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--- |
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datasets: |
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- AdamCodd/Civitai-2m-prompts |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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- roc_auc |
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inference: true |
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base_model: distilroberta-base |
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model-index: |
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- name: distilroberta-nsfw-prompt-stable-diffusion |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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metrics: |
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- type: loss |
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value: 0.3103 |
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- type: accuracy |
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value: 0.8642 |
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name: Accuracy |
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- type: f1 |
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value: 0.8612 |
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name: F1 |
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- type: precision |
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value: 0.8805 |
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name: Precision |
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- type: recall |
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value: 0.8427 |
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name: Recall |
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- type: ROC_AUC |
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value: 0.9408 |
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name: AUC |
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language: |
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- en |
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--- |
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## DistilRoBERTa-nsfw-prompt-stable-diffusion |
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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. |
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It achieves the following results on the evaluation set: |
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* Loss: 0.3103 |
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* Accuracy: 0.8642 |
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* F1: 0.8612 |
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* AUC: 0.9408 |
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* Precision: 0.8805 |
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* Recall: 0.8427 |
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## Model description |
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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. |
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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. |
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## Usage |
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```python |
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from transformers import pipeline |
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prompt_detector = pipeline("text-classification", model="AdamCodd/distilroberta-nsfw-prompt-stable-diffusion") |
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predicted_class = prompt_detector("masterpiece, 1girl, yellow sundress, looking at viewer") |
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print(predicted_class) |
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#[{'label': 'SFW', 'score': 0.9983291029930115}] |
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``` |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 150 |
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- num_epochs: 1 |
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- weight_decay: 0.01 |
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### Training results |
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Metrics: Accuracy, F1, Precision, Recall, AUC |
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``` |
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'eval_loss': 0.3103, |
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'eval_accuracy': 0.8642, |
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'eval_f1': 0.8612, |
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'eval_precision': 0.8805, |
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'eval_recall': 0.8427, |
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'eval_roc_auc': 0.9408, |
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``` |
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[Confusion matrix](https://huggingface.co/AdamCodd/distilroberta-nsfw-prompt-stable-diffusion/resolve/main/Confusion_matrix.png): |
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[[184931 23859] |
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[32820 175780]] |
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### Framework versions |
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- Transformers 4.36.2 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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- Evaluate 0.4.1 |
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If you want to support me, you can [here](https://ko-fi.com/adamcodd). |
