Describe short text without reference to an application

ARCHITECTURE.md

@@ -204,7 +204,7 @@ flowchart LR
   is (α = 1), where the largest languages fill every batch, and uniform (α = 0), where a
   100-passage language is seen until memorised. XLM-R and mT5 sample their languages the same way.
 - **How long.** A quarter of passages are cut to a 20 to 80 character snippet, on word boundaries
-  where the script has them: short text is the hard case, and what a document pipeline meets in
+  where the script has them: short text is the hard case, and what a document holds in its
   headers, captions and table cells.
 - **Why macro F1 chooses.** It weighs every language equally, the score a thousand-language model
   should be judged on. Top-1 accuracy, which the largest languages dominate, is reported beside it.

millid.py

@@ -657,7 +657,7 @@ class Sampler:
     A language is drawn with probability proportional to n^alpha, n being its passage count:
     alpha 1 is the data as it is, alpha 0 every language equally; 0.3 in between. Then a
     passage of it is drawn, and sometimes cut short, because a short snippet is where
-    language identification is hardest and what a document pipeline sees in table cells.
+    language identification is hardest and what a page holds in headers and table cells.
     """
 
     def __init__(self, texts: list[str], label_ids: np.ndarray, alpha: float, short_p: float, seed: int):