |
|:-------------:|:------:|:----:|:--------:|:------:|:---------:|:------:|:------:|:-------:|:------:|:--------:|:---------------:|
| 0.649         | 0.6297 | 500  | 0.8006   | 0.8195 | 0.7457    | 0.9095 | 0.6164 | 0.8010  | 0.7233 | 4.0119   | 0.4063          |
| 0.4104        | 1.2594 | 1000 | 0.8409   | 0.8294 | 0.8892    | 0.7772 | 0.6870 | 0.8406  | 0.8020 | 2.4398   | 0.4054          |
| 0.4101        | 1.8892 | 1500 | 0.8100   | 0.8359 | 0.7332    | 0.9722 | 0.6560 | 0.8107  | 0.7266 | 3.4982   | 0.4405          |
| 0.4046        | 2.5189 | 2000 | 0.7754   | 0.8120 | 0.6959    | 0.9747 | 0.6012 | 0.7762  | 0.6909 | 3.0282   | 0.5111          |
| 0.3992        | 3.1486 | 2500 | 0.8664   | 0.8625 | 0.8843    | 0.8418 | 0.7336 | 0.8663  | 0.8232 | 2.7164   | 0.3871          |
| 0.3691        | 3.7783 | 3000 | 0.8774   | 0.8850 | 0.8303    | 0.9475 | 0.7626 | 0.8777  | 0.8128 | 1.8936   | 0.3413          |
| 0.2581        | 4.4081 | 3500 | 0.8951   | 0.8984 | 0.8674    | 0.9316 | 0.7924 | 0.8952  | 0.8421 | 1.8813   | 0.2913          |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1