README.md

---
language:
- en
license: other
license_name: mixed-upstream-licenses
task_categories:
- text-classification
- zero-shot-classification
task_ids:
- intent-classification
- multi-class-classification
tags:
- fastbrain
- system-1
- non-autoregressive
- function-calling
- tool-use
- routing
- synthetic
- composite
- aggregation
- decontaminated
source_datasets:
- glaiveai/glaive-function-calling-v2
- NousResearch/hermes-function-calling-v1
- nvidia/Nemotron-SFT-Agentic-v2
- MaziyarPanahi/AgentToolDecisions-180K
- AnishJoshi/nl2bash-custom
- burak29/git-natural-language-commands
- AmazonScience/massive
- google-research-datasets/schema_guided_dstc8
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: train
    path: train.parquet
  - split: test
    path: test.parquet
---

# ⚡ FastBrain-Instruct: Aggregated Fine-Tuning Corpus for System 1 Decision Models

[![Data Origin](https://img.shields.io/badge/Origin-Aggregated%20%26%20Reformatted-amber.svg)](#️-provenance-not-an-original-dataset)
[![Samples](https://img.shields.io/badge/Samples-108%2C278-purple)](#-statistics)
[![Decontaminated](https://img.shields.io/badge/Benchmark-Decontaminated-blue)](#-benchmark-exclusion--decontamination)

> [!WARNING]
> ### ⚠️ Provenance: not an original dataset
>
> **FastBrain-Instruct is NOT written from scratch.** It aggregates existing open datasets, reformats them into one choice/routing schema, removes finance content, deduplicates, and decontaminates against public decision benchmarks. All query text and labels come from the upstream sources below (except 25 maintainer-written DevOps templates).
>
> | Upstream source | Author | License | Rows (train / test) |
> | :--- | :--- | :--- | ---: |
> | [Schema-Guided Dialogue (SGD)](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue) | Google Research (Rastogi et al., 2020) | CC BY-SA 4.0 | 34,455 / 3,889 |
> | [Nemotron-SFT-Agentic-v2](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2), via the `tool_selection` subset of [AgentToolDecisions-180K](https://huggingface.co/datasets/MaziyarPanahi/AgentToolDecisions-180K) | NVIDIA; transformed by Maziyar Panahi | CC BY 4.0 | 32,500 / 3,548 |
> | [MASSIVE](https://huggingface.co/datasets/AmazonScience/massive) (en, via `mteb/amazon_massive_intent`) | Amazon Science (FitzGerald et al., 2022) | CC BY 4.0 | 14,264 / 1,531 |
> | [Glaive Function Calling v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | Glaive AI | Apache 2.0 | 11,575 / 1,305 |
> | [NL2Bash](https://huggingface.co/datasets/AnishJoshi/nl2bash-custom) | Lin et al., 2018 (HF mirror by AnishJoshi) | see source | 2,809 / 345 |
> | [Hermes Function Calling v1](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1) | NousResearch | Apache 2.0 | 1,015 / 132 |
> | [Git natural-language commands](https://huggingface.co/datasets/burak29/git-natural-language-commands) | burak29 | see source | 810 / 75 |
> | DevOps / terminal templates | maintainer | Apache 2.0 | 22 / 3 |
>
> Glaive v2 and Nemotron-SFT-Agentic-v2 are themselves synthetically generated. Users must comply with each upstream license; SGD is CC BY-SA 4.0 (share-alike).

---

## 📌 What it is

Each row is a single decision: given a `query` (a user request, or for agent rows the recent conversation) and a list of candidate `choices` (name + description), pick the correct `target`. The format targets non-autoregressive "System 1" decision heads (e.g., the `JointSchemaHead` design used by Cloudflare Clef / Clef-Flash) that score candidates in one forward pass.

Two domains, balanced 50/50:
- **`code_mechanical`** — tool / function selection (Glaive, Hermes), multi-tool agent next-tool selection (Nemotron), terminal utility selection (NL2Bash), git subcommand selection, DevOps templates.
- **`daily_assistant`** — intent selection for voice-assistant and service dialogues (MASSIVE, SGD).

No model trained on this dataset is released, and no benchmark results are claimed.

