File size: 5,287 Bytes
b778663 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | #!/usr/bin/env python3
"""
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 # 50 per category = 200 total
# Load all cycle data
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)}")
# Classify and categorize
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")
# Sample balanced set
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)}")
# Write output
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
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)
# Push to HF Hub
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")
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