Remove cap documentation

README.md

@@ -63,7 +63,7 @@ through the Hugging Face revision or Git tag:
 |---|---|---|
 | v1 | `v1.0.0` | Original published datasets, including the original source text and AST graphs. |
 | v2.0.0 | `v2.0.0` | Reconstructed PROMISE, Defactors, BugsInPy, and Big-Vul from normalized source. Comments/docstrings and non-literal blank lines are removed; Python docstring-only suites are replaced with synthetic `pass` so every v1 bag/function remains. Labels are frozen and AST graphs are regenerated. Devign is not included. |
-| v2.0.1 | `v2.0.1` | Reconstructed the same four datasets without synthetic code. Python docstring-only functions/classes are deleted; bags that become empty, or positive bags with no remaining positive function, are deleted. Remaining bag/function labels are frozen and AST graphs/caps are regenerated. Devign is not included. |
+| v2.0.1 | `v2.0.1` | Reconstructed the same four datasets without synthetic code. Python docstring-only functions/classes are deleted; bags that become empty, or positive bags with no remaining positive function, are deleted. Remaining bag/function labels are frozen and AST graphs are regenerated. Devign is not included. |
 
 For example:
 
@@ -201,42 +201,8 @@ The following rules define the published MIL samples.
   function has its frozen positive `func_label`; all retained labels and
   function order are preserved.
 
-## Optional Training-time function caps
-
-The published JSONL files contain the full function lists. HAM-Net training,
-however, uses an optional deterministic cap of at most 16 functions per bag to
-control GPU memory and runtime. The cap does not modify the released JSONL files.
-It is applied only by the training data loader.
-
-For each bag, cap generation follows this procedure:
-
-1. Discard only candidate functions whose source is empty or whose AST has no nodes;
-   the remaining functions keep their original order and indices.
-2. Score each valid function as
-   `log(1 + node_count) + 0.3 * log(1 + edge_count)`.
-3. Rank candidates by score, then by node count, source start line, and original
-   index as deterministic tie-breakers. Select the top 12 candidates first.
-4. If more functions are available, select additional candidates by evenly covering
-   the remaining ranked list until the bag contains at most 16 functions. Bags with
-   16 or fewer valid functions retain all valid functions.
-5. Write the selected zero-based function indices and the policy metadata to the cap
-   file under the stable bag identifier. Cap files are generated separately for each
-   dataset revision and must be used only with the matching JSONL files.
-
-At training time, the data loader reads the cap entry for each bag and replaces the
-full function list with the recorded indices before constructing the model input.
-The same label-agnostic procedure is therefore shared across datasets and models.
-
 ## Files
 
 - Both versions: `promise_java.jsonl`, `defactors_python.jsonl`,
   `bugsinpy_python.jsonl`, and `bigvul_c.jsonl`.
 - v1 only: `devign_c.jsonl`.
-- Each revision contains a `caps/` directory. Its cap files are generated from the
-  JSONL files in that same revision using the deterministic `ast_topk_v1` policy.
-  v1 has caps for all five datasets; v2.0.0 and v2.0.1 have caps for their four
-  MIL datasets only.
-- `caps/caps_summary.json` records the input bag count, cap-entry count, selected
-  function count, and any bags omitted because they contain no valid function with
-  both non-empty code and a non-empty AST. A cap file must only be used with the
-  JSONL files from the same revision.