Create README.md

README.md

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+---
+datasets:
+- AdamCodd/Civitai-2m-prompts
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+- roc_auc
+inference: true
+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 was made to detect NSFW Stable-diffusion prompts by training it on 2M SFW and NSFW prompts (1043475 samples of each, perfectly balanced). One words prompts have been excluded to remove useless noise for the prediction.
+
+## 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).