Upload scripts/create-balanced-benchmark.py with huggingface_hub

scripts/create-balanced-benchmark.py

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+#!/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")