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Running
on
L40S
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app.py
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@@ -144,13 +144,13 @@ with gr.Blocks() as demo:
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1. How to use SynthID Text to apply a watermark to text generated by your
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model; and
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1. How to
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Note that this detector is trained specifically
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should maintain a specific watermarking configuration for every model you
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use and protect that configuration as you would any other secret. See the
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[end-to-end guide][synthid-hf-detector-e2e] for more on training your own
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detectors, and the [SynthID Text
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how this technology works.
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## Applying a watermark
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button. An example is provided to help get you started, but the cells are
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fully editable.
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Gemma will then generate watermarked and non-watermarked
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non-empty prompt you provided.
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[cloud-parameter-values]: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/adjust-parameter-values
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## Human recognition of watermarked text
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The primary goal of SynthID Text is to apply a watermark to generated text
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that generated text that carries a watermark should be imperceptible to
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you, the reader, but easily perceived by a watermark detector.
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see the true values.
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The [research paper][synthid-nature] has an in-depth study examining human
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perception of
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[synthid-nature]: https://www.nature.com/articles/s41586-024-08025-4
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'''
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@@ -277,7 +277,7 @@ with gr.Blocks() as demo:
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[end-to-end example][synthid-hf-detector-e2e] of how to train one of these
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detectors.
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You can see how your guesses compared to the
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above, the responses are displayed in checkboxes. If the box is checked,
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then the text carries a watermark. Your correct guesses are annotated with
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the "Correct" prefix.
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1. How to use SynthID Text to apply a watermark to text generated by your
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model; and
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+
1. How to identify that text using a ready-made detector.
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+
Note that this detector is trained specifically for this demonstration. You
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should maintain a specific watermarking configuration for every model you
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use and protect that configuration as you would any other secret. See the
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[end-to-end guide][synthid-hf-detector-e2e] for more on training your own
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+
detectors, and the [SynthID Text documentation][raitk-synthid] for more on
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how this technology works.
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## Applying a watermark
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button. An example is provided to help get you started, but the cells are
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fully editable.
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+
Gemma will then generate watermarked and non-watermarked responses for each
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non-empty prompt you provided.
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[cloud-parameter-values]: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/adjust-parameter-values
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## Human recognition of watermarked text
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The primary goal of SynthID Text is to apply a watermark to generated text
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without affecting generation quality. Another way to think about this is
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that generated text that carries a watermark should be imperceptible to
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you, the reader, but easily perceived by a watermark detector.
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see the true values.
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The [research paper][synthid-nature] has an in-depth study examining human
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+
perception of watermarked versus non-watermarked text.
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[synthid-nature]: https://www.nature.com/articles/s41586-024-08025-4
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'''
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[end-to-end example][synthid-hf-detector-e2e] of how to train one of these
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detectors.
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+
You can see how your guesses compared to the actual results below. As
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above, the responses are displayed in checkboxes. If the box is checked,
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then the text carries a watermark. Your correct guesses are annotated with
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| 283 |
the "Correct" prefix.
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