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  1. app.py +7 -7
app.py CHANGED
@@ -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 indetify that text using a ready-made detector.
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- Note that this detector is trained specifically fore 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 documentaiton][raitk-synthid] for more on
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  how this technology works.
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  ## Applying a watermark
@@ -218,7 +218,7 @@ with gr.Blocks() as demo:
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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 repsonses 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
@@ -244,7 +244,7 @@ with gr.Blocks() as demo:
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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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- wihtout 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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@@ -253,7 +253,7 @@ with gr.Blocks() as demo:
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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 watermared versus non-watermarked text.
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  [synthid-nature]: https://www.nature.com/articles/s41586-024-08025-4
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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 actaul 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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  1. How to use SynthID Text to apply a watermark to text generated by your
146
  model; and
147
+ 1. How to identify that text using a ready-made detector.
148
 
149
+ Note that this detector is trained specifically for this demonstration. You
150
  should maintain a specific watermarking configuration for every model you
151
  use and protect that configuration as you would any other secret. See the
152
  [end-to-end guide][synthid-hf-detector-e2e] for more on training your own
153
+ detectors, and the [SynthID Text documentation][raitk-synthid] for more on
154
  how this technology works.
155
 
156
  ## Applying a watermark
 
218
  button. An example is provided to help get you started, but the cells are
219
  fully editable.
220
 
221
+ Gemma will then generate watermarked and non-watermarked responses for each
222
  non-empty prompt you provided.
223
 
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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
248
  that generated text that carries a watermark should be imperceptible to
249
  you, the reader, but easily perceived by a watermark detector.
250
 
 
253
  see the true values.
254
 
255
  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.
279
 
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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,
282
  then the text carries a watermark. Your correct guesses are annotated with
283
  the "Correct" prefix.