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  - explicit-content-detection
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  - media-filter
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  ---
 
 
 
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  # **siglip2-mini-explicit-content**
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  > **siglip2-mini-explicit-content** is an image classification vision-language encoder model fine-tuned from **`siglip2-base-patch16-512`** for a single-label classification task. It is designed to classify images into categories related to explicit, sensual, or safe-for-work content using the **SiglipForImageClassification** architecture.
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  > \[!Note]
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  > This model is intended to promote positive, safe, and respectful digital environments. Misuse is strongly discouraged and may violate platform or regional guidelines. As a classification model, it does not generate unsafe content and is suitable for moderation purposes.
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  ```py
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  Classification Report:
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  precision recall f1-score support
 
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  - explicit-content-detection
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  - media-filter
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  ---
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+ ![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/GbdtFwysvOM4Nulmetrtq.png)
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  # **siglip2-mini-explicit-content**
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  > **siglip2-mini-explicit-content** is an image classification vision-language encoder model fine-tuned from **`siglip2-base-patch16-512`** for a single-label classification task. It is designed to classify images into categories related to explicit, sensual, or safe-for-work content using the **SiglipForImageClassification** architecture.
 
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  > \[!Note]
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  > This model is intended to promote positive, safe, and respectful digital environments. Misuse is strongly discouraged and may violate platform or regional guidelines. As a classification model, it does not generate unsafe content and is suitable for moderation purposes.
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+ > [!note]
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+ *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786
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  ```py
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  Classification Report:
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  precision recall f1-score support