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Running
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L40S
UX Optimizations (#3)
Browse files- Update the Generate button value and interactivity to clarify when the system is generating (7894bef49b93a70c3210ef5bbad5db2f1e516651)
- Provide only a single hands-on example, to expedite generation (8834ce585ba6b459b6d72c9c0b9850d24daaa16b)
- Fix typos (9706b48ae93bf82579e585fa19bc3c7e3195c335)
- Update the Space title, so that browser tabs use the standard capitalization (ee678658c03554a8e5ffa381d11c2f0ae4ba9b25)
Co-authored-by: Neal DeBuhr <[email protected]>
README.md
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---
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title:
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emoji: π
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colorFrom: pink
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colorTo: gray
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---
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title: SynthID Text
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emoji: π
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colorFrom: pink
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colorTo: gray
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app.py
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@@ -23,8 +23,8 @@ _DETECTOR_IDENTIFIER = 'google/synthid-spaces-demo-detector'
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_PROMPTS: Tuple[str] = (
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'Write an essay about my pets, a cat named Mika and a dog named Cleo.',
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-
'
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'
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)
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_TORCH_DEVICE = (
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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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Lets use [Gemma 2B IT][gemma] to help you understand how watermarking works.
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Using the text boxes below enter up to three prompts then click the generate
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button.
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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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-
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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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[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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}
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generate_btn.click(
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generate,
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inputs=prompt_inputs,
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outputs=[generate_btn, generations_col, generations_grp, reveal_btn]
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detections_col: gr.Column(visible=False),
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revealed_grp: gr.CheckboxGroup(visible=False),
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reset_btn: gr.Button(visible=False),
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generate_btn: gr.Button(visible=True),
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}
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reset_btn.click(
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_PROMPTS: Tuple[str] = (
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'Write an essay about my pets, a cat named Mika and a dog named Cleo.',
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'',
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'',
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)
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_TORCH_DEVICE = (
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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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Lets use [Gemma 2B IT][gemma] to help you understand how watermarking works.
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Using the text boxes below enter up to three prompts then click the generate
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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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|
|
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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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|
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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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the "Correct" prefix.
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}
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generate_btn.click(
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lambda: gr.update(value='Generating...', interactive=False), None, generate_btn
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).then(
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generate,
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inputs=prompt_inputs,
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outputs=[generate_btn, generations_col, generations_grp, reveal_btn]
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detections_col: gr.Column(visible=False),
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revealed_grp: gr.CheckboxGroup(visible=False),
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reset_btn: gr.Button(visible=False),
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generate_btn: gr.Button(value='Generate', interactive=True, visible=True),
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}
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reset_btn.click(
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