---
datasets:
- AdamCodd/Civitai-2m-prompts
metrics:
- accuracy
- f1
- precision
- recall
- roc_auc
inference: true
base_model: distilroberta-base
model-index:
- name: distilroberta-nsfw-prompt-stable-diffusion
  results:
  - task:
      type: text-classification
      name: Text Classification
    metrics:
    - type: loss
      value: 0.3103
    - type: accuracy
      value: 0.8642
      name: Accuracy
    - type: f1
      value: 0.8612
      name: F1
    - type: precision
      value: 0.8805
      name: Precision
    - type: recall
      value: 0.8427
      name: Recall
    - type: ROC_AUC
      value: 0.9408
      name: AUC
language:
- en
---
## DistilRoBERTa-nsfw-prompt-stable-diffusion

This model utilizes the [Distilroberta base](https://huggingface.co/distilroberta-base) architecture, which has been fine-tuned for a classification task on [AdamCodd/Civitai-2m-prompts](https://huggingface.co/datasets/AdamCodd/Civitai-2m-prompts) dataset, on the positive prompts.
It achieves the following results on the evaluation set:
* Loss: 0.3103
* Accuracy: 0.8642
* F1: 0.8612
* AUC: 0.9408
* Precision: 0.8805
* Recall: 0.8427

## Model description

This model is designed to identify NSFW prompts in Stable-diffusion, trained on a dataset comprising of ~2 million prompts, evenly split between SFW and NSFW categories (1,043,475 samples of each, ensuring a balanced dataset). Single-word prompts have been excluded to enhance the accuracy and relevance of the predictions.

Although this model demonstrates satisfactory accuracy, it is recommended to use it in conjunction with this [image NSFW detector](https://huggingface.co/AdamCodd/vit-base-nsfw-detector) to improve overall detection capabilities and minimize the occurrence of false positives.

## Usage

```python
from transformers import pipeline

prompt_detector = pipeline("text-classification", model="AdamCodd/distilroberta-nsfw-prompt-stable-diffusion")

predicted_class = prompt_detector("masterpiece, 1girl, yellow sundress, looking at viewer")
print(predicted_class)
#[{'label': 'SFW', 'score': 0.9983291029930115}]
```

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 1
- weight_decay: 0.01

### Training results

Metrics: Accuracy, F1, Precision, Recall, AUC

```
'eval_loss': 0.3103,
'eval_accuracy': 0.8642,
'eval_f1': 0.8612,
'eval_precision': 0.8805,
'eval_recall': 0.8427,
'eval_roc_auc': 0.9408,
```

[Confusion matrix](https://huggingface.co/AdamCodd/distilroberta-nsfw-prompt-stable-diffusion/resolve/main/Confusion_matrix.png):

[[184931  23859]

[32820  175780]] 

### Framework versions

- Transformers 4.36.2
- Datasets 2.16.1
- Tokenizers 0.15.0
- Evaluate 0.4.1

If you want to support me, you can [here](https://ko-fi.com/adamcodd).