Intro references tabs by name, Score tab notes Pythia-1.4B, remove all bold

app.py

@@ -124,8 +124,8 @@ def draw(dataset, which):
     feats = {k: float(r[k]) for k in FEAT_ORDER}
     X = np.nan_to_num(np.array([[feats[k] for k in b["feats"]]]), posinf=1e10, neginf=-1e10)
     risk = float(b["clf"].predict_proba(b["scaler"].transform(X))[0, 1])
-    verdict += (f"\n\n### Classifier risk score: **{risk:.3f}**\n"
-                f"Computed by the memorization classifier from base-model features measured *before* "
+    verdict += (f"\n\n### Classifier risk score: {risk:.3f}\n"
+                f"Computed by the memorization classifier from base-model features measured before "
                 f"fine-tuning. The plots below show where "
                 f"this example's feature values fall relative to memorized and not-memorized examples.")
     return r.prefix, gen, verdict, dist_figure(feats, dataset)
@@ -146,18 +146,19 @@ def predict(text, clf_name):
     X = np.nan_to_num(np.array([[feats[k] for k in b["feats"]]]), posinf=1e10, neginf=-1e10)
     risk = float(b["clf"].predict_proba(b["scaler"].transform(X))[0, 1])
     flag = "high" if risk >= 0.5 else "low"
-    md = (f"### Predicted memorization risk: **{risk:.3f}** ({flag})\n"
-          f"Forecast from base-model features using the classifier trained on **{clf_name}**. "
+    md = (f"### Predicted memorization risk: {risk:.3f} ({flag})\n"
+          f"Forecast from base-model features using the classifier trained on {clf_name}. "
           f"There is no ground truth for arbitrary text, so treat it as a relative risk score. "
           f"The plots below show where your text's features fall relative to memorized and not-memorized "
-          f"examples in **{clf_name}**.")
+          f"examples in {clf_name}.")
     return md, dist_figure(feats, clf_name)
 
 
 INTRO = """# Predicting Memorization Before Fine-Tuning
-A classifier trained only on **pre-fine-tuning** base-model features predicts which sequences a
-fine-tuned model will memorize. **Explore** verifies this against ground truth on held-out examples from
-a fresh fine-tuning run; **Score your own text** applies the same classifier to a passage you provide.
+A classifier trained only on pre-fine-tuning base-model features predicts which sequences a
+fine-tuned model will memorize. The "Explore held-out examples" tab verifies this against ground truth on
+held-out examples from a fresh fine-tuning run; the "Score your own text" tab applies the same classifier
+to a passage you provide.
 """
 
 with gr.Blocks(title="Predicting Memorization Before Fine-Tuning") as demo:
@@ -177,9 +178,9 @@ with gr.Blocks(title="Predicting Memorization Before Fine-Tuning") as demo:
         feat = gr.Plot(label="Where this example's features fall (memorized vs not-memorized)")
         go.click(draw, [d_in, f_in], [pref, gen, verdict, feat])
     with gr.Tab("Score your own text"):
-        gr.Markdown("Paste a passage (at least ~100 tokens). We compute its base-model features and "
-                    "return the classifier's **predicted** memorization risk, and show where your text's "
-                    "features fall relative to memorized and not-memorized examples.")
+        gr.Markdown("Paste a passage (at least ~100 tokens). We compute its base-model features using "
+                    "Pythia-1.4B and return the classifier's predicted memorization risk, and show where "
+                    "your text's features fall relative to memorized and not-memorized examples.")
         txt = gr.Textbox(label="Your text", lines=6)
         clf_in = gr.Dropdown(sorted(CLFS.keys()), value=sorted(CLFS.keys())[0],
                              label="Compare against dataset")