springofwindslabs/function-calling-ja-trial

🛑 Most Function-Calling Failures Are Not Schema Failures. They Are State Failures.

The model believes the world is still valid — when reality has already changed. エージェントの事故は「ツールの失敗」ではなく「古い状態を信じたまま正常終了する」ことで起きます。本データセットは多輪ツール呼び出し・エラー復帰・検証ステップを日本語で学習させます。

🔎 Explore the Evidence Behind This Dataset

Want to understand the dataset's design, validation, and limitations?

Explore the evidence, methodology, and validation results →

Review the supporting evidence and intended use before deciding whether this dataset fits your workflow.

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Japanese Function Calling Dataset — Official Open‑Source Evaluation Package (50 Rows Subset) by springofwindslabs

Full production volumes (1,000-row standard and 2,300+ row non-overlapping extended lots), commercial licensing, and the measured validation methodology are documented in the project documentation.

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🔎 You Have the Trial — Here Is What It Proves, and What It Doesn't

This free trial demonstrates the failure modes (the problem). It is fully usable under Apache-2.0 and validated by the same pipeline as the commercial lots.

What the full dataset adds: production-volume native-register Japanese (keigo / teineigo / casual) utterances mapped to industry-standard English tool schemas.

	Free trial (this repo)	Full dataset
Purpose	verify schema, quality & failure modes	systematic training / evaluation
Volume	sample	1,000 rows (1K) / 3,300 rows (2K combined) per track
Validation	same pipeline	28,922 rows measured, 100% schema-pass — published
Overlap	—	lots are hash / ID / 3-gram audited non-overlapping

→ Verify the measured evidence before you consider anything (independent evidence hub: full EVALS, row-level sample anatomy, and a reproduction script you can run against this trial — no signup, no tracking walls)

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📁 Validated Package Architecture

Every commercial delivery natively bundles the complete execution context stack. These assets are designed for enterprise procurement workflows while remaining fully accessible to research teams evaluating alignment quality:

├── dataset.jsonl   (Strict schema-validated native-Japanese tool-use rows)
├── README.md       (Operational constraints, schema docs & license terms)
├── stats.json      (Row counts, turn distribution & QC metrics)
└── SHA256SUMS.txt  (Cryptographic integrity manifest for audit trails)

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🧠 Data Philosophy: Why Enterprise LLM Teams Require Production-Grade Trajectories

When evaluating dataset scaling for fine-tuning open-weights models (Llama-3, Qwen-2.5, GLM-5.3, etc.), global AI engineering teams frequently hit major friction with high-volume public synthetic datasets (e.g., TOUCAN with 1.5M tracks).

While public synthetic datasets offer massive footprints, they naturally suffer from Success-Bias and Model Lock-In. Generated inside perfect closed environments, those models collapse instantly in production when encountering real-world network friction, state stale latency, or rigid permission controls.

The Springofwindslabs Baseline is Built for Resilient Production Deployment.
Instead of raw unverified entropy, our factory processes high-density, 100% schema-validated unique trajectories pre-loaded with hyper-realistic autonomous error-recovery tracks. Every single row forces the model to master real-world exceptions:

• 403 Insufficient Scope (Autonomous privilege discovery and token refreshment)
• 429 Rate Limit Backoff (Dynamic backoff algorithms, jitter injections, and token bucket tracking)
• Resource Lock Conflicts (Deterministic state checks and multi-step corrective retries)

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🧪 Open-Source Evaluation Package & Contents

This repository hosts the official 50‑row validation sample designed to run structural verification within your training framework (Axolotl, Unsloth, etc.):

• Format: Strict JSONL (1 row = 1 sample, UTF‑8)
• Schema Stability: 100% schema‑validated — no phantom tool‑calls, no argument‑type mismatches, no malformed trajectories.
• Robust Error Recovery: Enforces multi-turn execution resilience against high-context Japanese tool routing, honorific variations, and entity double-meanings without structural breakdown.

Schema (per row): Single-shot design — a Japanese user request followed by a strict-JSON tool call (optimized for high-precision JA tool-routing alignment).

