README.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
  - dataset-repair
  - fastbrain-v3
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
  - config_name: choice_v3
    data_files:
      - split: train
        path: data/choice_v3/train.parquet
      - split: validation
        path: data/choice_v3/validation.parquet
      - split: test
        path: data/choice_v3/test.parquet
  - config_name: legacy_replay_v3
    data_files:
      - split: train
        path: data/legacy_replay_v3/train.parquet
      - split: validation
        path: data/legacy_replay_v3/validation.parquet
      - split: test
        path: data/legacy_replay_v3/test.parquet
  - config_name: semantic_v3
    data_files:
      - split: train
        path: data/semantic_v3/train.parquet
      - split: validation
        path: data/semantic_v3/validation.parquet
      - split: test
        path: data/semantic_v3/test.parquet
  - config_name: tool_context_v3
    data_files:
      - split: train
        path: data/tool_context_v3/train.parquet
      - split: validation
        path: data/tool_context_v3/validation.parquet
      - split: test
        path: data/tool_context_v3/test.parquet
  - config_name: tool_environment_holdout_v3
    data_files:
      - split: validation
        path: data/tool_environment_holdout_v3/validation.parquet
      - split: test
        path: data/tool_environment_holdout_v3/test.parquet
  - config_name: hermes_actions_v3
    data_files:
      - split: train
        path: data/hermes_actions_v3/train.parquet
      - split: validation
        path: data/hermes_actions_v3/validation.parquet
      - split: test
        path: data/hermes_actions_v3/test.parquet
  - config_name: nlupp_hotels_v3
    data_files:
      - split: train
        path: data/nlupp_hotels_v3/train.parquet
      - split: validation
        path: data/nlupp_hotels_v3/validation.parquet
      - split: test
        path: data/nlupp_hotels_v3/test.parquet
  - config_name: hint3_native_v3
    data_files:
      - split: train
        path: data/hint3_native_v3/train.parquet
      - split: validation
        path: data/hint3_native_v3/validation.parquet
      - split: test
        path: data/hint3_native_v3/test.parquet

FastBrain-Instruct

The 2026-10-14 v3 release repairs documented supervision and evaluation defects in v2. It regenerates semantic candidate pairs, restores full Hermes requests and native call sets, recovers Nemotron schemas, restores legacy validation, and disables unsupported NLU++ Boolean labels. Model training and capability-retention experiments have not been run for this release.

from datasets import load_dataset

ds = load_dataset("TypeScript/FastBrain-Instruct", "choice_v3")

Configurations

Config	Train	Validation	Test	Purpose
choice_v3	113,163	9,505	21,502	Integrated repaired choice corpus; recommended for the choice head
legacy_replay_v3	61,131	825	7,181	Legacy coarse routing and restored Hermes single-call choices
semantic_v3	26,922	7,230	12,220	Regenerated MTOP/TOPv2 candidate-relative match/no-match pairs
tool_context_v3	25,110	1,450	2,101	Observed Funcdex/Nemotron tool choices with available full schemas
tool_environment_holdout_v3	—	534	561	Additional held-out Nemotron policy/catalog groups; evaluation only
hermes_actions_v3	868	24	123	Native observed call sets; separate auxiliary task
nlupp_hotels_v3	765	97	98	Native multi-label intents and masked slot values; separate auxiliary task
hint3_native_v3	801	68	1,348	Native brand-relative intent/OOS labels; separate auxiliary task
default	97,450	—	10,828	Frozen original release
choice_v2	116,440	8,206	24,639	Frozen previous integrated release

legacy_replay_v3, semantic_v3, and tool_context_v3 are exact subsets of choice_v3. Do not concatenate these subsets with the integrated config. tool_environment_holdout_v3 overlaps the tool validation/test splits. All three native auxiliary configs have their own schema and are not automatic choice-head training data. Some native Hermes single-call observations also appear in the choice view; included_in_choice_v3 records this overlap.

Calling load_dataset("TypeScript/FastBrain-Instruct") still loads default. All original and v2 data files remain unchanged from the pre-repair revision. The v2 data card is archived; its known defects are addressed by the explicit v3 configs, not by silently replacing old splits.

