---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- au_tex_tification
metrics:
- accuracy
model-index:
- name: roberta-base-autextification
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: au_tex_tification
      type: au_tex_tification
      config: detection_en
      split: train
      args: detection_en
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6296720410406742
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta-base-autextification

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the au_tex_tification dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3253
- Accuracy: 0.6297
- Roc Auc: 0.8980

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Roc Auc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|
| 0.4844        | 1.0   | 3385  | 0.2904          | 0.9057   | 0.9745  |
| 0.1311        | 2.0   | 6770  | 0.4360          | 0.8997   | 0.9817  |
| 0.1576        | 3.0   | 10155 | 0.5514          | 0.9088   | 0.9837  |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1