{
  "id": "unique sample identifier",
  "tools": "JSON string: array of available tool definitions (name, description, parameters)",
  "conversations": [
    {"from": "user",      "value": "日本語のタスク依頼"},
    {"from": "assistant", "value": "tool call as strict JSON"}
  ]
}

Load with 🤗 Datasets:

from datasets import load_dataset
ds = load_dataset("springofwindslabs/function-calling-ja-trial", split="train")
print(ds[0]["conversations"])

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🚀 Upgrade to Production Scale & Commercial Tiers

If these 50 rows validate your alignment script successfully, you can immediately access our mutually exclusive full production datasets with multi‑jurisdictional commercial licenses via the project documentation below:

🇯🇵 Japanese Function Calling Dataset (High-Context Domain)

• Standard Volume (1,000 High-Fidelity Rows): Available for immediate production deployment.
• Extended Volume (2,300+ Non-Overlapping Rows): Available now. Includes Full 1K Tier + 2K Tier (Total 3,300+ Unique Rows Full Package).
• ➔ Commercial licensing & documentation: https://springofwindslabs.github.io/springofwinds-datasets/?utm_source=huggingface&utm_medium=readme&utm_campaign=function-calling-ja-trial

⚖ Perpetual organizational usage rights granted immediately upon checkout. Standalone raw redistribution is strictly prohibited. For corporate NDAs, custom enterprise billing, or direct bank wire transfers, contact our data factory team directly at: springofwindslabs@gmail.com

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🧩 Complete springofwindslabs Dataset Family

Enterprise alignment pipelines rarely need just one capability. Teams that procure two or more branches report faster convergence across tool-routing, multilingual execution, and compliance reasoning:

Capability Branch	Free 50-Row Evaluation (Hugging Face)	Commercial documentation
🌐 MCP Agent Trajectories	mcp-agent-trajectory-benchmark	Details ➔
🛠️ Function Calling (EN)	function-calling-en-trial	Details ➔
🇯🇵 Function Calling (JA)	function-calling-ja-trial	Details ➔
⚖️ Regulatory Compliance CoT	regulatory-compliance-cot-trial	Details ➔

💼 Multi-branch procurement: for combined multi-dataset licensing or volume terms across two or more branches, contact springofwindslabs@gmail.com — consolidated invoicing available.

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🗺 Release Roadmap — Lot 2 & New Reasoning Domains

Our data factory ships on a continuous production cadence. Currently in the pipeline:

• 🏭 Lot 2 — In Active Production: a fresh wave of fully non-overlapping production rows (hash, ID and 3-gram Jaccard audited against Lot 1) across the dataset family.
• ⚖️ New Regulatory CoT Domains — Planned: additional Chain-of-Thought reasoning branches expanding jurisdictional and compliance-domain coverage.
• 🔔 Never miss a release: click Follow on springofwindslabs here on Hugging Face, and see the project documentation to receive release alerts the moment new lots go live.

Existing Lot 1 license holders can expand coverage incrementally — every new lot is guaranteed non-overlapping with prior purchases, so no budget is ever spent twice on the same row.

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🏢 Enterprise Institutional Procurement & Tier Transparency

Our production assets are curated exclusively for institutional LLM alignment pipelines and commercial system integration. To align with quarterly technology budgets, we offer tiered procurement caps based on transaction row density (Standard 1,000-row volumes vs. 2,300+ row non-overlapping lot packages — mutually exclusive by hash, ID and 3-gram Jaccard audit). Commercial invoicing, corporate wire clearance, and SLA terms are fully detailed upon gateway entry.

Enterprise teams evaluating Japanese function-calling alignment can advance directly to production deployment here:

🚀 Advance straight to production-grade LLM alignment today: ➔ Commercial licensing and package details: Commercial licensing and package details

📖 Citation

If this dataset supports your research or product evaluation, please cite:

@misc{springofwindslabs2026fcja,
  title  = {Japanese Function Calling Dataset for LLM Alignment: High-Context Native-Japanese Tool Routing},
  author = {springofwindslabs},
  year   = {2026},
  url    = {https://huggingface.co/datasets/springofwindslabs/function-calling-ja-trial}
}

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Enterprise Procurement

For licensing, corporate invoicing, procurement review, or custom commercial agreements: ✉️ springofwindslabs@gmail.com

🩺 Building with AI coding agents? (free tool from this lab)

Datasets like this one train agents to handle tool calls and recovery — but the repository you point an agent at matters just as much. Missing AGENTS.md, undiscoverable build/test commands, or absent constraints make even strong models fail.

We built repo-doctor — a zero-dependency CLI that scores a repository's AI readiness (plus docs, secrets exposure, tests/CI, dependencies, hygiene, git history) in one command, fully offline:

repo-doctor .

Free & MIT-licensed → github.com/springofwindslabs/repo-doctor