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ai-text-detection
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rishanthrajendhran commited on
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Card: list the false-positive rates this model's thresholds cover

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@@ -54,7 +54,7 @@ Or in one call: `records = il.run(texts, det)`. `classify` and `extract` use Gem
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  ## Thresholds
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- `thresholds.json` holds this model's cuts at 0.1%, 0.5%, 1%, 2% and 5% false-positive rates, fitted on the 80,000 human
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  documents of WildOutlines' `calibration` split: one global cut per rate, plus per-format and per-topic cuts. The package applies them. A cut
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  fitted for one model does not transfer to another model's scores. For documents unlike English web text, fit cuts on
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  human documents from your own domain with `idealens calibrate`.
 
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  ## Thresholds
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+ `thresholds.json` holds this model's cuts at 0.1%, 0.5%, 1%, 2%, 5%, 10% and 20% false-positive rates, fitted on the 80,000 human
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  documents of WildOutlines' `calibration` split: one global cut per rate, plus per-format and per-topic cuts. The package applies them. A cut
59
  fitted for one model does not transfer to another model's scores. For documents unlike English web text, fit cuts on
60
  human documents from your own domain with `idealens calibrate`.