[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["缺少我需要的資訊","missingTheInformationINeed","thumb-down"],["過於複雜/步驟過多","tooComplicatedTooManySteps","thumb-down"],["過時","outOfDate","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["示例/程式碼問題","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間:2025-03-24 (世界標準時間)。"],[],[],null,["# ShieldGemma\n\n\u003cbr /\u003e\n\n[ShieldGemma](/gemma/docs/shieldgemma) is a set of ready-made, instruction-tuned, open\nweights safety classifiers built on the Gemma family of models to determine\nwhether text or images violate a safety policy across input and output.\nShieldGemma is trained to identify across key harms across different models, see\nthe model cards for more information.\n\n- ShieldGemma 2 for image content moderation: Available in 4B. See the [model card](/gemma/docs/shieldgemma/model_card_2) for more details.\n- ShieldGemma 1 for text content moderation: Available in 2B, 9B, and 27B---allowing you to balance speed, performance, and generalizability to suit your needs across any deployment. See the [model card](/gemma/docs/shieldgemma/model_card) for more details.\n\n| **Note:** While ShieldGemma is trained on key harms, it may be generalizable to other harms related to your use case. It's important to experiment and regularly assess how well ShieldGemma fits your needs.\n\n**Safeguard your models with ShieldGemma**\n\n|---|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| | [Image moderation: ShieldGemma 2 in Transformers](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/responsible/docs/safeguards/shieldgemma2_on_huggingface.ipynb) | [Text moderation: ShieldGemma in Transformers](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/responsible/docs/safeguards/shieldgemma_on_huggingface.ipynb) | [Text moderation: ShieldGemma in Keras](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/responsible/docs/safeguards/shieldgemma_on_keras.ipynb) |\n\n\u003cbr /\u003e\n\nYou can use ShieldGemma models in the following frameworks.\n\n- [KerasNLP](https://keras.io/keras_nlp/), with model checkpoints available from [Kaggle](https://www.kaggle.com/search?q=shieldgemma+in%3Amodels).\n- [Hugging Face Transformers](https://github.com/huggingface/transformers), with model checkpoints available from [Hugging Face Hub](https://huggingface.co/collections/google/shieldgemma-release-66a20efe3c10ef2bd5808c79)."]]