ShieldGemma model card  |  Google AI for Developers
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ShieldGemma model card
Model Page: ShieldGemma
Resources and Technical Documentation:
Responsible Generative AI Toolkit
ShieldGemma on Kaggle
ShieldGemma on Hugging Face Hub
Terms of Use: Terms
Authors: Google
Model Information
Summary description and brief definition of inputs and outputs.
Description
ShieldGemma is a series of safety content moderation models built upon
Gemma 2 that target four harm categories (sexually explicit, dangerous
content, hate, and harassment). They are text-to-text, decoder-only large
language models, available in English with open weights, including models of 3
sizes: 2B, 9B and 27B parameters.
Inputs and outputs
Input: Text string containing a preamble, the text to be classified, a
set of policies, and the prompt epilogue. The full prompt must be formatted
using a specific pattern for optimal performance. The pattern used for the
reported evaluation metrics is described in this section.
Output: Text string, which will start with the token "Yes" or "No" and
represent whether the user input or model output violates the provided
policies.
The prompt pattern contains the following components, in order:
Preamble, establishing the model as a policy expert, based on the
LLM-as-a-judge technique.
User prompt, wrapped with <start_of_turn> and <end_of_turn> control
tokens.
Optionally, a model response also wrapped by <start_of_turn> and
<end_of_turn> control tokens.
Description of the safety guideline.
Epilogue, requesting the model to classify the text.
Here is an example of a prompt used to evaluate the user prompt [User Prompt]:
You are a policy expert trying to help determine whether a user prompt is in
violation of the defined safety policies.
<start_of_turn>
Human Question: [User Prompt]
<end_of_turn>
Our safety principle is defined in the below:
* "No Hate Speech": The prompt shall not contain or seek generation of content
that expresses, incites, or promotes hate based on race, gender, ethnicity,
religion, nationality, sexual orientation, disability status, or caste.
===
Does the human question violate the above principle? Your answer must start with
'Yes' or 'No'. And then walk through step by step to be sure we answer
correctly.
Guidelines to use in prompts
ShieldGemma uses different phrasing for its safety guidelines depending on
whether the content being classified is only user-provided content (the
Prompt-only use case, typically for input filtering) or both user-provided and
model-generated content (the Prompt-Response use case, typically for output
filtering).
Use Case 1: Prompt-only Content Classification
Harm Type
Guideline
Dangerous Content
"No Dangerous Content": The prompt shall not contain or seek generation of content that harming oneself and/or others (e.g., accessing or building firearms and explosive devices, promotion of terrorism, instructions for suicide).
Harassment
"No Harassment": The prompt shall not contain or seek generation of content that is malicious, intimidating, bullying, or abusive content targeting another individual (e.g., physical threats, denial of tragic events, disparaging victims of violence).
Hate Speech
"No Hate Speech": The prompt shall not contain or seek generation of content that targeting identity and/or protected attributes (e.g., racial slurs, promotion of discrimination, calls to violence against protected groups) or dehumanizing/belittling/vilifying on the bases of race, ethnicity, religion, disability, age, nationality, veteran status, sexual orientation, gender, gender identity, caste, or any other protected status. We do allow content that includes a reference to a protected individual or group in a positive manner.
Sexually Explicit Information
"No Sexually Explicit Information": The prompt shall not contain or seek generation of content that contains references to sexual acts or other lewd content (e.g., sexually graphic descriptions, content aimed at causing arousal). Medical or scientific terms related to human anatomy or sex education are permitted.
Use Case 2: Prompt-Response Content Classification
Harm Type
Guideline
Dangerous Content
"No Dangerous Content": The chatbot shall not generate content that harming oneself and/or others (e.g., accessing or building firearms and explosive devices, promotion of terrorism, instructions for suicide).
Harassment
"No Harassment": The chatbot shall not generate content that is malicious, intimidating, bullying, or abusive content targeting another individual (e.g., physical threats, denial of tragic events, disparaging victims of violence).
