Building Supervised Fine-Tuning Data from NVIDIA Open-SWE-Traces: Trajectory Parsing, Patch Analysis, Token Budgets, and Tool-Use Metrics
banner(“STEP 3 — Building the analysis DataFrame”) def process_example(ex): traj = normalize_trajectory(ex.get(“trajectory”)) rc = role_counts(traj) nf, add, dele, _files, _exts = parse_patch(ex.get(“model_patch”)) meta = normalize_metadata(ex.get(“metadata”)) full_text = “\n”.join(message_text(m) for m in traj) return { “instance_id”: ex.get(“instance_id”), “repo”: ex.get(“repo”), “language”: (ex.get(“language”) or “unknown”).lower(), “license”: ex.get(“license”), “resolved”: ex.get(“resolved”), “agent”: ex.get(“_agent”), “model”: ex.get(“_model”), “n_messages”: len(traj), “n_system”: rc.get(“system”, 0),…
