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merged 2 commits into from
Mar 18, 2025
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Fix Readme Snippet #2

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06210c1
Update shieldgemma2.md
ain-soph Mar 17, 2025
ed6c1d1
Update shieldgemma2.md
ain-soph Mar 17, 2025
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23 changes: 13 additions & 10 deletions docs/source/en/model_doc/shieldgemma2.md
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Original file line number Diff line number Diff line change
Expand Up @@ -35,47 +35,50 @@ This model was contributed by [Ryan Mullins](https://huggingface.co/RyanMullins)
- You can extend ShieldGemma's built-in in policies with the `custom_policies` argument to the Processor. Using the same key as one of the built-in policies will overwrite that policy with your custom defintion.
- ShieldGemma 2 does not support the image cropping capabilities used by Gemma 3.

### Classificaiton against Built-in Policies
### Classification against Built-in Policies

```python
from PIL import Image
import requests
from transformers import AutoProcessor, ShieldGemma2ForImageClassification

model_id = "google/shieldgemma-2-4b-it"
model = ShieldGemma2ForImageClassification.from_pretrained(model_id, device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)

image_1 = # An image you have loaded
image_2 = # An image you have loaded
image_3 = # An image you have loaded
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"
image = Image.open(requests.get(url, stream=True).raw)

inputs = processor(images=[image_1, image_2, image_3]).to(model.device)
inputs = processor(images=[image], return_tensors="pt").to(model.device)

output = model(**inputs)
print(output.probabilities)
```

### Classificaiton against Custom Policies
### Classification against Custom Policies

```python
from PIL import Image
import requests
from transformers import AutoProcessor, ShieldGemma2ForImageClassification

model_id = "google/shieldgemma-2-4b-it"
model = ShieldGemma2ForImageClassification.from_pretrained(model_id, device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)

image_1 = # An image you have loaded
image_2 = # An image you have loaded
image_3 = # An image you have loaded
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"
image = Image.open(requests.get(url, stream=True).raw)

custom_policies = {
"key_a": "descrition_a",
"key_b": "descrition_b",
}

inputs = processor(
images=[image_1, image_2, image_3],
images=[image],
custom_policies=custom_policies,
policies=["dangerous", "key_a", "key_b"],
return_tensors="pt",
).to(model.device)

output = model(**inputs)
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