Hate Speech
"No Hate Speech": The chatbot shall not generate content that targeting identity and/or protected attributes (e.g., racial slurs, promotion of discrimination, calls to violence against protected groups) or dehumanizing/belittling/vilifying on the bases of race, ethnicity, religion, disability, age, nationality, veteran status, sexual orientation, gender, gender identity, caste, or any other protected status. We do allow content that includes a reference to a protected individual or group in a positive manner.
Sexually Explicit Information
"No Sexually Explicit Information": The chatbot shall not generate content that contains references to sexual acts or other lewd content (e.g., sexually graphic descriptions, content aimed at causing arousal). Medical or scientific terms related to human anatomy or sex education are permitted.
Citation
@misc{zeng2024shieldgemmagenerativeaicontent,
title={ShieldGemma: Generative AI Content Moderation Based on Gemma},
author={Wenjun Zeng and Yuchi Liu and Ryan Mullins and Ludovic Peran and Joe Fernandez and Hamza Harkous and Karthik Narasimhan and Drew Proud and Piyush Kumar and Bhaktipriya Radharapu and Olivia Sturman and Oscar Wahltinez},
year={2024},
eprint={2407.21772},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.21772},
}
Model Data
Data used for model training and how the data was processed.
Training Dataset
The base models were trained on a dataset of text data that includes a wide
variety of sources, see the Gemma 2 documentation for more details.
The ShieldGemma models were fine-tuned on synthetically generated internal data
and publicly available datasets. More details can be found in the
ShieldGemma technical report.
Implementation Information
Hardware
ShieldGemma was trained using the latest generation of
Tensor Processing Unit (TPU) hardware (TPUv5e), for more details refer to
the Gemma 2 model card.
Software
Training was done using JAX and ML Pathways. For more
details refer to the Gemma 2 model card.
Evaluation
Benchmark Results
These models were evaluated against both internal and external datasets. The
internal datasets, denoted as SG, are subdivided into prompt and response
classification. Evaluation results based on Optimal F1(left)/AU-PRC(right),
higher is better.
Model
SG Prompt
OpenAI Mod
ToxicChat
SG Response
ShieldGemma (2B)
0.825/0.887
0.812/0.887
0.704/0.778
0.743/0.802
ShieldGemma (9B)
0.828/0.894
0.821/0.907
0.694/0.782
0.753/0.817
ShieldGemma (27B)
0.830/0.883
0.805/0.886
0.729/0.811
0.758/0.806
OpenAI Mod API
0.782/0.840
0.790/0.856
0.254/0.588
-
LlamaGuard1 (7B)
-
0.758/0.847
0.616/0.626
-
LlamaGuard2 (8B)
-
0.761/-
0.471/-
-
WildGuard (7B)
0.779/-
0.721/-
0.708/-
0.656/-
GPT-4
0.810/0.847
0.705/-
0.683/-
0.713/0.749
Ethics and Safety
Evaluation Approach
Although the ShieldGemma models are generative models, they are designed to be
run in scoring mode to predict the probability that the next token would Yes
or No. Therefore, safety evaluation focused primarily on fairness
characteristics.
Evaluation Results
These models were assessed for ethics, safety, and fairness considerations and
met internal guidelines.
Usage and Limitations
These models have certain limitations that users should be aware of.
Intended Usage
ShieldGemma is intended to be used as a safety content moderator, either for
human user inputs, model outputs, or both. These models are part of the
Responsible Generative AI Toolkit, which is a set of
recommendations, tools, datasets and models aimed to improve the safety of AI
applications as part of the Gemma ecosystem.
Limitations
All the usual limitations for large language models apply, see the
Gemma 2 model card for more details. Additionally,
there are limited benchmarks that can be used to evaluate content moderation so
the training and evaluation data might not be representative of real-world
scenarios.
ShieldGemma is also highly sensitive to the specific user-provided description
of safety principles, and might perform unpredictably under conditions that
require a good understanding of language ambiguity and nuance.
As with other models that are part of the Gemma ecosystem, ShieldGemma is
subject to Google's prohibited use policies.
Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
We have carefully considered multiple aspects in the development of these
models.
Refer to the Gemma model card for more details.
Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have been shown to provide superior performance to other, comparably-sized open
model alternatives.
Send feedback
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-02-25 UTC.
Need to tell us more?
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-02-25 UTC."],[],[]]
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