Datasets:

sraivante
/

home-commands-json-v1

license: apache-2.0
language:
  - en
  - hi
task_categories:
  - text-generation
size_categories:
  - 1M<n<10M
tags:
  - home-automation
  - text-to-json
  - hinglish
  - templated
  - experimental
configs:
  - config_name: prepared
    default: true
    data_files:
      - split: train
        path: data/train.jsonl.gz
      - split: validation
        path: data/validation.jsonl.gz
      - split: test
        path: data/test.jsonl.gz
  - config_name: original
    data_files:
      - split: source
        path: source/home-commands-3.4M.jsonl.gz
  - config_name: robotics_edge_v1
    data_files:
      - split: test
        path: evaluation/robotics_edge_v1/cases.jsonl

Home Commands JSON v1

English and Romanized Hindi/Hinglish home commands mapped to structured JSON. This release contains the exact source and processed snapshot associated with Tiny Home JSON 1M v1. It also includes a separately authored evaluation challenge set. Native-script Hindi appears only in a small later diagnostic, not as a supported training language claim.

Publisher: sraivante. Release date: 2026-09-25. Version: v1.0.0.

Configurations and counts

Configuration / split	Rows	Role
original / source	3,458,816	Original received JSONL, losslessly gzip-compressed
prepared / train	910,114	Actual training examples
prepared / validation	103,089	Validation split
prepared / test	104,723	Original held-out test split
robotics_edge_v1 / test	284	Later stress-test cases; never used to train this model

The source split is provenance data, not an additional training split. Do not combine it with the prepared splits: it contains their source examples and many duplicates. The prepared configuration is the default.

Load the data

from datasets import load_dataset

repo = "sraivante/home-commands-json-v1"
data = load_dataset(repo, "prepared", revision="v1.0.0")
print(data["train"][0])

# For a small initial download:
stream = load_dataset(repo, "prepared", split="train", streaming=True, revision="v1.0.0")
print(next(iter(stream)))

challenge = load_dataset(repo, "robotics_edge_v1", split="test", revision="v1.0.0")
source = load_dataset(repo, "original", split="source", streaming=True, revision="v1.0.0")

JSONL files are gzip-compressed without changing the decompressed bytes. Each line is one UTF-8 JSON object. Download and decompress directly if not using the Hugging Face Datasets library.

Schema

Original source:

{"instruction":"Please turn on the fan","output":{"activity":"air","subject":"fan","action":"ON"}}

This illustrates the schema rather than asserting the example's source line. The prepared files retain instruction and the object-valued output, and add:

group_id: canonical phrase-family string used for split assignment.
source_line: one-based line number of the retained example in the original decompressed JSONL.

Actions are ON, OFF, STATUS. There are 30 observed combinations across fan, light, motor, speaker, geyser, cooler, washing_machine, tv, ac, and sprinkler. motor refers to the water pump. Full valid combinations and the field schema are in metadata/allowed_outputs.json and metadata/output_schema.json.

Challenge cases have id, category, text, nullable expected JSON, expected_behavior, scope, reason, and overlap-audit fields. expected=null means no single supported command is acceptable: the declared policy calls for abstention/clarification. It does not mean the trained model supports a reject class. The four input-guard cases include empty/overlong text intentionally.

Preparation and split construction

Audit JSONL records, output schema and conflicting normalized inputs.
Normalize input identity with Unicode NFKC, whitespace normalization and case-folding. Remove 2,340,890 duplicate normalized-input rows. Retain one source example per distinct normalized input, leaving 1,117,926 examples.
Group phrase families after removing punctuation, common greetings/politeness prefixes and suffixes, and the article the. Stratify family assignment by the 30 target commands, with approximately 80/10/10 family proportions and seed 20260925. This produces 2,292 / 288 / 288 train/validation/test families.
Fit the byte-level BPE tokenizer only on training examples; serialize input and JSON response with explicit role tokens. The training objective includes input and response next-token targets, excluding padding and initial BOS.

Training contains 22,655,302 prediction tokens for one pass. Only 4,120 training examples have STATUS targets, versus 905,994 ON/OFF examples; command imbalance and heavy templating should be considered when evaluating models.

The preparation code is in preparation/. The exact tokenizer, token-ID memmaps, indexes and normalized splits used by training are also included in artifacts/home_commands_training_bundle.zip for reproduction.

Evaluation scope and known limitations

The associated model reached 100% exact JSON accuracy on the original 104,723 test rows, but only 68/140 (48.6%) on later supported-command challenges and 48/120 (40%) on those without source-phrase overlap. This is evidence that the original split is not a sufficient real-world generalization test.

The later 284-case suite was frozen before model inference. Twenty cases overlap an original source phrase (14 train, 3 validation, 3 test); the remaining 264 do not. It is a small assistant-authored diagnostic with correlated wording, not a probability sample, recorded-speech dataset or robotics safety benchmark.

The training output space has no reject/no-op class, negation semantics, device instances, speed/position parameters, schedules or multi-step commands. The associated model emitted recognized commands for all 130 requests requiring abstention, including 33 ON/OFF commands. Do not interpret valid JSON as safe actuation. Additional human-written data and independent testing are needed for practical command systems.

Snapshot identity

These are SHA-256 hashes of the decompressed original bytes:

File	SHA-256
Original source	`10822f6617d2cc94dca66d609a1f41769cd5ef2ebba224d85fc7ae79cfbbaeba`
Train JSONL	`63313b7aafeb2a2a32e87c660015d745a84698a0db1c23e83c8da94105547430`
Validation JSONL	`df6821e7165af193a09b6790de530c83ef9466d25b4a7e01d25f5b392ec3b580`
Test JSONL	`a43dc4afaa386e5d4825e26b495159b0beace648488c4d843f153c650adfaa06`
Later challenge JSONL	`30cb9a6de18cc12cefcbb6e1aac8a29c7371b0f4ea537e516f38d02b3f417d6c`

training_snapshot_manifest.json is the byte-identical manifest used by the run. release_manifest.json additionally hashes compressed/public artifacts and links the model version. The historical manifest's private/pre-authorization note describes its creation state; this release follows the publisher's later explicit authorization. No source JSONL or split content has been edited.

Provenance, license and attribution

The source file was supplied by sraivante for this project. Its upstream generation script, historical repository revision, and original creation date were not provided or independently verified. No external corpus was added in preparation. The exact file hash identifies the data actually used by the run; the release date is not a claim about the original source creation date.

Copyright (c) 2026 sraivante, limited to original contributions and original selection/arrangement, under Apache License 2.0. See LICENSE and NOTICE. Third-party ownership, attribution and terms remain unchanged. The material supplied to this project did not identify third-party source repositories; the original-contribution license does not purport to relicense any unidentified third-party material.

The later diagnostic was assistant-authored during evaluation for the publisher and is separately versioned and marked in the manifest. It was not mixed into the training snapshot. Models or revisions trained using this challenge must use a fresh independent evaluation set for new performance claims.