Repairs and evidence

• Semantic supervision: all retained pairs are regenerated with three repaired operation descriptions and a conservative conflict graph. All 44 previously confirmed false-no-match cases are repaired or explicitly quarantined. Known context-dependent/ambiguous requests and unsupported persistent call rules are excluded from the choice view. See case-level outcomes and the versioned catalog. The catalog still contains 68 operations; this is not a 151-operation natural intent dataset.
• Legacy replay: restore 825 protected validation rows across all seven legacy sources. Repair documented Git/shell/MASSIVE descriptions, replace a known equivalent pager distractor, and reserve lexical groups against existing holdouts. MASSIVE cooking-label boundaries and other unreviewed labels remain limitations. Missing context is not guessed.
• Hermes: all 1,147 original rows are uniquely mapped to the pinned native file, with full requests, system messages, tool schemas and original call sets recovered. Contract failures, documented semantic contradictions and specifically reviewed financial operations are quarantined. Multi-call responses are retained as observed sets in hermes_actions_v3; their first call is not made unique gold. Only eligible single-call records with at least two native tools enter the choice view. Source order does not certify parallel independence or a unique execution order.
• Tool context: quarantine 31 reviewed inconsistent/ambiguous parents, including contaminated preceding histories. Recover the primary catalog for every published Nemotron source step before filtering: 10,810 native conversations / 23,275 targets. In the final tool view, 23,259 Nemotron rows have full native schemas. Only 10,792 pass both the strict source-prefix check and native argument contract checks needed to expose argument supervision. Other recovered arguments remain explicitly unverified. No execution-correctness or unique-valid-action claim is made.
• NLU++: preserve all 960 native texts, intent lists and original slot dictionaries. Disable the generic Boolean export (head_questions_json={}, head_expected_json={}, use_for_head_training=false), because an absent native annotation does not certify a broadly worded predicate false. Mask the 22 reversed stay-date pairs from slots_for_value_training_json; retain the original values in slots_json.
• HINT3: add a separate native auxiliary view of Curekart/SOFMattress with pinned source files and ODbL/DbCL attribution. Source OOS refers to its original brand catalog, not FastBrain's global no-match option. Native test/OOS is never promoted into training. Financing, lexical overlap, empty text and duplicates are handled explicitly; this filtered view is not the canonical HINT3 benchmark.

Choice adapter and task contract

Use head_adapter_v3.py for the current Clef joint-schema format. It assigns deterministic random option IDs per record, preserves the ID-to-name answer mapping, and accounts for the encoder sorting IDs. Merely shuffling the choices list does not randomize the encoder's final order. encode_without_truncation raises on oversized input instead of letting the upstream encoder crop state.

The documented adapter includes the complete published query plus candidate names/descriptions; it does not append tools_json to state. All 144,170 choice records were encoded with the actual Clef tokenizer/encoder: maximum 11,482 tokens, below 16,384. See the measured token budget. Adding full schemas, more head questions or media requires a new budget check.

For observed tool records, target is the source-observed action and acceptable_targets is a declared positive label list, not an exhaustive set of valid actions. Do not turn unobserved alternatives into independently certified noul=false or score-zero labels. The supplied adapter exports only the observed choice task. Auxiliary call-set and multi-label records are rejected by that adapter.

Evaluation and retention

The v3 test has changed requests, descriptions, candidates and filtering; its accuracy is not directly comparable to v2 accuracy. Preserve old regression runs on frozen files, and re-evaluate both the baseline head and candidate head on the same v3 protocol.

The additional Nemotron environment holdout reserves 23 validation and 23 test policy/catalog groups absent from v3 tool training. Existing in-environment validation/test remains in tool_context_v3 and is identified by provenance. This is exact policy/catalog grouping within this dataset, not proof that a previous model has never seen the public source or semantically similar workflows.

The historical legacy validation is a restored reserved set, not newly unseen data. Historical MASSIVE native-split mixing and unavailable original conversation identities in several legacy sources remain disclosed. Conservative lexical grouping does not establish original dialogue or template ancestry.

Use fixed evaluation membership, evaluation contract, and ablation definitions. Report source/operation-family slices, candidate-count buckets, present/absent results and in-/out-of-environment tool results separately. Paired semantic rows share a parent; uncertainty estimates should group by parent/template/environment as applicable. Predeclare retention tolerances before training; a lower aggregate loss is not a retention gate.

Provenance, exclusions and limitations

Release counts and hashes · Validation · Repair recipe · Input evidence · Quarantine ledger · Historical membership · Licenses

The quarantine ledger distinguishes quarantined rows, split reservations, deduplication and records retained in a separate task. It is audit data, not a training config; its row count is not a label-error rate. Native source text and original labels remain available for traceability.

Existing excluded source families/benchmarks remain excluded. Finance filtering is scoped and recorded: it does not establish that every legacy row is finance-free. Ordinary retail price, refund and cash-on-delivery questions are not automatically equated with banking. The semantic 1:1 present/absent construction is an artificial sampling ratio, not a deployment prior. This release does not establish real no-call/clarification policy competence, exhaustive semantic correctness, human gold annotation, or a model-quality gain.