docs/v2-data-card.md

metadata

language:
  - en
license: other
license_name: mixed-upstream-licenses
license_link: >-
  https://huggingface.co/datasets/TypeScript/FastBrain-Instruct/blob/main/LICENSE.md
task_categories:
  - text-classification
  - zero-shot-classification
task_ids:
  - intent-classification
  - multi-class-classification
tags:
  - fastbrain
  - tool-use
  - function-calling
  - intent-classification
  - multi-label
  - synthetic
  - aggregated
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
  - WillHeld/mtop
  - WillHeld/top_v2
  - prem-research/Funcdex-MT-Function-Calling
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    default: true
    data_files:
      - split: train
        path: train.parquet
      - split: test
        path: test.parquet
  - config_name: choice_v2
    data_files:
      - split: train
        path: data/choice_v2/train.parquet
      - split: validation
        path: data/choice_v2/validation.parquet
      - split: test
        path: data/choice_v2/test.parquet
  - config_name: semantic_v2
    data_files:
      - split: train
        path: data/semantic_v2/train.parquet
      - split: validation
        path: data/semantic_v2/validation.parquet
      - split: test
        path: data/semantic_v2/test.parquet
  - config_name: tool_context_v2
    data_files:
      - split: train
        path: data/tool_context_v2/train.parquet
      - split: validation
        path: data/tool_context_v2/validation.parquet
      - split: test
        path: data/tool_context_v2/test.parquet
  - config_name: nlupp_hotels_v2
    data_files:
      - split: train
        path: data/nlupp_hotels_v2/train.parquet
      - split: validation
        path: data/nlupp_hotels_v2/validation.parquet
      - split: test
        path: data/nlupp_hotels_v2/test.parquet
  - config_name: semantic_original_labels
    data_files:
      - split: train
        path: data/semantic_original_labels/train.parquet
      - split: validation
        path: data/semantic_original_labels/validation.parquet
      - split: test
        path: data/semantic_original_labels/test.parquet

FastBrain-Instruct

FastBrain-Instruct aggregates public intent and tool-selection data for decision heads. The 2026-10-14 v2 release adds semantic boundary pairs, richer tool context and a separate multi-label task while preserving the original files.

Load the integrated release explicitly:

from datasets import load_dataset

ds = load_dataset("TypeScript/FastBrain-Instruct", "choice_v2")
print(ds["train"][0])

Configurations

Config	Train	Validation	Test	Use
default	97,450	—	10,828	Frozen original release
choice_v2	116,440	8,206	24,639	Integrated choice corpus: legacy replay + semantic pairs + tool context
semantic_v2	26,976	7,230	12,228	MTOP/TOPv2 intent boundaries and paired candidate-relative no-match
tool_context_v2	26,241	976	1,583	Funcdex and repaired Nemotron observed next-tool steps
nlupp_hotels_v2	765	97	98	Original multi-label intent modules and slots; separate task format
semantic_original_labels	21,966	3,151	9,131	Original labels for complex/ambiguous/unsupported/low-support cases; auxiliary only

semantic_v2 and tool_context_v2 are overlapping views of choice_v2, not additional examples to concatenate with it. nlupp_hotels_v2 and semantic_original_labels use their own schemas. Calling load_dataset("TypeScript/FastBrain-Instruct") still loads default.

The original train.parquet, test.parquet, train.jsonl and test.jsonl remain unchanged from the frozen baseline. All 10,828 original test rows also appear in choice_v2/test with their original seven core fields intact. The original data card is archived here; its historical claims should be read together with the new audit below.

What changed

Main training component	Rows
Retained legacy replay	63,223
MTOP/TOPv2 semantic pairs	26,976
Funcdex	4,010
Repaired Nemotron	22,231

• Semantic coverage: 68 retained intent classes after alias consolidation, split/template isolation, label screening and a minimum of 20 independent training requests per class. Each selected request has one positive and one answer-removed no-match example. Candidate budgets target 16/32/64 ordinary operations and shrink when conflict exclusions require it; __no_match__ is counted separately. This release does not provide a 151- or 256-intent natural-language catalog.
• Tool context: preserve the full available pre-answer prefix for Funcdex; restore Nemotron system policy and exclude steps with known missing earlier history or non-system truncation. Tools remain observed expert-action labels; schema validity and context recovery do not establish that every other tool is invalid. No text-only/no-call record is relabeled as no-match.
• Holdout isolation: the audit identified 2,687 raw Nemotron conversations shared by the original train and test sets. The v2 rebuild blocks 3,286 historical held-out parents before extracting training steps. The frozen default retains its historical split limitations.
• Multi-label preservation: NLU++ hotels keeps all annotated intent modules and slots. Only 576 of its 765 train rows are eligible for intent training; empty-intent context fragments have use_for_intent_training=false and no fabricated negative labels.

Record format

Choice configs retain id, domain, category, query, target, choices (a list of name/description objects), and source. Added fields record parent_id, source_family, source_repo, source_revision, source_split, source_row_id, task_type, label_status, is_rejection, candidate_count, catalog_id, provenance_json, context_json, and tools_json.

provenance_json contains the source file and row, construction method, group, and applicable license for new rows. For tool records, query renders the structured context_json. Funcdex retains full original schemas in tools_json; Nemotron retains only names, descriptions and required-parameter names because the intermediate source discarded the rest.

NLU++ uses text, intents, slots_json, source/fold identifiers, use_for_intent_training, head_questions_json and head_expected_json. The optional head fields contain 40 independent boolean module questions. Filter on use_for_intent_training before intent training. Auxiliary semantic rows retain source labels and parse trees and explicitly set use_for_single_choice=false.

Curation and use

Curation recipe and split policy · Source revisions and file hashes · Dataset counts and hashes · Validation report · Semantic catalog · Source exclusions

Use source, source_family, task_type and provenance_json.group to make explicit ablations. The paired semantic data has an artificial 1:1 present/absent ratio within that view; this is not a deployment class prior. The release does not establish model improvement or preservation of an existing head's capabilities; those require training and evaluation.

The source-family exclusions from the original corpus are retained. Additional lexical checks use the exact cached files recorded in the exclusion manifest. They do not certify absence of paraphrases or unobserved source ancestry. Curation uses assistant ontology review, deterministic rules and targeted training-example inspection, not exhaustive human annotation. Ambiguous single-choice cases are retained only in the explicitly auxiliary view when possible.

Attribution and licenses

This is an aggregation and adaptation of upstream data. MTOP and TOPv2 are CC BY-SA 4.0; NLU++ and the attributed Nemotron component are CC BY 4.0; Funcdex declares MIT. Legacy records retain their original source terms, including SGD's CC BY-SA 4.0. The collection is not licensed uniformly as MIT. See license and attribution notices, the pinned source manifest and the archived legacy card.