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---
license: mit
base_model: roberta-base-openai-detector
tags:
- generated_from_trainer
datasets:
- au_tex_tification
metrics:
- accuracy
model-index:
- name: roberta-base-openai-detector-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: 0.55
---
<!-- 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. -->
# roberta-base-openai-detector-autextification
This model is a fine-tuned version of [roberta-base-openai-detector](https://huggingface.co/roberta-base-openai-detector) on the au_tex_tification dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5239
- Accuracy: 0.55
- Roc Auc: 0.5000
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|
| 0.0003 | 1.0 | 10 | 2.8320 | 0.6 | 0.4896 |
| 0.0002 | 2.0 | 20 | 3.2171 | 0.55 | 0.5 |
| 0.0016 | 3.0 | 30 | 3.5239 | 0.55 | 0.5000 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|