## 🚫 Benchmark exclusion & decontamination

This dataset is intended for training models that will be compared on the benchmarks used in the Cloudflare Clef / Jev Decision Index table. Therefore:

1. **No benchmark is used as a source.** Excluded entirely: BFCL, ToolRet and all 35 of its source datasets (incl. APIGen/xLAM, ToolACE, ToolBench, Gorilla, MetaTool, API-Bank, …), API-Bank, When2Call (and derived data such as the When2Call part of AgentToolDecisions), BANKING77, CLINC150, BRIGHT, Amazon ESCI, PhishNChips.
2. **Text-level decontamination.** Every query (for agent rows: every user message) was checked against the texts of BFCL v3, ToolRet-Queries (35 subsets), When2Call (all files), API-Bank (all files), BANKING77 and CLINC150 (all configs). A row is removed on a normalized exact match **or** any shared 10-word n-gram. 2,674 rows were removed this way (e.g., Glaive: 899 ToolRet, 470 BFCL, 333 When2Call matches; SGD: 420 BFCL matches; MASSIVE: 212 CLINC150 matches).

Not checked at text level: Home appliances (no public copy found), BRIGHT, Amazon ESCI, PhishNChips (different domains from all sources used).

## 🔧 Processing

1. **Extraction**
   - Glaive v2: all 112,960 rows scanned; the user turn right before the first `<functioncall>` is the query; candidates are the tools listed in that sample's system prompt.
   - Hermes v1: first human turn; candidates are that sample's tools.
   - Nemotron agentic: conversation rendered as `role: content` lines (last 1,500 chars); candidates are all tools available to the agent (5–30).
   - NL2Bash: only single, non-compound commands (no pipes, `;`, `&&`, substitutions, loops); target is the utility name; `find` capped at 1,500 rows.
   - SGD: user turns where a service's active intent changes; candidates are the intents of that service plus intents from other services, with SGD's own intent descriptions.
   - MASSIVE: all splits; description generated from the label name.
2. **Candidates**: at least 5 per row; when a sample has fewer own candidates, random distractors from the same source are added, excluding names that heavily overlap with the target (token Jaccard ≥ 0.5).
3. **Finance removal**: MASSIVE `qa_currency`, `qa_stock`; SGD `Banks_*` and `Payment_*` services.
4. **Deduplication**: normalized exact `query` duplicates removed; queries with conflicting labels dropped (1,410).
5. **Balancing**: the larger domain is downsampled (largest source first) to match the smaller one.
6. **Split**: random 90/10 over unique queries; no `query` appears in both splits.

## 📊 Statistics

| | Train | Test | Total |
| :--- | ---: | ---: | ---: |
| **Rows** | **97,450** | **10,828** | **108,278** |
| `code_mechanical` | — | — | 54,139 (50%) |
| `daily_assistant` | — | — | 54,139 (50%) |
| Choices per row | 5–30 (median 5) | | |
| Distinct targets | | | 5,681 |

## ⚠️ Limitations

- **Easy distractors in some sources**: Glaive and NL2Bash rows usually have only one "own" candidate, so the other 4 are random and often unrelated.
- **Near-duplicates remain**: templated queries (Glaive, SGD) differing only in entities are kept; only exact duplicates were removed.
- **Weak descriptions**: MASSIVE descriptions are generated from label names (e.g., "User intention to play music"); Nemotron tool descriptions are kept as-is.
- **NL2Bash skew**: `find` dominates the terminal subset even after capping.
- **Decontamination is lexical**: paraphrased benchmark items would not be caught.

---

## 🛠️ Quickstart

```python
from datasets import load_dataset

ds = load_dataset("TypeScript/FastBrain-Instruct")
print(ds["train"][0])
```

| Field | Type | Description |
| :--- | :--- | :--- |
| `id` | string | Row id |
| `domain` | string | `code_mechanical` or `daily_assistant` |
| `category` | string | e.g. `api_tool_routing`, `agent_tool_selection`, `terminal_command`, `service_dialogue` |
| `query` | string | User request (or rendered recent conversation for agent rows) |
| `target` | string | Correct choice name |
| `choices` | list of `{name, description}` | Candidates, including `target` |
| `source` | string | Upstream source |

## ⚖️ Upstream Attribution

- **Google Research** — Schema-Guided Dialogue (Rastogi et al., AAAI 2020), CC BY-SA 4.0.
- **NVIDIA** — Nemotron-SFT-Agentic-v2, CC BY 4.0; decision-format transformation by **Maziyar Panahi** (AgentToolDecisions-180K).
- **Amazon Science** — MASSIVE (FitzGerald et al., 2022), CC BY 4.0.
- **Glaive AI** — Glaive Function Calling v2.
- **NousResearch** — Hermes Function Calling v1.
- **Lin et al.** — NL2Bash (LREC 2018).
- **burak29** — Git natural-language commands.

## 📜 Citation

```bibtex
@dataset{fastbrain_instruct_2026,
  author = {TypeScript},
  title = {FastBrain-Instruct: Aggregated Fine-Tuning Corpus for System 1 Decision Models},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {https://huggingface.co/datasets/TypeScript/FastBrain-Instruct}
}
```