| |
| """ |
| Create a balanced BFCL-style benchmark from ALL cycle data. |
| Different from all previous work: this is a PROPER EVAL BENCHMARK (not training data). |
| Balanced across simple/parallel/irrelevance categories, in exact bench-v2 format. |
| """ |
| import json, glob, random, collections |
| from pathlib import Path |
| from huggingface_hub import HfApi |
|
|
| random.seed(42) |
| TARGET_PER_CAT = 50 |
|
|
| |
| all_examples = [] |
| for d in ["cycle-100-output","cycle-100-v2","cycle-100-v3","cycle-100-v4", |
| "cycle-100-v5","cycle-100-v6","cycle-100-v7","cycle-100-v8", |
| "cycle-100-v9","cycle-100-v10","gap-filled","benchmark-targeted", |
| "augmented-output","safety-quality-fixes"]: |
| for f in sorted(glob.glob(f"{d}/*.jsonl")): |
| if "all-" in f or "push-" in f: continue |
| with open(f, encoding="utf-8") as fh: |
| try: |
| for line in fh: |
| line = line.strip() |
| if not line: continue |
| all_examples.append(json.loads(line)) |
| except: |
| pass |
|
|
| print(f"Total loaded: {len(all_examples)}") |
|
|
| |
| by_cat = {"simple": [], "parallel": [], "irrelevance_tools": [], "irrelevance_no_tools": []} |
|
|
| for ex in all_examples: |
| msgs = ex.get("messages", []) |
| tools = ex.get("tools", []) |
| |
| for i, m in enumerate(msgs): |
| if m.get("role") == "assistant": |
| gold_calls = [] |
| for tc_raw in (m.get("tool_calls") or []): |
| if isinstance(tc_raw, str): |
| try: tc_raw = json.loads(tc_raw) |
| except: continue |
| if not isinstance(tc_raw, dict): continue |
| fn = tc_raw.get("function", {}) |
| if isinstance(fn, str): |
| try: fn = json.loads(fn) |
| except: fn = {} |
| if not isinstance(fn, dict): continue |
| name = fn.get("name", "") |
| args = fn.get("arguments", "{}") |
| if isinstance(args, str): |
| try: args = json.loads(args) |
| except: args = {} |
| gold_calls.append({"name": name, "arguments": args}) |
| |
| nc = len(gold_calls) |
| if nc == 0 and tools: cat = "irrelevance_tools" |
| elif nc == 0 and not tools: cat = "irrelevance_no_tools" |
| elif nc == 1: cat = "simple" |
| else: cat = "parallel" |
| |
| row = { |
| "messages": msgs[:i+1], |
| "tools": tools, |
| "gold_calls": gold_calls, |
| "category": cat, |
| "held_out_tool": False, |
| "multi_turn": any(m2.get("role") == "tool" for m2 in msgs[:i]), |
| "in_v1": False, |
| } |
| by_cat[cat].append(row) |
| break |
|
|
| for cat, rows in by_cat.items(): |
| print(f" {cat:25s}: {len(rows)} available") |
|
|
| |
| bench_rows = [] |
| for cat in ["simple", "parallel", "irrelevance_tools", "irrelevance_no_tools"]: |
| pool = by_cat.get(cat, []) |
| random.shuffle(pool) |
| selected = pool[:min(TARGET_PER_CAT, len(pool))] |
| bench_rows.extend(selected) |
| print(f" Sampled {cat}: {len(selected)}") |
|
|
| random.shuffle(bench_rows) |
| print(f"\nTotal benchmark rows: {len(bench_rows)}") |
|
|
| |
| OUT = Path("sakthai-cycle-bench") |
| OUT.mkdir(exist_ok=True) |
|
|
| bench_path = OUT / "data/test.jsonl" |
| with open(bench_path, "w", encoding="utf-8") as f: |
| for row in bench_rows: |
| f.write(json.dumps(row, ensure_ascii=False) + "\n") |
|
|
| |
| summary_path = OUT / "summary.json" |
| cats = collections.Counter(r["category"] for r in bench_rows) |
| with open(summary_path, "w") as f: |
| json.dump({"total": len(bench_rows), "categories": dict(cats), |
| "multi_turn": sum(1 for r in bench_rows if r["multi_turn"])}, f, indent=2) |
|
|
| |
| api = HfApi() |
| repo = "Nanthasit/sakthai-cycle-bench" |
| api.create_repo(repo_id=repo, repo_type="dataset", exist_ok=True) |
|
|
| api.upload_file(path_or_fileobj=str(bench_path), path_in_repo="data/test.jsonl", |
| repo_id=repo, repo_type="dataset") |
| api.upload_file(path_or_fileobj=str(summary_path), path_in_repo="summary.json", |
| repo_id=repo, repo_type="dataset") |
|
|
| readme = f"""--- |
| license: apache-2.0 |
| tags: [sakthai, benchmark, tool-calling, function-calling] |
| --- |
| # SakThai Cycle Benchmark |
| |
| **{len(bench_rows)} balanced BFCL-style benchmark rows** derived from 10 cycle rounds. |
| |
| | Category | Count | |
| |---|---| |
| | simple | {cats.get('simple',0)} | |
| | parallel | {cats.get('parallel',0)} | |
| | irrelevance_tools | {cats.get('irrelevance_tools',0)} | |
| | irrelevance_no_tools | {cats.get('irrelevance_no_tools',0)} | |
| | **Total** | **{len(bench_rows)}** | |
| |
| Multi-turn: {sum(1 for r in bench_rows if r['multi_turn'])} rows |
| |
| Use with eval_bench.py: |
| ``` |
| SAK_MODELS=Nanthasit/sakthai-context-1.5b-merged \\ |
| SAK_BENCH=Nanthasit/sakthai-cycle-bench \\ |
| uv run python eval_bench.py |
| ``` |
| """ |
| api.upload_file(path_or_fileobj=readme.encode(), path_in_repo="README.md", |
| repo_id=repo, repo_type="dataset") |
|
|
| print(f"\nPushed: https://huggingface.co/datasets/{repo}") |
| print(f"Run: SAK_MODELS=model_id SAK_BENCH={repo} uv run python eval_bench.py") |
|
|
