how do i interpret the results

#2
by cuiyi0326 - opened

I'm using the default policies and got the following results. How should i interpret the results? if i understand correctly, the logits tensor shape (3,2) corresponds to 3 default policies and every police has two values yes and no. so my question is why do i always get two [nan, nan]?

ShieldGemma2ImageClassifierOutputWithNoAttention(loss=None, logits=tensor([[ nan, nan],
[ nan, nan],
[35.1562, 53.1972]], device='cuda:0'), hidden_states=None, probabilities=tensor([[ nan, nan],
[ nan, nan],
[1.4618e-08, 1.0000e+00]], device='cuda:0'))

Use Latest Version Transformers,I get results like this: tensor([[5.3998e-02, 9.4600e-01],
[3.6518e-01, 6.3482e-01],
[1.8238e-08, 1.0000e+00]]),The probability of looking pornographic, dangerous, bloody and violent

Google org

Hello, the policy outputs scores for Yes and No respectively for each policy (policy is provided by text as you put) @BITDDD
@cuiyi0326 can you put your inference code here for me to reproduce?

Google org

Hello again, I built a notebook on how to properly use ShieldGemma 2 with custom policies: https://github.com/merveenoyan/smol-vision/blob/main/ShieldGemma_2_for_Vision_LM_Safety.ipynb

Use Latest Version Transformers,I get results like this: tensor([[5.3998e-02, 9.4600e-01],
[3.6518e-01, 6.3482e-01],
[1.8238e-08, 1.0000e+00]]),The probability of looking pornographic, dangerous, bloody and violent

Here the order is "dangerous", "sexual", "violence", but I'm not sure. I guess inputing images of the three categories is the only way to find out?

Google org

Hi @cuiyi0326 ,

Apologies for the late reply, The reason your seeing [nan, nan] for two of the policies is most likely a bug in the older version of the transformers library your are using. This is known issue, and it means the model was unable to compute a valid output for those policies, which is why the values show up as "Not a Number".
The issue has been already identified and fixed in release of Latest version transformers.

Kindly use Latest Version pip install --upgrade transformers and let us know still if you have any concerns will assist you on this.

Thank you.

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