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2 changes: 1 addition & 1 deletion docs/mkdocs/how-to/analyze-evals.md
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@@ -1,4 +1,4 @@
# Test the cross-brain effect from evals
# Interpret the cross-brain effect from evals

You have run [`encoding analyze`](../reference/encoding.md) a few times and have a
handful of eval files. This guide turns them into an answer to the question
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6 changes: 3 additions & 3 deletions docs/mkdocs/tutorials/walkthrough.md
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Expand Up @@ -59,7 +59,7 @@ its inputs from the directory layout; you never pass individual file paths.
Whisper speech-recognition model.

```bash
hypline transcribe data/ --audio-ext .wav --model tiny
hypline transcribe data/ --audio-ext .wav --model small
```

```text
Expand All @@ -79,7 +79,7 @@ and voice-activity-detection messages.)
learning the workflow rather than analyzing the transcripts. For a real analysis,
omit `--model` to use the default `large-v2`, which is far more accurate but a
multi-GB download and much slower on CPU (pass `--device cuda` if you have a
GPU).
GPU). -- to be updated

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I will take care of this.


Only four files are transcribed, not one per run. These are the
reading-condition subset from [step 1](#1-get-the-example-dataset): each run's
Expand Down Expand Up @@ -446,7 +446,7 @@ choices side by side.
demonstrate the *effect*, since two runs of `--model tiny` transcripts are far
too little data for the cross-brain scores to mean anything. On a full study
they carry the shared-representation signal; here they only confirm the
command runs end to end.
command runs end to end. -- to be updated

!!! success "Check"

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