--- 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 [!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 `` 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} } ```